Systemic inflammatory response syndrome (SIRS) is a sepsis-associated inflammatory state and a self-defense mechanism against specific and nonspecific stimuli. Ketamine influences many key processes that are altered during sepsis. However, the underlying mechanisms remain incompletely understood. In this study, TNF-α-treated mice, as well as HT-29 and L929 cell models, were applied to characterize TNF-α-induced systemic and local cecal tissue inflammatory responses. Behavioral, biochemical, histological, and molecular biological approaches were applied to illustrate the related processes. Mice with TNF-α-induced SIRS showed systemic and local cecal tissue inflammatory responses, as indicated by increased levels of high mobility group box 1 protein (HMGB1), chemokines (C-X-C motif) ligand 10 (CXCL10), interleukin-6 (IL-6), and IL-10, as well as high mortality. Ketamine pretreatment alleviated death rates, symptoms, and the production of inflammatory cytokines induced by TNF-α in mice. Moreover, ketamine also protected the mice from TNF-α-induced cecal damage by suppressing the phosphorylation of receptor-interacting serine/threonine-protein kinase 3 (RIP3) and mixed lineage kinase domain-like protein (MLKL). In addition, our results showed that ketamine efficiently inhibited TNF-α-induced necroptosis in HT-29 and L929 cells. Furthermore, we explored the mechanism using different L929 cell lines. The results displayed that ketamine inhibited TNF-α-induced necroptosis by enhancing RIP1 ubiquitination and reducing the RIP1-RIP3 and RIP3-MLKL interactions, as well as the formation of necrosomes. Thus, our study may provide a new theoretical and experimental basis for treating diseases characterized by SIRS-associated inflammatory factor storms. Moreover, our exploration may provide potential molecular mechanisms and targets for therapeutic intervention and clinical application of ketamine. Systemic inflammatory response syndrome (SIRS) is characterized by innate immune system activation and the stimulation of inflammatory responses, followed by a cytokine storm in the circulation, which involves the generation and secretion of proinflammatory cytokines . As a coIt shall be noted that cecum was illustrated as especially sensitive to TNF-α-induced damage. Previous researches have strongly shown that TNF-α could trigger cecal epithelial necroptosis, a kind of programmed cell death associated with the proteins receptor-interacting protein kinase 1 (RIP1), RIP3, and mixed lineage kinase domain-like protein (MLKL) –13. MoreKetamine has been widely administered in clinical anesthesia as a noncompetitive glutamatergic N-methyl-D-aspartate receptor (NMDAR) antagonist . AccordiThe current research demonstrated that ketamine could ameliorate TNF-α-induced death in C57BL/6 mice as well as HT-29 and L929 cells. Furthermore, we showed that ketamine suppressed the phosphorylation of RIP3 and MLKL by enhancing the ubiquitination levels of RIP1. Thus, we concluded that ketamine could protect mice from TNF-α-induced death and will be a potent therapeutic drug for SIRS.vs. 10 mg/kg vs. 20 mg/kg: 0% vs. 10% vs. 30%. Supplementary Fig. vs. prophylactic ketamine: 70% vs. 71.4%. Supplementary Fig. p < 0.001), 12 h (p < 0.001) and 18 h (p < 0.001) after TNF-α administration , 12 h (p < 0.05) and 18 h (p < 0.05) after TNF-α administration  and 18 h (p < 0.05) after TNF-α administration  injection, all mice died within 24 h. In contrast, prophylactic ketamine greatly boosted the survival of mice in a dose-dependent manner. In particular, the percent survival of mice in the KET + TNF-α group was 71.4% Fig. . Besidesion Fig. . Furtherion Fig. . Line crion Fig. .Fig. 1KeAt 12 h after TNF-α administration, we measured the levels of HMGB1, IL-6, CXCL10, and IFN-γ in the serum of the mice. The results showed that compared with those in the sham group, the levels of HMGB1, IL-6, CXCL10, and IFN-γ in the TNF-α group increased significantly at 12 h after TNF-α administration. By comparing with those in the TNF-α group, the levels of HMGB1, IL-6, CXCL10, and IFN-γ decreased in the KET (20 mg/kg) + TNF-α group Fig. .Fig. 2Kep < 0.001). By comparing with that in the TNF-α group, the damage score of cecal tissue decreased in the KET + TNF-α group (p < 0.001)  Fig. . It has ed death , 32. AmoOur outcomes displayed that ZVAD/LBW242/TNF-α management greatly decreased cell viability, and ketamine obviously decreased ZVAD/LBW242/TNF-α-induced cell damage and inhibit LDH release in HT-29 cells in a dose-dependent way Fig. . MeanwhiNext, we wanted to clarify the mechanism by which ketamine protects against the TNF-α-induced necroptosis pathway. Because TNF-α can trigger necroptosis in L929 cells, we examined the role of ketamine in the inhibition of TNF-induced necroptosis in L929 cells. The experiment included ZVAD to exclude apoptosis. The results showed that ketamine could efficiently promote cell survival and inhibit LDH release in a dose-dependent manner at 3 h after TNF-α and ZVAD administration Fig. . StudiesThe results indicated that the TNF-α + ZVAD group showed more PI-positive cells than the normal group at 3 h after TNF-α and ZVAD administration. By comparison, the KET (500 μg/ml) + TNF-α + ZVAD group and KET (100 μg/ml) +l TNF-α + ZVAD group showed less PI-positive cells than the TNF-α + ZVAD group, indicating that ketamine prevented necroptosis Fig. . As antiBecause TNF-α-induced necroptosis in L929 cells is mediated through the TNFR1-RIP1-RIP3-MLKL signaling pathway, we examined whether the target of ketamine was downstream of TNFR1, RIP1, RIP3, and MLKL. It has been shown that the dimerization or oligomerization of TNFR1, RIP1, RIP3, or MLKL can dramatically induce necroptosis. Therefore, we used this system to clarify the role of ketamine in necroptosis. Artificial dimerization systems were on basis of the FK506 binding domain F36V mutant (FV) or hormone-binding domain G521R mutant (HBD*). We stably transfected FV-tTNFR1, RIP1ΔDD-HBD*, FV-RIP3, and MLKLΔPD-HBD* into the corresponding KO L929 cells. Upon treatment of L929 cells expressing RIP1ΔDD-HBD* or MLKLΔPD-HBD* with 4-OHT and L929 cells expressing FV-tTNFR1 or FV-RIP3 with AP20187, abundant cell death was found within several hours. According to Fig. After TNF binds to the receptor, TNFR1 will recruit multiple proteins to form the complex I. Polyubiquitination of RIP1 in complex I mediated by cIAP ligases is very important for the activation of NF-κB and mitogen-activated protein kinases (MAPKs). Inhibition of the ubiquitination of RIP1 will enhance cell death. Ketamine treatment delayed TNFR1 oligomerization- but not RIP1/RIP3/MLKL oligomerization-induced necroptosis, suggesting that ketamine treatment might affect the formation of complex I. Complex I was analyzed by Flag-TNF-α immunoprecipitation with or without ketamine. As shown in Fig. RIP1 ubiquitination has been reported to play an essential role in activating NF-κB phosphorylation and inhibiting the formation of necrosomes. NF-κB activation reduced TNF-α-induced necroptosis through the upregulation of some anti-death proteins, such as c-FLIP, while ketamine treatment enhanced the degradation of IκB-α and increased NF-κB activation  RIP1 and K376R RIP1 in RIP1-KO L929 cells and treated these cells without TNF-α or with ketamine. To exclude apoptosis induced by changes in RIP1 protein levels, ZVAD was added in combination with TNF-α Fig. . KetaminThe current research showed that ketamine alleviated the death and sickness response induced by TNF-α in mice. Then, we demonstrated that ketamine attenuated TNF-α-induced cecal damage by suppressing the phosphorylation of RIP3 and MLKL. Furthermore, this study showed that ketamine reduced the TNF-α-induced serum levels of inflammatory cytokines in mice. In addition, we investigated the underlying mechanisms by which ketamine inhibited necroptosis in vitro. We found that ketamine efficiently inhibited TNF-α-induced necroptosis by enhancing RIP1 ubiquitination and reducing the RIP1-RIP3 and RIP3-MLKL interactions, as well as the formation of necrosomes  cell activity , 49, 50.vs. CLP-induced chronic SIRS), optical activity (S-ketamine vs. R-ketamine), as well as the dose of ketamine (20 mg/kg vs. 10 ~ 15 mg/kg). Pathophysiologically, gastrointestinal damage and cell death play roles as initiators or accelerators of SIRS. The previous study has confirmed that TNF-α caused severe oxidative stress and activation of caspase pathway, resulting in serious apoptosis and necrosis of the intestinal and cecal cells. In addition, the physical barrier function of the gastrointestinal mucosa can be influenced by great HMGB1 released by necrotic cells [via the control of ROS generation and MLKL phosphorylation [The timing of ketamine treatment during SIRS still remained controversial. Previous studies indicated that ketamine has prophylactic and/or therapeutic effects against inflammatory responses induced by systemically administered LPS , 56, as ic cells . Follow-ic cells . Necroptic cells . Previouic cells . In addirylation . In the rylation . In the rylation , 68. In It has been reported that necroptosis takes part in some pathological processes including inflammation, atherosclerosis, and amyotrophic lateral sclerosis (ALS). Cecal damage was observed in TNF-α-treated mice, and this damage was dependent on RIP3-mediated necroptosis. Although necrostatin-1 has been shown to be an efficient inhibitor of necroptosis, no clinical drug has been developed for TNF-α-induced SIRS. This research investigated the protective roles of ketamine during TNF-α-induced SIRS. Unlike necrostatin-1, ketamine did not directly influence the kinase activity of RIP1 or its interaction with downstream proteins, such as RIP3. Ketamine improved the ubiquitination of RIP1 after TNF-α stimulation, but its mechanism is still unknown.In summary, our research uncovered the interesting role of ketamine in TNF-α-induced SIRS, which inhibits necroptosis by affecting RIP1 ubiquitination levels and the formation of necrosomes and reducing the release of inflammatory cytokines. Our information provides new insight into the effect of ketamine on controlling the disease progression of SIRS. Our study may provide a theoretical and experimental basis for treating diseases characterized by SIRS-associated inflammatory factor storms and provide ideas for the clinical application of ketamine, with certain theoretical and clinical significance.Abcam provided rabbit anti-MLKL (ab184718), rabbit anti-phospho-RIP3  (ab222320), rabbit anti-phospho-MLKL (S345)(ab196436), rabbit anti-TNFR1 (ab68160), rabbit anti-TRADD (ab110644), and rabbit anti-CIAP2 (ab32059) antibodies. Rabbit anti-RIP1 (3493 s) and rabbit anti-IκBα (9242) antibodies were purchased from Cell Signaling Technology. The mouse anti-GAPDH (AC033) antibody was purchased from AbClonal. Sigma provided the rabbit anti-RIP3 (PRS2283) antibody. Santa Cruz Biotechnology offered mouse anti-β-actin (sc-47778) antibody. Calbiochem  offered Benzyloxycarbonyl-Val-Ala-Asp-fluoromethylketone . Sigma-Aldrich offered propidium iodide . Murine TNF-α (PMC3015) was purchased from Thermo Fisher. Ketamine (1707031) was obtained from Gutian Pharmaceutical Co. Ltd.ad libitum at the Xiamen University Laboratory Animal Center. The Institutional Animal Care and Use Committee approved all mouse experiments according to good animal practice as defined by the Xiamen University Laboratory Animal Center . The binding of all investigators to the randomization assignment was made.The housing of male C57BL/6 mice  was made in a given pathogen-free environment with a 12-h light/dark cycle with adequate food and water The random assignment of mice to the following groups was made. Mice in the TNF-α group were intravenously injected with 15 µg of TNF-α (75 µg/ml) diluted in endotoxin-free phosphate-buffered saline (PBS). Mice in the control group were injected with PBS alone. In the TNF-α + KET pretreatment group, ketamine was diluted in endotoxin-free PBS and intravenously injected at a dose of 20 mg/kg 20 min before TNF-α injection. Under constant observation, the animals were checked every 30 min. The mice were sacrificed at the indicated time, and the binding of investigator was made when the mice were injected with TNF-α and when the deaths were counted.Previous studies have demonstrated that ear temperatures obtained using a convenient and noninvasive infrared thermometer are reliable, and the readings are similar and consistent with rectal temperatures , 70. MorThe sickness behavior score (SBS) uses physiological variables to evaluate the severity and mortality of sepsis. The hourly evaluation of animals is made for six indexes of sickness behavior: temperature change, water/sucrose preference, liquid intake, food intake, body weight, and movement. For each parameter, a score of 0 indicated healthy behavior, and a score of 1 indicated sickness behavior. Totally, the individual hourly SBS could differ from zero to six .The mice were placed at the center of a cube-shaped arena (50 cm (L) × 50 cm (W) × 40 cm (H)), which was manually divided into an average of 16 square areas (3.125 cm (L) × 3.125 cm (W)) in a dimly illuminated testing room with indirect white lighting. The free locomotion of the mice was automatically monitored and recorded by a Noldus EthoVision XT  for 5 min. The mean velocity was used as a parameter of locomotion, and the number of crossings of the areas (defined as entering the area with four paws) was a parameter to evaluate animal sickness by offline analysis . The habThe mice were euthanized with isoflurane for 12 h after tail vein injection of TNF-α, and then the cecum was collected and fixed in 4% paraformaldehyde in PBS for 24 h. After dehydration with ethanol, cleared with xylene, the fixed tissues were embedded in paraffin blocks. The sections were stained with hematoxylin and eosin (H&E). The extent of cecal damage was evaluated by a previously illustrated method . IdenticFor immunofluorescent staining, the cecal tissue was removed and post-fixed for 2 ~ 4 h after perfusion, followed by 48-h cryoprotection at 4 °C in 0.1 M PB with 30% (w/v) sucrose. Transverse frozen spinal sections (30 μm thick) were cut in a cryostat , followed by serial collection after being embedded in mounting medium . The rinsing of sections was made in 0.01 M phosphate-buffered saline  three times (10 min each), followed by 30 min blocking with 5% fetal bovine serum (FBS) in 0.01 M PBS with 0.3% (v/v) Triton X-100 at room temperature. The 4-h incubation of sections was performed at RT, followed by overnight incubation at 4 °C, thereafter, with rabbit anti-p-MLKL antibody  or rabbit anti-p-RIP3 antibody . After three washes with 0.01 M PBS (10 min each), the 1-hour incubation of sections was made at RT with Alexa 488 or 594 donkey anti-ribbit IgG . Finally, the sections were mounted with mounting medium containing DAPI (Abcam) after washing three times with 0.01 M PBS (10 min each), and then obtained with a confocal laser scan microscope .HMGB1 , IFN-γ , IL-6 , and CXCL10  concentrations in mouse serum were decided with enzyme-linked immunosorbent assay  kits according to the manufacturer’s instructions. The optical density of each sample was measured on an ELISA plate scanner  at 450 nm .Western blot analysis was used to determine p-MLKL and GAPDH levels in the cecal tissue of mice in each group at 12 h after being injected with 15 μg of TNF-α or determine p-RIP3, p-MLKL, and GAPDH levels in L929 cells treated with nothing , TNF-α + ZVAD, or TNF-α + ZVAD + ketamine for 3 h. Then, the cecum or L929 cells were collected and lysed for western blot analysis. To be brief, after separation by adopting 10% SDS‐PAGE gels with a marker , proteins were transferred onto PVDF membranes . 5% skim milk was adopted to block the membranes for 40 min to decrease nonspecific binding, followed by overnight incubation with primary antibodies  at 4 °C. After 2-h incubation with horseradish peroxidase-conjugated mouse  or rabbit  secondary antibodies  at room temperature, an improved chemiluminescence detection kit  was employed to visualize the protein bands. The band intensities were decided with ImageJ software , which represented the expression of every protein.rip1-knockout (KO) L929 cells or Flag-RIP3-expressing RIP3-KO L929 cells was made with TNF-α (10 ng/ml) or TNF-α (10 ng/ml) plus ketamine (500 µg/ml) for the given time. After the cells were treated according to the experimental design, the 60-min lysis of cells was made with lysis buffer  on ice. The 30-min centrifugal of cell lysates was made at 20,000 g. Antibody-coupled beads were employed to immunoprecipitate the supernatant at 4 °C overnight. After immunoprecipitation, the beads were washed three times in lysis buffer, and after subsequent elution with SDS sample buffer, the immunoprecipitated proteins were boiled for 10 min at 100 °C and explored by western blotting as previously described [3 × Flag-TNF-α (100 ng/ml) with or without ketamine (500 µg/ml) was employed to treat L929 cells for the given time. Then, the treatment of Flag-RIP1-expressing escribed .Colon cancer (HT-29) cell lines were cultivated in McCoy’s 5 A medium. The media was added with 10% FCS, 2 mM glutamine,100 units/ml penicillin, and 100 μg/ml streptomycin. The given amounts of recombinant human TNF (rhTNF) were added to induce Necroptosis. The 1-h treatment of HT-29 cells was made with 10 µM Smac mimetic LBW242 and 20 µM ZVAD before 24-h stimulation with 20 ng/ml rhTNF combining with various concentrations of ketamine. The CellTiter-Glo Luminescent Cell Viability Assay kit (Promega Corporation) was employed to determine cell survival on basis of the manufacturer’s guidance. Applying an ELISA kit to measure lactate dehydrogenase (LDH)  on basis of the manufacturer’s guidance. The Omega POLAR Star (BMG Labtech GmbH) was employed to record luminescence. Also, the collection and centrifugal of HT-29 cells were performed in 0.01 M PBS solution, and the removal of supernatant was realized. The dissolution of pellets was achieved in PRO-PREP solution by vortexing and ultrasonication for western blot analysis to test the level of MLKL phosphorylation.2.Moreover, mouse fibroblast L929 cells were obtained from the ATCC. RIP1-KO L929 cells, RIP3-KO L929 cells, NEMO-KO L929 cells, and K376R RIP1 mutation cells were described previously . All celCell death analysis was performed through the measurement of plasma membrane integrity and adding PI. After being added directly to the medium, PI (5 μg/ml) was incubated for 10 min to determine spontaneous cell death. The total number of PI-positive cells was counted under an inverted fluorescence microscope. Four fields of cells for every sample were counted. The information is shown as the percent of PI-positive cells per total cells counted. Cell death was also analyzed using an ELISA kit to measure lactate dehydrogenase (LDH)  or a CellTiter-Glo luminescent cell viability assay kit  according to the manufacturer’s protocols. Luminescent recording was performed with a POLAR star Omega .A fluorometric intracellular ROS kit  was employed to measure ROS levels on basis of the manufacturer’s guidance. A fluorescence microscope was adopted to view stained cells. The 3-hour treatment of cells was made with TNF-α + ZVAD or ketamine+TNF-α + ZVAD, and a CytoFLEXS flow cytometer  was used to measure fluorescence.HBD*(G521R), tTNFR1, RIP1ΔDD, RIP3-RHIMmut, and MLKLΔPD were amplified by standard PCR from the related templates. The addition of an inducer resulted in dimerization/polymerization of the fusion proteins in eukaryotic cells, leading to necroptosis. This method can specifically induce dimerization/polymerization interactions among TNFR1, RIP1, RIP3, and MLKL through the addition of small molecule polymerization inducers.TNFR1, RIP1, RIP3, and MLKL fusion protein gene sequences with inducible dimerization/polymerization joints were constructed as previously described . To prodp-values < 0.05 were regarded as significant.Prism software, version 7.0  was adopted to analyze the information. The data are shown as the mean ± SEM. One-way ANOVA was employed to compare groups followed by Bonferroni’s or Dunnett’s posttest. The log-rank test was adopted to analyze survival curves, and Supplemental FiguresOriginal WB scan


African Health Sciences which is rich in innovative research and practice papers from all over the world. We have purposely kept away from Covid-19 this season because it was distorting our perception of ill health in Africa and beyond.Welcome to this June 2021 issue of Escherichia coli and Klebsiella pneumoniae isolates from type 2 diabetic patients with urinary tract infections keeping the Klebsiella theme aliveKlebsiella again! It is to a paper on the prevalence and antimicrobial susceptibility of extended-spectrum beta lactamases-producing Escherichia coli and Klebsiella pneumoniae isolated in selected hospitals of Anyigba, NigeriaHence we have focused largely on infectious diseases. Our first paper comes from Egypt. El- Domany and colleaguesBut our next paper is on ‘bacterial profile and their antimicrobial susceptibility patterns in patients admitted at Madda Walabu University Goba Referral Hospital, Ethiopia.’6Asefa and others8This is followed by papers on urinary tract infections in TurkeyThen follows the prevalence and density of malaria parasitaemia amongst HIV individuals in Nigeria;16Muzanyi and colleagues19Now to cancer. We have several oncology papers. First, Uganda authorsWe continue this NCDs work with papers on diabetes mellitus. These include risk factors for type 2 diabetes mellitus in NigeriaNCDs continue. We have papers on ‘circulatory microRNA expression profile for coronary artery calcification in chronic kidney disease patients;’31Now to the clinical. Nigerian clinicians have a paper on the relationships between ‘cardiovascular signs and neurological signs in asphyxiated neonates.’37We have several papers on injury: traumatic spinal cord injury and clinical complications;42We bring you several papers on sexuality: a new grading system for female sexual dysfunction in Egyptian women49The remaining papers are on diverse issues such as availability of low vision services in Ghana;African Health Sciences: the home of truly free online publishing in Africa!We thank all our readers, authors, reviewers, our team of highly dedicated volunteers, collaborators, partners and colleagues for their immeasurable contribution to


The cell viability, cell proliferation, cell apoptosis, and tube formation were assessed. Subsequently the related mechanisms were analyzed by assays for JC-1 mitochondrial membrane potential (MMP), intracellular ROS, ATP, western blot and proteomics. Results: 27 main chemical components contained in HZQMY were identified. HZQMY significantly inhibited the viability and proliferation of HRCECs exposed to high glucose, and promoted the apoptosis. In addition, HZQMY also boosted the release of ROS and suppressed tube formation of HRCECs under high glucose exposure. Meanwhile, HRCECs treated with high glucose released more ROS than normal cells, which could be markedly inhibited by HZQMY in a dose-dependent manner. Additionally, western blot assay indicated that HZQMY increased the expression of proteins related to the P38 signaling pathway and inhibited nuclear factor kappa-B (NF-κB) pathway. Proteomic analysis predicted that HSPA4, MAPK3, ENO1, EEF2 and ERPS may be the candidate targets of HZQMY in HRCECs. Conclusions: HZQMY inhibited the proliferation and promoted the Mitochondria related apoptosis of HRCECs exposed to high glucose possibly through regulating P38 and NF-κB signaling pathway.Background: Diabetic retinopathy (DR) is one of the serious complications of diabetes and an important cause of blindness. Despite much research on the pathogenesis of DR, there is still a lack of safe and effective treatment methods. Hu-zhang-qing-mai-yin (HZQMY), a Chinese medicine formula, has been clinically used in the safe and effective treatment of DR for many years. However, the systematic pharmacological research is lacking. The aim of this study was to evaluate the anti-DR effects of HZQMY and explore the possible mechanism involved. Methods: The constituents of HZQMY were analyzed by LC-MS/MS. DR model was established by high glucose simulation on human retinal capillary endothelial cells (HRCECs)  Diabetes is one of the most common chronic metabolic diseases, with an estimated 592 million diabetics worldwide by 2035 . DiabetiIn recent years, clinical studies have shown that the application of Chinese medicine in the prevention and treatment of DR has significant advantages due to the definite clinical efficacy . Based oPolygonum cuspidatum Sieb. et Zucc., fruit of Forsythia suspensa, whole herb of Sedum sarmentosum Bunge, whole herb of Siegesbeckia orientalis L., rattan stem of Spatholobus suberectus Dunn, root of Glycyrrhiza uralensis Fisch, root of Stragalus membranaceus (Fisch.) Bunge and root of Ligusticum chuanxiong hort. The formula plays the function of clearing heat and detoxification, promoting blood circulation and removing blood stasis. However, its active components and mechanism of action are still unclear, and systematic pharmacological studies on the treatment of DR are lacking.Hu-zhang-qing-mai-yin (HZQMY) is an empirical Chinese medicine prescription produced by combining the theory of traditional Chinese theory with clinical experience, which is composed of root of in vitro, so as to provide reference for the prevention and treatment of DR.In our study, the main ingredients of HZQMY were identified by HPLC-Q-TOF-MS, its anti-DR effects were evaluated and the mechanism of action was explored Human retinal capillary endothelial cells (HRCECs) were acquired from the Cell Bank of Shanghai Academy of Chinese Sciences . Dulbecco’s modified eagle medium (DMEM) was purchased from HyClone , fetal bovine serum (FBS) was bought from GIBCO . Cell Counting Kit-8 (CCK-8) and PBS were obtained from meilun biotechnology co., Ltd. . ATP Assay Kit, Mitochondrial membrane potential kit and ROS assay kit were purchased from Beyotime Biotechnology . Apoptosis Detection Kit and cycle detection kit were provided by KeyGen Biotechnology co., Ltd. . Matrigel was provided by Corning . Primary antibody of p-NF-κB, NF-κB, p-P38, P38, BCL-XL, BCL-2 and GAPDH were bought from Cell Signaling Technology .Polygonum cuspidatum Sieb. et Zucc., fruit of Forsythia suspensa, whole plant of Sedum sarmentosum Bunge, whole plant of Siegesbeckia orientalis L., rattan stem of Spatholobus suberectus Dunn, root of Glycyrrhiza uralensis Fisch, root of Stragalus membranaceus (Fisch.) Bunge and root of Ligusticum chuanxiong hort. were provided by the ophthalmology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine  was 13 L/min. Gradient elution with mobile phase A (0.1 formic acid water), mobile phase B (acetonitrile): 99% A in 0–2 min; from 99 to 34% A in 2–20 min; from 34 to 5% A in 20–28 min. The mass spectrometry was performed in positive and negative ion scanning patter with a scanning range of 100–1700 m/z and a capillary voltage of 4000 V. The intercepting cone voltage is 60 V.Agilent 6530 four-stage rod-time-of-flight mass spectrometry (Q-TOF-MS) system was performed, chromatographic separations were carried out on an ACQUITY UPLC HSS T3  column at 25°C and the drying gas (N2. DR model was established by induction of HRCECs with high glucose in vitro (HRCECs were cultured in DMEM containing 10% FBS and 1% penicillin-streptomycin at 37°C and 5% COin vitro . The cel3 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then different concentrations of HZQMY extract  were added in 96-well plates for 24 and 48 h. Discarding the old medium, 100 μL of 10% CCK-8 kit was added into each well for incubation at 37°C for 30 min. OD value was detected at 450 nm with a microplate analyzer. Cell survival rate (%) = / ×100%.HRCECs were seeded into 96-well plates  = (amount of clones/number of inoculated cells) ×100%.HRCECs were seeded into 6-well plates (600 cells/well) for 24 h in a 5% CO5 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then each group was added with different concentrations of the extract for 24 h culture. Cells were harvested, 1 ml of precooled 70% ethanol was added to mix well, and fixed at 4°C for 2 h. The cells were collected, mixed with 0.5 ml PI into the sample tube, and incubated in the dark for 30 min. The red fluorescence was detected by flow cytometry at 488 nm, and the light scattering was recorded at the same time. The Beckman flow cytometer was used for detection.HRCECs were seeded into 6-well plates (5 × 1060 µL of the dissolved matrigel (9–12 mg/ml) was added in a well in 96-well plate and then placed in the incubator at 37°C for 1 h. HRCECs were cultured under different concentrations of HZQMY with or without HG. Subsequently, 50 μL of cell suspension was collected and added into the pre-solidified matrigel for 8 h in the 37°C incubator. The tube formation was observed under an optical microscope, and photographed. ImageJ was used to calculate the number of lumens and the length of tubules.4 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then each group was added with different concentrations of the extract for 24 h culture. Subsequently, the cells were washed by PBS three times, 150 µL of 4% paraformaldehyde was added to each well and fixed at room temperature for 30 min, and remove the fixing solution, add 150 µL of 0.1% Triton X-100 for 10 min. Finally, 150 µL DAPI solution was added in each well for incubation at room temperature for 10 min in dark. The samples were photographed by Operetta CLS high-content analysis system.HRCECs were seeded into 24-well plates (5 × 105 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then each group was added with different concentrations of drugs for 24 h culture. After that, the cells were digested with trypsin without Ethylene Diamine Tetraacetic Acid (EDTA) and centrifuged with a 15 ml centrifuge tube at 2000 rpm for 10 min at 4°C, and the supernatant was discarded. 100 μL 1 × FITC binding solution was added to mix well, then 5 μL FITC dye was added for 10 min incubation at room temperature in dark. Last, 5 μL PI dye was added for 5 min incubation in dark. The Beckman flow cytometer was used for detection.HRCECs were seeded into 6-well plates (5 × 105 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then each group was added with different concentrations of the extract for 6 h. DCFH-DA was added for 1 h incubation in a 37°C cell incubator. The Beckman flow cytometer was used for detection.HRCECs were seeded into a 96-well plate (1 × 105 cells/well) for 12 h in a 5% CO2 incubator at 37°C, then added with different concentrations of HZQMY extract to culture for 24 h. Cells were lysed to isolate total protein, then centrifuged at 12,000 g and 4°C for 10 min. Next, 20 µL sample or standard solution were added to 100 µL ATP detection solution, mixed, and luminescence was measured with a multifunctional enzyme plate analyzer.ATP concentration was detected by the ATP Assay Kit. HRCECs were seeded into 6-well plates (5 × 104 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then added with different concentrations of the extract for 24 h culture. Apoptotic cells should show green fluorescence after JC-1 staining, while normal cells show red fluorescence. The relative ratio of red-green fluorescence is often used to measure the proportion of mitochondrial depolarization, which was used as one of the early detection indicators of cell apoptosis. The fluorescence quantification was carried out using ImageJ software.HRCECs were seeded into 24-well plates (5 × 105 cells/well) for 24 h in a 5% CO2 incubator at 37°C, then each group was added with different concentrations of the extract for 24 h culture. Cells were collected and total proteins were extracted from HRCECs using RIPA lysate and PMSF (RIPA: PMSF = 100:1). Protein concentration was measured by using the BCA Protein Concentration Assay Kit. The proteins were separated by 10% SDS-polyacrylamide electrophoresis and transferred to PVDF membrane, which was then sealed with QuickBlock™ Blocking Buffer at room temperature for 1 h. After that, the membrane was incubated with the primary antibody overnight at 4°C, followed by washing 3 times with TBST, and the rabbit/mouse antibody was incubated at room temperature for 1 h. Then the membrane was washed 3 times with TBST. Finally, the gray values of the protein bands were detected and photographed by Chemi Scope Mini, and GAPDH was used as internal reference.HRCECs were seeded into 6-well plates  were seeded into 100 mm culture dishes and 100 µg/ml HZQMY extract was added until 60% confluent. After 24 h, cells were harvested and flash-frozen in liquid nitrogen. Identification and analysis were processed by Ouyi Biotechnology Co., Ltd. . The proteins were quantified by iTRAQ labeling, and based on UniProt, KEGG, go, KOG/cog databases, the annotated information of the identified proteins was extracted to mine the protein functions. Proteins with quantification changes >1.5 and t-test, and the comparison of the mean between multiple groups was carried out by one-way analysis of variance. p < 0.05 was considered statistically significant.All data were analyzed by SPSS 22.0 statistical software, and the results were expressed as mean ± standard deviation. The comparison of the mean between two groups was performed by trans-Anethole (19), Oleanic acid (20), Quercetin dihydrate (21), Physcion 8-β-D-glucoside (22), Hyperoside (23), 3-Hydroxy-4-methoxybenzoic acid (24), Luteolin (25), Formononetin (26) and Prunetin (27). The chromatogram of the compounds obtained is shown in HPLC-Q-TOF-MS was used to analyze the constituents of HZQMY, and a total of 27 compounds were identified by comparing databases and literatures, which are Forsythoside E (1), Heteroclitin D (2), Quercetin (3), Forsythoside (4), Pinoresinol 4-O-β-D-glucopyranoside (5), Quercitrin (6), Forsyithin (7), Arctiin (8), Polydatin (9), Liquiritin (10), Apigenin (11), Isorhamnetin (12), Calycosin-7-O-glucoside (13), Kaempferol (14), Afromosin (RG) (15), (-)-Catechin hydrate (16), Genistein (17), Eleutheroside A (18), As shown in 2/M phase accounted for a higher proportion in the HZQMY group than in the HG group  can be used for quantitative comparison. As shown in HZQMY regulated the proteins of P38 and NF-κB signaling pathway to inhibit proliferation of HRCECs.To explore the mechanism of inhibitory effect of HZQMY on the proliferation of HRCECs in high glucose environment, we studied its mitochondrial apoptosis pathway. The decreased MMP, ROS and ATP levels proved that mitochondrial function was impaired.As we all know, P38 is a classic pathway of apoptosis. In our study, P-P38 could be upregulated by HZQMY, and BCL-2 and BCL-XL could be downregulated by it, as shown in p value <0.05 and quantification fold changes >1.5. After analyze, the two groups between the HG-HRCECs group and the HZQMY-100 group have 852 differentially expression proteins, of which 122 were upregulated and 730 were downregulated. To find the potential target proteins of HZQMY on HRCECs, differentially expression proteins were compared using GO and KEGG analysis.The screening conditions of differentially expression proteins were set as GO analysis of differentially expression proteins mainly includes three aspects: biological processes, molecular functions and cellular component. As shown in The result of KEGG enrichment analysis showed that differentially expression proteins were significantly enriched in apoptosis, HIF-1 signaling pathway and AMPK signaling pathway . The disThe String database is a database of predicted functional correlations between proteins. The species was selected in the String database for analysis of the differentially expression proteins to obtain the interaction relationship between them. Top 25 proteins with connectivity degree were selected and the interaction network diagram was drawn . Top fivProliferative diabetic retinopathy (PDR) is the late manifestation of DR, which is one of the common serious complications of diabetic eyes, and is an important cause of vision loss and even blindness in patients . In the Traditional Chinese medicine (TCM) in the treatment of diabetic retinopathy has its unique advantages of safety, good efficacy and less side effects. Although TCM is used as a supplement and alternative medicine in many developed countries, more than 70 percent of people take it to prevent and treat diseases in China . HZQMY hin vitro tubulogenesis experiment was carried out to observe the effect of HZQMY on HRCECs exposed to high glucose. As a result, tube formation of the HRCECs could be inhibited by HZQMY. In addition, we also found that HZQMY could increase the apoptosis of HRCECs, which is displayed as accelerating the nuclear pyknosis and cell late apoptosis.In the present study, we demonstrated that high glucose increased cell viability and proliferation, while HZQMY could reverse these phenomena. What’s more, since angiogenesis is a property of endothelial cells, the 2/M phase accounted for a higher proportion in the HZQMY group than in the HG group, indicating that HZQMY could regulate P38 and NF-κB pathway and interfere with the cell cycle, thereby inhibiting cell proliferation and promoting apoptosis (We further explored the mechanism of action on the inhibition of cell proliferation and induction of cell apoptosis by HZQMY. The mitochondrial pathway is the most common apoptotic one . HZQMY wpoptosis .With the rapid development of proteomics and the establishment of bioinformatics theory, problems such as low detection sensitivity and few reliable evaluation indexes for clarifying the mechanism of action of numerous active compounds from Chinese medicines have been gradually solved, and a new strategy has been provided for the research on the target of these compounds in the treatment of diseases. KEGG analysis showed that the differentially expression proteins were mainly enriched in apoptosis, and the signaling pathways involved are most likely to be HIF-1. What’s more, we also screened out 5 most likely target proteins through PPI analysis, which were HSPA4, MAPK3, ENO1, EEF2 and ERPS. High glucose combined with palmitate can regulate the expression of HSPA4 . MAPK3 . In diabIn conclusion, HZQMY inhibited the proliferation and promoted the mitochondria related apoptosis of HRCECs under high glucose exposure possibly by regulating P38 and NF-κB pathway. The target proteins need to be verified further.


Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study is the first to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015). For this purpose, we employed supervised learning in a classification problem and computed Pearson's correlations and intraclass correlation coefficients on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages. Internet of Things made online communication vital for our lives in society. As the repository of psychologically relevant written language expanded massively at an accelerating pace, opening new possibilities for social science research worldwide, a pressure to automatize content analysis also arose  began in the early 1990s. Since then, two revised versions have been launched: LIWC2001  and the highest number until then. In the following years, the ascending trend in using LIWC dictionaries for impactful research continued, peaking in 2020 with 124 papers indexed in Web of Science, after a similar number was reached in 2019 . So far, scientists have used LIWC2015 for assessment in clinical psychology and psychotherapy , parts of speech , psychological constructs , various contents , informal language , and punctuation—for a comprehensive description of the LIWC2015 categories, see Pennebaker et al.  and function word categories . The former measure the discussed topics, whereas the latter help researchers understand how people approach their inner and outer experiences. In this regard, the brain area responsible for the content words is the temporal lobe, which deals with sensory input, emotions, and declarative memory  and strong (irregular), the last indicating the tense by an internal vowel change —or having a number of unique words in the vocabulary , changing their word-class  but without diacritics. Thus, some differences in the more linguistically specific grammar-related frequencies that the LIWC2015 software produces might vary from one dictionary version to another. For instance, previous research comparing the English LIWC2007 with the Serbian translation has already revealed such tendencies  might end up being more similar to the original version in terms of derived word frequencies than those referring to the morphological particularities of each language . For instance, English uses no article for generic reference, which is not the case with other European languages—e.g., English ch Swan, . Likewisper se, since the frequency of the dictionary words found in the input data determines the language analysis outcomes. In this regard, the words that frequently appear in the input text weigh more than the others, while some words, although correctly translated and adapted to the new language, might not even count in the analysis. Thus, ideally, the new dictionary should include words with frequencies similar to those of the English LIWC2015 words, which is an unfeasible task. In this vein, developing a perfectly equivalent LIWC2015 adaptation is not possibleIn a nutshell, the discussion on translation and adaptation barriers and solutions is ample, differs from one language to another, and would not fit in a single non-theoretical article. Moreover, obtaining valid LIWC2015 dictionaries in other languages is not limited to the translation and adaptation process Therefore, several important questions arise: “To what extent does the translation of LIWC alter the original instrument—especially in the case of languages that differ significantly from English?”; “To what extent can we rely on different linguistic versions of LIWC as one instrument in cross-cultural studies to validly address research hypotheses concerning psychosocial phenomena?.”Previous work has focused on validating a new LIWC translation against the English version. The usual procedure is to analyze corpora available in English and the targeted language and then confront the results. The correlational approach revealed high similarities between the English version of the dictionary and specific-language dictionaries, as happened with the Spanish LIWC2001  examining the equivalence issues of the English, Dutch, Brazilian Portuguese, and Romanian versions of LIWC2015 when disregarding language specificities; (2) testing the efficacy of an accessible corrective solution for the expected equivalence issues—i.e., within-language standardization. Additionally, we check whether excluding the grammar and informal language categories, which are more linguistically specific, improves the resemblance of the four LIWC2015 language versions.To reach the first goal, we will use two approaches for data analysis: a machine learning-based strategy that would provide a view on the quantitative differences between the four dictionaries and a correlational-based method that would allow us to analyze the extent to which the four instruments acquire similar trends in homologous corpora and demonstrate “inter-rater” reliability. Given the previous studies that revealed strong correlations between the word percentages obtained with the English LIWC2015 and those computed with a LIWC2015 translation  as if the data were acquired with the same LIWC2015 instrument from a monolingual corpus.-s or -en at the word ending, as in English , and the tenses of the verbs have more complicated structures than in English and Dutch —for thorough explanations of the Brazilian Portuguese particularities, see Whitlam . Then we will retrain the classification model and discuss its performance in distinguishing between the four languages. We expect our data to support the hypothesis that the four LIWC2015 translations produce similar results, even in this comparative framework, as an effect of within-language standardization.In line with our second research goal, following the recommendations of Meier et al.  for multI-statements and depression in a multilingual, cross-cultural study. In this example, the data structure is hierarchical: the first-person pronoun use extracted with LIWC2015 and the depression scores of people with different linguistic backgrounds are Level 1 variables; the participants' language  represents the Level 2 variable. Within-language standardization rescales the values of the Level 1 predictor  around the mean of each linguistic cluster to where the participants belong. On the other hand, grand mean standardization rescales the values of the predictor around the mean of the entire cross-cultural sample. The scores resulted from both standardization strategies have the proprieties of a normal distribution with the mean equal to zero and a standard deviation of one. However, within-language standardization alters the participants' within-cluster rank order compared to the raw data—whereas grand mean standardization not. In other words, the advantage of within-language standardization is that it repositions the I-statement percentages by having as reference points the other cases in the same language cluster. The uncentered scores or those centered or standardized around the grand mean produce unclear relationships between the person/text-level variables —because of the mixture of within- and between-language variations. In contrast, within-language standardization would provide outcomes uncorrelated with the language variable (Level 2), leading to a purer estimate of the Level 1 associations , which includes between-dictionaries differences and language particularities, would bias the outcomes. Thus, within-language standardization appears as a promising solution for handling multilingual data even when the languages, and implicitly the word percentages determined with distinct LIWC2015 versions, differ. Having quantitative between-language differences in the research corpus might not be uncommon because every language has its syntax and pragmatics specificities. For instance, a text translated from English to a Slavic language tends to have 20% fewer function words  the English, Dutch, Brazilian Portuguese, and Romanian transcripts of all-time conference talks held in English. TED hosts several types of events—e.g., TEDWomen, TEDSummit, independently organized local events, etc.—but we gathered only the transcripts of the talks labeled on the website as TED Conference. Then, we filtered out the talks for which the transcripts were not available in all languages. The data collection phase ended in July 2019. The final dataset comprised 7,012 transcripts . Each corpus was analyzed with the LIWC2015 version of the corresponding language.To create a multilingual setting, we automatically saved from the official TED website  meet two or more times a year to share meaningful, innovative ideas from their area of expertise. The talks have been recorded in audio-video format and made available online for free. The oldest records date from June 2006 and, considering that each meeting usually is a marathon of more than 50 talks, the repository accumulated to the present moment is quite large. Moreover, the TED website has a neat, well-organized structure, which facilitates automatic data collection. Thus, the multitude of TED data stored so far has become a valuable research resource, especially for language analysis, given that each talk has downloadable subtitles in multiple languages.https://www.ted.com/participate/translate). For instance, the applicants for a TED translator position must be fluent in both source and target languages, pass an onboarding application quiz and learn all TED guidelines and best practices. The translated texts are reviewed by experienced volunteers who also provide feedback to the less-experienced community members. The TED Translators team also includes supervisors—volunteers with expert language skills and advanced knowledge in TED guidelines—who give supplementary advice and support. In terms of guidelines, TED requires the translators to choose informal over formal terms, preserve the speaker's tone and energy rather than do literal translations, find expressions similar to the original ones but natural in the target language, or favor modern over traditional terms, to name a few illustrative rules that shape the style and contents of the transcripts.Furthermore, TED requests its volunteer translators meet several competency and quality standards  with a linear kernel as a multiclass classifier. The rationale for restricting the SVM input to two dimensions was to obtain a graphical visualization of how the dictionaries positioned one relative to another based on the SVM performance.standardization, we mean the basic statistical procedure of subtracting the sample mean from each observation and dividing by the standard deviation. We used two standardization methods—grand mean standardization and within-language standardization—that are different only in terms of the reference parameters for rescaling the values: (1) whole-dataset mean and standard deviation; (2) subsample mean and standard deviation. Moreover, we considered two sets of LIWC2015 variables for each standardization scenario, as explained in the following paragraph. Thus, the machine learning-based approach was repeated four times.This classification approach for comparing the four LIWC2015 dictionaries was implemented in two main research scenarios defined by what standardization method we used to prepare the data for multilingual analysis. As a general rule, by pronouns, other function words, other grammar, and informal language ones). Third, we reintroduced all LIWC2015 categories, standardized the data at the level of each subsample , and proceeded with the PCA and SVM. Fourth, we selected only the LIWC2015 content categories, applied within-language standardization, and rerun the machine learning algorithm for the last time.First, we included all LIWC2015 categories and standardized the data without considering the language of the transcripts (grand mean standardization) before applying the principal component analysis (PCA). Second, we also used grand mean standardization but selected only the content categories in the dictionary  but between variables that have the same metric and variance—i.e., variable that are part of the same class  is high enough to support between-dictionary agreement. To test this question, we applied a two-way mixed model for consistency and single scores.Then, we determined the intraclass correlation coefficients (ICC), which, as opposed to Pearson's correlation coefficients, show the relationship not between different variables  poor accuracy in determining the language of the transcripts in the classification framework, as reflected by four parameters: sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC); (2) high between-language Pearson's correlation coefficients for most categories; (3) good or excellent agreement as reflected by the ICCs. Specifically, to assess the between-dictionary equivalence based on the classification accuracy, we relied mainly on the AUCs and considered as a benchmark the 0.70 value, which is commonly used to discriminate between low and moderate accuracy  was more complicated in the absence of a generally approved standard for testing LIWC2015 dictionaries. Thus, the first hand reference value for appreciating whether the dictionaries produced similar outcomes was r et al.  to validr et al.  to test and Boot  obtainedand Boot , and theand Boot  for the Regarding the ICC-based criterion, establishing what value should be decisive for assessing the between-dictionary equivalence is similar to choosing a benchmark for Pearson's correlation. However, to interpret our findings, we relied mainly on the usual recommendations for clinical research and aimed at ICCs of at least 0.75 as indicators of good between-dictionary agreement, with ICCs > 0.90 showing excellent reliability Portney, . AccordiThe mean number of linguistic units counted with the LIWC2015 software ranged between 1,792.31 in the Romanian corpus and 1,980.93 for the English transcripts. The four dictionaries labeled roughly the same percentage of words, as shown in Analysis subsection. Before the dimensionality reduction procedure, we randomly divided the 7,012 transcripts into two parts: 75% of the data points were assigned to the training subset and the rest to the test subset. Next, we used the datasets containing the variable loadings on the two principal components and the language labels for fitting and assessing the classification model.We performed a principal component analysis in all classification scenarios to reduce the datasets from 62 to two independent variables for classification, as discussed in the 1 column of Table 5), with one exception—the classifier showed poor accuracy in distinguishing between the English and Dutch transcripts (AUC = 0.65).The accuracy parameters indicated that overall, the four LIWC2015 tools led to different results when the language specificities were disregarded  than when they were considered . In the grand mean standardization scenario, there were little signs of between-dictionary equivalence. Thus, while we obtained pretty low discrimination indices between English and Dutch transcripts, the Brazilian Portuguese and Romanian transcripts dramatically distanced one from another and the other corpora. The confusion matrices for all analysis conditions are presented in 2 values in Table 5). For instance, when we considered all LIWC2015 categories, the SVM tended to misclassify many English, Dutch, and Brazilian Portuguese transcripts as Romanian (low specificity for the Romanian transcripts). Overall, these results suggest that when language specificities were taken into account, the four tools showed a high level of equivalence. The AUC values ranged between 0.47 and 0.53 when all LIWC2015 categories were inputs in the analysis and between 0.49 and 0.50 when we excluded the ones focused on grammar and informal language.When we applied within-language standardization, the model had great difficulty identifying the correct language of the transcripts  for 83.87% of the pairwise associations across languages (312 out of 372 pairs). Thus, in general, for a high number of LIWC2015 components, the variance in the word percentages computed with one dictionary explained at least 25% of the variability in the outcomes obtained with another dictionary for corresponding linguistic contents. The most troublesome features for multilingual analysis were several grammar categories , the reward category, and the informal language categories except agreement. On the other hand, 167 out of 372 pairwise associations attained the stricter criterion of r ≥ 0.72, most of them, 44, belonging to the Dutch–English pair, followed by the Romanian–English pair with 33 effect sizes this large. The LIWC2015 categories that showed very high equivalence (r ≥ 0.72) across all language pairs were mainly of the content-type ones . Of the grammar-type categories, the I-statements and negations also demonstrated very strong associations across all language pairs. The first-person plural pronouns and the words related to numbers were also close to this performance.The Pearson's correlation coefficients in ICC = 0.63), with a lower consistency for the grammar and informal language categories (mean ICC = 0.54) than for the content-focused ones (mean ICC = 0.69). However, given the high number of LIWC2015 components and that we had more than two instruments, assessing the between-dictionary equivalence would require more fine-grained interpretations. Only 11 categories out of 62 did not have sufficient reliability for multilingual analysis (ICC < 0.50), at least not on our TED-talks dataset. Most of them were grammar categories—i.e., she/he, auxiliary verbs, adverbs, conjunctions, adjectives—indicating possible language specificities in the input texts, the composition of the dictionaries, or both. For instance, the mean percentages of she/he words and adverbs computed with the Brazilian Portuguese dictionary were significantly higher than the percentages detected with the other dictionaries, which means that the Brazilian Portuguese dictionary was probably the cause for the low ICCs on those two categories. Likewise, in terms of mean percentages, the Romanian dictionary showed the highest inconsistency with the other LIWC2015 tools and might have been the primary source of disagreement for auxiliary verbs and adjectives. The informal language categories demonstrated very low ICCs, too. The reason for this might have been the low variance across the transcripts—not necessarily some potential linguistic particularities—given the nature of the TED conferences . The poor reliability for the power and reward categories might have come from the differences in word coverage, lack of variance across transcripts, linguistic or cultural particularities that affected the construction of the dictionaries, or any other less evident reason.According to the mean ICC value across all LIWC2015 categories, the four dictionaries showed moderate agreement  as the upper bound of the confidence interval, and 11 other variables had near good ICCs  at the right end of the confidence interval. Among the last ones were three function words: first-person plural pronouns, second-person pronouns, and verbs. Thus, we might assume that on a different TED-talks sample, the four LIWC2015 dictionaries might achieve good reliability even on more linguistic variables. All the other undiscussed LIWC2015 categories reflected moderate between-dictionary agreement —for more details, see On the other hand, 16 LIWC2015 variables revealed good between-dictionary agreement . Consistently with the Pearson's correlation results, they mainly were categories with a clear content focus: In the current study, we aimed to assess the validity of LIWC2015 in a multilingual setting. Previous work has focused solely on the equivalence between a new LIWC language version and the original English dictionary, but testing how similar LIWC translations are one to each other has been overlooked. Multilingual analysis can enable exciting cross-cultural inquiries applicable to a wide range of research problems. However, it raises significant challenges due to the particularities of different languages that can easily affect the insights. Thus, a resounding question of how suitable is LIWC2015 for multilingual analysis arises. We addressed it in two ways: using a classification problem and a correlational framework of two types  on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian.The classification approach indicated that the Brazilian Portuguese and Romanian LIWC2015 departed significantly from the Dutch and English dictionaries when we did not apply within-language standardization. The results obtained with the Dutch LIWC2015 tended to mix with those extracted with the English LIWC2015, suggesting more similarity between the two tools. In this regard, Brazilian Portuguese and Romanian are Romance languages, whereas Dutch and English are Germanic languages. This fact might explain why the Brazilian Portuguese and Romanian classes distanced so much from the English-Dutch cluster. Modern Dutch is a typical Germanic language and, from a linguistics perspective, differs very little from modern English in terms of inflectional and derivational aspects Simpson,  and sharMoreover, Dutch has simpler rules for constructing the verb tenses than the Romanian and Brazilian Portuguese. Likewise, Romanian and Brazilian Portuguese have in common some grammar peculiarities, such as more different words for the same parts of speech  or words that transition to different parts of speech without changing their initial form , indicating that the grammar and informal language particularities might not be the only cause for concern when multiple LIWC2015 translations are used for assessment. However, within-language standardization emerged as a viable solution to alleviate the between-language specificities that could threaten the validity of the quantitative comparisons in a multilingual setting.power and reward categories, and informal language. This disagreement once again signaled potential language specificities in the input texts, the composition of the dictionaries, or both, and was attributable not necessarily to a general inconsistency across the four dictionaries.The correlation analyses provided evidence to support the equivalence between the four dictionaries. The pairwise Pearson's correlation coefficients revealed that, overall, the English, Dutch, Brazilian Portuguese, and Romanian LIWC2015 tended to capture similar content variations from one transcript to another, as expected based on previous research concerning the similarity between the English LIWC2015 and different adaptations. There were a few grammatical categories that showed statistically significant but lower correlations. The ICC-based reliability analysis indicated that the four instruments tended to agree on most LIWC2015 categories. Specifically, ICCs revealed significant between-dictionary inconsistency  only for several grammar features, the she/he words, leading to linguistic analysis results inconsistent with those produced with other dictionaries. The categories that reflected a good or almost good between-dictionary agreement referred to less linguistically specific function words  or to clear psychological- or other-type contents . For approximately half of the categories, the inter-dictionary reliability was moderate. However, these lower ICCs might also be favorable enough to signal equivalence, depending on the benchmark used for interpreting the values. The cut-point that we used for good agreement (ICC ≥ 0.75) is specific to clinical research, where the diagnostic decisions usually have more serious implications than in some types of psychological research.For instance, the outcomes obtained with the Brazilian Portuguese LIWC2015 stood out from the other three languages in terms of third-person singular pronouns, which could explain the low ICC. Furthermore, this result might be consistent with the tendency to repeat the subject in Brazilian Portuguese, which probably creates different linguistic patterns in the usage of Together, the results of our classification and correlational approaches sustain the recommendation of standardizing or centering the data for each language subsample before performing a comparative analysis in a multilingual setting, as Meier et al.  suggesteI-statements, after controlling for narcissism?”). Likewise, the multilevel approach could be used when both Level 1 and Level 2 predictors are part of the central research questions, mainly when text input is available from a significant number of languages.However, performing within-language standardization is more appropriate for some research questions than for others. For instance, if someone is gathering bilingual data to address a Level 1 question , within-language standardization is suitable to avoid the lack of equivalence between LIWC2015 tools. Conversely, such a corrective strategy would be less effective when the research aims at Level 2 questions . Moreover, our findings align with previous research and prove that within-language standardization is an efficient solution to attenuate the language particularities that could affect hypothesis testing in a multilingual setting. However, the present study contains several limitations that leave room for improvement and future quest.One important shortcoming stems from previous research showing that the communication context could produce variation in the frequency of both content and function words, which could impact the equivalence statistics for different LIWC2015 dictionaries . Therefore, the language analysis results obtained on TED transcripts might change on other datasets, given that some words are more suitable in some contexts than in others and that some underlying psychological mechanisms might shape the verbal behavior. In the same line of thought, notably, in any closed-vocabulary approach analysis, some highly frequent words used in the input text determine the outcomes for each dictionary category and, implicitly, the between-dictionary equivalence statistics or the relations to other variables. Hence, our findings might not necessarily refer to each LIWC2015 tool as a whole but only to parts of the dictionary, depending on what words the TED speakers and translators used.https://www.ted.com/participate/translate/guidelines). Such instructions might increase the quality of the translations in terms of how representative is the translation language for native speakers. However, we might argue that, at the same time, they could leave room for subjective interpretations and personal biases in the translation process, which could further lead to artificial differences between the translations of the same transcripts. Although the study of Meier et al.  affects the equivalence of tools like LIWC2015 emerges as a valid question suggesting that our findings might require even more nuances. For instance, the high equivalence between the English and Dutch LIWC2015 and the lower agreement between the English and the other two dictionaries in our study could be explained by the fact that the Dutch tool was a more precise translation of the original version, not necessarily because Dutch and English are similar languages. Likewise, lower equivalence between the English dictionary and a new version like the Romanian one, which includes synonyms and grammar adaptations, does not directly reflect the quality of content analysis on that particular language.per se could become inconsistent with the participants' psychological reality . However, studies such as the one conducted by Araújo et al.  and then analyzing it with the original LIWC2015 or other available and valid adaptation instead of translating the dictionary in another language. Such an approach could raise reasonable doubts considering that the dataset could suffer severe changes due to translation. Thus, the verbal behavior o et al.  on 14 too et al. , using sLIWC2015 is a valuable tool for multilingual analysis. However, special care is needed when the aim is to compare or classify contents extracted with multiple LIWC2015 versions or to address person-level questions using data with a hierarchical structure. Disregarding language specificities might lead to biased results, given that between-group differences may occur due to the differences between LIWC translations and between languages themselves. A viable solution to this problem is employing multilevel analysis with language as the Level 2 variable. A competitive alternative, primarily when the research focuses on linking Level 1 variables, is to perform within-language standardization on the LIWC2015 data before investigating the main research questions. This practice reduces the challenges posed by the language particularities.10.17605/OSF.IO/6TN9K.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Open Science Framework (OSF), doi: DPD generated the original ideas, gathered the data, conducted the analyses, and wrote the first draft of all the article sections. FAS refined the original ideas, gave feedback, and helped DPD edit the final draft of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Complex patterns of collective behaviour may emerge through self-organization, from local interactions among individuals in a group. To understand what behavioural rules underlie these patterns, computational models are often necessary. These rules have not yet been systematically studied for bird flocks under predation. Here, we study airborne flocks of homing pigeons attacked by a robotic falcon, combining empirical data with a species-specific computational model of collective escape. By analysing GPS trajectories of flocking individuals, we identify two new patterns of collective escape: early splits and collective turns, occurring even at large distances from the predator. To examine their formation, we extend an agent-based model of pigeons with a ‘discrete’ escape manoeuvre by a single initiator, namely a sudden turn interrupting the continuous coordinated motion of the group. Both splits and collective turns emerge from this rule. Their relative frequency depends on the angular velocity and position of the initiator in the flock: sharp turns by individuals at the periphery lead to more splits than collective turns. We confirm this association in the empirical data. Our study highlights the importance of discrete and uncoordinated manoeuvres in the collective escape of bird flocks and advocates the systematic study of their patterns across species. TheseComputational models are valuable for understanding the processes that underlie such complex patterns –17. ThesSturnus vulgaris) coordinate with their seven closest neighbours  with their mating partner and three other topological neighbours [Chaetura pelagica) with all individuals within a specific distance , jackdaighbours ,36 and chbours’, ). The nuhbours’, ,37. Morehbours’, ,38. Becahbours’, ,39–43. Shbours’, .Columba livia) [Whether patterns of collective escape differ among species is not yet known; given the unpredictability and rarity of predator attacks, as well as the difficulty of simultaneously tracking both predator and prey, studying collective escape in the field is challenging. Complete trajectories of airborne birds in flocks under predation have only recently been collected with a newly developed, remotely controlled robotic-falcon  attackia livia) . In the a livia)  with a sa livia) ) to invePatterns of collective escape in bird flocks have been extensively described only for large flocks of European starlings   and are known to underlie escape waves in other species (e.g. U-turns in fish) [Agitation waves emerged in a computational model of starlings only through a fixed escape manoeuvre, which was copied by close-by neighbours and thus propagated through the group . The manin fish) ,56–58. Ein fish) .To generate the early splits and collective turns (at large distance from the predator) that we see in pigeon flocks, we extend an agent-based model of pigeons' collective escape ,47,61 wi.We show that both splitting and collective turning emerge in our model from a single escape manoeuvre. Based on theories of turning propagation ,64,65 an. 2. 2.1et al. [We analysed the GPS data of flocks of homing pigeons, collected by Sankey et al. . The datet al.  for a spet al. ) from 43et al. .Falco peregrinus) in both appearance and motion [et al. [More specifically, the RobotFalcon resembled a male peregrine falcon  and noted two patterns: collective turns and splits. To further study these patterns, we followed a quantitative approach.−1, black lines in figure 2b3 and electronic supplementary material, figure S1). For each flight, we calculated the rate of turning (turns per second), the turning angle per second, and the proportion of time spent turning . We tested whether these measurements differ between flights with and without the RobotFalcon, and ensured that our angular velocity threshold does not alter our conclusions .We quantified collective turning by first building the trajectory of each flock from the average position of all flock members across each flight. We then divided the trajectory in intervals in which the flock's angular velocity had the same sign (continuous turning to the left or to the right). Over each interval, we measured the cumulative change of heading. Small changes of heading were considered proxies for straight motion of the flock . We cal. 2.2We extended our agent-based model of collective escape of homing pigeons, HoPE (Homing Pigeons Escape)  to study‘pigeon-oids’, after Reynolds [‘pred-oid’) that pursues them. Agents collect and update information about their environment (neighbours and predator) asynchronously at a given reaction rate, similar to previous realistic models of collective behaviour [HoPE consists of multiple pigeon-agents (referred to as Reynolds ) that coehaviour ,72. All . 2.2.1‘forces’ that represent these rules and has been adjusted to the specifics of flocking by pigeons [Flocking in HoPE is based on the rules of attraction, alignment and avoidance among topological neighbours ,30,43. C pigeons ,24. Each pigeons ,47 and a pigeons . Alignme pigeons ,73, e.g. pigeons ,43). Ali pigeons ,46. Each pigeons .. 2.2.2et al. [‘Catches’ of prey by the predator are not modelled.We modelled our predator-agent to resemble the motion of the RobotFalcon in the field experiment of Sankey et al. . The pre. 2.2.3In the original model , splittib and electronic supplementary material, figure S2). Following previous empirical findings, each flock member has a unique probability to manoeuvre based on (i) a unique baseline tendency to escape [Specifically, the discrete escape manoeuvre in our model involves a turn away from the heading of the predator  during mfigure 2o escape , and , simulating flocks of the same size as the empirical data . At each time step in the simulation, the larger flock was considered the sistency ). Result‘initiator’  causes, and subsequently to examine how the relative frequency of these patterns depends on the characteristics of the initiator. In detail, we study the initiator's position in the flock based on its ‘centrality’, i.e. an individual's spatial position relative to all its flock-mates . We further make the centrality of individuals comparable between members of small and large flocks by dividing it by the average centrality of all individuals in small and large flocks, respectively. Individuals with centrality less than the median of the distribution across all individuals and flights are characterized as ‘central’, opposite to individuals with higher centrality labelled as ‘edge’. Secondly, the direction of escape is labelled as ‘inwards’ or ‘outwards’ depending on whether the escape turn is towards the flock's centre  or away from it. We measured these initiator's traits at the beginning of each escape manoeuvre. Thirdly, we get the initiator's angular velocity based on the total change of heading during the manoeuvre divided by its duration, and we label as ‘low’ and ‘high’ turns with angular velocities higher and lower than the median, respectively. We studied the rate of the two collective patterns as the frequency of their appearance relative to the total number of escape manoeuvres. We confined ourselves to patterns generated in the main flock.The focus of our analysis was to first determine which pattern of collective escape an . 2.3‘collective turns’, during which the individual remains part of the flock, and (ii) ‘splitting turns’, during which the individual splits off from it. Note that here we focus on splitting individuals and not on identifying initiators of collective turns; we treat each individual's turn while being part of a flock as absence of splitting from it. As a measurement of centrality, we use the distance of each individual to the centre of the flock divided by the mean centrality of small or large flocks . We constructed generalized linear mixed models (GLMMs) on the binomial variable ‘turning type’ , and with fixed variables being the angular velocity and centrality of each individual at the beginning of each turn [To test the validity of our theoretical findings for real flocks of pigeons, we studied in the empirical data the association between the turning characteristics of individuals and the collective pattern when attacked by the RobotFalcon . Using tach turn ,78. Give. 2.4Our computational model is built with the programming language C++ 17, using OpenGL for visualizing the simulations. All data analyses and statistical modelling were performed in R (v. 3.6)  and all . 3. 3.1We identified two patterns of collective escape in the empirical data: the collective turn and the split . In detaThe rate of each pattern is significantly higher in the presence of the RobotFalcon than in its absence . Small f. 3.2The flocking model HoPE resembles real flocks of pigeons in several characteristics, namely their distributions of individual speed, nearest neighbour distance and relative position of neighbours . The relative frequency of collective turns and splits does not significantly differ between outward and inward manoeuvres  leads to a higher probability to split off from the group . Centrality does not show a significant effect on turning type . However, if outliers above the 90% quantile of distance to the centre of the flock are filtered out , centrality is positively associated with splitting , especially in close distances to the predator . For the validity of our fitted models see electronic supplementary material, figure S8, S9.We tested in the empirical data the hypothesis based on our modelling results : individ. 4To gain insight in what underlies patterns of collective escape in homing pigeons, we combined GPS data of flocks under attack by the RobotFalcon  with a s‘costly’  to perform and has been proven successful in the past [.Several factors may increase the angular velocity of a bird's turn in nature; first, past and present predatory risk , closer the past . Prey mathe past ,85,87. Tthe past  or depenthe past ). In ourThe occurrence of a split or a collective turn may also depend on how quickly a turn propagates through the group. The speed of this propagation (information transfer concerning the turning direction) may be heavily affected by the specifics of coordination. These specifics differ across species, for instance whether interactions are topological (with fixed number of closest neighbours) or metric , and the relative importance of alignment, attraction and repulsion ,36,37,46The position of initiators of collective turns in airborne flocks varies across bird species and ecological contexts. During roosting, mobbing and transit flights ,37,65, sThere may be several reasons that pigeon flocks turn away from a predator at large distances to it. Pigeons often form small flocks with low density. Therefore, their flocks may not confuse the predator enough to decrease its hunting success (no confusion effect ), and inThe role of uncoordinated escape by group members during collective motion has not yet been studied. Uncoordinated turning by an initiator, as implemented in our model, causes a collective turn to be larger, given that it is not a compromise between the headings of the initiator and its neighbours ,97. ThisThe consequences of a split for the survival of the prey and the success of the predator may depend on the strategy of both. The success of hunting by predators is higher when the flock is smaller and when attacking singletons, rather than flocks ,100. FroIn pigeon flocks, we identified fewer patterns of collective escape (splits and collective turns) than have been described for other species ,49. ThisThe rules of individual escape that were used in previous models did not suffice to produce splits at large distances to the predator and collective turns ,52. MostFuture work on empirical data of airborne flocks under attack should focus on individual escape reactions, the number and relative position of initiators of turns and information transfer in relation to flock shape and size ,90,106.


Although simvastatin has been shown to inhibit vascular permeability, which might be amplified via gap junction intercellular communication (GJIC), the underlying mechanism of action remains unclear. In the present study, we investigated the effects and mechanisms of simvastatin on endothelial cells GJIC. Specifically, human umbilical vein endothelial cells (HUVECs) were stimulated with TNF-α (10 ng/mL) alone or in combination with simvastatin (5 µM), and their effects on vascular endothelial cell GJIC tested via the scrape loading/dye transfer (SL/DT) assay. Next, we performed immunofluorescence, real-time PCR and western blot assays to analyze expression of Cx37, Cx40 and Cx43 in HUVECs. Results showed that GJIC activity in HUVECs was markedly elevated in HUVECs treated with TNF-α in combination with simvastatin. In addition, simvastatin treatment significantly upregulated expression of Cx37 and Cx40 but downregulated Cx43 mRNAs and proteins. Taken together, these marked changes indicated that simvastatin exerts its regulatory effects on gap junction function by upregulating Cx37 and Cx40 and downregulating Cx43 expression. Previous studies have shown that endothelial permeability increases and many inflammatory mediators contribute to elevated vascular permeability during early stages of atherosclerosis development Lee . Moreovein vitro endothelial cell model. Specifically, HUVECs were stimulated using TNF-α (10 ng/mL) alone or in combination with 5 µM simvastatin, and their effects on vascular endothelial cell GJIC tested via the scrape loading/dye transfer (SL/DT) assay. In addition, patterns of Cx37, Cx40 and Cx43 expression in HUVECs were analysed via immunofluorescence, real-time PCR and western blot analyses.The purpose of the present study was to investigate the effects and underlying mechanisms of simvastatin action on endothelial cells GJIC using an 5 cells/well and grown to 80% confluence. Subsequently, the cells were cultured in fresh medium (serum-free media) supplemented with TNF-α (10 ng/mL) alone or in combination with 5 μM simvastatin and incubated for 24 h at 37°C . Eighth-passage HUVECs were seeded in 6-well plates, at a density of 4 × 10Cell morphology was observed under a white light microscope (200× magnification). The SL/DT assay was used to measure gap junctional intercellular communication (GJIC) activity, and investigate the transfer of fluorescent Lucifer yellow dye from one cell into adjacent ones through functional gap junctions as previously described  as described by Cervellati et al. . Brieflyβ-actin.Detection of protein expression was performed as described by Cervellati et al. . BrieflyP < 0.05 and **P < 0.01.All of data were statistically analyzed using SPSS 13.0 software and presented as means ± standard deviations (SD) of the mean. Differences between groups (n = 6) were analyzed using the t-test, at significance levels of *Observation of cells under white light (200× magnification) revealed a higher number of pseudopods was increased in the TNF-α treatment group compared to . On the other hand, pseudo foots were significantly lower in TNF-α combined with simvastatin treatment, relative to the group treated with TNF-α alone . ConflueResults from SL/DT assay showed that simvastatin treatment suppressed TNF-α-induced inhibition of GJIC activity in HUVECs, and affected expression of Cx37, Cx40 and Cx43 in endothelial cells. These proteins play an important role in GJIC. Immunofluorescence results showed that TNF-α treatment downregulated expression of Cx37 and Cx40 gap junctions in HUVECs relative to those in the control group. However, exposure of HUVECs simvastatin upregulated Cx37 and Cx40 expression. In addition, TNF-α treatment upregulated while exposure to simvastatin downregulated Cx43 expression in HUVECs .Figure P < 0.01) and Cx40  relative to those treated with TNF-α alone. On the other hand, Cx43 was significantly downregulated in the combination relative to the TNF-α alone group   significantly upregulated Cx37  .Figure Western blots showed that treatment of HUVECs with TNF-α for 24 h significantly downregulated expression of Cx37 (145.85 ± 10.01 vs 90.94 ± 8.58) and Cx40 (64.25 ± 4.89 vs 28.50 ± 6.54) but upregulated that of Cx43 (93.70 ± 12.36 vs 112.69 ± 7.21) proteins relative to the control group. However, these patterns of expression were reversed after simvastatin treatment. In addition, Cx37 (90.94 ± 8.58 vs 98.75 ± 7.66) and Cx40 (28.50 ± 6.54 vs 37.69 ± 9.58) proteins were significantly upregulated, while Cx43 was significantly downregulated (112.69 ± 7.21 vs 81.03 ± 5.28) in HUVECs treated with TNF-α in combination with simvastatin, relative to those treated with TNF-α alone .Figure Gap junctions, first identified in the 1960s, have been shown to play an important role in maintenance of normal cellular functions (Qu et al. Results of the SL/DT assay showed that TNF-α significantly suppressed GJIC activity, which was consistent with previous studies that have shown that TNF-α could inhibit GJIC activity in vascular endothelial cells, liver epithelial cells and human corneal fibroblasts (Kimura et al. Results from a previous study showed that simvastatin improved sensitivity of Leydig tumor cells to chemotherapeutic toxicity by enhancing gap junction functions (Wang et al. Overall, these marked changes indicate that simvastatin exerts its regulatory effects on gap junction functions by upregulating Cx37 and Cx40 but downregulating Cx43 expression. These findings may also fortify the rationale underlying the atheroprotective mechanism of simvastatin therapy. Cx43 is the most predominant gap junction protein among various types of connexins, and has been shown to play essential roles in coordinating activities of cardiovascular tissues, key among them being selective inhibition of Cx43 hemichannels by Gap19 and its impact on myocardial ischemia/reperfusion injury %J Basic Research in Cardiology (Wang et al. To gain more mechanistic insights into the underlying biological mechanism, analyzing high-throughput molecular measurements at the functional level was selected. Especially, the knowledge base-driven pathway analysis is becoming the first choice for many investigators, which mainly exploit pathway knowledge in public repositories (Du et al., Numerous studies have described simvastatin’s effect on cadherin. Notably, adhesion junctions are extensively distributed in microvessels, so few focus beneficial effect of Simvastatin on tight junction (Zapolska-Downar et al.


The ongoing severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic had brought disastrous consequences throughout the entire world. While several manufactured vaccines have been approved for emergency use, continuous efforts to generate novel vaccines are needed. In this study, we developed SARS-CoV-2 virus-like particles (VLPs) containing the full length of spike (S) glycoprotein (S full), S1, or S2 together with the influenza matrix protein 1 (M1) as a core protein. Successfully constructed VLPs expressing the S full, S1, and S2 via Sf9 cell transfections were confirmed and characterized by Western blot and transmission electron microscopy (TEM). VLP immunization in mice induced higher levels of spike protein-specific IgG and its subclasses compared to naïve control, with IgG2a being the most predominant subclass. S full and S1 immune sera elicited virus-neutralizing activities, but these were not strong enough to fully inhibit receptor–ligand binding of the SARS-CoV-2. Neutralizing activities were not observed from the S2 VLP immune sera. Overall, our findings revealed that S full or S1 containing VLPs can be developed into effective vaccines. Nidovirales, family Coronaviridae, which can be subdivided into alphacoronavirus, betacoronavirus, and gammacoronavirus based on their antigenic and genetic characteristics [Coronaviruses are single-stranded RNA viruses belonging to the order eristics . While teristics ,2. In Deeristics ,4. Sinceeristics . Clinicaeristics . Yet, eferistics . Althougeristics , continueristics .Given the current circumstances with the COVID-19 pandemic, virus-like particles (VLPs) are actively being investigated as a potential vaccine platform against the COVID-19. VLPs are highly immunogenic and considered superior compared to other traditional vaccines for several reasons. Because VLPs are completely devoid of viral genetic material required for replication, the vaccine is non-infectious and is safer than live-attenuated or whole-inactivated vaccines . NotablySeveral SARS-CoV-2 studies involving VLPs have been conducted and have reported interesting findings. Previously, Xu et al.  demonstrSpodoptera frugiperda (Sf9) insect cells cultured in serum-free SF900 II medium  were used to generate recombinant baculovirus (rBV) and VLPs as described previously [n = 5 per group). All animals were housed in an approved facility with day and night cycle, with easy access to food and water. All animal experimental procedures have been approved and conducted following the guidelines set out by Kyung Hee University IACUC (Permit number: KHSASP-20-666).eviously . Twenty Codon-optimized S full construct , S1 construct 2055 bp), and S2 construct (1764 bp) were synthesized from GenScript . S1 construct was generated by fusing the transmembrane domain (TM) and cytoplasmic tail (CT) regions from the influenza virus hemagglutinin (HA) to the 3′ terminus of the S1 gene, and S2 construct was generated by fusing a signal peptide (SP) from honeybee melittin to the 5′ terminus of the S2 gene, respectively, as described 55 bp, an. After 3The polyclonal antibodies targeting the S RBD and the S2 domains were purchased from Sino Biological  and these were used as primary antibodies for the immunocytochemistry. Immunocytochemistry using rBVs was performed as described elsewhere . BrieflyVLP constructs were characterized using Western blotting and transmission electron microscopy (TEM). Briefly, VLPs of various concentrations were separated via SDS-PAGE, and proteins were transferred to a nitrocellulose membrane. Membranes were blocked for 1 h at RT using 5% skim milk, and probed with the polyclonal antibodies purchased from Sino Biological (1:1000 dilution) overnight at 4 °C. The next day, membranes were washed three times with TBST and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:2000 dilution) at RT for 1 h. After washing with TBST, bands were developed on X-ray film in the dark room using enhanced chemiluminescence (ECL). VLPs were observed under TEM. Briefly, samples were applied to glow-discharged carbon-coated copper grids and allowed to absorb for 2 min. After blotting off excess samples with Whatman paper, sample grids were stained with 2% uranyl acetate for 1 min. After removing excess reagents, results were recorded with Bio-High voltage EM system  at Korea Basic Science Institute.n = 5 per group): naïve (unimmunized), S full VLP, S1 VLP, and S2 VLP immunized. Mice were intramuscularly immunized through the quadriceps femoris with 100 μg of VLPs at 4-week intervals. Sera were collected 1 week after each immunization. All mice were sacrificed 4 weeks after the final immunization.Mice were divided into 4 groups .S1 and S2 antigens were purchased from Sino Biological . Antigen-specific antibody responses were determined using ELISA, as described previously . Briefly2SO4 and absorbance values at 450 nm were measured. Percentage of inhibition was calculated as follows: Inhibition (%) = (1 − sample OD450/control OD450) × 100.Surrogate virus neutralization assay, which allows assessment of virus inhibition without the BSL-3 facility requirement, was performed as described previously . HRP-conp values less than 0.05 were considered significant and are denoted using an asterisk.Data sets are presented as mean ± SEM. Statistical analyses were performed using GraphPad Prism 5 software . Statistical significance between groups were determined using one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Transmembrane protein topologies were predicted using the TMHMM Server v.2.0 database. Original sequences of the full-length SARS-CoV-2 S glycoprotein, S1, and S2 domains were used for prediction A. Since Codon-optimized sequences for S full gene, HA TM-CT, and melittin signal peptides were synthesized by GenScript. Nucleotide sequences for codon-optimization were acquired from the NCBI database .Codon-optimized S full, S1, and S2 genes were transformed into pFastBac vectors. Successful cloning of the genes was confirmed through gel electrophoresis. Total gene lengths for S full, S1, S2, and pFastBac vectors were 8.7 kbp, 6.7 kbp, 6.4 kbp, and 4.8 kbp, respectively A. VectorPolyclonal antibodies targeting the S protein RBD and the S2 were assessed for their binding capability. Immunocytochemistry was performed using these polyclonal antibodies and the recombinant baculoviruses. While Sf9 cell nuclei were stained with DAPI as expected, fluorescence on the cell surfaces was not observed even when incubated with the polyclonal antibodies. Since both S full and S1 proteins contain the RBDs, the polyclonal antibody raised against the S RBD was used to confirm successful rBV expression. Polyclonal S2 antibody also reacted with the S2 protein-expressing rBVs .As illustrated, two distinct opaque bands corresponding to the VLP1 and VLP2 were formed at the interfaces between the sucrose layers A. Pre-suTo confirm proper construction of the VLPs, Western blotting and TEM analyses were conducted. Consistent with the rBV immunocytochemistry data, the polyclonal antibodies used in the study successfully interacted with the VLPs expressing S full, S1, or S2 proteins. When VLPs transferred onto the nitrocellulose membranes were probed with the polyclonal S RBD antibody, S full and S1 VLPs around 140 kDa and 74 kDa were detected which corresponds to their molecular weight. Similarly, probing S2 VLPs with the S2 polyclonal antibody resulted in bands developing around 66 kDa. Probing the VLPs with the M1 monoclonal antibody revealed that the recombinant baculoviruses expressing the spike proteins were successfully displayed on the M1 protein A. TEM anTo confirm the immunogenicity of the VLPs, 100 μg of the VLPs were intramuscularly administered in mice. At 1 week after the second immunization, blood of mice was collected and ELISA was performed to confirm successful immunization and boosting effect. All of the sera collected from each immunized group reacted against the S full VLPs and induced potent antibody response, whereas naïve sera failed to elicit significant antibody responses A. To che2 dilution (2 diluted RBD-HRP mixture were selected for sVNT assay. Sera of naïve mice did not confer any neutralizing activity as expected. Interestingly, despite the potent antibody response induced by S2 VLPs, it failed to inhibit RBD binding to the hACE2 receptor. On the contrary, VLPs possessing the S1 domain conferred partial inhibition of RBD-hACE2 binding (To confirm whether the antibody responses induced by VLPs could inhibit the viruses, sVNT was conducted. Successful conjugation of HRP to the S RBD was assessed through ELISA. Upon reaction with the TMB substrate, a potent colorimetric response was immediately developed thereby indicating that HRP conjugation to the RBD has occurred A. To chedilution B. Based  binding C. The stThe COVID-19 pandemic continues to wreak havoc across the globe and vaccines are direly needed. In this study, we generated VLP vaccines expressing the S full, S1, or S2 glycoprotein of the SARS-CoV-2 using the insect cell-based baculovirus expression system. After characterization, the immunogenicities were evaluated in mice. Our results illustrate that baculovirus-expressed VLPs displaying the S or S1 proteins are capable of eliciting antibody responses contributing to viral inhibition as demonstrated through sVNT assay. With multiple reports of emerging SARS-CoV-2 variants that include mutations in the RBD ,28, rais+ and CD8+ T cells, is important for SARS-CoV-2 patients. Reportedly, T cell exhaustion and extremely low levels of peripheral T cells were common features observed in COVID-19 patients [Our ELISA results demonstrated that the Th1-associated IgG2a was induced to a significantly greater extent than the Th2-associated IgG1, particularly from S2 VLPs, thus implying potential Th1-bias in the elicited immune response. Similar to the present findings, orally administering a yeast-based vaccine expressing the spike glycoprotein of SARS-CoV-2 elicited a mixed Th1/Th2 immune response with a Th1 bias in BALB/c mice . The prepatients . Given tpatients , robust Antibodies raised against internal structural proteins such as M1 have been reported to be incapable of neutralizing the influenza virus, which stems from their relative inaccessibility of these proteins compared to the HA or the NA surface antigens . As suchOne possible explanation for the phenomenon described above is the presence of non-neutralizing antibodies. While sera acquired from SARS-CoV-2-invected patients and several monoclonal antibodies have been demonstrated to neutralize the virus, this was not necessarily always the case. A noticeable level of non-neutralizing antibodies has been detected in the sera of infected patients, and even some monoclonal antibodies targeting the S protein have been found to be non-neutralizing from multiple neutralization assays . AdditioWhile non-neutralizing antibodies are perceived as a detriment to protection against SARS-CoV-2, this may not necessarily be the case, as demonstrated in the case of other pulmonary viruses. Non-neutralizing antibodies confer protection via antibody-mediated effector functions including the antibody-dependent cellular cytotoxicity ,46. A stIn summary, our study revealed that immunizing mice with the VLPs expressing the S glycoprotein induces S protein-specific antibody responses, with those elicited by the S2 VLP being particularly potent. While future studies investigating the efficacy of VLP vaccines expressing a diverse array of SARS-CoV-2 neutralizing epitopes would be interesting and could possibly become a promising COVID-19 therapeutic option, additional studies are warranted to elucidate the role of non-neutralizing antibodies in protection against the ongoing pandemic.