UA and NLR were reciprocally correlated and associated with cardiometabolic risk factors. During a mean follow-up period of 27 ± 20 months, 99 ± 3.6% deaths, and 213 ± 7.8% HE were registered. The Kaplan–Meier survival estimates showed significantly worse outcomes in patients with elevated UA or NLR levels. Multivariate Cox regression analysis demonstrated that NLR independently predicted CD and HE. There was no multiplicative interaction between UA and NLR; however, the use of measures of additive interaction evidenced a positive additive interaction between UA and NLR for CD and HE. Although it is clear that correlation does not imply causation, the coexistence of NRL and UA appears to have a synergistic effect, providing further information for the risk stratification of CAD patients.Oxidative stress and inflammation are key factors in cardiometabolic diseases. We set out to evaluate the relationship between serum uric acid (UA) and the neutrophil-to-lymphocyte ratio (NLR) with cardiometabolic risk factors in coronary artery disease (CAD) patients, and their additive and multiplicative interactive effects on outcomes (cardiac death/CD and hard events (HE)—death plus reinfarction). A total of 2712 patients  who underwent coronary angiography was retrospectively analyzed and categorized into no-CAD patients ( Oxidative stress and inflammation are common features of cardiovascular and metabolic diseases . Uric acLymphocytes and neutrophils play a central role in the inflammatory response. The neutrophil-to-lymphocyte ratio (NLR), a simple hemochrome-derived index practically always available as part of the general patient evaluation, is an index of systemic inflammation and immunity and is emerging as a prognostic biomarker in many diseases, including the cardiovascular diseases . NLR seeAlthough an association between hyperuricemia and NLR has been observed in cardiometabolic diseases, the combined effect of NLR and uric acid increase on cardiometabolic components and prognosis in stable coronary artery disease (CAD) is inadequately defined ,7. Thus,A total of 2712 patients (mean age (SD): 67 (11) years, 1960 males) who underwent coronary angiography at the Cardiology Department of the Institute of Clinical Physiology–CNR  was retrospectively analyzed by using the Institute’s electronic databank , collecting detailed information about the demographic, clinical, laboratory, and instrumental data of consecutive patients hospitalized from 1977 to 2011 . PatientThe inclusion criterion in the study was the absence of history or clinical or instrumental evidence of atherosclerosis in vascular districts other than the coronary bed. Exclusion criteria were any predominant non-cardiac chronic diseases  or known malignant diseases. Patients taking UA-lowering medications  were also excluded. A complete clinical history of each subject was available, including cardiovascular risk factors such as smoking history (current or past-smoking habit), hypertension (blood pressure higher than 140/90 mmHg or the use of antihypertensive drugs), diabetes (T2D: fasting glycemia >126 mg/dl or use of an antidiabetic treatment), and dyslipidemia . Patient medications included nitrates, oral aspirins, calcium antagonists, angiotensin-converting enzyme inhibitors, and diuretics.Biochemical analysis was performed at patient admission, and blood samples were drawn after an overnight fast and immediately assessed by routine automated laboratory analyzers.All patients underwent coronary angiography. The severity of CAD was determined by the number of involved main coronary arteries  with a luminal diameter narrowing of >50%, and all angiograms were reviewed by expert cardiologists. As previously described, follow-up events were collected retrospectively from at least one of the following four methods: a review of the patient’s record, telephone interview conducted by trained personnel, personal communication with the patient’s physician, or medical visit to the outpatient clinic ,11. The t-test, the chi-square test and analysis of variance (ANOVA), and the post hoc Scheffe’s test. Multivariate regression analysis was performed to identify UA- and NLR-independent determinants.Data are expressed as mean standard deviation (SD). The statistical analyses performed included the Student’s Cumulative event rates were estimated by Kaplan–Meier survival curves, and probability values were determined with the log-rank test. For survival analysis, only one event was considered in each patient. Statistical analysis also included Cox proportional hazard models to determine independent predictors of cardiovascular events (CD and HE).To assess the biological interaction between UA and NLR, the following parameters were calculated :RERI was defined as the additional risk due to interaction  on an additive scale, and it was calculated as follows:RERI = HR 11−HR 01−HR 10 + 1, where HR 11 refers to the HR for high UA and high NLR; HR 01 is the OR for low UA and high NLR, and HR 10 represents exposure to high UA and low NLR. The RERI of zero indicates no additive interaction, whereas a RERI greater than 0 indicates an additive interaction.The proportion attributable to interaction (proportion of the combined effect that is due to interaction), the AP estimate, indicated the proportion of risk attributable to the interaction of the high UA and high NLR, and it was calculated according to the following formula:AP = (HR11−1)/((HR1−1) + (HR10−1)), with AP > 0, which indicates biological interaction.SI was calculated as the ratio between the combined effect and individual effects, according to the formula: SI = RERI/HR 11. SI estimates whether a synergistic (SI > 1) or antagonistic (SI < 1) interaction exists between two exposures, where SI = 1 means no interaction.The ratio of HRs = HR 11/(HR 10 × HR 01) if equal to one indicates there is no multiplicative interaction.p-value ≤ 0.05 was considered significant.Statistical analysis was performed using the statistical package Statview, version 5.0.1 . A n = 806 no-CAD, 1545 CAD, 361 AMI, respectively) are reported in Demographic and clinical characteristics of patients according to coronary angiography results and clinical presentation , BMI , and NLR , and inversely with EF . Moreover, UA was higher in males  in patients presenting hypertension  and T2D  with respect to those without these risk factors. Moreover, UA was higher in patients presenting multivessel disease concerning mono-a vessel disease or no-vessel disease .UA levels directly correlated with aging (p < 0.001).When the number of risk factors was considered , UA levels showed a progressive increase , and an inverse correlation was found between NLR and BMI  and EF . A significant difference in NLR levels was observed between patients with and without T2D , with and without dyslipidemia , and with and without hypertension . Moreover, NLR progressively increased in patients presenting no-vessel disease when compared to those with mono-vessel disease or multivessel disease . NLR did not change with the number of cardiovascular risk factors .A positive relationship was found between NLR and age  cardiac death were registered, and 213 (7.8%) had major adverse cardiovascular events (death or reinfarction). The Kaplan–Meier survival estimates showed significantly worse outcomes in patients with elevated UA (cardiac death) or NLR levels (cardiac death and HE) .The Multivariate Cox regression model identified elevated levels of NLR as an independent determinant associated with a higher risk of death and HE during follow-up . As shown in For HE, although the contribution of high UA/low NLR was negligible, a weak positive interaction was appreciable after the combination of UA with high NLR . UA and NLR, biomarkers of oxidative stress and inflammation, showed positive interactions to increase cardiac death risk in CAD, an effect that is greater than their summation. Instead, the risk of HE is essentially due to the NLR contribution, although an interactive effect may be appreciated also for this outcome, suggesting that patients presenting elevated levels of both biomarkers may have an increased risk.The role of UA in cardiometabolic disease is still controversial and discussed. Although previously viewed as an inert metabolic end-product of purine metabolism, UA is now considered a potent antioxidant, which may play a protective role against oxidative stress ,3. HowevIt is well known that chronic inflammation plays a key role in the development of insulin resistance, type 2 diabetes, atherosclerosis, and cardiovascular disease ,18. NLR,However, to date, no studies have explored the UA, NLR, and CAD relationship together, and the additive interaction of the two biomarkers in CAD. This is an innovative point and a major strength of this research. We observed that UA and NLR are reciprocally correlated and represent an independent determinant for each other. Moreover, the use of RERI > 0, AP > 0, and S > 1 indicate a biological interaction. In particular, among the index considered, RERI was the best tool to estimate the biological interaction using a proportional hazards model . RERI rer = 0.2, p < 0.001), and UA levels were higher in patients taking when compared with those not taking diuretics . Thus, these parameters merit evaluation in future studies to better assess their role in determining UA levels and the association of this biomarker with other cardiovascular risk factors. One limitation is that it is a retrospective and single-center study, and verifying this additive interaction requires application in more populations. Moreover, there are no shared cut-offs for UA and NLR. In particular, the cut-offs used for hyperuricemia (>6 mg/dL in women and 7 mg/dL in men) are mainly based on the the saturation point; however, some data suggest that the adverse effects of hyperuricemia on cardiometabolic conditions could occur even at lower levels [The present study presents some limits. UA is known to be affected by alcohol intake, diuretics, and renal function, but information related to alcohol intake was not available in the database. The use of diuretics and creatinine was not available in all subjects; thus, these variables could not be evaluated in the multivariate analysis. Nonetheless, as expected, in the subgroups of subjects in which this data were available, there was a significant association between UA and creatinine (r levels ,35,36. Ar levels ,38,39In conclusion, although it is clear that correlation does not mean causation, the reciprocal combination of UA and NLR could participate in the onset and development of cardiometabolic alterations showing an additive interaction. More prospective population-based studies are needed in the general population and in patients with cardiovascular disease to determine whether an intervention, such as lifestyle modifications or medications that reduce UA and/or inflammation, will decrease the cardiometabolic risk and improve the outcome.


Usnic acid (UA) is one of the most abundant and common metabolites of lichens, known for its numerous pharmacological properties. Nevertheless, it presents some criticalities that severely limit its use in therapy: poor solubility in water and significant hepatotoxicity. Soluplus and Solutol HS15 and D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS) were employed to develop polymeric micelles (UA–PM). The chemical and physical properties of the system were characterized, including the size, homogeneity, zeta potential, critical micellar concentration (CMC), encapsulation efficiency (EE%), and in vitro release. The freeze-drying process was considered to prevent agglomeration and improve the stability of the formulation. The stability of the micelles and the freeze-dried product (UA–PML) was also evaluated. The anti-migratory activity of UA and UA–PM was evaluated in human neuroblastoma SH-SY5Y cells using the wound healing assay. Their effect on the activity of metalloproteinases (MMP)-2/9 involved in the migration process of cells was verified by gelatin zymography. The optimized UA–PM contained Soluplus, Solutol HS15, and TPGS in a 1:4:0.5 weight ratio and increased the aqueous solubility to about 150-fold solubilized, solubilizing 0.5 mg/mL of UA. UA–PM has a small size (45.39 ± 0.31 nm), a polydispersity index (PDI) of 0.26 ± 0.01, and an EE% of 82.13 ± 5.57%. The colloidal dispersion was stable only for 9 days at 4 °C, while the freeze-drying process improved the stability for up to 30 days. UA was released for a prolonged period during the in vitro release study. The in vitro cell-based experiments showed that UA–PM (0.2 µg/mL) inhibited SH-SY5Y cell migration and the gelatinolytic activity of MMP-2/9 in culture media, while free UA at the same concentration exerted no biological activity. This study demonstrates that polymeric micelles are an excellent formulation for UA to manifest inhibitory action on neuroblastoma cell migration. In addition to plants, lichens are a rich source of bioactive compounds and have been used since ancient times in folk medicine to treat various diseases worldwide . LichensUsnea, Cladonia, Lecanora, and Alectoria -dibenzofurandione) is an abundant metabolite commonly found in a variety of lichen species such as lectoria .This natural compound might be of great relevance for pharmacological and clinical applications because of its different biological and physiological properties. It is known for its antibacterial and antiparasitic potentials, antiviral, antioxidant, anti-inflammatory, analgesic, and UV protection activities. This molecule is of potential interest for cancer therapy due to its antimitotic and antiproliferative action ,5,6,7,8.Neuroblastoma (NB) is a fairly common extracranial solid tumor occurring at pediatric age, with a clinical course dependent on the tumor biology ,10.NB is classified as low-risk if the tumor regresses spontaneously or with conventional treatment options and as aggressive high-risk NB if the disease is refractory or recurrent with widespread metastases at the time of diagnosis . In cancIn an effort to address the disadvantages of conventional cancer therapies, natural compounds are still widely studied for their anticancer properties in order to develop new complementary therapies .The literature proposes UA as a good candidate for anticancer therapy due to its inhibitory activity of cancer cell migration ,18,19. HNano- and micro-sized colloidal carriers have been proposed as drug delivery systems to increase solubility and ameliorate the therapeutic index, improving the efficacy and reducing the hepatotoxicity of UA after systemic administration .Liposomes ,24,25, nIn this study, for the first time, polymeric micelles were proposed as a nanocarrier to increase UA aqueous solubility (UA–PM). Polymeric micelles (PM) offer numerous clinical applications, such as the protection of the encapsulated drug and the solubilization of poorly soluble drugs. Here, the polymers selected for the PM preparations were Solutol HS15, Soluplus, and D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS), three tensides with high solubilizing properties ,38,39,40The effect of UA and UA–PM on cell migration and MMP-2/9 gelatinolytic activity were evaluated in SH-SY5Y human neuroblastoma cells, thus verifying the potential enhancement of the biological activity of UA delivered into the PM.PM has a core–shell structure due to the self-assembly of the amphiphilic block copolymers when added to an aqueous solvent. They have a hydrophobic core, capable of encapsulating poorly water-soluble compounds, and an external hydrophilic portion that prolongs the retention time in the bloodstream, preventing opsonization and absorption by macrophage phagocytosis and can be functionalized for drug targeting ,45,46,47The interaction of UA and PM was also investigated by UV-Vis absorption. The UV analyses were recorded for the UA solution, UA–PM, and PM alone . The blaPM and UA–PM had similar sizes of about 50 nm. The encapsulation of UA maintained a homogeneous system, as evidenced by the PDI value. The EE% was 80.04%, indicating the high affinity of the molecule for the hydrophobic core of the micelles.TEM analysis of UA–PM confirmed the DLS results, highlighting structures around 50 nm. a.The preparation of PM for the UA delivery has never been reported before in the literature, and the physical and chemical parameters of the system showed a very promising formulation to improve the biopharmaceutical characteristics of this compound. The theoretical critical micellar concentration (CMC) value for PM is 125 mg/L .The release profiles of UA from the micelles and from the solution in PBS:EtOH 70:30 at 37 °C are shown in The development of a formulation also implies an adequate stability study that includes checking the parameters susceptible to change during storage under different conditions, which could affect the quality of the product. The stability study of PM consisted of visual control and analytical evaluation of UA content and physical parameters such as sizes, PDI, and zeta potential. For this purpose, the micellar dispersion was stored at 4 °C for 30 days, while the freeze-dried product (UA–PML) at room temperature for 30 days. In the 1-month stability test, no sedimentation or phase separation was observed in the case of dispersion. The freeze-dried powder was stable without any collapse of the solid. The EE, particle size, PDI, and zeta potential of UA–PM and the freeze-dried micelles (UA–PML) were reported, and they are shown in To avoid aggregation and/or chemical instability, the authors considered the freeze-drying process as a method to transform the colloidal dispersion into a solid with better stability over time. Freeze-drying is used to remove water and increase or protect the stability of the micelles in favour of fewer transportation costs. Then, at predetermined time intervals, the solid was redispersed in water, and the reconstituted micelles were physically and chemically characterized. The physicochemical parameters of the UA–PML were comparable with those of dispersion, as evidenced by data at the beginning of the test  a,b. The Furthermore, by means of a visual check, the freeze-dried product showed easy reconstitution without the presence of any sediment. The re-dispersibility of UA–PML was also evaluated according to the previously reported method . The resThe effect of free UA and UA–PM on the SH-SY5Y cell viability cells was determined using an MTT assay. UA was assayed in a range of 0.2 to 10 µg/mL concentrations. As illustrated in Among the various biological activities of UA, the literature attributes anticancer properties to the dibenzofuran derivative , includiSome anticancer agents are cytotoxic with different sensitivities to actively growing cancer cells compared with non-cancer cells. However, anticancer agents are also generally toxic to non-cancer cells, thus causing adverse effects in cancer patients. In the process of metastasis, migrating and invasive cancer cells spread throughout the body, causing secondary sites of invasion and damage to organs and tissues . Since mIn this work, the potential inhibitory effect of UA on the human neuroblastoma SH-SY5Y cell migration was evaluated by wound healing assay using nontoxic concentrations of free UA (0.2 and 0.5 µg/mL).UA had no significant effect on cell migration a,b; the Since mixed polymeric micelles of Soluplus–Solutol and TPGS polymeric micelles are a largely used nanoformulation for biopharmaceutical studies ,58, hereConcentrations of 0.2 and 0.5 µg/mL of UA–PM corresponded to noncytotoxic dilutions of vehicle PM  that ruled out the possible interference of PM toxicity on cell migration. As shown in To rule out any potential effect of the vehicle on SH-SY5Y cell migration, PM at the two dilutions (corresponding to UA–PM 0.2 and 0.5 µg/mL) was tested. It is noteworthy that PM 1:2500 resulted in a slight reduction in cell migration while keeping a wound width of 30 ± 15% at 24 h after the initial scratch, whereas PM 1:1000 totally contributed to the inhibition of cell migration. In fact, at 24 h after the initial scratch, the cells treated with PM 1:1000 maintained a wound width of 86 ± 9% c,d.When taken together, these data highlight that UA loading in PM allows UA to exert its anti-migratory role at doses that are nontoxic (0.2 µg/mL) to cells. Thus, we can speculate that PM increases UA activity by affecting not only UA solubility but also its cellular permeability.ECM degradation is an important step in the cell migration process, allowing cells to migrate out of the primary tumor and metastasize. MMPs, particularly MMP-2/9 (or gelatinases), are involved in ECM degradation playing a key role in cell migration . TargetiAs shown in These data suggest that UA–PM can act on cell migration and inhibit the gelatinolytic activity of the SH-SY5Y neuroblastoma cells. Other natural compounds with migrastatic action on human neuroblastoma cells have demonstrated improved efficacy when loaded into nanocarrier ,63. Thus® UV Water Purification System was used to obtain distilled water . Sigma-Aldrich  provided (+)-usnic acid (purity 98%), Phosphate-buffered saline pH 7.4 (PBS), D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS), Methanol HPLC grade, Acetonitrile HPLC grade, Formic acid analytical grade, Dichloromethane (CH2Cl2) and dimethyl sulfoxide (DMSO). Phosphotungstic acid was purchased from Electron Microscopy Science . The cell culture material was purchased from Merck KGaA , including Ham’s F-12 Nutrient Mixture, Dulbecco’s Modified Eagle Medium (DMEM), Fetal Bovine Serum (FBS), L-glutamine, antibiotics (penicillin and streptomycin), 3--2,5-Diphenyltetrazolium Bromide (MTT), Coomassie Brilliant Blue G-250, gelatin, and all other chemicals and solvent for the in vitro tests. Bio-Rad supplied the electrophoresis reagents . Sarstedt  supplied the disposable plastics.Badische Anilin-und Soda Fabrik  kindly provided Soluplus and Solutol HS15. The Merck Millipore’s Simplicity3OH/CH2Cl2 mixture (1:4 v/v). The solvents were evaporated under vacuum, and the film was hydrated with 5 mL of deionized water while being sonicated for 3 min to form a micellar dispersion. UA was added in the organic phase to prepare UA–PM, obtaining a final concentration of 0.5 mg/mL.The thin-film hydration method was employed to prepare empty PM ,44,64. BThe optimization of the formulation involved numerous tests with different polymer ratios and different UA concentrations. Soluplus:TPGS in ratio 4:1, 3:2, 2:3, Soluplus:Solutol:TPGS 1:4:1 and 1:4:0.5 were tested. UA was loaded from 0.5 to 1 mg/mL. Based on the physical characteristics, loading capacity, and stability of the micelles, Soluplus, Solutol, and TPGS 1:4:0.5 weight ratio was selected. The amount of solvent was related to the previously published studies ,42,44,47The average diameter, size distribution , and zeta potential values were determined using a Zeta Sizer Pro instrument  at 25 °C. The results are reported as the mean of the three measurements.g rpm for 10 min. Finally, the supernatant was collected and analyzed by HPLC-DAD analysis.The encapsulation efficiencies (EE%) were determined using the dialysis method. Two mL of the samples were inserted in a previously hydrated dialysis bag (cut-off 12–14 kDa). The bag was placed in a beaker containing 1 L of water and kept under constant magnetic stirring for one hour at room temperature. After that, the contents of the bag were withdrawn and diluted to 1:50 with methanol, and the sample was placed for 30 min in an ultrasonic bath and ultracentrifuged at 14,000× R for UA was 7.28 min. Different concentrations ranging from 0.002 µg/µL to 0.99 µg/µL were used for the calibration curve. The linear correlation coefficient was >0.999.The amount of encapsulated UA was determined using the HPLC-DAD method, with the mobile phase containing (A) formic acid/water with pH 3.2 and (B) acetonitrile. The flow rate was set to 0.5 mL/min. The column was a Kinetex C18 , maintained at 25 °C. The gradient profile was: 0.10–2 min 75–85% B, 2–4 min 85–90% B, 4–6 min 90–95% B, 6–8 min 95–100% B. The acquisition of the chromatographic profiles was carried out at a wavelength of 280 nm. The tThe UV-vis measurements were performed using a UV/Vis 1900 Shimadzu Spectrophotometer . The samples were DMSO solutions of free UA (0.5 μg/mL), UA–PM (UA concentration 0.5 μg/mL), and PM in water.w/v in water) was dropped onto the grid as a staining medium, and the excess solution was removed using a filter paper. The samples were dried for 3 min, after which they were examined with the electron microscope and photographed at an accelerating voltage of 20 kV.The samples were analyzed using the Scanning Electron Microscope Gaia 3  FIB-SEM (Focused Ion Beam-Scanning Electron Microscope). The electron beam used for the TEM imaging had a voltage of 15 kV, and it was operated in a high-vacuum mode and with a Bright-field TEM detector. The samples (10 μL) were applied to a 150-mesh carbon film-covered copper grid. After that, 5 μL of phosphotungstic acid solution (1% theorCMC) value for PM [SoluplusX, SolutolX, and TPGSX are the molar fractions of Soluplus, Solutol, and TPGS, and SoluplusCMC, SolutolCMC, and TPGSCMC are the CMC values of Soluplus, Solutol, and TPGS, respectively. The molar fractions were determined from the ratio between the moles of the constituent and the total moles of the constituents of the mixture.The following equation was used to calculate the theoretical CMC  at −20 °C. The lyophilized sample (UA–PML) was kept at room temperature in a silica gel desiccator until the analysis. The powder was redispersed with the original amount of deionized water and shortly vortexed to ensure its complete dispersion. The average particle hydrodynamic diameter, homogeneity, surface charge, and EE% were evaluated.The physical and chemical stability of UA–PM as a colloidal dispersion and as a freeze-dried product (UA–PML) was investigated. The micellar dispersion was placed in glass bottles with plastic caps and stored at 4 °C for 10 days, while the freeze-dried powder was kept at room temperature for 30 days. At set times, the size, PDI, and zeta potential were evaluated by diluting UA–PM with deionized water. The chemical stability was checked to monitor the EE%. At 10, 20, and 30 days, UA–PML was solubilized in deionized water, and the physical  and chemical (EE%) parameters were checked. The appearance and re-dispersibility of the powder were evaluated.An in vitro release test was conducted to evaluate the percentage of UA release from UA–PM compared to the UA acetonitrile solution. In both cases, 2 mL of the sample, containing 0.5 mg/mL of UA, was placed inside a previously hydrated dialysis bag . Then, the bag was inserted in a beaker containing 200 mL of release medium consisting of PBS:EtOH 70:30 and placed under magnetic stirring at a temperature of 37 °C. To maintain the sink conditions, 1 mL of the release medium was withdrawn and replaced with an equal volume of the PBS:EtOH mixture at predetermined intervals. The collected aliquots were analyzed by HPLC-DAD to determine the amount of UA released during 24 h. All of the experiments were carried out in triplicate.® No. CRL-2266TM). The cells were grown in a 50% Ham’s F12 and 50% DMEM supplemented with L-glutamine (2 mM), streptomycin (100 μg/mL), penicillin (100 U/mL), and 10% FBS (complete medium) in a humidified 5% CO2 incubator. At 90% confluence, the SH-SY5Y cells were trypsinizated  and propagated. The following in vitro cell-based experiments were conducted in a heat-inactivated serum medium (HI-FBS medium). HI-FBS was obtained by incubating FBS at 55 °C for 30 min.The ATCC (American Type Culture Collection) provided the SH-SY5Y human neuroblastoma cells (ATCC3 cells/well) in a complete medium. The cells were then treated with UA (0.2–10 µg/mL) and UA–PM (0.2–10 µg/mL of loaded UA) in a HI-FBS medium. As controls, the untreated cells and the cells treated with empty PM were used. The cells were incubated with MTT solution (0.5 mg/mL) at 37 °C in the dark for 1 h. After washing with PBS, the insoluble formazan crystals were dissolved by adding 100 µL/well of dimethyl sulfoxide. The iMARK microplate reader was used to measure the absorbance values  at a wavelength of 595 nm. The data obtained from a biological triplicate are reported as percentages compared with the untreated control cells.The viability of the SH-SY5Y cells was analyzed using the MTT colorimetric assay. Briefly, the cells were seeded in 96-well plates (5 × 105 cells/well). A 200-µL sterile plastic tip was used to wound the monolayer longitudinally, and the following PBS washing removed the nonadherent cells. The SH-SY5Y cells were then treated with UA (0.2 and 0.5 μg/mL) and UA–PM (0.2 and 0.5 μg/mL of loaded UA) in a HI-FBS medium for 24 h. The untreated cells and the PM-treated cells were used as controls. Phase-contrast microscopes with a digital acquisition system  were used to observe the cell-free area . Wound width was measured and quantified by ImageJ image analysis software version 1.53e .SH-SY5Y cell migration was evaluated under various experimental conditions using a wound healing assay . The cel4 cells/well) and incubated overnight. The cells were then treated with UA (0.2 μg/mL) and UA–PM (0.2 μg/mL of loaded UA) in HI-FBS medium for 24 h, while the untreated cells and the cells treated with empty PM were used as controls.The gelatinolytic activity of MMP-2/9 was assessed using gelatin zymography, as previously described . Brieflyg at 4 °C for 1 min. The conditioned medium (2 μL/sample) was electrophoresed in an 8% polyacrylamide gel containing 1 mg/mL gelatin under nonreducing conditions. The 24-h-conditioned medium of the human fibrosarcoma HT1080 cells was used as a standard control activity of MMP-2/9 [Cell debris was removed by centrifuging the culture supernatants at 9700×  MMP-2/9 .v/v) Triton X-100 before being incubated for 30 min at room temperature in reaction buffer . Overnight incubation was performed in the reaction buffer. The gel was then incubated for 1 h at room temperature with a solution of 40% (v/v) methanol and 10% (v/v) acetic acid for protein fixation. Following two 10-min washes in distilled water, gel staining was performed with 0.05% colloidal Coomassie Brillant Blue G-250 dissolved in 1.6% (v/v) phosphoric acid, 8% (w/v) ammonium sulfate, and 20% (v/v) methanol. Gelatinase activity was visible as clear bands on a blue background after the staining was removed with 1% (v/v) acetic acid. The zymography images were captured using a digital scanner.To remove SDS, the gel was washed twice (30 min each) in 2.5%  followed by Tukey’s HSD test was used to analyze the in vitro data. The threshold for statistical significance was set at This study investigated the potential applicability of PM containing Soluplus, Solutol, and TPGS as a delivery system to increase the solubility of UA and its anti-migratory activity in human neuroblastoma SH-SY5Y cells. PM proved to be an excellent formulation by presenting good physical parameters, high encapsulation efficiency, and ensuring prolonged release. The freeze-drying process prolongs the formulation’s storage stability, preventing aggregation and/or chemical degradation.In addition, in vitro cell-based experiments showed that nontoxic doses of UA–PM inhibited cell migration compared with the same doses of free UA. UA–PM also had an inhibitory effect on the gelatinolytic activity of MMP-2/9, known to be involved in the process of cell migration. Thus, polymeric micelles influence not only the solubility of UA but also its anti-migratory activity promoting, via the polymeric constituents, permeability across cell membranes. Overall, this study sheds light on polymeric micelles as an optimal formulation to deliver lipophilic drugs that can increase their solubility and permeability. Further in vivo studies will be necessary to prove the efficacy of UA delivered in polymeric micelles as a migrastatic agent.


Regulatory T cells (Tregs), which execute their immunosuppressive functions by multiple mechanisms, have been verified to contribute to the tumor microenvironment (TME). Numerous studies have shown that the activation of the CBM complex/NF-κB signaling pathway results in the expression of hypoxia-inducible factor-1 (HIF-1α) and interleukin-6 (IL-6), which initiate the TME formation. HIF-1α and IL-6 promote regulatory T cells (Tregs) proliferation and migration through the MAPK/CDK4/6/Rb and STAT3/SIAH2/P27 signaling pathways, respectively. IL-6 also promotes the production of HIF-1α and enhances the self-regulation of Tregs in the process of tumor microenvironment (TME) formation. In this review, we discuss how the crosstalk between the CARMA1–BCL10–MALT1 signalosome complex (CBM complex)/NF-κB and MAPK/P27 signaling pathways contributes to the formation of the TME, which may provide evidence for potential therapeutic targets in the treatment of solid tumors. Programmed cell death 1 (PD-1) is an immunosuppressive co-stimulatory signal receptor that belongs to the CD28 family. PD-1 and PD-L1 blockage at immune checkpoints can rejuvenate patients’ T cells to achieve long-term remission . Howevervia a myriad of distinct but overlapping signaling cascades  are a class of serine/threonine kinases. As important signaling molecules that regulate transcription, CDK–cyclin complexes are involved in Treg growth, proliferation, dormancy, and apoptosis . During Therefore, it is a good strategy to control the TME by inhibiting Treg proliferation by targeting CDKs with some target drugs . NumerouThe IL-6 signaling pathway is associated with tumor angiogenesis, and previous studies have found that the suppression of IL-6 signaling led to suppression of angiogenesis and migration of breast cancer . IL-6 isSTAT3 is an important member of the STAT family, which also includes STAT1, STAT2, STAT4, STAT5a, and STAT5b. The STAT6 and STAT3 signal transduction pathways are closely related to cell proliferation, differentiation, and apoptosis. The pathways control the production of growth factors and cytokines, and the extracellular signal stimulation, thereby regulating target gene transcription. The pathway activation can lead to abnormal cell proliferation and malignant transformation . Jak–STASiah2−/− mice was blunted owing to P27-dependent suppression of CDK4/6 signaling activation . Then, P27, which inhibits CDK4/6 activation, is degraded by ubiquitination of SIAH2 . Accorditivation .STAT3 gene is highly expressed in hepatocellular carcinoma cells, and regorafenib (Stivarga), a drug targeting STAT2 for the treatment of hepatocellular carcinoma, has been identified as a second-line oral agent .The SIAH2/PHD/HIF-1α pathway plays an important role in the development of the TME. The experiments by Nakayama et al. have confirmed that the E3 ubiquitin ligase SIAH2 shows significant ubiquitin-dependent degradation of prolyl-hydroxylase 1 (PHD1) and prolyl-hydroxylase 3 (PHD3), while its effect on PHD2 is not significant . There a+ cytotoxic T cells, dendritic cells, and natural killer cells  binds to HIF-1α, resulting in the production of a variety of products, such as vascular endothelial growth factor (VEGF), which is associated with angiogenesis, and CXCR4, which is associated with cell migration . OverexpVEGF and HIF-1α are also closely related to the development of blood vessels. Currently, inhibitor drugs targeting these two proteins are approved by the FDA for the treatment of some tumors . Given tThe mitogen-activated protein kinase (MAPK) signaling pathway is a signal transduction pathway that is widely found in animal cells. This pathway plays an important role in regulating the Treg cell cycle and gene expression . The MAPde novo formation of the embryonic circulatory system) and angiogenesis (the growth of blood vessels from preexisting vasculature)  constitute a subfamily of growth factors that stimulate the growth of new blood vessels. VEGFs are important signaling proteins involved in both vasculogenesis (ulature) . VEGFs culature) . NRP-1, ulature) .RAS is a small GTP-binding protein, with the GTPase domain binding GDP in the inactive state and GTP in the active state; therefore, RAS plays the role of molecular switch. RAS adjusts T cell development, differentiation, and proliferation by inducing downstream signal transduction pathways. Inhibition of RAS has been found to be associated with an increased number and boosted function of Foxp3+ Tregs .ERKs adjust Treg proliferation, differentiation, and survival, and regulate the production of a variety of downstream growth factors  . The RASAs shown in CARMA1 (CARD11) proteins are composed of 1,147 amino acid residues. Their N-termini consist of a CARD domain and a coiled-coil structure, and the C-termini contain a PDZ domain, an SH3 domain, and a GUK domain. Activated downstream of protein kinase C (PKC), CARMA1 is coupled to lipid rafts on cell membranes . When MHIn addition, MALT1 interacts with CARMA1’s coiled spiral region. This complex has enzymatic activity . NF-κB uCARMA1 gene, can weaken the formed TME and improve sensitivity to PD-1/PD-L1 immune checkpoint therapy , TNF , and IL-6 (growth factor for multiple myeloma), thereby promoting the above signaling pathways regulated by VEGF and IL-6. Two small-molecule inhibitors regulate cell signaling pathways in a synergistic manner to inhibit the proliferation and migration of Tregs, thereby blocking the secretion of inhibitory cytokines by Tregs to aid in the formation of the TME. Currently, four clinical trials have been recruited on the In conclusion, Tregs are involved in the regulation of autoimmune diseases, allergic diseases, and graft rejection, and Treg-mediated immunosuppression has become a major obstacle to effective treatment of tumors. Tregs play an immunosuppressive role in the TME through various mechanisms. Based on our analyses , we thinvia either CBM or the MAPK/P27 signaling pathway and its underlying mechanisms need further exploration. Therefore, further exploration of the complex role of CBM and MAPK/P27 signaling pathway in the formation of TME will provide stronger evidence for potential therapeutic targets in solid tumor therapy.In this review, we discussed the crosstalk between the CBM and MAPK/P27 signaling pathways, in order to gain a better understanding of the complexity of the role of Tregs in the process of TME formation. However, the complex role of Tregs in the formation of the TME


The objective of the present work is to evaluate the influence of material color on mechanical properties of fused deposition modeling (FDM) parts. The performance of the products is evaluated by testing eight different colors of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) material in terms of tensile strength, compressive strength, and flexural strength. The analysis of data shows a significant difference in mechanical characteristics of prints depending on filament color. For different colors, these three strengths almost follow the same rising and falling tendency. In order to explore the relationship between mechanical strengths and filament colors, the color-mixing theory and the least-squares method are adopted to fit the best ratio coefficients of different color combinations. Results are presented showing that the strength value  of the mixed color can be evaluated through that of primary colors by fitting the other strength . It is shown that the predicted value is always no more than 7% error compared with the actual strength, in spite of two-color or three-color mixtures. An additional confirmation test with seven colored PLA filaments from different suppliers was conducted to focus on the extensibility. The outcomes show the maximum fitting errors of strengths for mixed colors in all cases are within 5%, proving the effectiveness and applicability of this predicted approach. This study can bring a detailed analysis that enables better estimation of the function of material color and contributes to improving the property of FDM printed products for consumers by choosing the suitable filament color. Additive manufacturing (AM), also known as 3D printing, is the process of creating parts directly from three-dimensional model data by depositing materials layer by layer gradually. Compared with traditional subtractive manufacturing, AM has the following advantages such as digital production, flexibility of complex geometry, high manufacturing efficiency, resource conservation and environmental friendliness . TherefoFDM is a complex process with a large number of parameters that significantly impact the quality of built parts. With increasing higher demands on the structural performance of products, many researchers devoted to investigating the influence of process parameters, such as build orientation , raster To meet this need, the present study aims to examine differences in mechanical behaviors  of ABS and PLA samples with different colors, which are the most popular filament materials for FDM users. Based on the experimental data, this study brings a view of colorimetry to explain the relationship of mechanical properties between different colors. The achieved results are helpful for FDM users to select the most suitable material color for improving the structural performance of products.® . Eight different colors ) were selected to determine the influence on the final properties of prints. All filaments were stored in a sealed package with desiccant packets to avoid moisture absorption.The ABS and PLA filaments were provided by eSUN® commercial FDM printer , as shown in The specimens were manufactured by FUNMAT HTThe tests were carried out using a MIT-20 electronic universal testing machine , with a measurement capacity of 20 kN, as shown in The shape and size of the tensile specimen with type I was determined according to the ASTM D638, and the test speed was 5 mm/min. The compressive specimen and flexural specimen were designed according to the aforementioned ASTM standard . The tesIn order to ensure the accuracy of the experimental result, three identical specimens for each color sample were prepared for each type of mechanical test, and the average value of data obtained was calculated as the outcome of strength . In totaThe results of tensile strength, compressive strength, and flexural strength of ABS and PLA in all colors are plotted in On the other hand, in the case of ABS, tensile strength ranges from 18.8 MPa to 24.9 MPa, compressive strength ranges from 36.0 MPa to 43.9 MPa, and flexural strength ranges from 32.5 MPa to 35.9 MPa for different colors. The maximum value of all relative differences in mechanical strengths between different colors can be up to 32% (tensile strength), and the minimum value can reach 10% . Similar to PLA, variance in color does impact the performance of the ABS material as well. However, the effect is more pronounced on tensible and compressive strength, compared with flexural strength. Moreover, the ABS material with red color gives the highest value, and black color gives the lowest value for both tensible and compressive strength. Nevertheless, for flexural strength, the highest value and lowest value are obtained in green color and black color, respectively. The results do not seem to be in line with the previous tendency from PLA, that is, extreme values are obtained in the same colors. One possible explanation for this phenomenon is that the difference in this group of data is small, which is easily affected by various errors, e.g., manufacturing error and experimental error. In addition, the relative difference in flexural strength between red color (34.9 MPa) and green color (35.9 MPa) is only 3%. Taking into account the factor of errors, the results obtained can be acceptable.The difference in material color is due to color additives (tint or pigment) processed into filaments. Actually, the “filler” added to the polymer can change the thermal properties, such as crystallization  and meltAccording to additive methods of color reproduction  or colorM donates the mixed color; R, Y, and B donate three primary colors: red, yellow, and blue, respectively; and r, y, and b donate the corresponding non-negative mixing ratio. For example, when red and yellow are mixed in the ratio of r:b = 1:1, orange will be obtained. In the case of r:b ≠ 1:1, the mixture color may be reddish-orange, yellowish-orange, etc., depending on the appropriate adjustment of relative quantity.The color-matching function can be written as :(1)M≡r to solve the mathematical problem as follows:n represents the number of experimental data. Since each colored sample has three specimens for the tensile test, there are in total 3 × 3 × 3 = 27 combinations of experimental data for three colors without repetition. In addition, including the group of the combination of average value, there are 28 groups in total, meaning n = 28, as shown in The coefficients λThe tensile strengths of all specimens for these three colors are tabulated in To better illustrate the internal relationship between different mechanical behaviors, the tensile strength, compressive strength, and flexural strength of PLA and ABS in various colors in 1 and λ2 obtained from different mechanical strengths. It is obvious to note that values are affected by the type of mechanical strength, due to varying degrees of data dispersion. Nevertheless, the difference has little effect on the predicted value, meaning the prediction method with color-matching theory is relatively robust and effective.In addition, In order to verify the practicality of the prediction method for other situations, two groups of other color combinations for PLA are tested. On the other hand, to examine the difference between different materials, mechanical tests for ABS specimens of the same combination of colors are also carried out. Without loss of generality, the ratio coefficients are obtained by applying the LSM for different mechanical strengths, and the parameters in the situation where the maximum error emerges are presented.1 and λ2 obtained are obviously different for ABS and PLA, even in the same combination of colors. In the case of “Red + Blue→Purple”, the parameters obtained from flexural strength for PLA are The data in TR, TB, TY, and TBk donate the tensile strength of red, blue, yellow and black, respectively. λ1, λ2, and λ3 represent the corresponding ratio coefficient for each primary color, which can be obtained by solving Formula (4):In this subsection, the case where three colors are mixed is tested. According to the color circle in ® , as shown in There is a large number of FDM material manufacturers available on the market. However, due to the lack of standards for AM, the same material from different manufacturers may exhibit significant discrepancy in property . Therefo®. Moreover, even in the same color, the PLA material from other manufactures exhibit very different performance. For example, the mechanical strengths of the red sample from eSUN® are 47.8 MPa for tension, 61 for compression and 63.3 for flexure, while the values become 37 MPa, 62.2 MPa and 44.1 MPa, respectively, for the sample from Rambery® in the same color, demonstrating the importance of considering different performances caused by manufacturers in the selection of filaments [A close examination of ilaments .® samples printed in different colors also follow almost the same rising and falling curve in spite of individual points. That is, in the order of red→yellow→blue→green→orange→purple→black, the values first increase and decrease, and then increase and decrease again, meaning there is a qualitatively good agreement of strength trend between the materials from two suppliers.In the interest of clarity, Similarly, we use the LSM and color-mixing theory to evaluate the properties of mixed colors, just as discussed in the section before. ® PLA material but in various colors show the maximum difference of 29% in mechanical characteristics (tensile strength). On the one hand, the highest values for three kinds of mechanical strengths were all reached by the samples with purple color. On the other hand, the lowest mechanical strengths were reached by the samples with natural color. While for ABS, the maximum difference between different colors can be up to 36%, which also appears in tensile strength. In addition, the black samples achieved the lowest mechanical strength in all cases. On the contrary, the red samples achieved the highest tensile and compressive strength, while the green samples achieved the highest flexural strength. In summary, the influence of material color cannot be negligible, especially when studying the effect of process parameters on the structural performance of FDM parts.This paper evaluated the influence of filament colors on mechanical characteristics of FDM products. The mechanical properties of ABS and PLA specimens printed using eight colors are characterized to determine tensile strength, compressive strength, and flexural strength. The analysis of experimental results presents that the color of the material plays a significant role in the performance of FDM products. Different filament colors affected the properties of the specimens to varying degrees. Samples chosen from the same eSUNAt the same time, the achieved data show that the distribution trends of different mechanical strengths for different colors are consistent. That is to say, if a color has a higher (or lower) tensile strength when compared with other colors, its compressive strength and flexural strength are generally higher (or lower) as well. Furthermore, this study presents a novel approach for predicting mechanical strength with different colors using the color-mixing theory. By fitting the ratio coefficients using the LSM, the mechanical strength  of mixed colors can be evaluated. The relative errors between the predicted value and actual value are always within the range of 7% for both PLA and ABS.® PLA material in an effort to verify the extensibility of this approach to filaments from different manufacturers. The experimental data indicates a good agreement of trend curves for three kinds of mechanical strengths from filaments of two brands. The statistical analysis indicates that the maximum fitting errors of strengths for all color mixture combinations are always within 5%, free from variances in filament manufacturer. The results presented clearly confirm the effectiveness and applicability of the prediction approach.In addition, a confirmation test was performed on seven colored RemberyThis study reveals the relationship between mechanical characteristics and mixture of colors. Besides the complex parametric optimization approach ,31, userFirst of all, the experimental materials are limited to ABS and PLA. Although previous studies have demonstrated that color additives to PLA material can lead to changes in mechanical properties, the degree of impact is different for different materials, as shown in this study. Therefore, it is necessary to further study the extent to which the color affects other FDM materials, such as nylon, polyetherimide, polycarbonates and others.Meanwhile, this study used four sets of color mixtures to verify the color-mixing theory. According to color reproduction, the three primary colors can be mixed to produce almost all other colors. Therefore, more color combinations need to be tested in future work to verify the applicability of the theory.1 and λ1. How to accurately describe the color of the filament is a challenge for both manufacturers and users.Furthermore, a question that is not considered when processing data is the error of color. Due to the wide range of the color spectrum, it is difficult to tell whether a red color is “pure” red. For example, the mixture of red and yellow can be orange, reddish-orange or yellowish-orange. Apparently, the properties of these three colored filaments are not the same, which will affect the value of the fit coefficients λAt last, it has been proved that the color filler in filaments leads to discrepancy in mechanical performance. However, all filament manufacturers keep their respective colorant additives as proprietary trade secrets, making it challenging to explain the results as the additives are unknown. For this reason, a more detailed experiment before printing is needed to be conducted to obtain colored filaments through a filament production process, by blending a natural filament and color additive with known chemical characterization . This wo


Clinicians and laboratories routinely use urinalysis (UA) results to help determine if urine cultures and/or antimicrobials are indicated. Yet, the performance of individual UA parameters and common clinical thresholds for action are not well defined and may vary across different patient populations. Our objective was to compare the performance of different UA parameters in predicting significant bacteriuria irrespective of symptoms, and to assess performance of pyuria based on age, sex, and presence of indwelling catheter.This retrospective review of UA and urine culture data from the Duke University Health System included all UAs ordered within 24 hours of a urine culture between 2015 and 2020 (no reflex urine cultures included). We defined significant bacteriuria as a urine culture with ≥1 uropathogen growing at ≥100,000 colony forming units/mL. Then, we used this definition to evaluate the performance of relevant UA parameters and result thresholds including sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV). We also combined 18 different UA criteria  and used receiver operating characteristic (ROC) curves to identify the top 5 performing models for predicting significant bacteriuria (sensitivity and specificity).18 Different Combinations of UA Parameters for Predicting Significant Bacteriuria on Urine CulturesOf 240,195 encounters during the 6-year study period, 38% were outpatient and 62% were inpatient. Twenty-nine percent had a urine culture with significant bacteriuria; 30.7% had a negative urine culture. No single UA parameter had both - high sensitivity and high specificity in predicting bacteriuria. Trace leukocyte esterase and low-level pyuria had a high NPV for significant bacteriuria (Table 1A). Combined UA parameters did not perform better than pyuria alone (Table 1B). The high NPV >=0.90 of pyuria was maintained among most patient age and sex subgroups with the exception of females ≥65 and patients with indwelling catheters (Table 2).UA parameters should be leveraged for their NPV instead of sensitivity, when used as a part of diagnostic workup. Future reflex urine culture workflows and diagnostic stewardship algorithms should incorporate population-specific UA criteria and/or focus on populations where NPV of pyuria is high.Sonali D. Advani, MBBS, MPH, FIDSA, Locus Biosciences: Advisor/Consultant|Locus Biosciences: Honoraria|Sysmex America: Advisor/Consultant Nicholas A. Turner, MD, MHSc, Aperio: Advisor/Consultant Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Valerie Vaughn, MD, MSc, Thermo Fisher Scientific: Honoraria.


Woody plant species represent an invaluable reserve of biochemical diversity to which metabolic engineering can be applied to satisfy the need for commodity and specialty chemicals, pharmaceuticals, and renewable energy. Woody plants are particularly promising for this application due to their low input needs, high biomass, and immeasurable ecosystem services. However, existing challenges have hindered their widespread adoption in metabolic engineering efforts, such as long generation times, large and highly heterozygous genomes, and difficulties in transformation and regeneration. Recent advances in omics approaches, systems biology modeling, and plant transformation and regeneration methods provide effective approaches in overcoming these outstanding challenges. Promises brought by developments in this space are steadily opening the door to widespread metabolic engineering of woody plants to meet the global need for a wide range of sustainably sourced chemicals and materials. Woody plants are resilient perennials defined by their characteristic woody stems and large root systems. Woody plants are a highly diverse group that is polyphyletic in origin and have both flowering and non-flowering members. Many species of woody plants have evolved since their origins around 380 million years ago, and they have since come to dominate various landscapes around the globe  in two studies despite piceid not occurring naturally in apple  in apple resulted in the accumulation of piceid in transgenic apple fruits  terpenoids, alkaloids, simple phenolics, coumarins, tannins, and lignins. There have been several examples of successful metabolic engineering efforts centered on the accumulation of such compounds in woody plants Table . ProanthCitrus sinensis) was engineered to produce less limonene (major VOC in oil glands of mature orange fruit) and instead accumulate increased monoterpene alcohols that share the common biosynthetic precursor geranyl pyrophosphate with limonene, increasing resistance to fungal pathogens that would otherwise lower yields and shelf life  is similarly applied to the protection of woody plants from pests and pathogens by disrupting their interactions with the host plant  in transient transgenic apple calli  overexpression cassette to considerably shorten the seed-to-seed time in the resulting lines  and the vaccine adjuvant QS-21 from Quillaja saponaria. Some chemical classes represented in woody plants used in herbalism are quinoline alkaloids {Dey, Berberis species), purine alkaloids , benzophenones (the major bioactive compounds in mango leaves which are used to treat diabetes), and flavonoids  expressing an RNAi construct suppressing 7-N-methylxanthine methyltransferase showed up to a 50% reduction in caffeine content in embryonic tissues and plantlets—opening the door to naturally decaffeinated coffee  and willow  grow quickly and tolerate coppicing well, making them good targets of metabolic engineering for biofuel production. As high lignin content constrains the effectiveness of chemical pulping and of lignocellulosic ethanol production, ideal woody feedstocks for this process would also have optimal cellulose and lignin compositions and benefit from the redirection of metabolites away from lignin biosynthesis  and Jatropha curcas are promising sources of biodiesel  catabolism was suppressed by downregulating a lipase via RNAi, resulting in higher storage lipid accumulation in seeds  and can be extended to metabolic phenotypes that are potential targets of metabolic engineering not only in walnut, but also in other highly heterozygous woody plant species for which a reference genome exists , mandarin orange (Citrus reticulata), apple, pear (Pyrus communis), and grape  endonuclease systems have been used in woody plants such as poplar  , transformation efficiencies using hairy root culture far exceeded aerial tissues, showing efficiencies over 80% alongside of higher regeneration rates , GRF-INTERACTING FACTOR 1 (GIF1), LEAFY COTYLEDON 1 (LEC1), WUSCHEL, and BABY BOOM (BBM) successfully improved the regeneration of Arabidopsis   shortened the generation time to one year in sweet orange and citrange . In the case of citrange, the transgenic plants were used in re-transformation to test gene stacking; the reporter genes were stably expressed in the re-transformant and were evaluated as early as one year  and passion fruit (Passiflora edulis)   , metabolic flux was redirected from carotenoid to taxadiene biosynthesis and resulted in high amounts of taxadiene in leaves and fruits of the transgenic plants  was boosted in multiple complementary plant systems that produce carotenoids such as tomato . To this end, biosynthetic precursors and their derivatives can be fed to the complementary systems to produce downstream products  uses multiple unidirectional site-specific recombinases to stack multiple genes in vivo. The GAANTRY ArPORT1 strain of A. rhizogenes was demonstrated to be effective at stacking up to ten genes in rice  are stable in plant cells and multiple genes on artificial chromosomes can be stably expressed. PACs have been the subject of research in non-woody plants such as barley, maize, and Arabidopsis, and promise remains for implementing PACs into woody plant species with established transformation and regeneration methods (Yu et al. Pivotal discoveries in herbaceous model systems such as Though gene regulatory networks have been extensively explored in herbaceous plants, considerable work remains to gain a more complete understanding of such regulatory networks in woody plants. The task of unraveling these networks in woody plant species is complicated by the diversity of tissue types present as well as the maturation time needed for such gene networks to emerge and be adequately explored. Poplar serves as a suitable woody model plant from which findings can be translated. Methods and knowledge developed in poplar can prove instrumental in exploring other woody plant systems to bolster our knowledge of their metabolic networks and to translate suitable approaches for metabolic engineering. Conquering the bottlenecks of transformation and regeneration in non-model woody plants will shorten the design-build-test-learn cycles in gene editing and transgene insertion to streamline their usage in metabolic engineering as well. Finally, a large amount of untapped diversity exists in domesticated cultivars across broad geographic ranges and non-domesticated genotypes. Comparative genomics can take advantage of the highly diverse genomes of woody species around the globe not only for breeding, but also to study the evolution of genes and gene families to facilitate gene mining for metabolic engineering (Tuskan et al. As woody plants are understudied, there is room to integrate current genetic and metabolic engineering strategies developed in other plant and microbial systems. Implementation of these strategies is finding increasing success in more studied woody plant systems such as poplar and citrus. Translating these tools into more woody plant systems not only leverages different native metabolic networks, but also expands the collection of ecosystem services available to the site of interest due to the broader species selection. These ecosystem services brought by woody plants are unique among plant chemical factories and include habitat, carbon sequestration, erosion reduction, and water retention. These ecosystem services can be viewed as the key benefits of a sustainable bioeconomy anchored in the used of long-lived woody plants. Low resource and labor demand further complement the vast ecosystem services of woody plant platforms. Metabolic engineering expedites our capability to simultaneously capitalize on the rich biochemical diversity of woody plants in tandem with the suite of environmental benefits associated with their usage. The transition from an oil-based economy to a sustainable bio-based economy will be facilitated by advances in this space.


The study aims to assess the usefulness of human T-cell leukemia virus type 1 (HTLV-1)-infected cell analysis using flow cytometry (HAS-Flow) as a monitoring method for adult T-cell leukemia (ATL) development in HTLV-1-positive patients with rheumatoid arthritis (RA) under treatment with antirheumatic therapies. A total of 13 HTLV-1-negative and 57 HTLV-1-positive RA patients participated in this study, which was used to collect clinical and laboratory data, including HAS-Flow and HTLV-1 proviral load (PVL), which were then compared between the two groups. CADM1 expression on CD4+ cells in peripheral blood (PB) was used to identify HTLV-1-infected cells. The population of CADM1+ CD4+ cells was significantly higher in HTLV-1-positive RA patients compared to HTLV-1-negative RA patients. The population of CADM1+ CD4+ cells was correlated with HTLV-1 PVL values. There were no antirheumatic therapies affecting both the expression of CADM1 on CD4+ cells and PVLs. Six HTLV-1-positive RA patients who indicated both high HTLV-1 PVL and a predominant pattern of CADM1+ CD7neg CD4+ cells in HAS-Flow can be classified as high-risk for ATL progression. HAS-Flow could be a useful method for monitoring high-risk HTLV-1-positive RA patients who are at risk of developing ATL during antirheumatic therapies. Standardization of antirheumatic therapies improves prognosis in patients with rheumatoid arthritis (RA). Several biologics and targeted-synthetic disease-modifying antirheumatic drugs (DMARDs) have transformed rheumatic disease treatment . HoweverHuman T-cell leukemia virus type 1 (HTLV-1) is the causative agent of adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). There are approximately 1 million HTLV-1 carriers in Japan, making it endemic,  and it iThe CD4-positive T-cells are the main target of HTLV-1. The incidence rate of ATL was estimated at approximately 5% of HTLV-1 carriers. The risk factors for ATL development have been reported to be advanced age and higher HTLV-1 proviral load (PVL) values ) in HTLV-1-asymptomatic carriers . An incrneg) cells have been reported to be enriched with clonally expanded cells and abnormal lymphocytes, which were suspected as ATL cells [dim < CADM1+ CD7neg; CADM1+ CD7neg-dominant pattern) [HTLV-1-infected cell analysis system flow cytometry (HAS-Flow) was developed as a novel method for monitoring HTLV-1-infected cells, including ATL cells. The cell adhesion molecule 1 (CADM1) versus CD7 plot accurately reflects disease progression in HTLV-1 infection, and CADM1-positive cells with downregulated CD7 in HTLV-1-asymptomatic carriers have properties similar to those in indolent ATL . For exaTL cells . Severalpattern) ,10,11. However, HAS-Flow has been established as an analysis system for HTLV-1-infected cells using PBMCsobtained from HTLV-1 carriers, including HTLV-1-associated disorders such as HAM/TSP and ATL ,10,11. BTherefore, this study aims to evaluate the usefulness of HAS-Flow in HTLV-1-positive RA patients receiving antirheumatic therapies. In addition, the correlation between HTLV-1 PVL values and the proportion of CADM1+ CD4+ T-lymphocytes in HTLV-1-positive RA patients was examined. The influence of RA disease activity and its antirheumatic therapies on the expression of CADM1 in CD4+ T-lymphocytes were also analyzed. The present study demonstrated that HAS-Flow may be an adequate and useful method for monitoring HTLV-1-infected cells and evaluating the high-risk cell population of ATL progression using HAS-Flow in HTLV-1-positive RA patients.The HTLV-1 RA Miyazaki registry was conducted from December 2017 at the University of Miyazaki Hospital and Miyazaki Zenjinkai Hospital in the Miyazaki Prefecture, Japan. This registry was started as inheritance research of the HTLV-1 RA Miyazaki Cohort Study . The regThis registry was used to choose the participants for the current investigation. The following are the study’s inclusion criteria: HTLV-1-positive RA participants who underwent HAS-Flow between April 2019 and December 2021 in this registry. Quantitative PCR was also used to determine HTLV-1 PVL levels. According to these inclusion criteria, 57 of 85 HTLV-1-positive RA patients were enrolled in this study. Furthermore, 13 HTLV-1-negative RA patients had performed HAS-Flow in this registry during the same observation period. All clinical data assessed, such as RA disease activity and antirheumatic regimen, was collected from the patient’s medical records. The study protocol was approved by the research ethics committees of the University of Miyazaki Hospital  and Miyazaki Zenjinkai Hospital, and followed the Ethical Guidelines for Medical and Health Research Involving Human Subjects.For HAS-Flow analysis, whole blood samples in EDTA tubes were obtained periodically from 57 participants with HTLV-1-positive RA and 13 participants with HTLV-1-negative RA. HAS-Flow analysis was performed to detect the expression levels of CADM1 and CD7 in CD4+ T-lymphocytes, according to previously reported methods . An unlaThe current study used HTLV-1 PVL levels measured immediately before or after performing HAS-Flow. The HTLV-1 PVL was measured using PBMCs separated by Ficoll-based density gradient centrifugation; DNA was purified from PBMCs; and real-time PCR was performed using the Light Cycler 2.0 thermal cycler  to measure the HTLV-1 pX region and the human albumin gene . The PVLClinical information was collected during the observation period while performing HAS-Flow. According to the European League Against Rheumatism (EULAR) guidelines, the Disease Activity Score of 28 joints (DAS28) using the erythrocyte sedimentation rate, Simple Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI) were used to evaluate RA disease activity . Physicap-value of <0.05 was considered significant.Statistical analyses were performed using EZR . EZR is a modified version of R commander (version 2.4-2), which was designed to add statistical functions that are frequently used in biostatistics . The medThe clinical characteristics of the HTLV-1-negative and HTLV-1-positive RA participants who participated in this investigation are shown in p = 0.002) (p = 0.006) (= 0.002) A. Furthe= 0.006) B. The pop = 0.0005) (p = 0.0000008) . The population of CADM1+ CD4+ cells (D + N) was correlated with the HTLV-1 PVL values in RA participants receiving several antirheumatic therapies  A. In add0000008) B. In theThis is the first study to show that HAS-Flow analysis may be used to assess HTLV-1-infected CD4+ cells in HTLV-1-positive RA patients under treatment with antirheumatic therapies. Among CD4+ cells the population expressing CADM1 was higher in HTLV-1-positive RA patients compared to those in HTLV-1-negative RA patients. CADM1, which was previously discovered as a tumor suppressor in lung cancer , has beeThe independent risk factors for the progression of ATL have been indicated as follows: a higher HTLV-1 PVL, advanced age, family history of ATL, and the first opportunity for HTLV-1 testing during treatment for other diseases . In addiA recent study developed and demonstrated the use of oligoclonality index (OCI)-Flow, which is another flow cytometry method to analyze HTLV-1-infected cells, based on the T-cell receptor (TCR) Vβ subunit diversity on T-cells infected with HTLV-1  . OCI-FloHowever, there are several limitations to the present study. Firstly, the sample size was small. Secondly, because this study has been performed as a cross-sectional observational study, there was no time-sequential evaluation of HAS-Flow in this study. Therefore, the impact of antirheumatic therapies on the population of CADM1+ CD4+ cells in patients with HTLV-1-positive RA is yet to be elucidated. It is necessary to investigate whether CADM1+ CD7neg dominant pattern is altered during antirheumatic therapies based on long-term observation. Thirdly, we used HAS-Flow and HTLV-1 PVL to assess the high-risk for ATL development in HTLV-1-positive RA patients; six patients were identified as high-risk in this study. However, whether these high-risk RA patients developed ATL is unknown. As a result, we intend to conduct a large-scale study in the future with prolonged follow-up periods to investigate the utility of HAS-Flow as a monitoring and/or predictive approach for ATL development in HTLV-1-positive RA patients.In conclusion, the analysis of CADM1+ CD4+ cells by HAS-Flow may be a useful method to observe HTLV-1-infected cells in HTLV-1-positive RA patients. Antirheumatic therapies, including immunosuppressants and biologics, may have less effect on the expression of CADM1. Because CD7neg HTLV-1-infected cells have a significant potential of ATL progression, examining the CADM1+ CD7neg predominant pattern in HTLV-1-positive RA patients may be a predictive finding of ATL development. However, whether antirheumatic medications affect the progression of ATL in HTLV-1-positive RA patients is unknown. Future research into the time-sequential HAS-Flow study could provide insights into the role impact of antirheumatic drugs in ATL progression.


This study aims to retrospectively analyze numerous related clinical data to identify three types of potential influencing factors of obstructive sleep apnea-hypopnea syndrome (OSAHS) for establishing three predictive nomograms, respectively. The best performing one was screened to guide further clinical decision-making.Correlation, difference and univariate logistic regression analysis were used to identify the influencing factors of OSAHS. Then these factors are divided into three different types according to the characteristics of the data. Lasso regression was used to filter out three types of factors to construct three nomograms, respectively. Compare the performance of the three nomograms evaluated by C-index, ROC curve and Decision Curve Analysis to select the best one. Two queues were obtained by randomly splitting the whole queue, and similar methods are used to verify the performance of the best nomogram.In total, 8 influencing factors of OSAHS have been identified and divided into three types. Lasso regression finally determined 6, 3 and 4 factors to construct mixed factors nomogram (MFN), baseline factors nomogram (BAFN) and blood factors nomogram (BLFN), respectively. MFN performed best among the three and also performed well in multiple queues.Compared with BAFN and BLFN constructed by single-type factors, MFN constructed by six mixed-type factors shows better performance in predicting the risk of OSAHS. As one of the well-known sleep apnea diseases, the typical pathophysiological feature of obstructive sleep apnea-hypopnea syndrome (OSAHS) is the complete or partial obstruction of the upper airway that occurs repeatedly during sleep, causing intermittent hypoxia, sleep fragmentation, sleep structure disorders, hypercapnia and sympathetic nerve excitation , 2. The The current diagnosis of OSAHS relies on polysomnography and clinical symptoms , 16. SnoIt is now generally believed that OSAHS is a complex, multi-factor and multi-gene disease –26, and As an independent risk factor for abnormal glucose and lipid metabolism , OSAHS hThis study aims to retrospectively analyze a large number of clinical data closely related to OSAHS to identify potential influencing factors of OSAHS for establishing the best performance predictive nomogram for OSAHS by complex analysis methods. It is hoped that this nomogram can accurately predict the risk of individuals suffering from OSAHS to guide further clinical decision-making.n = 118); (2) Received OSAHS related treatments  (n = 0); (3) With severe systemic disease (n = 0); (3) Unable to complete body index measurement (n = 1); (4) There is missing data (n = 0). Finally, 361 subjects recorded a complete prior history, have completed a whole blood index test, standard PSG and body index measurements.From January 2017 to September 2021, we retrospectively recruited 480 patients undergoing polysomnography (PSG) at the First Affiliated Hospital of Nanchang University and Shangrao People’s Hospital. 119 patients were excluded due to the following exclusion criteria: (1) No complete blood indicators needed for the research, including fasting blood glucose (FBG), glycosylated serum protein (GSP), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A-1 (ApoA1), apolipoprotein B (ApoB) and lipoprotein a (Lpa)  and complied with the Declaration of Helsinki.2.Height (m) and weight (kg) were measured and assigned readings by the professional measurers using the same measuring equipment (automatic height and weight instrument), based on the world health organization (WHO) standard method. BMI was calculated as weight/heightThe professional doctors asked and recorded the patient’s past medical history , age (year), gender (man or woman) and the prior history of smoking and alcohol intake (yes or no).All patients used polysomnography based on a laboratory PSG  to monitor and score respiratory events. Electroencephalogram, electromyogram, electrooculogram, electrocardiogram, nasal cavity and oral airflow, chest and abdominal cavity respiratory activity, snoring, pulse oximetry and other data were simultaneously recorded. All tests started before 10:00 at night and ended after 6:00 am the next day. After the monitoring, the professional and technical personnel will interpret and review according to the 2007 American Sleep Medicine Association Sleep and Related Event Interpretation Standards .AHI obtained through PSG was used to diagnose the patient with OSAHS. According to the standards set by the American Sleep Medicine Association, AHI ≥ 5 is defined as OSAHS , 46.We run spearman correlation analysis to explore the correlation between age, BMI, nine blood indicators, and AHI. Furthermore, the results were visualized through the lollipop graph. According to the grouping criteria: AHI ≥ 5, all patients were divided into the OSAHS and non-OSAHS groups. The differences of 16 indicators between the two groups were further compared and continuous variable results were visualized through the box plot. Univariate logistic regression was used to screen for the influencing factors of OSAHS further. The above analysis results are used to integrate to obtain eight influencing factors of OSAHS .K = 10. Similarly, we also identified three influencing factors from all baseline index factors . 4 influencing factors were also identified from all blood index factors . To compare the performance differences among the nomograms constructed by different types of factors, we use these three groups of influencing factors to construct nomograms to predict the patient’s risk of OSAHS, respectively. The nomogram constructed by six mixed factors was defined as the mixed factors nomogram (MFN). Similarly, baseline factors nomogram (BAFN) of 3 baseline factors and blood factors nomogram (BLFN) of 4 blood factors were defined separately.Finally, six influencing factors were screened from 8 influencing factors by choosing the best Lasso penalization parameter (λ) determined by the smallest k-fold cross-validation with n = 253) and test queue (n = 108) at a ratio of 7:3. To verify that randomization did not cause a deviation in the distribution of clinical indicators, we used a chi-square test to compare the differences in indicators between the training and test queues. Patients in these three queues were used in the following validation analysis simultaneously. We drew calibration curves to evaluate the accuracy of the nomogram in predicting the risk of OSAHS. Harrell’s concordance index (C-index) was measured to quantify the discrimination performance of the nomogram. The receiver operating characteristic (ROC) curve was used to visualize the predictive performance of the nomogram. We also ran a decision curve analysis (DCA) to determine the clinical benefit of the nomogram by quantifying the net benefits of different threshold probabilities for the three queues of patients.To repeatedly verify the predictive ability of MFN, 361 patients (whole queue) were randomly divided into training queue  and SPSS Statistics 22. R packages “ggpubr,” “reshape2,” “readr,” “glmnet,” “rms,” “ROCR,” “riskRegression” and “rmda” were used. Continuous data with a normal distribution are presented as the mean ± standard deviation; those with a skewed distribution are presented as the median [with interquartile range (IQR)]. Categorical data are presented as numbers (%). P < 0.05 was thought as statistically significant.Depending on the features of the distribution, differences between continuous variables were classified by the student’s The flow chart of the entire study is shown in Eight influencing factors are divided into three types . To compare the performance differences of the nomograms constructed by different influencing factors, we used three types of factors to run lasso regression analysis to filter the variables to construct the nomogram, respectively. Six mixed factors . The mixed internal calibration curve shows that the predicted probabilities of the three nomograms are all close to the actual probabilities, indicating that the prediction accuracy of the three nomograms is relatively high . CompareIt can also be seen from the three benefit curves that the overall benefit rate of MFN in the interval is higher than that of the other two types of nomograms . Based op > 0.05), which shows that the randomization did not cause the deviation of the indicator . These all support the conclusion that previous studies have regarded them as the vital risks factor for OSAHS , and HDL as protective factors of OSAHS .Previous studies have shown that OSAHS is positively correlated with TG, TC, LDL levels and negatively correlated with HDL levels . StudiesIneffective treatment and the serious harm of multiple systems reflect the extreme importance of early predictive diagnosis and early intervention of OSAHS. The nomogram constructs a scoring system based on the scores of each clinical member factor and the total score . DoctorsAlthough MFN performed well in multiple tests, this study still has many limitations. First of all, this study is considered a retrospective study based on existing data, not a prospective one. The influencing factors of OSAHS are determined only based on the results of the outcome indicators, not the specific process, which will also affect the accuracy of the results. Therefore, it is difficult for us to conduct an in-depth discussion on the specific mechanism of the identified influencing factors. Also limited by the shortcomings of retrospective analysis, the clinical variables included in our analysis are limited. For example, the available blood indicators are limited, which is reflected in fewer biochemical indicators and blood cell indicators. Many data of significant influencing factors of OSAHS, such as mechanical obstruction diseases of the upper airway, internal analysis system diseases , genetic factors have not been collected in our study. In addition, quantitative indicators such as the Epworth sleepiness score, which can well quantify the severity of OSAHS, were not included in this study. This will negatively affect the construction of the nomogram with more comprehensive variables more scientific prediction performance. Secondly, the limited sample size will also significantly affect the statistical efficiency, which will affect the reliability of the results and affect the performance of the test nomogram in this study. Finally, the excellent performance demonstrated by MFN still needs to be verified in a large number of clinical applications.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.XF and ML conceived and designed the study and wrote the manuscript. XF provided contributions to data analysis. XF and YZ prepared the figures. XF, XN, MH, and CT revised 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We evaluated changes of HTLV-1 proviral loads (PVLs) during treatment for rheumatoid arthritis (RA) and investigated whether these changes affect the clinical course in HTLV-1-positive RA patients.A total of 41 HTLV-1-positive RA patients were analyzed. Their clinical picture including disease activity  and comorbidity were evaluated over a 2-year period. PVLs from peripheral blood mononuclear cells were investigated by real-time polymerase chain reaction (PCR). We investigated whether HTLV-1 PVLs is altered, or which clinical characteristics affect changes of HTLV1-PVLs during 2-year treatment.Clinical disease activity was not changed during the 2-year observational period. The mean HTLV-1 PVL value change from baseline to 2 years was -1.2 copies/1000 PBMCs, which was not statistically significant. No baseline clinical characteristics influenced changes in HTLV-1 PVL. However, a numerical change of HTLV-1 PVLs was increased in 4 patients initiating the new biological/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) at 2−10 months after starting the new b/ts DMARDs . Infection occurred in 4 patients, and 3 of those patients showed an increased HTLV-1 PVL. Univariate analysis revealed an association between increase of HTLV-1 PVL and incidence of infection.Over 2 years, HTLV-1 PVL did not significantly change in our HTLV-1-positive RA patients. Individual changes in HTLV-1 PVL were correlated with incidence of infection but not disease activity which indicate that we may take precaution toward infection at the uptick of HTLV-1 PVL in HTLV-1-positive RA patients. Rheumatoid arthritis (RA) is an autoimmune disease characterized by symmetrical joint inflammation that results in progressive joint destruction. Treatments efficacy for RA have been changed by biological and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs), currently, the use of which enables patients to achieve low diseases activity in up to 40% and remission in up to 20% . HoweverHuman T-cell leukemia virus type 1 (HTLV-1) is an exogenous retrovirus and the etiological agent of adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM) . It infeSince immunosuppressive agents can weaken host immunity, there have been questions as to whether treatment of HTLV-1 infected RA patients with immunosuppressive agents increases their risk of ATL/HTLV-1 associated disease development. Indeed, several cases in which ATL developed during treatment for RA have been reported. The patients in these cases had been treated by various DMARDs including methotrexate (MTX), abatacept and etanercept –10.And, there is a possibility that HTLV-1 infection affects the clinical character of RA. Previously, we conducted a detailed investigation about the clinical features of HTLV-1-infected RA patients . That st® HTLV, Abbott Japan, Tokyo, Japan) was used for an HTLV-1 screening test and a line immunoassay (LIA) , was used for confirmation of HTLV-1 infection. Among these 83 patients, 22 patients were excluded because of low HTLV-1 PVL value [<1.9 copies per 1000 PBMCs; which indicated the lowest quartile level of PVLs in the previous study . All patients had a diagnosis of RA based on the 1987 American College of Rheumatology (ACR) classification criteria for RA or the 2010 ACR/European League against Rheumatism (EULAR) classification criteria for RA. We collected the enrolled patients’ data at the time of the first measurement of HTLV1-PVL, including the disease-modifying antirheumatic drugs (DMARDs) use history, their disease duration, positivity of rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA), and concomitant medications. The incidence of infection and development of HTLV-1-associated disease such as ATL were evaluated during a two-year follow-up period. Information about any changes of b/tsDMARDs was also collected during the study period.The patients’ clinical disease activity was assessed using the Disease Activity Score in 28 joints-erythrocyte sedimentation rate (DAS28-ESR), the Disease Activity Score in 28 joints-C-reactive protein (DAS28-CRP), simplified disease activity index (SDAI), and clinical disease activity index (CDAI) at baseline and 6, 12, 18 and 24 months after the first measurement of HTLV-1 PVL. The incidence of infections reported by the patients as well as on the findings of physical examinations were also assessed.Genomic DNA was obtained from samples by standard proteinase K treatment. To quantify the proviral load, we performed real-time PCR as described previously . The priGraphPad prism software  and JMP Statistical Software  were used for statistical analysis. Normal distribution of the data was confirmed using the Kolmogorov-Smirnov test. The distribution of baseline variables and proportion of disease activity in different patient subgroups were examined by Mann-Whitney U test and chi-square test. The Student’s paired t-test (for parametric data) or Wilcoxon signed rank test (non-parametric data) were used to detect statistically significant differences in HTLV-1 PVL change, inflammatory markers and disease activity. The Kruskal-Wallis test was used to compare baseline characteristics and disease course including incidence of infection and to estimate changes of disease activity among patient subgroups. Univariate analyses were used to identify factors contributing to the incidence of infection; our data were judged to be too few to perform multivariate analyses. All data are expressed as means ± standard deviations (SDs). P-values less than 0.05 were considered to indicate statistical significance.A total of 41 patients were analyzed in this study. The patients’ baseline demographic and clinical characteristics are summarized in Clinical disease activity as indicated by the DAS28-ESR, DAS28-CRP, SDAI, and CDAI, and the inflammatory markers were evaluated at baseline and 6, 12, 18 and 24 months after enrollment  in HTLV-1 PVL values from baseline at the 2-year point. Baseline characteristics investigated including b/tsDMARDs used at treatment and concomitant use of PSL were not different among these 4 groups   .However, the relation of the change of PVL with the risk of infection was not elucidated by these previous studies. Our study is the first report to show an association between increase of HTLV-1 PVL and incidence of infection. This result suggests that T cell dysfunction caused by HTLV-1 infection becomes more significant as the HTLV-1 viral load increases. Our data showed that HTLV-1 PVLs increased after new b/tsDMARD initiation. Taken together, physicians should observe HTLV-1-positive RA patients with special caution for infection in the first 2 years or more after initiation of a b/tsDMARD.No patient developed ATL or HTLV-1 related disease such as HAM, HTLV-1 uveitis in this study. Although immunosuppressive states have possibility to increase HTLV-1 PVL, it still remains unclear whether immunosuppressive agents affect the development of ATL in HTLV-1 carrier patients. In our previous study over a 24-week observational period, no ATL was developed among 50 HTLV-1-positive RA patients treated with a TNF-inhibitor and 27 HTLV-1-positive RA patients treated with a non-TNF-inhibitor , 26. MorChanges in HTLV-1 PVLs did not affect the disease activity of RA, and the disease activity of enrolled HTLV-1-positive RA patients was not significantly changed during the observational period. This result is similar with those of other studies. The above-mentioned 4-year observational study conducted by Umekita et al. showed no significant change of disease activity or HTLV-1 PVLs, and Endo et al. reported that the treatment response to non-TNF inhibitors was no different between HTLV-1 antibody-positive and -negative patients , 26. HowSeveral limitations of this study must be mentioned. The number of patients was small, and the number who initiated new b/tsDMARDs was even smaller. To confirm the tendency we found for the increment of HTLV-1 PVLs after initiation of b/tsDMARDs, a larger sample size is needed. Moreover, due to small number of infection cases, we could not perform multivariate logistic regression analysis to detect statistically related factors. Although this is the first study to show the possibility of increasing HTLV-1 PVL as a risk factor for infection, multivariable analysis with larger samples is important to rule out confounding factors. And we excluded the patients with low HTLV-1 PVL value because the PVL values in the patients with low PVL value at baseline had shown very little change during years in our previous experiences in clinical practice and we thought low PVL might less affect to clinical course of RA. So, the relation of changes in HTLV-1 PVL with clinical course of RA in RA patients with very low HTLV-1 PVL value remains unclear. Finally, with regard to the development of ATL, the study period of this study was relatively short. A very long latency period, as long as 50 years after HTLV-1 exposure, precedes ATL . TherefoIn conclusion, our present study demonstrated that in most of our HTLV-1-positive RA patients, HTLV-1 PVLs did not significantly changed during treatment and the changes in HTLV-1 PVLs did not affect the treatment response. However, the increase of HTLV-1 PVL was suggested to be related to the incidence of infection; thus, we should take special precautions against infection when HTLV-1-positive RA patients show an uptick of their HTLV-1 PVL.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Sasebo Chuo Hospital. The patients/participants provided their written informed consent to participate in this study.NI: conception and design of the study, analysis and interpretation of data, and drafting the article. KE: conception and design of the study, analysis and interpretation of data. NI, KE, and YU: collection and assembly of data. TA, AU, AT, ToA, KI, KT, YU, AK: analysis and interpretation of data, critical revision the manuscript. KE, YU, and AK: supervised the project. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.