Netarsudil, a Rho kinase inhibitor with norepinephrine transport inhibitory effect, lowers intraocular pressure, however, its effect on axon damage remains to be elucidated. The aim of the current study was to investigate the effect of netarsudil on TNF-induced axon loss and to examine whether it affects phosphorylated-AMP-activated kinase (p-AMPK) and autophagy in the optic nerve.Intravitreal administration of TNF or TNF with netarsudil was carried out on rats and quantification of axon number was determined. Electron microscopy determined autophagosome numbers. Localization of p-AMPK expression was examined by immunohistochemistry. The changes in p62, LC3-II, and p-AMPK levels were estimated in the optic nerve by immunoblot analysis. The effect of an AMPK activator A769662 or an AMPK inhibitor dorsomorphin on axon number was evaluated.Morphometric analysis revealed apparent protection by netarsudil against TNF-induced axon degeneration. Netarsudil increased autophagosome numbers inside axons. Netarsudil treatment significantly upregulated optic nerve LC3-II levels in both the TNF-treated eyes and the control eyes. Increased p62 protein level induced by TNF was significantly ameliorated by netarsudil. The netarsudil administration alone lessened p62 levels. Netarsudil significantly upregulated the optic nerve p-AMPK levels. A769662 exhibited obvious axonal protection against TNF-induced damage. A769662 treatment upregulated LC3-II levels and the increment of p62 level induced by TNF was significantly ameliorated by A769662. Immunohistochemical analysis revealed that p-AMPK is present in axons. Netarsudil-mediated axonal protection was significantly suppressed by dorsomorphin administration.Netarsudil upregulated p-AMPK and autophagy. Netarsudil-mediated axonal protection may be associated with upregulated p-AMPK. The Rho kinase (ROCK) inhibitors have been introduced as intraocular pressure (IOP)-lowering drugs, which improve the outflow of aqueous humor via the trabecular meshwork and Schlemm's canal.Autophagy research has received increasing attention and may need to be updated frequently. A very recent guideline has suggested that among the autophagy markers, microtubule-associated protein light chain 3 (LC3) has been used as a typically characterized autophagosome marker per se in many published studies, but the LC3 subfamily may be dispensable for autophagy regulation in certain cell types.,–Adenosine monophosphate-activated protein kinase (AMPK) is a multimeric serine/threonine protein kinase, and its activity is dependent on phosphorylation on Thr172. The role of AMPK in autophagy may differ dependent on physiological or pathological condition and cell or tissue type. For example, it was shown that activation of AMPK inhibits autophagy stimulation by amino acid starvation in HEK293 cells.Eight-week-old male Wistar rats were fed on a standard diet and water ad libitum and housed in cages . All experiments were approved by the Ethics Committee of the Institute of Experimental Animals of St. Marianna University School of Medicine and conducted according to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research.Under anesthetization with intramuscular injections of a mixture of ketamine-xylazine, 10 ng TNF  in 2 µL of phosphate-buffered saline (PBS) was administered intravitreally into the left eyes of the rats.2, and total area is 29,250 µm2 per eye) were used to quantify the axon number for each sample using an image processing software .In this experiment, we used 30, 12, and 20 rats for the netarsudil study, the A769662 study, and the dorsomorphin study, respectively. Optic nerves were isolated 2 weeks after intravitreal administration, fixed, processed, and embedded in acryl resin.2 each totaling 336.4 µm2 per optic nerve from each sample. The analysis was performed in three to four eyes per experimental condition.Ultrathin sections (100 nm) were made and incubated with saturated uranyl acetate. Ultrastructure of optic nerves was evaluated by a transmission electron microscope . Autophagosome numbers inside axons were determined as the sum in 10 different areas of 33.64 µmIn this experiment, we used 48 and 36 rats for the netarsudil study and for the A769662 study, respectively. Optic nerves were isolated 1 week after intravitreal administration and homogenized in protein extraction buffer. After being centrifuged, the supernatant was used for protein concentration measurement and processed in sample buffer (Bio-Rad Laboratories). Equal amounts of each sample were applied to SDS-PAGE gels (Bio-Rad Laboratories), ran, and transferred to an enhanced chemiluminescent membrane. Primary antibodies used were anti-LC3 antibody , anti-p62 antibody , anti-p-AMPK , and anti-β-actin . Tris buffered saline with Tween 20 (T-TBS) was used as dilution solution. Secondary antibodies were diluted 1:5000 in T-TBS and they were anti-rabbit and anti-mouse antibodies . An electrochemiluminescence (ECL) system was used for visualization of immunoblotting.Eyeballs (from 3 rats) were enucleated 1 week after intravitreal administration, fixed, processed, and embedded in paraffin. Five micrometer transverse sections were made through the optic nerve head. Primary antibodies included anti-p-AMPK , anti-Thy-1 , and anti-neurofilament . The dilution was done with 1% bovine serum albumin (BSA) in PBS. Secondary antibodies were diluted in 1% BSA in PBS and they were FITC-labeled anti-rabbit and rhodamine-labeled anti-mouse antibodies . The mount medium with DAPI was put on the slide glass with the cover glass and the images were obtained by a confocal microscopy system . Captured images of p-AMPK-positive fibers in the optic nerve were then analyzed by an image software . Ten different lines in one image can calculate the total image intensity. Fifty points were used to determine the background value in one image. Data from three sections of each eye were averaged for one eye, and three eyes were used for each experimental group.t-test. The results were considered statistically significant when P < 0.05.Data are expressed as mean ± SEM. Differences among groups were analyzed by 1-way ANOVA with post hoc Tukey's Honestly Significant Difference (HSD) test. Only immunohistochemical intensity data were analyzed by the Student's ,,P = 0.3728 vs. TNF; see P = 0.0414 vs. TNF; 200 pmol: P = 0.0004 vs. TNF; see Consistent with our previous findings,P = 0.3092), we used this concentration to test its effect on autophagy. Normal microtubules and neurofilaments were observed inside axons in the PBS group .In the eyes, p-AMPK has been found in the neural retina, including photoreceptor cells


Soluble tumor necrosis factor receptors 1 and 2 (sTNF-R1 and sTNF-R2) are reported to protect against excessive TNF-α, a primary mediator of systemic responses to infection. This study aimed to investigate the levels of TNF-α, sTNF-R1, and sTNF-R2 in saliva and to verify whether their dynamics are associated with periodontal health. The study population comprised 28 adult patients. Probing pocket depth, clinical attachment level, and bleeding on probing were assessed, and periodontal inflamed surface area (PISA) was calculated. Stimulated saliva was collected before the oral examinations. The levels of TNF-α, sTNF-R1, sTNF-R2, and total protein (TP) in saliva samples were determined. There were significant positive correlations between TNF-α, sTNF-R1, and sTNF-R2 to TP (/TP) in stimulated saliva. Moreover, there were significant positive correlations between PISA and sTNF-R2/TP. Stepwise multiple regression analysis revealed that PISA was significantly associated with sTNF-R2/TP in saliva; however, TNF-α/TP was not significantly associated with PISA. In conclusion, this study demonstrates that significant relationships exist between the salivary levels of TNF-α and sTNF-R1, and that salivary sTNF-R2 is associated with the expansion of inflamed periodontal tissue. Tumor necrosis factor-α (TNF-α) is a major mediator of inflammation and inflammation-related diseases . It actsThe effects of TNF-α are mediated by two membrane receptors, TNF receptor type 1 (TNF-R1) and TNF receptor type 2 (TNF-R2), that are carried on the surface of the target cell . These tA disintegrin and metalloproteinase 17 (ADAM17) is a TNF-α-converting enzyme, which is the principal protease involved in the activation of pro-TNF-α and is assumed to be the main protease responsible for the release of TNF-α from the transmembrane , and thaWhole saliva is a fluid containing components of the exocrine glands and GCF in the oral cavity and is considered a diagnostic fluid for periodontal disease screening. In fact, various mediators of chronic inflammation and tissue destruction have been detected in the saliva of patients with periodontitis and are thought to reflect the state of oral health, including periodontal disease status , and it The study population comprised 28 adult patients (11 men and 17 women), randomly selected from patients with slight or moderate chronic periodontitis, who visited the Kyushu Dental University Hospital from December 2019 to November 2020. Their average age was 67.2 (standard deviation (SD): 15.8) years, and the average number of teeth was 25.2 (3.4). Patients with medical disorders, those who had taken antibiotics or other antimicrobial therapy within the past 3 months, those who had fewer than 20 teeth, and pregnant women were excluded from the study.The human subject protocol was approved by the Ethics Committee of the Faculty of Dentistry at Kyushu Dental University  and was conducted in accordance with the Declaration of Helsinki involving human participants. Written informed consent was obtained from all participants after the purpose and procedures of the study were fully elucidated.g for 10 min at 4 °C to collect only the supernatant, which was further aliquoted and immediately stored at −30 °C until analysis. The participants refrained from eating, drinking, brushing, and washing their mouth for a minimum of 2 h before saliva collection.Before the clinical assessment, stimulated saliva was collected in 15-mL sterile tubes by chewing sugar-free and odorless gum  for 5 min. The saliva was subsequently centrifuged at 14,000× A dentist examined the clinical periodontal probing depth (PPD), clinical attachment level (CAL), and bleeding on probing (BOP) at six sites per tooth using the periodontal probe UNC 15  and diagnosed periodontal disease according to the clinical criteria stated in the consensus report of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions.Additionally, the periodontal inflamed surface area (PISA) was measured in all participants and was calculated using an automatically computable EXCEL form known as the Calculate PISA probing pocket depth as an alternative to the parsprototo.info website.The protein levels of TNF-α, sTNF-R1, and sTNF-R2 in the saliva samples were determined according to the manufacturer’s protocols using commercially available enzyme-linked immunosorbent assay kits , and the levels of total protein (TP) in the saliva samples were determined using a commercially available protein assay kit .In this study, the ratios of TNF-α, sTNF-R1, and sTNF-R2 protein levels to TP in 1 mL of saliva were expressed as TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP.The sample size was determined using the software G*Power 3.1.9.6 , resulting in a minimum of 26 subjects for correlation and linear multiple regression analyses .Variables between male and female participants were compared using a t-test or Mann-Whitney U test. The strength of correlations between the ratios of TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP and the clinical parameters and between the ratios of TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP were assessed using Pearson’s correlation and Spearman’s rank correlation analyses, respectively. The relationships between the ratios of TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP in saliva and between PISA and the ratios of TNFα/TP, sTNF-R1/TP, and sTNF-R2/TP in saliva were shown in the scatter plot graphs. Among the linear, quadratic, and logarithmic curve models, curve estimation by regression analysis was performed, and the quadratic curve model with the highest R-squared value and best fit was adopted as the approximation curve in each graph of the scatter plots. In addition, stepwise multiple linear regression analyses were performed using periodontal clinical parameters that were significantly correlated with TNF-α/TP, sTNF-R1/TP, or sTNF-R2/TP in Pearson’s correlation analysis as dependent variables. Each of them was analyzed using independent variables, including age, sex, number of present teeth, volume of stimulated saliva, TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP. All data analyses were performed using IBM SPSS Statistics .The profiles of the participants, including sex, are shown in p < 0.01, t-test), other clinical parameters besides saliva were not significantly different between the sexes.Although the mean volumes of stimulated saliva secreted for a minute  between men and women were significantly different (men (2.3 mL/min) vs. women (1.2 mL/min); The levels of TNF-α, sTNF-R1, sTNF-R2 proteins, and TP in 1 mL of saliva, including sex, are shown in The mean concentrations of TNF-α, sTNF-R1, sTNF-R2 proteins, and TP of all participants were 0.4 pg/mL (SD: 0.3), 244.1 pg/mL (165.4), 86.9 pg/mL (72.3), and 1625.5 µg/mL (768.8), respectively; there were no significant differences in the values between the sexes.−7 (2.3 × 10−7), 1.7 × 10−4 (1.1 × 10−4), and 0.6 × 10−4 (0.5 × 10−4), respectively, and all values were not significantly different between the sexes.The ratios to TP of TNF-α, sTNF-R1, and sTNF-R2 in the saliva of the participants, including sex, are shown as TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP in r = 0.488, p < 0.01; r = 0.477, p < 0.01; and r = 0.874, p < 0.001, respectively. In the scatter plot graph of TNF-α/TP and sTNF-R1/TP , between sTNF-R1 /TP and the volume of stimulated saliva , and between sTNF-R2/TP and the volume of stimulated saliva, the percentage of sites with BOP to total probed sites (BOP%), and PISA .There were significant correlations between TNF-α and the number of present teeth, and those of teeth with PPD > 5 mm (PPD5), and CAL > 5 mm (CAL5) (A stepwise multiple linear regression analysis was performed using age, sex, number of present teeth, volume of stimulated saliva, TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP as independent variables as well as BOP%, numbers of PPD5 and CAL5, and PISA, which showed significant correlations between TNF-α/TP, sTNF-R1/TP, or sTNF-R2/TP in Pearson’s correlation analysis, as the dependent variables .The numbers of PPD5 and CAL5 teeth showed a significant association with TNF-α and TP, respectively. BOP% indicated a significant association with sTNF-R1/TP, sTNF-R2/TP, stimulated saliva, and age, and increases in sTNF-R1/TP and stimulated saliva were associated with a reduction in BOP%. Furthermore, PISA was significantly associated with sTNF-R2/TP.2, the ratios of TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP increased linearly with the expansion of PISA. Furthermore, if the PISA exceeded 200 mm2, the ratio of TNF-α/TP was observed to decrease linearly with the expansion of PISA (2 (The relationships between PISA and the ratios of TNF-α/TP, sTNF-R1/TP, and sTNF-R2/TP in saliva are shown in the scatter plot graphs . In addi of PISA C, and sT of PISA A. Howeve PISA (2 B.In the present study, salivary TNF-α levels were associated with an increase in the number of teeth with deep PPD and a CAL of >5 mm. TNF-α contributes to the onset of periodontal inflammation, such as periodontitis. Elevated levels of TNF-α from various cells in gingival tissue are associated with the destruction of periodontal tissues, including bone resorption . TherefoAlthough the dynamics of sTNF-R1 and sTNF-R2 in saliva are unknown, it was demonstrated in the present study that the mean salivary level of sTNF-R1 was higher than that of sTNF-R2. Moreover, there was a strong positive correlation between the levels of sTNF-R1 and sTNF-R2 in saliva. With regard to the dynamics between TNF-α and sTNF-R1 and sTNF-R2 in saliva, it was found in this study that both levels of sTNF-R1 and sTNF-R2 were synergistically enhanced up to a certain level of salivary TNF-α, but decreased when TNF-α exceeded a certain level. Previously, the serum levels of sTNF-R1 and sTNF-R2 in patients with ankylosing spondylitis and rheumatoid arthritis were reported to be higher than those in healthy controls . GeneralThe present study is the first to depict the relationship between periodontal health and salivary levels of TNF-α, sTNF-R1, and sTNF-R2. Here, PISA was used as a parameter to quantify the amount of inflamed periodontal tissue to quantify the inflammatory burden, which indicated that salivary levels of sTNF-R2 were significantly associated with PISA in addition to BOP%, which reflects the quantitative evaluation of inflamed periodontal tissues. Although there was no significant correlation between PISA and the salivary levels of TNF-α and sTNF-R1, it seemed that increases in the levels of TNF-α, sTNF-R1, and sTNF-R2 in saliva were linked to the expansion of PISA in the early stages of inflammation, when the spread of inflamed periodontal tissue is small. Moreover, salivary TNF-α levels did not progress to a more advanced stage of inflammation, while only salivary sTNF-R2 levels tended to increase with the expansion of PISA. TNF-α was reported to upregulate the release of sTNF-R2 from human gingival fibroblasts but not that of sTNF-R1 . AdditioAs a result, the reduction in salivary TNF-α levels may be induced by sTNF-R2, which may prevent development linked to the destruction of inflamed periodontal tissues. In contrast, it was evident that the salivary level of sTNF-R1 was high and mutually correlated with that of sTNF-R2, and it seemed that the salivary level of sTNF-R1 was associated with the reduction of gingival inflammation in the present study, Naturally, TNF-R1 has a high affinity against soluble TNF-α . TherefoIn a preliminary study, we confirmed that there were no statistical differences in the proportions of TNF-α, sTNF-R1, and sTNF-R2, which were adjusted by TP in stimulated and resting saliva, and that the collection of stimulated saliva was easier and could be obtained in larger quantities than that of resting saliva, even if saliva secretion was limited. Thus, the present study used stimulated saliva and measured the concentrations of TNF-α, sTNF-R1, and sTNF-R2 in addition to TP. Furthermore, there is a significant difference in the mean volume of stimulated saliva between the sexes, as indicated in this study, because the saliva flow rate increases with an increase in salivary gland size, and the salivary gland size differs between the sexes . HoweverBiomarkers in GCF may be more useful for assessing the state of local periodontal health compared to those in saliva; however, collecting a sufficient amount of fluid and assessing some types of biomarkers can often be difficult . The preThe present study was a cross-sectional study that involved patients with slight or moderate chronic periodontitis and examined the relationships between the parameters of different periodontal health statuses of the subjects in addition to salivary TNF-α, sTNF-R1, and sTNF-R2 levels. The findings of this study are the first step in clarifying the dynamics and roles of TNF-α, sTNF-R1, and sTNF-R2 in the process of periodontal disease. In this study, data were presented separately for men and women to show the characteristics of the subjects. However, although the sample size in this study was suitable for correlation and linear multiple regression analyses, the results of the analysis obtained by comparison between the sexes may need to be considered as reference values because each sample size was small when comparing the two groups. This should be considered in the same way when comparing groups using other parameters, such as differences in age and severity of periodontal diseases in this study. Accordingly, the present study did not show other data for comparison by group, except for sex. However, the most significant limitation of this study is that we were unable to verify the same parameters in healthy subjects without periodontal disease or in patients with severe chronic periodontitis because the subjects in this study only included patients with slight or moderate chronic periodontitis. Thus, based on our findings, future studies are needed to further demonstrate the roles of sTNF-R1 and sTNF-R2 in the development of periodontal disease. Specifically, by increasing the sample size, including healthy controls and subjects with more severe periodontal health, and tracing the prognosis of the patients included in this study, it may be possible to obtain more reliable data on the status of TNF-α, sTNF-R1, and sTNF-R2 in saliva. Proinflammatory signaling activated by TNF-α is an important factor in the pathology of periodontal disease [The present study demonstrates for the first time that there are significant relationships between the salivary levels of TNF-α, sTNF-R1, and sTNF-R2, and that salivary sTNF-R2 is associated with the expansion of inflamed periodontal tissue.


Chronic kidney disease (CKD) has been recognized as a global public health problem. Despite the current advances in medicine, CKD-associated morbidity and mortality remain unacceptably high. Several studies have highlighted the contribution of inflammation and inflammatory mediators to the development and/or progression of CKD, such as tumor necrosis factor (TNF)-related biomarkers. The inflammation pathway driven by TNF-α, through TNF receptors 1 (TNFR1) and 2 (TNFR2), involves important mediators in the pathogenesis of CKD. Circulating levels of TNFRs were associated with changes in other biomarkers of kidney function and injury, and were described as predictors of disease progression, cardiovascular morbidity, and mortality in several cohorts of patients. Experimental studies describe the possible downstream signaling pathways induced upon TNFR activation and the resulting biological responses. This review will focus on the available data on TNFR1 and TNFR2, and illustrates their contributions to the pathophysiology of kidney diseases, their cellular and molecular roles, as well as their potential as CKD biomarkers. The emerging evidence shows that TNF receptors could act as biomarkers of renal damage and as mediators of the disease. Furthermore, it has been suggested that these biomarkers could significantly improve the discrimination of clinical CKD prognostic models. In the last decade, chronic kidney disease (CKD) has been recognized as a global public health problem, due to its increasing incidence and prevalence rates ,2. AdditCKD is a pathological condition that results from a gradual and permanent loss of renal function over time, characterized by the presence of kidney dysfunction and injury markers, over a period of at least three months. According to the ‘2012 Kidney Disease: Improving Global Outcomes’ (KDIGO) guidelines, the severity of CKD is classified into five stages, according to glomerular filtration rate (GFR) and urinary albumin excretion . IncreasThe etiology of CKD depends on the setting, with diabetes and hypertension being the two major causes of kidney injury in developed countries . HoweverCKD is associated with a decreased quality of life, increased risk of hospitalization, cardiovascular complications and mortality, independently of other risk factors ,3,6,7. ISeveral studies in the literature suggest that activation of inflammatory processes in the early stages of CKD drives kidney function impairment , meaningThe persistent low-grade inflammatory status that characterizes CKD plays a key role in the pathophysiology of the disease. Inflammation starts early in the onset of renal diseases ,14 and wInflammation is a well-established risk factor of both morbidity and mortality in CKD patients ,22,23, lThe initial inflammatory response occurs to overcome renal injury, promote tissue remodeling and wound healing. However, when this process outreaches the physiological limit, a chronic inflammatory state may arise, with undesirable systemic consequences . The dysThe inflammatory state is characterized by activation of inflammatory cells, releasing an array of acute phase proteins, cytokines, and chemokines ,25, whicCytokines and acute phase proteins are simultaneously key mediators and biomarkers of inflammation. Even though their circulating concentrations show a tendency to increase with the worsening of disease, the rate and magnitude of the increase depends on the molecule itself. It has been shown in several CKD models, that the classical proinflammatory signaling pathway, the NF-ĸB system ,30, is aTumor necrosis factor alpha (TNF-α), also known as TNF superfamily member 2 (TNFSF2) or simply TNF, is a pleiotropic cytokine that can mediate the inflammatory response, regulate immune function by promoting immune cells activation and recruitment, and may trigger cell proliferation, differentiation, apoptosis, and necroptosis . TNF-α iTNF-α can be found in two bioactive homotrimeric forms: as a 26 kDa transmembrane peptide, or as a 17 kDa soluble form that is released into circulation upon cleavage by the metalloproteinase TNF-α converting enzyme (TACE) ,42. The TNF-α exerts both homeostatic and pro-inflammatory roles. However, TNF-α binding to TNFR1 mostly promotes inflammation and tissue injury , while bTNFR1 and TNFR2 present different intracellular domains  that canThe pathways triggered upon TNFR1 activation are better known. TNFR1 contains an intracellular death domain (DD) that, in the absence of ligand, interacts with a cytosolic silencer of death domains (SODD) . Upon biUbiquitinated RIPK1 allows the activation of complex I, comprising TRADD, RIPK1, TRAF2, cellular inhibitor of apoptosis protein 1 or 2 (cIAP1/2), and linear ubiquitin chain assembly complex (LUBAC) ,56. BothThe activation of the IKK complex requires NEMO ubiquitination, by LUBAC ,58, and Alternatively, TNF-α binding to TNFR1 can induce two types of programmed cell death, apoptosis or necroptosis, when death-inducing signaling complexes  are assembled in the cytosol . TNF-TNFThe depletion of the cIAP1 and 2 also reduces, or prevents, RIPK1 ubiquitination , resultiFurthermore, the aggregation of RIPK1 and RIPK3 leads to the activation of mixed lineage kinase domain-like protein (MLKL) ,67, throUbiquitination of the proteins involved in TNFR1-signaling cascades has a major role in determining TNF-induced downstream outcomes. Ubiquitination status of RIPK1 determines whether TNF-TNFR1 signaling mediates cell survival or apoptosis, since RIPK1 ubiquitination prevents complex IIa and IIb from assembling. Several ubiquitin-modifying proteins that act on RIPK1 have been identified. The ubiquitin-modifying enzyme A20 is able to bind and remove polyubiquitin chains from RIPK1 and LUBAC, blocking NF-ĸB activation. Cylindromatosis (CYLD) is another deubiquitylating enzyme that acts on several proteins, such as TRAF2, RIPK1, and IKKγ, to regulate the NF-ĸB and JNK pathways ,69. In aUnlike TNFR1, TNFR2 does not have a DD, being unable to recruit TRADD . Upon TNHowever, TNFR2 may also trigger non-canonical NF-κB signaling , by promFurthermore, TNFR2 is able to activate JNK and protein kinase B (Akt) pathways ,76. TNFRIn endothelial cells, TNFR2 also signals through interferon regulatory factor-1 (IRF1), inducing interferon-β (IFN-β), promoting the transcription of inflammatory cytokines and monocytes recruitment during a TNF-induced inflammatory response .TNFR2 can induce cell death indirectly by crosstalk with TNFR1. Depletion of TRAF2 by TNFR2 inhibits the NF-kB and MAPK signaling pathways mediated by TNFR1, favoring the formation of death complexes .When TNFR1 and TNFR2 are co-expressed in the same cells, intracellular crosstalk between both signaling pathways seems to be mainly shaped by intracellular constraints, such as the availability of downstream effectors of each pathway, such as TRAF2 and ASK-1 ,82. HoweThe inflammation pathway driven by TNF-α is important in the pathogenesis of CKD ,84. HoweIn this review, we summarize the more important results from published studies on the contribution of TNF-α and its receptors to the development and/or progression of CKD . A searcAnimal studies are the primary source of evidence for the role of TNF-α in the development of kidney diseases . In the Studies addressing the deletion of TNFR1 and/or TNFR2 genes, in animal models, also illustrated the contributions of the TNFRs in the pathophysiology of kidney diseases. The deletion of TNFR1 was associated with an increase in GFR, in an angiotensin II-induced model of hypertension . Data frTNFR participation in diabetic kidney disease has been the subject of specific research. Previous studies reported that the TNF-α inhibition protects against tubular injuries  and prevTranscriptomics further showed that both oxidative stress and inflammation play a role in the pathogenesis of CKD, and are correlated with cellular alterations that lead to systemic complications . In ischIn clinical studies, circulating TNFR1 and TNFR2 were shown to be increased in several cohorts of patients, with different CKD etiologies and diverse age-groups and races . DespiteThe first study assessing the serum levels of a TNFR (unidentified either as TNFR1 or TNFR2) in CKD patients was published in 1994, and showed a strong correlation between the receptor levels and serum creatinine, in a group of 26 non-dialyzed CKD patients . TNFR1 aIn a group of 106 biopsy-proven IgA nephropathy patients, higher serum levels of TNFR1 and TNFR2 were present in patients with more severe renal interstitial fibrosis . IncreasThe predictive value of TNFRs was mostly described in diabetic nephropathy, as reviewed by Murakoshi et al. (2020) . SeveralCKD patients have an increased risk of mortality due to CVD, which is independent of the traditional risk factors, possibly due to the chronic inflammatory state. Both circulating TNFRs were described as predictors of CVD risk ,129 and In the last 15 years, proteomic and transcriptomic studies have proven useful in discovering new insights into the TNF-α signaling pathway in CKD, as well as the associated-comorbidities. In a proteomic analysis of human serum from patients with CKD, TNF-α was associated with disease severity ,135, as The huge amount of scientific evidence linking TNF signaling with the pathophysiology of CKD raises questions regarding the utility and safety of therapeutic strategies targeting TNF-α in humans with impaired kidney function.Nephrotoxicity is a rare side effect of anti-TNF-α medications, and a few reports of this occurrence have been described in the literature. Premužić et al. reported an association of TNF-α inhibitors  and the development of IgA nephropathy in three patients with both rheumatoid arthritis and diabetes, but without history of renal disease . MoreoveHowever, other studies demonstrated the therapeutic benefit of TNF-α blocking in improving renal inflammation and function. In patients with rheumatoid arthritis and CKD, the administration of anti-TNF-α was associated with less renal function decline . In addiThere is a limitation to the beneficial effects of anti-TNF-α agents, which seems to be related to their ability to induce autoimmunity by disrupting TNF-α normal immune regulation. Their use in clinical practice would require surveillance for complications. Indeed, the biological functions of cytokines are complex, and, thereby, blocking of cytokines might induce other unexpected and unclear effects in vivo. Furthermore, the effects of anti-TNF-α agents might be modulated by other factors, such as their distribution into diseased tissues, and degradation by proteases. Given the potential benefits of these therapies, a deeper understanding of the TNF signaling pathway and the mechanisms of action of the anti-TNF-α agents and their correlation with the clinical settings is needed for a more appropriate and personalized selection of therapeutic agents, and even for the development of new biological preparations, to be applied in the treatment of inflammatory diseases.Despite recent breakthroughs in CKD care, the rates of morbidity and mortality are still unacceptable. Chronic inflammation is a common feature in kidney diseases, regardless of its etiology, and which plays a key role in disease pathophysiology, progression, and development of associated complications. Unresolved inflammatory processes generally lead to renal fibrosis and ESRD.The role of TNF-α in the pathogenesis of kidney diseases depends on the engagement of receptor-specific and/or common signaling cascades. The differential expression of both receptors in different cell types, and the fact that soluble and transmembrane TNF-α present different affinities to each receptor, are other factors that contribute to the complexity of TNF-α signaling.Circulating TNFRs have been associated with renal damage in several animal and human studies. Based on the available data, increased levels of TNFRs associate with decreased eGFR and increased albuminuria. Overall, TNFRs have proven to be useful and effective in predicting renal function decline and CKD progression, as well as CKD-associated morbidity and mortality, among different cohorts of patients in both cross-sectional and longitudinal studies. The consistency of the published literature evidences their potential role as prognostic and risk-predictive biomarkers in CKD, along with the traditional markers already used in clinical practice.The mechanisms by which the TNFRs initiate and perpetuate renal damage are not completely understood. In fact, there is evidence that TNF-α is not the only molecule involved in the regulation of its receptors during renal function decline , suggestTo date, there are no anti-inflammatory treatments for CKD patients. Treating inflammation and preventing the progression of renal fibrosis is complex, due to the crosstalk between the inflammatory signaling pathways. The approved therapeutic use of anti-TNF monoclonal antibodies is currently limited to autoimmune diseases, such as rheumatoid arthritis, Chron’s disease, or psoriatic arthritis . ConsideFuture research should focus on validating the promising findings in large, multicentered studies, with standardized methodologies, to allow their translation into clinical practice. TNFRs could be important tools to improve CKD patient’s characterization and management, with direct implications for strategies to prevent or postpone the progression of CKD. This may possibly result in a better prognosis for patients, as well as in financial benefits; lowering healthcare costs in CKD management.


Micropterus salmoides). Five isonitrogenous and isolipidic micro-diets were formulated to contain graded inclusion levels of phospholipids . Results showed that the supplementation of dietary phospholipids linearly improved the growth performance of largemouth bass larvae. The inclusion of dietary phospholipids increased the activity of digestive enzymes, such as lipase, trypsin and alkaline phosphatase, and promoted the expression of tight junction proteins including ZO-1, claudin-4 and claudin-5. Additionally, dietary phospholipids inclusion alleviated the accumulation of intestinal triacylglycerols, and further elevated the activity of lysozyme. Dietary phospholipids inhibited the transcription of some pro-inflammatory cytokines, including il-1β, and tnf-α, but promoted the expression of anti-inflammatory cytokines tgf-β, with these modifications being suggested to be mediated by the p38MAPK/Nf-κB pathway. The analysis of bacterial 16S rRNA V3-4 region indicated that the intestinal microbiota profile was significantly altered at the genus level with dietary phospholipids inclusion, including a decreased richness of pathogenic bacteria genera Klebsiella in larval intestine. In summary, it was showed that largemouth bass larvae have a specific requirement for dietary phospholipids, and this study provided novel insights on how dietary phospholipids supplementation contributes to improving the growth performance, digestive tract development and intestinal health.While the beneficial roles of dietary phospholipids on health status and overall performances of fish larvae have been well demonstrated, the underlying mechanisms remain unclear. To address this gap, the present study was conducted to investigate the effects of dietary phospholipids on growth performance, intestinal development, immune response and microbiota of larval largemouth bass ( Phospholipids are phosphorus containing lipids with several important structural and functional roles , 2. It iDicentrarchus labrax) (Sander lucioperca) (Larmichthys crocea) (Sparus aurata) (Ctenopharyngodon idella)   , pikeperioperca)  and larg crocea) . Importa crocea) . The tig aurata) , grass c idella) , and larlmoides) . NeverthPhospholipids are important components of chylomicrons, which are central for lipid transport , but theAnother important consideration when it comes to digestive and intestinal health in larval fish is the intestinal microbiota . The divConsidering the points introduced above, and aiming at contributing to current knowledge of the roles of dietary phospholipids on the overall performance of fish larvae, the present study focused on assessing the effects of dietary phospholipids on growth performance, digestive enzymes activity, antioxidant capacity, immune response and intestinal microbiome of larval largemouth bass. Largemouth bass was chosen as target species as it has important socio-economic considerations, as its production in China has reached 600,000 tons in 2020 . PreviouFive isonitrogenous, isoenergetic and isolipidic diets were formulated with graded supplementation of soybean phospholipids, 0% , 2% (PL2), 4% (PL4), 6% (PL6) and 8% (PL8), and the resulting measured value of dietary phospholipids content was 1.69, 3.11, 5.23, 7.43, and 9.29%, respectively . Brieflyin vivo feeding trail was conducted at the joint laboratory of Shanghai Ocean University and Guangdong Evergreen Feed Industry Co., Ltd , and all procedures of this study were performed following the guide for the use of experimental animals of Shanghai Ocean University. Largemouth bass larvae  were provided by a local commercial hatchery in Foshan city . Larvae were fed with newly hatched artemia for three days, and then weaned to adapt a dried commercial micro-diet by alternate feeding artemia and micro-diet over a six days’ period. Then, larvae (17 DPH) with initial body weight of 9.61 ± 0.02 mg were divided into 15 tanks (water volume 1000 L) with the destiny of 2500 individuals per tank. Following a random allocation, triplicates groups of larvae were fed the experimental diets to apparent satiation five times daily  for 28 days. The larvae were gradually fed the diet with particle size of 180-250 μm (body weight ≤ 0.10g), 250-380 μm (0.10g < body weight ≤ 0.50g), 380-830 μm (0.50g < body weight ≤ 0.80g) and above 830 μm (body weight > 0.80g). The water quality was monitored during the feeding trail to maintain within optimal conditions: temperature, 27 ± 1°C; pH, 7.2 ± 0.2; dissolved oxygen ≥ 6 mg/L.The in vivo study, the weight and number of fish larvae were recorded to calculate the survival rate and growth performance. Before final weighing, fish were fasted for 24h and then anaesthetized with eugenol . Thirty larvae were randomly collected and sacrificed to measure the individual body weight and body length, and were then used for the body composition analysis. Another sixty fish were sacrificed and dissected to collect liver and intestinal samples for the analysis of enzyme activity, gene expression and microbiome.The initial body weight and body length of 100 randomly collected largemouth bass larvae were measured at the beginning of the feeding trail. At the termination of the The moisture content was determined through drying samples at 105°C to a constant weight . Crude pThe intestinal samples were homogenized in ice-cold phosphate buffer saline , and then centrifuged at 5000 rpm for 10 min at 4°C to separate the supernatant which was used for the analysis of digestive enzymes activity. The trypsin activity was measured by the trypsin assay kit , and L-arginine ethyl ester was used as the reaction substrate. The activity of lipase was determined, with triglyceride as the substrate, by the lipase assay kit (Nanjing Jiancheng Bio-Engineering Institute). The amylase activity was assayed by evaluating the reduced amylase content with a commercial kit (Nanjing Jiancheng Bio-Engineering Institute). The activity of alkaline phosphatase was determined using disodium phenyl phosphate as reaction substrate with a commercial kit (Nanjing Jiancheng Bio-Engineering Institute). The soluble protein content was determined with the coomassie brilliant blue method , which cMicrococcus lysodeikticus. Then, the mixture was reacted at 37°C for 5 min, and thereafter, the transmittance was recorded to calculate the lysozyme activity.The lysozyme activity of intestine was determined using the turbidimetric method with a commercial lysozyme assay kit (Nanjing Jiancheng Bio-engineering Institute). Briefly, the transmittance was recorded at 20s after samples being blended with the suspension of The triacylglycerols (TAG) content in intestine was analyzed spectrophotochemically at 546 nm according to the colorimetric enzyme-linked TAG detection method described by Schwartz and Wolins  with a cp38α mitogen-activated protein kinase (p38α mapk/mapk14), p38δ mapk (mapk 13), nuclear factor-kappa B p65 (nf-κb p65/rela), interleukin-1β (il-1β), tumor necrosis factor-α (tnf-α), 5-lipoxygenase (5-lox), transforming growth factor-β (tgf-β), zona occludens-1 (zo-1), claudin-1, claudin-4, claudin-5 and β-actin were designed based on the corresponding sequences or referred to previously published studies  following the instructions of manufacturer. The integrity of DNA extract was determined on 1% agarose gel, and concentration and purity of the obtained DNA were measured with NanoDrop 2000 UV-vis spectrophotometer . Then, the V3-V4 region of the bacterial 16S rRNA gene was amplified with the 338F/806R primer set in a PCR thermocycler . The PCR program was set as follows: 95°C for 3 min, followed by 27 cycles of 95°C for 30 s, 55°C for 30 s and 72°C for 45 s, and 72°C for 10 min. Subsequently, the PCR product was extracted from 2% agarose gel, and purified with the AxyPrep DNA Gel Extraction Kit , and quantified using Quantus™ Fluorometer . After that, the purified amplicons were sequenced on an Illumina MiSeq PE300 platform  by Majorbio Bio-Pharm Technology Co. Ltd. .The bacterial DNA of the larvae in both the control group and PL8 group were extracted using the E.Z.N.A.The raw DNA sequences were demultiplexed and quality-filtered to obtain the high-quality clean reads with the Quantitative Insights into Microbial Ecology (QIIME) quality filters , and theThe growth performance of largemouth bass in response to dietary phospholipids inclusion were calculated as follows:P < 0.05, and the results are presented as mean values, plus or minus standard errors of means. The intestinal bacterial diversity and OTU richness were analyzed by the Welch’s t-test with statistically significance being considered as P < 0.05.All of the statistical analysis were performed with SPSS version 19.0 . Polynomial contrasts (linear and quadratic) were used to test the effects of dietary phospholipids inclusion levels on the various variables measured in the present study after the data being tested for normality and homoscedasticity. When both linear and quadratic significant regressions were observed, linearity was selected as the simplest model for describing the observed trend. The level of significance was set at P < 0.05) (P < 0.05) (P < 0.05), and the maximum content was observed in the PL8 group  (P < 0.05), and the maximum value was observed in the PL4 group (P < 0.05) (The inclusion of phospholipids produced no significant influence on the moisture content of largemouth bass larvae (P > 0.05) (P < 0.05) (P < 0.05) (P < 0.05)  (claudin-1 in a negative linear manner (P < 0.05)  (The expression of P < 0.05) (P < 0.05) (mapk14 was significantly down-regulated in a linear manner with the supplementation of phospholipids (P < 0.05) (mapk13 (P > 0.05)  (il-1β expression (P < 0.05)   (tgf-β was linearly increased with the inclusion of dietary phospholipids (P < 0.05)  database under accession number PRJNA767234. The OTUs were assigned to 638 species, 466 genera, 244 families, 136 orders, 54 class, and 26 phyla. The alpha diversity index, including Simpson, Shannon, Ace and Chao index, of the intestinal microbiota in larvae of PL8 group was slightly higher than that of the control group, but no significant differences were observed   and PL8. The Proteobacteria, Firmicutes, and Actinomycetes were the dominant phyla in both treatments (Plesiomonas (51.49%) and Klebsiella (35.34%) , Macrococcus, Corynebacterium_1, Bifidobacterium, Acinetobacter, Thauera, and Thiobacillus (from order to genus) and Plesiomonas at the genus level , cobia (Rachycentron canadum), large yellow croaker, Dojo loach (Misgurnus anguillicaudatus), and hybrid grouper (Epinephelus fuscoguttatus ♀× E. lanceolatus ♂)  , 51–53. The immature digestive tract of fish larvae seriously restricts the digestion and absorption of nutrients, and that is one of the main reasons why the culture of larval fish relies on live prey. Previous studies have demonstrated that dietary phospholipids supplementation can promote the development of digestive tract in some fish larvae –6. In gezo-1 expression caused the impairment of tight junction  (Cyprinus carpio) (Acipenser stellatus)  , common  carpio)  and stelellatus) . Cytokinellatus) . The proow trout . In the  teleost . In gene teleost , 68; 69.tnf-α and il-1β, can be directly induced by the transcriptional factor, nf-κb  or not , in accordance with previous studies reviewed by Borges etal.  BioProject database under accession number PRJNA767234.The animal study was reviewed and approved by the Animal Care and Use Committee of the Shanghai Ocean University.SW: investigation, formal analysis, and writing - original draft. ZH: investigation, methodology, and data curation. GT: methodology and writing - review & editing. XW: investigation and methodology. ZF: investigation and methodology. NC: conceptualization, project administration, and funding acquisition. RX and HZ: investigation. SL: conceptualization, supervision, writing - review & editing, and funding acquisition. All authors contributed to the article and approved the submitted version.This work was financially supported by National Key R&D Program of China (2019YFD0900203), China Agriculture Research System of MOF and MARA (CARS-46), Shanghai Talent Development Fund (2019097), and National Natural Science Foundation of China (31802308).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.


The detection of ethanolamine ( Cell membranes are a key element of life because they keep the genetic material and metabolic machinery together. All present cell membranes are made of phospholipids, yet the nature of the first membranes and the origin of phospholipids are still under debate. We report here the presence of ethanolamine in space,  Life is based on three basic subsystems: a compartment, a metabolic machinery, and information-bearing molecules together with replication mechanisms , 2. AmonThe process through which the first phospholipids were formed remains unknown. Initial work proposed that phospholipids could be synthesized under possible prebiotic conditions –7, but tIn particular, some structural parts of phospholipids are known to be present in meteorites, such as fatty acids, alcohols, and phosphonic acids , 13, 14.al vents , consideal vents .EtA has been found in the Almahata Sitta meteorite , yet itsTex in the range of 5 to 15 K (We have detected EtA toward the molecular cloud G+0.693–0.027 (hereafter G+0.693), located in the SgrB2 complex in the Galactic Center, as shown in 21) shocks  induced  to 15 K . Since oTex measured in G+0.693 (Tex ∼5 to 15 K)  30-m and the Yebes 40-m radiotelescopes. Detailed information about the observations is presented in to 15 K) , 22.We have detected the 45 brightest transitions of EtA, as predicted by the LTE simulation  , 42, 47,(CH3NH2) .Moreover, form EtA . UnfortuThe penultimate step of the hydrogenation chain that results into EtA is aminoacetaldehyde  , we deriOnce EtA was available on Earth’s surface, it could form phospholipids  and the IRAM 30-m telescope . The observations were centered at the equatorial coordinates of G+0.693: RA(J2000) = 17 h 47 m 22s, DEC(J2000) = −28° 21′ 27”.The observations were carried out with the Yebes 40-m telescope located in Yebes , during six observing sessions in February 2020, as part of the project 20A008 . We used the new Nanocosmos Q-band (7-mm) high-electron-mobility transistor receiver that enables ultrabroad-band observations in two linear polarizations . The rec G+0.693 . The telhe beams . The noiWe have carried out a spectral survey at 3 mm using the IRAM 30-m telescope. The observations were performed in two observing runs during 2019: 10 to 16 April and 13 to 19 August, from project numbers 172-18  and 018-19 . We used the broad-band Eight MIxer Receiver (EMIR) and the fast Fourier transform spectrometers in FTS200 mode, which provided a channel width of http://hsqldb.org/) that contains the spectral line catalogs of the Jet Propulsion Laboratory (https://spec.jpl.nasa.gov/) (JPL) (https://cdms.astro.uni-koeln.de/) (The identification of the molecular lines was performed using the SLIM tool of the MADCUBA package./) (JPL)  and the eln.de/) , 58.For the case of EtA, we have used the spectroscopic entry 61004 (version September 2003) of the JPL database, based on different laboratory works –61. The To derive the physical parameters from the molecular emission, we have used the AUTOFIT tool of SLIM , which pFor the analysis of EtA, we fixed the linewidth  using the expression−2), and the The rotational diagram is calculated following the standard procedure  implemenions see . The errWe present in We have used the CDMS entry 041506 (June 2019), based on several spectroscopic works –67. We h species . We have


Illness scripts describe the mental model used by experienced clinicians to store and recall condition-specific knowledge when making clinical decisions. Studies demonstrate that novice clinicians struggle to develop and apply strong illness scripts. We developed the Integrated Illness Script and Mechanism of Disease (IIS-MOD) map framework to address this challenge. The term “illness script” describes the mental model by which a clinician organizes, stores, and retrieves from long-term memory key concepts and their relationships to clinical problems  . ExperieThe development of a holistic, organized knowledge base and a strong foundation of core illness scripts is a principal goal of health professions training. Initially, clinical learners must rely heavily on basic science understanding of core concepts and underlying pathophysiological mechanisms  to slowly reason through clinical hypotheses and competing diagnoses. Not surprisingly, effective cognitive integration of basic and clinical science knowledge has been shown to enhance diagnostic accuracy of novice clinicians , 4. Howewww.aquifersciences.org). The MOD map flows consistent with the manner a basic scientist would explain the occurrence of a given feature in a known disease and provides an integrated scientific view of the condition. In each section, annotated references are provided to supply supporting evidence for emerging and cutting-edge concepts and mechanisms.We developed the model of an Integrated Illness Script (IIS) and related Mechanism of Disease (MOD) map to address these challenges and accelerate the development and effective use of scripts by faculty and learners Fig. . The IISThe structure of the IIS-MOD map was created through an iterative multidisciplinary and multi-institutional consensus process of leading clinical and basic science educators and validated through multiple national workshops over a 3-year period. The design was advanced by six nationally selected pilot schools with teams of basic science and clinical educators and senior medical students working in rapid cycle prototypes to create content for fifty exemplar IIS-MOD maps. To date, six medical schools are utilizing the IIS and MOD map models as pedagogical tools to educate learners at all levels.For novice learners, the IIS and MOD map can provide a cognitively integrated framework with which to develop, apply, and elaborate basic science knowledge in a manner that supports emerging clinical decision-making skills. For experienced basic science and clinical faculty, the IIS and MOD map facilitate the difficult task of “unpacking” and making transparent deeply encapsulated knowledge to learners and collaborating colleagues. Importantly, the development and use of the IIS have enabled more effective collaborative instructional design and teaching between scientists and clinicians, and between scientists across disciplines. We believe the use of IIS-MOD maps has the potential to enhance curriculum development, teaching, and learning and to advance the value and safety of patient care by both novice and experienced clinicians.


Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones.As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for  S. Typhi, and demonstrate its use for antimicrobial resistance assignment and strain risk assessment.Whole genome sequencing data are increasingly becoming routinely available but generating actionable insights is challenging. Here, the authors describe Pathogenwatch, a web tool for genomic surveillance of  The ability to rapidly sequence microbial genomes facilitates the tracking of pathogen evolution in real-time and with a geographical context. Genomic surveillance provides the opportunity to identify the emergence of genetic signatures indicating antimicrobial resistance (AMR), or host adaptation, facilitating early intervention and minimizing wider dissemination. Consequently, genomic data has the ability to transform the way in which, we manage the emergence of microbes that pose a direct threat to human health in real time.Genomic data is being generated at a remarkable rate, but we need to bridge the gap between genome science and public health with tools that make these data broadly and rapidly accessible to those who are not expert in genomics. To maximize the impact of ongoing surveillance programs, these tools need to quickly highlight high-risk clones by assigning isolates to distinct lineages and identifying genetic elements associated with clinically relevant features such as AMR or virulence. In this way, new isolates can be examined against the backdrop of a population framework that is continuously updated, and that enables both the contextualization of local outbreaks and the interpretation of global patterns.Salmonella Typhi (S. Typhi) causes typhoid (enteric) fever, a disease that affects approximately 20–30 million people every year2. The disease is predominant in low-income communities, where public health infrastructure is poorly resourced. Similar to other infections, typhoid treatment is compromised by the emergence of S. Typhi with resistance to multiple antimicrobials, including those currently used for treatment2. Whole genome sequencing (WGS) has proven key to identify S. Typhi high-risk clones by linking the population structure to the presence of AMR elements. For example, the resurgence of multidrug resistant (MDR) typhoid  has been explained in part by the global spread of an MDR S. Typhi lineage known as haplotype H58 or subclade 4.3.14, which is associated with both acquired AMR genes and fluoroquinolone resistance mutations5.Salmonella spp.7, and for in silico typing and visualization of genome variation and relatedness based on WGS data12. Here, we describe Typhi Pathogenwatch, a web application to support genomic epidemiology and public health surveillance of S. Typhi. Typhi Pathogenwatch rapidly places new genomes within the broader geographic and population context, predicts their genotype according to established nomenclatures13, and detects the presence of AMR determinants and plasmid replicon genes to assess public health risk. Results can be downloaded or shared via a web address containing a unique collection identifier. Our approach allows the rapid incremental addition of new data and can be used to underpin the international surveillance of typhoid, MDR, and other public health threats.WGS is increasingly being implemented in local and national public health laboratories, and web applications can provide rapid analysis and access to actionable information for infection control in the context of a global population framework. Online resources are available for the identification of acquired AMR mechanisms in bacterial pathogens, including S. Typhi genomics that uses genome assemblies to perform three essential tasks for surveillance and epidemiological investigations, i.e., (i) placing isolates into lineages or clonal groups, (ii) identifying their closest relatives and linking to their geographic distribution, and (iii) detecting the presence of genes and mutations associated with AMR. The application can be accessed at https://pathogen.watch/styphi, where users can create an account to upload and analyse their genomes . User data remains private and stored in their personal account. Pathogenwatch provides compatibility with typing information for MLST13, cgMLST8, in silico serotyping (SISTR11), a SNP genotyping scheme (GenoTyphi4), and plasmid replicon sequences15. The results for a single genome are displayed in a genome report that can be downloaded as a PDF. The results for a collection of genomes can be viewed online and downloaded as trees and tables of genotypes, AMR predictions, assembly metrics, and genetic variation. Results can also be accessed at a later date and shared via a collection ID embedded in a unique weblink, thus facilitating collaborative surveillance.We developed a public health focused application for 4  under investigation. Pathogenwatch contextualizes the user-uploaded genomes with public genomes using a population tree of 19 diverse genome references  and from the Indian subcontinent , respectively  belonged to the globally dominant MDR genotype 4.3.1, although the five different genotypes comprising 4.3.1 showed different temporal distributions and relative abundance , cephalosporins (n = 2), chloramphenicol (n = 6), and sulfamethoxazole-trimethoprim (n = 7) were paralleled by the original genome studies22, and by an alternative bioinformatics method23. The 49 FN calls for ciprofloxacin were also in agreement with the in silico analyses reported in the original genome studies24, in which no QRDR mutations or qnr genes were detected. Only mutations outside of the QRDR of parE  or gyrA  were found in 20 genomes.Typhi Pathogenwatch queries genome assemblies with BLASTn = 55), two (n = 2), or three (n = 1) mutations in the QRDR of gyrA, gyrB, and/or parC, most frequently the single mutations gyrA_S83F (n = 25) and gyrB_S464F (n = 16). For 54 of these samples, the same mutations were reported in the original genome studies. For the remaining three genomes, no mutations were reported in the original studies, but we confirmed the presence of gyrB_S464F (n = 2) or gyrB_S464Y (n = 1) in the assemblies using Resfinder25.The specificity of the Pathogenwatch genotypic predictions was at least 0.95 for most antimicrobials Table , with thS. Typhi isolates from nine previous studies against the different combinations of resistance mechanisms identified by Pathogenwatch. The isolates with one or two QRDR mutations displayed mostly intermediate MICs against ciprofloxacin, and support reporting as intermediate in Pathogenwatch  and gyrB (S464F), however, were below the intermediate breakpoint, consistent with the lower specificity reported for ciprofloxacin in Table gyrA_S83F-gyrA_D87N-parC_S80I mutations, reported as resistant by Pathogenwatch28. However, the triple combination gyrA_S83F-gyrA_D87G-parC_E84K was represented by nine isolates with MICs in both the resistant (n = 6) and the intermediate (n = 3) ranges, and is reported by Pathogenwatch as intermediate. Further susceptibility testing of isolates with this combination of mutations is needed to refine genotypic predictions. Likewise, several other mechanisms potentially conferring insusceptibility to ciprofloxacin were found in the public genomes but had no or little associated MIC data, including seven additional triple mutations  S. Typhi20. Pathogenwatch identifies plasmid replicon sequences in the user genomes and reports them on the genome report and on the typing table in the collection view  was prevalent in MDR genomes from Southeast Asia and East Africa belonging to clade 4.3.1, while the same combination with the addition of IncFIA(HI1) was more prevalent in West Africa, and associated with clade 3.1  detects fragments of the repE gene that is present in a subset of IncHI1 plasmids, including the plasmid sequence type PST2 variant common in S. Typhi 3.1 in West Africa, but lacking from the PST6 variant that is widespread in S. Typhi 4.3.1 in East Africa and Asia30.MDR and XDR phenotypes have been associated with the acquisition of plasmids in iew Fig. . Pathogeiew Fig. . The cryS. Typhi. A non-synonymous point mutation in the gene encoding the efflux pump AcrB (R717Q) was recently singled out as a molecular mechanism of resistance to azithromycin in S. Typhi31. Pathogenwatch detected the acrB_R717Q mutation in a collection of 12 Bangladeshi genomes of genotype 4.3.1.1 isolated between 2013 and 2016 in which this mutation was first described , and one from an isolate recovered in Samoa in 20073. The Samoan genome 10349_1_30_Sam072830_2007 was typed as genotype 3.5.4, while the English genomes 65343 and 32480 (no travel information available) belonged to genotypes 4.3.1.1 and 4.3.2.1, respectively. Genome 65343 was closely related to the cluster of 12 genomes from Bangladesh where this mutation was first described, while genome 32480 belonged to a small cluster of five genomes from India or with travel history to India. Thus, reanalysis of public data with Pathogenwatch showed that the acrB_R717Q mutation has emerged in multiple genetic backgrounds, in multiple locations, and as early as 2007.Azithromycin is one of the last oral treatment options for typhoid for which resistance is currently uncommon, of particular importance in endemic areas with high rates of fluoroquinolone-resistance and outbreaks of XDR 33. Ceftriaxone-resistant typhoid fever was recently reported in developed countries from patients with travel history to Pakistan36. The isolates were associated to the recent outbreak of XDR S. Typhi in the Sindh province of Pakistan by the epidemiological data, the antibiograms, and information derived from WGS of the clinical isolate, such as presence of resistance genes and mobile genetic elements. In some cases, the genomes were contextualized with retrospective genomes by building a phylogenetic tree using an existing bioinformatic pipeline35.Typhoid fever is rare in countries with a good infrastructure for the provision of clean water and sanitation, with most cases arising from travel to endemic areashttps://www.ncbi.nlm.nih.gov/nuccore/RHPM00000000.1/]36). Pathogenwatch provides a printable genome report  and linked to isolates from India from 2008 to 201227 and are still circulating in the region37. The public data integrated in Pathogenwatch showed that, at the time of writing, this lineage is represented by 195 public genomes from seven countries  and from as early as 2006 . Linking the tree and the map highlights distinct clusters of genomes that show evidence of transmission across borders, for example between India–Pakistan and India–Nepal  and the IncN replicon in three genomes from the United Kingdom (two with travel history to India)26 and, additionally, in two related genomes from Japan with travel history to Nepal and India  surveillance programs in endemic regions; ii) implementation of WGS at laboratories in endemic regions; iii) analysis of WGS data with fast, robust, and scalable tools that deliver information for public health action; iv) dissemination of WGS data through networks of collaborating reference laboratories at national, international and global scales; and v) provision of WGS data and associated metadata through continuously growing databases that are amenable to interaction and interpretation41. Here, we introduced Typhi Pathogenwatch, a web application for genomic surveillance and epidemiology of S. Typhi, which enhances the utility of public WGS data and associated metadata by integration into an interactive resource that users can browse or query with their own genomes.Our understanding of the S. Typhi isolates analysed in a single reference laboratory reported only 0.03% discordant results28 versus 3.66% from our data. Similarly, AMRFinder7 and Resfinder 4.06 reported ≥98.0% overall concordance, but for two large collections of non-typhoidal Salmonella genomes. A limitation of our study is that it amalgamated published susceptibility data from thirteen different publications conducted in eight different countries. The availability of consistent laboratory antimicrobial susceptibility testing data is key for the periodic benchmarking and refinement of genomic predictions of AMR42, as made evident by the different mechanisms and combinations thereof identified for ciprofloxacin. Phenotypic resistance data consistently collected and reported could also be included in the Pathogenwatch metadata table. The unique combination of phenotypic and genotypic resistance with location, time, and population structure could aid the investigations of emerging resistance and discovery of novel resistance mechanisms.We demonstrated that genomic predictions of AMR in Pathogenwatch were highly concordant with the resistance phenotype. A previous study of 332 mcr-1 in S. enterica and Escherichia coli genomes from Public Health England43. With Pathogenwatch the entire Typhi community can access the updated AMR predictions, thus democratizing the reusability of the genomic data.The growing collection of public genomes is updated each time that a novel AMR mechanism is added to the curated Pathogenwatch AMR library. This can potentially reveal the presence of a newly identified gene or mutation in historic isolates, thus maximizing data reusability from which new insights into novel AMR mechanisms can be derived. The utility of maintaining a regularly updated archive of WGS data that can be rapidly “mined” for the presence of newly discovered AMR determinants was elegantly illustrated before by the retrospective discovery of the colistin resistance gene 44. Analyzing new genomes in the context of global genomes involves the retrieval, storage, and bioinformatic analysis of large amounts of sequence data and linked metadata, which is time-consuming and largely unfeasible for hospitals or public-health agencies with limited computing infrastructure. We demonstrated how Pathogenwatch circumvents this obstacle using the public genomes to exemplify outbreak investigations in endemic areas and patient management in non-endemic countries with travel history to endemic areas.Contextualizing new genomes with existing data has become a routine part of genomic epidemiology, as it can complement epidemiological investigations to place the new genomes in or out of an outbreak, link to past outbreaks, and determine if the success of a resistant phenotype is the result of a single clonal expansion or multiple independent introductions4, but local, national, and international genomic surveillance programs are needed for the real-time management of emerging lineages that pose a direct threat to human health45. Pathogenwatch does not currently support automated updates or submissions, which instead requires retrieval and curation of genome data and associated metadata. For example, as of November 2020 Pathogenwatch comprises 4234 of 4389 (96.5%) S. Typhi genomes with at least both year and country of isolation, while the same applies to 3473 of 7743 (44.9%) genomes on Enterobase12, 3936 of 5618 (70.1%) genomes on GenomeTrakr (14), and 2085 of 3100 (67.3%) genomes on PATRIC9. Pathogenwatch also displays patient travel information when available. While automated updates are needed to ensure the most up-to-date collection of genomes, the provision of genomes with available metadata maximizes the value that can be derived from the genomes. The metadata linked to the public genomes in Pathogenwatch can be expanded and retrospectively updated following recommendations of the expert community, and buy-in from international surveillance networks to make the metadata available.The interpretation of the genomic context relies heavily on the completeness of the public collection used for contextualization and of its metadata. The International Typhoid Consortium collected and sequenced around 40% of the global genomes available in Pathogenwatch for comparison47, several challenges to sharing genomic data and linked metadata remain in both the academic and public-health settings41. User-uploaded genomes, their metadata, and derived collections remain private in the Pathogenwatch user account, unless the user specifically shares them via a collection URL. Users can also integrate private and potentially confidential metadata into the display without uploading it to the Pathogenwatch servers. This private metadata will not be shared even if the collection is set to be shared via web link48.Pathogenwatch can facilitate collaborative surveillance in endemic areas via data integration and shared collections for the early detection and containment of high-risk clones. Collections can be set to off-line mode to work while disconnected from the internet, which may be advantageous in settings with unreliable internet connections. Despite recent efforts to promote data opennessS. Typhi, and the development of new typhoid conjugate vaccines have bolstered efforts to employ routine vaccination for the containment of typhoid fever49. Routine surveillance coupled with WGS can inform decisions on suitable settings for the introduction of vaccination programs and on the evolution of pathogens in response to them51. Pathogenwatch should be linked to routine genomic surveillance around typhoid vaccination initiatives to monitor the population dynamics in response to the deployment of new vaccines. The consistent provision of patient demographic data in the metadata would be of particular utility in this context.Recent improvements in our understanding of the disease burden and the dissemination of AMR in 52. Typhi Pathogenwatch combines accurate genomic predictions of AMR with broad geographic and population context within an easy-to-use interface delivered for the community and accessible to users of all bioinformatics skills levels to support ongoing typhoid surveillance programs. The modular architecture of Pathogenwatch allows new functionalities to be added to cater to the community needs.Rapid, timely access to information on local patterns of AMR may inform treatment regimens, which could ultimately lead to a reduction in morbidity and mortality associated with enteric fever53 single-page application with styling based on Material Design Lite v1.3.054. Phylocanvas55 is used for phylogenetic trees, Leaflet v1.4.056 is used for maps, and Sigma v1.2.157 is used for networks. The Pathogenwatch back-end, written in Node.js, consists of an API service for the user interface and four “Runner” services to perform analysis: species prediction, single-genome analyses, tree-building, and core genome multilocus sequence typing (cgMLST) clustering. Runner services spawn Docker containers for queued tasks, streaming a FASTA file or prior analysis through standard input and storing JSON data from standard output. Data storage and task queuing/synchronization are performed by a MongoDB cluster.The Pathogenwatch user interface is a React58, as described in the Pathogenwatch documentation59.Genome assemblies can be uploaded by the user in FASTA format or assembled de novo from high-throughput short read data with the Pathogenwatch pipelineS. Typhi genomes from 26 studies were available . An assembly improvement step was applied to the assembly with the best N50 and contigs were scaffolded using SSPACE v2.0 and sequence gaps filled using GapFiller v1.11. Assemblies were evaluated based on their metrics and the Pathogenwatch core genome stats . Seventeen public and published genomes were excluded as the assemblies either contained more than 700 contigs, more than 50,000 non-ATCG characters, a GC content below the smallest GC content or above than the largest GC content of the S. enterica subsp enterica genomes in RefSeq, or a total length that is <10% smaller than the smallest genome or >10% larger than the largest S. enterica subsp enterica genome in RefSeq, For five isolates, we used genome assemblies deposited in GenBank that met the same quality criteria. The metadata and assembly stats and method of the public genomes is available on  are available as public data and accessible to all users for browsing and for contextualization of their own datasets. As of November 2020, 4389 public 62 63.For both user-uploaded and public genomes, Pathogenwatch outputs a taxonomy assignment, a map of their locations, and assembly quality metrics. The taxonomy assignment is the best match to a microbial version of the RefSeq genome database release 78, as computed with Mash v2.1Salmonella serotyping (SISTR11), multi-locus sequence typing (MLST13), core-genome MLST (cgMLST8) and S. Typhi single-nucleotide polymorphism (SNP)-based genotyping (GenoTyphi4), as detailed in the documentation64. The MLST and cgMLST schemes are periodically downloaded from Enterobase66, and samples are typed as described in the documentation68. Exact allele matches are reported using their allele ID. Multiple allele hits for a gene are reported if present. Inexact allele matches and novel STs are reported by hashing the matching allele sequence and the gene IDs, respectively.Pathogenwatch also provides compatibility with Salmonella In Silico Typing Resource11), which produces serovar predictions from WGS assemblies by determination of antigen gene and cgMLST gene alleles using blastn v2.2.31+. Pathogenwatch uses the cgmlst_subspecies and serovar fields from the SISTR JSON output to specify the serovar.Pathogenwatch implements SISTR  and public genomes (population tree and subtrees). The trees are inferred using a curated core gene library of 3284 S. Typhi genes71 generated from a pan-genome analysis of 26 complete or high-quality draft genomes  only universal families with complete coverage of the representative were kept; b) all paralogues were removed; c) overlapping gene families were merged into a single, contiguous pseudo-sequence. A BLAST19 core library was then built with the representative genes, and a profile of variant sites determined for the core genes present in each reference genome. Each of the 4389 public genomes was then clustered with its closest reference genome based on this profile of variant sites, thus constituting each of the 19 population subtrees that Pathogenwatch employs to contextualize user-uploaded genomes.Pathogenwatch supports SNP-based neighbor joining trees of 74. User-provided assemblies are queried against the S. Typhi core gene library with blastn v2.2.3019 using an identity threshold of 90%. The core gene set of each query assembly is compared to the reference genome core that has the most variant sites in common. An overall relative substitution rate is determined, and loci that contain more variants than expected assuming a Poisson distribution are filtered out. Pairwise distances between assemblies (including user-provided and reference) are scored via a distance scoring algorithm that compares all variant positions from all pairs of core gene sets, SNPs are counted (generating a downloadable pairwise difference matrix) and normalized by the relative proportion of the core present (generating a downloadable pairwise score matrix). The pairwise score matrix is then used to infer a midpoint-rooted neighbor-joining tree using the Phangorn v2.4.075 and Ape v5.176 R packages. Trees are computed for the user assemblies only (collection tree), and for the user assemblies and public assemblies assigned to the same reference genome (public data subtrees), all of which are downloadable in Newick format.The relationships between genomes are represented with trees (dendrograms) based on the genetic distance computed from substitution mutations in the core gene library, as described in detail in the documentationS. Typhi defined by GenoTyphi  neighbor joining (NJ) with FastTree v2.1.878 using the option –noml on the same alignment as 2); and 4) ML with RAxML v8.2.8 on SNPs extracted from a previously published CT18-guided alignment3. Five hundred bootstrap replicates were computed for the ML trees (methods 2 and 4). We compared the topology of the trees thus generated using the treescape function from the Treescape v1.10.18 R package (now available as Treespace79) with the Kendall-Colijn distance and lambda parameter set to 0. The topology of the Pathogenwatch tree from dataset III was compared to the tree from method 4 using the Tanglegram algorithm of Dendroscope v3.580. The tree files used in the tree comparisons are provided in the ref. 81.We benchmarked the Pathogenwatch clustering method against other methods of SNP-based tree inference with three subsets of published genomes: Dataset I) 118 genomes spanning the population diversity of 82 is used to quickly group genomes into clusters at a given threshold. For a given genome, users are able to see how many other genomes it is clustered with at a range of distance thresholds, view the structure of the cluster as a network graph, and view the metadata and analysis for sequences in that cluster.Genomes can also be clustered in Typhi Pathogenwatch based on their cgMLST profile using single linkage clustering. Distance scores are calculated between each pair of samples by identifying the genes which have been found in both samples and by counting the number of differences in the alleles. The SLINK algorithm19 for the presence of genes and single point mutations known to confer resistance in S. Typhi to ampicillin (AMP), chloramphenicol (CHL), broad-spectrum cephalosporins (CEP), ciprofloxacin (CIP), sulfamethoxazole (SMX), trimethoprim (TMP), the combination antibiotic co-trimoxazole , tetracycline (TCY), azithromycin (AZM), colistin (CST), and meropenem (MEM) (Supplementary Table 283), as detailed in the documentation84.The Pathogenwatch AMR prediction module queries the genome assemblies with blastn v2.2.3085. False negative (FN) and false positive (FP) results were further investigated with alternative methods by querying the genome assemblies with Resfinder v3.2.125 and/or by mapping and local assembly of the sequence reads to the Bacterial Antimicrobial Resistance Reference Gene Database (Bioproject PRJNA313047) with ARIBA v2.14.423.The Pathogenwatch AMR prediction module also provides a prediction of AMR phenotype inferred from the combination of identified mechanisms. To benchmark the genotypic resistance predictions, we used a set of 1316 genomes from 16 published studies  with a script that is available at ref. 81.Seven studies reported ciprofloxacin MICs for a total of 889 Further information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


TNF-α) to quantitatively detect mTNF-α expression in the intestinal mucosa.While anti-tumor necrosis factor alpha (TNF-α) therapy has been proven effective in inflammatory bowel disease (IBD), approximately 40% of patients lose the response. Transmembrane TNF-α (mTNF-α) expression in the intestinal mucosa is correlated with therapeutic efficacy, and quantification of mTNF-α expression is significant for predicting response. However, conventional intravenous application of microbubbles is unable to assess mTNF-α expression in intestinal mucosa. Herein, we proposed intracolic ultrasound molecular imaging with TNF-α-targeted microbubbles  compared to that of the controls . Targeted US signal intensities and inflammatory lesions were topographically coupled in mouse colons. Linear regression analyses in specimens of mice and patients demonstrated significant correlations between the targeted ultrasound signal intensity and mTNF-α expression (both P < 0.001). Furthermore, TNF-α-targeted ultrasound imaging qualitatively distinguished the varying inflammatory severity in intestinal specimens from IBD patients.On quantitative TNF-α-targeted ultrasound images, a greater signal intensity was observed in the mouse colons with colitis ([1.96 ± 0.45] × 10TNF-α enables quantitative assessment of mTNF-α expression. It may be a potential tool for facilitating the implementation of personalized medicine in IBD.Intracolic ultrasound molecular imaging with MBThe online version contains supplementary material available at 10.1186/s10020-021-00379-z. Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), has become a global disease with increasing incidence . However, CLE has limited vision, cannot access the full length of the colon and small bowel and easily loses focus  and control MBs (MBcon) were synthesized according to a multistep biotin–streptavidin bridging chemistry method modified from a previously reported study  analysis. The specific conjunction of TNF-α antibody to the surface of MBs was determined by inverted fluorescence microscopy. The microbubble concentration was determined by using an automated cell counter . The amount of antibody on the surface of microbubbles was measured by a spectrofluorometer  according to a previous report  and followed the institutional ethical guidelines for animal experiments. All procedures and animal care were approved by the local Ethics Committee of the Third Affiliated Hospital, Sun Yat-sen University. Acute colitis was induced in 6–8-week-old male BALB/c mice  by rectal administration of 2,4,6-trinitrobenzenesulfonic acid (TNBS) in absolute ethanol (EtOH), while control mice were treated with 50% EtOH, as reported previously  score was determined according to loss of body weight (one point for each 5% loss of weight), consistency of stools  and presence of gross blood in stool  , colonic mesenterial adhesion  and colonic hyperemia (0 = none and 1 = present).2) was fixed in 4% formalin for histopathological evaluation and immunofluorescence staining, and the remaining part was snap-frozen for quantitative real-time polymerase chain reaction (RT-PCR). Quantitative RT-PCR and immunofluorescence analysis were performed to determine the expression of mTNF-α at the mRNA and protein levels, respectively  in 1 ml of phosphate buffered saline (PBS) were injected into the lumen. After a 1-min binding period, the lumen was perfused using 20 ml of normal saline, and US images were captured  were drawn around the bowel walls excluding the bowel lumen, and the signal intensity was determined by ImageJ software . The quantification of imaging signal intensity was achieved by the normalized intensity difference (NID [fold]), which represented the ratio of MB signal intensity after injection to baseline signal intensity. Quantification of mTNF-α expression was measured as the mean integrated optical density (IOD) under four representative fields (× 200) for each sample through Image-Pro Plus v6.0 software  as reported previously  of the First Affiliated Hospital of Sun Yat-sen University were obtained, in accordance with the rules and regulations concerning ethical issues on research use of human subjects in China.8 MBTNF-α or MBcon in random order in 1 ml of normal saline was sprayed onto the intestinal mucosa, and a 1-min binding period was maintained. Then, the cells were washed three times with normal saline to remove the free MBs, and images were acquired. After that, a high-frequency pulse was applied to destroy all MBs. The intestines were immersed in normal saline for washing three times. Targeted US imaging was performed using a clinical US imaging system  with a 5-12L high-frequency linear array probe operating with the following imaging parameters: mechanical index of 0.08, frequency of 12 MHz, and image depth of 4.0 cm. All intestines were imaged with consistent imaging settings.A total of 14 intestinal specimens from 8 IBD patients  who were not exposed to anti-TNF therapy but received other medications  were enrolled. After resection from IBD patients, the fresh intestines, including normal (at margin) and diseased areas (with a length of 2–3 cm), were preserved in UW solution (University of Wisconsin solution) . All data are presented as the mean ± standard deviation, but the clinical characteristics of IBD patients are presented as the mean (range), and TNF-α was 0.79 ± 0.05 μm, which was similar to that of MBcon   . The DAI and macroscopic damage score  were significantly increased in TNBS-treated mice compared to those of corresponding controls     Fig. B.Fig. 3QTNF-α, a higher targeted signal intensity was observed in the colon of colitis mice compared with that of control groups, and few signal intensities were shown in both colons of colitis and control mice after application of MBcon  compared to that with MBcon . In contrast, there was no significant difference in the ultrasound signal intensity between MBTNF-α ([0.56 ± 0.21] × 106 a.u.) and MBcon  in the control mice . However, no significant difference was observed in TNBS-treated (1.47 ± 0.37) and control colons (0.87 ± 0.39) after the injection of MBcon (P = 0.067)  and control colons (1.07 ± 0.10) after administration of MBIn addition, US mapping images of the inflammatory segment were obtained. The colon of TNBS-treated mice showed an obvious CEUS signal in the region where inflammation was evident in gross specimens and histopathology, which was consistent with mTNF-α expression confirmed by immunohistochemical analysis. A relatively weak targeted US signal and low mTNF-α expression were observed in the region with less inflammation in TNBS-treated colons. In contrast, control mice demonstrated no targeted US signal and negative expression of mTNF-α Fig. .Fig. 5In2 = 0.8267 at the mRNA level and R2 = 0.7395 at the protein level, both P < 0.001) represented the determined degree of NID relative to mTNF-α expression and demonstrated a favorable correlation between the targeted US signal intensity and pathological results.Furthermore, to confirm the accuracy of quantitative targeted US imaging, the expression of mTNF-α at the molecular level was determined by quantitative RT-PCR and immunohistochemical analysis. Linear regression equations regarding the NID and mTNF-α expression at the mRNA and protein levels were established Fig. . The coeThe baseline characteristics are shown in Table P < 0.001)  represented the feasibility of targeted TNF-α US imaging in quantitatively assessing mTNF-α expression in the intestinal mucosa of IBD patients.According to targeted TNF-α US imaging results, few signal intensities were observed in normal intestines compared with diseased intestines. Among the diseased intestines, stronger signal intensities were observed in the severely inflammatory lesions than in the moderately and mildly inflammatory lesions, and the signal intensities of moderately inflammatory lesions were higher than those of mildly inflammatory lesions Fig. A. Consis01) Fig. B. The coTNF-α, was developed, and ex vivo intracolic USMI experiments using MBTNF-α were successfully conducted to accurately quantify mTNF-α expression in colons excised from a murine model of colitis. The targeted US signal intensities and the inflammatory lesions were topographically coupled in colons, indicating its potential application in inflammation detection. Furthermore, when using MBTNF-α on ex vivo intestinal specimens of IBD patients, the feasibility of MBTNF-α for assessing mTNF-α expression on intestinal mucosa and characterizing bowel inflammatory lesions was validated again.To the best of our knowledge, this is the first study to date to use USMI in ex vivo intestinal specimens from a murine model of colitis and IBD patients to evaluate the expression of mTNF-α. In this study, a TNF-α-targeted US contrast agent, MB99mtechnetium–annexin V, which represented anti-TNF-induced apoptosis, correlated with the clinical benefit of anti-TNF treatment and might have potential for predicting responses. Nevertheless, SPECT was unsuitable for dynamic monitoring of CD patients due to radiation exposure. Another study and fluorescence molecular imaging, has emerged as an advanced approach for predicting the therapeutic efficacy of anti-TNF therapy in IBD with high specificity at the cellular and subcellular levels MBs were reported to be able to assess inflammatory severity in IBD models and a soluble form (sTNF-α)  (Kiessling et al. TNF-α. Second, the sample size of IBD patients was limited in this preliminary study. A larger group size and anti-TNF therapy experiments will be performed in our future studies. Finally, a biotin-streptavidin coupling chemistry method was utilized for the synthesis of MBTNF-α, which was not clinically translatable due to its potential immunogenicity. Targeted contrast agents available for clinical application with favorable biosafety will be developed in the future.There were several limitations in this study. First, we used ex vivo US imaging in colonic specimens of IBD mice and patients, which was unable to reflect the real physiological situation in vivo despite its great promise in clinical practice. Herein, future in vivo studies are needed to verify the predictive performance of MBTNF-α-based intracolic USMI strategy. Such noninvasive and quantitative technology provides a method to detect the expression of mTNF-α, which might open new avenues for personalized therapy in IBD.In summary, we proposed a MBAdditional file 1: Materials and Methods. Figure S1. Inflammation parameters after induction of acute colitis with TNBS in mice. Table S1. Histologic grading of biopsy specimens in Crohn’s disease. Table S2. Nancy index system in ulcerative colitis. Table S3. DAI of the control and TNBS-treated mice with acute colitis. Table S4. Macroscopic damage score of the control and TNBS-treated mice with acute colitis.