This study explored the effects and underlying mechanism of Raf kinase inhibitory protein(RKIP)on apoptosis in mast cells sensitized by Echinococcus granulosus cyst fluid.Bone marrow-derived mast cells(BMMCs)were isolated and cultured from RKIP knockout(KO)and wild-type(WT)C57BL/6 mice.Cells were divided into control and sensitized groups.The sensitized group was incubated for 24 h in RPMI1640 medium containing 10%serum from mice infected with E.granulosus,then activated for 3 h or 6 h with E.granulosus cyst fluid.The control group was incubated for 24 h in RPMI1640 medium,and then received an equal vol-ume of PBS.Cells and supernatants were collected for analysis.Flow cytometry was used to detect the expression of CD117 and FcεRⅠα on BMMCs.The levels of β-hexosaminidase,IL-4,and TNF-α in the supernatant were quantified with ELISA.Western blot analy-sis was used to assess expression changes in RKIP,apoptosis-related proteins,and pathway proteins in BMMC before and after sensi-tization.Flow cytometry analysis revealed that after 4 weeks of induction,the CD117 and FcεRⅠα double-positivity rates on both WT and KO BMMC exceeded 90%.ELISA indicated that the E.granulosus cyst fluid resulted in significantly greater β-hexosaminidase re-lease(F=16.88,P<0.05),and levels of IL-4(F=16.51,P<0.05)and TNF-α(F=9.78,P<0.05)in the KO sensitized group than the WT sensitized group.With respect to the WT control group,the WT sensitized group showed significantly down-regulated pro-tein expression levels of RKIP(F=8.20,P<0.05)and Bcl-2(F=101.40,P<0.01)after 3 h,but significantly up-regulated levels of p-PI3K(F=8.04,P<0.05),p-Akt(F=32.52,P<0.01),p-P65(F=13.29,P<0.05),and cleaved-caspase-3(F=46.34,P<0.01).With respect to the WT sensitized group,the KO sensitized group showed significantly up-regulated protein expression of p-PI3K(F=8.45,P<0.05),p-Akt(F=8.58,P<0.05),p-P65(F=11.02,P<0.05),and Bcl-2(F=84.50,P<0.001)after 3 h,but significantly down-regulated expression of cleaved-caspase-3(F=15.66,P<0.05).In conclusion,RKIP may inhibit the PI3K/Akt/NF-κB pathway,thereby inducing apoptosis in mast cells sensitized by E.granulosus cyst fluid.This process may help ease aller-gic reactions caused by mast cells in echinococcosis,thus offering a promising new approach for preventing and treating such reactions.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

AIM:To investigate the effects of early high-fat diet(HFD)on cognitive function and hippocam-pal lipidomic profile in transgenic mice bearing five familial Alzheimer disease mutant genes(5×FAD).METHODS:Eight-week-old SPF grade female wild-type(WT)mice were used as the contorl group,and 5×FAD mice were randomly divided into model(5×FAD)group and 5×FAD+HFD group,with 10 mice in each group.The 5×FAD+HFD group was orally given high-fat chow and the remaining 2 groups were given control chow for 12 weeks,and the change in body weight of the mice were recorded.Y-maze and Morris water maze tests were performed to measure the learning memory ability of the mice.Serum total cholesterol(TC),triglyceride(TG),low-density lipoprotein cholesterol(LDL-C)and high-density lipoprotein cholesterol(HDL-C)levels were measured using a biochemical analyzer.Immunohistochemistry was per-formed to visualize amyloid β-protein(Aβ)plaques in brain tissues.Hippocampal levels of tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1β),IL-6,and Aβ were measured by enzyme-linked immunosorbent assay(ELISA).Non-tar-geted lipidomic technology was used to measure the changes of hippocampal lipids.RESULTS:Compared with WT group,the mice in 5×FAD group lost significantly less weight(P<0.01)and spent significantly less time exploring the new arm of the Y-maze and the target quadrant of the water maze(P<0.05 or P<0.01).Brain Aβ plaques were significant-ly increased(P<0.01).Hippocampal levels of Aβ1-40,Aβ1-42,IL-1β and TNF-α were significantly elevated(P<0.05 or P<0.01).Compared with the 5×FAD group,the mice in the 5×FAD+HFD group showed significant increase in body weight(P<0.01)and time spent exploring the new arm of the Y-maze and the target quadrant of the water maze(P<0.01).Biochmeical analysis showed serum TC,LDL-C,HDL-C levels and HDL/TC ratio were significantly increased(P<0.05).Brain Aβ plaques were significantly reduced(P<0.05)and hippocampal Aβ1-40,Aβ1-42 and IL-1β levels were sig-nificantly decreased(P<0.05).Compared with the WT group,27 lipids were increased and 9 lipids were decreased in the 5×FAD group,involving the pathways such as cholesterol metabolism,fat digestion and absorption,regulation of lipolysis processes in adipocytes,and glycerophospholipid metabolism.Eighteen lipids were increased and 47 lipids were de-creased in the 5×FAD+HFD group compared to the 5×FAD group.Cardiolipin and TG were important lipids for separating the lipid profiles of the WT and 5×FAD groups,and TG was an important lipid for separating the lipid profiles of the 5×FAD and 5×FAD+HFD groups.Differential lipid enrichment analysis showed significant increase in TG lipid in the 5×FAD group compared with the WT group and significant decrease in TG lipid in the 5×FAD+HFD group compared with the 5×FAD group.CONCLUSION:Early HFD ameliorates cognitive function in 5×FAD mice by modifying TG metabolic disorder and attenuating neuroinflammation.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.