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.

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.

Objective To investigate the effectiveness of the integrated schistosomiasis control programme in Sichuan Province during the stage moving from transmission interruption to elimination (2015—2023), so as to provide insights into formulation of the schistosomiasis control measures during the post-elimination stage. Methods Schistosomiasis control data were retrospectively collected from departments of health, agriculture and rural affairs, forestry and grassland, water resources, and natural resources in Sichuan Province from 2015 to 2023, and a database was created to document examinations and treatments of human and livestock schistosomiasis, and snail survey and control, conversion of paddy fields to dry fields, ditch hardening, rivers and lakes management and building of forests for snail control and schistosomiasis prevention. The completion of schistosomiasis control measures was investigated, and the effectiveness was evaluated. Results A total of 20 545 155 person-times received human schistosomiasis examinations in Sichuan Province during the period from 2015 to 2023, and 232 157 person-times were seropositive, with a reduction in the seroprevalence from 2.10% (44 299/2 107 003) in 2015 to 1.12% (9 361/837 896) in 2023 (χ² = 7.68, P < 0.001). The seroprevalence of human schistosomiasis appeared a tendency towards a decline in Sichuan Province over years from 2015 to 2023 (b = −8.375, t = −10.052, P < 0.001); however, no egg positive individuals were identified during the period from 2018 to 2023, with the prevalence of human Schistosoma japonicum infections maintained at 0. Expanded chemotherapy was administered to 2 754 515 person-times, and medical assistance of advanced schistosomiasis was given to 6 436 persontimes, with the treatment coverage increasing from 46.80% (827/1 767) in 2015 to 64.87% (868/1 338) in 2023. Parasitological tests for livestock schistosomiasis were performed in 35 113 herd-times, and expanded chemotherapy was administered to 513 043 herd-times, while the number of fenced livestock decreased from 121 631 in 2015 to 103 489 in 2023, with a reduction of 14.92%. Snail survey covered 433 621.80 hm² in Sichuan Province from 2015 to 2023, with 204 602.81 hm² treated by chemical control and 4 637.74 hm² by environmental modifications. The area of snail habitats decreased from the peak of 5 029.80 hm² in 2016 to 3 709.72 hm² in 2023, and the actual area of snail habitats decreased from the peak of 8 585.48 hm² in 2016 to 473.09 hm² in 2023. The mean density of living snails remained low across the study period except in 2017 (0.62 snails/0.1 m²). Schistosomiasis control efforts by departments of agriculture and rural affairs in Sichuan Province included conversion of paddy fields to dry fields covering 153 346.93 hm², hardening of 6 110.31 km ditches, building of 70 356 biogas digesters, replacement of cattle with 227 161 sets of machines, and captive breeding of 21 161 070 livestock from 2015 to 2023, and the control efforts by departments of water resources included rivers and lakes management measuring 5 676.92 km and renovation of 2 331 irrigation areas, while the control efforts by departments of forestry and grassland included building of forests for snail control and schistosomiasis prevention covering 23 913.33 hm², renovation of snail control forests covering 8 720 hm² and newly building of shelterbelts covering 764 686.67 hm². All 63 endemic counties (cities and districts) had achieved the criterion for schistosomiasis elimination criteria in Sichuan Province by the end of 2023. Conclusion Following the integrated control efforts from 2015 to 2023, remarkable achievements have been obtained in the schistosomiasis control programme in Sichuan Province, with all endemic counties successfully attaining the schistosomiasis elimination target at the county level.

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal-derived tumors of the gastrointestinal tract. Tyrosine kinase inhibitors (TKIs) are the cornerstone of GIST therapy, but mutations in resistance genes pose many problems for treatment, especially the heterogeneity of KIT resistance mutations. In recent years, with the release of a number of GIST related drug research and experimental results, the great potential of targeted therapy, immunotherapy and combination therapy to treat GIST in different directions has been revealed, providing more therapeutic directions for GIST. This article will review the experimental research and future direction in recent years.

This paper summarized professor LIU Ruifen's clinical experience in treating endometrial hyperplasia. It is believed that the core pathogenesis of endometrial hyperplasia is internal obstruction of uterus and intrauterine collaterals by latent stasis. Based on the clinical manifestations， the menstrual cycle is divided into three stages， that is menstrual period， menstrual leakage period， and clear menstrual period， and the method of “three-staged stasis dispelling” is proposed. During the menstrual period， it is appropriate to move qi and invigorate blood， dissolve stasis and disperse concretions， with self-made Gongqing Formula （宫清方）. For menstrual leakage period， it is suggested to boost qi and clear heat， dispel stasis and stanch bleeding， with self-made Tongyong Zhixue Formula （通用止血方）. In terms of clear menstrual period， the method of dissolving phlegm and dissipating masses， moving qi and dispelling stasis is advised， and self-made Zhitong Tiaoxue Formula （止痛调血方） can be used. Simultaneously， attention should be paid to the combination of syndrome differentiation and disease differentiation， as well as collaborative diagnosis and treatment with traditional Chinese and Western medicine.

AIM To explore the mechanism of Yiqi Wenyang Huwei Decoction on airway inflammation improvement of rats with bronchial asthma based on IL-25/NF-κB signaling pathway.METHODS 60 rats were randomly divided into the control group,the model group,the dexamethasone group(0.2 mg/mL),the low-dose,medium-dose and high-dose Yiqi Wenyang Huwei Decoction groups(1,2,4 g/mL),with 10 rats in each group.Intraperitoneal injection of ovalbumin(OVA)and aluminum hydroxide suspension was applied to establish the rat asthma model,followed by 2-week corresponding dosing of the drugs.The rats of each group had their daily diet,mental status,hair growth and respiration observed;their differential count of inflammatory cells in bronchoalveolar lavage fluid(BALF)detected by automatic hematology analyzer;their pathological changes of lung tissue observed by HE staining;their pulmonary IL-25 protein expression detected by immunohistochemistry(IHC);their levels of IL-4,IL-5 and IL-13 in BALF measured by ELISA;their pulmonary expression of IL-25 and TRAF6 mRNA detected by RT-qPCR;and their pulmonary protein expressions of IL-25,TRAF6,IκBα,p-IκBα,NF-κB p65 and p-NF-κB p65 detected by Western blot.RESULTS Compared with the control group,the model group displayed severe damage of the lung tissue and infiltration of a large number of inflammatory cells;increased number of inflammatory cells and levels of IL-4,IL-5 and IL-13 in BALF(P<0.01);increased mRNA expressions of IL-25 and TRAF6,and pulmonary protein expressions of IL-25,TRAF6,p-IκBα/IκBα and p-NF-κB p65/NF-κB p65(P<0.01).Compared with the model group,all of the Yiqi Wenyang Huwei Decoction groups shared improved pulmonary infiltration of inflammatory cells;decreased number of inflammatory cells and levels of IL-4,IL-5 and IL-13 in BALF(P<0.05,P<0.01);and decreased mRNA expressions of IL-25 and TRAF6,and pulmonary protein expressions of IL-25,TRAF6,p-IκBα/IκBα and p-NF-κB p65/NF-κB p65(P<0.01).CONCLUSION Yiqi Wenyang Huwei Decoction can inhibit the airway inflammation in the rat model of bronchial asthma,which may be related to the inhibited activation of IL-25/NF-κB signaling pathway and the reduced expression of inflammatory factors.