Olfactory receptors (ORs) form the largest superfamily of G protein-coupled receptors (GPCRs). Traditionally recognized for their role in the nasal olfactory epithelium, where they mediate the sense of smell, accumulating evidence has firmly established their ectopic expression in non-olfactory tissues, including the intestine, lungs, and kidneys. The intestine, as the primary site for nutrient digestion and absorption, harbors a highly complex chemical environment. To adapt to this environment, the gut employs a sophisticated network of “chemosensors” to monitor luminal contents and maintain homeostasis. Among these sensors, intestinal ORs have emerged as crucial functional components, serving as a molecular bridge that connects environmental chemical signals—such as food-derived odorants—to specific physiological responses. This discovery has significantly deepened our understanding of how dietary flavors and compounds influence intestinal physiology at the molecular level. This review systematically summarizes the expression profiles, ligand classification, and biological functions of ORs within the gastrointestinal tract. Studies indicate that intestinal ORs exhibit distinct spatial distribution patterns across different gut segments and display cell-type specificity, particularly within enterocytes and enteroendocrine cells. These receptors function as versatile sensors capable of recognizing a wide variety of ligands, including exogenous dietary components, gut microbiota metabolites such as short-chain fatty acids, and endogenous small molecules like azelaic acid. Upon activation by specific ligands, intestinal ORs trigger intracellular signaling cascades, primarily involving the AC-cAMP-PKA pathway or calcium influx channels. A major focus of this review is to elucidate the molecular mechanisms by which these receptors regulate the secretion of gut hormones. Activation of specific ORs in enteroendocrine cells has been shown to stimulate the release of hormones such as glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and serotonin (5-HT), thereby modulating systemic energy metabolism, glucose homeostasis, and gastrointestinal motility. Furthermore, the review addresses the critical roles of ORs in immune regulation and pathology. Evidence suggests that specific ORs contribute to the maintenance of intestinal immune homeostasis and may offer protection against inflammation. Beyond their involvement in inflammatory responses, ORs such as Olfr78 have been shown to regulate the differentiation and function of intestinal endocrine cells. Similarly, Olfr544 has been demonstrated to alleviate intestinal inflammation by remodeling the gut microbiome and metabolome. These findings collectively suggest that specific ORs hold promise as therapeutic targets for mitigating intestinal inflammation and maintaining gut homeostasis. Additionally, the review explores the emerging role of ORs in cancer. Although OR expression is often downregulated in tumor tissues compared to normal mucosa, activation of specific ORs by certain ligands can inhibit tumor cell proliferation and migration and induce apoptosis via pathways such as MEK/ERK and p38 MAPK. Conversely, other receptors, such as OR7C1, may serve as biomarkers for cancer-initiating cells. In conclusion, intestinal ORs represent a vital component of the gut’s sensory network. The review also discusses the translational potential of these findings. By elucidating the precise pairing relationships between dietary components and specific ORs, novel therapeutic strategies could be developed. Intestinal ORs may thus emerge as promising targets for nutritional and pharmacological interventions in metabolic diseases, inflammatory bowel diseases, and malignancies.

Objective: To explore the value of contrast-enhanced computed tomography (CT) radiomics combined with machine learning algorithms in the preoperative prediction of perineural invasion (PNI) in oral squamous cell carcinoma (OSCC), aiming to provide evidence for assisting clinical treatment decision-making. Methods​: This study was approved by the Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. A total of 250 OSCC patients confirmed by postoperative pathology were included, comprising 128 PNI-positive and 122 PNI-negative cases. The dataset was randomly divided into training (n=175), validation (n=38), and independent testing (n=37) sets in a ratio of 7:1.5:1.5. Regions of interest were delineated on preoperative images, and radiomic features were extracted. After dimensionality reduction and feature selection using methods like Least Absolute Shrinkage and Selection Operator (LASSO) regression, multiple machine learning models, including support vector machine (SVM), random forest, Light gradient boosting machine (LightGBM), and a Stacking ensemble model, were constructed. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, calibration curves, and decision curve analysis (DCA). Model interpretability was analyzed using Shapley additive explanations (SHAP) and grouped permutation feature importance analysis. Results ​: ​ Among the 250 samples analyzed, the LightGBM model based on radiomics demonstrated the best performance on the independent test set, with an AUC of 0.781, outperforming models like SVM (AUC = 0.730) and Random Forest (AUC = 0.691), as well as clinical models (AUCs ranging 0.549-0.711). The LightGBM model showed good calibration (Brier score 0.198), and DCA indicated high clinical net benefit over a wide threshold probability range. Paired DeLong tests revealed no statistically significant differences in AUC between the ensemble (Stacking) model and the corresponding best-performing radiomics-based model. SHAP analysis and grouped permutation feature importance analysis further indicated that the primary discriminative information for the model came from radiomic texture features. Conclusion ​ The LightGBM model based on contrast-enhanced CT radiomics demonstrated good discriminative ability for preoperative prediction of PNI in OSCC. In the independent test set, it achieved the highest AUC. This model holds promise as a non-invasive auxiliary tool for preoperative risk assessment. Given the limited sample size of the independent test set, these results require further validation in larger cohorts and external datasets.

Objective:To explore the core categories of postpartum functional assessment based on the international classifica-tion of functioning,disability and health(ICF)and its brief version.Method:Using the method of evidence-based medicine,through literature search,the concepts contained in the literature indicators were extracted,linked with ICF,and referred to the relevant literature and WHO ICF checklist to develop the postpartum functional assessment scale for women.A total of 300 patients were investi-gated and evaluated clinically,and the frequency of each ICF item was counted.The items with dysfunction frequency rank≥30%were taken as the first ICF core set of postpartum function for women.Questionnaires were distributed to medical staff to count the ICF items that medical staff thought were closely related to wom-en's postpartum function,and more than 50%of the items were used as the second stage ICF item pool.The results of the two periods were integrated to construct the core category set of postpartum functional assessment for women based on ICF.In addition,the items with more than 50%dysfunction and 100%dysfunction were further selected as the brief version of the core category set for postpartum functional assessment of women.Result:A core category set of 63 items was initially constructed based on the ICF for postpartum functional as-sessment of women,including 27 physical function items,10 body structure items,15 activity and participa-tion items,and 11 environmental factors items.In addition,a brief version of the 43-item core set of function-al assessments for postpartum women was constructed,including 16 items in the physical function part,9 items in the body structure part,9 items in the activity and participation part,and 9 items in the environmen-tal factors part.Conclusion:Based on the ICF,this study preliminarily discusses the core categories of postpartum functional assessment and its brief version,so as to provide some guidance for clinical postpartum examination,function-al assessment and rehabilitation treatment of women.

Objective:To investigate the effect of mindfulness-based cognitive therapy on the sense of personal control and illness perception of young and middle-aged patients after percutaneous coronary intervention (PCI), so as to provide theoretical and practical guidance for the psychological care of patients after PCI.Methods:A randomized controlled trial was conducted. From January 2022 to December 2023, young and middle-aged patients after PCI admitted to the Department of Cardiology, Qinhuai Medical District, Eastern Theater Command General Hospital of the Chinese People′s Liberation Army were selected as the research subjects by convenient sampling method, patients were divided into the experimental group and the control group by the random number table method. The control group was given routine nursing, and the experimental group was given mindfulness-based cognitive therapy on the basis of routine nursing. The Personal Mastery Scale (PMS), Cardiac Self-Efficacy Questionnaire (CSEQ), and Brief Illness Perception Questionnaire (BIPQ) were used to evaluate personal sense of control, self-efficacy, and illness perception before and after the intervention respectively.Results:A total of 80 young and middle-aged patients after PCI were included. There were 40 cases in the control group, including 25 males and 15 females, aged (49.43 ± 5.55) years old. There were 40 cases in the experimental group, including 19 males and 21 females, aged (49.03 ± 4.19) years old. Before the intervention, there was no statistically significant difference in the PMS, CSEQ and BIPQ scores between the two groups of patients (all P>0.05). After the intervention, the PMS, CSEQ scores of the patients in the experimental group were (23.03 ± 2.54), (45.85 ± 8.16) points respectively, which were higher than (21.95 ± 2.28), (39.05 ± 8.78) points in the control group, the BIPQ score of the patients in the experimental group was(39.63 ± 8.12) points, which was lower the (45.45 ± 8.64) points in the control group, the differences were statistically significant ( t=-2.00, -3.59, 3.11, all P<0.05). Conclusions:Mindfulness-based cognitive therapy can effectively improve the sense of personal control and self-efficacy of young and middle-aged patients after PCI, reduce the sense of threat to the disease.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

Objective:To explore the clinical effects of sterilization of vas deferens by irrigation in clinic.Methods:Eighty-six male patients with voluntary sterilization were divided into control group(usual vasectomy,n=50)and observation group(sterilization of vas deferens by irrigation,n=36).The age,testicular volume,preoperative average concentration of spern,serum testosterone level,recovery duration evaluated by Artificial Obstruction Azoospermia(AOA)and degree of satisfaction were compared between the two groups of patients.Results:There were significant differences in recovery duration,degree of satisfaction between the two groups(P＜0.05).And there was no significant difference in age([32.0±5.5]years vs[31.0±6.3]years),testicular volume([16.0±4.8]mL vs[17.0±4.4]mL),preopera-tive average concentration of sperm([39.6±20.2]× 106/mL vs[40.2±22.6]× 106/mL)and levels of blood testosterone([4.3±0.8]ng/mL vs[4.4±0.8]ng/mL).There was significant difference in patency rate between the two sides of testicular ducts(91.7％vs 83.3％,P＜0.05).Conclusion:The method of sterilization of vas deferens by irrigation is worth popularizing in clinic.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To explore the core categories of postpartum functional assessment based on the international classifica-tion of functioning,disability and health(ICF)and its brief version.Method:Using the method of evidence-based medicine,through literature search,the concepts contained in the literature indicators were extracted,linked with ICF,and referred to the relevant literature and WHO ICF checklist to develop the postpartum functional assessment scale for women.A total of 300 patients were investi-gated and evaluated clinically,and the frequency of each ICF item was counted.The items with dysfunction frequency rank≥30%were taken as the first ICF core set of postpartum function for women.Questionnaires were distributed to medical staff to count the ICF items that medical staff thought were closely related to wom-en's postpartum function,and more than 50%of the items were used as the second stage ICF item pool.The results of the two periods were integrated to construct the core category set of postpartum functional assessment for women based on ICF.In addition,the items with more than 50%dysfunction and 100%dysfunction were further selected as the brief version of the core category set for postpartum functional assessment of women.Result:A core category set of 63 items was initially constructed based on the ICF for postpartum functional as-sessment of women,including 27 physical function items,10 body structure items,15 activity and participa-tion items,and 11 environmental factors items.In addition,a brief version of the 43-item core set of function-al assessments for postpartum women was constructed,including 16 items in the physical function part,9 items in the body structure part,9 items in the activity and participation part,and 9 items in the environmen-tal factors part.Conclusion:Based on the ICF,this study preliminarily discusses the core categories of postpartum functional assessment and its brief version,so as to provide some guidance for clinical postpartum examination,function-al assessment and rehabilitation treatment of women.