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 effects of alpha-mangostin(α-MG)on the in vitro osteoarthritis(OA)in rat chondrocytes induced by lipopolysaccharide(LPS)and the M1 polarization of macrophages.Methods An in vitro OA model of rat chondrocytes was induced by LPS.Both rat chondrocytes and macrophages were divided into the control group(normal culture),LPS group(stimulated with 1 μg/mL LPS for 24 hours),LPS+α-MG group(stimulated with 40 μmol/L α-MG combined with 1 μg/mL LPS for 24 hours),LPS+α-MG si-NC group(transfected with 70 μg/mL si-NC and stimulated with 40 μmol/L α-MG and 1 μg/mL LPS for 24 hours),and LPS+α-MG+si-BDNF group(transfected with 70 μg/mL si-BDNF and stimulated with 40 μmol/L α-MG and 1 μg/mL LPS for 24 hours).CCK-8 assay was used to detect the proliferative viability of chondrocytes and macrophages in each group,and flow cytometry was used to detect the apoptosis of chondrocytes and the polarization of macrophages.ELISA was used to detect the levels of interleukin(IL)-1β,IL-6,and tumor necrosis factor(TNF)-α of chondrocytes in each group.Western blot was used to detect the expression of BDNF,PI3K,and Akt in chondrocytes and macrophages.Results In chondrocytes,compared with the control group,the LPS group had decreased cell proliferation rate,increased cell apoptosis rate,down-regulated expression of BDNF,PI3K and Akt proteins,and up-regulated levels of IL-1β,IL-6 and TNF-α(P<0.05);compared with the LPS group,the LPS+α-MG group showed increased cell proliferation rate,decreased cell apoptosis rate,up-regulated expression of BDNF,PI3K and Akt proteins,and down-regulated levels of IL-1β,IL-6 and TNF-α(P<0.05);compared with the LPS+α-MG+si-NC group,the LPS+α-MG si-BDNF group demonstrated decreased cell proliferation rate,increased cell apoptosis rate,down-regulated expression of BDNF,PI3K and Akt proteins,and increased levels of IL-1β,IL-6 and TNF-α(P<0.05).In macrophages,compared with the control group,the LPS group had decreased cell proliferation rate,increased proportion of M1 cells,and down-regulated expression of BDNF,PI3K and Akt proteins(P<0.05);compared with the LPS group,the LPS+α-MG group showed increased cell proliferation rate,decreased proportion of M1 cells,and up-regulated expression of BDNF,PI3K and Akt proteins(P<0.05);compared with the LPS+α-MG+si-NC group,the LPS+α-MG+si-BDNF group showed decreased cell proliferation rate,increased proportion of M1 cells,and down-regulated expression of BDNF,PI3K and Akt proteins(P<0.05).Conclusion α-MG inhibits the M1 polarization of macrophages to improve the apoptosis and inflammation of rat chondrocytes by activating the BDNF/PI3K/Akt signaling pathway.

Objective:This multicenter retrospective study aimed to analyze the clinicopathological features of duodenal adenocarcinoma (DA) and identify prognostic factors for postoperative survival.Methods:Demographic characteristics, clinicopathological features, treatment outcomes and survival of DA patients undergoing surgical treatment at 18 Chinese medical centers from January 2012 to December 2023 were retrospectively analyzed.Results:Among the 2 056 DA patients included, 46.8% (963) had extra-ampullary DA (EA-DA), and 53.2% (1 093) had peri-ampullary DA (PA-DA). The 1-, 3-, and 5-year overall survival (OS) rates for patients who underwent radical surgery were 93.2%, 71.0%, and 57.2%, respectively. The median overall survival was 76 months, and the median progression-free survival (PFS) was 65 months. No differences in survival were observed between the laparotomy group and minimally invasive surgery (MIS) group either before or after propensity score matching (OS: 76 vs. 75 months before PSM, P=0.986; OS: 75 vs. 75 months after PSM, P=0.602). Furthermore, there were no significant differences between-group in operation time and postoperative complications ( P＞0.05). The MIS group experienced less intraoperative blood loss and shorter hospital stays. The multivariate Cox regression analysis revealed that advanced age ( HR=1.43,95% CI:1.18-1.73), elevated carbohydrate antigen 19-9 levels ( HR=1.24,95% CI:1.02-1.51), perineural invasion ( HR=1.44,95% CI:1.14-1.81), vascular invasion ( HR=1.35,95% CI:1.07-1.71), advanced T stage (T3-4 vs. T1-2: HR=1.86,95% CI:1.49-2.31), regional lymph node metastasis ( HR=1.93,95% CI:1.58-2.36), preoperative biliary drainage ( HR=1.26,95% CI:1.04-1.53), intraoperative blood loss ( HR=1.34,95% CI:1.11-1.62), clinically significant postoperative pancreatic fistulas ( HR=1.53,95% CI:1.12-2.09), and postoperative hemorrhage ( HR=1.62,95% CI:1.14-2.29) were independent risk factors for poor prognosis after surgery (all P＜0.05). Conclusions:Radical surgery is associated with favorable overall survival among DA patients, and no difference in survival is observed between EA-DA and PA-DA patients. MIS is a reliable alternative for DA treatment.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate the dosimetric differences in preoperative short-course volumetric modulated arc therapy(VMAT)for rectal cancer using different fluence smoothing(FS)levels in the Monaco Treatment Planning System(Monaco TPS).Methods Twenty rectal cancer patients who received preoperative neoadjuvant short-course VMAT at some hospital from September 2021 to December 2022 were retrospectively selected.Four groups of radiotherapy plans were formulated using the Monaco TPS for each case,which were classified into an off group,a low group,a medium group and a high group based on the FS levels.Then the four groups were compared in terms of the dosimetric parameters,monitor unit and number of the segments in the planning target volume(PTV)and organ at risk(OAR).Statistical analysis was performed using SPSS 27.0 software.Results All the four groups had the doses to the target volume meeting clinical requirements,which had no significant differences in the doses to 5％(D5％)and 95％(D95％)to the target volume and the maximum dose(Dmax),minimum dose(Dmin),mean dose(Dmean)and conformity index(all P＞0.05).Statistical differences were found between the homogeneity indexes of the four groups(P＜0.05),with the medium group behaving the best.The number of the segments rose while the mornitor units decreased siginificantly with the increase of FS levels,with the differences being statistically significant(P＜0.05).There were no significant differences between the V25,V20,V15 and V10 of the small intestine,the V25 and V20 of the bladder and the V15 and V10 of the left and right femur(all P＞0.05).Conclusion In preoperative short-course VMAT for rectal cancer,clinical requirements can be met with different FS levels in the Monaco TPS,and medium-level FS results in optimal overall dose distribution in terms of treatment planning.[Chinese Medical Equipment Journal,2025,46(5):48-53]

Iodine speciations in aquatic environments are affected by dissolved oxygen,redox potential,microbial activity,organic matter decomposition,light reaction,etc.Accurate quantification of iodine speciation can not only help to understand the geochemical cycle of iodine,but also help to trace and study environmental processes.Based on the combination of ion chromatography(IC)and inductively coupled plasma mass spectrometry(ICP-MS),a rapid and sensitive method was established for determining the speciations of iodine in environmental water samples including seawater,river water,lake water,rainwater,groundwater,etc.The results presented here showed that IO3?and I?in seawater were quickly separated and measured within 120 s when using guard column AG22 and 8 mmol/L(NH4)2CO3 as the mobile phase.While for lake water,river water and precipitation samples with high soluble organically bond iodine(SOI),an AS22 separation column(250 mm×4 mm)connected with a guard column and using 50 mmol/L(NH4)2CO3 as mobile phase could effectively separate unknown SOI from IO3? to achieve accurate quantification of IO3?.For accurate correction of iodine measurement signal fluctuations,133Cs was directly added to the(NH4)2CO3 mobile phase as an internal standard.The SOI content was calculated by the total iodine concentrations minus the sum of IO3?and I?.The precision of the established iodine speciation analytical method was better than 3.5%,and the standard addition experiment showed that the analytical method was accurate.When the injection volume was 25 μL,the detection limits were 0.011?0.025 μg/L for IO3? and 0.023?0.031 μg/L for I?,respectively.The method was successfully used to analyze IO3?,SOI and I? in environmental water samples,such as seawater,river water,rainwater and groundwater.

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 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.