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.

Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

OBJECTIVE To explore the antidepressant mechanism of Wenyang jieyu granules （WYJYG） via the NOD-like receptor thermal protein domain associated protein 3 （NLRP3）/apoptosis-associated speck-like protein containing a CARD （ASC）/Caspase-1 pathway. METHODS A rat model of depression was established by chronic unpredictable mild stress combined with single-housing for 42 consecutive days.The experiment set up blank group， model group， MCC950 （NLRP3 inflammasome inhibitor） group （10 mg/kg）， fluoxetine group （positive control，2.08 mg/kg），low-dose WYJYG（3.78 g/kg） and high-dose WYJYG group （7.56 g/kg），with 10 rats in each group. From the 22nd day of the experiment， rats in the fluoxetine group， low-dose and high-dose WYJYG groups were intragastrically administered with the corresponding drugs and intraperitoneally injected with an equal volume of normal saline. Rats in the MCC950 group were intraperitoneally injected with MCC950 at the corresponding concentration and intragastrically administered with an equal volume of distilled water. Rats in the blank group and model group were given an equal volume of distilled water by gavage and an equal volume of normal saline by intraperitoneal injection. All interventions were performed once a day for 21 consecutive days. Behavioral tests were conducted once a week. After the last administration， the contents of ASC， ionized calcium binding adaptor molecule 1 （Iba1）， interleukin-1β （IL-1β）， and IL-18 in hippocampal tissues were detected. The protein expressions of NLRP3， cluster of differentiation 68 （CD68）， Caspase-1， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein were determined， and neuronal apoptosis was observed. RESULTS After the last administration， compared with the model group， the open-field activity time was significantly prolonged （ P ＜0.05）， and the latency to feed in a novel environment was significantly shortened （ P ＜0.05） in rats of the high-dose WYJYG group. In hippocampal tissue， the contents of ASC， Iba1， IL-1β， and IL-18， as well as the protein expression levels of NLRP3， Caspase-1， and CD68， and the positive rate of neuronal apoptosis were all significantly decreased/downregulated （ P ＜0.05）. Bcl-2 protein expression was significantly upregulated （ P ＜0.05）， and the density of neuronal apoptosis-positive cells was significantly reduced （ P ＜0.05）. CONCLUSIONS WYJYG play on antidepressant role by inhibiting the NLRP3/ASC/Caspase-1 pathway， reducing microglia-mediated neuroinflammation， and inhibiting hippocampal neurons apoptosis.

This study aims to investigate the scientificity and efficacy of the compatibility of Jinlingzi San from pharmacokinetics. Liquid chromatography-tandem mass spectrometry(LC-MS/MS) was utilized to determine the plasma concentrations of the active components: toosendanin, tetrahydropalmatine A, and tetrahydropalmatine B at various time points following the gavage of Jinlingzi San and its single medicines in rats. Subsequently, WinNonlin was employed to calculate pertinent pharmacokinetic parameters. The pharmacokinetic parameters in rat plasma were compared between the single medicines and the compound formula of Jinlingzi San. It was discovered that the area under the curve(AUC_(all)) and peak concentrations(C_(max)) of tetrahydropalmatine A, and tetrahydropalmatine B were significantly elevated in the compound formula group compared with the single medicine groups. Conversely, the AUC_(all )and C_(max) of toosendanin notably decreased. Furthermore, the compound formula group had longer mean residence time(MRT) and lower apparent clearance(CL/F) of all three active ingredients than the single medicine groups(P<0.05). These findings indicated that Jinlingzi San enhanced the absorption of tetrahydropalmatine A and tetrahydropalmatine B in vivo, facilitating their pharmacological actions. Concurrently, it inhibited the absorption of toosendanin, thereby preventing potential toxic reactions. Moreover, the compatibility prolonged the residence time of the active ingredients in the body. This study provides a reference for exploring the compatibility rationality of Jinlingzi San.
Animals ; Rats ; Tandem Mass Spectrometry/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Chromatography, Liquid/methods* ; Berberine Alkaloids/blood* ; Liquid Chromatography-Mass Spectrometry

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

A rapid and accurate method for textile fiber identification was developed for quality control and consumer protection.This method utilized electric soldering iron burning-mesh collision enhanced microtube plasma ionization mass spectrometry(ESIB-MC-μTP-MS)to acquire textile fiber MS data and used a random forest(RF)prediction model to identify fiber composition based on these MS data.The MC-μTP device involved in the method was a homemade low-temperature plasma ionization device constructed using cost-effective and readily available components.The system was applicable for direct analysis of small amount of textile samples without any complex sample pretreatment processes.Characteristic thermal decomposition products of different fibers were generated via soldering iron burning(350℃)in ambient atmosphere,and were subsequently analyzed by a mass spectrometer,with each analysis completed within 5 s.Raw MS data underwent noise reduction,normalization,and global binning steps to form a dataset,and its intrinsic class separability was evaluated using principal component analysis(PCA)combined with k-means clustering.Then,the RF model was trained based on the dimensionality-reduced textile fiber dataset.After grid search optimization,this model demonstrated robust performance with a 0.9762 out-of-bag score,a 0.9683 cross-validation accuracy(5-fold),and a 0.9636 test accuracy,supported by precision,recall,and F1-scores exceeding 0.889 for all fiber classes.The method was applied to analysis of 30 luxury apparel samples from eight brands,among which 20 samples achieved 100%prediction confidence,aligning with labeled compositions.The identification result of two low-confidence samples was further confirmed using attenuated total reflection Fourier transform infrared spectroscopy(ATR-FT-IR).The method has been proven to be simple,portable and with minimal sample requirements for on-site customs inspections,providing a viable tool in the fight against counterfeit products,therefore supporting regulatory enforcement and consumer trust in the textile goods market.

Objective To investigate the relationship between serum Rho-associated coiled coil containing protein kinase 2(ROCK2),syndecan-1(SDC-1),Apelin-13 and renal function and prognosis in patients with diabetes nephropathy.Methods From November 2019 to November 2021,153 patients with diabetic nephrop-athy admitted to the hospital were selected as the study group,and 153 patients with simple diabetes were se-lected as the simple diabetes group.Another 153 healthy individuals who underwent physical examinations during the same period were selected as the healthy control group.The levels of serum ROCK2,SDC-1 and Apelin-13 were determined by enzyme-linked immunosorbent assay.Pearson correlation analysis was used to analyze the correlations between the levels of serum ROCK2,SDC-1,and Apelin-13 and the clinical data of pa-tients with urinary nephropathy.Univariate and multivariate Logistic regression analyses were conducted to analyze the influencing factors of poor prognosis in patients with diabetic nephropathy.The receiver operating characteristic(ROC)curve was drawn to analyze the predictive value of serum ROCK2,SDC-1 and Apelin-13 for poor prognosis of patients with diabetes nephropathy.Results Compared with the healthy control group,the levels of serum ROCK2 and SDC-1 in the simple diabetes group and the study group increased,the level of Apelin-13 decreased,the estimated glomerular filtration rate(eGFR)decreased,and the ratio of urinary albu-min to creatinine(UACR)increased,and the differences were statistically significant(P＜0.05).The levels of serum ROCK2,SDC-1,Apelin-13 and UACR in the study group were higher than those in the simple diabe-tes group,while eGFR was lower than that in the simple diabetes group,and the differences were statistically significant(P＜0.05).Pearson correlation analysis indicated that the levels of serum ROCK2,SDC-1,and Apelin-13 in the study group were negatively correlated with eGFR(P＜0.05),and positively correlated with renal disease stage,creatinine,24-hour proteinuria,and UACR index(P＜0.05).Compared with the good prognosis group,the levels of serum ROCK2,DCC-1,Apelin-13 and UACR in the poor prognosis group in-creased(P＜0.05),and eGFR decreased(P＜0.05).Univariate and multivariate Logistic regression analyses indicated that the levels of serum ROCK2,SDC-1,and Apelin-13 were all influencing factors for the prognosis of patients(P＜0.05).ROC curve analysis indicated that the area under the curve(AUC)for the combined prediction of poor prognosis in patients by serum ROCK2,DCC-1,and Apelin-13 levels was significantly grea-ter than that by ROCK2(Z=2.854,P=0.004)and DCC-1(Z=2.426,P=0.015)and Apelin-13(Z=2.172,P=0.030)were predicted separately.Conclusion The serum ROCK2,DCC-1 and Apelin-13 levels in patients with diabetic nephropathy increase and are related to renal function.The combination of the three has a relatively high predictive efficacy for the prognosis of patients.

Objective:To explore the therapeutic effect of compound Kuijie Ankang Decoction on ulcerative colitis (UC) model mice by non targeted metabonomics; To explore its mechanism.Compound Kuijie Ankang.Methods:The mice were randomly divided into blank control group, model group, Kuijie Ankang Decoction group and sulfasalazine group, with 12 mice in each group. Except the blank control group, the other groups were given 1.5% DSS solution for free drinking to prepare UC model. After successful modeling, Kuijie Ankang Decoction group was intragastrically administered with compound Kuijie Ankang Decoction of 9.68 g/kg, sulfasalazine group was intragastrically administered with sulfasalazine capsule suspension of 320 mg/kg, model group and blank control group were intragastrically administered with equal volume of purified water, once a day, for 7 consecutive days. The body mass and disease activity index (DAI) score of mice were measured. ELISA was used to measure the levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10) in the colon tissue of mice; the protein expressions of Claudin-1 and Zo-1 in colon tissue were detected by immunofluorescence method. HE staining was used to observe the pathological changes in the colon, and UHPLC-OE-MS technology was used to analyze the endogenous metabolite structure of mouse colon tissue, differential metabolites and related metabolic pathways were screened.Results:Compared with the model group, the colon length in Kuijie Ankang Decoction group and sulfasalazine group increased ( P<0.01), the DAI score decreased ( P<0.01), the levels of TNF-α, IL-1β and IL-6 in colon tissue decreased ( P<0.01), the level of IL-10 increased ( P<0.01), and the average optical density of Claudin-1 and Zo-1 protein increased ( P<0.01 or P<0.05). Metabolomics analysis identified 26 potential differential metabolites, including nicotinamide adenine dinucleotide, guanine, gamma aminobutyric acid, and thiamine, affecting 26 key metabolic pathways, including lysine biosynthesis, thiamine metabolism, cysteine and methionine metabolism. Conclusion:Kuaijie Ankang Decoction may improve metabolites such as Gamma aminobutyric acid and thiamine through metabolic pathways such as lysine biosynthesis to alleviate inflammatory reactions, thereby exerting therapeutic effects on ulcerative colitis in mice.

Objective To investigate the epidemiological characteristics of Mycoplasma pneumoniae(MP)infection among pediatric inpatients with respiratory tract infections(RTIs)at Children's Hospital of Hebei Province,providing evidence for clinical diagnosis and treatment.Methods A retrospective analysis was conducted on 79 546 children hospitalized for RTIs between January 2016 and February 2024.Nasopharyngeal aspirates or deep sputum samples were collected,and polymerase chain reaction(PCR)was used to detect nucleic acids of 13 respiratory pathogens,including MP and adenovirus.The epidemiological trends across different years,seasons,genders,and age groups were analyzed.Results Among the 79 546 enrolled cases(male:47 437,59.6%;female:32 109,40.4%),the MP-positive rate was 17.7%(14 106/79 546),peaking in 2023(28.8%)and reaching the lowest in 2021(7.0%).Except for the period from July 2020 to March 2021 with exceptionally low MP positivity,epidemic peaks consistently occurred between August and February or March of the following year.Seasonal analysis revealed significantly higher MP positivity in autumn and winter compared to spring(P<0.001).Female children exhibited a higher MP-positive rate(20.1%,6444/32 109)than males(16.2%,7662/47 437)(P<0.001).The MP-positive rate increased with age:infancy(3.2%,849/26 741),toddlerhood(9.3%,1935/20 763),preschool(21.2%,3918/18 448),and school-age(54.5%,7404/13 594)(P<0.001).Co-infections with other respiratory pathogens were observed in 37.3%(5264/14 106)of MP-positive cases,with human rhinovirus(HRV)being the most frequent co-pathogen(43.3%,2279/5264 of mixed infections).Conclusion MP is a major pathogen of RTIs in hospitalized children at Children's Hospital of Hebei Province.Infections occur year-round but predominantly in autumn,with higher susceptibility in females and school-age children.Targeted preventive measures should be implemented during peak seasons to mitigate transmission risks.