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.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

Objectives:To explore whether short-course radiotherapy (SCRT)-based total neoadjuvant therapy (TNT) combined with PD-1 inhibitors could further promote tumor regression and improve the prognosis.Methods:This is a prospective, multicenter, two-arm randomized controlled, seamless phase Ⅱ/Ⅲ trial for proficient mismatch repair or microsatellite stable (pMMR/MSS) locally advanced rectal cancer (LARC). Eligible patients were randomly assigned to the iTNT (TNT+PD-1) group or the TNT group. Patients in the TNT group received SCRT (5 Gy×5) followed by 4 cycles of CAPOX or 6 cycles of mFOLFOX chemotherapy, with the iTNT group receiving SCRT followed by the same regime in combination with 4 cycles of Sintilimab. Total mesorectal excision (TME) surgery or watch and wait (W&W) was performed after neoadjuvant therapy and then 2 cycles of same regimen as before were recommended. The primary endpoints are the complete response (CR) rate for phase Ⅱ trial and 3-year disease-free survival (DFS) for phase Ⅲ trial. A total of 588 patients will be enrolled for the phase Ⅱ/Ⅲ trial. Short-term efficacy and safety data from the initial 100 treated patients were analyzed as planned.Results:From 2022-8-31 to 2023-5-24 the initial 100 patients were enrolled from 10 hospitals in China, 76.0%(76/100) patients were male, and the median age was 61 years (21-74 years). More patients had tumors located in the lower rectum (78.0%, 78/100), staged T3-4 (97.0%, 97/100) and N1-2 (93.0%, 93/100), and about half of the tumors invaded the mesorectal fascia (52.0%, 52/100) and with extramural vascular invasion (51.0%, 51/100). Analyses were performed according to the per-protocal (PP) set. All patients in the iTNT group ( n=52) and the TNT group ( n=48) completed SCRT; The 4-cycle chemotherapy±Sintilimab completion rates were 86.5% and 100.0% in the iTNT and TNT groups, respectively. In the iTNT group, 82.7% (43/52), 11.5% (6/52), and 5.8% (3/52) of the patients received 4, 3, and 2 cycles of PD-1 inhibitor. After TNT, 68 patients underwent radical surgery and 15 patients achieved cCR and adopted W&W. The pathological complete response (pCR) rates were 48.5% (16/33) and 17.1% (6/35) in the iTNT and TNT groups, with CR rates of 50.0% (25/50) and 26.1% (12/46), respectively. The incidence of treatment-related grade 3-4 adverse events was 26.9% (14/52, iTNT group) and 18.8% (9/48, TNT group), with thrombocytopenia and leukopenia being the most common. Among patients receiving immunotherapy, grade 3 immunotherapy-related adverse events occurred in 2 (3.8%, 2/52) patients: one case was pancreatitis, another case was hepatitis combined with myositis and myocarditis. Conclusion:The preliminary results show that SCRT-based TNT combined with PD-1 inhibitors could further improve the CR rate for LARC without unexpected serious adverse events.

Objective:To explore the clinical value of autologous umbilical cord whole blood(UCB) priming of the cardiopulmonary bypass(CPB) circuit in neonatal cardiac surgery for congenital heart disease(CHD).Methods:This prospective non-randomized controlled trial included neonates undergoing CHD surgery at Guangdong Provincial People’s Hospital from August 2024 to January 2025. The experimental group used autologous UCB for CPB circuit priming, while the control group used adult allogeneic blood(AAB) priming when UCB was unavailable. Preoperative characteristics, intraoperative CPB and aortic cross-clamping(ACC) times, postoperative ICU stay duration, mechanical ventilation time, and hospitalization length were compared.Results:There were no significant differences in preoperative baseline characteristics between the two groups( P>0.05). At the end of surgery, red blood cell count(RBC), hemoglobin level(Hb), and creatine kinase(CK) showed no significant differences between the groups( P> 0.05). Additionally, perioperative left ventricular ejection fraction(LVEF) demonstrated no statistically significant variations( P>0.05). At surgery completion, the UCB group exhibited lower hematocrit(HCT) and higher blood lactic acid(Lac) levels but these differences resolved by 6 hours postoperatively( P>0.05). The UCB group had higher maximum vasoactive-inotropic scores(VISmax) within 48 hours and longer ICU stays, though total hospitalization and mechanical ventilation durations showed no significant differences( P>0.05). Conclusion:Autologous UCB priming reduces AAB requirements and has minimal impact on postoperative cardiac and pulmonary function recovery, or homeostasis., which is safe and feasible. This study provides evidence supporting the clinical application of UCB priming in CPB circuits.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

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 observe the effects of ear acupressure combined with acupoint application on serum inflammatory factors,pain and psychological status of children with limb fractures.Methods:By using random number table method,60 children with limb fracture who were admitted to Qiantang Campus of Zhejiang Provincial Traditional Chinese Medicine Hospital from February 2022 to August 2024 were divided into control group(received conventional treatment combined with acupoint application)and experimental group(combined with ear acupressure based on the control group),with 30 cases in each group.Pain and swelling degree of the affected limb,anxiety and depression[Screening Checklist for Childhood Anxiety Disorders(SCARED),Depression self-rating scale for children(DSRSC)],inflammatory factors[High sensitivity C-reactive protein(hs-CRP),tumor necrosis factor-α(TNF-α),interleukin-6(IL-6),interleukin-1(IL-1)]and family satisfaction rate were compared between the two groups.Results:The scores of visual analogue scale(VAS),SCARED and DSRSC in the experimental group were lower than those in the control group at 1 week and 4 weeks after treatment(P＜0.05).Swelling degree of the affected limb in the experimental group was better than that in the control group at 4 weeks after treatment(P＜0.05).hs-CRP,IL-1,IL-6 and TNF-α in the experimental group were lower than those in the control group at 4 weeks after treatment(P＜0.05).The family satisfaction rate in the experimental group was higher than that in the control group(P＜0.05).Conclusion:Ear acupressure combined with acupoint application therapy for children with limb fractures can significantly alleviate postoperative pain,promote swelling reduction,effectively reduce inflammation,and improve anxiety and depression,with high treatment satisfaction.

ObjectiveTo analyze the epidemiological and clinical characteristics of pertussis in Baoshan District, Shanghai from 2017 to 2024, so as to provide an evidence-based reference for optimizing prevention and control strategies. MethodsData on pertussis cases were collected from the China Disease Prevention and Control Information System, Shanghai Integrated Management and Immunization Service Information System, and follow-up epidemiological investigations. Descriptive epidemiological analyses were performed to analyze the epidemiological characteristics, clinical manifestations, and vaccine effectiveness. Joinpoint regression analyses were used to examine the temporal trends in incidence rates, and a Poisson model was constructed for spatiotemporal scan analyses. ResultsA total of 1 634 pertussis cases were reported in Baoshan District from 2017 to 2024, with a male-to-female ratio of 1.08∶1. More cases were observed in males than in females, with the age ranged from 20 days to 81 years. Among them, 59.92% were in the 6‒<11 years age group, and 63.34% were students. Low-level sporadic incidence persisted during 2017‒2023, followed by a sharp increase in 2024 (71.37/100 000). Starting in January 2024, the incidence rate showed an upward trend, peaking in May before declining. The majority of cases occurred between April and June. The trend in reported pertussis incidence rates in Baoshan District from 2017 to 2023 showed no statistically significant change (APC=10.039%, t=2.586, P=0.150). Incidence rate rose from January 2024, peaked in May (APC=133.641%, t=3.841, P=0.006), then declined significantly (APC=-47.816%, t=2.586, P<0.001). The 12 subdistricts of Baoshan District were divided into low, medium, and high population density areas, with an average annual reported incidence rate of 6.09/100 000, 8.19/100 000 and 11.96/100 000, respectively. The reported incidence rate increased with an increase in population density. Spatiotemporal scan analyses showed that cases clustered in the southwest and northeast of Baoshan District. Epidemiological follow-up investigations of 1 520 cases revealed that the main clinical symptoms were cough (97.63%) and sputum production (41.58%), and 98.13% of the cases were confirmed by positive nucleic-acid test results. Among the 1 475 cases with immunization records, 83.53% had completed the four-dose pertussis vaccine before onset. The complication incidence rates, from high to low, were in the 0-dose vaccination group, 1‒3-dose vaccination group and 4-dose vaccination group. The duration of cough, from long to short, was observed in the the 0-dose vaccination group, 1‒3-dose vaccination group and 4-dose vaccination group, correspondingly. ConclusionIt is recommended to improve the pertussis surveillance system in medical institutions and establish an active monitoring network, prioritizing deployment in school settings and areas with high population density. Enhancing diphtheria-tetanus-pertussis (DTP) vaccination coverage among 6-year-old children and further optimizing the pertussis immunization strategies are essential to prevent and reduce the risk of pertussis among school-aged children.

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.