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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

Objective:To construct a humanistic care nursing program for adult ICU patients, providing guidance for the clinical practice of humanistic care in ICUs.Methods:Based on a literature review and clinical practice experience, a preliminary humanistic care nursing program for adult ICU patients was drafted. From August to September 2024, the Delphi method was used to conduct two rounds of expert consultation with 16 experts to revise the content of each item and the overall program, resulting in the final version of the humanistic care nursing program for ICU adult patients. The experts' engagement was measured by the effective response rate of the questionnaires, their authority by the expert authority coefficient, and the coordination of expert opinions by the Kendall's coefficient of concordance.Results:The effective response rate for the Delphi expert consultation questionnaires was 100.00% (16/16) in both rounds. The expert authority coefficients were 0.872 and 0.875, respectively. After the second round of consultation, the Kendall's coefficients for the importance, applicability, and feasibility of each level of item ranged from 0.119 to 0.313 ( P<0.05). The final humanistic care nursing program for adult ICU patients included three first-level items, 12 second-level items, and 55 third-level items. Conclusions:The humanistic care nursing program for adult ICU patients constructed in this study is scientific, targeted, and feasible, providing guidance for the clinical practice of humanistic care in ICU settings.

Objective To investigate the predictive value of serum interleukin-17(IL-17)and soluble hemoglobin scavenger receptor 163(sCD163)for early anastomotic leakage in patients with colorectal cancer after laparoscopic surgery.Methods From June 2022 to June 2024,80 colorectal cancer patients who underwent laparoscopic surgery were selected as the study subjects.Patients were subdivided into an anastomotic leakage group(17 cases)and a non-anastomotic leakage group(63 cases)according to whether an anastomotic leakage occurred within 7 d postoperatively.Enzyme-linked immunosorbent assay(ELISA)was applied to detect the levels of serum IL-17 and sCD163 in patients on preoperative day 1,postoperative day 1,postoperative day 3,postoperative day 5,and postoperative day 7.The clinical data of the two groups were compared.Multivariate Logistic regression was applied to analyze the influencing factors of early anastomotic leakage in colorectal cancer patients after laparoscopic surgery.The receiver operating characteristic curve(ROC curve)was applied to analyze the predictive value of serum IL-17 and sCD163 levels for early anastomotic leakage in colorectal cancer patients after laparoscopic surgery.Results The percentage of distance between tumor and anus＜7 cm was higher in the anastomotic leakage group than that in the non-anastomotic leakage group,and the amount of drainage was more than that in the non-anastomotic leakage group at 7 d postoperatively,the differences were statistically significant(P＜0.05).There were statistically significant differences in the levels of serum IL-17 and sCD163 between the two groups of patients at different time points,as well as in time and interaction effects(P＜0.05).Distance between tumor and anus＜7 cm(O R=3.176,95％CI:1.128～8.940),serum IL-17 level ≥ 43.95 pg/mL at 3 d postoperatively(O R=2.145,95％CI:1.281～3.592),and serum sCD163 level ≥ 81.02 pg/mL at 5d postoperatively(OR=1.852,95％CI:1.225～2.801)were the risk factors of early anastomotic leakage in patients with colorectal cancer after laparoscopic surgery(P＜0.05).The area under the curve(AUC)of the combination of serum IL-17 and sCD163 in predicting early anastomotic leakage in colorectal cancer patients after laparoscopic surgery was 0.923,which was superior to the single diagnosis of serum IL-17 and sCD163(Zcombination-IL-17=2.77,Zcombination-sCD163=2.11,P＜0.05).Conclusion Serum IL-17 and sCD163 levels are elevated in colorectal cancer patients with early anastomotic leakage after laparoscopic surgery.The combined detection of the two has certain clinical value in predicting early anastomotic leakage in patients with colorectal cancer after laparoscopic surgery.

Objective To investigate the mechanism of benzyl isothiocyanate(BITC)combined with sorafenib(Sor)in the treatment of anaplastic thyroid cancer(ATC).Methods Two ATC cell lines,8505C and CAL-62,were treated with Sor at concentrations of 0,20,30,40,and 50 μmol/L.The cell survival rate was assessed using CCK-8 assay.The combined dose of BITC and Sor was determined by calculating combination index(CI).CAL-62 and 8505C cells were exposed to 10 μmol/L BITC(BITC group),10 μmol/L Sor(Sor group),or a combination of 10 μmol/L BITC and 10 μmol/L Sor(BITC+Sor group)for 24 hours.The control group was not treated.The effects of Sor and BITC on ATC cell viability were evaluated using the CCK-8 method.Apoptosis was analyzed via flow cytometry.Western blot assay was employed to detect the protein expression levels of LC3B Ⅱ,Beclin-1 and nuclear factor(NF)-κB.Real-time fluorescence quantitative PCR was used to quantify the mRNA levels of LC3B.Additionally,CAL-62 cells were subcutaneously injected into mice to establish tumor xenograft model.Mice were treated with BITC(100 mg/kg,intraperitoneal injection),Sor(30 mg/kg,intragastric administration)or a combination of BITC and Sor every other day for 21 days.Finally,the expression levels of LC3B Ⅱ,Beclin-1 and NF-κB in tumor tissue were analyzed by Western blot assay.Results Sor significantly inhibited the viability of CAL-62 and 8505C cells in a concentration-dependent manner.The combination index(CI)was 0.710 at BITC 10 μmol/L and Sor 10 μmol/L,indicating a moderate synergistic effect between the two drugs.In both 8505C and CAL-62 cells,compared with the control group,treatment with BITC or Sor resulted in the decreased cell viability,as well as reduced expression levels of Beclin-1 and NF-κB proteins(P＜0.05),and the apoptosis rate,LC3B mRNA and LC3B Ⅱ protein expression levels were significantly increased(P＜0.05).When BITC and Sor were combined,the cell viability,Beclin-1 and NF-κB protein expressions were further reduced compared to either drug alone,while the apoptosis rate,LC3B mRNA and LC3B Ⅱ protein expression levels were significantly elevated(P＜0.05).In the mouse xenograft tumor model,the BITC+Sor group exhibited increased LC3B Ⅱ expression,along with decreased Beclin-1 and NF-κB expression levels,tumor volume and tumor mass compared to the BITC or Sor groups(P＜0.05).Conclusion The combination of BITC and Sor can inhibit ATC cells through NF-κB pathway,induce autophagy and promote apoptosis in vitro and in vivo.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute（CLSI）. Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus（ n=1 544，35.2%），coagulase-negative Staphylococci（ n=1 441，32.9%）， Enterococcus faecium（ n=574，13.1%）， Enterococcus faecalis（ n=385，8.8%），and α-hemolytic Streptococci（ n=187，4.3%）. The prevalence of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）was 26.2%（405/1 544）and 69.8%（1 006/1 441），respectively. Notably，all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility（>97.0%）to cephalobiol，rifampicin，trimethoprim-sulfamethoxazole，linezolid，minocycline，tigecycline，and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium（resistance to vancomycin and teicoplanin：both 1.7%）compared to Enterococcus faecalis（both 0.3%）. The detection rates of MRSA and MRCNS exhibited significant regional variations across the country（ χ2=17.674 and 148.650，respectively，both P<0.001）. No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA（ χ2=14.111， P<0.001）and MRCNS（ χ2=4.828， P=0.028）in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA（ χ2=18.986， P<0.001）and MRCNS（ χ2=4.477， P=0.034）in less developed regions（per capita GDP

Objective:To report the results of bacterial resistant investigation collaborative system（BRICS）on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023，and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period，11 492 strains of Gram-negative bacteria were collected from 60 hospitals，of which 10 098（87.9%）were Enterobacterales and 1 394（12.1%）were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli（50.0%）， Klebsiella pneumoniae（26.1%）， Pseudomonas aeruginosa（5.1%）， Acinetobacter baumannii complex（5.0%）and Enterobacter cloacae complex（4.1%）. The ESBL-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus mirablilis were 46.8%（2 685/5 741），18.3%（549/2 999）and 44.0%（77/175），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（76/5 741）and 15.0%（450/2 999）；32.9%（25/76）and 78.0%（351/450）of CREC and CRKP were sensitive to ceftazidime/avibactam combination，respectively. 94.7%（72/76）and 90.2%（406/450）of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore，57.9%（44/76）and 79.1%（356/450）were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 64.6%（370/573），while more than 80.0% of CRAB complex was sensitive to tigecycline，eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 17.0%（99/581）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China，with statistically significant differences in the prevalence of CREC，CRKP，CRPA and CRAB complex（ χ2=10.6，28.6，10.8 and 19.3， P<0.05）. The prevalence of ESBL-producing Escherichia coli， CREC，CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals（ χ2=12.5，9.8，12.7 and 57.8，all P<0.01）. Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China，and Escherichia coli is ranked in the top，while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend，CREC infecton maintains a low prevalence level，and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.