ObjectiveTo validate the therapeutic effect of Yinqi Sanhuang Jiedu decoction （YQSH） on a mouse model of carbon tetrachloride （CCl₄）-induced liver fibrosis and to investigate its correlations with the Piezo-type mechanosensitive ion channel component 1 （Piezo1）-Yes-associated protein （YAP） mechanical signaling axis and macrophage polarization. MethodsFifty-four C57BL/6J mice were randomized into a blank control group， a 4-week model group， a 6-week model group， a 8-week model group， a positive drug （silymarin， 55 mg·kg^-1·d^-1） group， and low-， medium-， and high-dose （8.325， 16.65， 33.3 g·kg^-1·d^-1， respectively） YQSH groups. Except the 6-week model group （n=12）， each of the other groups had 6 mice. Mice in other groups except the blank control group received intraperitoneal injections of 10% CCl₄ twice weekly for the modeling of liver fibrosis. Drug interventions began one week after the initial modeling through gavage， and the blank control and model groups received 0.2 mL of normal saline via gavage. The histopathological changes and collagen deposition in the liver were observed via hematoxylin-eosin （HE）， Masson's trichrome， and Sirius red staining. Serum activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST）， as well as serum levels of total protein （TP）， albumin （ALB）， total bilirubin （TBIL）， hyaluronic acid （HA）， laminin （LN）， procollagen type Ⅲ （PCⅢ）， and collagen type Ⅳ （Ⅳ-C）， were measured. The levels of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β） in the liver tissue were determined by enzyme-linked immunosorbent assay （ELISA）. The protein and mRNA levels of Piezo1 and YAP1 in the liver tissue were determined by immunohistochemistry （IHC） and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. Co-localization of Piezo1 with YAP1， and YAP1 with inducible nitric oxide synthase （iNOS） was observed by the immunofluorescence （IF） assay. The proportions of M1-type （F4/80⁺CD80⁺） and M2-type （F4/80⁺CD206⁺） macrophages in the liver tissue were examined by flow cytometry. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses and gene set enrichment analysis （GSEA） were performed through transcriptomic sequencing. ResultsCompared with the blank control group， the model group exhibited gradually worsened liver fibrosis at the 4^th， 6^th， and 8^th weeks. The 6- and 8-week model groups showcased inflammatory cell infiltration and collagen deposition in the liver （P<0.01） and upregulated protein levels of both Piezo1 and YAP1 （P<0.01）. Compared with the model groups， treatment with YQSH improved the liver function （P<0.05， P<0.01）， alleviated liver fibrosis （P<0.05， P<0.01）， and reduced intrahepatic inflammatory cell infiltration and collagen deposition （P<0.01）. Furthermore， the treatment downregulated the protein and mRNA levels of Piezo1 and YAP1 （P<0.05， P<0.01）， lowered the levels of inflammatory factors TNF-α and IL-1β （P<0.05， P<0.01）， and decreased the ratio of CD80 （M1-type）/CD206 （M2-type） macrophage proteins （P<0.01）. The IF assay showed co-localization of YAP1 with Piezo1 and iNOS. Compared with the model groups， YQSH treatment downregulated the expression of Piezo1， YAP1， and iNOS （P<0.05， P<0.01）. Transcriptomic analysis suggested that the anti-liver fibrosis effect of YQSH may be related to the Hippo signaling pathway （P<0.05）. ConclusionThe anti-liver fibrosis effect of YQSH may be related to its inhibition of the Piezo1-YAP signaling axis and regulation of macrophage polarization.

ObjectiveTo validate the therapeutic effect of Yinqi Sanhuang Jiedu decoction （YQSH） on a mouse model of carbon tetrachloride （CCl₄）-induced liver fibrosis and to investigate its correlations with the Piezo-type mechanosensitive ion channel component 1 （Piezo1）-Yes-associated protein （YAP） mechanical signaling axis and macrophage polarization. MethodsFifty-four C57BL/6J mice were randomized into a blank control group， a 4-week model group， a 6-week model group， a 8-week model group， a positive drug （silymarin， 55 mg·kg^-1·d^-1） group， and low-， medium-， and high-dose （8.325， 16.65， 33.3 g·kg^-1·d^-1， respectively） YQSH groups. Except the 6-week model group （n=12）， each of the other groups had 6 mice. Mice in other groups except the blank control group received intraperitoneal injections of 10% CCl₄ twice weekly for the modeling of liver fibrosis. Drug interventions began one week after the initial modeling through gavage， and the blank control and model groups received 0.2 mL of normal saline via gavage. The histopathological changes and collagen deposition in the liver were observed via hematoxylin-eosin （HE）， Masson's trichrome， and Sirius red staining. Serum activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST）， as well as serum levels of total protein （TP）， albumin （ALB）， total bilirubin （TBIL）， hyaluronic acid （HA）， laminin （LN）， procollagen type Ⅲ （PCⅢ）， and collagen type Ⅳ （Ⅳ-C）， were measured. The levels of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β） in the liver tissue were determined by enzyme-linked immunosorbent assay （ELISA）. The protein and mRNA levels of Piezo1 and YAP1 in the liver tissue were determined by immunohistochemistry （IHC） and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. Co-localization of Piezo1 with YAP1， and YAP1 with inducible nitric oxide synthase （iNOS） was observed by the immunofluorescence （IF） assay. The proportions of M1-type （F4/80⁺CD80⁺） and M2-type （F4/80⁺CD206⁺） macrophages in the liver tissue were examined by flow cytometry. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses and gene set enrichment analysis （GSEA） were performed through transcriptomic sequencing. ResultsCompared with the blank control group， the model group exhibited gradually worsened liver fibrosis at the 4^th， 6^th， and 8^th weeks. The 6- and 8-week model groups showcased inflammatory cell infiltration and collagen deposition in the liver （P<0.01） and upregulated protein levels of both Piezo1 and YAP1 （P<0.01）. Compared with the model groups， treatment with YQSH improved the liver function （P<0.05， P<0.01）， alleviated liver fibrosis （P<0.05， P<0.01）， and reduced intrahepatic inflammatory cell infiltration and collagen deposition （P<0.01）. Furthermore， the treatment downregulated the protein and mRNA levels of Piezo1 and YAP1 （P<0.05， P<0.01）， lowered the levels of inflammatory factors TNF-α and IL-1β （P<0.05， P<0.01）， and decreased the ratio of CD80 （M1-type）/CD206 （M2-type） macrophage proteins （P<0.01）. The IF assay showed co-localization of YAP1 with Piezo1 and iNOS. Compared with the model groups， YQSH treatment downregulated the expression of Piezo1， YAP1， and iNOS （P<0.05， P<0.01）. Transcriptomic analysis suggested that the anti-liver fibrosis effect of YQSH may be related to the Hippo signaling pathway （P<0.05）. ConclusionThe anti-liver fibrosis effect of YQSH may be related to its inhibition of the Piezo1-YAP signaling axis and regulation of macrophage polarization.

Objective To investigate the expression characteristics, prognostic value, and correlation with immune infiltration of vasoactive intestinal peptide receptor 2 (VIPR2) in esophageal adenocarcinoma (ESAD). Methods Transcriptome data of 80 ESAD and 10 normal tissues were retrieved from the TCGA database. Immune-related differential genes were identified by integrating the ImmPort database. Cox regression analysis was applied to evaluate the prognostic significance of VIPR2. GSVA and ssGSEA were employed to assess the association between VIPR2 and immune-cell infiltration. Functional enrichment was performed using GO, KEGG, and GSEA. Results VIPR2 expression was significantly downregulated in ESAD tissues (P<0.05). High VIPR2 expression was associated with prolonged overall survival (HR=0.246, P=0.047), serving as an independent prognostic factor. Fifteen immune-cell types, including CD8⁺ T cells, NK cells, and dendritic cells, showed significantly higher infiltration in the VIPR2-high group, whereas Th17-cell abundance was decreased (P<0.05). Enrichment analyses revealed that VIPR2 participated in multiple tumor-immune pathways and was positively correlated with PD-1 and CTLA-4 expression (P<0.01). Conclusion VIPR2 may serve as a potential biomarker in the prognostic assessment and could modulate the tumor-immune microenvironment in ESAD.

Objective To observe the value of whole-brain CT perfusion(CTP)parameters at different time and clinical data for predicting delayed cerebral ischemia(DCI)and 3-month poor prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods Totally 127 aSAH patients were retrospectively enrolled.Clinical and CTP data within 24 h of symptom onset and during DCI time window(DCITW)were collected.The patients were divided into DCI group(n=34)and non-DCI group(n=93)based on DCI occurred or not during hospitalization,also into poor outcome group(modified Rankin scale[mRS]≥3,n=36)and good outcome group(mRS≤2)based on 3-month's follow-up.Multivariate logistic regression was performed to select independent predictive factors among variates being significantly different between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive performance of logistic regression model.Results Patients'age,modified Fisher score(mFS),subarachnoid hemorrhage early brain edema score(SEBES)and mean flow extraction product(mFEP)within 24 h of onset were all identified as independent predictive factors of DCI,and the AUC of their combination for predicting DCI during hospitalization was 0.817.Patients' age and mFS within 24 h of onset,alternatively,World Federation of Neurosurgical Societies(WFNS)grade and mFEP during DCITW were all independent predictive predictors of 3 months' prognosis,and the combination of the latter two showed better predictive performance(AUC=0.922)tahn the former two(AUC=0.822,P＜0.05).Conclusion Whole-brain CTP parameters combined with clinical data within 24 h of onset of aSAH could be used to predict the occurrence of DCI during hospitalization,whole-brain CTP parameters during DCITW could be used to predict 3 months'poor prognosis.

Malignant tumors(commonly referred to as cancer)represent a major global public health challenge and contribute significantly to the worldwide disease burden.Early screening plays a critical role in improving detection rates,enabling timely intervention,and enhancing pa-tient survival rates.However,current cancer screening guidelines primarily focus on site-specific screening,which may not fully address the need for comprehensive early detection.A scientifical-ly rational,multi-cancer screening approach offers several advantages:it optimizes the use of bio-logical samples,reduces time costs for participants,enhances the efficiency and comprehensive-ness of screening,and minimizes overall expenses.Such an approach also facilitates the rational allocation of healthcare resources,ultimately helping to reduce the societal burden of cancer.To address this need,the Cancer Epidemiology Committee of the Chinese Anti-Cancer Association has developed the Expert Consensus on Combined Screening for Common Cancers in China.This consensus integrates multidisciplinary expertise and synthesizes the latest domestic and interna-tional researches on cancer screening,early detection,and treatment for prevalent malignancies.Drawing upon China's unique demographic and healthcare context,as well as practical screening experiences,the consensus provides evidence-based recommendations on target populations,screening technologies,and procedural workflows for multi-cancer screening.These guidelines align with the principles and methodologies established by the World Health Organization(WHO),aiming to:enhance the effectiveness of combined cancer screening in China,improve early detec-tion rates,and provide a scientific foundation for national cancer prevention and control strategies.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

BACKGROUND: Atrial fibrillation (AF) is a common cardiac arrhythmia in the elderly. This study aimed to evaluate urban-rural disparities in its prevalence and management in elderly Chinese. METHODS: Consecutive participants aged ≥ 65 years attending outpatient clinics were enrolled for AF screening using handheld single-lead electrocardiogram (ECG) from April 2017 to December 2022. Each ECG rhythm strip was reviewed from the research team. AF or uninterpretable single-lead ECGs were referred for 12-lead ECG. Primary study outcome comparison was between rural and urban areas for the prevalence of AF. The Student's t-test was used to compare mean values of clinical characteristics between rural and urban participants, while the Pearson's chi-square test was used to compare between-group proportions. Multivariate stepwise logistic regression analysis was performed to estimate the association between AF and various patient characteristics. RESULTS: The 29,166 study participants included 13,253 men (45.4%) and had a mean age of 72.2 years. The 7073 rural participants differed significantly (P ≤ 0.02) from the 22,093 urban participants in several major characteristics, such as older age, greater body mass index, and so on. The overall prevalence of AF was 4.6% (n = 1347). AF was more prevalent in 7073 rural participants than 22,093 urban participants (5.6% vs. 4.3%, P < 0.01), before and after adjustment for age, body mass index, blood pressure, pulse rate, cigarette smoking, alcohol consumption and prior medical history. Multivariate logistic regression analysis identified overweight/obesity (OR = 1.35, 95% CI: 1.17-1.54) in urban areas and cigarette smoking (OR = 1.62, 95% CI: 1.20-2.17) and alcohol consumption (OR = 1.42, 95% CI: 1.04-1.93) in rural areas as specific risk factors for prevalent AF. In patients with known AF in urban areas (n = 781) and rural areas (n = 338), 60.6% and 45.9%, respectively, received AF treatment (P < 0.01), and only 22.4% and 17.2%, respectively, received anticoagulation therapy (P = 0.05). CONCLUSIONS In China, there are urban-rural disparities in AF in the elderly, with a higher prevalence and worse management in rural areas than urban areas. Our study findings provide insight for health policymakers to consider urban-rural disparity in the prevention and treatment of AF.