ObjectiveTo develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures. MethodsA retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsAmong the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT. ConclusionThe GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

ObjectiveTo investigate the effects of Anmeidan （AMD） on neuroinflammation in the hippocampus of sleep-deprived rats. MethodsSD rats were randomly divided into four groups （n = 10 per group）： control group， model group， AMD group， and melatonin group. A sleep deprivation model was established using the modified multiple platform water environment method. The AMD group received AMD at a dose of 18.18 g·kg^-1·d^-1， the melatonin group received melatonin at 100 mg·kg^-1·d^-1， and the control and model groups were given an equal volume of pure water. All treatments were administered by gavage for four weeks. Spontaneous activity was assessed using an animal behavior video system. Serum levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal pyramidal neuron morphology was examined using hematoxylin-eosin （HE） staining， and ultrastructural changes of hippocampal neurons were observed via transmission electron microscopy. Immunofluorescence was used to detect the expression of brain-derived neurotrophic factor （BDNF） and nerve growth factor （NGF） in the hippocampus. Western blot analysis was performed to measure the expression of nuclear factor-κB （NF-κB）， phosphorylated NF-κB （p-NF-κB）， NOD-like receptor protein 3 （NLRP3）， and Caspase-1 proteins. ResultsCompared with the control group， the model group showed a significant increase in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell structural damage and decreased cell count， aggravated hippocampal ultrastructural damage， mitochondrial cristae disruption， and exacerbated vacuolization. The expression of p-NF-κB p65， NLRP3， and Caspase-1 proteins was upregulated， serum IL-1β， IL-6， and TNF-α levels were significantly elevated （P<0.01）， and the fluorescence intensity of BDNF and NGF proteins was significantly reduced （P<0.01）. Compared with the model group， the AMD group showed a significant reduction in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell count with reduced structural damage， alleviated hippocampal ultrastructural damage， significantly downregulated p-NF-κB p65， NLRP3， and Caspase-1 protein expression （P<0.01）， decreased serum IL-1β， IL-6， and TNF-α levels （P<0.01）， and significantly increased the fluorescence intensity of BDNF and NGF proteins （P<0.01）. ConclusionAnmeidan alleviates hippocampal neuronal damage in sleep-deprived rats， potentially by downregulating the NLRP3 signaling pathway， reducing inflammatory cytokine release， and increasing neurotrophic factor levels.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

The iridoid glycosides from Veronica anagallis-aquatica L. alleviate heart failure by modulating the gut microbiota and influencing the production of two metabolites with potential antihypertrophic effects, HVA and 2OH-VA.Image 1.

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

The Guidelines for Occupational Hazard Assessment and Control of Active Pharmaceutical Ingredient in the Pharmaceutical Industry (T/WSJD 60—2024) is the first guiding standard in the field of health in China that focuses on occupational health protection for active pharmaceutical ingredient (API). It covers the general principles, work procedures, assessment methods, and control strategies for API occupational hazard assessment, providing practical guidance and recommendations for pharmaceutical enterprises to eliminate or reduce occupational health risks associated with API, improve working environment, and enhance refined management practices. This article interpreted and analyzed the background of standard establishment, formulation process, fundamental basis, and main content, to provide scientific and comprehensive technical support for occupational health managers in the pharmaceutical industry to better apply this standard.

Objective To construct a nomogram prediction model for predicting hemoglobin A1c(HbA1c)failure in type 2 diabetes mellitus(T2DM)patients.Methods A total of 936 inpatients with T2DM admitted to the Department of Endocrinology of the Affiliated Hospital of Shandong Second Medical University from January 2021 to January 2022 were selected as the research objects and divided into the non-standard group(HbA1c≥7%,n=801)and the standard group(HbA1c<7%,n=135).Univariate analysis was used to screen the related factors of HbA1c failure.Logistic regression multivariate model was used to analyze the influencing factors of HbA1c failure in T2DM patients.The R language was used to construct a nomogram,and the area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictive ability of the model.The C-index and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model.Results There were statistically significant differences in triglyceride(TG),low-density lipoprotein cholesterol,direct bilirubin,urinary albumin/creatinine ratio(UACR),self-monitoring of blood glucose(SMBG),meat and vegetable pairing,hot pot,whole grain and animal viscera consumption between the two groups(P<0.05).Logistic regression analysis showed that TG(OR 1.699,95%CI 1.298～2.222),UACR(OR 1.003,95%CI 1.001～1.005),SMGB(OR 0.480,95%CI 0.313～0.735),more meat and less vegetables(OR 1.432,95%CI 1.062～1.931)were the influencing factors of HbA1c failure.The AUC of the nomogram prediction model based on the influencing factors was 0.711,with C-index 0.710(95%CI 0.663～0.758)and good calibration(χ2=11.185,P=0.191).Conclusions The nomogram prediction model for HbA1c failure in T2DM patients established based on TG,UACR,SMGB,meat and vegetarian mix has good discrimination and calibration,which can provide certain reference value for warning of poor blood glucose control.

OBJECTIVES: To evaluate the safety and efficacy of thiotepa (TT)-containing conditioning regimens for allogeneic hematopoietic stem cell transplantation (HSCT) in children with inborn errors of immunity (IEI). METHODS: Clinical data of 22 children with IEI who underwent HSCT were retrospectively reviewed. Survival after HSCT was estimated using the Kaplan-Meier method. RESULTS: Nine patients received a traditional conditioning regimen (fludarabine + busulfan + cyclophosphamide/etoposide) and underwent peripheral blood stem cell transplantation (PBSCT). Thirteen patients received a TT-containing modified conditioning regimen (TT + fludarabine + busulfan + cyclophosphamide), including seven PBSCT and six umbilical cord blood transplantation (UCBT) cases. Successful engraftment with complete donor chimerism was achieved in all patients. Acute graft-versus-host disease occurred in 12 patients (one with grade III and the remaining with grade I-II). Chronic graft-versus-host disease occurred in one patient. The incidence of EB viremia in UCBT patients was lower than that in PBSCT patients (P<0.05). Over a median follow-up of 36.0 months, one death occurred. The 3-year overall survival (OS) rate was 100% for the modified regimen and 88.9% ± 10.5% for the traditional regimen (P=0.229). When comparing transplantation types, the 3-year OS rates were 100% for UCBT and 93.8% ± 6.1% for PBSCT (P>0.05), and the 3-year event-free survival rates were 100% and 87.1% ± 8.6%, respectively (P>0.05). CONCLUSIONS TT-containing conditioning for allogeneic HSCT in children with IEI is safe and effective. Both UCBT and PBSCT may achieve high success rates.
Humans ; Retrospective Studies ; Transplantation Conditioning/methods* ; Thiotepa/therapeutic use* ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Male ; Female ; Child, Preschool ; Infant ; Child ; Transplantation, Homologous ; Graft vs Host Disease ; Adolescent

Objective:To develop and validate an artificial intelligence-assisted system based on convolutional neural networks (CNN) for automatic lesion recognition in small intestinal capsule endoscopy.Methods:Three small intestinal capsule endoscopy datasets were used for training ( n=26 638), validating ( n=6 652), and testing ( n=1 013) the deep learning model, covering 12 lesion categories, including vascular malformations, hemorrhage, erosion, erythema, stenosis, lymphangiectasia, submucosal tumors, polyps, lymphoid follicles, foreign bodies, veins, and normal mucosa. CNN performance was measured by area under receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, and F1 score, with comparisons with endoscopists of different experience levels. Results:The top-performing model (EfficientNet-CE) achieved 86.28% sensitivity, 98.67% specificity, and AUC of 0.987 4 across all categories. It demonstrated high accuracy (86.28%) and a processing speed of 52.43 frames per second, approximately 42.4 times faster than junior endoscopists (<3 years' experience) and 40.3 times faster than senior endoscopists (>5 years' experience).Conclusion:The CNN-based model allows rapid, accurate identification of 12 small intestinal lesion types and effectively supports endoscopists in reviewing capsule endoscopy examinations due to its high sensitivity.