Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Objective:To analyze the risk factors associated with future exacerbations in patients with chronic obstructive pulmonary disease (COPD) who have no history of exacerbation in the past year.Methods:COPD patients with no exacerbation history in the past year, registered in the RealDTC study from January 2018 to December 2023, were enrolled. Demographic data, COPD Assessment Test (CAT) scores, modified Medical Research Council (mMRC) dyspnea questionnaire scores, forced expiratory volume in the first second predicted of percentage (FEV 1%pred), forced expiratory volume in one second (FEV 1) to forced vital capacity (FVC), Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, GOLD groups, and inhaled medication regimens were collected. All patients were followed up for one year, and the number of exacerbations was recorded. Patients were divided into an exacerbation group and a non-exacerbation group based on the occurrence of exacerbations during the follow-up period. Logistic regression analysis was used to screen the influencing factors for exacerbations in COPD patients. Results:A total of 2 901 COPD patients were included, among which 633 patients (21.8%) experienced exacerbations during the follow-up period. Compared with the non-exacerbation group, patients in the exacerbation group were older, with higher CAT and mMRC scores, lower body mass index (BMI), FEV 1%pred, and FEV 1/FVC. The proportions of patients with high school education or above and those using long-acting β 2-agonist (LABA) + long-acting muscarinic antagonist (LAMA) medications were also lower (all P<0.05). Logistic regression analysis showed that age ( OR=1.010, 95% CI: 1.000-1.021), CAT score ≥20 ( OR=1.415, 95% CI: 1.074-1.865), education level of junior high school or below ( OR=1.243, 95% CI: 1.003-1.540), LABA + LAMA inhalation ( OR=0.605, 95% CI: 0.432-0.848), and BMI ( OR=0.969, 95% CI: 0.943-0.995) were independent risk factors for future exacerbations in COPD patients with no exacerbation history in the past year (all P<0.05). Conclusions:The risk of future exacerbations remains high in COPD patients with no exacerbation history in the past year. High CAT scores, low education levels, and low BMI are associated with future exacerbations. Clinicians should pay close attention to the management of such patients and implement appropriate interventions.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To investigate the pharmacological mechanism of Ebselenin(Ebselen,EbSe)in the treat-ment of Escherichia coli(E.coli)infection,which had no significant inhibitory effect on Gram-negative bacte-ria,based on previous studies.Methods After EbSe intervention in E.coli infected Raw264.7 cells,the via-bility of Raw264.7 cells was determined by CCK-8 method,the morphology and structure of Raw264.7 cells were observed by electron microscope,and the in-tracellular bacterial load of Raw264.7 cells was calcu-lated by coated plate method.Polarization status of peritoneal macrophages,Raw264.7 intracellular NO and ROS content and intracellular HO-1 expression in Raw264.7 and E.coli acutely infected mice after E.co-li infection by flow cytometry.qPCR was used to detect the expression of related mRNAs in Raw264.7 cells.qPCR was used to detect the intracellular GSH content in Raw264.7 cells by spectrophotometric assay,and the state of cytoskeletal proteins was observed by immuno-fluorescence.Western blot assay was performed to de-tect the intracellular Txnrd1 expression level.Results Microtiter method,CCK-8,and electron microscopy observations showed that EbSe had no effect on the growth of E.coli and Raw264.7 cells in vitro.The re-sults of smear plate counting showed that EbSe reduced the intracellular bacterial load of Raw264.7 in the in-fected group.Flow cytometry results showed that EbSe upregulated the number of M2-type macrophages.The EbSe-treated infected group had reduced intracellular NO and ROS levels and increased GSH levels.The qPCR results showed that the expression of IL-6,IL-1β,and iNOS was decreased,and the expression of HO-1,Txnrd1,and Glut1 was increased in DHB4-in-fected Raw264.7 cells after EbSe treatment.Cytoskel-etal staining showed that the morphology of the EbSe-treated infected cells was similar to that of oxPAPC-in-duced cells.Western blot results showed the expres-sion of Txnrd1 protein in EbSe-treated infected cells in-creased.Conclusion EbSe exerts anti-E.coli acute infection effect by regulating macrophage polarization and inhibiting macrophage excessive inflammatory state.

Meniere's disease represents an idiopathic inner ear disorder characterized by endolymphatic hy-drops.Currently,the research methods for identifying genes associated with this disease mainly involve first-genera-tion sequencing and second-generation sequencing.This article reviews research on the genetic study of Meniere's disease,mainly summarizing the candidate genes with repeated pedigree validation in familial Meniere's disease,as well as those frequently reported in sporadic Meniere's disease.

Aim To study the therapeutic effect of al-licin on senna-induced diarrhea in mice and to explore the underlying mechanism.Methods Forty-eight C57BL/6J mice were randomly divided into six groups:control,model,loperamide positive control group(2 mg·kg-1),allicin low-dose group(6 mg·kg-1),allicin medium-dose group(12 mg·kg-1)and allicin high-dose group(18 mg·kg-1).Except for the con-trol group,the diarrhea model was induced in the other groups by intragastric administration of senna leaf ex-tract.After drug administration,several diarrhea indi-ces were measured:the rate of loose stools,diarrhea index,accumulated frequency of loose stools at differ-ent time points within 5 hours,and small intestine pro-pelling rate.Serum levels of TNF-α and IL-6 were de-tected by ELISA.Serum NO content was determined u-sing the Griess method.The activities of SOD and CAT,as well as MDA content in the ileum and colon,were measured.The pathological changes and the ex-pression of mRNA related to intestinal barrier proteins in the ileum and colon were evaluated using HE stai-ning and RT-qPCR.Results Allicin improved diar-rhea symptoms in mice induced by senna leaf.It re-duced the rate of loose stools,diarrhea index,cumula-tive number of loose stools in five hours,and the intes-tinal propulsion rate.Allicin also protected the intesti-nal mucosa,decreased serum TNF-α and IL-6 levels,and lowered MDA content in the intestines.It in-creased serum NO levels and enhanced SOD and CAT activities in the intestines.Additionally,allicin upreg-ulated the mRNA expression of AQP1,AQP4,and ZO-1 in intestinal tissues.Conclusions Allicin has a significant therapeutic effect on senna-induced diarrhea in mice.The underlying molecular mechanisms may involve anti-inflammatory and antioxidant effects,in-creased NO content,and upregulation of mRNA ex-pression of aquaporins and tight-junction proteins.

Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

Objective To develop a novel type of transparent simulation manikin as a surgical training model to meet the surgical treatment demand on the medical train.Methods A transparent manikin was developed with the steps of basic data collection,motherboard design and manufacture and module production and assembly.Firstly,basic data collection was carried out with reference to standardized human anatomy and parameters.Secondly,some software such as UG NX7.5 was used to construct the motherboard of the manikin.Finally,module production and assembly were performed with the materials of acrylic,transparent rubber,silicone and hydrogel and the technology of silicone infusion.Results The transparent manikin developed had its anatomy structure close to that of the real body and high visuality for its internal and external components,which simulated a variety of war wounds and thus could be integrated with the surgical training scenarios on the medical train effectively.Conclusion The transparent manikin developed is characterized by high visuality,modularity and blood flow,and meets the demands for surgical training on the medical train.[Chinese Medical Equipment Journal,2025,46(6):111-115]

Objective:To analyze the risk factors associated with future exacerbations in patients with chronic obstructive pulmonary disease (COPD) who have no history of exacerbation in the past year.Methods:COPD patients with no exacerbation history in the past year, registered in the RealDTC study from January 2018 to December 2023, were enrolled. Demographic data, COPD Assessment Test (CAT) scores, modified Medical Research Council (mMRC) dyspnea questionnaire scores, forced expiratory volume in the first second predicted of percentage (FEV 1%pred), forced expiratory volume in one second (FEV 1) to forced vital capacity (FVC), Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, GOLD groups, and inhaled medication regimens were collected. All patients were followed up for one year, and the number of exacerbations was recorded. Patients were divided into an exacerbation group and a non-exacerbation group based on the occurrence of exacerbations during the follow-up period. Logistic regression analysis was used to screen the influencing factors for exacerbations in COPD patients. Results:A total of 2 901 COPD patients were included, among which 633 patients (21.8%) experienced exacerbations during the follow-up period. Compared with the non-exacerbation group, patients in the exacerbation group were older, with higher CAT and mMRC scores, lower body mass index (BMI), FEV 1%pred, and FEV 1/FVC. The proportions of patients with high school education or above and those using long-acting β 2-agonist (LABA) + long-acting muscarinic antagonist (LAMA) medications were also lower (all P<0.05). Logistic regression analysis showed that age ( OR=1.010, 95% CI: 1.000-1.021), CAT score ≥20 ( OR=1.415, 95% CI: 1.074-1.865), education level of junior high school or below ( OR=1.243, 95% CI: 1.003-1.540), LABA + LAMA inhalation ( OR=0.605, 95% CI: 0.432-0.848), and BMI ( OR=0.969, 95% CI: 0.943-0.995) were independent risk factors for future exacerbations in COPD patients with no exacerbation history in the past year (all P<0.05). Conclusions:The risk of future exacerbations remains high in COPD patients with no exacerbation history in the past year. High CAT scores, low education levels, and low BMI are associated with future exacerbations. Clinicians should pay close attention to the management of such patients and implement appropriate interventions.