Myocardial infarction is a cardiovascular disease (CVD) with high morbidity and mortality, which can trigger a cascade of cardiac pathophysiological changes, including fibrosis, inflammation, ischemia-reperfusion injury (IRI), and ventricular remodeling, ultimately leading to heart failure (HF). While conventional pharmacological treatments and clinical reperfusion therapy may enhance short-term prognoses and emergency survival rates, both approaches have limitations and adverse effects. Natural products (NPs) are extensively utilized as therapeutics globally, with some demonstrating potentially favorable therapeutic effects in preclinical and clinical pharmacological studies, positioning them as potential alternatives to modern drugs. This review comprehensively elucidates the pathophysiological mechanisms during myocardial infarction and summarizes the mechanisms by which NPs exert cardiac beneficial effects. These include classical mechanisms such as inhibition of inflammation and oxidative stress, alleviation of cardiomyocyte death, attenuation of cardiac fibrosis, improvement of angiogenesis, and emerging mechanisms such as cardiac metabolic regulation and histone modification. Furthermore, the review emphasizes the modulation by NPs of novel targets or signaling pathways in classical mechanisms, including other forms of regulated cell death (RCD), endothelial-mesenchymal transition, non-coding ribonucleic acids (ncRNAs) cascade, and endothelial progenitor cell (EPC) function. Additionally, NPs influencing a particular mechanism are categorized based on their chemical structure, and their relevance is discussed. Finally, the current limitations and prospects of NPs therapy are considered, highlighting their potential for use in myocardial infarction management and identifying issues that require urgent attention.
Humans ; Myocardial Infarction/genetics* ; Biological Products/therapeutic use* ; Animals ; Oxidative Stress/drug effects* ; Signal Transduction/drug effects*

Steroid-induced osteonecrosis of the femoral head （SONFH） is a severe musculoskeletal disorder often induced by the prolonged or excessive use of glucocorticoids. Characterized by ischemia of bone cells， necrosis， and trabecular fractures， SONFH is accompanied by pain， femoral head collapse， and joint dysfunction， which can lead to disability in severe cases. The pathogenesis of SONFH involves hormone-induced osteoblast apoptosis， bone microvascular endothelial cell （BMEC） apoptosis， oxidative stress， and inflammatory responses. The phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） signaling pathway plays a pivotal role in the development of the disease. Modulating the PI3K/Akt signaling pathway can promote Akt phosphorylation， thereby stimulating the osteogenic differentiation of bone marrow mesenchymal stem cells and osteoblasts， promoting angiogenesis in BMECs， and inhibiting osteoclastogenesis. The research on the treatment of SONFH with traditional Chinese medicine （TCM） has gained increasing attention. Recent studies have shown that TCM monomers and compounds have potential therapeutic effect on SONFH by intervening in the PI3K/Akt signaling pathway. These studies not only provide a scientific basis for the application of TCM in the treatment of SONFH but also offer new ideas for the development of new therapeutic strategies. This review summarized the progress in Chinese and international research on the PI3K/Akt signaling pathway in SONFH over the past five years. It involved the composition and transmission mechanisms of the signaling pathway， as well as its regulatory effects on osteoblasts， mesenchymal stem cells， osteoclasts， BMECs， and other cells. Additionally， the review explored the TCM understanding of SONFH and the application of TCM monomers and compounds in the intervention of the PI3K/Akt pathway. By systematically analyzing and organizing these research findings， this article aimed to provide references and point out directions for the clinical prevention and treatment of SONFH and promote further development of TCM in this field. With in-depth research on the PI3K/Akt pathway and the modern application of TCM， it is expected to bring safer and more effective treatment options for patients with SONFH.

Knowledge graphs have been more popular in the medical industry in recent years due to the development of big data analysis technologies.Information reflecting a range of physiological markers of patients is gathered in laboratory medicine,which forms an essential basis for clinical decision-making.Knowledge graphs could uncover the hidden value in inspection and consequently furnish accurate inspection data.Knowledge graphs are currently used in laboratory medicine to forecast possible adverse drug responses,help with diagnosis,integrate historical trends of patient laboratory inspections,and identify correlations across detection projects.The interaction between inspection items,auxiliary diagnosis,and medication guidance are three angles from which the use of knowledge graphs in laboratory medicine is analyzed and evaluated.Further support the intelligent construction of clinical laboratory diagnosis,it is also suggested that the development of test medical knowledge graphs extend in the direction of semantic search,decision support,and intelligent question answer.

Objective:A predictive model for cirrhosis-associated hepatic encephalopathy (HE) was constructed and validated using a machine learning algorithm to evaluate the predictive efficacy of the model.Methods:Clinical data of patients with liver cirrhosis (4 537 cases) in the medical record system and laboratory information system of the First Hospital of Jilin University from January 2018 to December 2019 were collected and analyzed retrospectively. Based on the inclusion and exclusion criteria, 474 patients were finally included in the study. Cohort 1 included patients with cirrhosis without HE (113 cases) and patients with cirrhosis complicated with HE (108 cases) from January to December 2018, and was used for feature screening, model building, optimal algorithm selection, and internal validation of the cirrhosis complicated with HE risk prediction model. Cohort 2 included patients with cirrhosis without HE (133 patients) and patients with cirrhosis complicated with HE (120 patients) from January 2019 to December 2019 for external validation. Lasso regression was utilized to identify key predictive variables, and various models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), random forest (RF), and support vector machine (SVM) were employed for model building and internal validation. The DeLong test was used to compare the predictive efficacy of the four models for HE, and the optimal algorithm was selected by combining the specificity or sensitivity. The area under the ROC curve, calibration curve and decision curve were applied to evaluate the predictive efficacy, accuracy of predicted probabilities and clinical utility of the model.Results:The 46 tests with<30% missing data in Cohort 1 were extracted as variables to be selected for modeling. Seven characteristic variables were obtained using Lasso regression screening, including hemoglobin (Hb), total bile acid (TBA), cholinesterase, total bilirubin, creatinine, prothrombin activity, and circulating platelets. The prediction model built by the LightGBM algorithm (HE-Lab7 model) predicted HE with an area under the curve (AUC) of 0.880, which was higher than that of XGBoost, RF, and SVM (all P<0.05), with a sensitivity of 0.825 and a specificity of 0.836. The Brier score of the calibration curve was 0.147, indicating that the predicted probability of the model is in good agreement with the actual probability of occurrence. Decision curves indicate that the model has a high clinical benefit. In Cohort 2, the HE-Lab7 model predicted HE with an AUC of 0.775, a sensitivity of 0.927, and a specificity of 0.758. Conclusion:The predictive efficacy of the cirrhosis-associated HE risk prediction model developed based on the optimal LightGBM algorithm using the large-scale test data based on four machine learning algorithms is good, which provides a reference basis for early prediction and identification of cirrhosis-associated HE.

Metagenomic next-generation sequencing, as an unbiased detection technology, demonstrates higher diagnostic efficacy than traditional methods. Gut microorganisms are important flora for safeguarding health and have become a hot research topic. Modeling and analyzing the genomic data of intestinal flora using machine learning is very important in disease prediction and diagnosis. This paper briefly introduces the characteristics of metagenomic next-generation sequencing, key algorithms and evaluation indexes of machine learning, outlines the main steps of combining machine learning with metagenomic next-generation sequencing, and summarizes the application of the combination of machine learning and metagenomic next-generation sequencing technology in the study of intestinal flora, which will provide a more accurate method for diagnosis and prediction of the related diseases, and give more ideas for the future research and clinical practice.

With the rapid development of computer vision technology, deep learning models have demonstrated new research area and potential value in intelligent microbiological detection. By utilizing multilayer neural networks and large amounts of training data, these models are capable of automated extraction and analysis for complex features, thereby improving the efficiency and accuracy of detection. This paper introduces the research background of deep learning in microbiological image detection, and elaborates on the methods for constructing microbiological image datasets, including data collection, preprocessing, annotation, and partitioning, and introduces typical deep learning models as well as their application examples in various microbiological detection. Deep learning in microbiological image analysis faces numerous challenges which needs further development.

The reliability and practicality of research findings in health economics are gradually becoming core issues of close concern for clinical and public health experts. As healthcare resources remain under increasing pressure, conducting efficient cost-effectiveness analyses and achieving rational resource allocation are becoming ever more critical. In recent years, medical laboratory health economics has transitioned from purely academic discussions to integration into clinical practice, becoming a key tool for improving the efficiency and quality of healthcare services. It has demonstrated remarkable results in optimizing patient management and medical decision-making processes. So we invited experts from the fields of medical laboratory science and health economics to share valuable experiences and unique insights on topics such as cost assessment methods, pricing strategies, quality regulation, and the role of medical laboratory health economics in enhancing clinical practice and patient benefits. These experts generally agree that, while research in medical laboratory health economics has shown significant advantages and effectively addressed urgent clinical needs in resource allocation and cost control, it still faces multiple challenges, including limitations in research methodology application, high operational costs, and insufficient standardization of management systems.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

The threat of antimicrobial resistance is escalating, and traditional antimicrobial susceptibility testing methods have long turnaround times, which are inadequate for rapid clinical decision-making. Consequently, the development of rapid antimicrobial susceptibility testing (RAST) methods that are fast, efficient, and cost-effective is of great significance. This review discusses the principles, characteristics, and application potentials of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry RAST, European Committee on Antimicrobial Susceptibility Testing RAST, Microfluidic RAST, Flow Cytometry RAST, Raman Spectroscopy RAST, and ultra-rapid antimicrobial susceptibility testing. These technologies exhibit significant advantages in reducing detection time and improving detection accuracy, but also face challenges such as high technical complexity, high costs, and limited applicability. It is expected that an ideal RAST will be realized in the future to better respond to the challenges of global antimicrobial resistance.

Objective To analyze the procurement data of lipid-lowering drugs in hospitals at different levels in Jiangsu Province from October 2019 to September 2023,to evaluate the impact of the volume-based procurement(VBP)policy,and to provide references for clinical rational drug use and healthcare policy optimization.Methods Based on procurement data from the Jiangsu Provincial Health Information Center,statistical analyses of procurement expenditures,defined daily doses(DDDs),and defined daily cost(DDC)were conducted.Mixed-effects models were applied to assess changes in procurement expenditures,DDDs,and DDC before and after VBP implementation.Results From 2019 to 2023,statins dominated the market in Jiangsu Province,with rosuvastatin recording the highest DDDs(748 million).Statins,traditional Chinese medicines,and cholesterol absorption inhibitors ranked highest in procurement expenditures.Tertiary hospitals accounted for the largest share of usage(47.6%)and expenditures(55.8%),while secondary hospitals had the lowest DDC(1.22 yuan)and tertiary hospitals the highest(1.89 yuan).Post-VBP,procurement expenditures and DDC decreased by 53.9%and 35.4%,respectively.Primary hospitals showed the largest expenditure reduction(61.6%),and secondary hospitals exhibited the greatest DDC decline(53.9%).DDDs increased significantly in primary care settings(e.g.,pitavastatin surged by 239.79%in secondary hospitals),while tertiary hospitals saw reduced usage of some drugs(e.g.,amlodipine/atorvastatin decreased by 7.34%).Mixed-effects models confirmed that VBP significantly reduced expenditures(OR=-1.07,P<0.01)and DDC(OR=-2.70,P<0.01)while indirectly lowering prices of non-VBP drugs.After covariate adjustment,expenditure reductions for rosuvastatin and atorvastatin narrowed,ezetimibe expenditures increased(OR=0.13,P<0.01),and pitavastatin usage declined(OR=-0.10,P<0.01).Changes in amlodipine/atorvastatin and ezetimibe lacked statistical significance due to short VBP implementation periods.Tertiary hospitals demonstrated the strictest policy adherence,with the largest expenditure and DDC reductions(P<0.01).Subgroup analysis revealed that the policy did not significantly affect clinical demand(DDDs)in hospitals at different levels,though it was considered to have triggered adjustments in medication structure.Conclusion Jiangsu's lipid-lowering drug structure aligns with guidelines(statin-based,moderate-intensity preference).VBP effectively reduced costs,with tertiary hospitals prioritizing originator-to-generic substitution and primary hospitals reflecting cost-control and demand variations.Confounding factors influenced policy evaluation.The study recommends continuous monitoring and policy optimization to enhance procurement efficiency,ensure rational clinical use,and sustain cost savings,providing insights for further healthcare reform.