This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

OBJECTIVE To establish population pharmacokinetic model of levetiracetam （Lev） for Chinese patients with post- stroke epilepsy （PSE）， and provide reference for formulating individualized dosing regimens for Lev therapy in this specific population. METHODS Blood concentration data and clinical diagnosis and treatment information of PSE patients meeting the inclusion criteria were retrospectively collected and divided into model group and validation group at an 8∶2 ratio using a random number method. Based on the model group data， a population pharmacokinetic model was developed using nonlinear mixed-effects modeling. Internal evaluation was performed through goodness-of-fit tests and bootstrap analysis， while external validation was conducted using the validation group data. RESULTS A total of 75 blood concentration measurements from 70 PSE patients were collected， with 60 measurements from 55 patients used for model development and 15 measurements from 15 patients reserved for external validation. The final model estimated a population typical value of clearance at 2.98 L/h. Estimated glomerular filtration rate， daily dose， and homocysteine level significantly influenced clearance of Lev （P＜0.01）. The model demonstrated satisfactory predictive performance， as evidenced by goodness-of-fit tests， bootstrap analysis， and external validation results. CONCLUSIONS Daily dose， estimated glomerular filtration rate， and homocysteine level are identified as significant covariates influencing Lev clearance in Chinese PSE patients. When making clinical decisions， comprehensive consideration should be given to the patient’s treatment response， physiological and pathological conditions， and the occurrence of adverse reactions， etc. The dosage of Lev should be adjusted based on the results of population pharmacokinetic model.

As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.

Gentianopsis barbata (G. barbata) represents a significant plant species with considerable ornamental and medicinal value in China. This investigation sought to elucidate the primary constituents within the plant and investigate their pharmacological properties. Fifty triterpenoids (1-50), including nine previously undescribed compounds (1, 2, 7, 10, 20, 28, 29, 37, and 41) were isolated and characterized from the whole plants of G. barbata. Notably, compounds 1 and 2 exhibited the novel 3,4;9,10-diseco-24-homo-cycloartane triterpenoid skeleton. The isolated triterpenoids demonstrated substantial anti-inflammatory activity through inhibition of tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) cytokine secretion in LPS-induced RAW264.7 macrophages, and hepatoprotective effects by preventing tert-butyl hydroperoxide (t-BHP)-induced oxidative injury in HepG2 cells. These results demonstrate both the presence of diverse triterpenoids in G. barbata and their therapeutic potential for inflammatory and hepatic conditions, providing scientific evidence supporting the clinical application of this traditional Mongolian medicinal plant.
Triterpenes/isolation & purification* ; Mice ; Anti-Inflammatory Agents/isolation & purification* ; Animals ; Humans ; RAW 264.7 Cells ; Hep G2 Cells ; Interleukin-6/genetics* ; Tumor Necrosis Factor-alpha/genetics* ; Medicine, Mongolian Traditional ; Macrophages/immunology* ; Protective Agents/isolation & purification* ; Liver/drug effects* ; Gentianaceae/chemistry* ; Plant Extracts/chemistry* ; Molecular Structure

Objective: To investigate the prevalence of Dengue virus (DENV) infection among voluntary blood donors in Xishuangbanna Dai Autonomous Prefecture, and to evaluate the necessity of implementing nucleic acid testing (NAT) for blood donors during the rainy season (May-October). Methods: Prior to initiating donor screening, the Xishuangbanna Central Blood Center conducted in-house validation of reagent performance and participated in external quality assessment (EQA) organized by the National Center for Clinical Laboratories (NCCL). During the surveillance period (August-October 2024), a total of 2 919 donor samples were screened using a 6-sample mini-pool NAT strategy. Daily internal quality controls were recorded. Samples that tested positive in pooled screening were deconvoluted and retested in duplicate; only those reactive in both replicate wells were sent to the NCCL for confirmatory testing. At NCCL, samples underwent re-testing using five domestic NAT reagents, as well as serological assays for NS1 antigen and DENV-specific IgG/IgM. Confirmed positive samples were further characterized by serotyping, envelope (E) gene sequencing, and phylogenetic analysis using the maximum likelihood method. Results: The DENV NAT reagent demonstrated consistent detection of 40 copies/mL controls in individual donor (ID)-NAT test (mean CT: 35.61±0.40). During the 63-day quality control monitoring, DENV detection remained stable (mean CT: 22.53±0.72). The center achieved full marks in EQA assessments for 2023 and 2024. Three reactive pools were identified in initial screening, and subsequent individual testing confirmed three DENV RNA-positive donors (sample numbers: 2401, 2402, and 2403). The confirmatory test results from NCCL were: all five NAT platforms consistently detected DENV RNA in the three samples; for serological tests, 2 samples (2402, 2403) were positive for NS1 antigen, while all three samples were negative for both IgG and IgM antibodies. DENV serotyping reagents identified DENV-2 in all cases, which were further confirmed as DENV-2 Genotype Ⅱ-Cosmopolitan by E gene sequencing. Phylogenetic analysis indicated that samples 2401 and 2402 clustered with Southeast Asian strains (Thailand/MZ636802.1, Laos/PQ775621.1), while sample 2403 closely matched a previously reported local Yunnan strain (PV544686.1). Conclusion: DENV-2 infection was detected among blood donors in Xishuangbanna during the rainy season, indicating concurrent risks of imported and local transmission. We recommend implementing pooled NAT screening for blood donors in high-risk areas during dengue epidemic seasons, along with strengthened laboratory quality control, to enhance blood safety.

Objective:To investigate the association between frailty and prognosis of elderly patients in the emergency department, and to validate frailty screening tools suitable for the emergency department.Methods:This was a prospective cohort study. Clinical data of elderly patients over 60 years old treated in the emergency department of Beijing Bo'Ai Hospital from January to December 2021 were collected. The Frailty Screening Questionnaire (FSQ), FRAIL Scale (FRAIL) and Clinical Frailty Scale (CFS) were used to score patients, and patients were divided into frail or non-frail group according to the criteria of the above three scales. Twelve-month all-cause mortality was the primary endpoint, dependence and re-admission to the emergency department within 12 months were secondary outcomes. Receiver operating characteristic curves were used to evaluate the ability of the FSQ, FRAIL and CFS scores to predict the primary and secondary endpoints, and the areas under the curve (AUC) were calculated and compared. Survival analysis was performed using Cox hazard proportional regression model, and relative risk was expressed as hazard ratio ( HR) and 95% CI. Results:A total of 406 patients were included in the study. The AUCs (95% CI) of FSQ, FRAIL and CFS scores for predicting 12-month all-cause mortality were 0.879 (0.844-0.909), 0.838 (0.798-0.872), 0.906 (0.873-0.933), respectively (all P<0.001). The AUCs of 3 scores for predicting secondary endpoints ranged from 0.820 to 0.889 (all P<0.001). Pairwise comparisons of the AUCs showed that the CFS was superior to one or both of the other frailty screening scales in predicting 12-month all-cause mortality and dependence except for re-admission to emergency room within 12 months after discharge (all P<0.05). Cox regression analysis revealed that, after adjusting for sex, age, body mass index and comorbidities, frailty as defined by the FSQ, FRAIL, and CFS scales was independently associated with 12-month all-cause mortality, with the HRadj of 3.267 (95% CI: 2.406-4.435), 2.465 (95% CI: 1.819-3.341), 3.523 (95% CI: 2.648-4.687), respectively (all P<0.001). Conclusions:FSQ, FRAIL and CFS scores can predict adverse outcomes, the CFS is a practical frailty screening tool in the emergency department, and frailty screening can improve the risk stratification of older patients.

Objective:To develop and validate a prediction model by combining clinical data and biomarkers to evaluate the probability of frailty among older emergency patients.Methods:A cross-sectional study was conducted. From January 2021 to December 2021, patients aged 60 years and older admitted to the emergency department of China Rehabilitation Research Center were enrolled. Data of patient's clinical information were collected. The patients were divided into frail group and non-frail group according to the Fried's frailty phenotype and clinical data were compared between the two groups. LASSO regression was used to deal with dimension reduction and multivariate logistic regression was employed to construct a prediction model based on variables selected by the LASSO regression. Nomogram was used to visualize the prediction model. The area under the receiver operating characteristic curve, calibration curve, decision curve analysis and bootstrap were used to evaluate the discrimination, calibration, clinical applicability, and internal validity of the model respectively.Results:A total of 348 patients were enrolled, and the incidence of frailty was 53.74% (187/348). Education, coronary heart disease, chronic obstructive pulmonary disease, albumin, fibrinogen, N-terminal pro-brain natriuretic peptide, decreased creatinine, and underweight were independent predictors for frailty in older emergency patients ( P < 0.05). A nomogram model was built based on the above predictors and the model showed good discrimination, calibration and clinical applicability. Conclusions:The study utilized objective clinical data and biomarkers to establish a predictive model for the occurrence of frailty in elderly emergency department patients. This model aids in risk stratification and targeted intervention for elderly emergency patients, thereby improving patient outcomes.

OBJECTIVE: To analyze the clinical characteristics and genetic profile of patients with 46,XY Disorders of sex development (DSD) and a female phenotype in order to provide insights for the diagnosis and management of similar cases. METHODS: A retrospective analysis was conducted on 36 children with 46,XY DSD and a female phenotype who were treated at the Department of Endocrinology, Genetics and Metabolism of Henan Children's Hospital between March 1, 2016, and June 30, 2024. The evaluations included external genitalia scoring using the Prader scale and External Masculinization Score (EMS), imaging studies to assess gonadal development, and assessments of adrenal and gonadal function via adrenal hormone levels, sex hormone levels, and human chorionic gonadotropin (hCG) stimulation testing. Gender role behavior was assessed using gender role scales and sandplay therapy. Whole exome sequencing and Sanger sequencing were used to identify and validate genetic variants. A multidisciplinary team (MDT) comprehensively determined gender rearing based on molecular genetic diagnosis. This study was approved by the Medical Ethics Committee of Henan Children's Hospital (Ethics No.: 2024-K-105). RESULTS: The median age at initial consultation was 3 years and 1 month (range: 7 days to 16 years). Common symptoms included primary amenorrhea, clitoromegaly, and inguinal hernia. Fully feminized external genitalia were observed in 52.7% of the cases, and 80.5% had absence of the uterus. Internal gonads included absent gonads (5.6%), ovotestes (8.3%), streak gonads (5.6%), cryptorchidism (75.0%), and normally positioned testes (5.6%). At initial diagnosis, median luteinizing hormone (LH) was 1.305 IU/L, with elevated LH in 14 cases. Median follicle-stimulating hormone (FSH) was 4.87 IU/L, with elevated FSH in 17 cases. Median testosterone was 0.025 ng/mL. Median dihydrotestosterone (DHT) was 36.90 pg/mL. After hCG stimulation, median testosterone was 0.984 ng/mL and median DHT was 71.69 pg/mL. The testosterone/DHT ratio was elevated in one case (35.7). Testosterone levels remained below 1 ng/mL after hCG stimulation in 18 cases. Anti-Müllerian hormone (AMH) was decreased in 15 cases and increased in 3 cases. Inhibin B (InhB) was increased in 7 cases and decreased in 4 cases. Pathogenic variants were detected in 88.9% of the patients, involving AR (11 cases), CYP17A1 (4 cases), GATA4 (1 case), NR0B1 (1 case), NR5A1 (7 cases), SRD5A2 (1 case), WT1 (2 cases), STAR (4 cases), and LHCGR (1 case), totaling 34 variant sites. Among these, 9 variants were de novo, and 23 were inherited from parents. Sixteen variant sites were previously unreported. Gender assignment was male in 11 cases (30.6%) and female in 25 cases (69.4%). CONCLUSION Common symptoms in 46,XY DSD patients with a female phenotype include primary amenorrhea, clitoromegaly, and inguinal hernia. Elevated FSH, androgen deficiency, and decreased AMH and InhB may indicate testicular dysgenesis or impaired androgen synthesis. Adrenal insufficiency should raise suspicion for defects in steroid hormone synthesis pathway enzymes.
Humans ; Female ; Disorder of Sex Development, 46,XY/diagnosis* ; Child ; Male ; Phenotype ; Child, Preschool ; Retrospective Studies ; Adolescent ; Infant

Objective:To establish a 14-day prognosis model for emergency patients with acute ischemic cerebral stroke and evaluate its predictive efficacy.Methods:A prospective cohort study was conducted. Patients with acute ischemic stroke admitted to the emergency department of Beijing Bo’ai Hospital within 72 hours of onset from October 2018 to December 2020 were enrolled. Univariate and multivariate logistic regression analysis were used to screen the risk factors of poor prognosis. The ROC curve was drawn to determine the cut-off value of continuous variables and discretise data with reference to clinical practice. The corresponding scores were set up according to the β regression coefficient of each variable, and the clinical scale prediction model of short-term prognosis of acute cerebral infarction was established. Patients with ischemic stroke in the hospital from January to December 2021 were selected as the internal validation, to verify the constructed predictive model.Results:A total of 321 patients were included in the study, including 223 in the training set and 98 in the internal validation set. Multivariate logistic regression analysis showed that age, hypersensitive C-reactive protein, prealbumin (PA), infarct volume, Frailty Screening Questionnaire (FSQ) and National Institute of Health Stroke Scale (NIHSS) were independent risk factors for poor short-term prognosis of acute cerebral infarction. The total score of the clinical prediction scoring system for short-term prognosis of acute cerebral infarction in the emergency department was 15 points, including age ≥74 years (1 point), PA ≤373 mg/L (2 points), large artery atherosclerosis (1 point), cardiogenic embolism (2 points), infarct volume ≥ 2.18 cm 3 (2 points), FSQ ≥3 points (1 point), NIHSS ≥4 points (6 points); The area under the ROC curve (AUC) of the scoring system for predicting short-term poor prognosis of acute cerebral infarction was 0.927 (95% CI: 0.894-0.960). The optimal cut-off value was ≥5 points, and the sensitivity and specificity were 0.770 and 0.976, respectively. In the internal validation set, the scoring system had similar predictive value for poor outcomes (AUC=0.892, 95% CI:0.827-0.957). Conclusion:The scoring system for short-term prognosis prediction of acute ischemic cerebral infarction has good diagnostic efficacy, and could guide clinicians to judge the prognosis of emergency patients in the early stage.

Objective:To investigate the value of radiomics nomogram based on standardized pre-treatment chest enhanced CT in predicting the mutation status of epidermal growth factor receptor (EGFR) for patients with lung adenocarcinoma.Methods:A retrospective analysis was conducted on pre-treatment chest enhanced CT images and clinical data of 262 patients from the affiliated hospital of Jining Medical University with pathologically proven primary lung adenocarcinoma who received EGFR gene testing, including EGFR wild type ( n=122) and mutant type ( n=140). The patients were divided into training group ( n=183) and testing group ( n=79) according to a ratio of 7∶3 by stratified sampling method. Standardized pre-processed the images, delineated the ROI and extracted the radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension and select key features. The standardized radiomics model, clinical model and the combined model were established by Logistic Regression (LR) machine learning method. Calculated the Rad-score and drew the nomogram. ROC curve and Delong were used to evaluate and compare the predictive performance of different models. Results:23 standardized enhanced CT radiomics features and 4 clinical features were selected. The predictive performance of standardized radiomics model was better than that of non-standardized radiomics model [area under curve (AUC): 0.863 vs. 0.805, t=2.19, P<0.05]. The AUCs of the combined model and standardized radiomics model were higher than that of the clinical model (training group: 0.885, 0.863 vs. 0.774, t=3.57, 2.17, P<0.05; testing group: 0.873, 0.829 vs. 0.763, t=2.19, 2.02, P<0.05). The radiomics nomogram was built based on Rad-score, age, sex, smoking history and BMI. Conclusions:The combined model and standardized radiomics model could effectively predict the mutation status of EGFR gene in lung adenocarcinoma patients before treatment, providing valuable clinical insights.