Objective: To identify early clinical predictors associated with dysphagia and delayed swallowing recovery in patients with traumatic brain injury (TBI). Methods: In this retrospective study, we enrolled adult TBI patients admitted to the rehabilitation unit of a tertiary medical center between June 2019 and June 2023. Data on baseline characteristics, neurological status, imaging findings, and rehabilitation-related variables were collected. Swallowing function was assessed using two indicators: (1) nasogastric (NG) tube retention and (2) the Functional Oral Intake Scale (FOIS) scores at 1, 4, and 12 weeks post-injury. Regression analyses were conducted to identify predictors associated with dysphagia and swallowing recovery. Results: A total of 160 patients were included. At 1 week post-injury, longer intensive care unit (ICU) stay, poor initial sitting balance and use of sedative medication in ICU were associated with NG tube retention. At 4 weeks, lower initial Rancho Los Amigos Scale (RLAS) scores, immobility-related complications, longer hospitalization, and temporal lobe hematomas were associated with persistent NG tube dependence. By 12 weeks, older age, delayed ability to follow commands, and poor initial sitting balance remained associated with NG tube retention. FOIS outcomes were also associated with older age, delayed time to follow commands, impaired initial sitting balance, prolonged ICU stay, temporal lobe hematomas, lower initial RLAS scores, immobility-related complications, prolonged endotracheal tube placement and extended hospital stays. Conclusion Impaired cognitive status, poor physical function, immobility-related complications, and temporal lobe hematomas were key factors associated with dysphagia and delayed oral intake in individuals with TBI.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Objective:To construct an intelligent evaluation model based on the grey whitening weight cluster algorithm for risk in supply chain of medical consumables,and explore its application value in the management for medical consumables.Methods:The risk source of supply chain of medical consumables under the supply-processing-distribution(SPD)mode was analyzed,and the problems in the management for supply chain were evaluated by using grey whitening weight cluster,and an intelligent evaluation model for risk in supply chain of medical consumables was constructed to conduct control and management for risk in supply chain of medical consumables.A total of 10.61 million pieces of 400 types of medical consumables that were purchased and used by the National Center for Children's Health,China,Beijing Children's Hospital,Capital Medical University from 2022 to 2023 were selected.In them,the 5.15 million pieces of 200 types of medical consumables that were purchased and used during January and December 2022 were controlled and managed for risk through the management mode of assessment and prediction with experts.The 5.46 million pieces of 200 types of medical consumables that were purchased and used during January and December 2023 were controlled and managed for risk through used the intelligent evaluation model based on the gray whitening weight cluster algorithm under the SPD mode in supply chain of medical consumables for risk(prediction management mode with evaluation model).The incidences of risk,and the accuracy of data in supply chain between the two management modes were compared.A self-made satisfaction questionnaire was used to investigate the satisfaction rates of medical staffs,medical technicians,managers of department,and keepers of warehouse regarding to the supply of medical consumables.Results:The incidence rates of risks in suppliers,inventory,finance,technical support and information security of supply chain were respectively 1.8%,2.2%,0.4%,1.5%and 3.1%by adopting prediction management mode with evaluation model in 100,000 randomly inspected cases of medical consumables,all of which were lower than those of the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=9.239,23.013,11.706,21.141,42.331,P<0.05).The accuracy rates of supply data of spot check for the consumables of auxiliary examination,the nursing consumables in ward,the consumables of surgical treatment,the consumables of oral treatment and other disposable consumables of prediction management mode with evaluation model were all higher than those of the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=11.628,15.842,7.790,7.289,7.448,P<0.05).The satisfaction rates of medical staffs,medical technicians,managers of department and keepers of warehouse for clinical supply of medical consumables in prediction management mode with evaluation model were all higher than those in the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=4.824,5.703,5.529,5.143,P<0.05).Conclusion:The intelligent evaluation model based on the grey whitening weight cluster algorithm under the SPD mode for risk in supply chain of medical consumables can reduce the incidence rate of risk in the supply chain of medical consumables,and improve the accuracy of supply data of medical consumables,and enhance the satisfaction of staffs in hospital.

Objective To investigate the value of MRI of the sternal end of clavicle in bone age assessment in Chinese population,especially its applicability in the determination of criminal responsible age.Methods A total of 431 patients aged from 10.00 to 29.99 years with neck or chest MRI were retrospectively collected.According to the Schmeling grading method,the epiphyseal development of the clavicle MRI was divided into five grades.The consistency of methods was evaluated.The correlation and general descriptive analysis between MRI grades and age was analyzed.The sex difference was analyzed.Curve fitting was used to establish a nonlinear model between age and grades.Results The grades of clavicle MRI showed a significant age-related trend(Figure 2),and the correlation was 0.861(0.887 in males and 0.840 in females).Except for grade 1,there was no significant difference between males and females in other grades.The minimum age of male grade 3 was greater than 14 years old,and the minimum age of female grade 3 was greater than 16 years old.The minimum age in grade 4 and grade 5 was over 18 years old in both sexes.The best curve fitting model was cubic model for both sexes(R2=0.805 for men and 0.722 for women).Conclusion Clavicle MRI can be used for the assessment of bone age in Chinese population.Complete epiphyseal plate closure can be used as a reliable indicator for the determination of age at 18 years old,and it is expected to achieve radiation-free forensic bone age assessment.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Aim To investigate the effects of the fangchinoline derivative LYY-32 on the biological prop-erties of the BLM642-1290 DNA helicase,in order to lay a foundation for further research on its antitumor activity.Methods Fluorescence polarization assay,malachite green-phosphate and ammonium molybdate colorime-try,and fluorescein-labeled DNA gel electrophoresis experiments were conducted to study the effects of fangchinoline derivative LYY-32 on the DNA binding activity,ATPase activity,and DNA unwinding activity of BLM642-1290 DNA helicase.The effects of LYY-32 on the DNA unwinding activity of DNA helicase in cells were studied using fluorescent techniques and time-lapse microscopy.Ultraviolet spectral scanning was used to investigate the effects of LYY-32 on the confor-mation of the BLM642-1290 DNA helicase.Results At a concentration of 10 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to dsDNA was 53.17％.At a concentration of 5 μmol·L-1,the inhibition rate of LYY-32 on BLM642-1290 DNA helicase binding to ssDNA was 88.49％.The inhibition rate of LYY-32 on the ATPase activity of BLM642-1290 DNA he-licase was 89.3％at a concentration of 50 μmol·L-1.When the concentration of LYY-32 exceeded 5μmol·L-1,its inhibition rate on the DNA unwinding activity of BLM642-1290 DNA helicase was 100％.LYY-32 also significantly inhibited the DNA unwinding ac-tivity of DNA helicase in cells.However,LYY-32 had no effect on the conformation of BLM642-1290 DNA heli-case.Conclusion The DNA binding activity,AT-Pase activity,and DNA unwinding activity of BLM642-1290 DNA helicase could be significantly inhibi-ted by the fangchinoline derivative LYY-32.