ObjectiveTo investigate the significance of different non-treatment thresholds or upper limits of normal （ULN） of alanine aminotransferase （ALT） in evaluating significant liver pathological injury in patients with chronic hepatitis B virus （HBV） infection， and to provide guidance for clinical diagnosis and treatment. MethodsThis study was conducted among 733 patients with chronic HBV infection who were hospitalized in Ningbo No. 2 Hospital from January 2015 to December 2023 and underwent liver biopsy and histopathological examination， and all patients had a persistent ALT level of ≤40 U/L and positive HBV DNA （>30 IU/mL）. According to the treatment threshold or ULN of ALT， the patients were divided into group 1 with 575 patients （≤35 U/L for male patients， ≤25 U/L for female patients）， group 2 with 430 patients （≤30 U/L for male patients， ≤19 U/L for female patients）， group 3 with 443 patients （≤27 U/L for male patients， ≤24 U/L for female patients）， group 4 with 446 patients （≤25 U/L）， group 5 with 158 patients （>35 U/L for male patients， >25 U/L for female patients）， and group 6 with 145 patients （>30 — ≤35 U/L for male patients， >19 — ≤25 U/L for female patients）. Groups 2， 5， and 6 were compared to analyze the severity of liver pathological injury in patients with different ALT levels and the constituent ratio of patients with significant liver pathological injury， and groups 1， 2， 3， and 4 were compared to investigate the value of different ULN or non-treatment thresholds of ALT in determining liver inflammation grade （G）， liver fibrosis stage （S）， and the treatment indication based on liver pathology. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； a one-way analysis of variance was used for comparison between multiple groups， and the least significant difference t-test or the Tambane’s test was used for further comparison between two groups； the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the Kruskal-Wallis H test was used for comparison between multiple groups and further comparison between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups； a Ridit analysis was used for comparison of ranked data. A multivariate Logistic regression analysis （forward stepwise） was performed with whether liver pathology met the treatment indication （≥G2 and/or ≥S2） as the dependent variable and related factors with a significant impact on the dependent variable （P <0.05） as the independent variable. The receiver operating characteristic （ROC） curve was plotted， and the area under the ROC curve （AUC）， as well as sensitivity， specificity， positive predictive value， negative predictive value， positive likelihood ratio， and negative likelihood ratio， was used to assess the diagnostic value of different non-treatment thresholds of ALT. ResultsAmong the 733 patients， 259 （35.33%） had ≥G2 liver inflammation， 211 （28.79%） had ≥S2 liver fibrosis， and 306 （41.75%） had treatment indication （≥G2 and/or ≥S2）. There was a significant difference in liver inflammation grade （G0 — G4） between groups 2， 5， and 6 （χ²=22.869， P <0.001）， and there were also significant differences in the constituent ratios of patients with ≥G2 or ≥G3 liver inflammation between the three groups （χ²=21.742 and 14.921， P<0.001 and P=0.001）. There was a significant difference in liver fibrosis stage （S0 — S4） between groups 2， 5， and 6 （χ²=16.565， P<0.001）， and there were also significant differences in the constituent ratios of patients with ≥S2， ≥S3 or S4 liver fibrosis between the three groups （χ²=13.264， 13.050， and 6.260， P=0.001， 0.001， and 0.044）. There were significant differences between groups 2， 5， and 6 in the constituent ratios of patients with or without treatment indication based on liver pathology （χ²=20.728， P<0.001）. There were significant differences between groups 2， 5， and 6 in the constituent ratio of male patients （χ²=24.836， P<0.05）， age （F=5.710， P<0.05）， ALT （F=473.193， P<0.05）， aspartate aminotransferase （AST） （F=107.774， P<0.05）， ALT/AST ratio （F=40.167， P<0.05）， γ-glutamyl transpeptidase （GGT） （H=15.463， P<0.05）， aspartate aminotransferase-to-platelet ratio index （APRI） （H=63.024， P<0.05）， and LIF-5 （5 indicators for liver inflammation and fibrosis） （H=46.397， P<0.05）. In groups 1 — 4， compared with the patients without treatment indication， the patients with treatment indication had a significantly lower constituent ratio of patients with positive HBeAg， significantly lower levels of platelet count （PLT） and HBV DNA， and significantly higher age， ALT， AST， GGT， APRI， FIB-4， and LIF-5 （all P<0.05）. The Logistic regression analysis showed that age （odds ratio ［OR］=1.044， 95% confidence interval ［CI］： 1.025 — 1.063， P<0.001）， GGT （OR=1.022， 95%CI： 1.007 — 1.038， P=0.003）， and HBV DNA （OR=0.839， 95%CI： 0.765 — 0.919， P<0.001） were influencing factors for treatment indication based on liver pathology in group 1； HBeAg （OR=1.978， 95%CI： 1.269 — 3.082， P=0.003）， age （OR=1.048， 95%CI： 1.025 — 1.071， P<0.001）， GGT （OR=1.016， 95%CI： 1.001 — 1.031， P=0.041）， and PLT （OR=0.995， 95%CI： 0.991 — 1.000， P=0.049） were influencing factors in group 2； age （OR=1.040， 95%CI： 1.014 — 1.066， P=0.002）， ALT （OR=1.047， 95%CI： 1.005 — 1.092， P=0.029）， HBV DNA （OR=0.817， 95%CI： 0.736 — 0.907， P<0.001）， and LIF-5 （OR=7.382， 95%CI： 1.151 — 47.330， P=0.035） were influencing factors in group 3； age （OR=1.054， 95%CI： 1.031 — 1.077， P<0.001）， ALT （OR=1.061， 95%CI： 1.016 — 1.107， P=0.008）， and HBV DNA （OR=0.825， 95%CI： 0.743 — 0.917， P<0.001） were influencing factors in group 4. The diagnostic performance for identifying ≥G2 liver inflammation， ≥S2 liver fibrosis， and treatment indication in groups 1 — 4 had an AUC of >0.7； group 1 showed the lowest sensitivity （28.76%） and the highest specificity， positive predictive value， positive likelihood ratio， and negative likelihood ratio in judging treatment indication； group 2 had the highest sensitivity and negative predictive value and the lowest negative likelihood ratio； groups 3 and 4 had similar diagnostic indicators. ConclusionIn patients with chronic HBV infection and a persistently low ALT level， the severity of liver histopathological injury and the constituent ratio of significant liver histopathological injury decrease with the reduction in ALT level. A higher non-treatment threshold or ULN of ALT can help to identify the patients requiring treatment （with a higher specificity）， while a lower non-treatment threshold or ULN of ALT can help to identify the patients who do not require treatment （with a higher sensitivity）.

Objective To explore the sex difference in clinical manifestations, echocardiographic features and prognosis of patients with bicuspid aortic valve (BAV) combined with moderate to severe aortic stenosis (AS). Methods The clinical data and echocardiographic follow-up parameters of patients diagnosed with BAV combined with moderate to severe AS at Zhongshan Hospital, Fudan University, from July 30, 2010 to August 1, 2015 were retrospectively collected. The composite endpoint was defined as all-cause mortality or surgical intervention. Kaplan-Meier survival curve and log-rank test were used to compare the risk of composite endpoint events between male and female patients, and Cox regression models were used to analyze independent risk factors for composite endpoint events. Results A total of 144 patients were included, with 69 males and 75 females, and a median age of 60 years. Compared to males, female patients were younger (58[46, 65] years vs 61[54, 67] years, P＝0.046), had a lower proportion of aortic valve calcification (61.1% vs 81.1%, P＝0.009), and a higher peak transvalvular pressure gradient (72[59, 88] mmHg vs 63[55, 72] mmHg, P＝0.002). In terms of left ventricular remodeling types, the proportion of concentric remodeling was higher in females than in males (36.0% vs 15.9%, P＝0.006), while the proportion of eccentric hypertrophy was lower in females than in males (10.7% vs 29.0%, P＝0.006). The proportion of female patients classified as New York Heart Association (NYHA) functional class Ⅲ-Ⅳ was higher than that of males (45.3% vs 17.4%, P＜0.001), and left ventricular ejection fraction was higher in females (68%[65%, 72%] vs 65%[60%, 70%], P＝0.003). With a median follow-up of 49 months, Kaplan-Meier survival analysis showed that the risk of composite endpoint events was higher in females than in males (P＜0.001). Independent risk factors for composite endpoint events included female (HR＝1.74, 95%CI 1.02-2.96, P＝0.042), severe AS (HR＝1.99, 95%CI 1.18-3.36, P＝0.010), and NYHA functional class Ⅲ-Ⅳ (HR＝2.08, 95% CI 1.24-3.47, P＝0.005). Conclusions Sex significantly affects the clinical manifestations, echocardiographic characteristics, and prognosis of patients with BAV combined with moderate to severe AS, suggesting that sex-specific echocardiographic assessment and follow-up monitoring should be conducted for these patients.

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

With the widespread application of robotic technology in surgery, multiple robot-assisted laparoscopic endoscopic surgical systems are currently in the R&D phase, highlighting an urgent need for establishing a scientific and standardized quality evaluation framework. In 2024, the National Medical Products Administration issued the standard YY/T 1941-2024, entitled Robotically-assisted Laparoscopic Surgical System, which specifies performance requirements and test methods for such systems. This standard provides critical guidance for the development of innovative products, technological advancement, and regulatory oversight. The standard offers clear definitions of relevant terminology and systematically standardizes the quality evaluation methods for these products across six key aspects: manual controller performance, instrument arm performance, system operational performance, safety protection functions, robotic surgical instruments, and software. This article interprets the core indicators outlined in the standard, aiming to assist clinicians in gaining a deeper understanding of the performance parameters of these systems and to support manufacturers in conducting quality assessments and risk analyses during product development, thereby promoting high-quality progress in the industry and related products.

The innate immune system serves as the body’s first line of defense against pathogens and plays a central role in inflammation regulation, immune homeostasis, and tumor immunosurveillance. In recent years, with the growing recognition of the concept “exercise is medicine”, increasing attention has been paid to the immunoregulatory effects of physical activity. Accumulating evidence suggests that regular, moderate-intensity exercise significantly enhances innate immunity by strengthening the skin-mucosal barrier, increasing levels of secretory immunoglobulin A (sIgA), and improving the functional capacity of key immune cells such as natural killer (NK) cells, neutrophils, macrophages, and dendritic cells. It also modulates the complement system and various inflammatory mediators. This review comprehensively summarizes the effects of exercise on each component of the innate immune system and highlights the underlying molecular mechanisms, including activation of AMP-activated protein kinase (AMPK), inhibition of nuclear factor-kappa B (NF-κB), enhancement of mitochondrial function via the PGC-1α/TFAM axis, and initiation of autophagy through the ULK1/mTOR pathway. Emerging mechanisms are also discussed, such as exercise-induced epigenetic modifications (e.g., histone acetylation and miRNA regulation), modulation of the gut microbiota, and metabolite-mediated immune programming (e.g., short-chain fatty acids (SCFAs), β‑hydroxybutyrate). The effects of exercise on innate immunity vary considerably among individuals, depending on factors such as age, sex, and comorbidities. For example, adolescents exhibit enhanced NK cell mobilization, whereas older adults benefit from reduced chronic inflammation and immune aging. Sex hormones and metabolic conditions (e.g., obesity, diabetes, chronic obstructive pulmonary disease, cancer) further modulate the immune response to exercise. Based on these insights, we propose a personalized approach to exercise prescription guided by the FITT (frequency, intensity, time, and type) principle, aiming to optimize immune outcomes across diverse populations. Importantly, given the dual role of exercise in immune activation and regulation, caution is warranted: while moderate exercise enhances immune defense, excessive or high-intensity activity may induce transient immunosuppression. In pathological contexts such as infection, autoimmune diseases, or tissue injury, exercise intensity and timing must be carefully adjusted. This review provides practical guidelines for exercise-based immune modulation and underscores the need for dose-response studies and advancements in precision exercise medicine. In conclusion, exercise represents a safe and effective strategy for enhancing innate immune function and mitigating chronic inflammatory diseases.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female