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）.

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.].

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

ObjectiveTo understand the current status of Cronobacter sakazakii (Cronobacter spp.) infection and its molecular epidemiological characteristics among patients with diarrhea, so as to provide evidence for the prevention and control of diarrhea disease caused by infection with Cronobacter spp. in Changping District, Beijing. Methods760 stool samples were collected from the diarrhea patients in a sentinel hospital in 2019, for the detection of Cronobacter spp., Salmonella, diarrheogenic Escherichia coli (DEC), and Vibrio Parahaemolyticus. Meanwhile, drug sensitivity experiment and pulsed-field gel electrophoresis (PFGE) typing analysis were conducted on the Cronobacter spp. strains isolated. ResultsA total of 20 Cronobacter spp. strains (2.63%) were isolated, with a lower detection rate than that of Salmonella and Vibrio Parahaemolyticus (χ²=9.052, P=0.011). However, there were no statistically significant differences between the detection rates in Cronobacter spp. and DEC (χ²=1.076, P=0.300). Seasonal characterization analysis showed that Cronobacter spp. could be detected in spring (1.00%), summer (4.17%), autumn (3.00%) and winter (1.67%), and the differences were statistically significant (χ²=662.700, P<0.001). The PFGE analysis showed that 20 PFGE banding patterns were found in 20 Cronobacter spp. strains, with a similarity coefficient ranging from 56.30% to 90.09% and a diverse PFGE banding pattern. The drug sensitivity experiment results showed that 18 (90.00%) strains were resistant to cefazolin, and2 (10.00%) strains were intermediate. While, as for cefoxitin, 2 (10.00%) strains were resistant to it, and 5 (25.00%) strains were intermediate. All the 20 strains were 100.00% sensitive to the other 11 antibiotics. ConclusionIn the study, Cronobacter spp. is detected in all seasons through the year, with a high resistance rate to cefazolin, no multi-drug resistant bacteria appeared, and diverse PFGE banding patterns.

Objective:Bibliometric analysis was performed to map scientific knowledge landscape, so that to explore the research status and future trends in the field of carbapenem-resistant Gram-negative bacteria (CRGNB) over the past decade.Methods:Literature on CRGNB published between January 1st, 2015 and December 31st, 2024 was retrieved from the China National Knowledge Internet (CNKI) database and Web of Science Core Collection (WoSCC). VOSviewer and CiteSpace were used for bibliometric analysis.Results:A total of 3 340 Chinese and 10 761 English publications were included in this study. The annual Chinese publications remained stable, while English publications exhibited a linear growth. It was anticipated that the English publications would decline in the forthcoming years, although remaining high. China contributed the highest number of publications, and Zhejiang University was the institution with the largest number of publications. Bonomo RA, Chen L, etc. were high-impact authors in the field of CRGNB and had formed a stable cooperative group. Antimicrobial Agents and Chemotherapy was the journal with the largest number of publications. High-frequency keywords in the domain of CRGNB were comprehensively categorized into four distinct clusters, including carbapenem resistance mechanisms and gene transmission, antimicrobial drugs and combination therapy, management of critically ill patients, and infections and colonization. It was imperative to acknowledge the significance of all of these research areas. Burst word analysis suggested that carbapenem-resistant Enterobacterales virulence genes as well as new isoforms of Klebsiella pneumoniae carbapenemase (KPC) had become a research hotspot. Conclusions:The issue of carbapenem resistance remains a significant concern. Current research focus on the resistance mechanisms and antimicrobial agents, highlighting its significant academic advancement and practical applications. Fostering international collaboration through academic exchanges between research teams worldwide is imperative to establish robust cooperative relationships, facilitate multidisciplinary cooperation, and promote high-quality research.

AIM To investigate the clinical effects of Yifei Tongluo Decoction combined with azithromycin on patients with childern severe Mycoplasma pneumoniae pneumonia due to Toxic Heat Blocking Lung.METHODS One hundred and fifty-six patients were randomly assigned into control group(78 cases)for 7-day administration of azithromycin,and observation group(78 cases)for 7-day administration of both Yifei Tongluo Decoction and azithromycin.The changes in clinical effects,disappearance time of local symptoms,mycoplasmas,pulmonary imaging score,Toxic Heat Blocking Lung score,pulmonary function indices(PEF,VPTEF,MMF,TPTEF),inflammatory factors(sB7-H3,Cgp-39,sICAM-1,CCL5),immune function indices(RBC-C3bR,RBC-ICR,CD3+,CD4+)and safety indices were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05),along with shorter disappearance time of local symptoms(P＜0.05).After the treatment,the two groups displayed decreased mycoplasmas,pulmonary imaging score,Toxic Heat Blocking Lung score,inflammatory factors,RBC-ICR(P＜0.05),and increased pulmonary function indices,RBC-C3bR,CD3+,CD4+(P＜0.05),especially for the observation group(P＜0.05).No obvious difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with childern severe Mycoplasma pneumoniae pneumonia due to Toxic Heat Blocking Lung,Yifei Tongluo Decoction combined with azithromycin can safely and effectively alleviate clinical symptoms,improve pulmonary functions,and reduce body inflammatory responses.