Guidelines for Digital Ancient Books of TCM Indexing(T/CIATCM 119-2024)is based on the theoretical knowledge,disciplinary methods,and practical applications of TCM classical cataloging.Taking digital ancient books of TCM as the object,it systematically reveals the content of TCM knowledge,which is an essential indexing processing standard for building an intelligent retrieval system for TCM ancient books,and can provide support for the deep development and innovative utilization of TCM knowledge.It can not only promote the co-construction and sharing of ancient book resources in the TCM industry,but also promote the standardization construction and application of TCM information.This standard specifies the principles,methods,and examples of free indexing of digital ancient books of TCM based on their original content.It is applicable to the indexing and processing of digital ancient books of TCM for TCM professional libraries and related institutions,and to the data processing and construction of various types of TCM ancient book databases.

Lung cancer has the highest incidence and mortality among malignant tumors in China.Persistent subsolid nodules(SSNs)are closely associated with early-stage lung adenocarcinoma.Artificial intelligence(AI),as an emerging technology,is capable of performing in-depth analysis of large-scale imaging data through autonomous learning and possesses the ability to predict outcomes from new data,demonstrating great potential and application prospects in the assessment of SSNs.AI can not only effectively assist radiologists in diagnosis and treatment,but also improve work efficiency while reducing misdiagnosis and missed diagnosis rates.This review summarizes the recent applications and research progress of AI in the assessment of SSNs,to provide new insights for the diagnosis and treatment of SSNs.

Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.

Objective:To investigate the molecular characteristics of low-risk acute myeloid leukemia(AML)at recurrence,and analyze the factors affecting retreatment efficacy and prognosis.Methods:A retrospective analysis was conducted on the clinical and laboratory data of 31 patients with newly diagnosed low-risk AML who relapsed during consolidation treatment or follow-up after treatment in our hospital from April 2017 to January 2023.Gene mutations before and after relapse were compared,retreatment efficacy following relapse was evaluated,and univariate and multivariate analyses were performed to identify factors influencing treatment efficacy and prognosis.Results:Gene sequencing results after relapse showed that the most common newly acquired mutation was FLT3-ITD,while RAS mutation detected at initial diagnosis were predisposed to loss of expression during relapse.The median overall survival(OS)after relapse for the entire cohort was 349(170-528)days,with non-hematopoietic stem cell transplantation(HSCT)group and HSCT group demonstrating median survival times of 210(106-314)days and not reached,respectively(P=0.001).Multivariate analysis revealed that age ≥60 years was a significant risk factor for achieving remission after retreatment in initially diagnosed low-risk AML patients who experienced relapse(OR=18.222,95％CI:1.188-279.597,P=0.037).Additionally,DNMT3A mutation was identified as an independent risk factor for OS(HR=13.165,95％CI:2.018-85.877,P=0.007),while HSCT post-relapse demonstrated significant survival benefits(HR=0.133,95％CI:0.025-0.698,P=0.017)and served as an independent protective factor for OS.Conclusion:Relapsed low-risk AML is often associated with loss of RAS and novel mutations in FLT3-ITD.Age ≥ 60 years and DNMT3A mutations were identified as independent adverse factors for achieving subsequent remission and post-relapse survival,respectively,while HSCT significantly improved patient outcomes.

Objective To explore a nomogram of intratumor and peritumor radiomics based on enhanced CT venous phase to pre-operatively predict the pathological grade of poorly differentiated esophageal squamous cell carcinoma(ESCC).Methods A retro-spective selection was made of 266 ESCC patients confirmed by pathology(76 cases of poorly differentiated;190 cases of non-poorly differentiated),and all patients were randomly divided into training set(n=186),validation set(n=80),and full data set(n=266).Tumors were segmented on the enhanced CT venous phase to create three-dimensional region of interest(ROI)of intratumor,peritu-mor 0.3 cm,and intratumor+peritumor 0.3 cm.A total of 2 553 radiomics features were extracted.After feature dimensionality reduc-tion,XGboost machine learning algorithm was utilized to rank the top fifteen features.Stepwise forward multiple logistic regression was employed to identify the most significant features.The radiomics scores of the intratumor,peritumor 0.3 cm,and intratumor+peritu-mor 0.3 cm were calculated.The diagnostic efficacy of the model was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results The nomogram constructed based on radiomics scores of intratumor,peritumor 0.3 cm,intratumor+peritumor 0.3 cm in the training set for preoperative prediction of poorly differentiated ESCC had an AUC of 0.899[95％confidence interval(CI)0.846-0.938],and it was well validated in the vali-dation set(AUC 0.869,95％CI 0.775-0.934)and the full data set(AUC 0.889,95％CI 0.845-0.924).Additionally,calibration curves and DCA indicated that the nomogram achieved good calibration ability in the three cohorts and offered greater clinical net benefit.Conclusion The nomogram based on enhanced CT venous phase intratumor and peritumor radiomics achieves a high and stable diagnostic efficacy for preoperatively predicting poorly differentiated ESCC,which may help with individualized surgical selec-tion and management before surgery.

Objective:To evaluate the efficacy of multimodal prehabilitation in patients with refractory functional constipation undergoing Jinling procedure(modified Duhamel surgery).Methods:In this prospective randomized controlled trial,80 patients with refractory functional constipation scheduled for Jinling procedure at the Department of General Surgery,the General Hospital of Eastern Theater Command between January 2020 and December 2021 were enrolled.Participants were randomly assigned to either the observation group(n=40,multimodal prehabilitation)or control group(n=40,routine nursing care).Outcome measures included:time to first flatus,time to first ambulation,defecation volume on postoperative day 5,length of hospitalization,nutritional markers(hemoglobin,albumin,total protein at postoperative day 7),anxiety/depression scores(Hospital Anxiety and Depression Scale,HADS),and total complication rates.Results:Compared to controls,the first ventilation time(48.02±6.15)h,first ambulation time(49.92±5.58)h,defecation volume on the fifth day(234.50±51.03)mL,hospital stay(13.15±2.64)d,anxiety score(43.68±3.45)points,depression score(43.81±1.58)points,and the total incidence of postoperative complications(15%)were significantly lower in the observation group(all p values<0.05).By contrast,the serum levels of hemoglobin(115.60±11.60)g/l,albumin(41.19±5.79)g/L and total protein(61.64±4.94)g/L on day 7 post-operatively were significantly higher in the observation group than those in the control group(P<0.05).Conclusions:Multimodal prehabilitation enhances postoperative intestinal recovery,reduces complications,improves nutritional status,and shortens hospital stays in refractory functional constipation patients undergoing Jinling procedure,supporting its clinical adoption.

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.
Artificial Intelligence ; Bibliometrics ; Humans ; China ; Equipment and Supplies ; United States ; Biomedical Research

OBJECTIVE: To explore the current research status and hotspots in the field of biomechanics of diabetic foot by bibliometric analysis methods. METHODS: Literatures related to biomechanics of diabetic foot published in the Web of Scienc Core Collection (WoSCC) from 1981 to 2024 were searched. CiteSpace software and R language bibliometrics plugin were used to conduct a visual analysis of annual publication volume of the literature, including publication volume of each country and region, the publication situation of authors and institutions, the citation situation of individual literature, and the co-occurrence network of keywords. RESULTS: Totally 996 literatures were included, and the number of published papers increased steadily. The United States (261 papers) and China (89 papers) were the top two countries in terms of the number of published papers. The mediating centrality of the United States was 0.94, and that of China was 0.01. Scholars such as Cavanagh and institutions like the Cleveland Clinic were at the core of research in this field. High-frequency keywords include plantar pressure (plantar pressure), diabetic foot (diabetic foot), ulceration (ulcer), etc. The research focuses on plantar pressure, ulcer formation and prevention, etc. CONCLUSION Biomechanical research on diabetic foot mainly focuses on the pressure distribution on the sole of the foot, callus formation, mechanical analysis of soft tissues on the sole of the foot, and the study of plantar decompression caused by Achilles tendon elongation. The research trend has gradually shifted from focusing on joint range of motion to gait and the design of braces and assistive devices, and has begun to pay attention to muscle strength, gait imbalance and proprioception abnormalities.
Humans ; Diabetic Foot/physiopathology* ; Biomechanical Phenomena ; Bibliometrics

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.