Objective To explore an artificial intelligence (AI)-based method for automated hand hygiene monitoring and to compare the effectiveness of three algorithms (UniFormerV2, TDN, C3D) in recognizing hand hygiene steps in surgical settings, thereby aiding hospital infection control. Methods From April to October 2024, we non-invasively collected 641 video recordings of healthcare staff performing hand hygiene at four-bay scrub sinks in two tertiary hospitals using overhead HD cameras. The dataset was annotated by five trained experts for model training and validation. Results Following training on 385 samples, internal validation (n=119) showed the C3D model achieved 81% accuracy, 87% recall, and an 83% F1-score. The TDN model achieved 93%, 91%, and 92% for the same metrics. The UniFormerV2 model outperformed both, with an accuracy, recall, and F1-score of 93%—an improvement of over 10 percentage points compared to traditional CNNs (TDN, C3D). It also achieved an 84% accuracy in external validation, demonstrating strong generalization. Conclusion The UniFormerV2 model is more accurate than CNN-based models for hand hygiene step recognition and shows robust performance in external validation. It presents a viable tool for healthcare facilities to enhance hand hygiene management, ultimately improving medical quality and patient safety.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective:To explore the impact of a three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women.Methods:It was a randomized controlled trial. A simple random sampling method was used to select 460 singleton pregnant women with a pre-pregnancy body mass index≥24 kg/m2 who had regular prenatal check-ups at the Obstetrics and Gynecology Center of the Third Affiliated Hospital of Chongqing Medical University from June 1, 2018, to December 31, 2022. The women were randomly divided into an experimental group and a control group (230 cases each) using a computer-generated random number table. The control group received regular prenatal check-ups according to the pregnancy care guidelines (once every 4 weeks during mid-pregnancy, once every 2 weeks during late pregnancy, with additional frequency as needed based on the condition). The control group also received a one-time body composition analysis and dietary guidance from a nutritionist at the time of registration. In addition to the control group′s interventions, the experimental group received three-dimensional management based on a perinatal one-day clinic. It included an intensive one-day clinic session, a traditional plus internet-based re-education model (as needed based on the condition), individualized guidance from obstetrics and clinical nutrition clinics (once every 2 weeks), a free body composition test at 24 weeks of pregnancy, and weekly WeChat group push of health care knowledge during pregnancy. A total of 55 cases dropped out, leaving 200 cases in the experimental group and 205 cases in the control group for analysis. General information, pregnancy-related, and postpartum indicators were collected in the two groups. The effect of three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women was analyzed using t-tests and chi-square tests. Results:There was no significant differences in baseline age, pre-pregnancy body mass index, initial blood glucose, initial glycated hemoglobin, or initial gestational age between the two groups (all P>0.05). The experimental group showed significantly lower gestational weight gain, neonatal weight, and proportion of excessive pregnancy weight gain compared to those in the control group [(11.41±5.23) vs (13.22±4.51) kg, (3 352.1±465.3) vs (3 489.5±464.0) g, 48.00% vs 73.17%] (all P<0.05). There were no significant differences in hospitalization days, gestational age at delivery, incidence of gestational diabetes, incidence of hypertensive disorders of pregnancy, incidence of premature rupture of membranes, incidence of preterm birth, incidence of macrosomia, vaginal delivery rate and rate of neonatal transfer to the pediatric department between the two groups (all P>0.05). Conclusion:Early intervention with the three-dimensional management based on the one-day perinatal clinic can effectively control gestational weight gain and neonatal weight in overweight and obese pregnant women.

AIM To establish the near-infrared spectroscopy quantitative models for moisture,23-acetylalismol B and 23-acetylalismol C in Alismatis Rhizoma decoction pieces.METHODS The near-infrared spectroscopy(NIRS)data were collected in 95 batches of decoction pieces,after which drying method was adopted in the content determination of moisture,HPLC was applied to determining the contents of 23-acetylalismol B and 23-acetylalismol C,the quantitative models were established by partial least squares method combined with feature extraction algorithms.RESULTS The model training determination coefficients were 0.952 6,0.958 1 and 0.920 8,along with the prediction determination coefficients of 0.930 0,0.905 2 and 0.906 4,the residual prediction deviations(PRD)of 4.00,3.58 and 3.46,and the root mean square error ratios of prediction values to calibration values(RMSEP/RMSEC)of 1.15,1.11 and 1.06,respectively.CONCLUSION The quantitative models based on NIRS exhibit good prediction effects,which can be used for the rapid quality detection of Alismatis Rhizoma decoction pieces.

Objective:To investigate how age and nail-tract volumetric bone mineral density (vBMD) are associated with postoperative mechanical performance of proximal femoral nail anti-rotation (PFNA-Ⅱ) fixation for geriatric intertrochanteric fractures using a finite-element analysis.Methods:Fifteen elderly patients with intertrochanteric fracture of the femur were selected for this study. They were 11 females and 4 males and divided into 5 groups based on their ages ( n=3): 55-year-old, 65-year-old, 75-year-old, 85-year-old, and 95-year-old groups. After three-dimensional models of the proximal femur were constructed using the preoperative CT data of their healthy contralateral hip, 31-A1.2 fractures of the AO/OTA type were created and PFNA-Ⅱ fixations simulated. Two loading schemes were created: graded quasi-static axial loads (700 N, 1,400 N, 2,100 N, and 2,800 N) were applied to compute equivalent plastic strain volumes in the femoral head region; displacement-controlled loading was applied to failure to derive load-displacement curves for stiffness and the maximum failure load. Nail-tract vBMD and regional hip vBMDs were measured by quantitative CT. Pearson correlation analysis was conducted to investigate the associations of age and nail-tract vBMD with the aforementioned mechanical indicators. Results:Under the same load, compared with the 55-year-old, 65-year-old, and 75-year-old groups, the plastic strain unit volumes of the fracture models in the 85-year-old and 95-year-old groups increased significantly. Under a load of 700 N, no plastic strain was observed in the fracture models in the 55-year-old, 65-year-old, and 75-year-old groups, while an average plastic strain of approximately 50 mm 3 was observed in the fracture models in the 85-year-old group. Under a load of 2,800 N, the high strain areas in the fracture models in the 85-year-old and 95-year-old groups were mainly concentrated at the tip of the head nail and the junction between the head nail and the main nail. Load-displacement curves showed a marked reduction in the failure load in patients aged ≥85 years. Under loads of 1,400 N, 2,100 N, and 2,800 N, there was a strong association between the nail-tract vBMD and the volume of the plastic strain unit ( r=-0.82, -0.88, -0.89, respectively), which was stronger than those for total-hip vBMD. Conclusions:Finite-element analysis indicates that age and nail-tract vBMD are closely related to local plastic strain and overall stiffness of the proximal femur after PFNA-Ⅱ fixation for the geriatric intertrochanteric fractures. Patients aged ≥85 years old are more prone to plastic yielding, which compromises fixation stability.

At present, robotic surgery system, as a cutting-edge technique in minimally invasive surgery, has become a crucial component in the training programs for outstanding surgeons. Given the relatively late start of this technique and immature faculty resources, there is an urgent need to optimize and improve the teaching philosophies and methodologies for robotic skill training in China, and Sino-foreign cooperation has emerged as a significant pathway to promote this reform. Taking the "International Robotic Surgery Training Center (IRSTC)" jointly established by The First Affiliated Hospital of Sun Yat-sen University and University of California as an example, this article provides a comprehensive analysis of collaboration background, construction of the training system, curriculum design, cooperation channels, and localization practices, in order to provide a reference and valuable experiences for Sino-foreign cooperation in clinical skill training.

Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.

Systemic lupus erythematosus （SLE） may lead to secondary gynecological and obstetric disorders such as decreased ovarian reserve function， menstrual abnormalities， and adverse pregnancy outcomes. Based on "bi （痹） of both body and viscera" theory， this paper proposed that the core mechanism of SLE secondary gynecological and obstetric diseases lies in the mutual transformation between "body bi" and "viscera bi"， which together affect the uterus. Physiologically， uterus forms an internal-external network with the body and viscera through the meridians and blood vessels. Pathologically， when the healthy qi is deficient， nourishment of the body and viscera is impaired； when toxins and stasis accumulate， pathogenic factors disturb the uterus through the chong （冲） and ren （任） meri-dians. The resulting obstruction in the uterus can， in turn， manifest externally and aggravate damage to the body and viscera. Therefore， the pathogenesis of SLE secondary gynecological and obstetric diseases follows a dynamic trajectory of "body bi first， body bi affecting viscera， and then bi of both body and viscera". In treatment， the principle of harmonizing and balancing the healthy qi is emphasized. The main approach is to regulate the viscera， stabilize the body， and nourish the uterus， with the coordination of nourishing the viscera through the body， thereby achieving simultaneous treatment of both body and viscera. This highlights the guiding significance of the "bi of both body and viscera" theory in preventing and treating SLE secondary gynecological and obstetric diseases.