[Objective] To explore the effectiveness of applying the PDCA (Plan-Do-Check-Act) cycle to enhance the compatibility rate of five Rh blood group antigen phenotypes between donors and recipients across multiple hospital campuses. [Methods] Clinical blood transfusion data from May to July 2022 were selected. Specific improvement measures were formulated based on the survey results, and the PDCA cycle management model was implemented from August 2022. The post-intervention phase spanned from August 2022 to October 2023. The Rh phenotype compatibility rate, the detection rate of Rh system antibodies, and the proportion of Rh system antibodies among unexpected antibodies were compared between the pre-intervention phase (May to July 2022) and the post-intervention phase. [Results] After the continuous improvement with the PDCA cycle, the compatibility rate for the five Rh blood group antigen phenotypes between donors and recipients from August to October 2023 reached 81.90%, significantly higher than the 70.54% recorded during the pre-intervention phase (May to July 2022, P<0.01), and displayed a quarterly upward trend (β=0.028, P<0.05). The detection rate of Rh blood group system antibodies (β=-9.839×10^-5, P<0.05) and its proportion among all detected antibodies (β=-0.022, P<0.05) showed a quarterly decreasing trend, both demonstrating a negative correlation with the enhanced compatibility rate (r values of -0.981 and -0.911, respectively; P<0.05). [Conclusion] The implementation of targeted measures through the PDCA cycle can effectively increase the compatibility rate of five Rh blood group antigen phenotypes between donors and recipients, reduce the occurrence of unexpected Rh blood group antibodies, thereby lowering the risk of transfusion and enhancing the quality and safety of medical care.

BACKGROUND:We have successfully established an animal model of small ear pig in southern Yunnan province with internal tension relieving technique combined with autologous Achilles tendon for anterior cruciate ligament reconstruction,and verified the stability and reliability of the model.However,whether internal tension relieving technique can promote the ligamentalization process of autologous Achilles tendon graft has not been studied. OBJECTIVE:To investigate the differences in the process of ligamentalization between conventional reconstruction and internal reduction reconstruction of the anterior cruciate ligament by gross view,histology and electron microscopy. METHODS:Thirty adult female small ear pigs in southern Yunnan province were selected.Anterior cruciate ligament reconstruction was performed on the left knee joint with the ipsilateral knee Achilles tendon(n=30 in the normal group),and anterior cruciate ligament reconstruction was performed on the right knee joint with the ipsilateral knee Achilles tendon combined with the internal relaxation and enhancement system(n=30 in the relaxation group).The autogenous right forelimb was used as the control group;the anterior cruciate ligament was exposed but not severed or surgically treated.At 12,24,and 48 weeks after surgery,10 animals were sacrificed,respectively.The left and right knee joint specimens were taken for gross morphological observation to evaluate the graft morphology.MAS score was used to evaluate the excellent and good rate of the ligament at each time point.Hematoxylin-eosin staining was used to evaluate the degree of ligament graft vascularization.Collagen fibers and nuclear morphology were observed,and nuclear morphology was scored.Ultrastructural remodeling was evaluated by scanning electron microscopy and transmission electron microscopy. RESULTS AND CONCLUSION:(1)The ligament healing shape of the relaxation group was better at various time points after surgery,and the excellent and good rate of MAS score was higher(P<0.05).Moreover,the relaxation group could obtain higher ligament vascularization score(P<0.05).(2)The arrangement of collagen bundles and fiber bundles in the two groups gradually tended to be orderly,and the transverse fiber connections between collagen gradually increased and thickened,suggesting that the strength and shape degree of the grafts were gradually improved,but the ligament remodeling in the relaxation group was always faster than that in the normal group at various time points after surgery.(3)The diameter,distribution density,and arrangement degree of collagen fibers in the relaxation group were better than those in the normal group at all time points,especially in the comparison of collagen fiber diameter between and within the relaxation group(P<0.05).

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

BACKGROUND:Observational studies have shown that intervertebral disc degeneration affects sedentary and physical activity levels;however,the causal relationship between sedentary and physical activity levels in the Oswestry disability index score and intervertebral disc degeneration is unclear. OBJECTIVE:To explore the causal relationship between sedentary and physical activity levels in the Oswestry disability index score and intervertebral disc degeneration using the Mendelian randomization method. METHODS:Five features associated with behavioral correlations in the Oswestry disability index score,including time spent watching TV,time spent on the computer,and light/moderate/vigorous physical activity,were selected from large-scale population-based genome-wide association studies,and instrumental variables were extracted for each of these behaviorally related features.Mendelian randomization analyses were performed in conjunction with the extraction of intervertebral disc degeneration as an outcome from the Finn Gen latest version 9 database.The results were analyzed using the inverse variance weighted,MR-Egger regression,simple mode,weighted mode,weighted median estimator,and regression model odds ratios(OR)and 95%confidence interval(CI)to assess the causal relationship between sedentary and physical activity levels in the Oswestry disability index scoring and intervertebral disc degeneration.Cochran's Q was used to test for heterogeneity,MR-Egger intercept to test for multiplicity,and leave-one-out to test the sensitivity of single nucleotide polymorphisms to the causal relationship between exposure factors and disc degeneration. RESULTS AND CONCLUSION:The results of the Mendelian randomization analysis using inverse variance weighted method showed a positive causal association between time spent watching TV/on the computer and the risk of intervertebral disc degeneration(OR=1.775,95%CI:1.418-2.221,P<0.001)/(OR=1.384,95%CI:1.041-1.839,P<0.001),an inverse causal association between light physical activity and the risk of intervertebral disc degeneration(OR=1.000,95%CI:0.999-1.000,P=0.020).MR-Egger intercept analysis indicated there was potential horizontal polytropy between light physical activity and intervertebral disc degeneration(P=0.005),while there was no horizontal pleiotropy between time spent watching TV,time spent on the computer and intervertebral disc degeneration(P=0.521,P=0.851).Cochran's Q analysis showed that heterogeneity was observed between time spent watching TV,time spent on the computer and intervertebral disc degeneration(P=3.33×10-11,P=0.001),and no significant heterogeneity was observed between light physical activity and intervertebral disc degeneration(P=0.186).Overall,there is a bidirectional causal relationship between sedentary and physical activity levels in the Oswestry disability index score and intervertebral disc degeneration,i.e.,not only does intervertebral disc degeneration affect sedentary and physical activity levels in the Oswestry disability index score,but sedentary and physical activity levels in the Oswestry disability index score also affect intervertebral disc degeneration.These findings add to the genetic evidence for a positive effect of light physical activity on intervertebral disc degeneration,indicate that moderate/vigorous physical activity shows no significant causal relationship with intervertebral disc degeneration,and expand the evidence base for sedentary behaviors such as prolonged time spent watching TV/on the computer as a risk factor for intervertebral disc degeneration.

Objective To introduce an innovative technique, the "balance-shaped sternal elevation device" and its application in the subxiphoid uniportal video-assisted thoracoscopic surgery (VATS) for anterior mediastinal masses resection. Methods Patients who underwent single-port thoracoscopic assisted anterior mediastinal tumor resection through the xiphoid process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from May to June 2024 were included, and their clinical data were analyzed. Results A total of 7 patients were included, with 3 males and 4 females, aged 28-72 years. The diameter of the tumor was 1.9-17.0 cm. The operation time was 62-308 min, intraoperative blood loss was 5-100 mL, postoperative chest drainage tube retention time was 0-9 days, pain score on the 7th day after surgery was 0-2 points, and postoperative hospital stay was 3-12 days. All patients underwent successful and complete resection of the masses and thymus, with favorable postoperative recovery. Conclusion The "balance-shaped sternal elevation device" effectively expands the retrosternal space, providing surgeons with satisfactory surgical views and operating space. This technique significantly enhances the efficacy and safety of minimally invasive surgery for anterior mediastinal masses, reduces trauma and postoperative pain, and accelerates patient recovery, demonstrating important clinical significance and application value.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

Objective: To classify the ABO blood group phenotypes of 5 cases from a family, and to explore the molecular mechanism for reduced B antigen expression in ABO blood group system. Methods: Serological identification of the ABO blood group was performed using microcolumn gel assay and saline tube method. The soluble antigens in saliva were detected by the agglutination inhibition assay. The full-length sequences and upstream promoter regions of ABO gene were sequenced for genotyping using PacBio SMRT sequencing technology. Results: The results of serological tests indicated the expression of B antigen decreased in 3 out of 5 blood samples. A mixed-field agglutination was observed with anti-B antibody. B antigen was not detected in all 5 saliva samples. The ABO genotype for all samples were ABO B.01/ABO O.01.02, and a novel mutation c. 28+5875C>T within the DNA-binding region of RUNX1 in +5.8-kb site were found in the B allele for 3 samples with reduced expression of B antigen. Conclusion: Results of serological and genetic analyses classify the 3 cases with reduced B antigen expression as B phenotype. The novel mutation c. 28+5875C>T of RUNX1 could be the key reason for reduced B antigen expression in 3 cases with B phenotype.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.