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.

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 evaluate the clinical efficacy of Huayu Tongluo Formula （化瘀通络方， HTF） in patients with mid-stage diabetic kidney disease of blood stasis syndrome and explore its potential mechanisms. MethodsA multi-center， randomized， double-blind， placebo-controlled clinical trial was conducted. Ninety patients of mid-stage diabetic kidney disease of blood stasis syndrome were divided into a control group of 46 cases and a treatment group of 44 cases. Both groups received conventional western medicine treatment， the treatment group additionally taking HTF， while the control group taking a placebo of the formula. The treatment was administered once daily for 24 weeks. The primary outcomes included 24-hour urine total protein （24 h-UTP）， serum albumin （Alb）， glycated hemoglobin （HbA1c）， and serum creatinine （Scr）.The secondary outcomes included changes in levels of endothelin-1 （ET-1）， nitric oxide （NO）， vascular endothelial growth factor （VEGF）， and traditional Chinese medicine （TCM） syndrome scores before and after treatment. Clinical efficacy was evaluated based on TCM syndrome scores and overall disease outcomes. Adverse reactions and endpoint events were recorded. ResultsIn the treatment group after treatment， 24 h-UTP， ET-1， and VEGF levels significantly decreased （P＜0.05）， Alb and NO levels significantly increased （P＜0.05）； while the TCM syndrome scores for edema， lumbar pain， numbness of limbs， dark purple lips， dark purple tongue or purpura， and thin， rough pulse all significantly decreased （P＜0.05）. In the control group， no significant changes were observed in any of the indicators after treatment （P＞0.05）.Compared with the control group， the treatment group showed significant reductions in 24 h-UTP， ET-1， and VEGF levels， and increases in Alb and NO levels （P＜0.05）. The TCM syndrome scores for edema， lumbar pain， dark purple tongue or purpura， and thin， rough pulse were all lower in the treatment group than in the control group （P＜0.05）. The total effective rate of TCM syndrome in the treatment group was 59.09% （26/44）， and the overall clinical effective rate was 45.45% （20/44）. In the control group， these rates were 15.22% （7/46） and 8.7% （4/46）， respectively， with the treatment group showing significantly better outcomes （P＜0.05）. A total of 7 adverse events occurred across both groups， with no significant difference （P＞0.05）. No endpoint events occurred during the study. ConclusionOn the basis of conventional treatment of Western medicine， HTF can further reduce urinary protein levels and improve clinical symptoms in patients with mid-stage diabetic kidney disease of blood stasis syndrome. The mechanism may be related to its effects on endothelial function.

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.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Objective To explore the valve regurgitation status of left heart after the implantation of left ventricular assist device (LVAD) and its effect on prognosis of patients with LVAD implantation. Methods A total of 35 patients with cardiomyopathy who underwent magnetic levitation LVAD implantation at Zhongshan Hospital, Fudan University from February 2021 to July 2024 were retrospectively selected. Clinical data during hospitalization were collected, including preoperative basic data and postoperative valve regurgitation status. Telephone follow-ups were conducted to monitor patients’ survival status and transthoracic echocardiography was used to assess left valve function. Kaplan-Meier survival curves and log-rank test were employed to compare the survival rate of patients with different levels of valve regurgitation. Results The 35 patients had a mean age of (53.9±11.1) years, with 85.7% male, and 3 patients (8.6%) died during hospitalization. Preoperatively, 17 patients (48.6%) had moderate or greater mitral regurgitation, while all 35 patients had less than moderate aortic regurgitation. One month postoperatively, thirty patients were followed up, among which 24 patients (80%) had less than moderate mitral regurgitation, including 11 cases with alleviated regurgitation compared to pre-surgery; 6 patients (20%) had moderate or greater mitral regurgitation, including 4 cases with stable regurgitation and 2 cases with progression of regurgitation compared to pre-surgery; 2 patients (6.7%) had progression of aortic regurgitation to moderate or greater. The follow-up time was 1.2 (1.0, 2.1) years, with 1-year survival rate of 91.4% and 3-year survival rate of 71.1%. Survival analysis showed that the 3-year survival rate of patients with moderate or greater mitral regurgitation one month postoperatively was significantly lower than that of patients with less than moderate regurgitation (66.7% vs 83.3%, P＝0.046). Conclusions After the implantation of magnetic levitation LVAD, most patients showed improvement in mitral regurgitation, while aortic regurgitation remained unchanged. The degree of mitral regurgitation one month postoperatively is associated with prognosis.

Abstract Hyperuricemia (HUA) is a metabolic disorder syndrome caused by purine metabolism dysregulation, and its prevalence increases year by year. The development and progression of HUA are accompanied by significant alterations in the composition of intestinal microbiota, making probiotics a potential and safe method to reduce serum uric acid. Probiotics ameliorate HUA through three pathways: competing with intestinal epithelial cells for purine absorption to decrease uric acid synthesis, inhibiting xanthine oxidase activity through modulation of inflammatory cytokines to reduce the conversion of purine to uric acid, as well as restoring and maintaining an orderly state of the gut microbiota to facilitate normal uric acid excretion. This article reviews the role of probiotics in ameliorating HUA, so as to provide the reference for the application of probiotics in the prevention and intervention of HUA.