Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using phase Ⅰ and phaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPⅣ), BP values from this phase were also recorded.Results The study enrolled 106 volunteers with 70 males and 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPⅣ), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.

In the era of big data, neuroimaging and algorithmic analyses have propelled brain science research and brain mapping. Acupuncture, widely recognized as an effective surface stimulation therapy, has demonstrated therapeutic efficacy for various brain conditions such as stroke and depression. However, the mechanisms linking acupuncture to brain function and its modulatory effects on brain activity require systematic exploration. Additionally, there is an urgent need to scientifically reinterpret traditional meridian theory and enhance its clinical applicability. Therefore, we propose the initiative of constructing a "brain mapping atlas of meridian, collateral and body surface stimulation" to explore the patterns linking the therapeutic effects of stimulating the twelve meridians, eight extraordinary vessels, divergent channels, collateral channels, sinew channels, and skin regions to brain function. This initiative aims to provide a scientific interpretation of traditional Chinese medicine meridian theory and enhance its practical applicability. This paper begins by reviewing the current state of brain mapping. It then summarizes existing research on the relationship between acupuncture and the brain, highlighting the necessity of constructing this atlas. The paper further analyzes the methodologies and technical challenges involved. Finally, the potential applications of the brain mapping atlas of meridian, collateral and body surface stimulation, and its main significance in advancing traditional meridian theory to keep pace with the times are prospected.
Humans ; Acupuncture Therapy ; Meridians ; Big Data ; Brain/physiology* ; Brain Mapping

Objective To explore the application effect of 3D printed heart models in the training of young cardiac surgeons, and evaluate their application value in surgical simulation and skill improvement. Methods Eight young cardiac surgeons were selected form West China Hospital as the trainees. Before training, the Hands-On Surgical Training-Congenital Heart Surgery (HOST-CHS) operation scores of the 8 cardiac surgeons were obtained after operating on 2 pig heart models of ventricular septal defect (VSD). Subsequently, simulation training was conducted on a 3D printed peri-membrane VSD heart model for 6 weeks, once a week. After the training, all trainees completed 2 pig heart VSD repair surgeries. The improvement of doctors’ skills was evaluated through survey questionnaires, HOST-CHS scores, and operation time after training. Results Before the training, the average HOST-CHS score of the 8 trainees was 52.2±6.3 points, and the average time for VSD repair was 54.7±7.1 min. During the 6-week simulation training using 3D printed models, the total score of HOST-CHS for the 8 trainees gradually increased (P<0.001), and the time required to complete VSD repair was shortened (P<0.001). The trainees had the most significant improvement in scores of surgical cognition and protective awareness. The survey results showed that trainees were generally very satisfied with the effectiveness of 3D model simulation training. Conclusion The 3D printed VSD model demonstrates significant application advantages in the training of young cardiac surgeons. By providing highly realistic anatomical structures, 3D models can effectively enhance surgeons’ surgical skills. It is suggested to further promote the application of 3D printing technology in medical education, providing strong support for cultivating high-quality cardiac surgeons.

Modulating Tankyrases (TNKS), interactions with USP25 to promote TNKS degradation, rather than inhibiting their enzymatic activities, is emerging as an alternative/specific approach to inhibit the Wnt/β-catenin pathway. Here, we identified UAT-B, a novel neoantimycin analog isolated from Streptomyces conglobatus, as a small-molecule inhibitor of TNKS-USP25 protein-protein interaction (PPI) to overcome multi-drug resistance in colorectal cancer (CRC). The disruption of TNKS-USP25 complex formation by UAT-B led to a significant decrease in TNKS levels, triggering cell apoptosis through modulation of the Wnt/β-catenin pathway. Importantly, UAT-B successfully inhibited the CRC cells growth that harbored high TNKS levels, as demonstrated in various in vitro and in vivo studies utilizing cell line-based and patient-derived xenografts, as well as APC^min/+ spontaneous CRC models. Collectively, these findings suggest that targeting the TNKS-USP25 PPI using a small-molecule inhibitor represents a compelling therapeutic strategy for CRC treatment, and UAT-B emerges as a promising candidate for further preclinical and clinical investigations.

Objective To propose a heart sound segmentation method based on multi-feature fusion network. Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset (a total of 3 153 recordings from 764 patients, about 91.93% of whom were male, with an average age of 30.36 years). Firstly the features were extracted in time domain and time-frequency domain respectively, and reduced redundant features by feature dimensionality reduction. Then, we selected optimal features separately from the two feature spaces that performed best through feature selection. Next, the multi-feature fusion was completed through multi-scale dilated convolution, cooperative fusion, and channel attention mechanism. Finally, the fused features were fed into a bidirectional gated recurrent unit (BiGRU) network to heart sound segmentation results. Results The proposed method achieved precision, recall and F1 score of 96.70%, 96.99%, and 96.84% respectively. Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance, which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.

Mental disorders are characterized by disturbances in behavior,volition,emotion,and cognition and are considered emotional diseases in traditional Chinese medicine.Acupuncture is one of the most widely used complementary alternative therapies for the treatment of mental disorders.Recently,there has been growing interest in the use of the Tong Du Tiao Shen(Dredging Du meridian to regulate the spirit)as a primary treatment.However,a comprehensive summary of the establishment and related acupuncture methods of Tong Du Tiao Shen is lacking.This paper aims to address this gap by exploring the origin and development of Tong Du Tiao Shen,its application in treating mental disorders,and the modern biological mechanisms involved.Ultimately,this paper seeks to expand the clinical application of Tong Du Tiao Shen acupuncture and provide a scientific basis for future research in this field.

Objective To explore the relationship between the expression levels of serum cingulate protein(CGN)and polyligand glycan 1(SDC-1)and the disease condition and outcome of hypertensive basal ganglia hemorrhage(HBGH).Methods A total of 123 patients with HBGH admitted to the Second People's Hospi-tal of Lianyungang from February 2019 to February 2022 were selected as the study objects,and 120 healthy volunteers who underwent physical examination in the hospital during the same period were selected as the health group.Serum CGN and SDC-1 expression levels were detected in the two groups.According to the dis-ease outcome,the patients were divided into the improved group(92 cases)and the deteriorated group(31 ca-ses).Receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to analyze the predictive value of serum CGN and SDC-1 expression levels on the disease outcome of patients with HB-GH.Results Serum CGN and SDC-1 expression levels in the severe group were higher than those in the mod-erate group and the mild group,and serum CGN and SDC-1 levels in the moderate group were higher than those in the mild group,and the differences were statistically significant(P＜0.05).Serum CGN and SDC-1 expression levels in HBGH patients in three groups were higher than those in health group,and the differences were statistically significant(P＜0.05).Serum CGN and SDC-1 expression levels in the deteriorated group were higher than those in the improved group,and the differences were statistically significant(P＜0.05).The AUC of serum CGN and SDC-1 for predicting the disease outcome of HBGH patients was 0.742(95％CI:0.792-0.697)and 0.861(95％CI:0.906-0.910),respectively,and the AUC of the combination of the two was 0.917(95％CI:0.962-0.870).The amount of blood loss and ventricular rupture in the deteriorated group were higher than those in the improved group,and the Glasgow Coma Scale(GCS)score on admission was lower than that in the improved group,and the differences were statistically significant(P＜0.05).Multi-variate Logistic regression analysis showed that serum CGN≥51.63 pg/mL(OR=3.815),serum SDC-1≥450.67 μg/L(OR=4.230)and GCS score ≤8(OR=5.333)were the influencing factors for disease outcome of HBGH patients(P＜0.05).Conclusion The increased expression levels of serum CGN and SDC-1 are closely related to the disease aggravation and the deterioration of the disease outcome in patients with HBGH,and they have certain predictive value for the disease outcome in patients with HBGH.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.