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 discuss the performance of visual score and heart-to-contralateral lung (H/CL) ratio of 99Tc m-pyrophosphate (PYP) SPECT/CT scintigraphy for diagnosing transthyretin-related cardiac amyloidosis (ATTR-CA) by using Meta-analysis. Methods:Relevant studies on 99Tc m-PYP SPECT/CT diagnosis of ATTR-CA published before August 20, 2023 from databases including Pubmed, EMbase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang and China Science and Technology Journal Database (VIP) were retrieved. Articles were screened and indices which reflect the diagnostic efficiency such as sensitivity and specificity were extracted. Forest plots were drawn, and subgroup analysis was performed to analyze the heterogeneity. Results:A total of 160 articles were retrieved, and 11 articles involving 1 802 patients were enrolled, of whom 605 were diagnosed with ATTR-CA. All 11 articles were enrolled when analyzing the diagnostic efficiency of visual score for diagnosing ATTR-CA. After integration, the sensitivity and specificity were 0.95(95% CI: 0.91-0.97) and 0.95(95% CI: 0.90-0.98), respectively. Ten articles (1 611 patients) were enrolled when analyzing the diagnostic efficiency of H/CL ratio for diagnosing ATTR-CA. After integration, the sensitivity and specificity were 0.93(95% CI: 0.82-0.98) and 0.99(95% CI: 0.90-1.00), respectively. Subgroup analysis indicated that lack of uniformity in diagnostic criteria was the primary source of heterogeneity. Conclusion:99Tc m-PYP SPECT/CT scintigraphy exhibits high diagnostic efficiency for ATTR-CA.

Replantation of traumatic amputation in children has its own characteristics. This consensus primarily focuses on the issues related to the treatment of traumatically amputated limb injuries in children. Organised along a timeline, the consensus summarises domestic and international clinical experiences in emergency care and injury assessment of traumatic limb amputation limbs, indications and contraindications for replantation surgery, principles and procedures of replantation surgery, postoperative medication and management, as well as rehabilitation in children. The aim of this consensus is to propose standardise the treatment protocols for limb replantation for children therefore to serve as a reference for clinical practitioners in medical practices, and further improve the treatment and care for the traumatic limb amputations in children.

BACKGROUND: The burden of esophageal cancer varies across different regions of the world. The aim of this study is to analyze the current burden of esophageal cancer in 185 countries in 2022 and to project the trends up to the year 2050. METHODS: We extracted data on primary esophageal cancer cases and deaths from the GLOBOCAN 2022 database, which includes data from 185 countries. Age-standardized incidence rates (ASIR) and mortality rates (ASMR) per 100,000 person-years were calculated by stratifying by Human Development Index (HDI) levels and regions. Considering changes in population size and age structure, we assumed that the risks of incidence and mortality remain constant at the levels of 2022 to forecast the number of new cases and deaths from esophageal cancer globally by 2050. RESULTS: In 2022, an estimated 511,054 people were diagnosed with esophageal cancer globally, and 445,391 died from the disease. The global ASIR and ASMR for esophageal cancer were 5.00 and 4.30 per 100,000, respectively. The highest rates were observed in East Africa (7.60 for incidence, 7.20 for mortality per 100,000), East Asia (7.60 for incidence, 5.90 for mortality per 100,000), Southern Africa (6.30 for incidence, 5.90 for mortality per 100,000), and South Central Asia (5.80 for incidence, 5.50 for mortality per 100,000). Among the 185 countries worldwide, esophageal cancer was among the top five causes of cancer incidence in 18 countries and among the top five causes of cancer mortality in 25 countries. In 2022, China had 224,012 new cases and 187,467 deaths from esophageal cancer, accounting for approximately 43.8% and 42.1% of the global total, respectively, which is higher than the proportion of China's population to the global population (17.9%). ASIR was 8.30 per 100,000, and ASMR was 6.70 per 100,000. The highest burden of esophageal cancer was in high HDI countries, with new cases and deaths accounting for 51.3% and 50.0% of the global total, respectively. The ASIR and ASMR were highest in the high HDI group (6.10 and 5.10 per 100,000, respectively), also exceeding the global averages. There was a trend of decreasing mortality to incidence ratio with increasing HDI, but no correlation was observed between HDI and ASIR or ASMR. In all regions worldwide, the incidence and mortality rates were higher in males than in females (with a male-to-female ASR ratio ranging from 1.10 to 28.7). Compared to 2022, it is projected that by 2050, the number of new esophageal cancer cases will increase by approximately 80.5%, and deaths will increase by 85.4% due to population growth and aging. CONCLUSIONS The burden of esophageal cancer remains heavy. Adopting a healthy lifestyle, including reducing tobacco and alcohol intake, avoiding moldy foods, and increasing intake of fresh fruits and vegetables, can help reduce the risk of stomach and esophageal cancer. In addition, the development and implementation of evidence-based and effective public health policies are critical to reducing the global disease burden of esophageal cancer.
Esophageal Neoplasms/mortality* ; Humans ; Male ; Incidence ; Female ; Middle Aged ; Global Health ; Aged ; Adult

Currently, in precision cardiac surgery, there are still some pressing issues that need to be addressed. For example, cardiopulmonary bypass remains a critical factor in precise surgical treatment, and many core aspects still rely on the experience and subjective judgment of cardiopulmonary bypass specialists and surgeons, lacking precise data feedback. With the increasing elderly population and rising surgical complexity, precise feedback during cardiopulmonary bypass becomes crucial for improving surgical success rates and facilitating high-complexity procedures. Overcoming these key challenges requires not only a solid medical background but also close collaboration among multiple interdisciplinary fields. Establishing a multidisciplinary team encompassing professionals from the medical, information, software, and related industries can provide high-quality solutions to these challenges. This article shows several patents from a collaborative medical and electronic information team, illustrating how to identify unresolved technical issues and find corresponding solutions in the field of precision cardiac surgery while sharing experiences in applying for invention patents.

DNA methylation is an important epigenetic regulatory mechanism, including gene promoter methylation and gene body methylation. Abnormal DNA methylation is closely related to the development and progression of malignant tumors, and the correlation between promoter methylation and tumors has been more clearly described previously. However, with the in-depth study of genome-wide DNA methylation, it has been found that there are more extensive methylation levels in gene body regions, which also play an important role in tumor-related gene expression, cell differentiation and tumor development. In this paper, we review the effects of gene bodymethylation on tumors in recent years and provide clues for the research and application of gene body methylation in the field of tumors by elaborating the role and regulatory mechanism of gene body methylation on tumors.

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.

bjective    To use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods    A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results    The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion    Machine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.

Objective     To establish a machine learning based framework to rapidly screen out high-risk patients who may develop atrial fibrillation (AF) from patients with valvular heart disease and provide the information related to risk prediction to clinicians as clinical guidance for timely treatment decisions. Methods     Clinical data were retrospectively collected from 1 740 patients with valvular heart disease at West China Hospital of Sichuan University and its branches, including 831 (47.76%) males and 909 (52.24%) females at an average age of 54 years. Based on these data, we built classical logistic regression, three standard machine learning models, and three integrated machine learning models for risk prediction and characterization analysis of AF. We compared the performance of machine learning models with classical logistic regression and selected the best two models, and applied the SHAP algorithm to provide interpretability at  the population and single-unit levels. In addition, we provided visualization of feature analysis results. Results    The Stack model performed best among all models (AF detection rate 85.6%, F1 score 0.753), while XGBoost outperformed the standard machine learning models (AF detection rate 71.9%, F1 score 0.732), and both models performed significantly better than the logistic regression model (AF detection rate 65.2%, F1 score 0.689). SHAP algorithm showed that left atrial internal diameter, mitral E peak flow velocity (Emv), right atrial internal diameter output per beat, and cardiac function class were the most important features affecting AF prediction. Both the Stack model and XGBoost had excellent predictive ability and interpretability. Conclusion    The Stack model has the highest AF detection performance and comprehensive performance. The Stack model loaded with the SHAP algorithm can be used to screen high-risk patients for AF and reveal the corresponding risk characteristics. Our framework can be used to guide clinical intervention and monitoring of AF.

Objective    To evaluate the use of machine learning algorithms for the prediction and characterization of cardiac thrombosis in patients with valvular heart disease and atrial fibrillation. Methods    This article collected data of patients with valvular disease and atrial fibrillation from West China Hospital of Sichuan University and its branches from 2016 to 2021. From a total of 2 515 patients who underwent valve surgery, 886 patients with valvular disease and atrial fibrillation were included in the study, including 545 (61.5%) males and 341 (38.5%) females, with a mean age of 55.62±9.26 years, and 192 patients had intraoperatively confirmed cardiac thrombosis. We used five supervised machine learning algorithms to predict thrombosis in patients. Based on the clinical data of the patients (33 features after feature screening), the 10-fold nested cross-validation method was used to evaluate the predictive effect of the model through evaluation indicators such as area under the curve, F1 score and Matthews correlation coefficient. Finally, the SHAP interpretation method was used to interpret the model, and the characteristics of the model were analyzed using a patient as an example. Results    The final experiment showed that the random forest classifier had the best comprehensive evaluation indicators, the area under the receiver operating characteristic curve was 0.748±0.043, and the accuracy rate  reached 79.2%. Interpretation and analysis of the model showed that factors such as stroke volume, peak mitral E-wave velocity and tricuspid pressure gradient were important factors influencing the prediction. Conclusion    The random forest model achieves the best predictive performance and is expected to be used by clinicians as an aided decision-making tool for screening high-embolic risk patients with valvular atrial fibrillation.