The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.

The American Heart Association (AHA) officially released the "2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association" on January 27, 2025. This report systematically compiles the latest statistics on major cardiovascular diseases worldwide, while simultaneously integrating relevant outcome indicators, including quality of care, procedures, and economic costs, and updating the global prevalence patterns and evolving trends of diverse risk factors impacting cardiovascular health, providing essential guidance for the prevention, diagnosis, and treatment of cardiovascular diseases. Synthesizing insights from this pivotal report and other relevant studies, this article highlights key findings concerning the global prevalence and mortality of heart diseases, associated risk factors, and emerging diagnostic and therapeutic technologies.

Objective To evaluate the feasibility and safety of Remodeling+Ring (modified Yacoub) for patients with aortic root aneurysm. Methods The clinical data of patients who underwent modified Yacoub surgery at West China Hospital of Sichuan University from July 2020 to May 2023 were retrospectively analyzed. Results Four male patients were enrolled, with an average age of (47.3±10.3) years and body surface area of (1.9±0.2) m2. One patient had bicuspid aortic valve. Aortic valve regurgitation was mild in three patients and moderate in one patient. Preoperative New York Heart Association (NYHA) heart function was gradeⅠin one patient and gradeⅡin three patients. The maximum diameter of the aortic sinus was (59.3±8.1) mm. All four patients recovered and were discharged without a second thoracotomy. No postoperative complications such as brain injury, infection, respiratory failure or renal insufficiency occurred. During the follow-up of (17.0±13.1) months, two patients showed no regurgitation of the aortic valve, two patients exhibited mild regurgitation. Three patients had a heart function of gradeⅠ and one patient of gradeⅡ. Conclusion Modified Yacoub technique is safe and effective for patients with aortic root aneurysm.

Objective To systematically evaluate the risk factors for postoperative delirium (POD) in patients undergoing pneumonectomy. Methods PubMed, Web of Science, Cochrane Library, CNKI, Wanfang, and VIP databases were searched from the inception to November 7, 2024 for cross-sectional studies, case-control studies, and cohort studies on POD in patients undergoing pneumonectomy. Two researchers independently screened the literature, extracted data, and evaluated the quality of the literature. RevMan 5.4.1 software was used for meta-analysis. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the literature. Results A total of 12 studies were included, with 5 574 patients. The NOS scores of the literature were all≥6 points. Meta-analysis results showed that age (≥60 years) [OR=2.43, 95%CI (2.01, 2.93), P<0.01], American Society of Anesthesiologists (ASA) classification (Ⅳ) [OR=8.74, 95%CI (5.23, 14.61), P<0.01], history of diabetes [OR=12.81, 95%CI (10.45, 15.71), P<0.01], history of cerebrovascular disease [OR=3.00, 95%CI (2.46, 3.67), P<0.01], depression [OR=7.27, 95%CI (5.46, 9.67), P<0.01], squamous cell carcinoma [OR=4.79, 95%CI (1.83, 12.51), P<0.01], malnutrition [OR=5.25, 95%CI (3.35, 8.25), P<0.01], sleep disorders [OR=2.79, 95%CI (2.28, 3.42), P<0.01], and duration of one-lung ventilation during surgery [OR=1.32, 95%CI (1.11, 1.57), P<0.01] were all risk factors for POD, while high body mass index (BMI) [OR=0.96, 95%CI (0.95, 0.97), P<0.01] was a protective factor for POD. Conclusion Age (≥60 years), ASA classification (Ⅳ), history of diabetes, history of cerebrovascular disease, depression, squamous cell carcinoma, malnutrition, sleep disorders, and duration of one-lung ventilation during surgery are independent risk factors for POD, while high BMI is a protective factor.

Neoadjuvant therapy has become the standard treatment for locally advanced resectable esophageal cancer, significantly improving long-term survival compared to surgery alone. Neoadjuvant therapy has evolved to include various strategies, such as concurrent chemoradiotherapy, chemotherapy, immunotherapy, or targeted combination therapy. This enriches clinical treatment options and provides a more personalized and scientific treatment approach for patients. This article aims to comprehensively summarize current academic research hot topics, review the rationale and evaluation measures of neoadjuvant therapy, discuss challenges in restaging methods after neoadjuvant therapy, and identify the advantages and disadvantages of various neoadjuvant therapeutic strategies.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

The implantation of left ventricular assist device (LVAD) has significantly improved the quality of life for patients with end-stage heart failure. However, it is associated with the risk of complications, with unplanned readmissions gaining increasing attention. This article reviews the influencing factors, prediction methods and models, and intervention measures for unplanned readmissions in LVAD patients, aiming to provide scientific guidance for clinical practice, assist healthcare professionals in accurately assessing patients' conditions, and develop rational care plans.

In audiovisual emotion recognition, representational learning is a research direction receiving considerable attention, and the key lies in constructing effective affective representations with both consistency and variability. However, there are still many challenges to accurately realize affective representations. For this reason, in this paper we proposed a cross-modal audiovisual recognition model based on a multi-head cross-attention mechanism. The model achieved fused feature and modality alignment through a multi-head cross-attention architecture, and adopted a segmented training strategy to cope with the modality missing problem. In addition, a unimodal auxiliary loss task was designed and shared parameters were used in order to preserve the independent information of each modality. Ultimately, the model achieved macro and micro F1 scores of 84.5% and 88.2%, respectively, on the crowdsourced annotated multimodal emotion dataset of actor performances (CREMA-D). The model in this paper can effectively capture intra- and inter-modal feature representations of audio and video modalities, and successfully solves the unity problem of the unimodal and multimodal emotion recognition frameworks, which provides a brand-new solution to the audiovisual emotion recognition.
Emotions ; Humans ; Attention ; Algorithms

Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural network (CNN) and Transformer-based networks perform well but have limitations: CNN have constrained receptive fields, and Transformer underutilize image features. This work proposes an improved pancreas segmentation method by combining CNN and Transformer. Point-wise separable convolution was introduced in a stage-wise encoder to extract more features with fewer parameters. A densely connected ensemble decoder enabled multi-scale feature fusion, addressing the structural constraints of skip connections. Consistency terms and contrastive loss were integrated into deep supervision to ensure model accuracy. Extensive experiments on the Changhai and National Institute of Health (NIH) pancreas datasets achieved the highest Dice similarity coefficient (DSC) values of 76.32% and 86.78%, with superiority in other metrics. Ablation studies validated each component's contributions to performance and parameter reduction. Results demonstrate that the proposed loss function smooths training and optimizes performance. Overall, the method outperforms other advanced methods, enhances pancreas segmentation performance, supports physician diagnosis, and provides a reliable reference for future research.
Humans ; Neural Networks, Computer ; Pancreas/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Deep Learning