Objective: To explore the association between the HLA antibody positivity rate in female blood donors and pregnancy history, number of pregnancies, interval from the last pregnancy to blood donation, and age, to identify associated variables using a univariate generalized additive model (GAM), and to further analyze the predictive role of characteristic variables for HLA antibody positivity using a random forest model. Methods: HLA antibody detection was performed on 391 female blood donors using the Luminex immunomagnetic bead method. The correlation between pregnancy-related factors and HLA antibodies was analyzed using the Chi-square test. Based on R software, a univariate GAM was first constructed to analyze the association types between characteristic variables and the HLA antibody positivity rate, followed by the construction of a random forest model to evaluate the predictive value of the variables. Results: Among the 391 female blood donors without a transfusion history, the overall HLA antibody positivity rate was 26.34%. The positivity rate in donors with a pregnancy history was significantly higher than that in those without (30.09% vs 9.72%, P<0.05), and HLA antibody positivity rate increased linearly with the number of pregnancies (P<0.05). In the univariate GAM, age and number of deliveries exhibited a non-linear association with the HLA antibody positivity rate (the positivity rate increased sharply between 25-35 years of age and stabilized after 3 deliveries). Besides, the interval from the last pregnancy to blood donation showed a linear association with the HLA antibody positivity rate, and the positivity rate decreased as the interval prolonged (P<0.05). In the random forest model, age (mean decrease gini=29.26) and interval from the last pregnancy to blood donation (mean decrease gini=22.02) were core predictive variables: age was more conducive to identifying positive samples, while the interval from the last pregnancy to blood donation was more helpful for excluding negative samples. The number of deliveries (mean decrease accuracy=16.98) made a significant contribution to predicting positive samples, whereas the number of abortions had no impact. The model had an AUC of 0.583 (95% CI: 0.593 8-0.770 2), indicating a certain predictive value. Conclusion: The associated variables identified by the univariate GAM model, including age, interval from the last pregnancy to blood donation, and number of deliveries, provide a basis for key variables in the random forest model. All three variables have predictive value for HLA antibody positivity, which can provide evidence-based support for personalized transfusion management and stratified screening of female blood donors in this region.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

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

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

The Department of Thoracic Surgery of Shanghai Chest Hospital has performed esophageal function testing for over 30 years, being the only department of its kind in China with this capability. The pressure testing and 24-hour pH/impedance monitoring of the esophagus is of great help to assist in the diagnosis and treatment of benign and malignant esophageal diseases related to it. Thanks to the esophageal function test, in addition to the routine various endoscopic anti-reflux procedures, our hospital has taken the lead in China in recent years to carry out a series of clinical and research work for benign esophageal diseases, such as the development of magnetic ring, double nedoscopic combination and new anti-reflux endoscopic techniques. In recent years, we have carried out high-resolution esophageal manometry and 24-hour pH/impedance monitoring for patients with interstitial pneumonia and pulmonary fibrosis suspected to be caused by gastroesophageal acid reflux. We can better assess the correlation between gastroesophageal reflux and pulmonary fibrosis, and to provide the different clinical treatments and even surgical interventions. The Bravo capsule is used more often in the United States, and it has obvious advantages over traditional approach for acid measurement. We strongly call for the collaboration between industry and academic institutions in this field, and the development of our own related products with independent intellectual property rights.

Micro/nanoplastics (MNPs), recognized as emerging environmental pollutants, are widely distributed in natural environments. Due to their small particle size and significant migratory capacity, MNPs can infiltrate diverse environmental matrices, then invade and accumulate in the organism via the skin, respiration, and digestion. Recently, concerns have grown over the detrimental effects and potential toxicity of MNPs on reproductive health. This review summarized published epidemiological and toxicological studies related to MNPs exposure and their effects on reproductive health. Firstly, this review critically examined the current landscape of epidemiological evidence and found that MNPs (e.g., polystyrene, polypropylene, polyvinyl chloride, polyethylene, etc.) are present in various biological specimens from both males and females, and their presence may be associated with an increased risk of reproductive disorders. Secondly, extensive toxicological studies revealed that MNPs exposure induces reproductive health damage through mechanisms such as disrupting the microstructure of reproductive organs and altering molecular-level expressions. Oxidative stress, inflammatory responses, and apoptosis are identified as potential links between MNPs exposure and reproductive damage. Finally, this review addressed the prevalent shortcomings in existing studies and proposed future directions to tackle the challenges posed by MNPs-induced reproductive harm. These insights aim to inform strategies for safeguarding public reproductive health and ecological security, providing a scientific foundation for mitigating risks associated with MNPs pollution.

Objective:To construct a Harvard cancer index-based risk predictive model for perioperative venous thromboembolism (VTE) in elderly patients with femoral neck fracture and assess its predictive effectiveness.Methods:A retrospective cohort study was conducted to analyze the clinical data of 610 elderly patients with femoral neck fracture admitted to Peking Union Medical College Hospital between January 2013 and December 2022, including 193 males and 417 females, aged 60-99 years [(77.3±9.0)years]. The patients were divided into VTE group ( n=125) and non-VTE group ( n=485) according to occurrence of VTE during the perioperative period. The two groups were compared in terms of gender, age, body mass index, smoking status, alcohol consumption, time from fracture to admission, surgical waiting time, comorbidities, perioperative electrolyte disorders, past or present history of malignancy, past history of deep vein thrombosis (DVT) or pulmonary embolism (PE), and preoperative use of oral anticoagulants. Univariate analysis and multivariable stepwise Logistic regression analysis were conducted to evaluate and identify independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. A perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed using the Harvard cancer index: (1) assigning a risk score to each variable according to the corresponding conversion criteria of the Harvard cancer index and risk score, based on the magnitude of their ORs; (2) determining the exposure rate of each risk factor based on the population distribution observed in this study; (3) calculating the average population risk score; (4) computing the individual VTE risk score; (5) deriving the ratio (X) of each individual ′s VTE risk score to the population average. Based on the Harvard cancer index classification criteria for disease risk levels, individual VTE risk categories were determined. The predictive performance of the risk stratification was evaluated by comparing the incidence of VTE across different risk levels. The predictive performance of the model was evaluated based on sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) test and internal validation was performed using the bootstrap resampling method with 1000 iterations. Results:Univariate analysis showed that gender, age, time from fracture to admission, surgical waiting time, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, history of DVT or PE, and preoperative use of oral anticoagulant drug were moderately associated with the occurrence of VTE in elderly patients with femoral neck fracture ( P<0.10). Multivariable stepwise logistic regression analysis demonstrated that female gender ( OR=2.26, 95% CI 1.34, 3.80, P<0.01), time from fracture to admission>1 day ( OR=3.70, 95% CI 2.24, 6.12, P<0.01), surgical waiting time>70 hours ( OR=2.06, 95% CI 1.29, 3.30, P<0.01), previous cerebral infarction ( OR=3.78, 95% CI 1.04, 13.76, P<0.05), stroke within the past month ( OR=11.57, 95% CI 1.21, 110.44, P<0.05), Alzheimer′s disease ( OR=3.26, 95% CI 1.12, 9.49, P<0.05), primary Parkinson ′s syndrome ( OR=3.47, 95% CI 1.22, 9.85, P<0.05), previous hysterectomy with bilateral adnexectomy ( OR=4.75, 95% CI 2.09, 10.80, P<0.01), perioperative electrolyte disorders ( OR=2.73, 95% CI 1.39, 5.35, P<0.01), and preoperative oral anticoagulant use ( OR=3.86, 95% CI 1.18, 12.67, P<0.05) were significantly associated with the occurrence of perioperative VTE in elderly patients with femoral neck fracture. Based on the above 10 risk factors, a perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed with the Harvard cancer index. The formula was as follows: X=[10×(female gender)+25×(time from fracture to admission>1 day)+10×(surgical waiting time>70 hours)+25×(previous cerebral infarction)+50×(stroke within the past month)+25×(Alzheimer′s disease)+25×(primary Parkinson′s disease)+25×(previous hysterectomy with bilateral adnexectomy)+10×(perioperative electrolyte disorders)+25×(preoperative use of oral anticoagulant drug)]/33. Individualized VTE risk was classified into five levels: very low, low, moderate, high, and very high, with corresponding VTE rates of 4.8%, 11.8%, 14.9%, 32.3%, and 73.5%, respectively ( χ2=87.71, P<0.01). The VTE risk predictive model demonstrated an AUC of 0.74 (95% CI 0.69, 0.79, P<0.01), with a sensitivity of 63.2% and specificity of 74.8%. The H-L goodness-of-fit test indicated satisfactory model calibration ( P>0.05). The internal validation with the bootstrap method confirmed that the AUC remained 0.74. Conclusions:Female gender, time from fracture to admission>1 day, surgical waiting time>70 hours, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, and preoperative use of oral anticoagulant drug are independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. Based on these factors, the perioperative VTE risk predictive model constructed using the Harvard cancer index demonstrates good clinical predictive value. Individualized VTE risk stratification can effectively identify high-, intermediate-, and low-risk populations, providing a valuable reference for tailoring anticoagulant prophylaxis strategies and enhancing postoperative surveillance.

Spinal cord injury represents a severe central nervous system trauma characterized by prolonged treatment duration, limited neural regeneration, and delayed functional recovery, greatly affecting patients′ quality of life. The impaired neural tissue struggles to recover effectively due to both the hostile microenvironment and its own compromised state. Current clinical interventions, including early reduction, laminectomy decompression, and intravenous or intrathecal methylprednisolone administration, fail to simultaneously modulate the microenvironment and improve the neural status. Zinc, a trace element abundant in the central nervous system, plays a critical role in gene expression regulation, synaptic plasticity, and neuronal activity. Clinical evidences have indicated that lower serum zinc concentration in patients with spinal cord injury correlates with poorer outcomes and animal experiments have also demonstrated the neuroprotective effects of zinc. In fact, zinc supplementation therapy has not yet been developed into a mature clinical protocol. Besides, related animal studies still lack comprehensive understanding. To this end, the authors reviewed the biological characteristics of zinc, its administration routes, neuroprotective effects and mechanisms in the management of spinal cord injury, aiming to provide references for future basic research and clinical practice.

Spinal cord injury is a highly disabling central nervous system disorder whose pathological progression is closely associated with blood-spinal cord barrier (BSCB) disruption. Physical trauma to the spine or spinal cord may compromise BSCB integrity, triggering secondary damage including spinal edema, hemorrhage, oxidative stress, and excessive inflammatory responses. For the pivotal role of protecting the spinal cord microenvironment, the repair of BSCB is crucial in the treatment of spinal cord injury. Although present studies have explored BSCB repair strategies such as biological factor regulation, biomaterial applications, and traditional Chinese medicine interventions, most studies focus on improving the overall barrier function and fail to systematically elucidate how these strategies target the core functional units of BSCB, namely the endothelial cells and their junction structures, to achieve functional and structural restoration of the barrier. Therefore, the authors reviewed the composition and key repair targets of BSCB, along with research advances in BSCB repair strategies based on endothelial cells and junction structures, aiming to provide insights for basic research and clinical treatment of spinal cord injury.

Gut microbiota,implicated in obesity,insulin resistance,depression,and cardiovascular risk,can be modulated by probiotics to mitigate cardiovascular disease risk.Vascular calcification(VC),a regulated process involving mineral deposits in vessels and valves,is a significant risk factor.Diet profoundly impacts both gut micro-biota and VC,influencing cardiovascular health via microbial metabolites.While the link between gut microbiota and VC is established,the precise dietary effects on vascular health remain unclear.This article reviews mecha-nisms through which dietary patterns shape gut microbiota and metabolites,influence VC and highlights directions for future research on VC diagnosis and treatment.