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ObjectiveThis study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients.MethodsA total of 18,408 DAPT patients were included. Data on patients’ demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC).ResultsThe XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847–0.875) in the development cohort and 0.877 (95% CI 0.856–0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model.ConclusionsThis study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

Abstract Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857–0.874) in the training dataset and 0.861 (95% CI 0.848–0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.

Background Aspirin is a widely used antiplatelet medication to prevent blood clots, reducing the risk of cardiovascular event. Healthcare providers need to be mindful of the risk of aspirin-induced bleeding and carefully balancing its benefits against potential risks. The objective of this study was to create a practical nomogram for predicting bleeding risk in patients with a history of myocardial infarction treating with aspirin. Methods A total of 2099 myocardial infarction patients with aspirin were enrolled. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation. Boruta analysis was utilized to identify clinically significant features associated with bleeding. Logistic regression model based on independent bleeding risk factors was constructed and presented as a nomogram. Model performance was assessed from three aspects: identification, calibration, and clinical utility. Results Boruta analysis identified eight clinical features from 25, and further multivariate logistic regression analysis selected four independent risk factors: hemoglobin, platelet count, previous bleeding, and sex. A visual nomogram was created based on these variables. The model achieved an area under the curve of 0.888 (95% CI: 0.845-0.931) in the training dataset and 0.888 (95% CI: 0.808-0.968) in the test dataset. Calibration curve analysis showed close approximation to the ideal curve. Decision curve analysis demonstrated favorable clinical net benefit for the model. Conclusions Our study focused on creating and validating a model to evaluate bleeding risk in patients with a history of myocardial infarction treated with aspirin, which demonstrated outstanding performance in discrimination, calibration, and net clinical benefit.

ObjectiveThe purpose of this retrospective study was to establish a numerical model for predicting the risk of pulmonary embolism (PE) in neurology department patients.MethodsA total of 1,578 subjects with suspected PE at the neurology department from January 2012 to December 2021 were considered for enrollment in our retrospective study. The patients were randomly divided into the training cohort and the validation cohort in the ratio of 7:3. The least absolute shrinkage and selection operator regression were used to select the optimal predictive features. Multivariate logistic regression was used to establish the numerical model, and this model was visualized by a nomogram. The model performance was assessed and validated by discrimination, calibration, and clinical utility.ResultsOur predictive model indicated that eight variables, namely, age, pulse, systolic pressure, hemoglobin, neutrophil count, low-density lipoprotein, D-dimer, and partial pressure of oxygen, were associated with PE. The area under the receiver operating characteristic curve of the model was 0.750 [95% confidence interval (CI): 0.721–0.783] in the training cohort and 0.742 (95% CI: 0.689–0.787) in the validation cohort, indicating that the model showed a good differential performance. A good consistency between the prediction and the real observation was presented in the training and validation cohorts. The decision curve analysis in the training and validation cohorts showed that the numerical model had a good net clinical benefit.ConclusionWe established a novel numerical model to predict the risk factors for PE in neurology department suspected PE patients. Our findings may help doctors to develop individualized treatment plans and PE prevention strategies.

This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning. Methods: A total of 26,030 elderly aspirin users (aged over 65) were retrospective included in the study. Data on patient demographics, clinical features, underlying diseases, medical history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. Patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation, respectively. Least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and multivariate logistic regression were employed to develop prediction models. Model performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). Results: The XGBoost model exhibited the highest AUC among all models. It consisted of six clinical variables: HGB, PLT, previous bleeding, gastric ulcer, cerebral infarction, and tumor. A visual nomogram was developed based on these six variables. In the training dataset, the model achieved an AUC of 0.842 (95% CI: 0.829– 0.855), while in the test dataset, it achieved an AUC of 0.820 (95% CI: 0.800– 0.840), demonstrating good discriminatory performance. The calibration curve analysis revealed that the nomogram model closely approximated the ideal curve. Additionally, the DCA curve, CIC, and NRC demonstrated favorable clinical net benefit for the nomogram model. Conclusion: This study successfully developed a predictive model to estimate the risk of bleeding in elderly aspirin users. This model can serve as a potential useful tool for clinicians to estimate the risk of bleeding in elderly aspirin users and make informed decisions regarding their treatment and management. [ABSTRACT FROM AUTHOR]

Background: The emergence and wide spread of carbapenemase-producing Enterobacteriaceae (CPE) poses a growing threat to global public health. However, clinically derived carbapenemase-producing Citrobacter causing multiple infections has rarely been investigated. Here we first report the isolation and comparative genomics of two blaNDM-5 carrying Citrobacter freundii (C. freundii) isolates from a patient with bloodstream and urinary tract infections. Results: Antimicrobial susceptibility testing showed that both blaNDM-5 carrying C. freundii isolates were multidrug-resistant. Positive modified carbapenem inactivation method (mCIM) and EDTA-carbapenem inactivation method (eCIM) results suggested metallo-carbapenemase production. PCR and sequencing confirmed that both metallo-carbapenemase producers were blaNDM-5 positive. Genotyping and comparative genomics analyses revealed that both isolates exhibited a high level of genetic similarity. Plasmid analysis confirmed that the blaNDM-5 resistance gene is located on IncX3 plasmid with a length of 46,161 bp, and could successfully be transferred to the recipient Escherichia coli EC600 strain. A conserved structure sequence (ISAba125-IS5-blaNDM-5-trpF-IS26-umuD-ISKox3) was found in the upstream and downstream of the blaNDM-5 gene. Conclusions: The data presented in this study showed that the conjugative blaNDM-5 plasmid possesses a certain ability to horizontal transfer. The dissemination of NDM-5-producing C. freundii isolates should be of close concern in future clinical surveillance. To our knowledge, this is the first study to characterize C. freundii strains carrying the blaNDM-5 gene from one single patient with multiple infections. [ABSTRACT FROM AUTHOR]

The existing intelligent condition monitoring model needs a large number of historical data and corresponding tags under different health states to train the model. It is difficult to collect abnormal samples in some actual system. A new system anomaly detection method trained with no abnormal sample is proposed. The proposed method combines the reward function model with maximum entropy and generative adversarial networks (GAN). Firstly, the GAN is trained with expert samples to generate virtual expert samples. The non-expert samples are generated by using random strategy on this basis. The mixed sample set with expert and non-expert samples is constructed. Combined with the maximum entropy probability model, the reward function is computed, and the gradient descent method is used to solve the optimal reward function. Secondly, the proposed model is trained by the normal samples collected in the early stage, and then used to detect the unknown state. Finally, the system is monitored by observing the change of the difference index generated by GAN with maximum entropy. The experimental analysis results verify the effectiveness of the method. Compared with the traditional algorithm, the proposed method detects the system anomaly earlier, and the difference index increases more rapidly when the anomaly occurs. [ABSTRACT FROM AUTHOR]

AbstractMotor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study’s findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion. [ABSTRACT FROM AUTHOR]

Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin's minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net's capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Siqiong Yao et al.)

This study aims to explore alterations in corticomuscular and cortical coupling during the rehabilitation of stroke patients. We initiated the analysis by employing variational modal decomposition (VMD) on electromyography (EMG) data, followed by the application of VDM-transfer entropy (VMD-TE) to quantify the coupling strength between electroencephalogram (EEG) and EMG signals. Subsequently, we constructed the VMD-TE connection matrix and analyzed the clustering coefficient and small-world attributes within the cortico-muscular functional network (CMFN). Finally, a random forest algorithm was employed to extract features from the VMD-TE connection matrix across different rehabilitation periods. Beta waves in EEG were emerged as the key information carrier between the cortex and muscle, and the CMFN of patients with the beta frequency band has small-world characteristics. During rehabilitation, we observed a decrease in coupling between the initially affected motor cortex and muscle, accompanied by an increase in coupling between the frontal region and muscle. Our findings suggest potential neuro-remodeling in stroke patients after rehabilitation, with CFMN serving as a valuable metric for assessing cortico-muscular coupling.

Endoplasmic reticulum (ER) stress-induced apoptosis occurs in the spinal cord following traumatic spinal cord injury (SCI). Dl-3-n-butylphthalide (NBP) exerts an neuroprotective effects against both ischemic brain injury and neurodegenerative diseases; however, the relationship between ER stress-induced apoptosis and the therapeutic effect of NBP in SCI remains unclear. In this study, moderate spinal cord injuries were induced in Sprague-Dawley (SD) rats with a vascular clip. NBP was administered by oral (80 mg/kg/d) gavage 2 h before injury and then once daily for 28 d thereafter. Neurological recovery was assessed using the Basso, Beattie, and Bresnahan (BBB) locomotion rating scale, the inclined plane test, and the footprint analysis. Neuronal cell death was examined by TUNEL staining at 7 days post-injury. ER stress and apoptosis-related proteins were quantified by immunofluorescence staining and western blotting both in vivo and in vitro . Our results showed that NBP significantly decreased spinal cord lesion cavity area and improved locomotor recovery in SD rats after SCI. NBP also decreased neuronal apoptosis and inhibited activation of the caspase 3 cascade. Upregulation of ER stress-related proteins, such as GRP78, ATF-6, ATF-4, PDI, XBP-1, and CHOP, was reversed by NBP treatment in SD rats with SCI. Similarly, NBP effectively ameliorated ER stress and apoptosis-related protein expression induced by incubation with thapsigargin (TG) in PC12 cells. Our findings demonstrate that NBP treatment alleviates secondary SCI by inhibiting ER stress-induced apoptosis, thereby promoting neurological and locomoter functional recovery.