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Aspirin is commonly utilized in the management and prevention of various diseases. However, in specific individuals, particularly those with low body mass index (BMI), aspirin can elevate the risk of bleeding. Achieving a delicate equilibrium between the desirable antiplatelet effects and potential bleeding complications is a notable consideration. The objective of this study was to create a novel bleeding risk prediction tool for aspirin users with a low BMI. A total of 2436 aspirin users with a low BMI were included in this study conducted at the Affiliated Dongyang Hospital of Wenzhou Medical University. Patient data, comprising demographics, clinical characteristics, comorbidities, medical history, and laboratory tests, were collected. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation purposes. The identification of clinically significant features associated with bleeding was achieved through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression and boruta analysis. Subsequently, these important features underwent multivariate logistic regression analysis. Based on independent bleeding risk factors, a logistic regression model was constructed and presented as a nomogram. Model performance was evaluated using metrics such as the area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC) in both the training and testing sets. LASSO analysis identified two clinical features, while Boruta analysis identified nine clinical features out of a total of 21 features. Subsequent multivariate logistic regression analysis selected significant independent risk factors. The boruta model, which demonstrated the highest AUC, consisted of six clinical variables: hemoglobin, platelet count, previous bleeding, tumor, smoke, and diabetes mellitus. These variables were integrated into a visually represented nomogram. The model exhibited an AUC of 0.832 (95% CI: 0.788–0.875) in the training dataset and 0.775 (95% CI: 0.698–0.853) in the test dataset, indicating excellent discriminatory performance. Calibration curve analysis revealed close alignment with the ideal curve. Furthermore, DCA, CIC, and NRC demonstrated favorable clinical net benefit for the model. This study has successfully created a novel risk prediction tool specifically designed for aspirin users with a low BMI. This tool enables the stratification of low BMI patients based on their anticipated bleeding risk.

Blockchain technology is increasingly being used in personal data protection. Inspired by the importance of data security, this paper proposes a personal data protection mechanism based on blockchain, combined with distributed hash tables and cryptography, to enhance users' control over the data generated using web applications. This paper designs this mechanism's system model and describes the three aspects in detail: data storage mechanism, data encryption mechanism, and data trading mechanism. Among them, the data storage mechanism restricts user data to be stored only in the local storage space of the user terminal, the decentralized blockchain network, and the distributed hash table network to ensure that enterprises providing network applications cannot privately store user interaction data, the encryption mechanism is responsible for encrypting all user data recorded in the network and allows users to control the key of the data to ensure the security of the user data in the blockchain and distributed hash tables, the data transaction mechanism allows users to trade their data, and to incentivize enterprises to assist users in collecting personal data, data transaction contracts are built into the data transaction mechanism, allowing enterprises to receive a share of the revenue from user data transactions. Then, for data transactions, use the Stackelberg game to simulate the revenue sharing between users and service providers in data trading to incentivize enterprises providing web services to assist users in collecting their data. The simulation results show that when the number of users is 1000, the revenues of this scheme for service providers are 31%, 561%, and 19% higher than the existing scheme. Finally, the personal data protection platform is implemented by code to verify the feasibility of the theory proposed in this paper in personal data protection.

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 Carbapenem-resistant Pseudomonas aeruginosa (CRPA) constitutes a serious source of global healthcare-associated infections, and the exploration of its resistance mechanism represents an important approach to address this issue. Because current research on antibiotic resistance predominantly focuses on multidrug-resistant P. aeruginosa which is widely isolated clinically and the resistance mechanism is complicated. CRPA generally has a higher tolerance to other antibiotics than carbapenem-sensitive P. aeruginosa , yet the specific mechanism of resistance remains poorly understood. Results This study delves into the specific antibiotic resistance mechanisms of carbapenem-resistance-only P. aeruginosa (CROPA), a rare kind of pathogen that shows resistance exclusively to carbapenem antibiotics. We collected 11 clinical isolates of CROPA, performed genome sequencing. Our analysis revealed numerous amino acid mutations and premature termination of OprD expression in the CROPA strains. The insertion of IS256 element into OprD in P. aeruginosa was a novel finding. Validation via qPCR and SDS-PAGE affirmed diminished OprD expression levels. Interestingly, common carbapenemases were not detected in our study, and there was no observed upregulation of relevant efflux pumps. The expression of wild-type OprD in CROPA strains restored the sensitivity to carbapenem antibiotics. Conclusions Compared with previous studies on MDR-CRPA, the emergence of CROPA may be directly linked to changes in OprD, while other resistance mechanisms could contribute to broader antibiotic resistance profiles. By focusing on the antibiotic resistance mechanisms of CROPA, this study illuminates the relationship between specific antibiotic resistance mechanisms and antibiotic resistance, providing a theoretical foundation for guiding clinical treatment and developing novel anti-infective agents. Highlights The focal point of this study rests on the uncommon CROPA strain. Compared with MDR-CRPA, the development of CROPA is directly linked to the inactivation of OprD. Reviving OprD enhances CROPA’s susceptibility to carbapenems.

Background Pancreatic cancer is the 12th most common type of cancer, and the sixth leading cause of cancer-related mortality, worldwide. Up-to-date statistics on pancreatic cancer would provide us with a better understanding of epidemiology and identify the causative risk factors for the prevention of this disease. Methods The degree and change patterns of exposure as well as the attributable cancer burden, including incidence, mortality, disability-adjusted life years (DALYs), and prevalence in global and regional, by sex, age, year, for pancreatic cancer, with the data extracted from the Global Burden of Diseases Study (GBD) 2021. All data analyses were conducted using linear regression analysis and the Joinpoint software (version 5.0.1). Results In 2021, 508,533 new cases of pancreatic cancer have been reported; the mortality and prevalence rate increased to 5.95, and 5.12 respectively; and the global DALYs rate increased to 130.33 this year. Besides, the pancreatic cancer-associated rates of incidence, mortality, DALYs, and prevalence were higher in males than in females. In addition, these indicators in the high SDI (Sociodemographic index) region were higher than the global mean. To date, the high fasting plasma glucose remained the major risk factor that influenced the incidence, mortality, DALYs, and prevalence of pancreatic cancer, followed by tobacco and high body mass index (BMI). Conclusions Results of this study suggest that the burden of pancreatic cancer is increasing generally, therefore, more attention and measures should be taken to cope with this situation.

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.

Transcranial direct current stimulation (tDCS) is an emerging brain intervention technique that has gained growing attention in recent years in the rehabilitation area. In this paper, we investigated the efficacy of tDCS in the rehabilitation process of stroke patients, utilizing corticomuscular coupling (CMC) and brain functional network analysis. Specifically, we examined changes in CMC relationships between the treatment and control groups before and after rehabilitation by transfer entropy (TE), and constructed brain functional networks by TE. We further calculated features of the functional networks, including node degree, global efficiency, clustering coefficient, characteristic path length, and small world index. Our results demonstrate that CMC in patients increased significantly after treatment, with greater improvements in the tDCS group, particularly within the beta and gamma bands. In addition, the functional brain network analysis revealed enhanced connectivity between brain regions, improved information processing capacity, and increased transmission efficiency in patients as their condition improved. Notably, treatment with tDCS resulted in more significant improvements than the sham group, with a statistically significant difference observed after rehabilitation treatment (p < 0.05). These findings provide compelling evidence regarding the role of tDCS in the treatment of stroke and highlight the potential of this approach in stroke rehabilitation.The use of tDCS for therapeutic interventions in stroke rehabilitation can significantly improve the coupling of patients' functional brain networks. Also, using Transfer Entropy (TE) as a characteristic of CMC, tDCS was found to significantly enhance patients' TE, i.e. enhanced CMC.

Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption. Artificial intelligence (AI) is becoming increasingly relevant to assist with oncology imaging, but diagnosing rare tumours remains challenging. Here, the authors develop an AI approach to detect rare thyroid cancer subtypes by integrating clinical knowledge with image generation based on ultrasound imaging data from large patient cohorts.

Abstract 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 bla NDM-5 carrying Citrobacter freundii (C. freundii) isolates from a patient with bloodstream and urinary tract infections. Results Antimicrobial susceptibility testing showed that both bla NDM-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 bla NDM-5 positive. Genotyping and comparative genomics analyses revealed that both isolates exhibited a high level of genetic similarity. Plasmid analysis confirmed that the bla NDM-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-bla NDM-5-trpF-IS26-umuD-ISKox3) was found in the upstream and downstream of the bla NDM-5 gene. Conclusions The data presented in this study showed that the conjugative bla NDM-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 bla NDM-5 gene from one single patient with multiple infections.

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve – receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676–0.766 and 0.633–0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.