Discover medicine最新文献

{"title":"Prevalence and factors associated with respiratory symptoms in an urban population: findings from BOLD study in Karachi, Pakistan.","authors":"Asaad Ahmed Nafees, Adeel Ahmed Khan, Unaib Rabbani, Aysha Zahidie, Rooman Ul Haq, Muhammad Irfan, Andre Amaral","doi":"10.1007/s44337-025-00432-4","DOIUrl":"10.1007/s44337-025-00432-4","url":null,"abstract":"<p><strong>Objective: </strong>Our study investigated the prevalence and risk factors of respiratory symptoms (RS) in an adult population aged ≥ 40 years in urban Karachi, Pakistan.</p><p><strong>Methods: </strong>A population based cross-sectional study was conducted. Multi-stage cluster random sampling technique was used to enroll 1052 participants. Questionnaire based on the Burden of Obstructive Lung Disease (BOLD) study protocol was used to assess chronic cough, chronic phlegm, wheezing and shortness of breath (SOB). Logistic regression analyses were conducted to determine the risk factors associated with RS.</p><p><strong>Results: </strong>The overall weighted prevalence of RS in our study was; chronic cough: 12% (95% CI: 9.7-14.8), chronic phlegm: 11.8% (95% CI: 9.4-14.7), wheezing in last 12 months: 11.5% (95% CI: 9.4-14.0), and shortness of breath (SOB): 30.4% (95% CI: 25.6-35.7). Increasing age and ever smoker were significant risk factors for all RS. Females had higher risk of chronic cough. Lower level of education and higher BMI were associated with wheezing and SOB. Exposure to passive smoking was found to be significant risk factor for SOB.</p><p><strong>Conclusion: </strong>We found a higher prevalence of respiratory symptoms among adult population in urban Karachi, specifically about 30.4% reported shortness of breath (SOB). Certain preventable risk factors (smoking and being exposed to passive smoking) were identified. Targeted interventions such as community education and localized air quality monitoring could help mitigate the burden of respiratory symptoms.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s44337-025-00432-4.</p>","PeriodicalId":520361,"journal":{"name":"Discover medicine","volume":"2 1","pages":"194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal skeletal muscle and myocardial vasoreactivity manifests prior to heart failure in a diabetic cardiomyopathy rat model.","authors":"Sadi Loai, Hai-Ling Margaret Cheng","doi":"10.1007/s44337-025-00192-1","DOIUrl":"https://doi.org/10.1007/s44337-025-00192-1","url":null,"abstract":"<p><strong>Background: </strong>Microvascular dysfunction (MVD) is a recognized sign of disease in heart failure progression. Intact blood vessels exhibit abnormal vasoreactivity in early stage, subsequently deteriorating to rarefaction and reduced perfusion. In managing heart failure with preserved ejection fraction (HFpEF), earlier diagnosis is key to improving management. In this study, we applied a steady-state blood-pool magnetic resonance imaging (MRI) method to investigate if it can sensitively detect abnormal leg muscle vasoreactivity, a sign of MVD, posited to manifest before structural and functional cardiac changes emerge in a diabetes model of HFpEF.</p><p><strong>Methods: </strong>Male and female Sprague-Dawley rats were maintained on either a high-fat, high-sugar diet or a control diet for 6 months after the induction of diabetes (<i>n</i> = 5 per group). Beginning at month 1 or 2 post-diabetes and every 2 months thereafter, rats underwent steady-state blood-pool MRI to assess vasoreactivity in the heart or skeletal muscle, respectively. A T1-reducing blood-pool agent was administered and the T1 relaxation time dynamically measured as animals breathed in elevated CO₂ levels to modulate vessels.</p><p><strong>Results: </strong>In male rats, the normally unresponsive heart to 10% CO₂ revealed a pro-vasoconstriction response beginning at 5 months post-diabetes. Abnormal leg skeletal muscle vasoreactivity appeared even earlier, at 2 months: the usual vasodilatory response to 5% CO₂ was interrupted with periods of vasoconstriction in diseased rats. In female rats, differences were observed between healthy and diseased animals only in the first 2 months post-diabetes and not later. In the heart, vasodilation to 10% CO₂ seen in healthy females was abolished in diabetic females. In skeletal muscle, 5% CO₂ was suboptimal in inducing reproducible vasoreactivity, but young diabetic females responded by vasodilation only.</p><p><strong>Conclusions: </strong>Abnormal vasoreactivity presented earlier than overt functional changes in both heart and skeletal muscle in diabetic cardiomyopathy, and steady-state blood-pool MRI offered early diagnosis of microvascular dysfunction.</p>","PeriodicalId":520361,"journal":{"name":"Discover medicine","volume":"2 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel recommender framework with chatbot to stratify heart attack risk.","authors":"Tursun Wali, Almat Bolatbekov, Ehesan Maimaitijiang, Dilbar Salman, Yasin Mamatjan","doi":"10.1007/s44337-024-00174-9","DOIUrl":"https://doi.org/10.1007/s44337-024-00174-9","url":null,"abstract":"<p><p>Cardiovascular diseases are a major cause of mortality and morbidity. Fast detection of life-threatening emergency events and an earlier start of the therapy would save many lives and reduce successive disabilities. Understanding the specific risk factors associated with heart attack and the degree of association is crucial in the clinical diagnosis. Considering the potential benefits of intelligent models in healthcare, many researchers have developed a variety of machine learning (ML)-based models to identify patients at risk of a heart attack. However, the common problem of previous works that used ML concepts was the lack of transparency in black-box models, which makes it difficult to understand how the model made the prediction. In this study, an automated smart recommender system (Explainable Artificial Intelligence) for heart attack prediction and risk stratification was developed. For the purpose, the CatBoost classifier was applied as the initial step. Then, the SHAP (SHapley Additive exPlanation) explainable algorithm was employed to determine reasons behind high or low risk classification. The recommender system can provide insights into the reasoning behind the predictions, including group-based and patient-specific explanations. In the final step, we integrated a Large Language Model (LLM) called BioMistral for chatting functionally to talk to users based on the model output as a digital doctor for consultation. Our smart recommender system achieved high accuracy in predicting a patient risk level with an average AUC of 0.88 and can explain the results transparently. Moreover, a Django-based online application that uses patient data to update medical information about an individual's heart attack risk was created. The LLM chatbot component would answer user questions about heart attacks and serve as a virtual companion on the route to heart health, our system also can locate nearby hospitals by applying Google Maps API and alert the users. The recommender system could improve patient management and lower heart attack risk while timely therapy aids in avoiding subsequent disabilities.</p>","PeriodicalId":520361,"journal":{"name":"Discover medicine","volume":"1 1","pages":"161"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}