Cardiovascular Medicine eJournal最新文献

{"title":"Heart Failure Prediction Using Machine Learning Techniques","authors":"P. K. Sahoo, Pravalika Jeripothula","doi":"10.2139/ssrn.3759562","DOIUrl":"https://doi.org/10.2139/ssrn.3759562","url":null,"abstract":"In this modern era people are very busy and working hard in order to satisfying their materialistic needs and not able to spend time for themselves which leads to physical stress and mental disorder. There are also reports that heart suffer because of global pandemic corona virus. Inflammation of the heart muscle can be caused by corona virus. Thus heart disease is very common now a day’s particularly in urban areas because of excess mental stress due to corona virus. As a result Heart disease has become one of the most important factors for death of men and women in the so called material world. It has emerged as the top killer that has affected both urban and rural population. CAD (Coronary artery disease) is one of the most common types of heart disease. In the medical field predicting the heart disease has become a very complicated and challenging task, requires patient previous health records and in some cases they even need Genetic information as well. So, in this contemporary life style there is an urgent need of a system which will predict accurately the possibility getting heart disease. Predicting a Heart Disease in early stage will save many people’s Life. There were many heart disease prediction systems available at present, the Authors have been researched well and proposed different Classification and prediction algorithms but each one has its own limitations. The main objective of this paper is to overcome the limitations and to design a robust system which works efficiently and will able to predict the possibility of heart failure accurately. This paper uses the data set from the UCI repository and having 13 important attributes. This work is implemented using many algorithms such as SVM, Naive Bayes, Logistic Regression, Decision Tree and KNN. It is found that SVM gave the best result with accuracy up to 85.2%. A comparative statement of all the algorithms also presented in the implementation part of the paper. This research also uses model validation technique to design a best suitable model fitting in the current scenario.","PeriodicalId":166464,"journal":{"name":"Cardiovascular Medicine eJournal","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131070820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"p62/SQSTM1 Accumulation Resulting from Degradation Inhibition and Transcriptional Activation is Essential in Silica Nanoparticle-Induced Pulmonary Inflammation Through NF-κB Activation","authors":"Yifan Wu, Yang Jin, Tianyu Sun, Piaoyu Zhu, Jinlong Li, Qingling Zhang, Xiaoke Wang, Yu Han, Junkang Jiang, Gang Chen, Xinyuan Zhao","doi":"10.2139/ssrn.3446990","DOIUrl":"https://doi.org/10.2139/ssrn.3446990","url":null,"abstract":"Most nanoparticles (NPs) are reported to block autophagic flux, accompanied by accumulated p62/SQSTM1 resulting from degradation inhibition. p62 also acts as a multifunctional scaffold protein that contains multiple domains, involved in various cellular processes. However, the autophagy substrate-independent role and regulation at a transcriptional level of p62 upon NPs exposure are ignored. Here, we exposed BEAS-2b cells to silica nanoparticles (SiNPs), and found that p62 degradation was inhibited due to autophagic flux blockade. Mechanically, SiNPs blocked autophagy flux through lysosomal capacity impairment rather than defective autophagosome fusion with lysosomes. Moreover, SiNPs stimulated translocation of NF-E2-related factor 2 (Nrf2) to the nucleus from the cytoplasm, and upregulated p62 transcriptional activation through direct binding of Nrf2 to p62 promoter. Nrf2 siRNA dramatically decreased both mRNA and protein levels of p62. Above two mechanisms led to p62 protein accumulation, therefore increasing <i>IL-1</i> and <i>IL-6</i> expression. SiNPs activated nuclear Factor kappa B (NF-κB), which can be alleviated by p62 knockdown. In summary, SiNPs accumulated p62 by both pre- and post-translational mechanisms, resulting in pulmonary inflammation. These findings improve our understanding of SiNP-induced pulmonary damage and molecular targets to antagonise it.","PeriodicalId":166464,"journal":{"name":"Cardiovascular Medicine eJournal","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122378255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not a Good Association: Diary Intake and Cardiovascular Disease in the PURE Study","authors":"S. Lindner","doi":"10.2139/ssrn.3355844","DOIUrl":"https://doi.org/10.2139/ssrn.3355844","url":null,"abstract":"In this comment, I argue that a recent study on dairy food intake and cardiovascular disease published in The Lancet is misleading because the authors fail to account for an important confounder.","PeriodicalId":166464,"journal":{"name":"Cardiovascular Medicine eJournal","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126727683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Effectiveness of SGLT2i Versus DPP4i on Cardiovascular, Kidney and Hyperkalemia Outcomes in Individuals from Routine Clinical Practice: Observational Cohort Study","authors":"Edouard L. Fu, Marco Trevisan, Vivek Lanka, C. Clase, Yang Xu, M. van Diepen, F. Dekker, M. Jardine, J. Carrero","doi":"10.2139/ssrn.3947641","DOIUrl":"https://doi.org/10.2139/ssrn.3947641","url":null,"abstract":"Background: While clinical trials have demonstrated efficacy for SGLT2 inhibitors (SGLT2i) on preventing cardiovascular and kidney damage, few high-quality studies have expanded to routine-care settings of low-risk patients. Previous observational studies were limited by immortal time bias or did not adjust for laboratory measurements. Methods: We compared clinical outcomes of adults who started SGLT2i or DPP4i therapy in Stockholm, Sweden, during 2013-2019. The primary outcome was a composite of cardiovascular (CV) death and hospitalization for heart failure (HHF). Secondary outcomes included major adverse cardiovascular events (MACE), all-cause mortality, atrial fibrillation, hyperkalemia and kidney disease progression (composite kidney failure and doubling of serum creatinine). Propensity score weighted Cox regression was used to estimate hazard ratios and balance 56 covariates. Results: We included 16,537 individuals (5526 SGLT2i; 11,011 DPP4i users), followed for median 1.9 years. Median age was 64 years (36% women), median estimated glomerular filtration rate 87 ml/min/1.73m2 and 31% had albuminuria. After weighting, patients starting SGLT2i therapy were at lower risk for the composite of CV death/HHF (HR 0.65; 95% CI 0.47-0.89) and hyperkalemia (HR 0.41; 95% CI 0.20-0.83) compared with DPP4i, without an increase in hypokalemia (HR 0.98; 95% CI 0.72-1.34). The adjusted HRs (95% CI) were 0.82 (0.64-1.06) for MACE, 0.74 (0.52-1.06) for all-cause mortality, 0.95 (0.68-1.33) for atrial fibrillation and 0.54 (0.27-1.08) for kidney disease progression. Conclusions: SGLT2i use compared with DPP4i was associated with a reduction in cardiovascular and kidney outcomes similar in magnitude to trials, as well as a lower risk of hyperkalemia. Funding: Research reported in this publication was supported by the Swedish Research Council (#2019-01059), the Swedish Heart and Lung Foundation and the Westman Foundation. ELF acknowledges support by a Rubicon Grant of the Netherlands Organization for Scientific Research (NWO). Declaration of Interests: JC acknowledges consultancy for Baxter and AstraZeneca, and grant support to Karolinska Institutet from AstraZeneca, Viforpharma and Astellas, all outside the submitted work. CMC has received consultation, advisory board membership or research funding from the Ontario Ministry of Health, Sanofi, Johnson & Johnson, Pfizer, Leo Pharma, Astellas, Janssen, Amgen, Boehringer-Ingelheim and Baxter, all outside the submitted work. None of the other authors declare relevant financial interests that would represent a conflict of interest. MJJ is responsible for research programs that have received unrestricted funding from Gambro, Baxter, Commonwealth Serum Laboratories (CSL), Amgen, Eli Lilly, and Merck; has served on advisory boards and steering committees sponsored by Akebia, Baxter, Boehringer Ingelheim, CSL, Janssen, and Vifor; and spoken at scientific meetings sponsored by Janssen, Amgen, and Roche, with any consultan","PeriodicalId":166464,"journal":{"name":"Cardiovascular Medicine eJournal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}