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.
{"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}
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 IL-1 and IL-6 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.
{"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}
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.
{"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}
Edouard L. Fu, Marco Trevisan, Vivek Lanka, C. Clase, Yang Xu, M. van Diepen, F. Dekker, M. Jardine, J. Carrero
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
背景:虽然临床试验已经证明SGLT2抑制剂(SGLT2i)在预防心血管和肾脏损害方面的有效性，但很少有高质量的研究扩展到低风险患者的常规护理设置。以前的观察性研究受到不朽时间偏差的限制，或者没有对实验室测量进行调整。方法:我们比较了2013-2019年在瑞典斯德哥尔摩开始SGLT2i或DPP4i治疗的成年人的临床结果。主要结局是心血管(CV)死亡和因心力衰竭(HHF)住院的综合结果。次要结局包括主要不良心血管事件(MACE)、全因死亡率、心房颤动、高钾血症和肾脏疾病进展(复合性肾衰竭和血清肌酐加倍)。采用倾向评分加权Cox回归估计风险比，平衡56个协变量。结果:我们纳入了16,537例个体(5526例SGLT2i;11,011名DPP4i用户)，平均随访1.9年。年龄中位数为64岁(36%为女性)，肾小球滤过率中位数为87 ml/min/1.73m2, 31%患有蛋白尿。加权后，开始SGLT2i治疗的患者CV死亡/HHF复合风险较低(HR 0.65;95% CI 0.47-0.89)和高钾血症(HR 0.41;95% CI 0.20-0.83)，与DPP4i相比，低钾血症未增加(HR 0.98;95% ci 0.72-1.34)。MACE校正后的hr (95% CI)为0.82(0.64-1.06)，全因死亡率为0.74(0.52-1.06)，房颤为0.95(0.68-1.33)，肾病进展为0.54(0.27-1.08)。结论:与DPP4i相比，SGLT2i的使用与心血管和肾脏预后的降低有关，其程度与试验相似，并且高钾血症的风险较低。资助:本出版物中报道的研究得到了瑞典研究理事会(#2019-01059)、瑞典心肺基金会和韦斯特曼基金会的支持。ELF得到了荷兰科学研究组织(NWO)的卢比孔河基金的支持。利益声明:JC承认为百特和阿斯利康提供咨询服务，并同意阿斯利康、Viforpharma和阿斯泰来为卡罗林斯卡研究所提供支持，所有这些都在提交的工作之外。CMC已获得安大略省卫生部、赛诺菲、强生、辉瑞、利奥制药、安斯泰来、杨森、安进、勃林格殷格翰和百特的咨询、顾问委员会成员或研究资助，所有这些都是提交的工作。其他作者均未声明存在利益冲突的相关经济利益。MJJ负责的研究项目获得了Gambro、Baxter、Commonwealth Serum Laboratories (CSL)、Amgen、Eli Lilly和Merck的无限制资助;曾在阿克比亚、百特、勃林格殷格翰、CSL、杨森和Vifor赞助的咨询委员会和指导委员会任职;并在杨森、安进和罗氏赞助的科学会议上发言，她的机构获得任何咨询、酬金或旅行支持。伦理批准声明:该研究仅使用了去识别数据，因此被认为不需要知情同意，并得到了区域伦理审查委员会和瑞典国家福利委员会的批准。
{"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}
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