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Mapping COVID-19 in India:Southern states at the forefront of new JN.1 variant

Asian Pacific Journal of Tropical Medicine 2024年第1期17卷 1-3页

作者： Rabin Debnath arshdeep singh Kushal Seni Anjali Sharma Viney Chawla Pooja A Chawla Department of Pharmaceutical Chemistry ISF College of PharmacyG.T RoadGhal KalanMogaPunjab142001India Department of Pharmaceutical Analysis ISF College of PharmacyG.T RoadGhal KalanMogaPunjab142001India University Institute of Pharmaceutical Sciences and Research Baba Farid University of Health SciencesFaridkotPunjabIndia

A new variant,JN.1,stemming from the omicron subvariant BA.2.86,garnered the attention of the World Health Organization(WHO)as a"variant of interest."Despite its rapid global spread,especially in the US,Canada,France,Singapore,Sweden[1],and the UK,JN.1 is considered to pose minimal danger.Current vaccinations are believed to remain effective against it.The WHO underscores the importance of maintaining immunization records amid co-occurring respiratory illnesses,and epidemiologists recommend monitoring hospitalizations,particularly in areas with low vaccination rates.

关键词： Canada Mapping spite

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Cardiovascular implications of inflammatory bowel disease: An updated review

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World Journal of Cardiology 2023年第11期15卷 553-570页

作者： Arshia Bhardwaj arshdeep singh Vandana Midha Ajit Sood Gurpreet singh Wander Bishav Mohan Akash Batta Department of Gastroenterology Dayanand Medical College and HospitalPunjabLudhiana 141001India Department of Internal Medicine Dayanand Medical College and HospitalPunjabLudhiana 141001India Department of Cardiology Dayanand Medical College and HospitalPunjabLudhiana 141001India

Emerging data highlights the heightened risk of atherosclerotic cardiovascular diseases(ASCVD)in patients with chronic inflammatory disorders,particularly those afflicted with inflammatory bowel disease(IBD).This review delves into the epidemiological connections between IBD and ASCVD,elucidating potential underlying mechanisms.Furthermore,it discusses the impact of current IBD treatments on cardiovascular risk.Additionally,the cardiovascular adverse effects of novel small molecule drugs used in moderate-to-severe IBD are investigated,drawing parallels with observations in patients with rheumatoid arthritis.This article aims to comprehensively evaluate the existing evidence supporting these associations.To achieve this,we conducted a meticulous search of PubMed,spanning from inception to August 2023,using a carefully selected set of keywords.The search encompassed topics related to IBD,such as Crohn’s disease and ulcerative colitis,as well as ASCVD,including coronary artery disease,cardiovascular disease,atrial fibrillation,heart failure,conduction abnormalities,heart blocks,and premature coronary artery disease.This review encompasses various types of literature,including retrospective and prospective cohort studies,clinical trials,meta-analyses,and relevant guidelines,with the objective of providing a comprehensive overview of this critical intersection of inflammatory bowel disease and cardiovascular health.

关键词： Inflammatory bowel diseases Cardiovascular disorders Pericarditis myocarditis Thromboembolism Chronic inflammation Oxidative stress Endothelial dysfunction

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Whispered Tuning: Data Privacy Preservation in Fine-Tuning LLMs through Differential Privacy

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Journal of Software Engineering and Applications 2024年第1期17卷 1-22页

作者： Tanmay singh Harshvardhan Aditya Vijay K. Madisetti arshdeep Bahga School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Cybersecurity and Privacy Georgia Institute of Technology Atlanta USA Cloudemy Technology Labs Chandigarh India

The proliferation of Large Language Models (LLMs) across various sectors underscored the urgency of addressing potential privacy breaches. Vulnerabilities, such as prompt injection attacks and other adversarial tactics, could make these models inadvertently disclose their training data. Such disclosures could compromise personal identifiable information, posing significant privacy risks. In this paper, we proposed a novel multi-faceted approach called Whispered Tuning to address privacy leaks in large language models (LLMs). We integrated a PII redaction model, differential privacy techniques, and an output filter into the LLM fine-tuning process to enhance confidentiality. Additionally, we introduced novel ideas like the Epsilon Dial for adjustable privacy budgeting for differentiated Training Phases per data handler role. Through empirical validation, including attacks on non-private models, we demonstrated the robustness of our proposed solution SecureNLP in safeguarding privacy without compromising utility. This pioneering methodology significantly fortified LLMs against privacy infringements, enabling responsible adoption across sectors.

关键词： NLP Differential Privacy Adversarial Attacks Informed Decisions

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Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

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Journal of Software Engineering and Applications 2024年第5期17卷 421-447页

作者： Harshvardhan Aditya Siddansh Chawla Gunika Dhingra Parijat Rai Saumil Sood Tanmay singh Zeba Mohsin Wase arshdeep Bahga Vijay K. Madisetti School of Computer Science Engineering & Technology Bennett University Greater Noida India Cloudemy Technology Labs Chandigarh India School of Cybersecurity and Privacy Georgia Institute of Technology Atlanta USA

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

关键词： Large Language Models PII Leakage Privacy Memorization Overfitting Membership Inference Attack (MIA)

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