{"title":"The Role of Disease-Associated Microglia in Neurodegenerative Disease: A Review","authors":"Victoria Labuda, Masilan A. Sundara","doi":"10.26685/urncst.575","DOIUrl":"https://doi.org/10.26685/urncst.575","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"118 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017331","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":"Examining the Neural Basis of Pain Tolerance and Fearlessness About Death in Suicide Risk: A Research Protocol","authors":"Sarina Rain","doi":"10.26685/urncst.555","DOIUrl":"https://doi.org/10.26685/urncst.555","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221425","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}
Introduction: Arthritis is one of the most common chronic diseases. Early detection of arthritis and its progression can facilitate early intervention measures, lowering disease severity in patients. As electronic health records (EHR) become more accessible, this study assesses whether general health information and arthritis-related questionnaires can be used in arthritis diagnosis, without the involvement of costly imaging methods. Therefore, we created deep learning (DL) and machine learning (ML) models to explore the feasibility of combining EHR and modern computational tools to diagnose arthritis. Methods: A total of 782 arthritis patients and 4014 control patients were identified from the Osteoarthritis Initiative (OAI) – a ten-year-long observational study that included patient EHR in five time points. Six hundred variables were filtered by random forest classifier followed by manual filtering. Data were split properly to training, testing and validation set, and the training set was balanced. Sequential, nonsequential DL models, and five independent DL models for each time points were used. The accuracy, positive prevalence value (PPV), negative prevalence value (NPV), and area under curve (AUC), were assessed and compared with four classical ML models. SHAP (SHapley Additive exPlanations) summary analysis was also conducted. Results: Sequential and non-sequential deep learning models showed accuracies of ~ 0.97, and the four classical machine learning approaches showed accuracies of above 0.9. High positive and negative predicted values (> 0.90) for all of the models suggested the potential clinical applicability of the model, while the SHAP analysis demonstrated its interpretability. Discussion: We tested various models and showed the ability to use machine learning methods for early diagnosis of arthritis with EHR. The models can be used as a screening tool to select susceptible patients for confirmatory tests such as X-ray and MRI. Identification of early disease states could facilitate protective measures that slow disease progression.
{"title":"Exploring the Feasibility of Applying Deep Learning for the Early Prediction of Arthritis","authors":"Jiaxuan Chen, Xiangxuan Kong","doi":"10.26685/urncst.562","DOIUrl":"https://doi.org/10.26685/urncst.562","url":null,"abstract":"Introduction: Arthritis is one of the most common chronic diseases. Early detection of arthritis and its progression can facilitate early intervention measures, lowering disease severity in patients. As electronic health records (EHR) become more accessible, this study assesses whether general health information and arthritis-related questionnaires can be used in arthritis diagnosis, without the involvement of costly imaging methods. Therefore, we created deep learning (DL) and machine learning (ML) models to explore the feasibility of combining EHR and modern computational tools to diagnose arthritis. Methods: A total of 782 arthritis patients and 4014 control patients were identified from the Osteoarthritis Initiative (OAI) – a ten-year-long observational study that included patient EHR in five time points. Six hundred variables were filtered by random forest classifier followed by manual filtering. Data were split properly to training, testing and validation set, and the training set was balanced. Sequential, nonsequential DL models, and five independent DL models for each time points were used. The accuracy, positive prevalence value (PPV), negative prevalence value (NPV), and area under curve (AUC), were assessed and compared with four classical ML models. SHAP (SHapley Additive exPlanations) summary analysis was also conducted. Results: Sequential and non-sequential deep learning models showed accuracies of ~ 0.97, and the four classical machine learning approaches showed accuracies of above 0.9. High positive and negative predicted values (> 0.90) for all of the models suggested the potential clinical applicability of the model, while the SHAP analysis demonstrated its interpretability. Discussion: We tested various models and showed the ability to use machine learning methods for early diagnosis of arthritis with EHR. The models can be used as a screening tool to select susceptible patients for confirmatory tests such as X-ray and MRI. Identification of early disease states could facilitate protective measures that slow disease progression.","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"58 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234206","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}
Hypertrophic cardiomyopathy (HCM), a genetic cardiovascular disease, is the leading cause of cardiac death in young people, often due to atrial fibrillation (AF). AF is generally treated using antiarrhythmics and anticoagulants, which have adverse side effects after long-term use, and are therefore unsuitable for young HCM patients. AF is characterized by a rapid and irregular atrial heartbeat, marked by a short action potential duration and atrial effective refractory period in atrial cardiomyocytes. Prior studies have indicated that the renin-angiotensin system is involved in lowering both, so it has been hypothesized that angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), which inhibit the renin-angiotensin system, could prevent AF. Therefore, in this study, we propose a research protocol to examine the viability of ACE inhibitors and ARBs as prophylactic measures against the development of AF in HCM patients. To test this, we suggest extracting atrial cardiomyocytes from HCM patients by performing enzyme dissociation on myocardial tissue. The isolated cardiomyocytes will then be treated in vitro with an ACE inhibitor, an ARB, a combination of both, or a control saline solution, and the patch-clamp technique will be used to determine the frequency and duration of their action potentials. We expect action potential duration and atrial effective refractory period to be longer in treated cells, while neither medication will provide a greater advantage, and, as prior research suggests, the combination will not yield significant benefits. The study will continue by testing the effects of ACE inhibitors and ARBs on the function of atrial myocardial organoids created from differentiated stem cells with an HCM mutation. The results of this study could present a new preventative measure against AF for HCM patients which would be safe for long-term use.
{"title":"Preventing Atrial Fibrillation in Hypertrophic Cardiomyopathy using Angiotensin-Converting Enzyme (ACE) Inhibitors and Angiotensin Receptor Blockers (ARBs)","authors":"Abiramee Kathirgamanathan, Akshita Nair","doi":"10.26685/urncst.543","DOIUrl":"https://doi.org/10.26685/urncst.543","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM), a genetic cardiovascular disease, is the leading cause of cardiac death in young people, often due to atrial fibrillation (AF). AF is generally treated using antiarrhythmics and anticoagulants, which have adverse side effects after long-term use, and are therefore unsuitable for young HCM patients. AF is characterized by a rapid and irregular atrial heartbeat, marked by a short action potential duration and atrial effective refractory period in atrial cardiomyocytes. Prior studies have indicated that the renin-angiotensin system is involved in lowering both, so it has been hypothesized that angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), which inhibit the renin-angiotensin system, could prevent AF. Therefore, in this study, we propose a research protocol to examine the viability of ACE inhibitors and ARBs as prophylactic measures against the development of AF in HCM patients. To test this, we suggest extracting atrial cardiomyocytes from HCM patients by performing enzyme dissociation on myocardial tissue. The isolated cardiomyocytes will then be treated in vitro with an ACE inhibitor, an ARB, a combination of both, or a control saline solution, and the patch-clamp technique will be used to determine the frequency and duration of their action potentials. We expect action potential duration and atrial effective refractory period to be longer in treated cells, while neither medication will provide a greater advantage, and, as prior research suggests, the combination will not yield significant benefits. The study will continue by testing the effects of ACE inhibitors and ARBs on the function of atrial myocardial organoids created from differentiated stem cells with an HCM mutation. The results of this study could present a new preventative measure against AF for HCM patients which would be safe for long-term use.","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"7 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241140","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}
Sanjmi Khurana, Sophia Joulaei, Alishba Mansoor, Esther Zhou, Anne Chow, Anne Huynh
{"title":"WISE National Conference 2024: Endless Exploration","authors":"Sanjmi Khurana, Sophia Joulaei, Alishba Mansoor, Esther Zhou, Anne Chow, Anne Huynh","doi":"10.26685/urncst.586","DOIUrl":"https://doi.org/10.26685/urncst.586","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"71 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239788","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}
Lisa R. MacLeod, Carina Lopez, Aduratomi Etuk, Prabashi Wickramasinghe
{"title":"Richard E. Peter 15th Annual Biology Conference (2024)","authors":"Lisa R. MacLeod, Carina Lopez, Aduratomi Etuk, Prabashi Wickramasinghe","doi":"10.26685/urncst.585","DOIUrl":"https://doi.org/10.26685/urncst.585","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"427 2‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246875","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}
Eileen Danaee, Adam Renato Carbonara, Neleah Lavoie
{"title":"2024 NeuGeneration Case Competition: Neurodiversity","authors":"Eileen Danaee, Adam Renato Carbonara, Neleah Lavoie","doi":"10.26685/urncst.583","DOIUrl":"https://doi.org/10.26685/urncst.583","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248549","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}
Introduction: Regulatory T cells (Tregs) are a subpopulation of CD4+ T lymphocytes that contribute to immune homeostasis by suppressing excessive immune activation. However, these immunosuppressive properties can lead to the suppression of anti-tumor immune responses. Depletion or blocking of Tregs through therapeutics has emerged as a possible method for enhancing anti-tumor immunity. However, the lack of selective targeting of Tregs in the tumor microenvironment is a significant limitation to the effectiveness of Treg therapies. Therefore, this investigation aims to review current literature on how Tregs suppress the antitumor immune response and how they can be targeted to promote anti-tumor immunity. Methods: This review examines recent literature on Tregs in the tumor microenvironment, focusing on both cell-contact dependent and independent mechanisms. Clinical trial studies were also included to assess therapeutic targeting of Tregs. The PubMed database was systematically searched for English articles from 2010 to present, supplemented by manual searches without date restrictions. Boolean expressions ensured comprehensive study retrieval. Results: The involvement of Tregs in the development of multiple cancer types is evident, and targeting these cells could potentially enhance the efficacy of antitumor immunity. In addition, we compiled a list of the novel approaches currently being used for Treg targeting in the context of cancer. Discussion: This review has identified the most promising targets for Treg-based therapies, opening avenues for accelerating the development of innovative cancer treatments. Conclusion: Our literature review offers insights into the complex interplay between the immune system and cancer. The understanding of this interaction is not just an endpoint but could potentially act as a steppingstone towards new scientific discoveries.
简介调节性 T 细胞（Tregs）是 CD4+ T 淋巴细胞的一个亚群，它通过抑制过度的免疫激活来促进免疫平衡。然而，这些免疫抑制特性会导致抗肿瘤免疫反应受到抑制。通过疗法消耗或阻断Tregs已成为增强抗肿瘤免疫力的一种可能方法。然而，缺乏对肿瘤微环境中 Tregs 的选择性靶向是 Treg 疗法有效性的一大限制。因此，本研究旨在回顾目前有关 Tregs 如何抑制抗肿瘤免疫反应以及如何靶向 Tregs 促进抗肿瘤免疫的文献。方法：本综述研究了肿瘤微环境中 Tregs 的最新文献，重点关注细胞接触依赖机制和独立机制。此外还包括临床试验研究，以评估Tregs的治疗靶点。本文在PubMed数据库中系统检索了2010年至今的英文文章，并辅以无日期限制的人工检索。布尔表达确保了研究检索的全面性。结果Tregs参与多种癌症类型的发展是显而易见的，以这些细胞为靶点有可能提高抗肿瘤免疫的效果。此外，我们还汇编了一份目前用于癌症Treg靶向的新方法清单。讨论：本综述为基于 Treg 的疗法确定了最有前景的靶点，为加速开发创新癌症疗法开辟了道路。结论我们的文献综述让我们深入了解了免疫系统与癌症之间复杂的相互作用。对这种相互作用的理解不仅仅是一个终点，还有可能成为新科学发现的垫脚石。
{"title":"The Role of Regulatory T cells (Tregs) in Tumorigenesis: A Comprehensive Literature Review","authors":"Kian Torabiardakani","doi":"10.26685/urncst.517","DOIUrl":"https://doi.org/10.26685/urncst.517","url":null,"abstract":"Introduction: Regulatory T cells (Tregs) are a subpopulation of CD4+ T lymphocytes that contribute to immune homeostasis by suppressing excessive immune activation. However, these immunosuppressive properties can lead to the suppression of anti-tumor immune responses. Depletion or blocking of Tregs through therapeutics has emerged as a possible method for enhancing anti-tumor immunity. However, the lack of selective targeting of Tregs in the tumor microenvironment is a significant limitation to the effectiveness of Treg therapies. Therefore, this investigation aims to review current literature on how Tregs suppress the antitumor immune response and how they can be targeted to promote anti-tumor immunity. Methods: This review examines recent literature on Tregs in the tumor microenvironment, focusing on both cell-contact dependent and independent mechanisms. Clinical trial studies were also included to assess therapeutic targeting of Tregs. The PubMed database was systematically searched for English articles from 2010 to present, supplemented by manual searches without date restrictions. Boolean expressions ensured comprehensive study retrieval. Results: The involvement of Tregs in the development of multiple cancer types is evident, and targeting these cells could potentially enhance the efficacy of antitumor immunity. In addition, we compiled a list of the novel approaches currently being used for Treg targeting in the context of cancer. Discussion: This review has identified the most promising targets for Treg-based therapies, opening avenues for accelerating the development of innovative cancer treatments. Conclusion: Our literature review offers insights into the complex interplay between the immune system and cancer. The understanding of this interaction is not just an endpoint but could potentially act as a steppingstone towards new scientific discoveries.","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"61 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252030","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":"Mechanisms of AML1-ETO Induced Transcription Factor Dysregulation, Epigenetic Modification, and Immune System Evasion in Pre-Leukemic Stem Cells","authors":"Karoll Kaveen Thanaraj, Likitha Busanelli","doi":"10.26685/urncst.542","DOIUrl":"https://doi.org/10.26685/urncst.542","url":null,"abstract":"","PeriodicalId":245521,"journal":{"name":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","volume":"32 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257547","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：