Development of Automated Triggers in Ambulatory Settings in Brazil: Research Protocol for Design Thinking and Machine Learning (Preprint) DOI Creative Commons
Claire Nierva Herrera, Fernanda Raphael Escobar Gimenes, João Paulo Herrera

et al.

JMIR Research Protocols, Journal Year: 2024, Volume and Issue: 13, P. e55466 - e55466

Published: June 17, 2024

Background The use of technologies has had a significant impact on patient safety and the quality care increased globally. In literature, it been reported that people die annually due to adverse events (AEs), various methods exist for investigating measuring AEs. However, some have limited scope, data extraction, need standardization. Brazil, there are few studies application trigger tools, this study is first create automated triggers in ambulatory care. Objective This aims develop machine learning (ML)–based outpatient health settings Brazil. Methods A mixed research will be conducted within design thinking framework principles applied creating triggers, following stages (1) empathize define problem, involving observations inquiries comprehend both user challenge at hand; (2) ideation, where solutions problem generated; (3) prototyping, construction minimal representation best solutions; (4) testing, feedback obtained refine solution; (5) implementation, refined solution tested, changes assessed, scaling considered. Furthermore, ML adopted tailored local context collaboration with an expert field. Results protocol describes its preliminary stages, prior any gathering analysis. was approved by members organizations institution January 2024 ethics board University São Paulo take place. May 2024. As June 2024, stage 1 commenced qualitative research. separate paper focused explaining method considered after outcomes 2 study. Conclusions After development setting, possible prevent identify potential risks AEs more promptly, providing valuable information. technological innovation not only promotes advances clinical practice but also contributes dissemination techniques knowledge related safety. Additionally, professionals can adopt evidence-based preventive measures, reducing costs associated hospital readmissions, enhancing productivity care, contributing safety, quality, effectiveness provided. future, if outcome successful, apply all units, as planned institutional organization. International Registered Report Identifier (IRRID) PRR1-10.2196/55466

Language: Английский

Emerging Role of Mesenchymal Stromal Cell and Exosome Therapies in Treating Cognitive Impairment DOI Creative Commons

Vick Key Tew,

Muttiah Barathan, Fazlina Nordin

et al.

Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(3), P. 284 - 284

Published: Feb. 20, 2025

Cognitive aging, characterized by the gradual decline in cognitive functions such as memory, attention, and problem-solving, significantly impacts daily life. This is often accelerated neurodegenerative diseases, particularly Alzheimer’s Disease (AD) Parkinson’s (PD). AD marked accumulation of amyloid-beta plaques tau tangles, whereas PD involves degeneration dopaminergic neurons. Both conditions lead to severe impairment, greatly diminishing quality life for affected individuals. Recent advancements regenerative medicine have highlighted mesenchymal stromal cells (MSCs) their derived exosomes promising therapeutic options. MSCs possess regenerative, neuroprotective, immunomodulatory properties, which can promote neurogenesis, reduce inflammation, support neuronal health. Exosomes, nanosized vesicles from MSCs, provide an efficient means delivering bioactive molecules across blood–brain barrier, targeting underlying pathologies PD. While these therapies hold great promise, challenges variability MSC sources, optimal dosing, effective delivery methods need be addressed clinical application. The development robust protocols, along with rigorous trials, crucial validating safety efficacy exosome therapies. Future research should focus on overcoming barriers, optimizing treatment strategies, exploring integration lifestyle interventions. By addressing challenges, MSC- exosome-based could offer transformative solutions improving outcomes enhancing individuals aging diseases.

Language: Английский

Citations

0
IADL for identifying cognitive impairment in Chinese older adults: insights from cross-lagged panel network analysis DOI Creative Commons

X. Y. Zhai,

Ruizhe Wang, Ran Liu

et al.

BMC Geriatrics, Journal Year: 2025, Volume and Issue: 25(1)

Published: May 22, 2025

As China has entered an aging society, the prevention of cognitive impairment is great importance. The progression usually a slow and continuous process, with Instrumental Activities Daily Living (IADL) serving as sensitive indicator for early prediction decline. objective this study was to utilize longitudinal network analysis pinpoint most indicators IADLs identify in different populations, offer practical recommendations preventing among older adults China. A total 2,781 participants were selected from Chinese Longitudinal Healthy Longevity Survey (CLHLS 2014-2018). Cognitive function assessed by Mini-mental State Examination (MMSE) modified Lawton scale, respectively. In study, cross-lagged panel (CLPN) model employed construct three separate networks all adults, male female Two centrality indices used quantify symptom directed CLPN: In-Expected-Influence (IEI) Out-Expected-Influence (OEI). networks, "Use public transit," "Make food" "Walk 1 km" emerged influential important indicators. edge transit → Attention Calculation" strongest connection networks. Among adult males, "General ability" exhibited influence on other domains, followed "Language," while "Attention had weaker influence. Conversely, females, factor, "Language." This provides new insights into associations between specific IADL activities domains adults. Concentrate monitoring limitations related km," promoting broader life-space mobility may be beneficial decline function. findings underscore importance targeting interventions not only but also potentially gender. Not applicable.

Language: Английский

Citations

0
Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study DOI Creative Commons
Collin Sakal, Ting Li, Juan Li

et al.

npj Aging, Journal Year: 2024, Volume and Issue: 10(1)

Published: Nov. 25, 2024

Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring detect changes in function. Factors known be associated with cognition that can gathered from accelerometers, user interfaces, and other sensors within wearable devices could used train machine learning models develop wearable-based systems. Using data over 2400 the National Health Nutrition Examination Survey (NHANES) we developed prediction differentiate normal those poor based on outcomes three tests measuring different domains During repeated cross-validation CatBoost, XGBoost, Random Forest performed best when predicting processing speed, working memory, attention (median AUCs ≥0.82) compared immediate delayed recall ≥0.72) categorical verbal fluency AUC ≥ 0.68). Activity sleep parameters were also more strongly assessing subdomains. Our work provides proof concept collatable through such as age, education, parameters, activity summaries, light exposure metrics between versus cognition. We further identified targets future causal studies seeking better understand how influence function adults.

Language: Английский

Citations

1
Development of Automated Triggers in Ambulatory Settings in Brazil: Research Protocol for Design Thinking and Machine Learning (Preprint) DOI Creative Commons
Claire Nierva Herrera, Fernanda Raphael Escobar Gimenes, João Paulo Herrera

et al.

JMIR Research Protocols, Journal Year: 2024, Volume and Issue: 13, P. e55466 - e55466

Published: June 17, 2024

Background The use of technologies has had a significant impact on patient safety and the quality care increased globally. In literature, it been reported that people die annually due to adverse events (AEs), various methods exist for investigating measuring AEs. However, some have limited scope, data extraction, need standardization. Brazil, there are few studies application trigger tools, this study is first create automated triggers in ambulatory care. Objective This aims develop machine learning (ML)–based outpatient health settings Brazil. Methods A mixed research will be conducted within design thinking framework principles applied creating triggers, following stages (1) empathize define problem, involving observations inquiries comprehend both user challenge at hand; (2) ideation, where solutions problem generated; (3) prototyping, construction minimal representation best solutions; (4) testing, feedback obtained refine solution; (5) implementation, refined solution tested, changes assessed, scaling considered. Furthermore, ML adopted tailored local context collaboration with an expert field. Results protocol describes its preliminary stages, prior any gathering analysis. was approved by members organizations institution January 2024 ethics board University São Paulo take place. May 2024. As June 2024, stage 1 commenced qualitative research. separate paper focused explaining method considered after outcomes 2 study. Conclusions After development setting, possible prevent identify potential risks AEs more promptly, providing valuable information. technological innovation not only promotes advances clinical practice but also contributes dissemination techniques knowledge related safety. Additionally, professionals can adopt evidence-based preventive measures, reducing costs associated hospital readmissions, enhancing productivity care, contributing safety, quality, effectiveness provided. future, if outcome successful, apply all units, as planned institutional organization. International Registered Report Identifier (IRRID) PRR1-10.2196/55466

Language: Английский

Citations

0