Climate change, biodiversity loss, and Indigenous Peoples' health and wellbeing: A systematic umbrella review.

Indigenous Peoples worldwide are experiencing a cascade of impacts on their health and wellbeing as a result of climate change and biodiversity loss. Existing literature at the interface of climate change, biodiversity loss, and Indigenous health tells us that Indigenous Peoples are among those most disproportionately and acutely affected by these impacts. Yet, a gap exists with respect to comprehensively and critically synthesizing the impacts reported across this literature and identifying Indigenous-led responses. Guided by an Indigenous advisory group, we employed a systematic umbrella review methodology, following PRISMA guidelines, to characterize the global secondary literature (PROSPERO registration #: CRD42023417060). In so doing, we identified the proximal, intermediate, distal, and gendered impacts of climate change and biodiversity loss on Indigenous health and wellbeing as well as Indigenous-led responses. Five databases were searched for published reviews, along with a grey literature search that focused on underrepresented geographic regions in the academic literature. Two independent reviewers conducted two-stage screening, data extraction, and quality assessment of retrieved records. Basic descriptive statistics were calculated. Qualitative data were analyzed thematically, using a constant comparative approach. A total of 38 review articles met the eligibility criteria and 37 grey literature records were retrieved and included in the review. Reviews were published between 2010-2023 and geographically clustered in the Circumpolar North. Intersecting proximal, intermediate, and distal impacts were characterized as place-based and specific, and linked to colonialism as an antecedent to and driver of these impacts. Gendered impacts were underexplored within reviews. Reviewed literature underscored the value of engaging diverse knowledge systems; platforming localized, community-led adaptation to climate change and biodiversity loss, while addressing sociopolitical constraints to these efforts; and applying a broader conceptualization of health that aligns with Indigenous frameworks. Going forward, we must foreground equity- and rights-based considerations within integrated responses to climate and biodiversity crises.

Precise prediction of phase-separation key residues by machine learning.

Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.

Investigating the nexus of metabolic syndrome, serum uric acid, and dementia risk: a prospective cohort study.

BACKGROUND: The global dementia prevalence is surging, necessitating research into contributing factors. We aimed to investigate the association between metabolic syndrome (MetS), its components, serum uric acid (SUA) levels, and dementia risk. METHODS: Our prospective study comprised 466,788 participants without pre-existing MetS from the UK Biobank. We confirmed dementia diagnoses based on the ICD-10 criteria (F00-03). To evaluate the dementia risk concerning MetS, its components, and SUA levels, we applied Cox proportional hazards models, while adjusting for demographic factors. RESULTS: Over a median follow-up of 12.7 years, we identified 6845 dementia cases. Individuals with MetS had a 25% higher risk of all-cause dementia (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.19-1.31). The risk increased with the number of MetS components including central obesity, dyslipidemia for high-density lipoprotein (HDL) cholesterol, hypertension, hyperglycemia, and dyslipidemia for triglycerides. Particularly for those with all five components (HR = 1.76, 95% CI = 1.51-2.04). Dyslipidemia for HDL cholesterol, hypertension, hyperglycemia, and dyslipidemia for triglycerides were independently associated with elevated dementia risk (p < 0.01). MetS was further linked to an increased risk of all-cause dementia (11%) and vascular dementia (VD, 50%) among individuals with SUA levels exceeding 400 µmol/L (all-cause dementia: HR = 1.11, 95% CI = 1.02-1.21; VD: HR = 1.50, 95% CI = 1.28-1.77). CONCLUSIONS: Our study provides robust evidence supporting the association between MetS, its components, and dementia risk. These findings emphasize the importance of considering MetS and SUA levels in assessing dementia risk, offering valuable insights for prevention and management strategies.

Climate change, biodiversity loss, and Indigenous Peoples' health and wellbeing: a systematic umbrella review protocol.

BACKGROUND: Research that examines the intersections of Indigenous Peoples' health and wellbeing with climate change and biodiversity loss is abundant in the global scholarship. A synthesis of this evidence base is crucial in order to map current pathways of impact, as well as to identify responses across the global literature that advance Indigenous health and wellbeing, all while centering Indigenous voices and perspectives. This protocol details our proposed methodology to systematically conduct an umbrella review (or review of reviews) of the synthesized literature on climate change, biodiversity loss, and the health and wellbeing of Indigenous Peoples globally. METHODS: A multidisciplinary team of Indigenous and non-Indigenous scholars will conduct the review, guided by an engagement process with an Indigenous Experts group. A search hedge will be used to search PubMed®, Scopus®, Web of Science™, CINAHL (via EBSCOHost®), and Campbell Collaboration databases and adapted for use in grey literature sources. Two independent reviewers will conduct level one (title/abstract) and level two (full-text) eligibility screening using inclusion/exclusion criteria. Data will be extracted from included records and analyzed using quantitative (e.g., basic descriptive statistics) and qualitative methods (e.g., thematic analysis, using a constant comparative method). DISCUSSION: This protocol outlines our approach to systematically and transparently review synthesized literature that examines the intersections of climate change, biodiversity loss, and Indigenous Peoples' health and wellbeing globally. SYSTEMATIC REVIEW REGISTRATION: This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on April 24, 2023 (registration number: CRD42023417060).

Effect of health literacy and shared decision-making on choice of weight-loss plan among overweight or obese participants receiving a prototype artificial intelligence robot intervention facilitating weight-loss management decisions.

Implementation of artificial intelligence (AI) in medical decision-making is still in early development. We developed an AI robot intervention prototype with a health literacy-friendly interface that uses interactive voice response (IVR) surveying to assist in decision-making for weight loss. The weight-specific health literacy instrument (WSHLI) and Shared Decision-Making Questionnaire (SDMQ) were used to measure factors influencing weight-loss decisions. Factors associated with participants choosing to lose weight were analyzed using logistic regression, and factors influencing the selection of specific weight-loss plans were examined with one-way analysis of variance. Our study recruited 144 overweight or obese adults (69.4% women, 58.3% with body mass index (BMI) ≥ 24). After interacting with the AI robot, 78% of the study population made the decision to lose weight. SDMQ score was a significant factor positively influencing the decision for weight-loss (odds ratio [OR]: 2.16, 95% confidence interval [CI]: 1.09-4.29, p = 0.027). Individuals who selected self-monitored lifestyle modification (mean ± SD: 11.52 ± 1.95) had significantly higher health literacy than those who selected dietician-assisted plan (9.92 ± 2.30) and physician-guided treatment (9.60 ± 1.52) (both p = 0.001). The study results demonstrated that our prototype AI robot can effectively encourage individuals to make decisions regarding weight management and that both WSHLI and SDMQ scores affect the choice of weight-loss plans.

Reorganization of prefrontal network in stroke patients with dyskinesias: evidence from resting-state functional near-infrared spectroscopy.

Stroke usually causes multiple functional disability. To develop novel rehabilitation strategies, it is quite necessary to improve the understanding of post-stroke brain plasticity. Here, we use functional near-infrared spectroscopy to investigate the prefrontal cortex (PFC) network reorganization in stroke patients with dyskinesias. The PFC hemodynamic signals in the resting state from 16 stroke patients and 10 healthy subjects are collected and analyzed with the graph theory. The PFC networks for both groups show small-world attributes. The stroke patients have larger clustering coefficient and transitivity and smaller global efficiency and small-worldness than healthy subjects. Based on the selected network features, the established support vector machine model classifies the two groups of subjects with an accuracy rate of 88.5%. Besides, the clustering coefficient and local efficiency negatively correlate with patients' motor function. This study suggests that the PFC of stroke patients with dyskinesias undergoes specific network reorganization.

THE VALUE OF A REGIONAL 'LIVING' COVID-19 REGISTRY AND THE CHALLENGES OF KEEPING IT ALIVE.

Background: The availability of accurate, reliable, and timely clinical data is crucial for clinicians, researchers, and policymakers so that they can respond effectively to emerging public health threats. This was typified by the recent SARS-CoV-2 pandemic and the critical knowledge and data gaps associated with novel Coronavirus 2019 disease (COVID-19).We sought to create an adaptive, living data mart containing detailed clinical, epidemiologic, and outcome data from COVID-19 patients in our healthcare system. If successful, the approach could then be used for any future outbreak or disease. Methods: From 3/13/2020 onward, demographics, comorbidities, outpatient medications, along with 75 laboratory, 2 imaging, 19 therapeutic, and 4 outcome-related parameters, were manually extracted from the electronic medical record (EMR) of SARS-CoV-2 positive patients. These parameters were entered on a registry featuring calculation, graphing tools, pivot tables, and a macro programming language. Initially, two internal medicine residents populated the database, then professional data abstractors populated the registry. Clinical parameters were developed with input from infectious diseases and critical care physicians and using a modified COVID-19 worksheet from the U.S. Centers for Disease Control and Prevention (CDC). Registry contents were migrated to a browser-based, metadata-driven electronic data capture software platform. Eventually, we developed queries and used various business intelligence (BI) tools which enabled us to semi-automate data ingestion of 147 clinical and outcome parameters from the EMR, via a large U.S. hospital-based, service-level, all-payer database. Statistics were performed in R and Minitab. Results: From March 13, 2020 to May 17, 2021, 549,691 SARS-CoV-2 test results on 236,144 distinct patients, along with location, admission status, and other epidemiologic details are stored on the cloud-based BI platform. From March 2020 until May 2021, extraction of clinical-epidemiologic parameter had to be performed manually. Of those, 543 have had >/=75 parameters fully entered in the registry. Ten clinical characteristics were significantly associated with the need for hospital admission. Only one characteristic was associated with a need for ICU admission. Use of supplemental oxygen, vasopressors and outpatient statin were associated with increased mortality.Initially, 0.5hrs -1.5 hours per patient chart (approximately 450-575 person hours) were required to manually extract the parameters and populate the registry. As of May 17, 2021, semi-automated data ingestion from the U.S. hospital all-payer database, employing user-defined queries, was implemented. That process can ingest and populate the registry with 147 clinical, epidemiologic, and outcome parameters at a rate of 2 hours per 100 patient charts. Conclusion: A living COVID-19 registry represents a mechanism to facilitate optimal sharing of data between providers, consumers, health information networks, and health plans through technology-enabled, secure-access electronic health information. Our approach also involves a diversity of new roles in the field, such as using residents, staff, and the quality department, in addition to professional data extractors and the health informatics team.Initially, due to the overwhelming number of infections that continues to accelerate, and the labor/time intense nature of the project, only a small fraction of all patients with COVID-19 had all parameters entered in the registry. Therefore, this report also offers lessons learned and discusses sustainability issues, should others wish to establish a registry. It also highlights the registry's local and broader public health significance. Beginning in June 2021, whole-genome sequencing results such as lineages harboring important viral mutations, or variants of concern will be linked to the clinical meta-data.