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Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.
BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. METHODS: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000-17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program-Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000-16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FINDINGS: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10-4 for Pakistan, 4·86 × 10-6 for India, and 1·32 × 10-5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. INTERPRETATION: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FUNDING: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.
The effects of lacosamide, pregabalin, and tapentadol on peripheral nerve excitability: A randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects.
BACKGROUND: Chronic pain is a leading cause of disability globally, with limited treatment options and frequent adverse effects. The IMI-PainCare-BioPain project aimed to enhance analgesic drug development by standardizing biomarkers. This study, IMI2-PainCare-BioPain-RCT1, evaluated the effects of lacosamide, pregabalin, and tapentadol on peripheral nerve excitability in healthy subjects through a randomized, double-blind, placebo-controlled crossover trial. METHODS: The study included 43 healthy participants aged 18-45 years. Participants underwent four treatment periods where they received single doses of lacosamide (200 mg), pregabalin (150 mg), tapentadol (100 mg), or placebo. High-frequency stimulation was applied to induce hyperalgesia. The two primary endpoints were changes in Strength Duration Time Constant (SDTC) in large sensory and motor fibers between lacosamide and placebo periods at the first post-dose timepoint compared to baseline (60 min). Other predefined endpoints included recovery cycle, threshold electrotonus (TEd), and S2 accommodation as well as effects of pregabalin and tapentadol. RESULTS: Lacosamide statistically significantly reduced SDTC in large sensory fibers (mean reduction 0.04 (95% CI 0.01-0.08), p = 0.012) and in motor fibers (mean reduction 0.04 (95% CI 0.00-0.07), p = 0.039) but had no effect on small sensory fibers at the first timepoint compared to placebo. There were no effects of pregabalin and tapentadol on SDTC. Of other predefined endpoints, lacosamide produced statistically significant changes in subexcitability, S2 accommodation TEd(peak), and TEd40(Accom) in large sensory fibers. No statistically significant changes were observed in refractoriness, relative refractory period, or accommodation half-time at the first timepoint compared to placebo. CONCLUSIONS: This study demonstrates that nerve excitability testing can detect pharmacodynamic effects on large myelinated fibers in healthy subjects. Lacosamide statistically significantly reduced peripheral nerve excitability, particularly in large sensory fibers.
Sjögren’s disease autoantigen TRIM21/Ro52 is exposed during lytic cell death, facilitates immune complex formation and activates macrophages
Sjögren’s disease (SjD) causes localised inflammation of the lacrimal and salivary glands (SGs), and autoantibody production against ubiquitously expressed intracellular proteins such as TRIM21/Ro52 and TROVE2/Ro60. TRIM21 has vital intracellular roles, as an Fc receptor and E3 ubiquitin ligase. It binds antibody Fc domains on opsonised pathogens, which have escaped extracellular immunity and entered cytosols. TRIM21 ubiquitinates these pathogenic targets, driving their proteasomal degradation. However, whilst the intracellular functions of TRIM21 are well-established, how and why TRIM21 becomes an autoantigen in SjD remains unclear. Previous studies suggest apoptosis promotes self-antigen release, triggering germinal centre (GC) reactions for autoantibody production. However, apoptosis is generally anti-inflammatory and thus more recently, lytic cell death has been proposed as a mechanism for driving inflammation in autoimmune diseases. Therefore, we first aimed to identify the location and regulation of TRIM21 expression in murine and human cells. We then tested whether TRIM21 is preferentially released during lytic forms of cell death (pyroptosis, necroptosis or necrosis), which may promote autoimmune responses upon concomitant release of inflammatory alarmins. We found that TRIM21 is expressed ubiquitously in cells and tissues and can be further upregulated following pathogenic (e.g. LPS and Poly I:C) or cytokine (e.g. IFN) stimulation. TRIM21 is released upon lytic cell death (pyroptosis/necroptosis) but not apoptosis. Instead, during apoptosis, TRIM21-GFP punctae localised within apoptotic blebs and were not released into the cell supernatant. Earlier research demonstrates that intracellular TRIM21 binds antibody Fc with very high affinity. However, whether extracellular TRIM21 could maintain its antibody-binding capacity was unknown. We analysed TRIM21-antibody binding by ELISA, immunoprecipitation (IP) and non-denaturing protein-complex separation (Native Blue-PAGE). We showed that TRIM21 forms immune complexes (ICs) with circulating antibodies via its PRYSPRY Fc-interacting domain. Importantly, these complexes were even larger with plasma antibodies from TRIM21/Ro52-seropositive SjD patients, where TRIM21-antibody interactions were mediated via both Fc- and F(ab’)2 domains. ICs are pathogenic in autoimmune diseases such as systemic lupus erythematosus (SLE), and may promote inflammation and antigen presentation. Thus we sought to explore the uptake and functional consequences of TRIM21-ICs in vitro. We fed TRIM21 and TRIM21-ICs of different sizes to macrophages. We showed that larger ICs are taken up more efficiently and facilitate stronger pro-inflammatory cytokine secretion, compared to soluble, free TRIM21. This was also validated by gene expression analyses, with macrophages showing inflammatory and metabolic transcriptional changes. Antigen presentation is an important mechanism for driving autoimmune, antigen-specific immune reactions. Therefore, we tested whether TRIM21 could drive antigen cross-presentation, by employing the OT-I ovalbumin (OVA) TCR-transgenic mouse system. We generated TRIM21-OVA ICs, which we fed to murine macrophages and then detected CD8+ T cell activation, as evidence for cross-presentation. TRIM21-OVA ICs were more efficiently cross-presented that OVA alone, suggesting that extracellular TRIM21 may be able to bind opsonised antigens and enhance their cross-presentation. We suggest such TRIM21-dependent mechanisms of inflammation and antigen presentation may perpetuate additional SG damage in SjD. This may drive cycles of further tissue destruction, TRIM21 antigen release, IC formation and cross-presentation. Therefore, TRIM21’s ability to bind antibody Fc and form ICs causes it to be inherently autoimmunogenic.
Time to recovery following open and endoscopic carpal tunnel decompression: meta-analysis.
BACKGROUND: Carpal tunnel release (CTR) can be performed using either an open or endoscopic approach. The patient recovery trajectories remain poorly understood. This study aimed to define and compare patient-reported recovery following unilateral open and endoscopic CTR. METHODS: A PRISMA-compliant, preregistered (CRD42023427718) systematic review was conducted, searching PubMed, Embase, and Cochrane databases on 4 July 2023 and 21 August 2024. Studies were included if they reported recovery data (patient-reported outcome measures (PROMs)) at predefined time points for adults undergoing unilateral CTR. Boston Carpal Tunnel Questionnaire and Quick Disabilities of Arm, Shoulder, and Hand scores were extracted. Standardized mean change (SMC) scores from baseline were pooled using random-effects meta-analysis. An innovative modification of the National Institutes of Health quality assessment tools was used to evaluate the risk of bias. RESULTS: In all, 49 studies were included (4546 participants included in the analysis; 3137 open CTR, 1409 endoscopic CTR). Both approaches improved PROM scores over 12 weeks, with early (4-week) outcomes strongly correlating (>0.89) with later (12-week) outcomes. Symptoms continued improving up to 104 weeks. At 1 week, open CTR showed symptomatic deterioration (SMC 10.29; 95% confidence interval (c.i.) 6.35 and 14.21 respectively), comparatively, endoscopic CTR demonstrated an improvement (SMC -2.83; 95% c.i. -7.80 and 2.14 respectively). By 2 weeks, symptom severity remained slightly worse in open CTR, but confidence intervals overlapped from week 3 and thereafter open CTR showed greater symptomatic improvement. Most studies had a high risk of bias and measured outcomes too infrequently for a granular comparison. CONCLUSIONS: Patient-reported recovery trajectories for CTR can inform patient counselling and future research. Endoscopic CTR may result in fewer symptoms in the first 2 weeks, but open CTR may offer comparable or potentially greater improvement thereafter. Future trials with high-frequency PROM capture should prioritize early (first 3 weeks) and long-term (≥24 weeks) outcomes.
Building a Collaborative Ecosystem Across the IDeA-CTR Networks in Response to a Public Health Emergency
Introduction: The urgency and scale of the COVID-19 pandemic demanded a coordinated response from public health agencies and the biomedical research community. The National COVID Cohort Collaborative (N3C) was established as a centralized enclave in 2020 to support the study of COVID-19 across the U.S. The Institutional Development Award for Clinical and Translational Research (IDeA-CTR) centers enhanced N3C's national response by bringing representation from rural and medically underserved communities. This improved the representation of our diverse populations in the N3C Enclave and its use for research by IDeA state investigators. Methods: We developed an organizational structure across the IDeA-CTRs to improve research productivity in resource-challenged areas of the U.S. This socio-technical ecosystem, informed by community input, included a governance committee and two workstreams. The operations workstream focused on data management and regulatory compliance, while the navigation, education, analysis, and training (NEAT) workstream supported educational and analytical activities for the N3C Enclave. Results: Our collaborative approach led to participation by 12 IDeA-CTRs, representing over 400 investigators from 23 sites. The shared governance, investigator engagement, and resource pooling enhanced research productivity and engagement with researchers across IDeA states. Participation in this IDeA-CTR N3C consortium enhanced informatics research capacity and collaboration across the IDeA-CTRs for participating networks. Conclusions: This collaborative model provides a roadmap and framework for future efforts among IDeA-CTRs and other academic partnerships. The socio-technical ecosystem fostered collectivism and Team Science, enabling the consortium to achieve far more than isolated efforts could, offering valuable insights for interdisciplinary research across geographically dispersed communities.
FULLY AUTOMATED MEASUREMENT OF PAEDIATRIC CEREBRAL PALSY PELVIC RADIOGRAPHS USING MACHINE LEARNING: EXTERNAL VALIDATION USING A NATIONAL SURVEILLANCE DATABASE
The radiographic analysis required for a national cerebral palsy (CP) hip surveillance programme is resource intensive. BoneFinder® is a machine-learning tool that can automatically calculate Reimer's migration percentage (RMP) from pelvic radiographs. HipScreen is a smartphone application that can partially automate RMP measurement.Three RMP measurement methods were compared across the same set of radiographs: 1) routine manual measurements performed by clinical experts from the CP Integrated Pathway Scotland (CPIPS) database, 2) automated measurements using BoneFinder® and 3) measurements performed by two clinicians using HipScreen.509 AP pelvic radiographs (1,018 hips; mean age:7.4 years) were selected at random from the CPIPS database. GMFCS levels were I (n=69), II (n=37), III (n=97), IV (n=120) and V (n=186). The absolute mean difference in RMP between BoneFinder® and CPIPS measurements, BoneFinder® and HipScreen and CPIPS and HipScreen measurements was 6.3%, 4.6% and 5.2% respectively.Interobserver reliability (ICC) of RMP measurement across the three methods was excellent (ICC = .92, P<.001, 95% CI .90–.93). Good to excellent ICC and correlation were found between BoneFinder® and CPIPS measurements (ICC = .87, P<.001, 95% CI .75–.93, r=.90) and HipScreen and CPIPS measurements (ICC = .91, P<.001, 95% CI .87–.94, r=.93). The area under the receiver operating characteristic curve for BoneFinder®'s and HipScreen's ability to detect a RMP ≥30/≥40% was .96/.98 and .97/.99, respectively.Fully automated RMP measurements were highly reliable with clinically acceptable measurement error. BoneFinder® appears to perform well in analysis of radiographs in CP children who may have challenging radiographic anatomy.
Reporting Guideline for Chatbot Health Advice Studies: The CHART Statement.
IMPORTANCE: The rise in chatbot health advice (CHA) studies is accompanied by heterogeneity in reporting standards, impacting their interpretability. OBJECTIVE: To provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice. DESIGN, SETTING, AND PARTICIPANTS: CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, 3 synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. RESULTS: CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include title (subitem 1a), abstract or summary (subitem 1b), background (subitems 2ab), model identifiers (subitem 3ab), model details (subitems 4abc), prompt engineering (subitems 5ab), query strategy (subitems 6abcd), performance evaluation (subitems 7ab), sample size (subitem 8), data analysis (subitem 9a), results (subitems 10abc), discussion (subitems 11abc), disclosures (subitem 12a), funding (subitem 12b), ethics (subitem 12c), protocol (subitem 12d), and data availability (subitem 12e). CONCLUSIONS AND RELEVANCE: The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.
Reporting guideline for Chatbot Health Advice studies: the CHART statement.
BACKGROUND: The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies. METHODS: CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. RESULTS: CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include Title (subitem 1a), Abstract/Summary (subitem 1b), Background (subitems 2ab), Model Identifiers (subitems 3ab), Model Details (subitems 4abc), Prompt Engineering (subitems 5ab), Query Strategy (subitems 6abcd), Performance Evaluation (subitems 7ab), Sample Size (subitem 8), Data Analysis (subitem 9a), Results (subitems 10abc), Discussion (subitems 11abc), Disclosures (subitem 12a), Funding (subitem 12b), Ethics (subitem 12c), Protocol (subitem 12d), and Data Availability (subitem 12e). CONCLUSION: The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.
Reporting guideline for chatbot health advice studies: the Chatbot Assessment Reporting Tool (CHART) statement.
The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as chatbot health advice studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and method in chatbot health advice studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary, modified, asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of chatbot health advice studies. These include title (subitem 1a), abstract/summary (subitem 1b), background (subitems 2a,b), model identifiers (subitems 3a,b), model details (subitems 4a-c), prompt engineering (subitems 5a,b), query strategy (subitems 6a-d), performance evaluation (subitems 7a,b), sample size (subitem 8), data analysis subitem 9a), results (subitems 10a-c), discussion (subitems 11a-c), disclosures (subitem 12a), funding (subitem 12b), ethics (subitem 12c), protocol (subitem 12d), and data availability (subitem 12e). The CHART checklist and corresponding diagram of the method were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of chatbot health advice studies.
The roles of placental senescence, autophagy and senotherapeutics in the development and prevention of pre-eclampsia: A focus on ergothioneine
Cellular senescence is a well-established biological phenomenon in eukaryotes. It involves DNA damage, telomere shortening, a senescence-associated secretory phenotype (SASP), and the inability of cells to replicate. It is associated with ageing, and also with oxidative stress. Given the importance of oxidative stress in pre-eclampsia, there is considerable evidence, that we review, that senescence plays an important role in both normal placental development and in the development of both early- and late-term pre-eclampsia. Autophagy is capable of delaying or even reversing the development of senescence, and certain small molecules such as sulforaphane and spermidine can stimulate autophagy, including via the redox-sensitive transcription factor Nrf2. Ergothioneine is a thiohistidine antioxidant that is protective against a variety of cardiovascular and other diseases. Ergothioneine also interacts with Nrf2, and pre-eclampsia occurs far less frequently in individuals with higher plasma ergothioneine levels. Together, these elements provide a self-consistent, molecular and systems biology explanation for at least one mechanism by which ergothioneine may be protective against pre-eclampsia.
Multiomic phenotyping of the airways in health and disease at single-cell resolution to discover molecular mechanisms of asthma
Asthma is a heterogenous disease of the airways showing phenotypic diversity across patients, requiring varied approaches to treatment. However, the underlying pathophysiological processes which drive the differences have not been comprehensively studied. To profile the varied molecular pathways involved in its pathogenesis, I present a collection of single cell and spatial transcriptomic datasets, together spanning over 400,000 cells captured from upper and lower airway tissue sites from 62 individuals (asthma patients and healthy controls) across multiple molecular modalities (transcriptome, immune repertoire, protein expression). In addition to profiling the cell surface proteome, I developed a technique to interrogate antigen binding in allergic asthma with house dust-mite sensitisation. This rich omics data resource is accompanied by detailed clinical phenotyping, thereby providing the largest single-cell atlas of asthma and its key phenotypes. Analysis of this resource reveals that in patients with allergic sensitisation, contrary to past concepts of allergic asthma pathogenesis, IgE-producing plasma cells were not found in the lung. However, IgE-binding cells were detected (e.g. basophils and mast cells), suggesting that IgE may be entering from the periphery. Notably, the allergens thought to cause IgE secretion were found to bind to immune cells, including antigen-presenting cells, suggesting the IgE secretion may be localised to lymphoid tissues. Profiling the differences between asthmatic and healthy individuals in the spatial context reveals probable mast cell - plasma cell and CD4+ - B cell niches, which may contribute to disease mechanism. Differential abundance and gene expression analyses between the whole asthma cohort and healthy individuals showed features predominantly related to type 2 eosinophilic inflammation. Those changes correlated with clinical variables describing lung function, such as FEV1 and FVC. In contrast, comparisons between the asthma phenotypes allowed identification of features associated with neutrophilic inflammation. These features included IL-1 signalling, antibacterial defence, and inflammasome activation. My findings underscore critical importance of careful phenotyping to uncover underlying molecular changes in asthma, as well as providing a rich resource to study it further.