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The integration of diverse clinical data sources requires standardization through models such as Observational Medical Outcomes Partnership (OMOP). However, mapping data elements to OMOP concepts demands significant technical expertise and time. While large health care systems often have resources for OMOP conversion, smaller clinical trials and studies frequently lack such support, leaving valuable research data siloed. This study aims to develop and validate a user-friendly tool that leverages large language models to automate the OMOP conversion process for clinical trials, electronic health records, and registry data. We developed a 3-tiered semantic matching system using GPT-3 embeddings to transform heterogeneous clinical data to the OMOP Common Data Model. The system processes input terms by generating vector embeddings, computing cosine similarity against precomputed Observational Health Data Sciences and Informatics vocabulary embeddings, and ranking potential matches. We validated the system using two independent datasets: (1) a development set of 76 National Institutes of Health Helping to End Addiction Long-term Initiative clinical trial common data elements for chronic pain and opioid use disorders and (2) a separate validation set of electronic health record concepts from the National Institutes of Health National COVID Cohort Collaborative COVID-19 enclave. The architecture combines Unified Medical Language System semantic frameworks with asynchronous processing for efficient concept mapping, made available through an open-source implementation. The system achieved an area under the receiver operating characteristic curve of 0.9975 for mapping clinical trial common data element terms. Precision ranged from 0.92 to 0.99 and recall ranged from 0.88 to 0.97 across similarity thresholds from 0.85 to 1.0. In practical application, the tool successfully automated mappings that previously required manual informatics expertise, reducing the technical barriers for research teams to participate in large-scale, data-sharing initiatives. Representative mappings demonstrated high accuracy, such as demographic terms achieving 100% similarity with corresponding Logical Observation Identifiers Names and Codes concepts. The implementation successfully processes diverse data types through both individual term mapping and batch processing capabilities. Our validated large language model-based tool effectively automates the transformation of clinical data into the OMOP format while maintaining high accuracy. The combination of semantic matching capabilities and a researcher-friendly interface makes data harmonization accessible to smaller research teams without requiring extensive informatics support. This has direct implications for accelerating clinical research data standardization and enabling broader participation in initiatives such as the National Institutes of Health Helping to End Addiction Long-term Initiative Data Ecosystem.

The integration of diverse clinical data sources requires standardization through models like OMOP (Observational Medical Outcomes Partnership). However, mapping data elements to OMOP concepts demands significant technical expertise and time. While large healthcare systems often have resources for OMOP conversion, smaller clinical trials and studies frequently lack such support, leaving valuable research data siloed.BACKGROUNDThe integration of diverse clinical data sources requires standardization through models like OMOP (Observational Medical Outcomes Partnership). However, mapping data elements to OMOP concepts demands significant technical expertise and time. While large healthcare systems often have resources for OMOP conversion, smaller clinical trials and studies frequently lack such support, leaving valuable research data siloed.To develop and validate a user-friendly tool that leverages large language models to automate the OMOP conversion process for clinical trial, electronic health record, and registry data.OBJECTIVETo develop and validate a user-friendly tool that leverages large language models to automate the OMOP conversion process for clinical trial, electronic health record, and registry data.We developed a three-tiered semantic matching system using GPT-3 embeddings to transform heterogeneous clinical data to the OMOP common data model. The system processes input terms by generating vector embeddings, computing cosine similarity against precomputed OHDSI vocabulary embeddings, and ranking potential matches. We validated the system using two independent datasets: a development set of 76 NIH HEAL Initiative clinical trial common data elements (CDEs) for chronic pain and opioid use disorders, and a separate validation set of electronic health record concepts from the NIH N3C COVID-19 enclave. The architecture combines UMLS semantic frameworks with asynchronous processing for efficient concept mapping, made available through an open-source implementation.METHODSWe developed a three-tiered semantic matching system using GPT-3 embeddings to transform heterogeneous clinical data to the OMOP common data model. The system processes input terms by generating vector embeddings, computing cosine similarity against precomputed OHDSI vocabulary embeddings, and ranking potential matches. We validated the system using two independent datasets: a development set of 76 NIH HEAL Initiative clinical trial common data elements (CDEs) for chronic pain and opioid use disorders, and a separate validation set of electronic health record concepts from the NIH N3C COVID-19 enclave. The architecture combines UMLS semantic frameworks with asynchronous processing for efficient concept mapping, made available through an open-source implementation.The system achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9975 for mapping clinical trial CDE terms. Precision ranged from 0.92 to 0.99 and recall from 0.88 to 0.97 across similarity thresholds from 0.85 to 1.0. In practical application, the tool successfully automated mappings that previously required manual informatics expertise, reducing the technical barriers for research teams to participate in large-scale data sharing initiatives. Representative mappings demonstrated high accuracy, such as demographic terms achieving 100% similarity with corresponding LOINC concepts. The implementation successfully processes diverse data types through both individual term mapping and batch processing capabilities.RESULTSThe system achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9975 for mapping clinical trial CDE terms. Precision ranged from 0.92 to 0.99 and recall from 0.88 to 0.97 across similarity thresholds from 0.85 to 1.0. In practical application, the tool successfully automated mappings that previously required manual informatics expertise, reducing the technical barriers for research teams to participate in large-scale data sharing initiatives. Representative mappings demonstrated high accuracy, such as demographic terms achieving 100% similarity with corresponding LOINC concepts. The implementation successfully processes diverse data types through both individual term mapping and batch processing capabilities.Our validated LLM-based tool effectively automates the transformation of clinical data into OMOP format while maintaining high accuracy. The combination of semantic matching capabilities and researcher-friendly interface makes data harmonization accessible to smaller research teams without requiring extensive informatics support. This has direct implications for accelerating clinical research data standardization and enabling broader participation in initiatives like the NIH HEAL Data Ecosystem.CONCLUSIONSOur validated LLM-based tool effectively automates the transformation of clinical data into OMOP format while maintaining high accuracy. The combination of semantic matching capabilities and researcher-friendly interface makes data harmonization accessible to smaller research teams without requiring extensive informatics support. This has direct implications for accelerating clinical research data standardization and enabling broader participation in initiatives like the NIH HEAL Data Ecosystem.

This study attempts to reproduce the salient features of the variability in the depth of the thermocline in the marginally eddy-resolving Parallel Ocean Climate Model (POCM) of Semtner and Chervin, using a simple linear vorticity model that only permits local Ekman pumping and the propagation of long Rossby waves.

During theDeepwater Horizonoil well blowout in the Gulf of Mexico, the application of 7 million liters of chemical dispersants aimed to stimulate microbial crude oil degradation by increasing the bioavailability of oil compounds. However, the effects of dispersants on oil biodegradation rates are debated. In laboratory experiments, we simulated environmental conditions comparable to the hydrocarbon-rich, 1,100 m deep plume that formed during theDeepwater Horizondischarge. The presence of dispersant significantly altered the microbial community composition through selection for potential dispersant-degradingColwellia,which also bloomed in situ in Gulf deep waters during the discharge. In contrast, oil addition to deepwater samples in the absence of dispersant stimulated growth of natural hydrocarbon-degradingMarinobacter.In these deepwater microcosm experiments, dispersants did not enhance heterotrophic microbial activity or hydrocarbon oxidation rates. An experiment with surface seawater from an anthropogenically derived oil slick corroborated the deepwater microcosm results as inhibition of hydrocarbon turnover was observed in the presence of dispersants, suggesting that the microcosm findings are broadly applicable across marine habitats. Extrapolating this comprehensive dataset to real world scenarios questions whether dispersants stimulate microbial oil degradation in deep ocean waters and instead highlights that dispersants can exert a negative effect on microbial hydrocarbon degradation rates.

Organochlorine (OC) pesticides and the more persistent polychlorinated biphenyls (PCBs) have well-established dose-dependent toxicities to birds, fish and mammals in experimental studies, but the actual impact of OC pollutants on European marine top predators remains unknown. Here we show that several cetacean species have very high mean blubber PCB concentrations likely to cause population declines and suppress population recovery. In a large pan-European meta-analysis of stranded (n = 929) or biopsied (n = 152) cetaceans, three out of four species:- striped dolphins (SDs), bottlenose dolphins (BNDs) and killer whales (KWs) had mean PCB levels that markedly exceeded all known marine mammal PCB toxicity thresholds. Some locations (e.g. western Mediterranean Sea, south-west Iberian Peninsula) are global PCB \"hotspots\" for marine mammals. Blubber PCB concentrations initially declined following a mid-1980s EU ban, but have since stabilised in UK harbour porpoises and SDs in the western Mediterranean Sea. Some small or declining populations of BNDs and KWs in the NE Atlantic were associated with low recruitment, consistent with PCB-induced reproductive toxicity. Despite regulations and mitigation measures to reduce PCB pollution, their biomagnification in marine food webs continues to cause severe impacts among cetacean top predators in European seas.

Childhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. We used two to three models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption). Reduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal A vaccination, short-term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1- to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns. The impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination. Bill and Melinda Gates Foundation and Gavi, the Vaccine Alliance.

Mutations in C9ORF72 are the most common cause of familial amyotrophic lateral sclerosis (ALS). Here, through a combination of RNA-Seq and electrophysiological studies on induced pluripotent stem cell (iPSC)-derived motor neurons (MNs), we show that increased expression of GluA1 AMPA receptor (AMPAR) subunit occurs in MNs with C9ORF72 mutations that leads to increased Ca 2+ -permeable AMPAR expression and results in enhanced selective MN vulnerability to excitotoxicity. These deficits are not found in iPSC-derived cortical neurons and are abolished by CRISPR/Cas9-mediated correction of the C9ORF72 repeat expansion in MNs. We also demonstrate that MN-specific dysregulation of AMPAR expression is also present in C9ORF72 patient post-mortem material. We therefore present multiple lines of evidence for the specific upregulation of GluA1 subunits in human mutant C9ORF72 MNs that could lead to a potential pathogenic excitotoxic mechanism in ALS. Repeat expansion mutation in C9ORF72 is the most common cause of familial ALS. Here, the authors generate motor neurons from cells of patients with C9ORF72 mutations, and characterize changes in gene expression in these motor neurons compared to genetically corrected lines, which suggest that glutamate receptor subunit GluA1 is dysregulated in this form of ALS.

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA . Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer’s disease and Parkinson’s disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition. Whole-genome sequence analysis identifies five independent risk loci for Lewy body dementia and demonstrates overlapping genetic architecture with Alzheimer’s and Parkinson’s diseases.

Reproductive failure in mammals due to exposure to polychlorinated biphenyls (PCBs) can occur either through endocrine disrupting effects or via immunosuppression and increased disease risk. To investigate further, full necropsies and determination of summed 25 polychlorinated biphenyls congeners (∑PCBs lipid weight) in blubber were undertaken on 329 UK-stranded female harbour porpoises (1990-2012). In sexually mature females, 25/127 (19.7%) showed direct evidence of reproductive failure (foetal death, aborting, dystocia or stillbirth). A further 21/127 (16.5%) had infections of the reproductive tract or tumours of reproductive tract tissues that could contribute to reproductive failure. Resting mature females (non-lactating or non-pregnant) had significantly higher mean ∑PCBs (18.5 mg/kg) than both lactating (7.5 mg/kg) and pregnant females (6 mg/kg), though not significantly different to sexually immature females (14.0 mg/kg). Using multinomial logistic regression models ΣPCBs was found to be a significant predictor of mature female reproductive status, adjusting for the effects of confounding variables. Resting females were more likely to have a higher PCB burden. Health status (proxied by \"trauma\" or \"infectious disease\" causes of death) was also a significant predictor, with lactating females (i.e. who successfully reproduced) more likely to be in good health status compared to other individuals. Based on contaminant profiles (>11 mg/kg lipid), at least 29/60 (48%) of resting females had not offloaded their pollutant burden via gestation and primarily lactation. Where data were available, these non-offloading females were previously gravid, which suggests foetal or newborn mortality. Furthermore, a lower pregnancy rate of 50% was estimated for \"healthy\" females that died of traumatic causes of death, compared to other populations. Whether or not PCBs are part of an underlying mechanism, we used individual PCB burdens to show further evidence of reproductive failure in the North-east Atlantic harbour porpoise population, results that should inform conservation management.

Frameshift insertion/deletions (fs-indels) are an infrequent but highly immunogenic mutation subtype. Although fs-indels are degraded through the nonsense-mediated decay (NMD) pathway, we hypothesise that some fs-indels escape degradation and elicit anti-tumor immune responses. Using allele-specific expression analysis, expressed fs-indels are enriched in genomic positions predicted to escape NMD, and associated with higher protein expression, consistent with degradation escape (NMD-escape). Across four independent melanoma cohorts, NMD-escape mutations are significantly associated with clinical-benefit to checkpoint inhibitor (CPI) therapy ( P meta  = 0.0039). NMD-escape mutations are additionally found to associate with clinical-benefit in the low-TMB setting. Furthermore, in an adoptive cell therapy treated melanoma cohort, NMD-escape mutation count is the most significant biomarker associated with clinical-benefit. Analysis of functional T cell reactivity screens from personalized vaccine studies shows direct evidence of fs-indel derived neoantigens eliciting immune response, particularly those with highly elongated neo open reading frames. NMD-escape fs-indels represent an attractive target for biomarker optimisation and immunotherapy design. The transcripts generated by frameshifts and indels in cancer are frequently degraded by nonsense mediated decay. Here, the authors show that some of these transcripts can escape this degradation mechanism and their prevalence correlates with tumour response to immunotherapy.