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Animal toxicity testing is time and resource intensive, making it difficult to keep pace with the number of substances requiring assessment. Machine learning (ML) models that use chemical structure information and high-throughput experimental data can be helpful in predicting potential toxicity . However, much of the toxicity data used to train ML models is biased with an unequal balance of positives and negatives primarily since substances selected for in vivo testing are expected to elicit some toxicity effect. To investigate the impact this bias had on predictive performance, various sampling approaches were used to balance in vivo toxicity data as part of a supervised ML workflow to predict hepatotoxicity outcomes from chemical structure and/or targeted transcriptomic data. From the chronic, subchronic, developmental, multigenerational reproductive, and subacute repeat-dose testing toxicity outcomes with a minimum of 50 positive and 50 negative substances, 18 different study-toxicity outcome combinations were evaluated in up to 7 ML models. These included Artificial Neural Networks, Random Forests, Bernouilli Naïve Bayes, Gradient Boosting, and Support Vector classification algorithms which were compared with a local approach, Generalised Read-Across (GenRA), a similarity-weighted k-Nearest Neighbour (k-NN) method. The mean CV F1 performance for unbalanced data across all classifiers and descriptors for chronic liver effects was 0.735 (0.0395 SD). Mean CV F1 performance dropped to 0.639 (0.073 SD) with over-sampling approaches though the poorer performance of KNN approaches in some cases contributed to the observed decrease (mean CV F1 performance excluding KNN was 0.697 (0.072 SD)). With under-sampling approaches, the mean CV F1 was 0.523 (0.083 SD). For developmental liver effects, the mean CV F1 performance was much lower with 0.089 (0.111 SD) for unbalanced approaches and 0.149 (0.084 SD) for under-sampling. Over-sampling approaches led to an increase in mean CV F1 performance (0.234, (0.107 SD)) for developmental liver toxicity. Model performance was found to be dependent on dataset, model type, balancing approach and feature selection. Accordingly tailoring ML workflows for predicting toxicity should consider class imbalance and rely on simpler classifiers first.
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Radial neck fractures with radial head rotation are very rare and extremely difficult to manage. We present the case of an 11-year-old girl who fell on her outstretched left upper extremity and damaged her left elbow in a road traffic accident. An arthrotomy was performed under a C-Arm fluoroscope, which confirmed the radial head displacement of 180° along with the fracture. The fracture site was reduced and fixed with two Kirschner wires, cutting the wire short at its distal end for a complete closure. Open reduction and internal fixation were followed by casting for five weeks. After two years of follow-up, she had complete pain free range of motion of the affected limb. No post-operative complications have been observed till date. Open reduction and internal fixation with two Kwires is a viable option for such complex injuries. However, further evaluation of outcomes and post-operative complications are required.
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Fijación Interna de Fracturas , Fracturas Radiales de Cabeza y Cuello , Niño , Femenino , Humanos , Accidentes de Tránsito , Hilos Ortopédicos , Lesiones de Codo , Articulación del Codo/cirugía , Articulación del Codo/diagnóstico por imagen , Fijación Interna de Fracturas/métodos , Reducción Abierta/métodos , Fracturas Radiales de Cabeza y Cuello/diagnóstico por imagen , Fracturas Radiales de Cabeza y Cuello/cirugía , Rango del Movimiento Articular , RotaciónRESUMEN
Adaptive stress response pathways (SRPs) restore cellular homeostasis following perturbation but may activate terminal outcomes like apoptosis, autophagy, or cellular senescence if disruption exceeds critical thresholds. Because SRPs hold the key to vital cellular tipping points, they are targeted for therapeutic interventions and assessed as biomarkers of toxicity. Hence, we are developing a public database of chemicals that perturb SRPs to enable new data-driven tools to improve public health. Here, we report on the automated text-mining pipeline we used to build and curate the first version of this database. We started with 100 reference SRP chemicals gathered from published biomarker studies to bootstrap the database. Second, we used information retrieval to find co-occurrences of reference chemicals with SRP terms in PubMed abstracts and determined pairwise mutual information thresholds to filter biologically relevant relationships. Third, we applied these thresholds to find 1206 putative SRP perturbagens within thousands of substances in the Library of Integrated Network-Based Cellular Signatures (LINCS). To assign SRP activity to LINCS chemicals, domain experts had to manually review at least three publications for each of 1206 chemicals out of 181,805 total abstracts. To accomplish this efficiently, we implemented a machine learning approach to predict SRP classifications from texts to prioritize abstracts. In 5-fold cross-validation testing with a corpus derived from the 100 reference chemicals, artificial neural networks performed the best (F1-macro = 0.678) and prioritized 2479/181,805 abstracts for expert review, which resulted in 457 chemicals annotated with SRP activities. An independent analysis of enriched mechanisms of action and chemical use class supported the text-mined chemical associations (p < 0.05): heat shock inducers were linked with HSP90 and DNA damage inducers to topoisomerase inhibition. This database will enable novel applications of LINCS data to evaluate SRP activities and to further develop tools for biomedical information extraction from the literature.
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Minería de Datos , Humanos , Estrés Fisiológico/efectos de los fármacos , Bases de Datos FactualesRESUMEN
Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.
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Simulación por Computador , Programas Informáticos , Xenobióticos , Xenobióticos/metabolismo , Humanos , AnimalesRESUMEN
Introduction: Sport-related concussions (SRC) have been a concern in all sports, including soccer. The long-term effects of soccer-related head injuries are a public health concern. The Concussion in Sport Group (CISG) released a consensus statement in 2017 and several soccer governing associations have published their own SRC guidelines while referring to it but it is unclear whether this has been universally adopted. Research question: We aimed to investigate whether guidelines published by soccer associations have any discrepancies; and the extent to which they follow the CISG recommendations. Materials and methods: A scoping review of available soccer-specific SRC guidelines was performed via databases PubMed, Google Scholar, and official soccer association websites via web browser Google. The inclusion criteria were soccer-specific SRC guidelines. Comparisons between guidelines were made concerning the following index items: initial (on-site) assessment, removal from play, re-evaluation with neuroimaging, return-to-sport protocol, special populations, and education. Results: Nine soccer associations with available guidelines were included in this review. Guidelines obtained were from official associations in the United Kingdom, United States of America, Canada, Australia, and New Zealand. When compared to each other and the CISG recommendations, discrepancies were found within guidelines regarding the index items. Additionally, major soccer associations in some countries famous for soccer were found to have not published any publicly available guidelines. Discussion and conclusion: SRC guidelines from different soccer associations contain discrepancies which may be detrimental to athletes, both short and long-term. We recommend that all major soccer governing associations publish guidelines that are standardised and accessible to all athletes.
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Regulatory authorities aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not explicitly defined by structural rules or physicochemical property information. This is largely due to how these groupings are developed, namely, a manual expert curation process, which in turn makes updating and refining groupings, as new substances are evaluated, a practical challenge. Herein, machine learning methods were leveraged to build models that could preliminarily assign substances to predefined groups. A set of 86 groupings containing 2,184 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity Structure Database (DSSTox) content to extract chemical and structural information. Substances were represented using Morgan fingerprints, and two machine learning approaches were used to classify test substances into 56 groups containing at least 10 substances with a structural representation in the data set: k-nearest neighbor (kNN) and random forest (RF), that led to mean 5-fold cross-validation test accuracies (average F1 scores) of 0.781 and 0.853, respectively. With a 9% improvement, the RF classifier was significantly more accurate than KNN (p-value = 0.001). The approach offers promise as a means of the initial profiling of new substances into predefined groups to facilitate prioritization efforts and streamline the assessment of new substances when earlier groupings are available. The algorithm to fit and use these models has been made available in the accompanying repository, thereby enabling both use of the produced models and refitting of these models, as new groupings become available by regulatory authorities or industry.
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Algoritmos , Aprendizaje Automático , Estados Unidos , United States Environmental Protection Agency , Bases de Datos FactualesRESUMEN
The aim of this study was to investigate the effectiveness of continuous cold flow and compression device as against traditional icing regimen and without icing after anterior cruciate ligament (ACL) reconstruction. All patients undergoing ACL reconstruction from June 2021 to August 2021 were enrolled in this study. Patients were randomly allocated to three groups: A control group (n=10) with no ice regimen post-operatively, a second control group (n=10) with ice bag, and a third group (n=10) with continuous cold flow and compression device (physiolab). All patients who had isolated ACL tear evident on magnetic resonance imaging were included. Pain intensity, limb girth, Oxford Knee Score, and 12-item survey form were measured pre- and post-operatively. Significant difference was noted between pain scores in all groups at two- and six-week follow-ups with p-value of 0.004 and 0.01. The test for "between subject effects" showed significant difference (p=0.007) in limb girth between the two groups. Cold and compression device can be used to reduce swelling immediately after ACL reconstruction.
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Lesiones del Ligamento Cruzado Anterior , Reconstrucción del Ligamento Cruzado Anterior , Humanos , Ligamento Cruzado Anterior/cirugía , Lesiones del Ligamento Cruzado Anterior/cirugía , Proyectos Piloto , Resultado del Tratamiento , Reconstrucción del Ligamento Cruzado Anterior/métodos , Articulación de la RodillaAsunto(s)
Fútbol Americano , Intolerancia a la Glucosa , Humanos , Voluntarios Sanos , Comidas , Ingestión de AlimentosRESUMEN
Computational modeling can be a critical tool to predict deployment behavior for transcatheter aortic valve replacement (TAVR) in patients with aortic stenosis. However, due to the mechanical complexity of the aortic valve and the multiphysics nature of the problem, described by partial differential equations (PDEs), traditional finite element (FE) modeling of TAVR deployment is computationally expensive. In this preliminary study, a PDEs-based reduced order modeling (ROM) framework is introduced for rapidly simulating structural deformation of the Medtronic Evolut R valve stent frame. Using fifteen probing points from an Evolut model with parametrized loads enforced, 105 FE simulations were performed in the so-called offline phase, creating a snapshot library. The library was used in the online phase of the ROM for a new set of applied loads via the proper orthogonal decomposition-Galerkin (POD-Galerkin) approach. Simulations of small radial deformations of the Evolut stent frame were performed and compared to full order model (FOM) solutions. Linear elastic and hyperelastic constitutive models in steady and unsteady regimes were implemented within the ROM. Since the original POD-Galerkin method is formulated for linear problems, specific methods for the nonlinear terms in the hyperelastic case were employed, namely, the Discrete Empirical Interpolation Method. The ROM solutions were in strong agreement with the FOM in all numerical experiments, with a speed-up of at least 92% in CPU Time. This framework serves as a first step toward real-time predictive models for TAVR deployment simulations.
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Estenosis de la Válvula Aórtica , Dietilestilbestrol/análogos & derivados , Prótesis Valvulares Cardíacas , Reemplazo de la Válvula Aórtica Transcatéter , Humanos , Válvula Aórtica/cirugía , Estenosis de la Válvula Aórtica/cirugía , Stents , Diseño de Prótesis , Resultado del TratamientoRESUMEN
Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.
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Perfilación de la Expresión Génica , Transcriptoma , Humanos , Ensayos Analíticos de Alto Rendimiento/métodos , Estrógenos , Línea Celular , Medición de Riesgo/métodosRESUMEN
Read-across continues to be a popular data gap filling technique within category and analogue approaches. One of the main issues hindering read-across acceptance is the notion of addressing and reducing uncertainties. Frameworks and formats have been created to help facilitate read-across development, evaluation, and residual uncertainties. However, read-across remains an expert-driven approach with each assessment decided on its own merits with no objective means of evaluating performance or quantifying uncertainties. Here, the underlying motivation of creating an algorithmic approach to read-across, namely the Generalised Read-Across (GenRA) approach, is described. The overall objectives of the approach were to quantify performance and uncertainty. Progress made in quantifying the impact of each similarity context commonly relied upon as part of read-across assessment are discussed. The framework underpinning the approach, the software tools developed to date and how GenRA can be used to make and interpret predictions as part of a screening level hazard assessment decision context are illustrated. Future directions and some of the overarching issues still needed in this field and the extent to which GenRA might facilitate those needs are discussed.
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Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine's service life by improving the turbine blades' fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade's fatigue life is calculated using Morrow's equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade's fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.
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In 2019, over 21% of an estimated 10 million new tuberculosis (TB) patients were either not diagnosed at all or diagnosed without being reported to public health authorities. It is therefore critical to develop newer and more rapid and effective point-of-care diagnostic tools to combat the global TB epidemic. PCR-based diagnostic methods such as Xpert MTB/RIF are quicker than conventional techniques, but their applicability is restricted by the need for specialized laboratory equipment and the substantial cost of scaling-up in low- and middle-income countries where the burden of TB is high. Meanwhile, loop-mediated isothermal amplification (LAMP) amplifies nucleic acids under isothermal conditions with a high efficiency, helps in the early detection and identification of infectious diseases, and can be performed without the need for sophisticated thermocycling equipment. In the present study, the LAMP assay was integrated with screen-printed carbon electrodes and a commercial potentiostat for real time cyclic voltammetry analysis (named as the LAMP-Electrochemical (EC) assay). The LAMP-EC assay was found to be highly specific to TB-causing bacteria and capable of detecting even a single copy of the Mycobacterium tuberculosis (Mtb) IS6110 DNA sequence. Overall, the LAMP-EC test developed and evaluated in the present study shows promise to become a cost-effective tool for rapid and effective diagnosis of TB.
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Técnicas Biosensibles , Microelectrodos , Tuberculosis , Mycobacterium tuberculosis/genética , Mycobacterium tuberculosis/aislamiento & purificación , Tuberculosis/diagnóstico , Tuberculosis/microbiología , Técnicas Biosensibles/economía , Técnicas Biosensibles/instrumentación , Técnicas Biosensibles/métodos , Técnicas Biosensibles/normas , Carbono/química , Microelectrodos/normas , Sensibilidad y Especificidad , Microscopía Electrónica de Rastreo , Reproducibilidad de los Resultados , ADN Bacteriano/análisisRESUMEN
The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.
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Mild steel welded products are widely used for their excellent ductility. Tungsten inert gas (TIG) welding is a high-quality, pollution-free welding process suitable for a base part thickness greater than 3 mm. Fabricating mild steel products with an optimized welding process, material properties, and parameters is important to achieve better weld quality and minimum stresses/distortion. This study uses the finite element method to analyze the temperature and thermal stress fields during TIG welding for optimum bead geometry. The bead geometry was optimized using grey relational analysis by considering the flow rate, welding current, and gap distance. The welding current was the most influential factor affecting the performance measures, followed by the gas flow rate. The effect of welding parameters, such as welding voltage, efficiency, and speed on the temperature field and thermal stress were also numerically investigated. The maximum temperature and thermal stress induced in the weld part were 2083.63 °C and 424 MPa, respectively, for the given heat flux of 0.62 × 106 W/m2. Results showed that the temperature increases with the voltage and efficiency of the weld joint but decreases with an increase in welding speed.
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'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to â¼100 µM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.
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Bioensayo , Ensayos Analíticos de Alto Rendimiento , Humanos , Medición de Riesgo/métodos , Ensayos Analíticos de Alto Rendimiento/métodos , Células Cultivadas , Bioensayo/métodosRESUMEN
Coronary stent underexpansion is associated with restenosis and stent thrombosis. In clinical studies of atherosclerosis, high wall shear stress (WSS) has been associated with activation of prothrombotic pathways, upregulation of matrix metalloproteinases, and future myocardial infarction. We hypothesized that stent underexpansion is predictive of high WSS. WSS distribution was investigated in patients enrolled in the prospective randomized controlled study of angulated coronary arteries randomized to undergo percutaneous coronary intervention with R-ZES or X-EES. WSS was calculated from 3D reconstructions of arteries from intravascular ultrasound (IVUS) and angiography using computational fluid dynamics. A logistic regression model investigated the relationship between WSS and underexpansion and the relationship between underexpansion and stent platform. Mean age was 63±11, 78% were male, 35% had diabetes, mean pre-stent angulation was 36.7°±14.7°. Underexpansion was assessed in 83 patients (6,181 IVUS frames). Frames with stent underexpansion were significantly more likely to exhibit high WSS (> 2.5 Pa) compared to those without underexpansion with an OR of 2.197 (95% CI = [1.233-3.913], p = 0.008). There was no significant association between underexpansion and low WSS (< 1.0 Pa) and no significant differences in underexpansion between R-ZES and X-EES. In the Shear Stent randomized controlled study, underexpanded IVUS frames were more than twice as likely to be associated with high WSS than frames without underexpansion.
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Enfermedad de la Arteria Coronaria , Intervención Coronaria Percutánea , Humanos , Masculino , Persona de Mediana Edad , Anciano , Femenino , Estudios Prospectivos , Valor Predictivo de las Pruebas , Stents , Vasos Coronarios/diagnóstico por imagen , Intervención Coronaria Percutánea/efectos adversos , Estrés Mecánico , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/terapiaRESUMEN
BACKGROUND: Lower respiratory tract infections (LRTIs) in early childhood are known to influence lung development and lifelong lung health, but their link to premature adult death from respiratory disease is unclear. We aimed to estimate the association between early childhood LRTI and the risk and burden of premature adult mortality from respiratory disease. METHODS: This longitudinal observational cohort study used data collected prospectively by the Medical Research Council National Survey of Health and Development in a nationally representative cohort recruited at birth in March, 1946, in England, Scotland, and Wales. We evaluated the association between LRTI during early childhood (age <2 years) and death from respiratory disease from age 26 through 73 years. Early childhood LRTI occurrence was reported by parents or guardians. Cause and date of death were obtained from the National Health Service Central Register. Hazard ratios (HRs) and population attributable risk associated with early childhood LRTI were estimated using competing risks Cox proportional hazards models, adjusted for childhood socioeconomic position, childhood home overcrowding, birthweight, sex, and smoking at age 20-25 years. We compared mortality within the cohort studied with national mortality patterns and estimated corresponding excess deaths occurring nationally during the study period. FINDINGS: 5362 participants were enrolled in March, 1946, and 4032 (75%) continued participating in the study at age 20-25 years. 443 participants with incomplete data on early childhood (368 [9%] of 4032), smoking (57 [1%]), or mortality (18 [<1%]) were excluded. 3589 participants aged 26 years (1840 [51%] male and 1749 [49%] female) were included in the survival analyses from 1972 onwards. The maximum follow-up time was 47·9 years. Among 3589 participants, 913 (25%) who had an LRTI during early childhood were at greater risk of dying from respiratory disease by age 73 years than those with no LRTI during early childhood (HR 1·93, 95% CI 1·10-3·37; p=0·021), after adjustment for childhood socioeconomic position, childhood home overcrowding, birthweight, sex, and adult smoking. This finding corresponded to a population attributable risk of 20·4% (95% CI 3·8-29·8) and 179 188 (95% CI 33 806-261 519) excess deaths across England and Wales between 1972 and 2019. INTERPRETATION: In this prospective, life-spanning, nationally representative cohort study, LRTI during early childhood was associated with almost a two times increased risk of premature adult death from respiratory disease, and accounted for one-fifth of these deaths. FUNDING: National Institute for Health and Care Research Imperial Biomedical Research Centre, Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, Royal Brompton and Harefield Hospitals Charity and Imperial College Healthcare NHS Trust, UK Medical Research Council.