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The cellular and molecular mechanisms underlying the health impacts of climate change must be better understood in order to plan interventions that mitigate harm.
The US Food and Drug Administration (FDA) has approved the first drug, resmetirom, for metabolic dysfunction-associated steatohepatitis (MASH), but much work remains for the industry, practitioners and health systems so that this approval will benefit all patients.
An antibody screen of two distinct multiple sclerosis cohorts reveals an autoantibody signature that is detectable years before symptom onset and linked to a common microbial motif.
Causal machine learning methods could be used to predict treatment outcomes for subgroups and even individual patients; this Perspective outlines the potential benefits and limitations of the approach, offering practical guidance for appropriate clinical use.
The QR4 algorithm for prediction of 10-year cardiovascular disease risk, developed, tested and externally validated in datasets comprising 16.8 million people from the United Kingdom, improves upon the QRISK3 algorithm that is in current use by incorporating new risk factors.
Recent developments in bioengineering and organic chemistry have enabled targeting of the previously ‘undruggable’ KRAS; this review summarizes the successes, challenges and future of KRAS therapeutics in the clinic.
A self-amplifying mRNA vaccine shows promise in this new modality by eliciting neutralizing antibodies against the SARS-CoV-2 Omicron (BA.1) variant in a phase 2/3 trial.
Using plasma samples collected over several time points during pregnancy from three different cohorts, associations between circulating placental IGFBP1 levels, metabolic traits and birth anthropometric measurements were measured, with low IGFBP1 levels identified as a potential risk factor for gestational diabetes mellitus.
Developed on cytology images of hydrothorax and ascites from 57,220 cases at four hospitals, a deep-learning model shows high accuracy in tumor origin prediction and presents prognostic value when patient treatment is consistent with the cancer origin predicted by the model.
By learning to pair dermatological images and related concepts in a self-supervised manner, a visual-language foundation model is shown to have comparable performance to supervised models for concept annotation and is used to scrutinize model decisions for enhanced interpretability and accountability of medical imaging applications.
An exploratory analysis of the 1-year clinical trial PASADENA in individuals with early-stage Parkinson’s disease suggests that prasinezumab might reduce motor signs progression to a greater extent in those with more rapidly progressing disease.
Researchers developed an AI model that designs novel, synthesizable antibiotic compounds — several of which showed potent in vitro activity against priority pathogens.
Diversity in clinical trials must be accompanied by justice and equity, including benefits for underrepresented participants, in order to boost population health.
A small, prospective clinical study shows that ex vivo drug screening of pediatric cancer samples can identify effective therapeutic options. If validated, these findings could herald a new approach to precision medicine in this setting.
In an observational study evaluating functional precision medicine in children and adolescents with relapsed or refractory solid and hematologic malignancies, it was feasible to provide personalized treatment recommendations to treating physicians on the basis of genomic profiling and ex vivo drug sensitivity testing within 4 weeks.