A deep learning algorithm trained on a linked data set of mammograms and electronic health records achieved breast cancer detection accuracy comparable to radiologists as defined by the Breast Canc... BackgroundComputational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a ma...

Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.

Oxytocin (OT) is a neurohypophyseal peptide hormone containing a disulphide-bridged pseudocyclic conformation. The biomedical use of OT peptides is limited amongst others by disadvantageous pharmacokinetic parameters. To increase the stability of OT by replacing the disulphide bridge with the stable and more rigid [1,2,3]triazol-1-yl moiety, we employed the Cu²⁺-catalysed side chain-to-side chain azide-alkyne 1,3-cycloaddition. Here we report the design, synthesis, conformational analysis, and in vitro pharmacological activity of a homologous series of Cα¹-to-Cα⁶ side chain-to-side chain [1,2,3]triazol-1-yl-containing OT analogues differing in the length of the bridge, location, and orientation of the linking moiety. Exploiting this macrocyclisation approach, it was possible to generate a systematic series of compounds providing interesting insight into the structure-conformation-function relationship of OT. Most analogues were able to adopt similar conformation to endogenous OT in water, namely, a type I β-turn. This approach may in the future generate stabilised pharmacological peptide tools to advance understanding of OT physiology.

Background The 2019 novel coronavirus (SARS-CoV-2) is reported to result in both respiratory and non-respiratory severe health outcomes, but quantitative assessment of the risk – while adjusting for underlying risk driven by comorbidities – is not yet established. Methods A retrospective observational study using electronic health records of 9,344,021 individuals across the U.S. with at-least 1 year of clinical history and followed up throughout 2020. Results 131,329 individuals were associated with SARS-CoV-2 infection by January 6, 2021 in three distinct surges. While the age and number of preexisting conditions had decreased throughout the pandemic, the characteristics of those who experienced severe health events did not. During the second surge, between June 7 and November 18, 2020, 425,988 individuals in the base cohort were admitted to emergency rooms or hospitals. Among them, 15,486 were detected with SAR-CoV-2 within few days of admission. Significant adjusted odds ratios were observed between SARS-CoV-2 infection and the following severe health events: respiratory (4.38, 95% confidence interval 4.16– 4.62), bacterial pneumonia (3.25, 2.76–3.83), sepsis (1.71, 1.53–1.91), renal (1.69, 1.57–1.83), hematologic/immune (1.32, 1.20–1.45), neurological (1.23, 1.09–1.38). Conclusions SARS-CoV-2 infection among hospitalized patients is associated with non-negligible increased risk of severe events including multiple non-respiratory ones. These associations, which complement recent studies, are persistent even after accounting for sources of selection and confounding bias, increasing the confidence they are not spurious.