Browse Articles

Bastard et al. estimate the extent of zoonotic human infections during the 2018–2019 Rift Valley Fever outbreak in Mayotte, France, combining passive surveillance and serological data. Estimating the case reporting fraction shows that syndromic surveillance fails to capture most infections during the epidemic.

D’Hondt et al. perform a qualitative and quantitative study on the implementation of machine learning (ML) in the intensive care unit (ICU). The authors interview hospital- and industry-based stakeholders to understand barriers in ML implementation and perform a number of ML experiments to quantify the impact of issues raised on model performance.

HIV remains a major global health issue, with the burden of the epidemic disproportionately falling on low- and middle-income countries. Progress in HIV prevention, most notably pre-exposure prophylaxis (PrEP), has been slow to reach those most in need.

Lynggaard et al. profile the salivary proteome and metaproteome in patients with head and neck cancer who have received radiation therapy and an intraglandular mesenchymal stem cell (MSC) treatment for radiation-induced xerostomia and in healthy controls. MSC therapy impacts the composition of the salivary proteome in the longer-term.

Ieki et al. train a deep learning model to estimate patients’ age from chest X-ray images. X-ray age is found to be an indicator of poor prognosis in patients with heart failure and patients admitted to the intensive care unit with cardiovascular disease.

Bicher, Zuba et al. consolidate the output of three epidemiological models to perform weekly forecasts of COVID-19 cases and required hospital beds. The predictions are more accurate than the individual models and enable planning of health care and COVID-19 interventions by national decision-makers.

McCartney, Borras et al. analyse the metabolites present in breath samples collected from people with or without COVID-19. The authors find that the breath metabolite signature differs based on the predominant SARS-CoV-2 variant circulating at the time, and that breath test accuracy improves when modelling samples from each variant wave separately.

Xu et al. develop a model that predicts the disclosure of self-harm and suicide ideation based on data from text-based counselling services. This model could potentially improve the preparedness of crisis workers.

Ewart et al. assess the performance of a human Liver-Chip for predictive toxicology. They also perform an economic analysis to demonstrate its potential financial value for the pharmaceutical industry.

Heskamp et al. perform quantitative MRI analysis of end-to-end muscle fat fractions in a series of patients with facioscapulohumeral dystrophy. The authors report that disease initiation commonly occurs at the distal end of affected muscles, with wave-like progression to the proximal end.

Bayisenge et al. describe teaching One Health approaches to medical students at the University of Global Health Equity in Rwanda. Wider implementation of this approach should enable a better response to the health challenges of our changing planet.

Nagata, Utsumi, Asaka, Maeda et al. describe a nanobody, TP86, that potently neutralizes both BA.1 and BA.2 Omicron SARS-CoV-2 variants, and, when combined with the TP17 nanobody, broadly neutralizes all SARS-CoV-2 variants. This nanobody cocktail also suppresses weight loss and prolongs survival of SARS-CoV-2 infected human ACE2 transgenic mice.

Li et al. evaluate the immunogenicity and safety of the CoronaVac inactivated COVID-19 vaccine in elderly patients with underlying medical conditions in China. Most patients show comparable neutralizing antibody and SARS-CoV-2-specific T cell responses to healthy controls, and no serious adverse effects are seen.

Divard, Raynaud et al. compare artificial intelligence (AI)-based predictions of kidney allograft failure based on electronic health records with those made by transplant physicians of varying levels of experience. The ability of physicians to predict allograft failure is limited, with superior performance seen for the AI system.

Adam et al. evaluate the impact of biased AI recommendations on emergency decisions made by respondents to mental health crises. They find that descriptive rather than prescriptive recommendations made by the AI decision support system are more likely to lead to unbiased decision-making.

Doan et al. characterize and assess the stability of film matrix embedded Adeno-Associated Virus 9 (AAV9) vectors during storage and transport at ambient temperatures. High and low viscosity formulations of AAV9 stored at 25 °C maintain titer for 6 months.

Frank, Gassert et al. use dark-field chest X-ray imaging to assess COVID-19-pneumonia. Dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and provides an ultralow dose alternative to computed tomography imaging for that purpose.

Teslya et al. examine the importance of sustained compliance with physical distancing during COVID-19 vaccination rollout. Using mathematical models, the authors find that waning compliance may contribute to increased hospitalisations in initial phases of the rollout and that behavioural interventions would likely help to control transmission.

Dastidar et al. discuss how recent policy measures, and favourable technological and infrastructural landscapes are bolstering India’s ability to implement virtual care. These developments were accelerated by the need for an alternative model of care during the COVID-19 lockdowns and may provide a bridge to Universal Healthcare in India.

Colella, Wongnak et al. test pet dogs and cats from metropolitan areas of eight countries in East and Southeast Asia for zoonotic parasites. The authors identify factors associated with potential exposure to zoonotic parasites, including animal characteristics and human living conditions.