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The cover illustration showcases the concept and functionality of the machine-learning model developed by Liu et al. to identify and assist in the diagnosis of skin conditions in routine clinical practice through everyday technology such as cellphones and laptops. By leveraging these devices, this research potentially expands universal access to high-quality information about skin conditions and may accelerate the deployment of digital solutions into clinical practice.
Government officials who disseminate unsupported claims about the novel coronavirus undermine public trust in science and in the public-health efforts that are essential to bringing the COVID-19 pandemic under control.
Francesco Paolo Russo, an Associate Professor of Gastroenterology at the Department of Surgery, Oncology and Gastroenterology of the University Hospital in Padua, Italy, talks about carrying out research, teaching and clinical work in the midst of the COVID-19 pandemic.
Necessity has been the mother of invention in the response to the COVID-19 pandemic, triggering many an innovation, often without the luxury of time to test these makeshift solutions to pressing problems. But there is much to be learned from times of crisis for times of plenty.
Researchers starting clinical trials of prevention measures for COVID-19 have a unique window of opportunity for collecting blood from the participants, at baseline and at the end of the trial, to be able to incorporate critical data into their analysis once serological tests for the causative coronavirus become available.
In the USA and around the world, the COVID-19 pandemic arrived as the population was fighting a devastating opioid overdose epidemic. Urgent and decisive action is needed to protect particularly vulnerable populations, such as those with opioid use disorder, to prevent a compounding effect on public health.
Testing drug safety in people who are pregnant remains a wicked problem, but in the transition toward big data and machine learning, target trials could afford a viable alternative to randomized, controlled trials.
Modeling an approach in which people who have recovered from COVID-19 are returned to society to reduce interactions between infected people and vulnerable people indicates the effectiveness of such an approach in reducing deaths.
Therapeutic interventions in colorectal cancer are dependent on immune responses to dying epithelial cells that are modulated by specific members of the gut microbiota.
Combined culture-dependent and culture-independent metagenomic sequencing methods reveal the pervasive and long-lasting presence of antimicrobial resistance genes within a tertiary hospital environment.
Single-cell transcriptome and T cell receptor analysis of bronchoalveolar lavage fluid suggests enrichment of proinflammatory macrophages in patients with severe COVID-19 and the presence of clonally expanded CD8+ T cells in patients with moderate COVID-19.
A cross-sectional study of hospitalized patients with COVID-19 and a longitudinal follow-up study of patients with COVID-19 suggest that SARS-CoV2-specific IgG or IgM seroconversion occurs within 20 days post symptom onset.
A new study models the potential effects of preferentially deploying recovered individuals, who are seropositive for anti-SARS-CoV-2 antibodies, into the community to reduce the number of interactions between susceptible and infected people, thereby limiting transmission of the virus.
A new epidemiological model, termed SIDARTHE, distinguishes between diagnosed and undiagnosed cases of SARS-CoV-2 infection, as well as modeling effects of social distancing and widespread testing, to predict possible outcomes of the COVID-19 epidemic in Italy.
Detailed clinical and virologic characteristics of the first 12 individuals with COVID-19 in the United States from the US Centers for Disease Control and Prevention.
Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery.
Results from the phase IIa COMBAT trial combining CXCR4 and PD-1 inhibition in patients with metastatic cancer show encouraging clinical responses in association with enhanced antitumor immune activation.
By using data from electrocardiograms, a deep learning algorithm outperforms traditional risk scores in predicting death over the course of the next year and identifies at-risk individuals with seemingly normal electrocardiograms.
In individuals diagnosed with age-related macular degeneration in one eye, a deep learning model can predict progression to the ‘wet’, sight-threatening form of the disease in the second eye within a 6-month time frame.
A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice
A pooled genetic, transcriptomic and immunopathologic analysis of over 500 tumors from patients with advanced renal cell cancer suggests that response to PD-1 blockade depends on both CD8+ T cell infiltration and enrichment of tumor-intrinsic somatic alterations.
Local microbiome composition influences treatment efficacy of chemotherapy in colon cancer via modulation of tolerogenic versus immunogenic ileal intestinal epithelial cell death, which in turn influences follicular helper T cell priming.
An HIV vaccine that elicits both antibodies and cellular immune responses confers long-lasting protection against viral challenge in nonhuman primates.
Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.
An iPSC-based three-dimensional model of the human blood–brain barrier reveals that NFAT and APOE dysregulation in pericyte-like mural cells contributes to cerebral amyloid angiopathy and can potentially be targeted to treat Alzheimer’s disease.
The PREDICT 1 trial shows large inter-individual variations in postprandial metabolic responses to standardized meals in over 1,000 participants, demonstrating potential for development of personalized nutrition strategies.
A meta-analysis comparing primary efficacy outcomes of phase 2 and phase 3 randomized controlled trials in rheumatoid arthritis and psoriatic arthritis shows that phase 2 studies consistently overestimate the effect sizes anticipated in subsequent phase 3 trials.