T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma.

Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs.

Giant Magnetoresistive Nanosensor Analysis of Circulating Tumor DNA Epidermal Growth Factor Receptor Mutations for Diagnosis and Therapy Response Monitoring.

Background: Liquid biopsy circulating tumor DNA (ctDNA) mutational analysis holds great promises for precision medicine targeted therapy and more effective cancer management. However, its wide adoption is hampered by high cost and long turnaround time of sequencing assays, or by inadequate analytical sensitivity of existing portable nucleic acid tests to mutant allelic fraction in ctDNA.

Methods: We developed a ctDNA Epidermal Growth Factor Receptor (EGFR) mutational assay using giant magnetoresistive (GMR) nanosensors.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence.

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems.

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: A mountable toilet system for personalized health monitoring via the analysis of excreta.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Purpose: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

A mountable toilet system for personalized health monitoring via the analysis of excreta.

Technologies for the longitudinal monitoring of a person's health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health. The 'smart' toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user's urine using a standard-of-care colorimetric assay that traces red-green-blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel.

A high quantum yield molecule-protein complex fluorophore for near-infrared II imaging.

Fluorescence imaging in the second near-infrared window (NIR-II) allows visualization of deep anatomical features with an unprecedented degree of clarity. NIR-II fluorophores draw from a broad spectrum of materials spanning semiconducting nanomaterials to organic molecular dyes, yet unfortunately all water-soluble organic molecules with >1,000 nm emission suffer from low quantum yields that have limited temporal resolution and penetration depth. Here, we report tailoring the supramolecular assemblies of protein complexes with a sulfonated NIR-II organic dye (CH-4T) to produce a brilliant 110-fold increase in fluorescence, resulting in the highest quantum yield molecular fluorophore thus far.