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The extent and depth of burn injury may mandate temporary use of cadaver skin (allograft) to protect the wound and allow the formation of granulation tissue while split-thickness skin grafts (STSGs) are serially harvested from the same donor areas. However, allografts are not always available and have a high cost, hence the interest in identifying more economical, readily available products that serve the same function. This study evaluated intact fish skin graft (IFSG) as a temporary cover to prepare the wound bed for STSG application. Thirty-six full-thickness (FT) 5 × 5 cm burn wounds were created on the dorsum of six anesthetized Yorkshire pigs on day −1. To mimic the two-stage clinical situation, on day 0, wounds were excised down to a bleeding wound bed and a temporary cover (either IFSG or cadaver porcine skin) was applied; then, on day 7, wounds were debrided to a viable wound bed prior to the application of autologous 1.5:1 meshed STSG (mSTSG). Rechecks were performed on days 14, 21, 28, 45, and 60 with digital images, non-invasive measurements, and punch biopsies. The IFSG created a granulated wound bed receptive to the application of an mSTSG. FT burn wounds treated with an IFSG had similar outcome measures, including contraction rates, trans-epidermal water loss (TEWL) measurements, hydration, and blood perfusion levels, compared to cadaver skin-treated burn wounds. Pathology scoring indicated significant differences between the allograft- and IFSG-treated wounds on day 7, with the IFSG having increased angiogenesis, granulation tissue formation, and immune cells. Pathology scoring indicated no significant differences once mSTSGs were applied to wounds. The IFSG performed as well as cadaver skin as a temporary cover and was not inferior to the standard of care, suggesting the potential to transition IFSGs into clinical use for burns.

Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners. The Center for Artificial Intelligence in Medicine and Imaging uses the following 4 key tactics to support AI/ML research: project-based learning opportunities that build interdisciplinary collaboration; internal grant programs that catalyze extramural funding; infrastructure that facilitates the rapid creation of large multimodal AI-ready clinical data sets; and educational and open data programs that engage the broader research community. The center is based on the premise that foundational and applied research are not in tension but instead are complementary. Solving important biomedical problems with AI/ML requires high-quality foundational team science that incorporates the knowledge and expertise of clinicians, clinician scientists, computer scientists, and data scientists. As AI/ML becomes an essential component of research and clinical care, multidisciplinary centers of excellence in AI/ML will become a key part of the scholarly portfolio of academic medical centers and will provide a foundation for the responsible, ethical, and fair implementation of AI/ML systems.

Stem cells derived from the subcutaneous adipose tissue of debrided burned skin represent an appealing source of adipose-derived stem cells (ASCs) for regenerative medicine. Traditional tissue culture uses fetal bovine serum (FBS), which complicates utilization of ASCs in human medicine. Human platelet lysate (hPL) is one potential xeno-free, alternative supplement for use in ASC culture. In this study, adipogenic and osteogenic differentiation in media supplemented with 10% FBS or 10% hPL was compared in human ASCs derived from abdominoplasty (HAP) or from adipose associated with debrided burned skin (BH). Most (95–99%) cells cultured in FBS were stained positive for CD73, CD90, CD105, and CD142. FBS supplementation was associated with increased triglyceride content and expression of adipogenic genes. Culture in hPL significantly decreased surface staining of CD105 by 31% and 48% and CD142 by 27% and 35% in HAP and BH, respectively (p

Microsoft Excel™’s default method of displaying probability values observed in the sample, in the case of very small values, is confusing to beginning students in statistics. This is because Excel displays them in engineering notation (3.52955E-.05, as opposed to .0000352955, for example). In addition, high quality statistical software programs, such as SAS/STAT™, never display an actual default probability value as small as .0000352955. For any probability value in a sample less than 1-in-10,000, SAS will display the given probability value as

Photoelectrochemical (PEC) water splitting, which utilizes sunlight and water to produce hydrogen fuel, is potentially one of the most sustainable routes to clean energy. One challenge to success is that, to date, similar materials and devices measured in different labs or by different operators lead to quantitatively different results, due to the lack of accepted standard operating procedures and established protocols for PEC efficiency testing. With the aim of disseminating good practices within the PEC community, we provide a vetted protocol that describes how to prepare integrated components and accurately measure their solar-to-hydrogen (STH) efficiency (ηSTH). This protocol provides details on electrode fabrication, ηSTH test device assembly, light source calibration, hydrogen evolution measurement, and initial material qualification by photocurrent measurements under monochromatic and broadband illumination. Common pitfalls in translating experimental results from any lab to an accurate STH efficiency under an AM1.5G reference spectrum are discussed. A III–V tandem photocathode is used to exemplify the process, though with small modifications, the protocol can be applied to photoanodes as well. Dissemination of PEC best practices will help those approaching the field and provide guidance for comparing the results obtained at different lab sites by different groups.

Abstract Introduction Many healthcare delivery systems have developed clinician‐led quality improvement (QI) initiatives but fewer have also developed in‐house evaluation units. Engagement between the two entities creates unique opportunities. Stanford Medicine funded a collaboration between their Improvement Capability Development Program (ICDP), which coordinates and incentivizes clinician‐led QI efforts, and the Evaluation Sciences Unit (ESU), a multidisciplinary group of embedded researchers with expertise in implementation and evaluation sciences. Aim To describe the ICDP‐ESU partnership and report key learnings from the first 2 y of operation September 2019 to August 2021. Methods Department‐level physician and operational QI leaders were offered an ESU consultation to workshop design, methods, and overall scope of their annual QI projects. A steering committee of high‐level stakeholders from operational, clinical, and research perspectives subsequently selected three projects for in‐depth partnered evaluation with the ESU based on evaluability, importance to the health system, and broader relevance. Selected project teams met regularly with the ESU to develop mixed methods evaluations informed by relevant implementation science frameworks, while aligning the evaluation approach with the clinical teams' QI goals. Results Sixty and 62 ICDP projects were initiated during the 2 cycles, respectively, across 18 departments, of which ESU consulted with 15 (83%). Within each annual cycle, evaluators made actionable, summative findings rapidly available to partners to inform ongoing improvement. Other reported benefits of the partnership included rapid adaptation to COVID‐19 needs, expanded clinician evaluation skills, external knowledge dissemination through scholarship, and health system‐wide knowledge exchange. Ongoing considerations for improving the collaboration included the need for multi‐year support to enable nimble response to dynamic health system needs and timely data access. Conclusion Presence of embedded evaluation partners in the enterprise‐wide QI program supported identification of analogous endeavors (eg, telemedicine adoption) and cross‐cutting lessons across QI efforts, clinician capacity building, and knowledge dissemination through scholarship.

Study objective: Since the 1990s, national guidelines have recommended hospitals develop STEMI treatment protocols and monitor quality. A 2003 survey of Minnesota hospitals without cardiac catheterization laboratories (CCL) found

Summary: Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. The bigger picture: Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data.

Sharing of whole genome sequencing (WGS) data improves study scale and power, but data from different groups are often incompatible. Here, US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Aromatase inhibitors are used to treat oestrogen-receptor-positive breast cancer. Here, the authors use genomic approaches to analyse tumours before and after neo-adjuvant treatment and find that treatment alters the clonal landscape of the tumours.

Metal oxide semiconductors are promising materials for solar energy capture but can suffer from stability problems. Here, the authors present a methodology for evaluating corrosion mechanisms and apply it to BiVO4, revealing chemical instabilities that are not predicted from thermodynamic considerations alone.