Impact of whole-genome duplications on structural variant evolution in Cochlearia.

Polyploidy, the result of whole-genome duplication (WGD), is a major driver of eukaryote evolution. Yet WGDs are hugely disruptive mutations, and we still lack a clear understanding of their fitness consequences. Here, we study whether WGDs result in greater diversity of genomic structural variants (SVs) and how they influence evolutionary dynamics in a plant genus, Cochlearia (Brassicaceae).

Utility of wastewater genomic surveillance compared to clinical surveillance to track the spread of the SARS-CoV-2 Omicron variant across England.

The world has moved into a new stage of managing the SARS-CoV-2 pandemic with minimal restrictions and reduced testing in the population, leading to reduced genomic surveillance of virus variants in individuals. Wastewater-based epidemiology (WBE) can provide an alternative means of tracking virus variants in the population but decision-makers require confidence that it can be applied to a national scale and is comparable to individual testing data. We analysed 19,911 samples from 524 wastewater sites across England at least twice a week between November 2021 and February 2022, capturing sewage from >70% of the English population.

Evaluation of variant calling algorithms for wastewater-based epidemiology using mixed populations of SARS-CoV-2 variants in synthetic and wastewater samples.

Wastewater-based epidemiology has been used extensively throughout the COVID-19 (coronavirus disease 19) pandemic to detect and monitor the spread and prevalence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and its variants. It has proven an excellent, complementary tool to clinical sequencing, supporting the insights gained and helping to make informed public-health decisions. Consequently, many groups globally have developed bioinformatics pipelines to analyse sequencing data from wastewater.

A framework for real-time monitoring, analysis and adaptive sampling of viral amplicon nanopore sequencing.

The ongoing SARS-CoV-2 pandemic demonstrates the utility of real-time sequence analysis in monitoring and surveillance of pathogens. However, cost-effective sequencing requires that samples be PCR amplified and multiplexed barcoding onto a single flow cell, resulting in challenges with maximising and balancing coverage for each sample. To address this, we developed a real-time analysis pipeline to maximise flow cell performance and optimise sequencing time and costs for any amplicon based sequencing.

Hypoxia signatures in closed-circuit rebreather divers.

Introduction: Faults or errors during use of closed-circuit rebreathers (CCRs) can cause hypoxia. Military aviators face a similar risk of hypoxia and undergo awareness training to determine their 'hypoxia signature', a personalised, reproducible set of symptoms. We aimed to establish a hypoxia signature among divers, and to investigate their ability to detect hypoxia and self-rescue while cognitively overloaded.

Paternal low protein diet perturbs inter-generational metabolic homeostasis in a tissue-specific manner in mice.

The underlying mechanisms driving paternally-programmed metabolic disease in offspring remain poorly defined. We fed male C57BL/6 mice either a control normal protein diet (NPD; 18% protein) or an isocaloric low protein diet (LPD; 9% protein) for a minimum of 8 weeks. Using artificial insemination, in combination with vasectomised male mating, we generated offspring using either NPD or LPD sperm but in the presence of NPD or LPD seminal plasma.

Retrospective screening of routine respiratory samples revealed undetected community transmission and missed intervention opportunities for SARS-CoV-2 in the United Kingdom.

In the early phases of the SARS coronavirus type 2 (SARS-CoV-2) pandemic, testing focused on individuals fitting a strict case definition involving a limited set of symptoms together with an identified epidemiological risk, such as contact with an infected individual or travel to a high-risk area. To assess whether this impaired our ability to detect and control early introductions of the virus into the UK, we PCR-tested archival specimens collected on admission to a large UK teaching hospital who retrospectively were identified as having a clinical presentation compatible with COVID-19. In addition, we screened available archival specimens submitted for respiratory virus diagnosis, and dating back to early January 2020, for the presence of SARS-CoV-2 RNA.

Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.

Study Objective: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.

Objective: The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.

Methods: During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California.

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

Background: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

Research Question: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance?

Study Design And Methods: We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio, and pH) were used to assess future need for MV.

Predicting progression to septic shock in the emergency department using an externally generalizable machine learning algorithm.

Objective: Machine-learning (ML) algorithms allow for improved prediction of sepsis syndromes in the ED using data from electronic medical records. Transfer learning, a new subfield of ML, allows for generalizability of an algorithm across clinical sites. We aimed to validate the Artificial Intelligence Sepsis Expert (AISE) for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.

Leveraging Remote Research Associates During a Pandemic.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has seriously impacted clinical research operations in academic medical centers due to social distancing measures and stay-at-home orders. The purpose of this paper is to describe the implementation of a program to continue clinical research based out of an emergency department (ED) using remote research associates (RA).

Methods: Remote RAs were trained and granted remote access to the electronic health record (EHR) by the health system's core information technology team.

Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

Importance: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.

Objective: To develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.

Design: Observational cohort study SETTING: Two academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).

Virtual Visits: Managing prenatal care with modern technology.

Purpose: To implement and evaluate a novel model of prenatal care for low-risk pregnant women that intersperses in-person physician visits with nurse practitioner visits conducted via videoconference.

Methods: This Quality Improvement initiative gave low-risk pregnant women the option of enrolling in a Traditional (N = 941) or Virtual Visit (N = 117) track for their prenatal care. Traditional patients had 14 physician visits and a postpartum visit.

Rationale for the prevention of oral diseases in primary health care: an international collaborative study in oral health education.

Ensuring that members of society are healthy and reaching their full potential requires the prevention of oral diseases through the promotion of oral health and well-being. The present article identifies the best policy conditions of effective public health and primary care integration and the actors who promote and sustain these efforts. In this review, arguments and recommendations are provided to introduce an oral health collaborative promotion programme called Live.

Extraction and nonradioactive detection of small RNA molecules.

The emergence of small RNAs as key and potent regulators of gene expression has prompted the need for robust detection and assay protocols to be developed for investigating their generation and tissue distribution. The physicochemical nature of these RNAs allows traditional assay methods to be employed; however, due to the relatively small size of endo-siRNAs, key changes to these protocols are required. Here, we present a method for the nonradioactive detection of endo-siRNAs in mouse tissue and microinjected Xenopus oocytes.

Contribution of natural antisense transcription to an endogenous siRNA signature in human cells.

Background: Eukaryotic cells express a complex layer of noncoding RNAs. An intriguing family of regulatory RNAs includes transcripts from the opposite strand of protein coding genes, so called natural antisense transcripts (NATs). Here, we test the hypothesis that antisense transcription triggers RNA interference and gives rise to endogenous short RNAs (endo-siRNAs).

Reducing the effect of variable starch levels in biomass recalcitrance screening.

Cell wall recalcitrance is the largest contributor to the high expense of lignocellulose conversion to biofuels (Himmel ME et al., Science 315:804-807, 2007). In response to this problem, researchers at the BioEnergy Science Center (BESC) are working to determine the contributing factors of biomass recalcitrance.

Mapping biological to clinical phenotypes during the development (21 days) and resolution (21 days) of experimental gingivitis.

Aim: To characterize and map temporal changes in the biological and clinical phenotype during a 21-day experimental gingivitis study.

Materials And Methods: Experimental gingivitis was induced over 21 days in healthy human volunteers (n = 56), after which normal brushing was resumed (resolution phase). Gingival and plaque indices were assessed.

Prophylaxis for venous thromboembolism during rehabilitation for traumatic brain injury: a multicenter observational study.

Background: Deep venous thrombosis (DVT) is a major cause of mortality and morbidity after traumatic brain injury (TBI). There is no consensus regarding appropriate screening, prophylaxis, or treatment during acute rehabilitation.

Methods: This prospective observational study evaluated prophylactic anticoagulation during rehabilitation in patients with TBI aged 16 years or older admitted to 12 TBI Model Systems rehabilitation centers (July 2004-December 2007).

Strand selective generation of endo-siRNAs from the Na/phosphate transporter gene Slc34a1 in murine tissues.

Natural antisense transcripts (NATs) are important regulators of gene expression. Recently, a link between antisense transcription and the formation of endo-siRNAs has emerged. We investigated the bi-directionally transcribed Na/phosphate cotransporter gene (Slc34a1) under the aspect of endo-siRNA processing.

What do natural antisense transcripts regulate?

In human and mouse up to 72% of all genomic loci show evidence of transcription from both sense and antisense strands. The benefit of the resulting natural antisense transcripts (NATs) remains unclear, largely because of a lack of significant correlation between gene ontology and antisense transcription. Here we suggest that a well defined group of NATs may be identified based on structural characteristics.

Impact of age on long-term recovery from traumatic brain injury.

Objective: To determine whether older persons are at increased risk for progressive functional decline after traumatic brain injury (TBI).

Design: Longitudinal cohort study.

Setting: Traumatic Brain Injury Model Systems (TBIMS) rehabilitation centers.