Neurotrauma clinicians' perspectives on the contextual challenges associated with long-term follow-up following traumatic brain injury in low-income and middle-income countries: a qualitative study protocol.

INTRODUCTION: Traumatic brain injury (TBI) is a global public health concern; however, low/middle-income countries (LMICs) face the greatest burden. The WHO recognises the significant differences between patient outcomes following injuries in high-income countries versus those in LMICs. Outcome data are not reliably recorded in LMICs and despite improved injury surveillance data, data on disability and long-term functional outcomes remain poorly recorded. Therefore, the full picture of outcome post-TBI in LMICs is largely unknown. METHODS AND ANALYSIS: This is a cross-sectional pragmatic qualitative study using individual semistructured interviews with clinicians who have experience of neurotrauma in LMICs. The aim of this study is to understand the contextual challenges associated with long-term follow-up of patients following TBI in LMICs. For the purpose of the study, we define 'long-term' as any data collected following discharge from hospital. We aim to conduct individual semistructured interviews with 24-48 neurosurgeons, beginning February 2020. Interviews will be recorded and transcribed verbatim. A reflexive thematic analysis will be conducted supported by NVivo software. ETHICS AND DISSEMINATION: The University of Cambridge Psychology Research Ethics Committee approved this study in February 2020. Ethical issues within this study include consent, confidentiality and anonymity, and data protection. Participants will provide informed consent and their contributions will be kept confidential. Participants will be free to withdraw at any time without penalty; however, their interview data can only be withdrawn up to 1 week after data collection. Findings generated from the study will be shared with relevant stakeholders such as the World Federation of Neurosurgical Societies and disseminated in conference presentations and journal publications

Using honey to heal diabetic foot ulcers

Diabetic ulcers seem to be arrested in the inflammatory/proliferative stage of the healing process, allowing infection and inflammation to preclude healing. Antibiotic-resistant bacteria have become a major cause of infections, including diabetic foot infections. It is proposed here that the modern developments of an ancient and traditional treatment for wounds, dressing them with honey, provide the solution to the problem of getting diabetic ulcers to move on from the arrested state of healing. Honeys selected to have a high level of antibacterial activity have been shown to be very effective against antibiotic-resistant strains of bacteria in laboratory and clinical studies. The potent anti-inflammatory action of honey is also likely to play an important part in overcoming the impediment to healing that inflammation causes in diabetic ulcers, as is the antioxidant activity of honey. The action of honey in promotion of tissue regeneration through stimulation of angiogenesis and the growth of fibroblasts and epithelial cells, and its insulin-mimetic effect, would also be of benefit in stimulating the healing of diabetic ulcers. The availability of honey-impregnated dressings which conveniently hold honey in place on ulcers has provided a means of rapidly debriding ulcers and removing the bacterial burden so that good healing rates can be achieved with neuropathic ulcers. With ischemic ulcers, where healing cannot occur because of lack of tissue viability, these honey dressings keep the ulcers clean and prevent infection occurring

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation

Purpose Internationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs). Methods CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules. Results We benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P = 1.1  ×  10−18), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data. Conclusion CardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines

Characterising the loss-of-function impact of 5' untranslated region variants in 15,708 individuals

Upstream open reading frames (uORFs) are tissue-specific cis-regulators of protein translation. Isolated reports have shown that variants that create or disrupt uORFs can cause disease. Here, in a systematic genome-wide study using 15,708 whole genome sequences, we show that variants that create new upstream start codons, and variants disrupting stop sites of existing uORFs, are under strong negative selection. This selection signal is significantly stronger for variants arising upstream of genes intolerant to loss-of-function variants. Furthermore, variants creating uORFs that overlap the coding sequence show signals of selection equivalent to coding missense variants. Finally, we identify specific genes where modification of uORFs likely represents an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in neurofibromatosis. Our results highlight uORF-perturbing variants as an under-recognised functional class that contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data in studying non-coding variant classes. Upstream open reading frames (uORFs), located in 5' untranslated regions, are regulators of downstream protein translation. Here, Whiffin et al. use the genomes of 15,708 individuals in the Genome Aggregation Database (gnomAD) to systematically assess the deleteriousness of variants creating or disrupting uORFs.Peer reviewe

Organic waste as a sustainable feedstock for platform chemicals

Biorefineries have been established since the 1980s for biofuel production, and there has been a switch lately from first to second generation feedstocks in order to avoid the food versus fuel dilemma. To a lesser extent, many opportunities have been investigated for producing chemicals from biomass using by-products of the present biorefineries, simple waste streams. Current facilities apply intensive pre-treatments to deal with single substrate types such as carbohydrates. However, most organic streams such as municipal solid waste or algal blooms present a high complexity and variable mixture of molecules, which makes specific compound production and separation difficult. Here we focus on flexible anaerobic fermentation and hydrothermal processes that can treat complex biomass as a whole to obtain a range of products within an integrated biorefinery concept

Recurrent, low-frequency coding variants contributing to colorectal cancer in the Swedish population

<div><p>Genome-wide association studies (GWAS) have identified dozens of common genetic variants associated with risk of colorectal cancer (CRC). However, the majority of CRC heritability remains unclear. In order to discover low-frequency, high-risk CRC susceptibility variants in Swedish population, we genotyped 1 515 CRC patients enriched for familial cases, and 12 108 controls. Case/control association analysis suggested eight novel variants associated with CRC risk (OR 2.0–17.6, p-value < 2.0E-07), comprised of seven coding variants in genes <i>RAB11FIP5</i>, <i>POTEA</i>, <i>COL27A1</i>, <i>MUC5B</i>, <i>PSMA8</i>, <i>MYH7B</i>, and <i>PABPC1L</i> as well as one variant downstream of <i>NEU1</i> gene. We also confirmed 27 out of 30 risk variants previously reported from GWAS in CRC with a mixed European population background. This study identified rare, coding sequence variants associated with CRC risk through analysis in a relatively homogeneous population. The segregation data suggest a complex mode of inheritance in seemingly dominant pedigrees.</p></div

A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

Background Genomic variants which disrupt splicing are a major cause of rare genetic diseases. However, variants which lie outside of the canonical splice sites are difficult to interpret clinically. Improving the clinical interpretation of non-canonical splicing variants offers a major opportunity to uplift diagnostic yields from whole genome sequencing data. Methods Here, we examine the landscape of splicing variants in whole-genome sequencing data from 38,688 individuals in the 100,000 Genomes Project and assess the contribution of non-canonical splicing variants to rare genetic diseases. We use a variant-level constraint metric (the mutability-adjusted proportion of singletons) to identify constrained functional variant classes near exon–intron junctions and at putative splicing branchpoints. To identify new diagnoses for individuals with unsolved rare diseases in the 100,000 Genomes Project, we identified individuals with de novo single-nucleotide variants near exon–intron boundaries and at putative splicing branchpoints in known disease genes. We identified candidate diagnostic variants through manual phenotype matching and confirmed new molecular diagnoses through clinical variant interpretation and functional RNA studies. Results We show that near-splice positions and splicing branchpoints are highly constrained by purifying selection and harbour potentially damaging non-coding variants which are amenable to systematic analysis in sequencing data. From 258 de novo splicing variants in known rare disease genes, we identify 35 new likely diagnoses in probands with an unsolved rare disease. To date, we have confirmed a new diagnosis for six individuals, including four in whom RNA studies were performed. Conclusions Overall, we demonstrate the clinical value of examining non-canonical splicing variants in individuals with unsolved rare diseases