367 research outputs found
DNA Methylation: Basic Biology and Application to Traumatic Brain Injury.
This article reviews the literature pertinent to epigenetic changes, and in particular, DNA methylation following traumatic brain injury (TBI). TBI is a heterogeneous disease that is a major cause of death and long-term disability. The links between TBI and epigenetics, the process by which environmental factors alter gene expression without changing the underlying DNA sequence, is an expanding area of research that may have profound consequences for understanding the disease, and for clinical care. There are various epigenetic changes that may occur as a direct result of TBI, including DNA methylation, histone modification, and changes in the levels of non-coding RNA. This review focuses on DNA methylation, its potential to alter the degree of injury, and the extent of recovery, including development of post-traumatic neurodegeneration, response to therapies, and the hereditable consequences of injury. The functional consequences of non-coding RNA and histone modifications are well described in the literature; however, the mechanism by which these three mechanisms interact are often overlooked. Here, we briefly describe the interaction of DNA methylation with the two other key epigenetic changes, and highlight key work being performed to understand the functional relevance of those mechanisms. The field of epigenetics is rapidly advancing as a result of the advent of less invasive and more versatile methods for measuring epigenetic proteins and their functional impact on cells; however, the evidence specific to TBI is limited. This review identifies several important outstanding questions that remain from the work already conducted, and highlights directions for the future
Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis
OBJECTIVE: In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic. DESIGN: Descriptive survey. SETTING: All non-specialist secondary care providers in England from 27 March27to 5 June 2020. PARTICIPANTS: Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195). MAIN OUTCOME MEASURES: Two thresholds for 'safe occupancy' were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement. RESULTS: At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1-17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. CONCLUSIONS: Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above 'safe-occupancy' thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves
A novel AhR ligand, 2AI, protects the retina from environmental stress.
Various retinal degenerative diseases including dry and neovascular age-related macular degeneration (AMD), retinitis pigmentosa, and diabetic retinopathy are associated with the degeneration of the retinal pigmented epithelial (RPE) layer of the retina. This consequently results in the death of rod and cone photoreceptors that they support, structurally and functionally leading to legal or complete blindness. Therefore, developing therapeutic strategies to preserve cellular homeostasis in the RPE would be a favorable asset in the clinic. The aryl hydrocarbon receptor (AhR) is a conserved, environmental ligand-dependent, per ARNT-sim (PAS) domain containing bHLH transcription factor that mediates adaptive response to stress via its downstream transcriptional targets. Using in silico, in vitro and in vivo assays, we identified 2,2'-aminophenyl indole (2AI) as a potent synthetic ligand of AhR that protects RPE cells in vitro from lipid peroxidation cytotoxicity mediated by 4-hydroxynonenal (4HNE) as well as the retina in vivo from light-damage. Additionally, metabolic characterization of this molecule by LC-MS suggests that 2AI alters the lipid metabolism of RPE cells, enhancing the intracellular levels of palmitoleic acid. Finally, we show that, as a downstream effector of 2AI-mediated AhR activation, palmitoleic acid protects RPE cells from 4HNE-mediated stress, and light mediated retinal degeneration in mice
Corrigendum to “Determination of Metals Present in Textile Dyes Using Laser-Induced Breakdown Spectroscopy and Cross-Validation Using Inductively Coupled Plasma/Atomic Emission Spectroscopy”
A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems
Objectives:
The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers.
Materials and Methods:
We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models.
Results:
We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured.
Discussion and conclusion:
Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms
The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study
BACKGROUND:
The literature paints a complex picture of the association between mortality risk and ICU strain.
In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.
METHODS:
A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).
RESULTS:
One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].
CONCLUSIONS:
Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation
Suitability of various plant derived gelling agents as agar substitute in microbiological growth media
Eleven putative gelling agents were investigated as agar substitutes. These included arrowroot (Maranta arundinaceae), coconut powder (Cocos nucifera), corn flour (Zea mays var. amylacea), gel rite (a water-soluble polysaccharide produced by Sphingomonas elodea), glue (Cyanoacrylates), katira gum (Cochlospermum religiosum), guar gum (Cyamopsis tetragonolobus L.), isubgol husk (Plantago ovata), pectin and rice (Oryza sativa L.) powder. Among these, guar gum was found a promising alternate candidate for agar. Media solidified with 2.8% guar gum was transparent and supportive for the growth of three test fungi (Trichoderma harzianum, Alternaria alternata and Alternaria solani) as good as agar. Guar gum also excelled in terms of cost benefit ratio when compared with agar. Guar gum fortified media was found to cost 1.17/L. Further, guar gum is easily available and can be added with ease thereby serving as a suitable and inexpensive substitute of agar and thus, can be adopted for routine microbiological testing in resource poor countries.Key words: Guar gum, media, agar, gelling agents
Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging
While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers
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