Reported outcome measures in necrotising soft tissue infections: a systematic review.

Introduction: There are inconsistencies in outcome reporting for patients with necrotising soft tissue infections (NSTI). The aim of this study was to evaluate reported outcome measures in NSTI literature that could inform a core outcome set (COS) such as could be used in a study of hyperbaric oxygen in this indication. Methods: A systematic review of all NSTI literature identified from Cochrane, Ovid MEDLINE and Scopus databases as well as grey literature sources OpenGrey and the New York Academy of Medicine databases which met inclusion criteria and were published between 2010 and 2020 was performed. Studies were included if they reported on > 5 cases and presented clinical endpoints, patient related outcomes, or resource utilisation in NSTI patients. Studies did not have to include intervention. Two independent researchers then extracted reported outcome measures. Similar outcomes were grouped and classified into domains to produce a structured inventory. An attempt was made to identify trends in outcome measures over time and by study design. Results: Three hundred and seventy-five studies were identified and included a total of 311 outcome measures. Forty eight percent (150/311) of outcome measures were reported by two or more studies. The four most frequently reported outcome measures were mortality without time specified, length of hospital stay, amputation performed, and number of debridements, reported in 298 (79.5%), 260 (69.3%), 156 (41.6%) and 151 (40.3%) studies respectively. Mortality outcomes were reported in 23 different ways. Randomised controlled trials (RCTs) were more likely to report 28-day mortality or 90-day mortality. The second most frequent amputation related outcome was level of amputation, reported in 7.5% (28/375) of studies. The most commonly reported patient-centred outcome was the SF-36 which was reported in 1.6% (6/375) of all studies and in 2/10 RCTs. Conclusions: There was wide variance in outcome measures in NSTI studies, further highlighting the need for a COS.

EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring.

Computational prediction of molecule-protein interactions has been key for developing new molecules to interact with a target protein for therapeutics development. Previous work includes two independent streams of approaches: (1) predicting protein-protein interactions (PPIs) between naturally occurring proteins and (2) predicting binding affinities between proteins and small-molecule ligands [also known as drug-target interaction (DTI)]. Studying the two problems in isolation has limited the ability of these computational models to generalize across the PPI and DTI tasks, both of which ultimately involve noncovalent interactions with a protein target. In this work, we developed Equivariant Graph of Graphs neural Network (EGGNet), a geometric deep learning (GDL) framework, for molecule-protein binding predictions that can handle three types of molecules for interacting with a target protein: (1) small molecules, (2) synthetic peptides, and (3) natural proteins. EGGNet leverages a graph of graphs (GoG) representation constructed from the molecular structures at atomic resolution and utilizes a multiresolution equivariant graph neural network to learn from such representations. In addition, EGGNet leverages the underlying biophysics and makes use of both atom- and residue-level interactions, which improve EGGNet's ability to rank candidate poses from blind docking. EGGNet achieves competitive performance on both a public protein-small-molecule binding affinity prediction task (80.2% top 1 success rate on CASF-2016) and a synthetic protein interface prediction task (88.4% area under the precision-recall curve). We envision that the proposed GDL framework can generalize to many other protein interaction prediction problems, such as binding site prediction and molecular docking, helping accelerate protein engineering and structure-based drug development.

Cerebrospinal fluid neurofilament light predicts longitudinal diagnostic change in patients with psychiatric and neurodegenerative disorders.

OBJECTIVE: People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown. METHODS: We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other. RESULTS: Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone. CONCLUSIONS: CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

Plasma neurofilament light in behavioural variant frontotemporal dementia compared to mood and psychotic disorders.

OBJECTIVE: Blood biomarkers of neuronal injury such as neurofilament light (NfL) show promise to improve diagnosis of neurodegenerative disorders and distinguish neurodegenerative from primary psychiatric disorders (PPD). This study investigated the diagnostic utility of plasma NfL to differentiate behavioural variant frontotemporal dementia (bvFTD, a neurodegenerative disorder commonly misdiagnosed initially as PPD), from PPD, and performance of large normative/reference data sets and models. METHODS: Plasma NfL was analysed in major depressive disorder (MDD, n = 42), bipolar affective disorder (BPAD, n = 121), treatment-resistant schizophrenia (TRS, n = 82), bvFTD (n = 22), and compared to the reference cohort (Control Group 2, n = 1926, using GAMLSS modelling), and age-matched controls (Control Group 1, n = 96, using general linear models). RESULTS: Large differences were seen between bvFTD (mean NfL 34.9 pg/mL) and all PPDs and controls (all < 11 pg/mL). NfL distinguished bvFTD from PPD with high accuracy, sensitivity (86%), and specificity (88%). GAMLSS models using reference Control Group 2 facilitated precision interpretation of individual levels, while performing equally to or outperforming models using local controls. Slightly higher NfL levels were found in BPAD, compared to controls and TRS. CONCLUSIONS: This study adds further evidence on the diagnostic utility of NfL to distinguish bvFTD from PPD of high clinical relevance to a bvFTD differential diagnosis, and includes the largest cohort of BPAD to date. Using large reference cohorts, GAMLSS modelling and the interactive Internet-based application we developed, may have important implications for future research and clinical translation. Studies are underway investigating utility of plasma NfL in diverse neurodegenerative and primary psychiatric conditions in real-world clinical settings.

Biopsychosocial factors of quality of life in individuals with moderate to severe traumatic brain injury: a scoping review.

PURPOSE: Individuals with moderate to severe traumatic brain injury (TBI) experience changes in their quality-of-life (QOL) post-injury. Given the vast literature that exists about QOL after TBI, a scoping review was performed to identify the different biopsychosocial factors that affect a person's QOL after a moderate to severe TBI. METHODS: A scoping review was conducted using the following electronic databases: MEDLINE, CINAHL, Embase, and PsycINFO. Terms relating to TBI and QOL were used. RESULTS: There were 7576 articles obtained from the databases, resulting in 535 full-text articles. Ultimately, 52 articles were extracted, which consisted of biopsychosocial QOL factors after TBI. The biopsychosocial factors of QOL after TBI included 19 biological factors (i.e., sex, TBI severity, cognition), 16 psychological factors (i.e., depression, self-efficacy, coping styles), and 19 social factors (i.e., employment, social participation, social support). Factors such as fatigue, self-awareness, transition, and discharge from hospitals are known issues in TBI literature but were minimally reported in studies in this review, identifying them as potential gaps in research. CONCLUSION: Identifying biopsychosocial factors relating to QOL after TBI can enable health services to develop targeted rehabilitation programs for individuals with TBI.

Successful novel use of tofacitinib for type II refractory coeliac disease.

Refractory coeliac disease (RCD) occurs when patients with confirmed CD have continuous or recurrent malabsorption and enteropathy after at least 12 months on a gluten-free diet. Differentiating between type I and type II RCD is key as the latter is associated with T-cell aberrancy and considered prelymphoma, with high mortality rates. Current treatment regimens for type II RCD include corticosteroids, biologics and chemotherapy, but there are no proven therapies for this serious condition. Our patient is a middle-aged woman who developed postpartum type II RCD. When she failed multiple drug classes, we did a trial of tofacitinib. Our clinical experience with use of a janus kinase inhibitor was successful, with no associated adverse events. This is the first report in the literature of RCD remission in response to tofacitinib. The use of this novel agent shows promise in reversing this potentially fatal condition.

The impact of whole genome and transcriptome analysis (WGTA) on predictive biomarker discovery and diagnostic accuracy of advanced malignancies.

In this study, we evaluate the impact of whole genome and transcriptome analysis (WGTA) on predictive molecular profiling and histologic diagnosis in a cohort of advanced malignancies. WGTA was used to generate reports including molecular alterations and site/tissue of origin prediction. Two reviewers analyzed genomic reports, clinical history, and tumor pathology. We used National Comprehensive Cancer Network (NCCN) consensus guidelines, Food and Drug Administration (FDA) approvals, and provincially reimbursed treatments to define genomic biomarkers associated with approved targeted therapeutic options (TTOs). Tumor tissue/site of origin was reassessed for most cases using genomic analysis, including a machine learning algorithm (Supervised Cancer Origin Prediction Using Expression [SCOPE]) trained on The Cancer Genome Atlas data. WGTA was performed on 652 cases, including a range of primary tumor types/tumor sites and 15 malignant tumors of uncertain histogenesis (MTUH). At the time WGTA was performed, alterations associated with an approved TTO were identified in 39 (6%) cases; 3 of these were not identified through routine pathology workup. In seven (1%) cases, the pathology workup either failed, was not performed, or gave a different result from the WGTA. Approved TTOs identified by WGTA increased to 103 (16%) when applying 2021 guidelines. The histopathologic diagnosis was reviewed in 389 cases and agreed with the diagnostic consensus after WGTA in 94% of non-MTUH cases (n = 374). The remainder included situations where the morphologic diagnosis was changed based on WGTA and clinical data (0.5%), or where the WGTA was non-contributory (5%). The 15 MTUH were all diagnosed as specific tumor types by WGTA. Tumor board reviews including WGTA agreed with almost all initial predictive molecular profile and histopathologic diagnoses. WGTA was a powerful tool to assign site/tissue of origin in MTUH. Current efforts focus on improving therapeutic predictive power and decreasing cost to enhance use of WGTA data as a routine clinical test.

Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.

Young and under the influence: A systematic literature review of the impact of cannabis on the driving performance of youth.

BACKGROUND: Young drivers ages 15-24 continue to constitute a high-risk population for fatal motor vehicle collisions (MVCs) compared to all other age groups. Driving under the influence of cannabis is an important contributor to the high rates of MVCs among youth. Understanding the specific impact of cannabis on the driving performance outcomes of young drivers can inform injury prevention, education, and intervention strategies. OBJECTIVES: This systematic literature review (SLR) aims to determine the Class (I- highest to IV-lowest) of evidence and level of confidence (A-high to U-insufficient) in the effects of cannabis on the driving performance of young drivers. METHODS: Registered in PROSPERO (#CRD42020180541), this SLR searched seven data bases and appraised the quality and confidence in the evidence using an established research methodology. RESULTS: Class II evidence suggests that THC is likely to reduce mean speed, headway distance, and reaction time; and increase lane and steering wheel position variability among young drivers (Level B, moderate confidence). CONCLUSIONS: This study shows that there is a moderate to low level of confidence on the impact of cannabis on the specific driving performance outcomes of young drivers. A need remains for Class I and II studies that focus on the specific effects on young drivers, distinguish between the biological and socially constructed variables of sex and gender, and includes larger and more representative samples.

Casting a Wider NET: An Unusual Cause of Acute Liver Failure in a Pregnant Patient.

We present a case patient in her second trimester of pregnancy who developed acute liver failure from metastatic neuroendocrine tumor (NET). Although she underwent prompt induction of a non-viable fetus due to initial concerns of hemolysis, elevated liver enzymes, and low platelet count syndrome, her liver function continued to deteriorate postpartum. She was subsequently transferred to our institution in order to undergo further evaluation that included a transjugular liver biopsy and subsequent diagnosis of high-grade NET. She was given salvage carboplatin-based chemotherapy, as she was not a liver transplant candidate. Unfortunately, the patient expired from cardiovascular collapse as a component of multiorgan failure.

Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes.

Advanced and metastatic tumors with complex treatment histories drive cancer mortality. Here we describe the POG570 cohort, a comprehensive whole-genome, transcriptome and clinical dataset, amenable for exploration of the impacts of therapies on genomic landscapes. Previous exposure to DNA-damaging chemotherapies and mutations affecting DNA repair genes, including POLQ and genes encoding Polζ, were associated with genome-wide, therapy-induced mutagenesis. Exposure to platinum therapies coincided with signatures SBS31 and DSB5 and, when combined with DNA synthesis inhibitors, signature SBS17b. Alterations in ESR1, EGFR, CTNNB1, FGFR1, VEGFA and DPYD were consistent with drug resistance and sensitivity. Recurrent noncoding events were found in regulatory region hotspots of genes including TERT, PLEKHS1, AP2A1 and ADGRG6. Mutation burden and immune signatures corresponded with overall survival and response to immunotherapy. Our data offer a rich resource for investigation of advanced cancers and interpretation of whole-genome and transcriptome sequencing in the context of a cancer clinic.

Drug-Induced Autoimmune Hepatitis From Hydralazine Leading to Acute Liver Failure and Liver Transplantation.

We describe a woman with no previous liver disease who developed drug-induced autoimmune hepatitis from hydralazine prescribed to her for hypertension. Despite the discontinuation of the medication, she developed acute liver failure and subsequently underwent successful liver transplantation. She survived and had a good clinical outcome.

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database.

BACKGROUND: Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. METHODS: To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. RESULTS: We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. CONCLUSIONS: Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.