Systematic comparison of ranking aggregation methods for gene lists in experimental results

MOTIVATION: A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS: In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION: The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Demographics and Outcomes of Initial Phase of COVID-19 Medicines Delivery Units Across 4 UK Centers During Peak B1.1.529 Omicron Epidemic: A Service Evaluation

Background: COVID-19 medicines delivery units (CMDU) were established in late December 2021 to deliver early antiviral therapy to patients classified as at risk with the aim of preventing hospitalization. Methods: We performed a service evaluation at 4 CMDUs in England. We assessed demographics and triage outcomes of CMDU referral, uptake of antiviral therapy, and the rate of subsequent hospitalizations within 2 weeks of CMDU referral. Results: Over a 3-week period, 4788 patients were referred and 3989 were ultimately assessed by a CMDU. Overall, 832 of the patients referred (17%) were judged eligible for treatment and 628 (13%) were ultimately prescribed an antiviral agent. The overall rate of admission within 14 days was 1%. Patients who were admitted were significantly older than those who did not require hospitalization. Of patients prescribed molnupiravir and sotrovimab, 1.8% and 3.2%, respectively, were admitted. Conclusions: There was a high volume of referrals to CMDU service during the initial surge of the Omicron wave in the United Kingdom. A minority of patients were judged to be eligible for therapy. In a highly vaccinated population, the overall hospitalization rate was low

Clinical management of hospitalized patients with high-consequence infectious diseases in England

Infectious disease physicians in England have been diagnosing and managing occasional cases of viral hemorrhagic fever since 1971, including the United Kingdom's first case of Ebola virus disease in 1976. Specialist isolation facilities to provide safe and effective care have been present since that time. Following the emergence of Middle East respiratory syndrome (MERS) in 2012, and the avian influenza A (H7N9) outbreak in 2013, and the 2014-2016 Ebola virus disease outbreak in West Africa, clinical and public health preparedness and response pathways in England have been strengthened for these types of diseases, now called high-consequence infectious diseases (HCIDs). The HCID program, led by NHS England and Public Health England between 2016 and 2018, helped to deliver these enhancements, which have since been used on multiple occasions for new UK cases and outbreaks of MERS, mpox, avian influenza, and Lassa fever. Additionally, HCID pathways were activated for COVID-19 during the first 3 months of 2020, before the pandemic had been declared and little was known about COVID-19 but HCID status had been assigned temporarily to COVID-19 as a precaution. The HCID program also led to the commissioning of a network of new airborne HCID treatment centers in England, to supplement the existing network of contact HCID treatment centers, which includes the United Kingdom's only 2 high-level isolation units. In this case study, the authors describe the airborne and contact HCID treatment center networks in England, including their formation and structures, their approach to safe and effective clinical management of patients with HCIDs in the United Kingdom, and challenges they may face going forward

A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest x-rays

There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS-CoV-2 positive patients, respectively. External validation of the final model was conducted on 741 Accident and Emergency (A&E) admissions with suspected SARS-CoV-2 infection from a separate NHS Trust. The LUCAS mortality score included five strongest predictors (Lymphocyte count, Urea, C-reactive protein, Age, Sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the area under the receiving operating characteristics curve (AUC-ROC) in development cohort 0.765 (95% confidence interval (CI): 0.738–0.790), in internal validation cohort 0.744 (CI: 0.673–0.808), and in external validation cohort 0.752 (CI: 0.713–0.787). The discriminatory power of LUCAS increased slightly when including the CXR image data. LUCAS can be used to obtain valid predictions of mortality in patients within 60 days of SARS-CoV-2 RT-PCR results into low, moderate, high, or very high risk of fatality

A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest x-rays

There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS-CoV-2 positive patients, respectively. External validation of the final model was conducted on 741 Accident and Emergency (A&E) admissions with suspected SARS-CoV-2 infection from a separate NHS Trust. The LUCAS mortality score included five strongest predictors (Lymphocyte count, Urea, C-reactive protein, Age, Sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the area under the receiving operating characteristics curve (AUC-ROC) in development cohort 0.765 (95% confidence interval (CI): 0.738–0.790), in internal validation cohort 0.744 (CI: 0.673–0.808), and in external validation cohort 0.752 (CI: 0.713–0.787). The discriminatory power of LUCAS increased slightly when including the CXR image data. LUCAS can be used to obtain valid predictions of mortality in patients within 60 days of SARS-CoV-2 RT-PCR results into low, moderate, high, or very high risk of fatality

Demographics and Outcomes of Initial Phase of COVID-19 Medicines Delivery Units Across 4 UK Centers During Peak B1.1.529 Omicron Epidemic: A Service Evaluation

BACKGROUND: COVID-19 medicines delivery units (CMDU) were established in late December 2021 to deliver early antiviral therapy to patients classified as at risk with the aim of preventing hospitalization. METHODS: We performed a service evaluation at 4 CMDUs in England. We assessed demographics and triage outcomes of CMDU referral, uptake of antiviral therapy, and the rate of subsequent hospitalizations within 2 weeks of CMDU referral. RESULTS: Over a 3-week period, 4788 patients were referred and 3989 were ultimately assessed by a CMDU. Overall, 832 of the patients referred (17%) were judged eligible for treatment and 628 (13%) were ultimately prescribed an antiviral agent. The overall rate of admission within 14 days was 1%. Patients who were admitted were significantly older than those who did not require hospitalization. Of patients prescribed molnupiravir and sotrovimab, 1.8% and 3.2%, respectively, were admitted. CONCLUSIONS: There was a high volume of referrals to CMDU service during the initial surge of the Omicron wave in the United Kingdom. A minority of patients were judged to be eligible for therapy. In a highly vaccinated population, the overall hospitalization rate was low

NRF2 activation reprogrammes defects in oxidative metabolism to restore macrophage function in COPD

Rationale: Chronic obstructive pulmonary disease (COPD) is a disease characterized by persistent airway inflammation and disordered macrophage function. The extent to which alterations in macrophage bioenergetics contribute to impaired antioxidant responses and disease pathogenesis has yet to be fully delineated. Objectives: Through the study of COPD alveolar macrophages (AMs) and peripheral monocyte-derived macrophages (MDMs), we sought to establish if intrinsic defects in core metabolic processes drive macrophage dysfunction and redox imbalance. Methods: AMs and MDMs from donors with COPD and healthy donors underwent functional, metabolic, and transcriptional profiling. Measurements and Main Results: We observed that AMs and MDMs from donors with COPD display a critical depletion in glycolytic- and mitochondrial respiration-derived energy reserves and an overreliance on glycolysis as a source for ATP, resulting in reduced energy status. Defects in oxidative metabolism extend to an impaired redox balance associated with defective expression of the NADPH-generating enzyme, ME1 (malic enzyme 1), a known target of the antioxidant transcription factor NRF2 (nuclear factor erythroid 2-related factor 2). Consequently, selective activation of NRF2 resets the COPD transcriptome, resulting in increased generation of TCA cycle intermediaries, improved energetic status, favorable redox balance, and recovery of macrophage function. Conclusions: In COPD, an inherent loss of metabolic plasticity leads to metabolic exhaustion and reduced redox capacity, which can be rescued by activation of the NRF2 pathway. Targeting these defects, via NRF2 augmentation, may therefore present an attractive therapeutic strategy for the treatment of the aberrant airway inflammation described in COPD.status: publishe

Clinical features and management of individuals admitted to hospital with monkeypox and associated complications across the UK: a retrospective cohort study

BACKGROUND: The scale of the 2022 global mpox (formerly known as monkeypox) outbreak has been unprecedented. In less than 6 months, non-endemic countries have reported more than 67 000 cases of a disease that had previously been rare outside of Africa. Mortality has been reported as rare but hospital admission has been relatively common. We aimed to describe the clinical and laboratory characteristics and outcomes of individuals admitted to hospital with mpox and associated complications, including tecovirimat recipients. METHODS: In this cohort study, we undertook retrospective review of electronic clinical records and pathology data for all individuals admitted between May 6, and Aug 3, 2022, to 16 hospitals from the Specialist and High Consequence Infectious Diseases Network for Monkeypox. The hospitals were located in ten cities in England and Northern Ireland. Inclusion criteria were clinical signs consistent with mpox and MPXV DNA detected from at least one clinical sample by PCR testing. Patients admitted solely for isolation purposes were excluded from the study. Key outcomes included admission indication, complications (including pain, secondary infection, and mortality) and use of antibiotic and anti-viral treatments. Routine biochemistry, haematology, microbiology, and virology data were also collected. Outcomes were assessed in all patients with available data. FINDINGS: 156 individuals were admitted to hospital with complicated mpox during the study period. 153 (98%) were male and three (2%) were female, with a median age of 35 years (IQR 30-44). Gender data were collected from electronic patient records, which encompassed full formal review of clincian notes. The prespecified options for data collection for gender were male, female, trans, non-binary, or unknown. 105 (71%) of 148 participants with available ethnicity data were of White ethnicity and 47 (30%) of 155 were living with HIV with a median CD4 count of 510 cells per mm3 (IQR 349-828). Rectal or perianal pain (including proctitis) was the most common indication for hospital admission (44 [28%] of 156). Severe pain was reported in 89 (57%) of 156, and secondary bacterial infection in 82 (58%) of 142 individuals with available data. Median admission duration was 5 days (IQR 2-9). Ten individuals required surgery and two cases of encephalitis were reported. 38 (24%) of the 156 individuals received tecovirimat with early cessation in four cases (two owing to hepatic transaminitis, one to rapid treatment response, and one to patient choice). No deaths occurred during the study period. INTERPRETATION: Although life-threatening mpox appears rare in hospitalised populations during the current outbreak, severe mpox and associated complications can occur in immunocompetent individuals. Analgesia and management of superimposed bacterial infection are priorities for patients admitted to hospital. FUNDING: None