A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants.

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.

Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults.

We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.

INTRODUCTION: This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aß) status of registry participants. METHODS: We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aß positivity using the results of positron emission tomography as ground truth. RESULTS: Study partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization. DISCUSSION: Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aß positivity.

Impaired immune signaling and changes in the lung microbiome precede secondary bacterial pneumonia in COVID-19.

Secondary bacterial infections, including ventilator-associated pneumonia (VAP), lead to worse clinical outcomes and increased mortality following viral respiratory infections including in patients with coronavirus disease 2019 (COVID-19). Using a combination of tracheal aspirate bulk and single-cell RNA sequencing we assessed lower respiratory tract immune responses and microbiome dynamics in 23 COVID-19 patients, 10 of whom developed VAP, and eight critically ill uninfected controls. At a median of three days (range: 2-4 days) before VAP onset we observed a transcriptional signature of bacterial infection. At a median of 15 days prior to VAP onset (range: 8-38 days), we observed a striking impairment in immune signaling in COVID-19 patients who developed VAP. Longitudinal metatranscriptomic analysis revealed disruption of lung microbiome community composition in patients with VAP, providing a connection between dysregulated immune signaling and outgrowth of opportunistic pathogens. These findings suggest that COVID-19 patients who develop VAP have impaired antibacterial immune defense detectable weeks before secondary infection onset.

Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm.

INTRODUCTION: There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years. METHODS: Data from 1737 patients were processed using the "All-Pairs" technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). RESULTS: A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. DISCUSSION: Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.

Bull-related incidents: their prevalence and nature.

The hazards associated with breeding livestock were well documented in some of the earliest annals of literature. With the exception of horses, bulls have probably caused more livestock- related deaths and injuries to human beings throughout recorded history than any other domesticated animal. A review of the literature might suggest that attacks by bulls were more of a problem in the past than today. However, a bull-related injury surveillance project conducted by the authors documented that bulls continue to contribute to an unacceptable number of serious injuries and deaths. In 2006, following an increase in the number of bull-attack cases identified during ongoing surveillance of agricultural work-related injuries, a search of agricultural injury data was initiated to gain a better perspective of the bull-incidence problem. Approximately 3 years of data, gathered from daily reviews of online sources plus a review of more than 12,000 prior injury reports, were combined, coded, and summarized. A total of 287 cases primarily from the United States were documented and analyzed. Where reported, contributing factors were identified, including age of victim, type of bull, type and condition of handling facility, experience of handler, and time of year. Analysis of the literature and data indicates that (1) the risk of injury associated with hours of exposure to bulls is higher than that of working around cows; (2) the risk of a bull-related fatality, based upon the hours of exposure, appears to be higher than other known hazards, such as tractor operation; (3) victims generally appeared to have had considerable experience with handling bulls; (4) bulls raised from calves on-site appeared more aggressive; and (5) most of the incidents involved the victim being inside the bull holding area. Recommendations are presented for reducing the potential of bull attacks on humans.