Genomic Epidemiology of Mycobacterium abscessus on the Island of Montréal Not Suggestive of Healthcare-associated Person-to-Person Transmission.

BACKGROUND: Mycobacterium abscessus complex (MABC), an opportunistic nontuberculous mycobacteria (NTM), can lead to poor clinical outcomes in pulmonary infections. Conflicting data exist on person-to-person transmission of MABC within and across healthcare facilities. To investigate further, a comprehensive retrospective study across five healthcare institutions on the Island of Montréal was undertaken. METHODS: We analyzed the genomes of 221 MABC isolates obtained from 115 individuals (2010-2018) to identify possible links. Genetic similarity, defined as ≤25 single-nucleotide polymorphisms (SNPs), was investigated through a blinded epidemiological inquiry. RESULTS: Bioinformatics analyses identified 28 sequence types (STs), including globally observed dominant circulating clones (DCCs). Further analysis revealed 210 isolate pairs within the SNP threshold. Among these pairs, there was one possible lab contamination where isolates from different patients processed in the same lab differed by only 2 SNPs. There were 37 isolate pairs from patients who had provided specimens from the same hospital; however, epidemiological analysis found no evidence of healthcare-associated person-to-person transmission between these patients. Additionally, pan-genome analysis showed higher discriminatory power than core genome analysis for examining genomic similarity. CONCLUSIONS: Genomics alone is insufficient to establish MABC transmission, particularly considering the genetic similarity and wide distribution of DCCs, although pan-genome analysis has the potential to add further insight. Our findings indicate that MABC infections in Montréal are unlikely attributable to healthcare-associated person-to-person transmission.

Digital cough monitoring - A potential predictive acoustic biomarker of clinical outcomes in hospitalized COVID-19 patients.

PURPOSE: Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death. METHODS: One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes. RESULTS: In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6 h (0·792) and 24 h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value. INTERPRETATION: Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.

Incidence of cough from acute exposure to fine particulate matter (PM2.5) in Madagascar: A pilot study.

Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children's MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications.