Performance of Xpert MTB/RIF in comparison with light-emitting diode-fluorescence microscopy and culture for detecting tuberculosis in pulmonary and extrapulmonary specimens in Bamako, Mali.

Background: The diagnosis of tuberculosis (TB) has mostly been relied on a long-used method called sputum smear microscopy. In 2010, Xpert MTB/RIF assay was approved by the World Health Organization for simultaneous TB diagnosis and detection of resistance. Our current study was undertaken to compare the diagnostic performance of Xpert MTB/RIF assay to auramine staining-based light-emitting diode-Fluorescence Microscopy (LED-FM) considering culture as the gold standard method for pulmonary and extrapulmonary TB. Method: Pulmonary and extrapulmonary specimens of suspected TB patients were examined in this study. From January 2016 to June 2019, sputum, urine, superficial swabs, gastric aspirates, and pleural infusion specimens were collected from new and previously treated TB individuals. Specimens were examined using Xpert MTB/RIF, LED-FM, and Mycobacterium culture techniques to evaluate their performance. Results: A total of 697 suspected TB samples were included in this analysis, and of these, 469 (67.29%) were positive for all three used methods. The overall sensitivities, specificities, and positive and negative predictive values were 99.6%, 62.0%, 88.4%, and 98.2% for Xpert MTB/RIF and 88.0%, 95.6%, 99.0%, and 60.7% for LED-FM, respectively, compared to culture method. Conclusion: The sensitivity of Xpert MTB/RIF assay was observed to be higher than the LED-FM method, thus suggesting this molecular technique as a promising tool for the diagnosis of pulmonary and extrapulmonary TB, which will help in the management of TB infections in developing countries such as Mali.

Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of COVID-19

A new guideline for mitigating indoor airborne transmission of COVID-19 prescribes a limit on the time spent in a shared space with an infected individual (Bazant and Bush, 2021). Here, we rephrase this safety guideline in terms of occupancy time and mean exhaled carbon dioxide concentration in an indoor space, thereby enabling the use of CO2 monitors in the risk assessment of airborne transmission of respiratory diseases. While CO2 concentration is related to airborne pathogen concentration (Rudnick and Milton, 2003), the guideline developed here accounts for the different physical processes affecting their evolution, such as enhanced pathogen production from vocal activity and pathogen removal via face-mask use, filtration, sedimentation and deactivation. Critically, transmission risk depends on the total infectious dose, so necessarily depends on both the pathogen concentration and exposure time. The transmission risk is also modulated by the fractions of susceptible, infected and immune persons within a population, which evolve as the pandemic runs its course. A mathematical model is developed that enables a prediction of airborne transmission risk from real-time CO2 measurements. Illustrative examples of implementing our guideline are presented using data from CO2 monitoring in university classrooms and office spaces. O_TEXTBOXImpact StatementThere is mounting scientific evidence that COVID-19 is primarily transmitted through indoor airborne transmission, as arises when a susceptible person inhales virus-laden aerosol droplets exhaled by an infectious person. A safety guideline to limit indoor airborne transmission (Bazant and Bush, 2021) has recently been derived that complements the public health guidelines on surface cleaning and social distancing. We here recast this safety guideline in terms of total inhaled carbon dioxide, as can be readily monitored in most indoor spaces. Our approach paves the way for optimizing air handling systems by balancing health and financial concerns, informs policy for safely re-opening schools and businesses as the pandemic runs its course, and may be applied quite generally in the mitigation of airborne respiratory illnesses, including influenza. C_TEXTBOX