Simple and low-cost antibiotic susceptibility testing for Mycobacterium tuberculosis using screen-printed electrodes

One quarter of the global population is thought to be latently infected by Mycobacterium tuberculosis (TB) with it estimated that 1 in 10 of those people will go on to develop active disease. Due to the fact that M. tuberculosis (TB) is a disease most often associated with low- and middle-income countries, it is critical that low-cost and easy-to-use technological solutions are developed, which can have a direct impact on diagnosis and prescribing practice for TB. One area where intervention could be particularly useful is antibiotic susceptibility testing (AST). This work presents a low-cost, simple-to-use AST sensor that can detect drug susceptibility on the basis of changing RNA abundance for the typically slow-growing M. tuberculosis (TB) pathogen in 96 h using screen-printed electrodes and standard molecular biology laboratory reactionware. In order to find out the sensitivity of applied sensor platform, a different concentration (10⁸ -10³  CFU/mL) of M. tuberculosis was performed, and limit of detection and limit of quantitation were calculated as 10^3.82 and 10^11.59  CFU/mL, respectively. The results display that it was possible to detect TB sequences and distinguish antibiotic-treated cells from untreated cells with a label-free molecular detection. These findings pave the way for the development of a comprehensive, low-cost, and simple-to-use AST system for prescribing in TB and multidrug-resistant tuberculosis.

Mycobacterial Infections in Patients With Chronic Kidney Disease and Kidney Transplantation

Patients with chronic kidney disease have impaired immunity that increases their risk of infection. Increased incidence of mycobacterial infections, in particular Mycobacterium tuberculosis, is described in patients undergoing hemodialysis and peritoneal dialysis as well as after kidney transplantation in low-prevalence and high-prevalence settings. Diagnosis of this infection can be challenging because of atypical presentations that may lead to treatment delay and, consequently, increased mortality; however, recent advances in molecular testing have improved diagnostic accuracy. It is imperative to try to identify those patients at increased risk and offer adequate prophylaxis. There are controversies and insufficient data regarding treatment agents, duration, and dosages. Most studies in nontuberculous mycobacteria are based on case series and retrospective studies.

Automatic detection of mycobacterium tuberculosis using artificial intelligence

Background

Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus.

Methods

We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice.

Results

Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity.

Conclusions

TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified.