Back

A high centrifugal force-enhanced Ziehl-Neelsen method for improved detection of Mycobacterium tuberculosis

Chaula, G. T.; Namkinga, L.; Sabiiti, W.; Ntiningya, N. E.; Mtafya, B.; Mahadhy, A.

2026-01-23 infectious diseases
10.64898/2026.01.21.26344580 medRxiv
Show abstract

Ziehl-Neelsen (ZN) smear microscopy remains central to tuberculosis (TB) diagnosis and treatment monitoring, yet its sensitivity is limited by incomplete recovery of Mycobacterium tuberculosis during pre-analytical processing. This study evaluated whether modifying centrifugation force and duration improves bacillary recovery and ZN smear performance. Laboratory experiments were conducted using M. tuberculosis H37Rv and pulmonary TB sputum samples. Following NALC-NaOH decontamination, samples were centrifuged at 2,000, 3,000, or 6,000 x g for 40 min, and the effect of centrifugation time at 3,000 x g was assessed by comparing 20 and 40 min using the same specimens. ZN smear grading and positivity were evaluated in triplicate and compared statistically, with significance set at P < 0.05. In H37Rv suspensions, smear grading increased with higher centrifugal force, while smear positivity plateaued at 3,000 x g. In contrast, clinical sputum samples showed progressive increases in both smear grading and positivity with increasing centrifugal force, with statistically significant improvements in positivity (p = 0.0097). Extending centrifugation time at 3,000 x g did not change smear positivity in laboratory suspensions or clinical sputum (P = 0.30). These findings indicate that standard centrifugation conditions are sufficient for laboratory strains, whereas higher centrifugal forces enhance bacillary recovery from clinical sputum, improving ZN smear sensitivity. Optimizing relative centrifugal force during pre-analytical processing may therefore reduce false-negative results and strengthen the diagnostic and treatment-monitoring performance of ZN smear microscopy in routine TB laboratories.

Published in PLOS One · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.