Neisseria meningitidis is a common commensal bacterium found in the respiratory tract, but it can also cause severe, invasive disease. Vaccines have been employed which have been successful in helping to prevent invasive disease caused by encapsulated N. meningitidis from the A, C, W, Y, and B serogroups.

The SARS-CoV-2 genome has mutated during the coronavirus disease 2019 (COVID-19) pandemic. Some of these mutations have impacted the performance of nucleic acid amplification tests like PCR, which are commonly used as diagnostic tools to detect an infection.

Background The Paris System for Reporting Urinary Cytology (TPS) uses hyperchromasia as major diagnostic criterion for high‐grade urothelial carcinoma (HGUC). The purpose of the study was to evaluate cases that were diagnosed as HGUC by TPS and determine whether there are different chromatin distribution patterns (ie, subsets). Methods Digital image annotations were performed on microscopic images of HGUC urine specimens with surgical biopsy/resection follow‐up. Median gray values were generated for each cell. Neutrophils (polymorphonuclear leukocyte [PMN]) were also enumerated in each case to serve as an internal control. A HGUC/PMN ratio was generated for each case, and the cases were distributed. Results Sixty‐nine HGUC cases yielded 2660 cells, including 2078 HGUC (30.1 cells/case) and 582 PMNs (8.4 cells/case). The average median gray value of an HGUC was 50.6 and of a PMN was 36.8 (P < .0001). Eight of 69 cases (11.6%) contained nuclei that, on average, were darker than or as dark as a PMN (extremely dark, ie, “India ink”). Fifty‐one of 69 cases (74.0%) contained nuclei that, on average, were slightly brighter than a PMN (hyperchromatic). Ten of 69 cases (14.5%) contained nuclei that, on average, were much brighter than a PMN (hypochromatic). Within a single case, all cases showed heterogeneity with the hypochromatic cases showing the most dramatic effect. Conclusions Digital image analysis reveals that there are large variations in chromasia between cases including a subset of cases with hypochromasia and another with extremely dark or “India ink” nuclei. There was much heterogeneity of chromasia seen within a single sample.

The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial intelligence (AI) approaches such as machine learning (ML) are being integrated into clinical microbiology practice.