Utility of MGG and Papanicolaou stained smears in the detection of Mucormycosis in nasal swab/scraping/biopsy samples of COVID 19 patients.

BACKGROUND: COVID 19 has been rapidly spreading across the globe. As a result of alteration of the immune milieu by COVID 19 and its treatment, there has been a rise in opportunistic fungal infections particularly Mucormycosis in these patients. Delay in diagnosis of these fungal infections can be fatal. The usual diagnostic modalities used to detect Mucor include potassium hydroxide (KOH) mount, fungal culture, and histopathology. Since histopathology and fungal culture have a long turnaround time we are dependent on KOH mount for rapid results. Here we investigate the role of stained cytology smears in the rapid diagnosis of Mucormycosis. METHODS: A prospective observational study was conducted in a tertiary health care hospital on samples of patients clinically suspected to have Mucormycosis. We performed May Grunwald Giemsa (MGG) and Papanicolaou (PAP) stains on the remnant samples of nasal swabs/scrapings/biopsies after KOH test and fungal culture. We took 16 KOH positive and 16 KOH negative samples. We also examined 16 fresh samples from patients whose earlier samples were reported to be negative on KOH test. RESULTS: The 6/16 KOH positive samples were found to be positive on stained cytology smears and 2 were mixed infections wherein both Mucor and Aspergillus were seen. The 4/16 KOH negative samples were positive for Mucor with one sample having both Mucor and Aspergillus. The 3/16 repeat samples which were earlier negative on KOH test were positive for Mucor. CONCLUSION: Stained cytology smears if used in conjunction with KOH test can increase the overall sensitivity of detection of Mucormycosis and mixed infections.

A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?

PURPOSE: To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. METHODS: In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. RESULTS: Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. CONCLUSION: During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.

Risks and features of secondary infections in severe and critical ill COVID-19 patients.

Objectives Severe or critical COVID-19 is associated with intensive care unit admission, increased secondary infection rate, and would lead to significant worsened prognosis. Risks and characteristics relating to secondary infections in severe COVID-19 have not been described. Methods Severe and critical COVID-19 patients from Shanghai were included. We collected lower respiratory, urine, catheters, and blood samples according to clinical necessity and culture and mNGS were performed. Clinical and laboratory data were archived. Results We found 57.89% (22/38) patients developed secondary infections. The patient receiving invasive mechanical ventilation or in critical state has a higher chance of secondary infections (P<0.0001). The most common infections were respiratory, blood-stream and urinary infections, and in respiratory infections, the most detected pathogens were gram-negative bacteria (26, 50.00%), following by gram-positive bacteria (14, 26.92%), virus (6, 11.54%), fungi (4, 7.69%), and others (2, 3.85%). Respiratory Infection rate post high flow, tracheal intubation, and tracheotomy were 12.90% (4/31), 30.43% (7/23), and 92.31% (12/13) respectively. Secondary infections would lead to lower discharge rate and higher mortality rate. Conclusion Our study originally illustrated secondary infection proportion in severe and critical COVID-19 patients. Culture accompanied with metagenomics sequencing increased pathogen diagnostic rate. Secondary infections risks increased after receiving invasive respiratory ventilations and intravascular devices, and would lead to a lower discharge rate and a higher mortality rate.