An investigation of clinical characteristics and antimicrobial agent susceptibility patterns in clinical Comamonas testosteroni isolates: An increasingly prevalent nosocomial pathogen.

BACKGROUND: Comamonas testosteroni is a gram-negative bacillus, known before 1987 as Pseudomonas testosteroni. Although considered as a rare pathogen, its frequency has been increasing. Data regarding its antibiotic susceptibility are insufficient. To date, forty-four cases have been reported in the literature. In this study, we identified the C. testosteroni infections observed in our hospital and evaluated their antimicrobial agent susceptibility patterns compared with cases reported in the literature. METHODS: For the purposes of the present study, patients admitted to hospital between November 2019 and December 2020 were screened. Those with clinical and laboratory signs of infection with positive C. testosteroni growth in culture were enrolled. Clinical isolates obtained from the samples processed in accordance with standard microbiological examination procedures in our laboratory were defined by MALDI-TOF mass spectrometry with 99.9% probability as C. testosteroni. RESULTS: C testosteroni infection was detected between November 2019 and December 2020 in eight patients in our hospital. Six of them had a bloodstream infection (BSI), one had pneumonia, and one had urinary tract infection due to C. testosteroni. Coexistence of COVID-19 was detected in four patients. Six out of the eight cases with BSI had hospital-acquired infection and all of the infections were healthcare-associated. When antimicrobial agent susceptibility results reported in the literature were evaluated in combination with the current results, ceftazidime and meropenem were found to be the most susceptible agents (96.1% and 80%, respectively). CONCLUSIONS: The frequency of nosocomial C. testosteroni infections and resistance to antimicrobial agents are gradually increasing. While resistance to carbapenems is on the upswing, third-generation cephalosporins are still seen as suitable treatment options.

Assessment of Galectin-1, Galectin-3, and Prostaglandin E2 Levels in Patients with COVID-19.

It is important to determine the inflammatory biomarkers in the severity of coronavirus disease 2019 (COVID-19) with the emergence of the pandemic. Galectins and prostaglandins play important roles in the regulation of immune and inflammatory responses. Therefore, this study aimed to investigate Galectin-1 (Gal-1), Galectin-3 (Gal-3), and prostaglandin E2 (PGE2) levels in patients with COVID-19. Serum concentrations of Gal-1, Gal-3, and PGE2 were measured using enzyme-linked immunosorbent assay on 84 patients with COVID-19 (severe = 29 and nonsevere = 55) and 56 healthy controls. In this study, increased levels of Gal-1 (median, 9.86, 6.35, and 3.67 ng/mL), Gal-3 (median, 415.31, 326.33, and 243.13 pg/mL), and PGE2 (median, 193.17, 192.58, and 124.62 pg/mL) levels were found in patients with COVID-19 than in healthy controls (P < 0.001 for all). In the severe disease group, Gal-3 levels were higher, while no differences were noted in Gal-1 and PGE2 levels (P = 0.011, P = 0.263, and P = 0.921, respectively). Serum levels of Gal-1 were positively correlated with those of Gal-3 (P = 0.871 and P < 0.001). Gal-3, C-reactive protein, lymphocyte count, and age were found as independent predictors of disease severity (P = 0.002, P = 0.001, P = 0.007, and P = 0.003, respectively). With the emergence of effective drug needs in the COVID-19 pandemic, differentiation of severe disease is important. Therefore, Gal-3 could be a potential prognostic biomarker of COVID-19.

Lymphocyte Subset Alteration and Monocyte CD4 Expression Reduction in Patients with Severe COVID-19.

The spectrum of coronavirus disease 2019 (COVID-19) severity, related to cellular immune functions, has not been fully clarified yet. Therefore, this study aimed to investigate the alteration of peripheral blood cells in patients with COVID-19. The flow cytometric characterization of immune cell subset was performed on 69 COVID-19 patients and 21 healthy controls. These data were evaluated based on the disease severity. A total of 69 patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were classified as asymptomatic infection (n = 14), nonsevere (n = 39), and severe (n = 16) groups. Decreased lymphocytes and increased CD14 + 4- monocytes are found in patients with severe COVID-19. Decreased CD4 expression level was observed in the monocytes of patients with severe COVID-19. The total lymphocytes, B and T lymphocytes, CD4+ cells and CD8+ cells, and natural killer (NK) and natural killer T (NKT) cells were found to be decreased in patients with severe COVID-19. The CD4+/CD8+ ratio was not significantly different between patients with COVID-19 and healthy controls. The percentage of activated T cells (CD3+HLA-DR+) and B cells (CD19+CD38+) was lower in patients with severe COVID-19. Age and CD4- monocytes were independent predictors of disease severity. The SARS-CoV-2 infection may affect lymphocyte subsets, resulting in decreased T and B cells, monocytes, and NK and NKT cells. Decreased CD4 expression level by monocytes was significantly correlated with disease severity. Further studies on the host immune response to SARS-CoV-2 infection are necessary to predict the disease severity and protect against the virus.

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.

Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study.

The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic.

The role of haematological parameters in patients with COVID-19 and influenza virus infection.

SARS-CoV-2, the causative agent of coronavirus disease 19 (COVID-19), was identified in Wuhan, China. Since then, the novel coronavirus started to be compared to influenza. The haematological parameters and inflammatory indexes are associated with severe illness in COVID-19 patients. In this study, the laboratory data of 120 COVID-19 patients, 100 influenza patients and 61 healthy controls were evaluated. Lower lymphocytes, eosinophils, basophils, platelets and higher delta neutrophil index (DNI), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were found in COVID-19 and influenza groups compared to healthy controls. The eosinophils, lymphocytes and PLR made the highest contribution to differentiate COVID-19 patients from healthy controls (area under the curves (AUCs): 0.819, 0.817 and 0.716, respectively; P-value is <0.0001 for all). The NLR, the optimal cut-off value was 3.58, which resulted in a sensitivity of 30.8 and a specificity of 100 (AUC: 0.677, P < 0.0001). Higher leucocytes, neutrophils, DNI, NLR, PLR and lower lymphocytes, red blood cells, haemoglobin, haematocrit levels were found in severe patients at the end of treatment. Nonsevere patients showed an upward trend for lymphocytes, eosinophils and platelets, and a downward trend for neutrophils, DNI, NLR and PLR. However, there was an increasing trend for eosinophils, platelets and PLR in severe patients. In conclusion, NLR and PLR can be used as biomarkers to distinguish COVID-19 patients from healthy people and to predict the severity of COVID-19. The increasing value of PLR during follow-up may be more useful compared to NLR to predict the disease severity.

Clinical and laboratory features of COVID-19: Predictors of severe prognosis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged first in December 2019 in Wuhan, China and quickly spread throughout the world. Clinical and laboratory data are of importance to increase the success in the management of COVID-19 patients. METHODS: Data were obtained retrospectively from medical records of 191 hospitalized patients diagnosed with COVID-19 from a tertiary single-center hospital between March and April 2020. Prognostic effects of variables on admission among patients who received intensive care unit (ICU) support and those who didn't require ICU care were compared. RESULTS: Patients required ICU care (n = 46) were older (median, 71 vs. 43 years), with more underlying comorbidities (76.1% vs. 33.1%). ICU patients had lower lymphocytes, percentage of large unstained cell (%LUC), hemoglobin, total protein, and albumin, but higher leucocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocytes ratio (PLR), urea, creatinine, aspartate amino transferase (AST), lactate dehydrogenase (LDH), and D-dimer when compared with non-critically ill patients (p < 0.001). A logistic regression model was created to include ferritin, %LUC, NLR, and D-dimer. %LUC decrease and D-dimer increase had the highest odds ratios (0.093 vs 5.597, respectively) to predict severe prognosis. D-dimer, CRP, and NLR had the highest AUC in the ROC analysis (0.896, 0.874, 0.861, respectively). CONCLUSIONS: The comprehensive analysis of clinical and admission laboratory parameters to identify patients with severe prognosis is important not only for the follow-up of the patients but also to identify the pathophysiology of the disease. %LUC decrease and D-dimer, NLR, and CRP increases seem to be the most powerful laboratory predictors of severe prognosis.