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.

Comparison of anakinra and tocilizumab in management of severe COVID-19: a retrospective cohort study.

BACKGROUND: Studies regarding effectiveness of anakinra and tocilizumab treatments in coronavirus disease 2019 (COVID-19) have contradictory results. Furthermore, there is scarce comparative data regarding superiority of any agent. To further elucidate any superiority between these two agents, we retrospectively investigated and compared outcomes in hospitalized COVID-19 patients of our inpatient cohort who received anakinra or tocilizumab. METHODS: This study was designed as a single-center, retrospective, cross-sectional cohort study. Hospitalized patients with confirmed diagnosis of COVID-19 who had Brescia-COVID respiratory severity scale score ≥3 and hyperinflammation (defined as elevation of C reactive protein ≥50 g/L or ferritin ≥700 ng/mL) and received anakinra or tocilizumab in addition to standard care were enrolled in the study. Length of hospital stay after initiation of antiinflammatory treatment, need for mechanical ventilation, need for intensive care unit admission, mortality were set as primary outcomes and compared between tocilizumab and anakinra recipients after propensity score matching. RESULTS: One hundred and six patients were placed in each group after propensity score matching. In the anakinra group, relative risk reduction for intensive care unit admission was 50% when compared to the tocilizumab group and the number needed to treat to avert an intensive care unit admission was 3 (95% CI, 2-5). In terms of mortality, a 52% relative risk reduction was observed with anakinra treatment and the number needed to treat to avert an intensive care unit admission was 8 (95% CI, 4-50). Significantly more patients were observed to receive glucocorticoids in the anakinra group. DISCUSSION: Anakinra administration in severe COVID-19 patients was significantly associated with better survival and greater clinical improvement compared to the tocilizumab administration in our study. Increased rate of glucocorticoid use in the anakinra group might have contributed to better outcomes.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

Are angiotensin converting enzyme (ACE1/ACE2) gene variants associated with the clinical severity of COVID-19 pneumonia? A single-center cohort study.

OBJECTIVE: The impact of the coronavirus disease 2019 (COVID-19) pandemic has been unceasingly ongoing worldwide. Recent bioinformatics analysis and epidemiologic studies have highlighted that the functional polymorphisms on the angiotensin converting enzyme (ACE) gene may have an impact on the clinical progress of COVID-19. In this study, we aimed to determine the impact of the ACE1 gene I/D polymorphism and ACE2 peptidase-2 domain variants on disease severity. METHODS: Hundred patients with confirmed COVID-19 related pneumonia [50 patients with severe disease in intensive care unit (ICU) and 50 patients not in ICU] were compared on the basis of genetic and clinical characteristics. Genomic DNA was purified from peripheral blood lymphocytes with an automated QIA symphony DSP DNA Mini-Kit. The Sanger sequencing analysis was performed. The frequencies of ACE1 gene polymorphism and ACE2 PD variants were compared in patients hospitalized in ICU and those not in ICU. The Statistical Package for Social Sciences version 22.0 was used for statistical analysis. RESULTS: The sequencing analysis of the ACE2 gene exon 1 and 2 revealed none of the polymorphisms investigated or any other variants in the present cohort. The frequencies of the ACE1 ID, DD, and II genotypes were 51%, 31%, and 18%, respectively. The frequency of the D allele was similar between the ICU and non-ICU groups (50.4% versus 49.6%). Older age and the presence of advanced stage radiologic abnormalities on admission were detected as independent predictors of ICU requirement. CONCLUSION: No effect of any ACE1 gene polymorphism on predicting ICU requirement was detected. To the best of our knowledge, this is the first study investigating the impact of ACE gene polymorphisms on clinical severity of COVID-19 in a Turkish cohort.

The changing dynamics of neutralizing antibody response within 10 months of SARS-CoV-2 infections.

There are limited data on how long neutralizing antibody (NAb) response elicited via primary SARS-CoV-2 infection will last. Eighty-four serum samples were obtained from a prospective cohort of 42 laboratory-confirmed COVID-19 inpatients at the time of discharge from the hospital and in the late convalescent phase. A virus neutralization assay was performed to determine the presence and titers of NAbs with authentic SARS-CoV-2. Long-term dynamics of NAbs and factors that may have an impact on humoral immunity were investigated. Mild and moderate/severe patients were compared. The mean sampling time was 11.12 ± 5.02 days (4-28) for the discharge test and 268.12 ± 11.65 days (247-296) for the follow-up test. NAb response was present in 83.3% of the patients about 10 months after infection. The detectable long-term NAb rate was significantly higher in mild patients when compared to moderate/severe patients (95.7% vs. 68.4%, p = 0.025). In the follow-up, NAb-positive and -negative patients were compared to determine the predictors of the presence of long-term humoral immunity. The only significant factor was disease severity. Patients with mild infections have more chance to have NAbs for a longer time. Age, gender, and comorbidity did not affect long-term NAb response. NAb titers decreased significantly over time, with an average rank of 24.0 versus 19.1 (p = 0.002). Multivariate generalized estimating equation analysis revealed that no parameter has an impact on the change of NAb titers over time. The majority of the late convalescent patients still had detectable low levels of neutralizing antibodies. The protective effect of these titers of NAbs from re-infections needs further studies.

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.

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases.

BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

Tocilizumab treatment in severe COVID-19: a multicenter retrospective study with matched controls.

Coronavirus disease-2019 (COVID-19) associated pneumonia may progress into acute respiratory distress syndrome (ARDS). Some patients develop features of macrophage activation syndrome (MAS). Elevated levels of IL-6 were reported to be associated with severe disease, and anti-IL-6R tocilizumab has been shown to be effective in some patients. This retrospective multicenter case-control study aimed to evaluate the efficacy of tocilizumab in hospitalized COVID-19 patients, who received standard of care with or without tocilizumab. Primary outcome was the progression to intubation or death. PSMATCH (SAS) procedure was used to achieve exact propensity score (PS) matching. Data from 1289 patients were collected, and study population was reduced to 1073 based on inclusion-exclusion criteria. The composite outcome was observed more frequently in tocilizumab-users, but there was a significant imbalance between arms in all critical parameters. Primary analyses were carried out in 348 patients (174 in each arm) after exact PS matching according to gender, ferritin, and procalcitonin. Logistic regression models revealed that tocilizumab significantly reduced the intubation or death (OR 0.40, p = 0.0017). When intubation is considered alone, tocilizumab-users had > 60% reduction in odds of intubation. Multiple imputation approach, which increased the size of the matched patients up to 506, provided no significant difference between arms despite a similar trend for intubation alone group. Analysis of this retrospective cohort showed more frequent intubation or death in tocilizumab-users, but PS-matched analyses revealed significant results for supporting tocilizumab use overall in a subset of patients matched according to gender, ferritin and procalcitonin levels.

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.

SARS-CoV-2 Detection with De Novo-Designed Synthetic Riboregulators.

SARS-CoV-2 is a human pathogen and the main cause of COVID-19 disease, announced as a global pandemic by the World Health Organization. COVID-19 is characterized by severe conditions, and early diagnosis can make dramatic changes for both personal and public health. Low-cost, easy-to-use diagnostic capabilities can have a very critical role in controlling the transmission of the disease. Here, we are reporting a state-of-the-art diagnostic tool developed with an in vitro synthetic biology approach by employing engineered de novo riboregulators. Our design coupled with a home-made point-of-care device can detect and report the presence of SARS-CoV-2-specific genes. The presence of SARS-CoV-2-related genes triggers the translation of sfGFP mRNAs, resulting in a green fluorescence output. The approach proposed here has the potential of being a game changer in SARS-CoV-2 diagnostics by providing an easy-to-run, low-cost diagnostic capability.

Multiple system inflammatory syndrome associated with SARS-CoV-2 infection in an adult and an adolescent.

Multisystem inflammatory syndrome in adults (MIS-A) is a new syndrome related with COVID-19. A case-based review was performed to present real-life experiences in terms of main findings and treatment options. We described two cases with the diagnosis of MIS and searched the literature to review all reported ≥ 18-year-old cases. The PubMed, Scopus, and Web of Science databases were searched. All relevant articles from January 2020 to February 2021 were reviewed. An adolescent and an adult patient (18 and 40 years-old, respectively) with the diagnosis of MIS were presented. Both had the consistent clinical findings with the case definition criteria. Although steroid, intravenous immunoglobulin (IVIG) and supportive care treatments have been suggested in the literature, there exists no treatment guideline for MIS-A. The clinical and laboratory findings of the patients progressively improved with the implementation of the IVIG and the pulse steroid treatments. A total of 51 cases (≥ 18 years-old) with MIS were analyzed. Mean age was 29.4 ± 10 years. Fever (80.4%), gastrointestinal (72.5%), and respiratory symptoms (54.9%) were the predominant symptoms. Cardiovascular abnormalities were the most frequent reported findings (82.4%, 42/51). The dermatological and conjunctival findings were reported in 39.2% and 35.3% of the patients, respectively. The increased level of inflammatory biomarkers was remarkable. Most of the patients were treated successfully with steroid and IVIG. Clinicians managing adult patients should keep in mind the development risk of MIS related with SARS-CoV-2 infection to perform necessary interventions properly without delay. IVIG and pulse steroid treatments are the effective options on clinical improvement.

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.

Comparing ICU admission rates of mild/moderate COVID-19 patients treated with hydroxychloroquine, favipiravir, and hydroxychloroquine plus favipiravir.

BACKGROUND: In this study, we aimed to compare the intensive care unit (ICU) admission rate of hospitalized mild/moderate COVID-19 patients treated with hydroxychloroquine (HCQ), favipiravir, and HCQ plus favipiravir. METHODS: Single center retrospective designed observational study conducted in Ankara City Hospital. Patients who were hospitalized between March 15, 2020 and June 1, 2020 in COVID-19 inpatient clinics with laboratory confirmed diagnosis of COVID-19 were included in the study. An inverse probability of treatment weighting (IPTW) for multiple treatment groups approach was used to balance the differences in several variables on admission. RESULTS: Among 2441 patients hospitalized with diagnosis of COVID-19 during the study period, 824 were eligible for the analysis. Median age of patients was 42 (18-93 years). Among all, 347 (43.2%) of the patients had mild disease, 470 (56.8%) had pneumonia. Propensity scores ranged from 0.1841 to 0.9381 in the HCQ group, from 0.03643 to 0.29885 in the favipiravir group, and from 0.03542 to 0.56184 in the HCQ plus favipiravir group. After IPTW for multiple treatment groups was applied, all the covariates in the planned propensity score had weighted standardized effect sizes below 10% which were ranged from 0.005 to 0.092. Multivariate analysis of treatment effect (adjusted effect of treatment) was indicated that there is no statistically significant difference between HCQ, favipiravir, and HCQ plus favipiravir treatment. After using combination of SMOTE and Bootstrap resampling approach, we found no statistically significant difference between HCQ and HCQ plus favipiravir groups in terms of ICU admission. However, compared with the HCQ group, ICU admission rate was statistically significantly higher in the favipiravir group. We obtained the similar results after the sensitivity analysis. CONCLUSIONS: HCQ with or without favipiravir treatment is associated with reduced risk of ICU admission compared to favipiravir alone in mild to moderate COVID-19 adult patients.

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.

Virolactia in an Asymptomatic Mother with COVID-19.

Background: Limited data are available on the perinatal and postnatal transmission of novel coronavirus disease 2019 (COVID-19). The Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) recommended breastfeeding with necessary precautions to mothers with COVID-19. Case Presentation: A 20-year-old pregnant woman with no symptoms of COVID-19 presented to the hospital for delivery at 39 weeks of gestation. She was tested for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by reverse transcriptase polymerase chain reaction (RT-PCR) because her father had been diagnosed with COVID-19. A nasopharyngeal swab RT-PCR test was positive for SARS-CoV-2. Therefore, the baby and the mother were cared for separately after delivery. Breast milk obtained after first lactation was tested by real-time RT-PCR and was positive for SARS-CoV-2. Conclusions: In this article, we aimed to report the presence of SARS-CoV-2 in breast milk. Although further studies are needed, this situation may have an impact on breastfeeding recommendations.

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.