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the causative agent was identified as a new Coronavirus (2019-nCoV), which had not previously been detected in humans [1]. Later, the name of 2019-nCoV disease was accepted as COVID-19, and the virus was named SARS-CoV-2 because of its close resemblance to SARS CoV. After this date, the number of patients increased rapidly, and the WHO declared an "International Public Health Emergency" regarding the coronavirus outbreak at its meeting on January 30, 2020.Although the world was caught unprepared to the sudden emergence and rapid spread of the COVID-19 outbreak, Turkey managed to postpone the emergence of the disease within its borders through the implementation of effective preventive measures until March 11, 2020, when the first case was detected. After cases seen in China, Italy, and Spain starting from January, Turkey executed a meticulous monitoring and evaluation process to decide, implement, and follow up with comprehensive and timely measures. These measures have given time to be prepared for both the community and the healthcare system in this pandemic. Furthermore, since the beginning of the pandemic, Turkish citizens infected with COVID-19 have Background/aim: The aim of this study is to evaluate the epidemiological and clinical characteristics and parameters that determined the clinical course and prognosis of the COVID-19 patients admitted to Ankara City Hospital during the first month of the pandemic in Turkey.Materials and methods: SARS-CoV-2 PCR positive patients who were hospitalized between March 10 and April 10, 2020 were included.Results: Among 222 patients, mean age was higher in severe acute respiratory illness (SARI)/critical disease group (P < 0.001). Median time from illness onset to admission and presence of comorbidity, especially coronary artery disease and chronic obstructive pulmonary disease, were significantly higher in the SARI/critical disease group (P < 0.05). Cough and fever were the most common symptoms, while anosmia and loss of taste were observed in 8.6% and 7.7% patients, respectively. The mortality rate was 5.4%. A high neutrophil/lymphocyte ratio; low lymphocyte, monocyte, and platelet count; elevated liver enzymes; low GFR; and high levels of muscle enzymes, ferritin, and IL-6 on admission were found to be associated with SARI/critical disease (P < 0.05). Bilateral ground-glass opacity and patchy infiltration were more frequently seen in the SARI/critical disease group (P < 0.001). Patients older than 65 years had an 8-fold increased risk for development of SARI/critical disease. Conclusion:This cohort study regarding COVID-19 cases in Turkey reveals that older age, presence of comorbidity, bilateral infiltration on CT, high neutrophil/lymphocyte ratio, low monocyte and platelet count, elevated liver enzymes, low GFR, high levels of muscle enzymes, and high levels of ferritin and IL-6 on admission are predictors of SARI and severe disease.

Background/aim: The aim of this study is to evaluate the distribution, sources, clinical features, and mortality rates of bacteremia due to evaluation of extensively drug-resistant (XDR) gram negative among solid-organ transplant (SOT) recipients. Materials and methods:A retrospective study of SOT recipients with bacteremia due to XDR gram-negative pathogens in 11 centers between 2016 and 2018 was conducted. Patients' records were evaluated.Results: Of 171 bacteremia that occurred in 164 SOT recipients, 93 (56.7%) were liver, 46 (28%) kidney, 14 (8.5%) heart, and 11 (6.7%) lung recipients. Bacteremia episodes were recorded in the first year in 63.7% of the patients (n = 109), early-onset bacteremia was recorded in 45% (n = 77) of the episodes. In multivariate analysis, catheter-associated bacteremia was an independent risk factor for 7-day mortality (p = 0.037), and early-onset bacteremia was found as an independent risk factor for 30-day mortality (p = 0.017). Conclusion:Difficult-to-treat infections due to XDR bacteria in SOT recipients shadow the success of transplantation. Central venous catheters seem to be the main risk factor. Judicious use of medical devices is of pivotal importance.

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.

Coronavirus Disease-19 (COVID-19) is a highly contagious infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The development of rapid antigen tests has contributed to easing the burden on healthcare and lifting restrictions by detecting infected individuals to help prevent further transmission of the virus. We developed a state-of-art rapid antigen testing system, named DIAGNOVIR, based on immune-fluorescence analysis, which can process and give the results in a minute. In our study, we assessed the performance of the DIAGNOVIR and compared the results with those of the qRT-PCR test. Our results demonstrated that the sensitivity and specificity of the DIAGNOVIR were 94% and 99.2%, respectively, with a 100% sensitivity and 96.97% specificity, among asymptomatic patients. In addition, DIAGNOVIR can detect SARS‑CoV‑2 with 100% sensitivity up to 5 days after symptom onset. We observed that the DIAGNOVIR Rapid Antigen Test’s limit of detection (LoD) was not significantly affected by the SARS‑CoV‑2 variants including Wuhan, alpha (B1.1.7), beta (B.1.351), delta (B.1.617.2) and omicron (B.1.1.529) variants, and LoD was calculated as 8 × 102, 6.81 × 101.5, 3.2 × 101.5, 1 × 103, and 1 × 103.5 TCID50/mL, respectively. Our results indicated that DIAGNOVIR can detect all SARS-CoV-2 variants in just seconds with higher sensitivity and specificity lower testing costs and decreased turnover time.