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.

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.

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.

Eggerthia catenaformis-related peritonitis in a patient with peritoneal dialysis.

Eggerthia catenaformis is a Gram-positive bacilli and an anaerobic and non-spore-forming bacterium, which rarely causes infections in humans. We present a case of peritonitis caused by E. catenaformis in a peritoneal dialysis patient. The isolate was identified as E. catenaformis with the MALDI-TOF MS method as in other cases in the literature. To the best of our knowledge, this is the first case of peritonitis caused by E. catenaformis in a human host.

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.

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.

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.

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.

Efficacy and cost-effectivity analysis of outpatient parenteral antimicrobial therapy unit in infectious disease clinical practices: Turkey perspective.

BACKGROUND: Outpatient parenteral antimicrobial treatment (OPAT) has become a common treatment modality in developed countries. OPAT units are not widespread in Turkey, and their cost-effectivity analysis has not been studied, yet. AIMS: To analyze the clinical efficacy and cost-effectiveness of the OPAT unit, based on a 1000-bed teaching hospital. METHODS: The records of patients, who were treated between October 2013 and December 2017, in an OPAT unit of a tertiary hospital in Ankara, were obtained retrospectively. The cost that would arise if the patients were hospitalized for the same treatment period with the same diagnosis was calculated and compared with the actual treatment cost of the patients in the OPAT unit. RESULTS: A total of 594 patients who received antimicrobial treatment at the OPAT unit were enrolled. The mean age of the patients was 55.39 ± 16.37 years and 313 (52.7%) were males. Based on the end-of-treatment goals, 98.5% of the patients reached the treatment goal. An indirect cost analysis revealed that the OPAT unit was 487.625 94 TL/129.008 78 $ less costly than inpatient parenteral antibiotic treatment. In other words, OPAT cost was 75% of the equivalent inpatient costs. It was also determined that a total of 7078 bed days and 11.9 bed days per person were saved. CONCLUSIONS: OPAT units should be expanded increasingly in Turkey. The evaluation together with the health care system conditions in Turkey revealed that the OPAT program is safe, effective, and cost-efficient.

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.

A micro-elimination approach to addressing hepatitis C in Turkey.

BACKGROUND: In 2016, WHO passed the Global Health Sector Strategy on Viral Hepatitis (GHSS), calling for its elimination by 2030. Two years later, Turkey approved a strategy to reach the WHO targets. This study reports new national prevalence data, breaks it down by subpopulation, and models scenarios to reach HCV elimination. METHODS: Literature was reviewed for estimates of HCV disease burden in Turkey. They were discussed with stakeholders and used as inputs to develop a disease burden model. The infected population was estimated by sequelae for the years 2015-2030. Three scenarios were developed to evaluate the disease burden in Turkey: a Base 2017 scenario, representing the current standard of care in Turkey; an increased treatment scenario, representing the impact of improved access to DAAs; and a WHO targets scenario, which meet the WHO GHSS viral hepatitis targets of a 65% reduction in mortality and 90% diagnosis rate of the infected population by 2030. RESULTS: At the beginning of 2017, 271,000 viremic infections were estimated. Of these, 58,400 were diagnosed and 10,200 treated. Modelling results showed that, with the current treatment paradigm in Turkey, by 2030 the total number of viremic HCV infections would decline by 35%, while liver-related deaths, hepatocellular carcinoma (HCC), and decompensated cirrhosis would decrease by 10-25%. In the increased treatment scenario, by 2030 viremic HCV infections would decrease by 50%; liver-related deaths, HCC and decompensated cirrhosis would decrease by 45-70%. In the WHO targets scenario, HCV infections would decrease by 80%; sequelae would decrease by 80-85%. Data on disease burden in micro-elimination target subpopulations are largely unavailable. CONCLUSIONS: To meet the WHO Global Health Sector Strategy targets for the elimination of HCV, Turkey needs to increase treatment. Better data are needed as well as countrywide access to DAAs.

MicroRNA expression profiles in patients with acute Crimean Congo hemorrhagic fever reveal possible adjustments to cellular pathways.

Several viral diseases are associated with altered microRNA (miRNA) expression, which can provide vital information about how cellular pathways respond to infection. However, the miRNA profile of Crimean Congo Hemorrhagic Fever (CCHFV) infections are not known. To address this gap, we performed real-time PCR-based miRNA analysis in individuals with acute Crimean Congo Hemorrhagic Fever (CCHFV) infections, with the goal of identifying pathways that might be associated with this disease. Peripheral blood mononuclear cells were analysed in eight individuals with detectable viral RNA and compared to five healthy subjects. A total of 106 differentially expressed miRNAs were identified, of which 19 miRNAs were either fivefold prominently up- or down-regulation. Several miRNAs associated with cytokine expression, some of which were previously associated with Dengue and Hantavirus infections were revealed. Moreover, possible mechanisms related to secretion of adhesion molecules and viral escape from innate immunity were identified. Pathway enrichment analyses further revealed the putative involvement of TNF-alfa, TGF-beta, MAPK, WNT, and neurotrophin signaling pathways in disease pathogenesis. J. Med. Virol. 89:417-422, 2017. © 2016 Wiley Periodicals, Inc.

Mortality predictors of Staphylococcus aureus bacteremia: a prospective multicenter study.

BACKGROUND: Staphylococcus aureus is one of the causes of both community and healthcare-associated bacteremia. The attributable mortality of S. aureus bacteremia (SAB) is still higher and predictors for mortality and clinical outcomes of this condition are need to be clarified. In this prospective observational study, we aimed to examine the predictive factors for mortality in patients with SAB in eight Turkish tertiary care hospitals. METHODS: Adult patients with signs and symptoms of bacteremia with positive blood cultures for S. aureus were included. All data for episodes of SAB including demographics, clinical and laboratory findings, antibiotics, and outcome were recorded for a 3-year (2010-2012) period. Cox proportional hazard model with forward selection was used to assess the independent effect of risk factors on mortality. A 28-day mortality was the dependent variable in the Cox regression analysis. RESULTS: A total of 255 episodes of SAB were enrolled. The median age of the patients was 59 years. Fifty-five percent of the episodes were considered as primary SAB and vascular catheter was the source of 42.1 %. Healthcare associated SAB was defined in 55.7 %. Blood cultures yielded methicillin-resistant S. aureus (MRSA) as a cause of SAB in 39.2 %. Initial empirical therapy was inappropriate in 28.2 %. Although overall mortality was observed in 52 (20.4 %), 28-day mortality rate was 15.3 %. Both the numbers of initial inappropriate empirical antibiotic treatment and the median hours to start an appropriate antibiotic between the cases of fatal outcome and survivors after fever onset were found to be similar (12/39 vs 60/216 and 6 vs 12 h, respectively; p > 0.05). High Charlson comorbidity index (CCI) score (p = 0.002), MRSA (p = 0.017), intensive care unit (ICU) admission (p < 0.001) and prior exposure to antibiotics (p = 0.002) all were significantly associated with mortality. The Cox analysis defined age [Hazard Ratio (HR) 1.03; p = 0.023], ICU admission (HR 6.9; p = 0.002), and high CCI score (HR 1.32; p = 0.002) as the independent predictive factors mortality. CONCLUSIONS: The results of this prospective study showed that age, ICU stay and high CCI score of a patient were the independent predictors of mortality and MRSA was also significantly associated with mortality in SAB.

Candidemia in critically ill COVID-19 patients: Risk factors and impact on mortality.

Background: Risk factors of candidemia are well-described in intensive care units (ICUs) before the Coronavirus disease 2019 (COVID-19) pandemic. The increased rates of admission to ICUs have appeared during the pandemic. Methods: Patient characteristics and laboratory data of 80 candidemia with COVID-19, 101 candidemia without COVID-19, and 100 non-candidemia with COVID-19 patients were evaluated, in this study. Results: Systemic inflammatory response syndrome (SIRS) ≥ 2, solid malignancy, total parenteral nutrition (TPN), central venous catheterization (CVC), hypotension, fever, urea, alanine aminotransferase (ALT), D-dimer, procalcitonin, ferritin, and delta neutrophil index (DNI) was found to be associated with candidemia in COVID-19 patients. TPN, hypotension, and fever were identified as independent predictors of candidemia in COVID-19, and candidemia in COVID-19 is characterized by significantly high mortality rates. Urea, lactate, and procalcitonin were defined as independent predictors of hospital mortality in candidemia patients with COVID-19. Conclusion: The presence of candidemia increases mortality in COVID-19. TPN, fever, and hypotension werefound to be the most powerful predictors of candidemia in COVID-19. Overall, these data show that candidemia in COVID-19 is characterized by significantly high mortality rates. Determination of distinctive features can prevent candidemia and mortality.

Should we review our prophylaxis approach for increased antibiotic resistance in transrectal prostate biopsy?

INTRODUCTION: This study aims to show the bacteriologic picture of acute prostatitis and bacteremia caused by infective agent after transrectal ultrasound-guided prostate biopsy (TRUSBx) and to determine the resistance rates of the infections in patients undergoing transrectal biopsy and to guide prophylaxis approach before biopsy. METHODOLOGY: The retrospective data of 935 patients who underwent TRUSBx between January 2010 to January 2019 were reviewed. Pre-biopsy urine cultures and antimicrobial susceptibility were obtained. Subsequently, patients admitted to the hospital with any complaint after biopsy were examined for severe infection complications. RESULTS: Of the 430 (61.7%) patients who underwent urine culture before the procedure, 45 (10.5%) had growth; 30 (66.7%) of the growing microorganisms were Escherichia coli. Twenty (44.4%) of all Gram-negative agents in pre-biopsy urine culture were susceptible to quinolone. Post TRUSBx bacteremia was present in 18.2%, urinary system infection in 83.6%, and hospitalization in 61.8% of 55 patients who were admitted to the hospital. In the isolated gram-negative microorganisms, fluoroquinolones resistance in urinary system infections was seen in 40% and bacteremia was seen in 70% of the cases. ESBL-producing Gram-negative bacteria were determined in 40% of infections in blood and 38.5% of urinary system infections in the post biopsy period in the current study. CONCLUSIONS: These high antibiotic resistance rates suggest that we better review our pre-procedure prophylaxis approaches.

Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.

All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.

Clinical and microbiological efficacy and toxicity of colistin in patients infected with multidrug-resistant gram-negative pathogens.

Polymyxins have recently again become important because of multidrug-resistant (MDR) gram-negative pathogens. The aim of this study was to evaluate the clinical and microbiological efficacy and toxicity of different dosages of colistin in patients infected with MDR microorganisms that were sensitive only to colistin. The study was conducted in the 1,200-bed Ankara Numune Training and Research Hospital. Patients with normal renal function who received colistin for 48 h or more were retrospectively evaluated. Clinical response was defined as resolution of fever and clinical and laboratory findings. Microbiological response was defined as bacteriological eradication from the infection site. Nephrotoxicity was defined as at least two consecutive serum creatinine measurements with an increase of 0.5 mg/dl from baseline at least 24 h apart after 2 or more days of colistin therapy. Twenty-four patients were included in the study: total clinical response was obtained in 17 of 24 (70.8 %) patients and microbiological response in 15 of 24 (62.5 %) patients. Patients were grouped according to colistin dosage of 3 × 1 million units (MU) versus 3 × 2 MU. Clinical response rates were 69.2 % and 72.7 %, respectively (p = 0.65). Microbiological response rate was similar (p = 0.62). Nephrotoxicity was revealed in 1 of 13 patients (7.7 %) for the 3 × 1 MU group and 2 of 11 patients (18.2 %) in the 3 × 2 MU group (p = 0.57). The nephrotoxicity rate was greater with higher dosages of colistin, but the difference was not statistically significant. Renal function of patients receiving higher dosages of colistin should be more closely monitored.

The usefulness of hematological parameters and cerebrospinal fluid indexes in the differential diagnosis of acute bacterial from viral meningitis.

Central nervous system (CNS) infection is a medical emergency with an important cause of mortality worldwide. The 79 patients with confirmed acute CNS infection (48 bacterial and 31 viral meningitis) were evaluated. Bacterial meningitis score, cerebrospinal fluid (CSF)/serum glucose ratio, and CSF/serum albumin ratio had the highest area under the curves (0.873, 0.843, 0.810, respectively) for discriminating bacterial meningitis. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte ratio (PLR) and CSF lactate dehydrogenase have a good ability for the differential diagnosis of bacterial meningitis. CSF/serum glucose ratio, NLR (with a cut-off value> 8.87), large unstained cell, total protein, albumin, and procalcitonin levels were found to be predictors for mortality. NLR can be used as a biomarker to differentiate bacterial meningitis from viral meningitis and to predict the prognosis of CNS infection. CSF/serum albumin ratio and CSF lactate dehydrogenase can be used to predict bacterial meningitis as well as CSF/serum glucose ratio.

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.

Subclinical infections with Crimean-Congo hemorrhagic fever virus, Turkey.

To investigate Crimean-Congo hemorrhagic fever virus in Turkey, we conducted a seroepidemiologic survey during January-April 2009. Seroprevalence of infection was 10% in a sample from an outbreak region and increased with patient age, indicating that the virus had been previously present in Turkey. We also estimated that 88% of infections were subclinical.