In this multicentre study, which is the largest case series ever reported, we aimed to describe the features of tularaemia to provide detailed information. We retrospectively included 1034 patients from 41 medical centres. Before the definite diagnosis of tularaemia, tonsillitis (n = 653, 63%) and/or pharyngitis (n = 146, 14%) were the most frequent preliminary diagnoses. The most frequent clinical presentations were oropharyngeal (n = 832, 85.3%), glandular (n = 136, 13.1%) and oculoglandular (n = 105, 10.1%) forms. In 987 patients (95.5%), the lymph nodes were reported to be enlarged, most frequently at the cervical chain jugular (n = 599, 58%), submandibular (n = 401, 39%), and periauricular (n = 55, 5%). Ultrasound imaging showed hyperechoic and hypoechoic patterns (59% and 25%, respectively). Granulomatous inflammation was the most frequent histological finding (56%). The patients were previously given antibiotics for 1176 episodes, mostly with β-lactam/β-lactamase inhibitors (n = 793, 76%). Antituberculosis medications were provided in seven (2%) cases. The patients were given rational antibiotics for tularaemia after the start of symptoms, with a mean of 26.8 ± 37.5 days. Treatment failure was considered to have occurred in 495 patients (48%). The most frequent reasons for failure were the production of suppuration in the lymph nodes after the start of treatment (n = 426, 86.1%), the formation of new lymphadenomegalies under treatment (n = 146, 29.5%), and persisting complaints despite 2 weeks of treatment (n = 77, 15.6%). Fine-needle aspiration was performed in 521 patients (50%) as the most frequent drainage method. In conclusion, tularaemia is a long-lasting but curable disease in this part of the world. However, the treatment strategy still needs optimization.

Vaccines have been seen as the most important solution for ending the coronavirus disease 2019 (COVID‐19) pandemic. The aim of this study is to evaluate the antibody levels after inactivated virus vaccination. We included 148 healthcare workers (74 with prior COVID‐19 infection and 74 with not). They received two doses of inactivated virus vaccine (CoronaVac). Serum samples were prospectively collected three times (Days 0, 28, 56). We measured SARS‐CoV‐2 IgGsp antibodies quantitatively and neutralizing antibodies. After the first dose, antibody responses did not develop in 64.8% of the participants without prior COVID‐19 infection. All participants had developed antibody responses after the second dose. We observed that IgGsp antibody titers elicited by a single vaccine dose in participants with prior COVID‐19 infection were higher than after two doses of vaccine in participants without prior infection (geometric mean titer: 898 and 607 AU/ml). IgGsp antibodies, participants with prior COVID‐19 infection had higher antibody levels as geometric mean titers at all time points (p < 0.001). We also found a positive correlation between IgGsp antibody titers and neutralizing capacity (rs = 0.697, p < 0.001). Although people without prior COVID‐19 infection should complete their vaccination protocol, the adequacy of a single dose of vaccine is still in question for individuals with prior COVID‐19. New methods are needed to measure the duration of protection of vaccines and their effectiveness against variants as the world is vaccinated. We believe quantitative IgGsp values may reflect the neutralization capacity of some vaccines.

Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription–polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. Results The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study’s data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). Conclusions ML models presented in this study can be used as clinical decision support tools to contribute to physicians’ clinical judgment for COVID-19 diagnoses.

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The epidemiological and antifungal susceptibility data for 35 episodes of candidemia in intensive care units (ICU) in 2007 were evaluated by prospective active surveillance. The incidence of fungaemia was 39.1 cases per 1000 ICU admissions and 2.85 cases per 1000 patient-days. The crude mortality was 65.7%; 70.8% of the fatalities occurred within 7 days of admission to the ICU. Only 2 species were isolated, Candida parapsilosis (77.1%) and Candida albicans (22.9%). There was no association between mortality and patient characteristics, prior antifungal usage, Candida subspecies or antifungal resistance (p > 0.05). Of the isolates, 5.7% were resistant to fluconazole and caspofungin, and 3.4% to voriconazole and amphotericin B. In molecular analysis of the isolates, 2 clusters of C. parapsilosis in the neurology and anaesthesiology ICUs were detected by randomly amplified polymorphic DNA (RAPD), suggesting a nosocomial transmission. In conclusion, a high incidence and high mortality rate of C. parapsilosis candidaemia were found in the ICUs. An excessive use of invasive procedures, total parenteral nutrition and broad-spectrum antibiotics in the ICUs, combined with a lack of proper infection control measures, may possibly explain the high incidence of C. parapsilosis candidaemia in our hospital.