Introduction: The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19). First COVID-19 case was detected in March, 10, 2020 in Turkey and as of May, 18, 2020 148,067 cases have been identified and 4096 citizens have died. Tuberculosis (TB) is a worldwide public health concern, incidence of tuberculosis (per 100,000 people) in Turkey was reported at 14, 1 in 2018. During pandemic COVID-19 was the main concern in every clinic and as we discuss here overlapping respiratory diseases may result in delaying of the diagnosis and treatment. Methodology: There were 4605 respiratory samples examined between March 23 and May 18 for COVID-19 and 185 samples for Mycobacterium tuberculosis in our laboratory. The Xpert Ultra assay was performed for the diagnosis of pulmonary tuberculosis; SARS-CoV-2 RNA was determined by real-time PCR (RT-PCR) analysis in combined nasopharyngeal and deep oropharyngeal swabs of suspected cases of COVID-19. Results: Both of SARS-CoV-2 and M. tuberculosis tests were requested on the clinical and radiological grounds in 30 patients. Here we discussed 2 patients who were both COVID-19 and TB positive. One patient already diagnosed with tuberculosis become COVID-19 positive during hospitalization and another patient suspected and treated for COVID-19 received the final diagnosis of pulmonary TB and Human Immunodeficiency Virus infection. Conclusions: We want to emphasize that while considering COVID-19 primarily during these pandemic days, we should not forget one of the “great imitators”, tuberculosis within differential diagnoses.

Genotype distribution of hepatitis C virus (HCV) can vary over the years between different patient groups and regions. The prevalence of intravenous drug users (IVDU) is known to increase in our country, yet there are a limited number of studies investigating the distribution of HCV genotypes in this group. These data are essential for monitorization of the changes in HCV epidemiology. The present study aimed to evaluate the five-year results of HCV genotyping among patients infected with HCV related to IVDU and unrelated to drug use. Plasma samples of 720 patients (HCV antibody, HCV RNA positive), which were sent to our laboratory for HCV genotyping between January 2014-March 2019 were analyzed. HCV RNA extraction from plasma samples was performed in the automated-extraction system of EZ1 advanced (Qiagen, Germany) using the EZ1 virus mini kit v2.0 (Qiagen, Germany). Amplicons were obtained by amplifying the 5’NCR and core gene region in the Rotorgene 6000 real-time PCR (Qiagen, Germany) device with the HCV RNA real-time quantitative 2.0 (NLM, Italy) kit. For the genotyping, a commercial line probe assay (LIPA) based on in vitro reverse hybridization GEN-C2.0 kit (NLM, Italy) which can distinguish 1, 2, 3, 4, 6 genotypes and 1a, 1b, 2a/c, 2b, 3a, 3b, 3c, 3k, 4a, 4b, 4c/d, 4e, 4f, 4h, 5a, 6a/b, 6g, 6f/q, 6m, 7a subtypes of HCV, based on variations in the 5’-NCR and core regions was used. HCV genotype distribution of 266 IVDU (93.2%: male; median age: 25 ± 6.82) and 454 non-drug users (51.3%: male; median age: 56.5 ± 16.06) were examined. In order of frequency in the group with IVDU; genotype 1a, 3a, 1b, 4c/d, 2b, 4, 3 were observed and genotype 1, 2a/c and mixed genotype (1+3a) were detected in one patient. In the group without IVDU, in order of frequency; genotype 1b, 1a, 3a, 1, 2a/c, 4 were observed and genotype 2b, 4c/d, 5a, 6a/b, 6 and mixed genotype (3+4) were detected in one patient. Genotypes 1a and 3a were significantly higher in the IVDU group (p< 0.00001, p< 0.00001), while 1b was significantly higher in patients without IVDU (p< 0.00001). Genotypes 1a and 3a were more common in young men (p< 0.00001, p= 0.000163), while 1b was higher in middleaged women (p< 0.00001). The incidence of genotypes 1b (p= 0.021) and 3a (p= 0.012) was higher in foreign nationals than the Turkish patients. When the HCV genotype distribution was examined by years, it was observed that the percentages of genotype 1b and 1a were decreasing, while the percentage of genotype 3a was increasing. As a result, in this study, HCV genotype distribution among IVDU was observed to be different from the general population without IVDU. It was found that genotypes 1a and 3a were more common in the IVDU group. As in the other regions of our country, genotype 1b was found most frequently in the general population. Genotype 3a increases significantly compared to years. In our study, the determination of genotypes existing in different parts of the world may be due to the foreign nationals living in our city and our region is a tourism center. It is also necessary to investigate whether there is an increase in IVDU over the years.

Background: The World Health Organization (WHO) recommends the surveillance of transmitted drug resistance mutations (TDRMs) to ensure the effectiveness and sustainability of HIV treatment programs. Objective: Our aim was to determine the TDRMs and evaluate the distribution of HIV-1 subtypes using and compared next-generation sequencing (NGS) and Sanger-based sequencing (SBS) in a cohort of 44 antiretroviral treatment-naive patients. Methods: All samples that were referred to the microbiology laboratory for HIV drug resistance analysis between December 2016 and February 2018 were included in the study. After exclusions, 44 treatment-naive adult patients with a viral load of >1000 copies/mL were analyzed. DNA sequencing for reverse transcriptase and protease regions was performed using both DeepChek ABL single round kit and Sanger-based ViroSeq HIV-1 Genotyping System. The mutations and HIV-1 subtypes were analyzed using the Stanford HIVdb version 8.6.1 Genotypic Resistance software, and TDRMs were assessed using the WHO surveillance drug-resistance mutation database. HIV-1 subtypes were confirmed by constructing a maximum-likelihood phylogenetic tree using Los Alamos IQ-Tree software. Results: NGS identified nucleos(t)ide reverse transcriptase inhibitor (NRTI)-TDRMs in 9.1 % of the patients, non-nucleos(t)ide reverse transcriptase inhibitor (NNRTI)-TDRMs in 6.8 % of the patients, and protease inhibitor (PI)-TDRMs in 18.2 % of the patients at a detection threshold of >= 1 %. Using SBS, 2.3 % and 6.8 % of the patients were found to have NRTI- and NNRTI-TDRMs, respectively, but no major PI mutations were detected. M41L, L74I, K65R, M184V, and M184I related to NRTI, K103N to NNRTI, and N83D, M46I, I84V, V82A, L24I, L90M, I54V to the PI sites were identified using NGS. Most mutations were found in low-abundance (frequency range: 1.0 % - 4.7 %) HIV-1 variants, except M41L and K103N. The subtypes of the isolates were found as follows; 61.4 % subtype B, 18.2 % subtype B/CRF02_AG recombinant, 13.6 % subtype A, 4.5 % CRF43_02G, and 2.3 % CRF02_AG. All TDRMs, except K65R, were detected in HIV-1 subtype B isolates. Conclusion: The high diversity of protease site TDRMs in the minority HIV-1 variants and prevalence of CRFs were remarkable in this study. All minority HIV-1 variants were missed by conventional sequencing. TDRM prevalence among minority variants appears to be decreasing over time at our center.

Numerous vaccines have been generated to decrease the morbidity and mortality of COVID-19. This study aims to evaluate the immunogenicity of the heterologous boosts by BioNTech against homologous boosts by CoronaVac at three-month intervals in two health care worker (HCW) cohorts, with or without prior COVID-19, for one year post-vaccination. This is a prospective cohort study in which the humoral responses of 386 HCWs were followed-up longitudinally in six main groups according to their previous COVID-19 exposure and vaccination status. Anti-SARS-CoV-2 spike-RBD total antibody levels were measured and SARS-CoV-2 neutralization antibody (NAbs) responses against the ancestral Wuhan and the Omicron variant were evaluated comparatively using international standard serum for Wuhan and Omicron, as well as with the aid of a conversion tool. The anti-SARS-CoV-2 spike-RBD total Ab and Nab difference between with and without prior COVID-19, three months after two-dose primary vaccination with CoronaVac, was statistically significant (p = 0.001). In the subsequent follow-ups, this difference was not observed between the groups. Those previously infected (PI) and non-previously infected (NPI) groups receiving BioNTech as the third dose had higher anti-SARS-CoV-2 spike total Ab levels (14.2-fold and 17.4-fold, respectively, p = 0.001) and Nab responses (against Wuhan and Omicron) than those receiving CoronaVac. Ab responses after booster vaccination decreased significantly in all groups at the ninth-month follow-up (p < 0.05); however, Abs were still higher in all booster received groups than that in the primary vaccination. Abs were above the protective level at the twelfth-month measurement in the entire of the second BioNTech received group as the fourth dose of vaccination. In the one-year follow-up period, the increased incidence of COVID-19 in the groups vaccinated with two or three doses of CoronaVac compared with the groups vaccinated with BioNTech as a booster suggested that continuing the heterologous CoronaVac/BioNTech vaccination, revised according to current SARS-CoV-2 variants and with at least a six-month interval booster would be an effective and safe strategy for protection against COVID-19, particularly in health care workers.

Objective: We aimed to analyse the positivity rate and cycle threshold values indicating viral loads for SARS CoV-2 among different respiratory specimens. Additionally, we evaluated the diagnostic efficacy of saliva samples. Patients and Methods: We included combined oropharyngeal and nasopharyngeal swab (cONS), sputum, and tracheal aspirate (TA) specimens of patients. Unpreserved saliva samples were collected prospectively from hospitalized patients within 72 hours of admission. SARS CoV-2 RNA was extracted by using Bio-Speedy viral nucleic acid buffer than RT-PCR was performed with Bio- Speedy COVID-19 qPCR detection kit. Results: Retrospective evaluation revealed SARS CoV-2 RNA in 19.66% of cONS (n: 5819), 30.77% of sputum (n: 39), 29.41% of TA samples (n: 34) from 4812 patients. In the majority (86.72%) of the samples, the first cONS sample was positive. Consecutive cONS and sputum/TA samples were investigated in 52 patients of whom 11 were positive with either of these samples. Saliva positivity was detected in 60% of cONS positive (n: 20) and 30% of cONS negative (n: 12) patients. Conclusion: Although, cONS samples show the greatest diagnostic guidance, repeated sampling from multiple sites of the respiratory tract increases the possibility of COVID-19 diagnosis. Saliva samples might be considered as an alternative specimen.