The human microbiome is increasingly recognized as a key determinant of health and disease, yet methodological variability continues to limit reproducibility and clinical translation of findings. This review synthesizes current approaches in microbiome research, critically evaluating each step from sampling to sequencing and downstream bioinformatics. Pre-analytical factors such as sample type, collection method, preservation, and storage conditions profoundly affect microbial community profiles and remain a major source of bias. Nucleic acid extraction protocols and quality assessment strategies are discussed with emphasis on optimized lysis techniques, contamination controls, and DNA yield evaluation. Advances in sequencing technologies are highlighted, including 16S rRNA amplicon sequencing, shotgun metagenomics, third-generation long-read platforms, and emerging single-cell and minimal-input methods, each with specific advantages and limitations in taxonomic and functional resolution. Bioinformatics pipelines for taxonomic profiling, variant detection, phylogenetic inference, and functional annotation are compared, with attention to widely used reference databases such as RefSeq, GTDB, and SILVA. Integrative multi-omics approaches, including metatranscriptomics, metabolomics, and genome-scale metabolic modeling, are presented as powerful tools for linking microbial community structure to host physiology and disease mechanisms. Despite these advances, the lack of standardized workflows across pre-analytical, sequencing, and computational steps continues to hinder inter-study comparability and biomarker validation. This review aims to provide a methodological framework that highlights both strengths and limitations of current technologies while underlining the need for harmonized protocols to ensure reproducibility and accelerate the translation of microbiome research into clinical practice.

The human microbiome, which encompasses microbial communities and their genetic material, significantly influences health and disease, including cancer. The urogenital microbiota, naturally present in the urinary and genital tracts, interact with factors such as age, lifestyle, and health conditions to affect homeostasis and carcinogenesis. Studies suggest that alterations in this microbiota contribute to the development and progression of genitourinary cancers, emphasizing the concept of oncobiome, which refers to microbial genetic contributions to cancer. Similarly, gut microbiota can influence hormone levels and systemic inflammation, impacting cancers such as cervical and prostate cancer. Advanced studies indicate that microbial communities in genitourinary cancers have distinct profiles that may serve as diagnostic biomarkers or therapeutic targets. Dysbiosis of the urinary microbiota correlates with bladder and kidney cancer. Additionally, gut microbiota influence the effectiveness of cancer treatments. However, further research is necessary to clarify causality, the role of microbial metabolites, and hormonal regulation. The aim of this review is to understand that these dynamics present opportunities for innovative cancer diagnostics and therapies, highlighting the need for integration of microbiology, oncology, and genomics to explore the role of microbiota in genitourinary cancers. For this, a comprehensive search of relevant databases was conducted, applying specific inclusion and exclusion criteria to identify studies examining the association between microbiota and urogenital cancers. Research into the mechanisms by which microbiota influence urogenital cancers may pave the way for new diagnostic and therapeutic approaches, ultimately improving patient outcomes.

In the oral cavity, which plays an important role in the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is possible to reduce the viral load of SARS-CoV-2 with antiseptics, thereby minimizing the transmission of the virus during dental procedures. The aim of this study was to clinically evaluate the effect of the hypochlorous acid (HClO) and povidone-iodine (PVP-I) solutions on the oral viral load of SARS-CoV-2. This randomized controlled trial was conducted on 75 patients hospitalized in the COVID-19 ward of a local hospital. All the patients included in the study were within the first 24 h of hospitalization and the first 5 days of coronavirus disease 2019 (COVID-19) symptoms. The viral load of mouthwash samples was measured with the cycle threshold (Ct) value of SARS-CoV-2 through a realtime reverse transcription polymerase chain reaction (RT-PCR). The patients were divided into 3 groups. The effect on the patient's SARS-CoV-2 viral load was investigated after gargling the mouths and throats for 30 s with HClO, PVP-I and isotonic saline. First, a sample was taken after gargling with isotonic saline, then another sample was taken after gargling for 30 s with a particular antiseptic to determine the viral load of SARS-CoV-2. Comparing the before and after mouthwash samples from all 3 groups, there were no statistically significant differences in the Ct values before and after gargling (p > 0.05). However, there were statistically significant differences in the number of negative samples after the use of HClO and PVP-I, which were positive before gargling (p < 0.05). In the light of the data obtained in this study, there is insufficient evidence that gargling with HClO or PVP-I reduces viral load. Taken together, these findings imply no role for antiseptics in the transmission of SARS-CoV-2 by the aerosol generated during dental procedures, or more generally, SARS-CoV-2 infection control.

Cytomegalovirus (CMV) colitis is a critical condition associated with severe complications in ulcerative colitis (UC). This study aimed to investigate the diagnostic value of the presence of CMV DNA in intestinal mucosa tissue and blood samples in patients with active UC. This study included 81 patients with exacerbated symptoms of UC. Patient data were obtained from the Hospital Information Management System. CMV DNA in colorectal tissue and plasma samples were analyzed using a real-time quantitative PCR assay. CMV markers were detected using immunohistochemistry and hematoxylin–eosin staining. Immunohistochemistry positivity was observed in tissue samples from eight (9.9%) patients. Only one (1.2%) patient showed CMV-specific intranuclear inclusion bodies. CMV DNA was detected in 63.0% of the tissues (median: 113 copies/mg) and in 58.5% of the plasma samples (median: 102 copies/mL). For tissues, sensitivity and the negative predictive value (NPV) for qPCR were excellent (100.0%), whereas specificity and the positive predictive value (PPV) were low (41.9% and 15.7%, respectively). For plasma, sensitivity and NPV were high (100.0%) for qPCR, whereas specificity and PPV were low (48.6% and 24.0%, respectively). CMV DNA ≥392 copies/mg in tissue samples (sensitivity 100.0% and specificity 83.6%) and ≥578 copies/mL (895 IU/mL) in plasma samples (sensitivity 66.7% and specificity 100.0%) provided an optimal diagnosis for this test. The qPCR method improved patient management through the early detection of CMV colitis in patients with UC. However, reliance on qPCR positivity alone can lead to overdiagnosis. Quantification of CMV DNA can improve diagnostic specificity, although standardization is warranted.

Introduction: Our knowledge has gaps regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication levels and its association to severity of Coronavirus disease 2019 (COVID-19). The aim of this study was to investigate the association of SARS-CoV-2 viral load with disease severity and serum biomarkers in COVID-19 patients. Methodology: Viral load was determined via cycle threshold (Ct) values of SARS-CoV-2 real-time reverse transcriptase-polymerase chain reaction (RT-PCR) in 214 adult patients. Ct values were compared with clinical severity, biochemical and hematological biomarkers. Results: Clinical course of the disease was mild (49.1%), moderate (40.2%), and severe (10.7%). Median Ct value was 28.2 (IQR: 22.2–33.8) during the first week of the disease. Ct values were lower within five days after symptom onset [lowest Ct value on the third day (median: 24, IQR: 20.6–32.3)], but they increased significantly during the second and third weeks. No association was detected between admission Ct values and disease severity. Gender, age, co-morbidity, and mortality did not differ significantly in patients with low (≤ 25) and high (> 25) Ct values. White blood cell, neutrophil, platelet, and especially lymphocyte counts, were significantly lower in patients with low Ct values. Conclusions: No definitive/clear correlation between SARS-CoV-2 viral load and severity and mortality was found in the studied COVID-19 patients. However, neutrophil, platelet, and especially lymphocyte count were significantly lower in patients with a high viral load.

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.