Tuberculosis (TB) is a chronic, granulomatous and necrotizing disease caused by microorganisms belonging to the Mycobacterium tuberculosis complex group. In 2017, 6.4 million new TB cases have been reported according to the World Health Organization 2018 Global Tuberculosis Report. TB remains among the major health problems of our time due to the increasing drug resistance problem and the difficulties in definitive diagnosis in recent years. It is stated by clinicians that intensive use of quinolone group drugs with oral form in simple indications such as respiratory or urinary tract infections may lead to resistance and this may result in treatment failures. The aim of this study was to determine the moxifloxacin susceptibility of M.tuberculosis isolates obtained from clinical specimens by phenotypical methods, to determine the resistance rates of moxifloxacin and to investigate the relationship between phenotypical resistance and mutations in the gyrA gene. A hundred (n= 100) consecutive non-multidrug resistant and 37 non-consecutive multidrug resistant M.tuberculosis strains isolated from the clinical specimens of patients with pulmonary tuberculosis were included in the study. The moxifloxacin susceptibility of the isolates was determined by using Löwenstein-Jensen medium and their epidemiological properties were investigated and also mutations detected by gyrA region were compared with drug susceptibility rates. Of the 137 isolates tested for phenotypical susceptibility, 25 (18.2%) were found to be resistant to moxifloxacin. Resistance rate among non-multidrug resistant and multidrug resistant isolates were determined as 17% and 21.6%, respectively. According to the results of the sequencing analysis, of the gyrA regions of all the isolates included in the study, a single base mutation was found in a total of six samples. The location positions of the mutations were determined as D94Y, D94G, A90V, G88A and among two strains as D89N. Two of the isolates with mutations were found to be phenotypically susceptible to moxifloxacin. In our study, it was found that moxifloxacin resistance in M.tuberculosis isolates was higher than similar studies and it was found that different mechanisms may be responsible for the existing resistance other than the mutations in the gyrA gene. It was concluded that the data obtained from the study should be shared with all clinicians in the country due to the possibility of resistance development to this group of drugs in a short time and considering this drug will have an important role in the treatment of TB, it should be used more limited in non-specific indications. Further studies using larger case groups and isolates are needed for the continuation of the research.

The concept of Artificial Intelligence (AI) is increasingly important in the healthcare sector today. Components of AI such as machine learning and deep learning are being utilized in various applications within the field of microbiology. This study examines the uses of AI in microbiology and its role in healthcare applications. Machine learning enables computer systems to analyze data using algorithms that mimic human intelligence, while deep learning processes information through multi-layered artificial neural networks. These technologies are used in many areas such as microbiological diagnosis, drug discovery, infection control, and patient monitoring. For instance, AI-supported systems are used in microbiological diagnosis to shorten diagnosis times and increase accuracy compared to traditional methods. Additionally, smart systems developed for preventing hospital-acquired infections alert hospital staff, thus reducing the risk of infection. AI also plays a significant role in the diagnosis of microorganisms such as viruses and fungi. Especially, AI-supported image analysis methods are utilized for rapid and accurate diagnosis. However, there are some challenges in the use of AI. Issues related to data privacy and ethics are among the factors limiting the applications of AI in microbiology and healthcare. Furthermore, the cost and complexity of algorithm implementation pose additional challenges. By discussing the applications of AI in microbiology and its potential in the future, this study sheds light on innovative developments in the healthcare sector.

Amaç : Tüberküloz (TB) tüm dünyada ciddi bir problem olmaya devam etmektedir. Türkiye hala TB insidansı orta derecede olan ülkeler arasında yer almaktadır. Dünya Sağlık Örgütü (WHO)’nün 2019 yılı küresel verem raporuna göre, 2018 yılında Türkiye’de ki TB insidansı 100.000’de 18 dir.2018 yılında yeni ve yeniden tedavi edilen olguların çoklu ilaç direnci (ÇİD) oranı Türkiye'den % 2,9 ve % 16 idi. Adıyaman ilinde izole edilen Mycobacterium tuberculosis kompleksi (MTBK) suşlarına ait ilaç direnci profili ve genotip dağılımı hakkında veri bulunmamaktadır. Bu çalışma ilimizde izole edilen MTBK suşlarının direnç profili ve genotip dağılımının belirlenmesi amacı ile yapılmıştır. Materyal ve Metod: Adıyaman Üniversitesi Eğitim ve Araştırma Hastanesi Mikobakteriyoloji Laboratuvarında Ocak 2015-Aralık 2016 tarihleri arasında izole edilen toplam 46 MTBK suşunun ilaç duyarlılık testi BACTECTM MGITTM 960 TB ilaç duyarlılık testi SİRE kiti kullanılarak yapıldı. İzolatların genotiplemesi spoligotipleme ve MIRU- VNTR yöntemleri ile yapıldı. Bulgular: Streptomisin, izoniazid, rifampin ve etambutole duyarlılık testleri SIRE Kiti kullanılarak MGIT 960 sistemi ile yapıldı. 46 MTBK izolatı içinde rifampisin, INH ve çoklu ilaca direnç %6,52 olarak saptandı. 46 izolattın 37’sinde 6 spoligotip ailesi saptanmış olup, en fazla T ailesinde kümelenme olduğu tespit edildi. MIRU sonuçlarına göre güncellenmiş bir veri tabanında mevcut MIRU uluslararası türleri ile eşleşen 12 orphan ve 21 pattern tanımlandı. Sonuç: Tüm dünyada önemli halk sağlığı problemlerinden biri olan tüberkülozun kontrol altına alınmasında ve direncin en kısa sürede saptanmasında mikrobiyolojik çalışmaların önemi büyüktür. Tüberkülozun kontrol altına alınmasında mutlaka ilaç duyarlılık testlerinin yapılması ve genotip düzeyindeki epidemiyolojik verilerin önemli bir yeri vardır.

Brucellosis, caused by intracellular bacteria of the Brucella genus, is one of the most neglected zoonotic infections globally. Transmission primarily occurs through the are consumption of contaminated milk and dairy products and occupational exposure to infected animals or their biological products. Therefore, livestock breeders, veterinarians and laboratory personnel working with clinical materials containing highly contagious pathogens are among the most risky occupational groups in terms of brucellosis. Despite ranking among the most common laboratory-acquired infections due to its low infectious dose, high potential for aerosolization, and insufficient awareness among laboratory personnel, the number of reported laboratory-acquired brucellosis cases remains notably low in our country, where the disease is endemic. In this study, a case of brucellosis acquired in the laboratory setting by a laboratory technician, exhibiting a clear occupational origin was presented.

The application of next-generation sequencing (NGS) technologies has enabled the identification of both culturable and non-culturable microorganisms in blood samples, revealing their potential roles in systemic infections and immune responses. However, the complexity and high dimensionality of microbiome data present significant challenges for analysis. In this study, it was evaluated the performance of various machine learning (ML) algorithms, including logistic regression, random forest (RF), decision tree, and support vector machines (SVM), in classifying 16S rRNA gene sequencing data of blood microbiota into cultured and uncultured groups. The dataset used in this study, obtained from Kalfin and Panaiotov, consists of 16S rRNA gene sequences from a total of 18,093 OTUs and 62 observations, including control samples. After excluding the six control samples, 56 samples from target sequencing of cultured and non-cultured blood samples of healthy individuals were analyzed. Results show that the random forest (RF) algorithm exhibits the highest classification performance, successfully distinguishing between cultured and uncultured blood microbiota. In the study, the potential of ML techniques in microbiome research was evaluated and the effectiveness and accuracy of these techniques in the analysis of microbiome data were investigated.

Background: Bedaquiline (BDQ), delamanid (DLM), and linezolid (LZD) are core drugs for multidrug-resistant tuberculosis (MDR-TB) treatment. Comprehensive regional data on phenotypic susceptibility and genotypic resistance-associated mutations are essential to guide optimized therapy. This study aimed to characterize phenotypic and genotypic resistance in treatment-naïve MTBC isolates by determining MICs of BDQ, DLM, and LZD and identifying associated genetic mutations. Methods: Minimum inhibitory concentrations (MICs) of BDQ, DLM, and LZD were determined for 71 Mycobacterium tuberculosis isolates (30 fully drug-susceptible and 41 MDR-TB) collected in the Cukurova region of Southern Türkiye between 2021 and 2023 using the MGIT 960/EpiCenter system. Sequencing of resistance-associated genes was analyzed: atpE and Rv0678 for BDQ, ddn and fbiA for DLM, and rrl and rplC for LZD. Results: MICs ranged from ≤0.025–6.4 µg/mL for BDQ, 0.005–0.32 µg/mL for DLM, and 0.125–2 µg/mL for LZD. According to WHO breakpoints, resistance rates were 7.0% for BDQ, 4.2% for DLM, and 7% for LZD, all confined to MDR-TB isolates. Nine MDR-TB isolates (12.7%) harbored atpE mutations, including both previously reported and putatively novel substitutions. A single ddn mutation was detected in a DLM-susceptible isolate. No mutations were observed in Rv0678, fbiA, rrl, or rplC. Conclusion: BDQ, DLM and LZD showed strong activity against MDR-TB isolates. However, the presence of atpE variants in both resistant and susceptible isolates demonstrated genotype-phenotype discordance of resistance. These results underline the necessity of the combination of genetic and phenotypic testing for detection of resistance and guidance for treatment.