The steady rise in the spread of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) requires rapid and reliable methods to identify resistant strains. The current molecular methods to detect MTB resistance to second-line drugs either do not cover an extended spectrum of mutations to be identified or are not easily implemented in clinical laboratories. A rapid molecular technique for the detection of resistance to second-line drugs in M. tuberculosis has been developed using hybridisation analysis on microarrays.

Aspirin resistance (AR) is a pressing problem in current ischemic stroke care. Although the role of genetic variations is widely considered, the data still remain controversial. Our aim was to investigate the contribution of genetic features to laboratory AR measured through platelet aggregation with arachidonic acid (AA) and adenosine diphosphate (ADP) in ischemic stroke patients. A total of 461 patients were enrolled. Platelet aggregation was measured via light transmission aggregometry. Eighteen single-nucleotide polymorphisms (SNPs) in ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1 and PEAR1 genes and the intergenic 9p21.3 region were determined using low-density biochips. We found an association of rs1330344 in the PTGS1 gene with AR and AA-induced platelet aggregation. Rs4311994 in ADRA2A gene also affected AA-induced aggregation, and rs4523 in the TBXA2R gene and rs12041331 in the PEAR1 gene influenced ADP-induced aggregation. Furthermore, the effect of rs1062535 in the ITGA2 gene on NIHSS dynamics during 10 days of treatment was found. The best machine learning (ML) model for AR based on clinical and genetic factors was characterized by AUC = 0.665 and F1-score = 0.628. In conclusion, the association study showed that PTGS1, ADRA2A, TBXA2R and PEAR1 polymorphisms may affect laboratory AR. However, the ML model demonstrated the predominant influence of clinical features.

Background: Gilbert’s syndrome is a common metabolic dysfunction characterized by elevated levels of unconjugated bilirubin in the bloodstream. This condition is usually caused by additional (TA) insertions in a promoter region of the uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) gene, which instead of the sequence А(TА)₆TАА contains А(TА)₇TАА. While the condition itself is benign, it presents elevated risk for patients treated with irinotecan, a common chemotherapy drug. Methods: The technique is based on hybridization analysis of a pre-amplified segment of the UGT1A1 gene promoter performed on a microarray. Specific probes containing locked nucleic acids (LNA) were designed and immobilized on the microarray to provide accurate identification. Results: A microarray has been developed to identify both common and rare variants of UGT1A1(TA)n polymorphisms. In total, 108 individuals were genotyped. Out of these, 47 (43.5%) had homozygous wild-type genotypes (TA)₆/(TA)₆; 41(38%) were heterozygotes (TA)₆/(TA)₇; and 18 (16.7%) – homozygotes (TA)₇/(TA)₇. In two cases (1.8%), rare genotypes (TA)₅/(TA)₇and (TA)₅/(TA)₆were found. The results were in full agreement with the sequencing. In addition, synthetic fragments corresponding to all human allelic variants [(TA)₅, (TA)₆, (TA)₇, (TA)₈] were successfully tested. Conclusions: The developed microarray-based approach for identification of polymorphic variants of the UGT1A1 gene is a promising and reliable diagnostic tool that can be successfully implemented in clinical practice.