Background: Candida spp. are opportunistic fungi whose pathogenic potential arises from interactions among multiple phenotypic traits that may not be captured by conventional single-factor analyses. This study aimed to identify multidimensional phenotypic virulence profiles among clinical Candida isolates using unsupervised machine learning. Methods: A total of 107 nasopharyngeal isolates recovered from COVID-19 convalescent patients in Almaty, Kazakhstan, during 2021–2022 were evaluated for biofilm formation, phospholipase, proteinase, hemolytic activities and susceptibility to fluconazole, voriconazole, itraconazole, and amphotericin B. Phenotypic data were analyzed using principal component analysis, Uniform Manifold Approximation and Projection, hierarchical clustering, permutational multivariate analysis of variance (PERMANOVA), Random Forest, and SHAP analysis. Results: Candida albicans predominated among the isolates (69.2%), and non-albicans Candida species accounted for 30.8%. Interspecies differences were observed in biofilm formation (p = 0.048) and itraconazole minimum inhibitory concentrations (p < 0.001). Three phenotypic clusters independent of species were identified (PERMANOVA: pseudo-F = 6.72, p = 0.0002). Across 20 repeated 5-fold cross-validation runs, the Random Forest model achieved an aggregated accuracy of 0.785, a mean balanced accuracy of 0.518 (95% CI: 0.461–0.577), and a macro-F1 score of 0.504 (95% CI: 0.444–0.559). SHAP identified itraconazole susceptibility, biofilm formation, and phospholipase activity as the strongest predictors. Conclusions: These findings demonstrate that integrating microbiological phenotyping with machine learning can reveal multidimensional virulence profiles beyond conventional species-based classification.

Thyroid diseases arise from both internal and external factors. Changing environmental and radiation conditions are increasing their prevalence and altering thyroid disease patterns. This review highlights current insights into how environmental influences affect thyroid structure and function, outlining recent discoveries, mechanisms, and areas needing further research. The purpose of the study. To review scientific literature that investigates the effects of adverse industrial environmental factors on thyroid gland function. Materials and Methods. A search for relevant studies was conducted in electronic databases, including PubMed, Web of Science, and Scopus, using specific terms and keywords. Only original observational studies examining the impact of harmful environmental factors on the development of thyroid pathology were included. Results. High levels of heavy metals (Pb, Hg, Cd, As) are linked to increased thyroid cancer risk. In oil and gas regions, children exhibit elevated levels of manganese, boron, vanadium, and silicon in their hair, which can interfere with iodine uptake and lead to iodine deficiency. Cadmium lowers thyroid-stimulating hormone, contributing to hyperthyroidism. Radiation-exposed areas (e.g., Chernobyl, Semipalatinsk) show more nodules and autoimmune thyroiditis. Fine particles (PM2.5, PM10) increase the risk of thyroid cancer, and prenatal air pollution affects newborn T4 levels. Organic and inorganic pollutants disrupt thyroid hormone production and promote cancer. Conclusion. Environmental contaminants, including heavy metals, radiation exposure, industrial residues and pollutants, and endocrine-disrupting chemicals, play a significant role in the occurrence and progression of thyroid disorders. The rise in chronic diseases, including thyroid pathologies, serves as a marker of environmental pollution. Further research is needed to explore the mechanisms by which environmental factors affect thyroid function. It is essential to develop prevention and monitoring strategies, particularly in environmentally unfavorable regions.

BACKGROUND AND IMPORTANCE: Microsurgical DREZotomy (MDT) involves lesioning the dorsal root entry zone at the cervical spinal level, carrying a potential risk of iatrogenic spinal cord injury. Misidentifying the C4 root can lead to impaired diaphragmatic contractions and respiratory compromise. This report is the first MDT procedure in our Central Asian region and introduces the simultaneous analysis of 3 ventilator waveforms—capnography, pressure time, and flow time—as a novel, rapid-response intraoperative neurophysiological monitoring for definitive C4 root identification. CLINICAL PRESENTATION: Traumatic brachial plexus avulsion is an upper limb injury resulting in severe complications such as paralysis and neuropathic pain. Patients with refractory, pharmaco-resistant pain may be candidates for cervical MDT. Intraoperative neurophysiological monitoring during MDT is beneficial because it allows for anatomical mapping from C4 to Th1 nerve roots. The C4 root should not be targeted during MDT because it is cardinal for diaphragmatic muscular activity. Three ventilation curves simultaneously displayed synchronous changes during C4 ventral root stimulation, indicative of phrenic excitation and the diaphragm's reaction against the anesthesia machine. The expected C4 ventral root responses completely aligned with the stimulation patterns observed across all 3 monitored parameters. CONCLUSION: The simplicity of this method and its high practical applicability make it valuable for identifying phrenic nerve fibers during various surgical procedures where nerve roots responsible for diaphragmatic muscle contraction may be at risk.