Background/Objectives: Klebsiella pneumoniae ST307 is emerging as a significant global high-risk antimicrobial-resistant (AMR) clone with a notable capacity to acquire and disseminate resistance genes. However, there is limited research on the pathogenicity, virulence, and adaptation of ST307 strains and on the clinical characteristics of infected patients. Methods: In this study, a carbapenem-resistant K. pneumoniae (CRKP) ST307 strain named U989 was isolated from a urine culture of a hospitalized patient in Volos, Greece, in July 2024. Whole-genome sequencing was performed to identify resistance genes to β-lactams bla_KPC-2, bla_CTX-M-15, bla_TEM-1B, bla_OXA-1, bla_OXA-10, bla_SHV-106, and bla_VEB-1 and resistance genes to other antibiotics. Results: A genomic analysis also revealed the presence of virulence factors such as iutA, clpK1, fyuA, fimH, mrkA, Irp2, and TraT and an IncFiB(pQil)/IncFII(K) replicon, which harbors the bla_KPC-2 gene. Additionally, the transposable element Tn4401 was identified as a key vehicle for the mobilization of the bla_KPC-2 resistance gene. Finally, this is the report of a high-risk CRKP ST307 clone expressing KPC-2, SHV-106, CTX-M-15, and VEB-1 bla genes in Greece. Conclusions: The coexistence of these resistance genes in addition to aminoglycoside, quinolone, and other resistance genes results in difficult-to-treat infections caused by respective carrier strains, often requiring the use of last-resort antibiotics and contributing to the global challenge of antimicrobial resistance.

The incidence of fungaemia caused by rare yeasts, particularly Trichosporon species, is rising globally, posing diagnostic and therapeutic challenges—especially in immunocompromised patients. This systematic review aims to analyse the epidemiology, diagnostic approaches, antifungal susceptibility, and clinical outcomes of Trichosporon fungaemia (TF) in South-Eastern Europe, including Turkey. A comprehensive search was conducted in PubMed and Scopus in August 2025. A total of 59 cases from 12 studies were identified, with Trichosporon asahii being the most prevalent species (86.4%). Cases originated from Turkey (79.7%), Greece (18.6%), and Croatia (1.7%). Diagnostic techniques varied, with phenotypic methods still widely used. MALDI-TOF MS and DNA sequencing were mainly applied as confirmatory methods. Haematologic disorders were the most frequently reported underlying conditions among the patients. Sixteen breakthrough fungaemia cases—occurring despite empirical or prophylactic antifungal therapy—were identified. Although rare, TF represents a severe infection with significant mortality in South-Eastern Europe. Early and accurate species identification—facilitated by advanced diagnostic tools—is crucial for effective management. In addition to diagnostic difficulties, treatment is also challenging. Voriconazole appears to be the preferred antifungal agent, even in breakthrough fungaemia cases. Enhanced awareness, routine use of molecular diagnostics, and ongoing epidemiological monitoring are essential to improve patient outcomes.

Objectives: Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders characterized by ineffective hematopoiesis, genomic instability, and a high risk of progression to acute myeloid leukemia. Oxidative stress (OS) has emerged as a central factor in MDS pathophysiology, contributing to DNA damage, altered cellular signaling, and disease progression. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a transformative approach for integrating multidimensional datasets including oxidative stress markers, hematologic parameters, and molecular profiles to enhance diagnosis, prognostication, and therapeutic monitoring in MDS. Methods: A comprehensive literature search was conducted in PubMed and Scopus, using the keywords “OS biomarkers,” “AI,” and “MDS’’. Results: Modified redox biomarkers can be correlated with oxidative imbalance and disease progression. ML models such as neural networks, decision trees, and support vector machines effectively capture complex relationships among redox biomarkers, enhancing risk stratification and prediction of treatment response. AI-driven proteomic analyses further revealed OS-related protein signatures linked to MDS pathophysiology. Overall, AI and ML enable the transformation of multidimensional OS data into clinically actionable tools for personalized management in MDS. Conclusions: Integrating biomarker research with AI-based analytics holds promise for advancing personalized diagnostics, prognostication, and therapeutic strategies in MDS, paving the way toward precision medicine.