The prevalence of hepatitis E virus (HEV) in the Bulgarian population remains underestimated. The aim of the present study was to evaluate age and gender trends in HEV prevalence in the heterogeneous Bulgarian population. Stored serum samples from blood donors and different patient sub-populations—kidney recipients (KR), patients with Guillain–Barre syndrome (GBS), Lyme disease (LD), patients with liver involvement and a clinical diagnosis other than viral hepatitis A and E (non-AE), hemodialysis (HD) and HIV-positive patients (HIV)—were retrospectively investigated for markers of past and recent/ongoing HEV infection. The estimated overall seroprevalence of past infection was 10.6%, ranging from 5.9% to 24.5% for the sub-populations evaluated, while the seroprevalence of recent/ongoing HEV infection was 7.5%, ranging from 2.1% to 20.4%. The analysis of the individual sub-populations showed a different prevalence with respect to sex. In regard to age, the cohort effect was preserved, as a multimodal pattern was observed only for the GBS sub-population. Molecular analysis revealed HEV 3f and 3e. The type of the population is one of the main factors on which the anti-HEV prevalence depends, highlighting the need for the development of guidelines related to the detection and diagnosis of HEV infection with regard to specific patient populations.

Rhinoviruses (RV) are one of the most common causative agents of respiratory infections, with significant socioeconomic impact. RV infections are not notifiable in Bulgaria, and little is known about the different RV genotypes circulating in the country. This study aims to investigate the diversity of RV genotypes that were circulating in Bulgaria in the period 2018–2021 in samples from ILI/ARI patients. Genotype assignment was based on sequencing and phylogenetic analysis of the 5′ untranslated region and the VP4-VP2 region. Out of a total of 1385 nasopharyngeal swabs tested, 166 were RV-positive (RV detection rate: 11.99% (166/1385)). Those with a cycle threshold <25 were selected for genotyping (n = 63). RV isolates were successfully genotyped and classified into 34 genotypes within Rhinovirus A (RV-A), Rhinovirus B (RV-B) and Rhinovirus C (RV-C) species. Presumptive recombination events between the 5′UTR and VP4-VP2 regions were detected in three of the isolates. RV-A and RV-C were the prevalent RV species, with significantly more frequent detections of RV-A in the years before the COVID-19 pandemic compared to the post-pandemic period, when RV-C prevailed. The present study is the first to determine RV genotypes in Bulgaria and the circulation of RV-C has been described for the first time in the country.

In 2018, an increase in echovirus 30 (E30) detections was reported in some European countries. To assess the circulation and phylogenetic relationships of E30 in Bulgaria, E30 samples identified at the National Reference Laboratory for Enteroviruses, National Centre of Infectious and Parasitic Diseases, Bulgaria (NRL for Enteroviruses) in 2017 and 2018 were subjected to sequencing and phylogenetic analysis. The present study revealed that sample positivity did not significantly increase in Bulgaria during the European upsurge. E30 was identified in six patients, two of whom were epidemiologically linked. The maximum-likelihood phylogenetic tree showed that sequences from five patients belonged to the G1 lineage (clades G1a and G1b). The sequence from one patient belonged to the G2 lineage and was grouped closer to sequences from the last E30 outbreak in Bulgaria in 2012. No recombination events were detected. The European E30 upsurge in 2018 was caused by two clades, and one of them was G1. The fact that the majority of the Bulgarian samples belonged to G1 indicated that the virus was present in the country but did not cause a local upsurge. Phylogenetic and epidemiological data indicated sporadic E30 cases and a possible shift towards G1 lineage in 2017 and 2018.

The design of software architecture remains a critical and complex task, often requiring expert knowledge, significant time investment, and the ability to interpret evolving requirements. This paper presents Archescape, a prototype system that supports the automated generation of software architecture from structured requirements using a hybrid approach that integrates machine learning algorithms with generative language models, specifically ChatGPT. The proposed system guides users through an interactive interface to collect detailed project requirements and produces architectural suggestions based on both algorithmic analysis and natural language generation. The system aims to assist both experienced architects seeking alternative perspectives and less experienced developers in need of guidance. Experimental results show that combining machine learning with large language models yields more adaptive, context-aware, and user-friendly architectural solutions than using either technique in isolation. Additionally, the platform supports educational purposes by enhancing understanding of the link between requirements and architecture. Limitations and future improvements, including architecture validation and domain specific tuning, are discussed. The results demonstrate the potential of AI-assisted tools to streamline the architectural design process and improve communication between technical and non-technical stakeholders.