Advancing Open Science

Background/objectives: A rise in infections associated with carbapenem-resistant Providencia species (CRPS) has been observed worldwide. This study presents a genomic analysis of CRPS isolates from four hospitals in Croatia and the outpatient setting, in order to determine the extent of the spread of CRPS in Croatia. In the present study, we applied a combination of phenotypic characterization and molecular analysis of resistance traits to determine the mechanisms and the routes of spread of CRPS. Material and methods: The antibiotic susceptibility testing was performed using disk-diffusion and broth dilution methods. The nature of extended-spectrum β-lactamases (ESBLs), carbapenemases, and fluoroquinolone resistance determinants was investigated by polymerase chain reaction (PCR). In order to obtain an insight into the whole resistome, selected isolates were subjected to the Interarray Genotyping Kit CarbaResist and whole genome sequencing (WGS). Results: In total, 30 isolates were collected from four centers, located in different geographic regions of Croatia. There was uniform resistance to piperacillin-tazobactam, cefuroxime, expanded-spectrum cephalosporins (ESCs), imipenem, ertapenem, meropenem, and ciprofloxacin. Immunochromatographic testing and PCR revealed OXA-48 and NDM carbapenemase in 15 isolates, respectively. Phenotypic tests for ESBLs were positive in all OXA-48 and one NDM-positive organism (16 isolates). The isolates were categorized as extensively drug-resistant (XDR). OXA-48-producing isolates were susceptible only to ceftazidime-avibactam, whereas NDM producers were susceptible to cefiderocol and, in the majority of cases, also to amikacin. WGS identified a plethora of genes encoding resistance to aminoglycosides, such as aadA1 and aadA2, (aph(3″)-Ib and aph(6)-Id, sulfonamides sul1 and sul2, trimethoprim dfrA1, dfrA10, and dfrA12, tetracyclines tet(A) and tet(B), and chloramphenicol catA3 and catA5. Conclusions: Providencia spp., in spite of being a rare pathogen, should be included in the surveillance studies across the medical centers in Croatia.

Genetic variation underlies the capacity of populations to adapt, yet what drives how this variation is generated and maintained in natural populations remains poorly understood. Fundamental processes such as mutation, ploidy, and recombination are known to shape genetic variation and adaptive potential but are typically studied in isolation and under controlled laboratory conditions. How these processes act together under varying environmental conditions to structure genetic variation across complex natural populations remains unresolved. In yeasts, these processes are dependent on reproductive mode, ploidy shifts, and environmental stressors, which jointly shape genomic stability and adaptive potential. Here, we review our current knowledge on the roles of mutation, ploidy, and recombination in adaptation in the model yeasts Saccharomyces cerevisiae and the human pathogenic Cryptococcus. We highlight heterogeneity in mutation rates, recombination, and ploidy states across strains, environments, and populations, challenging the assumption that these parameters are uniform. We argue that fluctuating environments, increasingly driven by climate change, are likely to intensify interactions among these processes to impact evolution in ways that remain difficult to predict. Integrating population genomics with ecologically realistic frameworks will be essential for understanding natural evolutionary dynamics and anticipating fungal adaptation and disease emergence.

The accurate monitoring and dynamic analysis of metal ions are of considerable practical significance in environmental toxicology and life sciences. Colorimetric analysis and surface-enhanced Raman scattering (SERS) sensing technologies, utilizing the aggregation effect of gold and silver nanoparticles (Au/Ag NPs), have emerged as prominent methods for rapid metal ion detection. While sharing a common plasmonic basis, these two techniques serve distinct yet complementary analytical roles: colorimetric assays offer rapid, instrument-free visual screening ideal for point-of-care testing (POCT), whereas SERS provides superior sensitivity and structural fingerprinting for precise quantification in complex matrices. Furthermore, the synergistic integration of these modalities facilitates the development of dual-mode sensing platforms, enabling mutual signal verification for enhanced reliability. This article evaluates contemporary optical sensing methodologies utilizing aggregation effects and their advancements in the detection of diverse metal ions. It comprehensively outlines methodological advancements from nanomaterial fabrication to signal transduction, encompassing approaches such as biomass-mediated green synthesis and functionalization, targeted surface ligand engineering, digital readout systems utilizing intelligent algorithms, and multimodal synergistic sensing. Recent studies demonstrate that these techniques have attained trace-level identification of target ions regarding analytical efficacy, with detection limits generally conforming to or beyond applicable environmental and health safety regulations. Moreover, pertinent research has enhanced detection linear ranges, anti-interference properties, and adaptability for POCT, validating the usefulness and developmental prospects of this technology for analysis in complicated matrices.

Background/Objectives: Phelan–McDermid syndrome (PMS), caused by either chromosome 22q13.3 deletions or pathogenic/likely pathogenic variants in the SHANK3 gene, is a rare neurodevelopmental disorder. Behavioral issues greatly impair the quality of life for affected individuals and their families. This genotype–phenotype study intended to further characterize key behavioral features and their genetic and metabolic correlates in PMS. Methods: We conducted a cross-sectional analysis of data on 56 individuals with PMS. Autistic and related behaviors were assessed with the Autism Diagnosis Interview—Revised (ADI-R) and adaptive behavior skills were assessed with the Vineland Adaptive Behavior Scales-Third Edition (Vineland-3), both covering multiple aspects of communication, socialization and abnormal behaviors. Genetic diagnostic information on deletions or pathogenic variants was supplemented with the sequencing data of nine candidate genes on 22q13.3. Metabolic data were obtained using the Biolog Phenotype Mammalian MicroArray plates (PM-M). Results. Every subject in the cohort presented either prominent autistic behavior or adaptive behavior impairment, 55.4% of them meeting the ASD cutoff in every ADI-R domain and 92.9% scoring in the lowest level of adaptive behavior (range of 20–70). Individuals with SHANK3 variants had lower adaptive behavioral skills than those with 22q13 deletions regardless of deletion size, while genomic parameters were largely unrelated to ADI-R scores. Metabolic profiling identified unique profiles of individuals with PMS compared with controls, while distinct profiles distinguished those who met or did not meet the ADI-R ASD cutoff. Cluster analyses revealed groups of individuals with ASD and other clinical features. Conclusion. This study highlighted the importance of SHANK3 in adaptive behavioral skills and uncovered potential metabolic biomarkers of therapeutic relevance.

Background/objectives: Grapevine (Vitis vinifera L.) is one of the most economically and culturally important fruit crops worldwide and hosts more than 100 viruses. Viral infections can cause severe yield losses, but plants can adapt to infection through changes in miRNA-mediated regulatory pathways. MicroRNAs are key regulators of plant development and stress responses. Several prediction tools are available for miRNA detection from small RNA sequencing data, each relying on different algorithms. The aim of this study was to compare miRNA predictions generated by three widely used tools (miRador, ShortStack, and miRDeep2) and to evaluate how viral coinfections influence miRNA expression in grapevine. Methods: Two grapevine cultivars, Refošk (“Terrano”) and Zeleni Sauvignon (“Sauvignon Vert”), were analyzed. Small RNA sequencing was performed on virus-free plants and plants coinfected with grapevine Pinot gris virus (GPGV), grapevine rupestris stem pitting-associated virus (GRSPaV), and grapevine rupestris vein feathering virus (GRVFV). Three miRNA prediction tools were used to identify miRNAs annotated in public databases. Differential expression analysis was performed separately for each tool and by using an integrated approach that combined all three datasets. The expression of selected miRNAs was further evaluated using stem-loop RT-qPCR. Results: The three prediction tools detected markedly different numbers of miRNAs, resulting in largely distinct sets of differentially expressed miRNAs and limited overlap between individual analyses. The integrated approach yielded a separate set of differentially expressed miRNAs, most of which overlapped with at least one individual dataset. Stem-loop RT-qPCR analysis supported the differential expression of several selected miRNAs. Conclusions: This study provides new insight into miRNA expression in grapevine under mixed-virus infection and demonstrates that miRNA profiling outcomes are strongly influenced by the choice of bioinformatic prediction tool. Our results highlight the importance of integrated analytical strategies combined with experimental validation to obtain robust and biologically meaningful interpretations of miRNA expression in plants.

As hydrogen mobility gains increasing importance, the number of hydrogen refueling stations (HRSs) worldwide is expanding rapidly. Hydrogen compression is a critical component of every HRS, exerting a direct and decisive influence on operability, performance, economic viability, downtime, safety, and public acceptance. Given this central role, this work presents a comprehensive overview of the hydrogen compression landscape, critically examining both conventional mechanical systems—such as piston and diaphragm compressors—and emerging non-mechanical technologies, including electrochemical and metal hydride compressors. The analysis also addresses novel hybrid approaches that combine methods to exploit their respective strengths. Each technology is assessed against a consistent set of practical criteria, encompassing not only fundamental performance metrics such as maximum discharge pressure and flow capacity but also key considerations relevant to real-world deployment. This review provides a detailed comparison of all hydrogen compression technologies with respect to energy efficiency, maintenance needs and intervals, capital expenditures (CAPEX), operating expenditures (OPEX), and Technology Readiness Level (TRL). Additional factors—including physical size, noise levels, and effects on hydrogen purity—are also evaluated, as they strongly influence the suitability for applications in urban or remote areas. By synthesizing recent scientific literature, industry data, and applicable technical standards, this work develops a structured multi-criteria framework that translates technical insights into practical guidance and a clear technology selection roadmap. The overarching objective is to equip engineers, station developers, operators, and policymakers with the knowledge needed to make informed and optimized decisions about hydrogen compression during HRS planning and design.

In lung cytology, screeners and pathologists examine many cells in cytological specimens and describe their corresponding imaging findings. To support this process, our previous study proposed an image-finding generation model based on convolutional neural networks and a transformer architecture. However, further improvements are required to enhance the accuracy of these findings. In this study, we developed a cytology-specific image-finding generation model using a vision transformer and open-source large language models. In the proposed method, a vision transformer pretrained on large-scale image datasets and multiple open-source large language models was introduced and connected through an original projection layer. Experimental validation using 1059 cytological images demonstrated that the proposed model achieved favorable scores on language-based evaluation metrics and good classification performance when cells were classified based on the generated findings. These results indicate that a task-specific model is an effective approach for generating imaging findings in lung cytology.

In fields such as noise control, medical ultrasound, and acoustic communication, the flexible regulation of reflected sound waves has significant application value. In this work, a dual-band acoustic metasurface was designed using a split hollow cuboid with an open-hole plate (OPSHC) structure, which simultaneously achieves the direction control of reflected sound waves in both frequency bands. An OPSHC is a series structural unit, and the two center frequencies are mainly controlled by the diameters of the two openings in the structure and the position of the open-hole plate. Through finite element simulation, the influence of the center frequency of the metasurface and the position of the open-hole plate on the bandwidth of the anomalous reflection was studied. The results show that when the low-frequency center frequency is fixed, the low-frequency bandwidth of the metasurface increases with the increase in the high-frequency center frequency. When the position of the plate is moved, the low-frequency bandwidth increases and the high-frequency bandwidth decreases. This type of metasurface provides a new technical approach for broadband acoustic metasurface applications in noise control and underwater detection systems.