Search Results (11,051)

Newborn screening (NBS), one of the most important public health care prevention programs, aims at the early identification of asymptomatic newborns at increased risk for inherited disorders, facilitating timely intervention to reduce morbidity and mortality. NBS in Hungary is celebrating the 50th anniversary of the nationwide implementation of screening for phenylketonuria and galactosemia, as well as the 40th anniversary of congenital hypothyroidism screening. The present paper reviews the early years, the present situation, and future perspectives for the Hungarian NBS program. Today, screening for 27 disorders (opt-out) plus spinal muscular atrophy (opt-in) is supported by two centralized and well-equipped laboratories in Budapest and Szeged, in-depth laboratory knowledge, a robust follow-up system, and governmental financial support. Since 1975, 3,289 patients have been confirmed with a screened condition from over 5.6 million newborns screened. The 50-year anniversary of the Hungarian NBS program highlights the dedication of both past and current professionals, ongoing advancements in analytical methods and laboratory information management systems, and alignment with international standards. The equitable provision of screening services continues to be prioritized for all newborns nationwide and within the broader Euro-regional context. Full article

Objectives: The present study aimed to examine the relationships between screen time (ST), sleep time (SLT), physical activity engagement (PA), Mediterranean diet (MD) adherence, body mass index (BMI), self-esteem (SE) and academic performance (AP) in high-socioeconomic-status (SES) school-aged youth in southern Spain. Methods: A descriptive, comparative, non-experimental and cross-sectional research study was conducted with a total sample of 217 Spanish students (13.88 ± 1.32). Structural equation modelling (SEM) was employed to analyse relationships between study variables as a function of sex. Results: SLT was positively associated with MD adherence and negatively related with BMI. Both PA and MD adherence were positively linked to SE, whilst MD adherence and SE were also positively related to AP. Regarding sex differences, ST was a stronger determinant among girls, showing negative associations with PA (β = −0.378; p < 0.001) and MD adherence (β = −0.315; p < 0.001), with this pattern not being observed in boys. PA was positively associated with SE in both sexes, but more strongly in boys (β = 0.332; p < 0.001) than in girls (β = 0.190; p = 0.034). In girls, both MD adherence (β = 0.295; p < 0.001) and SE (β = 0.224; p = 0.008) were positively associated with AP, with these associations not being found in boys. Conclusions: The findings underscore the complex interplay between lifestyle behaviours, psychosocial factors, and AP in school-aged youth. Regardless of SES, interventions should focus on reducing ST, promoting PA and MD adherence, and enhancing SE whilst considering sex-specific patterns. Full article

Evaluating the coherence of visual narrative sequences extracted from image collections remains a challenge in digital humanities and computational journalism. While mathematical coherence metrics based on visual embeddings provide objective measures, they require computational resources and technical expertise to interpret. We propose using vision-language models (VLMs) as judges to evaluate visual narrative coherence, comparing two approaches: caption-based evaluation that converts images to text descriptions and direct vision evaluation that processes images without intermediate text generation. Through experiments on 126 narratives from historical photographs, we show that both approaches achieve weak-to-moderate correlations with mathematical coherence metrics (r = 0.28–0.36) while differing in reliability and efficiency. Direct VLM evaluation achieves higher inter-rater reliability ($\mathrm{ICC}\left(\right)=0.718$ vs. $0.339$) but requires $10.8\times$ more computation time after initial caption generation. Both methods successfully discriminate between human-curated, algorithmically extracted, and random narratives, with all pairwise comparisons achieving statistical significance ($p<0.05$, with five of six comparisons at $p<0.001$). Human sequences consistently score highest, followed by algorithmic extractions, then random sequences. Our findings indicate that the choice between approaches depends on application requirements: caption-based for efficient large-scale screening versus direct vision for consistent curatorial assessment. Full article

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Monofractal analysis offers a promising framework for characterizing cardiac dynamics, particularly in the early detection of heart failure. However, most existing approaches rely on long-duration physiological signals and do not explore the classification of disease severity. In this study, we propose a hybrid CNN-ELM model trained exclusively on synthetic monofractal time series of short length (128 to 512 samples), aiming to assess its ability to distinguish between healthy individuals and varying degrees of heart failure defined by the NYHA functional classification. Our results show that Hurst exponent distributions reflect the progressive loss of complexity in cardiac rhythms as heart failure severity increases. The model successfully classified both binary (healthy vs. sick) and multiclass (NYHA I–IV) scenarios by grouping Hurst exponent values ($H_{0.1}$ to $H_{0.9}$) into clinical categories, achieving peak accuracy ranges of 97.3–98.9% for binary classification and 96.2–98.8% for multiclass classification across signal lengths of 128, 256, and 512 samples. Importantly, the CNN-ELM architecture demonstrated fast training times and robust generalization, outperforming previous approaches based solely on support vector machines. These findings highlight the clinical potential of monofractal indices as non-invasive biomarkers of cardiovascular health and support the use of short synthetic signals for scalable, low-cost screening applications. Future work will extend this framework to multifractal and real-world clinical data and explore its integration into intelligent diagnostic systems. Full article

Atrial Fibrillation (AF) is a common yet often undiagnosed cardiac arrhythmia with serious clinical consequences, including increased risk of stroke, heart failure, and mortality. In this work, we present a novel Embedded Edge system performing real-time AF detection on a low-power Microcontroller Unit (MCU). Rather than relying on full Electrocardiogram (ECG) waveforms or cloud-based analytics, our method extracts Heart Rate Variability (HRV) features from RR-Interval (RRI) and performs classification using a compact Long Short-Term Memory (LSTM) model optimized for embedded deployment. We achieved an overall classification accuracy of 98.46% while maintaining a minimal resource footprint: inference on the target MCU completes in 143 ± 0 ms and consumes 3532 ± 6 μJ per inference. This low power consumption for local inference makes it feasible to strategically keep wireless communication OFF, activating it only to transmit an alert upon AF detection, thereby reinforcing privacy and enabling long-term battery life. Our results demonstrate the feasibility of performing clinically meaningful AF monitoring directly on constrained edge devices, enabling energy-efficient, privacy-preserving, and scalable screening outside traditional clinical settings. This work contributes to the growing field of personalised and decentralised cardiac care, showing that Artificial Intelligence (AI)-driven diagnostics can be both technically practical and clinically relevant when implemented at the edge. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

Sesame seed oil is a bioactive-rich lipid source, notable for lignans, tocopherols, and unsaturated fatty acids that underpin its antioxidant and cardioprotective properties. This study optimized two innovative, non-thermal extraction techniques—pulsed electric field (PEF) and ultrasound bath-assisted extraction (UBAE)—to maximize yield and preserve oil quality for functional food applications. A blocked definitive screening design combined with response surface methodology modeled the effects of energy power (X₁, 60–100%), liquid-to-solid ratio (X₂, 10–20 mL/g), and extraction time (X₃, 10–30 min) on fat content, DPPH antiradical activity, and oxidative stability indices (Conjugated Dienes, CDs/Conjugated Trienes, CTs). UBAE achieved the highest fat yield—59.0% at low energy (60%), high X₂ (20 mL/g), and short X₃ (10 min)—while PEF maximized DPPH to 36.0 μmol TEAC/kg oil at high energy (100%), moderate X₂ (17 mL/g), and short X₃ (10 min). CDs were minimized to 19.78 mmol/kg (UBAE, 60%, 10 mL/g, 10 min) and CTs to 3.34 mmol/kg (UBAE, 60%, 12 mL/g, 10 min). Partial least squares analysis identified X₂ and X₃ as the most influential variables (VIP > 0.8), with energy–time interplay (X₁ × X₃) being critical for antioxidant capacity. Compared to cold-pressing and Soxhlet extraction, PEF and cold-pressing retained higher antioxidant activity (~19 μmol TEAC/kg) and oxidative stability (TBARS ≤ 0.30 mmol MDAE/kg), while Soxhlet—though yielding 55.65% fat—showed the poorest quality profile (Totox value > 560). Both non-thermal techniques can deliver bioactive-rich sesame oil with lower oxidative degradation, supporting their application in functional foods aimed at improving dietary antioxidant intake and mitigating lipid oxidation burden. PEF at high energy/short time and UBAE at low energy/short time present complementary, scalable options for producing high-value edible oils aligned with human health priorities. As a limitation, we did not directly quantify lignans or tocopherols in this study, and future work will address their measurement and bioaccessibility. Full article

Background: Human papillomavirus (HPV)-associated oral and oropharyngeal squamous cell carcinomas have risen dramatically in incidence over recent decades. Yet, unlike cervical neoplasia, there is no established screening paradigm for HPV-driven oropharyngeal dysplasia, as precursor lesions are often occult and are not easily accessible for examination. This drives an urgent need for non-invasive biomarkers to enable early detection, risk stratification, and timely intervention. Objective of this review is to highlight advances in liquid biopsy modalities, specifically saliva- and blood-based biomarkers—in the context of HPV-driven oral carcinogenesis—and to evaluate their utility in early cancer detection, prognostic, post-treatment surveillance, and recurrence monitoring. Methods: We performed a narrative review of PubMed-indexed studies (2015–2025) focusing on HPV-positive oral and oropharyngeal squamous cell carcinomas. and liquid biopsy analytes. Key sources were high-impact original studies and meta-analyses from 2020–2025 examining circulating tumor DNA (ctDNA), viral nucleic acids, circulating tumor cells (CTCs), extracellular vesicles (EVs), and related biomarkers in saliva and blood. Reported data on assay performance, biases, and validation were reviewed to highlight how oral cancer findings align with trends seen in other solid tumors. Results: In reviewing recent studies (2015–2025), we found consistent evidence that saliva best captures locoregional tumor signals while plasma circulating tumor HPV DNA (ctHPV DNA) reflects systemic disease, and that using both matrices improves detection over either alone. Dual-fluid testing will potentially enable earlier identification of molecular residual disease with clinically meaningful lead time before radiographic recurrence, supporting risk-adapted surveillance. Overall, literature favors standardized pre-analytics and combined saliva plus plasma workflows to enhance early detection and follow-up in HPV-positive oral and oropharyngeal squamous cell carcinomas. Conclusions: Liquid biopsy approaches offer promising tools for the early, non-invasive detection and real-time monitoring of HPV-associated oral cancers. Realizing their full clinical potential will require robust prospective validation and standardization of pre-analytical protocols. Integrating salivary and blood biomarkers into tailored surveillance programs may further support earlier intervention and improved patient outcomes, while potentially reducing reliance on unnecessary invasive procedures. Full article

Background: Diarrheagenic Escherichia coli (DEC) remains relevant to public health and agri-food chains. The context in Brazil, as a major food producer and exporter, reinforces the need for genomic surveillance. Objective: We aimed to characterize Brazilian diffusely adhering (DAEC), enteropathogenic (EPEC), and Shiga toxin-producing E. coli (STEC) sequences in silico across O-serogroups, in addition to sequence-type (ST), virulence, resistome, and phylogenomic relationships. Methodology: We retrieved 973 genomes assigned to Brazil from NCBI Pathogen Detection Database and performed virtual-PCR screening for key DEC-genes. We then typed O-serogroups (ABRicate/EcOH), Multi-Locus Sequencing Type (MLST), virulome (Ecoli_VF), resistome (ResFinder), and characterized stx genes. Results: DEC represented 18.7% of genomes, driven primarily by EPEC. In EPEC, the eae β-1 subtype was most common; we detected, for the first time in Brazilian sequences, ξ-eae subtype and ST583/ST301. Seventy-eight percent of DAEC isolates were multidrug-resistant (MDR), and two ST were newly reported in the country (ST2141/ST500). In STEC, O157 formed a largely susceptible clade with uniform eae γ-1, whereas 57% of non-O157 were MDR. New STs (ST32/ST1804) were observed, and three genomes were closely related to international isolates. Conclusions: Despite the low DEC representation in the dataset, new STs and eae subtypes were detected in Brazil. Also, MDR in DAEC and non-O157 STEC reinforces the need for antimicrobial-resistance genomic surveillance. Full article

Background/Objectives: The prison environment presents a unique context for examining the impact of addiction on physical and psychological functioning. Individuals with substance use disorders (SUDs) are overrepresented in correctional facilities and often experience greater emotional difficulties and impaired physical capacity. This study aimed to conduct a comparative analysis of psychological and functional profiles between addicted and non-addicted male inmates in a semi-open correctional facility. Methods: The study included 47 male prisoners (19 addicted, 28 non-addicted). Physical performance was assessed using the Countermovement Jump (CMJ), handgrip strength, the Functional Movement Screen (FMS), and the FitLight reaction time test. Psychological functioning was evaluated using six standardized questionnaires: problem-focused, emotion-focused, and avoidant coping strategies, depression (PHQ-9), perceived stress (PSS-10), and self-compassion (SCS). Results: No statistically significant differences (p > 0.05) were found between addicted and non-addicted inmates in physical performance parameters. Addicted individuals demonstrated slightly higher handgrip strength with lower variability, while non-addicted inmates showed slightly better lower-body power in the CMJ test. Functional movement quality and reaction speed were similar between groups. Psychological assessments also revealed no significant differences between the groups. Coping styles, depressive symptoms, perceived stress levels, and self-criticism scores were comparable in both populations. In the addicted group, deeper squats correlated with lower stress (rho = −0.46, p = 0.047), and better hurdle step performance correlated with emotion-focused coping (rho = 0.46, p = 0.048). Conclusions: Although no statistically significant differences were found between addicted and non-addicted male inmates in the assessed physical and psychological outcomes, the limited sample size and context-specific nature of this pilot study suggest that these findings should be viewed as preliminary and interpreted with caution. Nonetheless, the observed associations between physical performance and psychological variables indicate subtle interconnections between motor capacity, stress perception, and coping mechanisms that merit further investigation in larger, longitudinal studies. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Background: This study investigates the association between diagnostic delay (DD) and clinical and behavioral variables among Japanese women with endometriosis, and explores an optimal cut-off point distinguishing short and long DD. Methods: a cross-sectional online survey was conducted among 220 Japanese women aged 18–49 diagnosed with endometriosis. Data on healthcare behaviors, economic expenditures, and disease-specific outcomes were analyzed by stratifying participants based on DD length. Multivariate logistic regression models were applied. Results: the mean age at initial symptom onset was 24.3 years, and at diagnosis, 27.7 years. The median DD was 1.5 years, with significant differences between short and long DD groups (p < 0.001). Longer DD was significantly associated with greater use of over-the-counter (OTC) pain medication (p = 0.008) and a higher proportion of Stage IV endometriosis (p = 0.022). Conclusions: diagnostic delays longer than 1.5 years may contribute to disease progression and reliance on self-management, potentially postponing medical consultation. Early intervention strategies, including screenings and public awareness, may promote timely healthcare-seeking behavior. Future studies should prioritize clinical assessments and early diagnosis to reduce the burden of advanced disease. Full article

Raceway ponds would allow the sustainable production of algal biomass because of their lower cost. However, for successful cultivation, the target organism needs to prevail despite unavoidable contamination by environmental strains. The development of efficient methods to control cyanobacterial proliferation is thus highly desirable. With the aim to identify new cyanobactericidal substances, a set of natural compounds was screened for the ability to inhibit the growth of a model cyanobacterial strain, Synechococcus elongatus PCC 6301. Three compounds, namely hydroquinone, juglone and plumbagin, were found to be active in the 10⁻⁶ to 10⁻⁴ M range. Activity was confirmed on a panel of 10 other cyanobacteria that showed different sensitivity, with concentrations causing 50% growth inhibition varying up to 2 orders of magnitude. Co-cultivation experiments showed that the growth of Microcystis aeruginosa PCC 7941 was almost completely suppressed at quinone concentrations at which that of Tolypothrix PCC 7601 was substantially unaffected. Juglone and plumbagin in the micromolar range also exerted toxic effects on eukaryotic microalgae, bacteria and yeast, whereas the growth of higher plants was affected only at higher concentrations. In the case of juglone, activity was lost with time after being dissolved, allowing water discharge/recycling. The results point at the aromatic 1.4-quinone/diol ring as a lead moiety for the development of chemicals to help maintaining monospecificity of microalgae cultures. Full article

Background: Methicillin-resistant Staphylococcus aureus (MRSA) colonization is a risk factor for potential staphylococcal infection and outbreaks. Although it is recommended to obtain a swab culture to detect nasal colonization its necessity in low-prevalence countries is debated. The aim of this study was to determine the prevalence of MRSA nasal colonization, the rate of invasive infection development, and the risk factors for invasive infections in patients admitted to the intensive care unit. Materials and Methods: This retrospective study included patients who were followed up in one of the adult intensive care units at Kayseri City Training and Research Hospital between 1 January 2019 and 31 December 2024 (6 years) and from whom a culture was taken at the time of hospital admission to detect MRSA colonization in the nose. MRSA carriers were examined for the development of any invasive infection caused by MRSA within 28 days of their relevant admission. Results: Over a total period of six years, nasal swab samples were collected from 22,913 patients, and MRSA colonization was detected in 939 (4.0%). Of the patients with MRSA colonization, 32 (3.4%) were excluded from the analysis because they already had invasive MRSA infection. Additionally, 431 patients (45.8%) were excluded from the analysis because they were discharged or died within the first seven days of their admission. Consequently, invasive MRSA infection developed within 28 days in 29 of the 476 patients with MRSA colonization (6.0%). Patients who developed invasive infection had a higher rate of chronic renal failure (p < 0.001), hemodialysis (p < 0.001), central venous catheter (p = 0.028), staying in nursing home (p = 0.001), and a history of hospitalization within the last 90 days (p = 0.015). In the multivariable regression analysis, routine hemodialysis (OR: 5.216, p = 0.015), nursing home stay (OR: 3.668, p = 0.014), and a history of hospitalization within the last 90 days (OR: 2.458, p = 0.028) were found to be risk factors for developing invasive infection. The most common invasive infections were ventilator-associated pneumonia (n = 9), surgical site infection (n = 7), and catheter-related bloodstream infection (n = 6). All 29 strains were susceptible to vancomycin, linezolid, and daptomycin, while one strain was resistant to teicoplanin (3.5%). Conclusions: MRSA colonization has been detected in 4% of patients admitted to the intensive care unit. Screening should be performed because MRSA colonization may be a risk factor for invasive infections; however, screening all patients would be prohibitively expensive and labor-intensive. Instead, it may be more appropriate to identify risk factors and then screen select patients. Full article