Fatigue is a common non-motor symptom in Parkinson’s disease (PD), but even so, it may still be underdiagnosed or misdiagnosed in current practice due to its non-specific manifestations. The aims of this study were to investigate the prevalence of fatigue in PD patients compared to healthy controls and to identify the main characteristics and associations of fatigue with other non-motor symptoms and the impact of fatigue on sleep disturbances in Parkinson’s disease. Materials and methods: case–control study in which 131 PD patients and 131 age- and sex-matched controls were enrolled. Main characteristics of fatigue, sleep, and other non-motor symptoms were assessed using specific validated questionnaires. Results: According to the Chalder fatigue scale, fatigue is more prevalent in PD patients (38.16%) compared to healthy controls (26.71%). Fatigue was identified in 46.54% of the PD patients using the Parkinson’s Fatigue Scale (PFS-16). PD patients with fatigue presented a worse motor status, more sleep disturbances (insomnia, daytime sleepiness), a broader spectrum of non-motor symptoms (pain, anxiety, urinary disturbances), worse cognitive performances, a lower level of happiness, and worse quality of life compared to PD patients without fatigue. Conclusion: Fatigue is a common symptom of PD and needs to be assessed, considering its consequences on quality of life. Sleep disturbances have a great influence over fatigue in PD patients.

The tendency in the past few years has been to introduce tyres with lower rolling resistance coefficients to the market. This paper presents a mathematical method for determining the rolling resistance coefficients variation depending on the speed. The method uses power balance which results from automobile dynamics while rolling on chassis dynamometer. The rolling resistance coefficients of tyres obtained through ‘drum test method’, for which the rolling resistance coefficients variation is known in terms of vehicle speed, are considered as reference values, while than rolling resistance coefficients of tyres obtained through ‘MAHA roller dynamometer’ using the recorded lost drag power in the roll-out phase on the stand are considered as tested values. The rolling resistance coefficients variation could be determined up to the maximum permissible speed of the tyre, for all wheels trained on the stand and not just for one tyre, as determined in laboratory conditions. The test conditions are similar to those in real road conditions, where the temperature of the environment and wheels cannot be controlled. The values obtained by the authors’ proposed method were compared with the values obtained by the ‘drum test method’. The main contribution of the proposed method is to estimate the rolling resistance coefficients without using a very expensive test facility.

Background: Diarrheagenic Escherichia coli (E. coli) is an important cause of diarrheal diseases in both developing countries and industrialized countries. An outbreak of hemolytic uremic syndrome (HUS) in young children from southern Romania was reported in early 2016 and was attributed to Shiga toxin producing E. coli (STEC) O26 infection. The aim of this study was to determine the prevalence, demographic and clinical characteristics of STEC infections in children hospitalized with diarrhea in Brașov in the central region of Romania. We also described the occurrence of HUS among hospitalized children, close in time to the 2016 HUS outbreak in southern Romania. Methods: A prospective study was conducted between March and December 2016 among 722 children aged 1–30 months hospitalized with acute diarrhea. Stool samples obtained from patients with diarrhea were tested for the presence of Shiga toxin type 1 (STX1) and type 2 (STX2) by an immunochromatographic assay, and other enteropathogens. Demographic and clinical information on cases of HUS diagnosed in the same hospital was obtained from medical records. Results: Overall 46/722 (6.4%) children (mean age 10.3 months, 32.6% females) hospitalized with diarrhea tested positive for STX1 or STX2; of these 79% were positive for both STX1 and STX2, 16% for STX2 only, and 5% for STX1 only. Bloody diarrhea, vomiting and fever were documented in 32.6%, 52.1% and 50.0%, respectively of patients with STEC infection. Eleven confirmed HUS cases (mean age 20 months, five females) were identified between 2014 and 2016 with prodromal diarrhea reported in 10 of them. Three of the 11 HUS patients required hemodialysis. Conclusions: STEC prevalence among young children with diarrhea in Romania was high and the risk of HUS is emerging. The establishment of a systematic laboratory-based surveillance program including identification of the circulating STEC strains coupled with epidemiological investigation of HUS patients is warranted to determine the source and mode of transmission of STEC and prevent of STEC-associated diarrhea and HUS.

Skin cancer is one of the most common malignancies worldwide with melanoma being the deadliest form due to its high metastatic potential. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic methods, including visual inspection and dermoscopic evaluation are limited by inter-observer variability and require substantial clinical expertise. In recent years, artificial intelligence (AI) has shown promise in enhancing dermatological diagnostics by automating skin lesion analysis with high accuracy. This systematic review aims to evaluate and synthesize current literature on the application of AI for the detection and classification of skin cancer focusing on current trends, AI technologies, data sources, clinical relevance, limitations and future direction. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science for articles published between January 2010 and May 2025. The PRISMA guidelines were followed, and quality assessment was performed using the PROBAST tool. Out of 6296 identified records, 163 studies met the inclusion criteria. Convolutional neural networks (CNNs) were the most commonly used AI models, frequently trained on publicly available datasets such as ISIC and HAM10000. The reported classification accuracy ranged from 82% to 95%, with several models achieving dermatologist-level performance. However, the generalizability of these models was often limited due to dataset bias, lack of external validation, and insufficient reporting of demographic diversity. Few studies addressed model explainability or clinical integration. AI-based approaches demonstrate strong potential for enhancing the detection and classification of skin cancer with several models showing performance comparable to expert clinicians. Performance metrics including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were discussed alongside the skin image datasets and the relevant AI tools used in the studies. However, significant challenges remain, including the need for standardized evaluation frameworks, diverse and well-annotated datasets, and rigorous clinical validation. Future research should prioritize model transparency, fairness, and integration into real-world clinical workflows to ensure safe and equitable deployment.

This project is an observational, descriptive study evaluating frequencies of rotavirus disease in hospitalized children aged less than 5 years old between 2015 and 2021 in the Pediatric Hospital of Brasov, Central Romania. The study compares socio-demographic (age, sex, place of living and ethnicity), clinical, and treatment aspects between community-acquired rotavirus gastroenteritis (CARG) and hospital-acquired rotavirus gastroenteritis (HARG). During that period, 1913 hospitalized children had a rapid positive immunichromatographic rotavirus test from stool specimens. Among them, 1620 (84.6%) were CARG and 293 (15.4%) were HARG. CARG conditions represented 28.5% of all acute hospitalized gastroenteritis (n = 5673) whereas HARG represented 5.2%. Around the same percentage of urban children were seen in CARG as in HARG (58.5% (n = 948) for CARG and 56.3% (n = 164) for HARG). About 64.9% (n = 1052) of CARG cases were from Roma population, and 66.5% (n = 195) in HARG. The age group with the highest frequency of the disease was 12 to 24 months old for both CARG and HARG. The average hospital duration was 5.09 days for CARG and 7.62 days for HARG. Diarrhea was the principal symptom in both CARG and HARG (92.6% (n = 1500) for CARG and 93.9% (n = 275) for HARG). Most CARG patients (61% (n = 989)) were treated for symptomatic management with iv fluids. Most HARG (60.4% (n = 177)) were treated for symptomatic management with iv fluids and antibiotics. A significant seasonal shift to a later period in the year was observed during the last registration year of 2021, possibly due to the COVID-19 epidemic. The seasonal disease burden of rotavirus infection in children remains high in hospital care in Romania, which may justify the systematic introduction of rotavirus vaccination across the whole country.

Background/Objectives: Admission hyperglycemia is frequent in COVID-19 and may serve as an early marker of severity. We assessed whether admission hyperglycemia predicts severe disease and poor outcomes in adults without diabetes. Methods: We performed a retrospective cohort study at the Clinical Hospital of Pneu-mophthisiology and Infectious Diseases, Brașov, Romania, including adults hospital-ized with RT-PCR/antigen–confirmed COVID-19 between August 2020 and July 2021. Patients < 18 or >80 years, with prior diabetes, or on corticosteroids were excluded. Hyperglycemia was defined as fasting glucose >106 mg/dL and classified as mild (107–180 mg/dL), moderate (181–300 mg/dL), and severe (>300 mg/dL). Clinical, laboratory, imaging, treatment, utilization, and cost parameters were analyzed. Results: Of 1,009 patients, 734 (72.7%) were hyperglycemic at admission. Compared with normoglycemic patients, hyperglycemics more often developed respiratory fail-ure (67.7% vs. 38.2%), required CPAP (9.4% vs. 1.5%), had severe/critical disease (46.9% vs. 25.1%), ICU transfer (6.5% vs. 1.5%), and mortality (3.8% vs. 1.1%) (all p≤0.0256). They also showed lymphopenia, eosinopenia, higher inflammatory and co-agulation markers, longer hospitalization (12.1 vs. 10.1 days), and increased costs (€1,846 vs. €1,043) (all p< 0.001). Severe hyperglycemia (>300 mg/dL) strongly corre-lated with inflammation, coagulopathy, tissue injury, and radiologic severity. Conclusions: Admission hyperglycemia is a robust, easily measurable predictor of se-vere COVID-19 and adverse outcomes in non-diabetic adults and is associated with greater resource utilization and higher costs. Early identification may improve risk stratification, while prospective studies should assess whether glycemic control modi-fies prognosis. Keywords: COVID-19; SARS-CoV-2; admission hyperglycemia; severity; mortality; ICU;