Vaccine hesitancy remains a significant public health challenge, particularly during pandemics when high immunization rates are crucial. While individual psychological antecedents of vaccine hesitancy have been extensively studied, limited empirical evidence exists on how contextual determinants, such as socioeconomic status, political trust, and digital literacy, collectively shape vaccine-related behaviors, particularly in Central European populations. This study explores the key determinants of COVID-19 vaccine hesitancy among Czech adults. A cross-sectional study was conducted using data from the 48th wave of the Czech national panel survey Život během pandemie [Life During Pandemic], carried out in March 2023. The data were obtained via an online questionnaire administered to a nationally representative sample of Czech adults (n = 1,708). Sociodemographic, socioeconomic, and anamnestic variables were examined alongside political attitudes. Psychological antecedents of vaccination were assessed using the 5C model (confidence, complacency, constraints, risk calculation, and collective responsibility), and digital vaccine literacy was measured using seven items covering trust in official sources, trust in social media, and the ability to evaluate and apply vaccine information. Statistical analyses included bivariate tests and multivariable regression models to identify vaccine uptake and intent determinants. Higher trust in constitutional institutions, including the president (OR = 1.55; 95/ CI: 1.38-1.74), government (1.60; 1.38-1.85), Chamber of Deputies (1.73; 1.48-2.02), and Senate (1.47; 1.29-1.69), was significantly associated with higher vaccine uptake. Similarly, positive attitudes toward the integration of Ukrainian refugees into Czech society - across domains such as work (1.63; 1.39-1.90), housing (1.59; 1.36-1.86), school (1.64; 1.41-1.92), language (1.57; 1.34-1.84), and culture (1.74; 1.50-2.03) - were positively associated with uptake. Greater confidence in vaccine safety and effectiveness was also a significant predictor (1.51; 1.44-1.58). In contrast, lower education (0.64; 0.56-0.73), lower income (0.91; 0.86-0.95), female sex (0.60; 0.47-0.76), and higher complacency (0.76; 0.73-0.80) were associated with reduced uptake. Respondents with better digital vaccine literacy, particularly those more adept at identifying misinformation, showed significantly greater vaccine confidence (mean score: 3.62 vs. 3.30, p < .001). Beyond psychological antecedents, institutional trust, political orientation, and digital vaccine literacy significantly shape COVID-19 vaccination behaviors. These findings underscore the importance of targeted interventions that address political and digital influences on vaccine hesitancy, and they highlight the need for future research to examine the causal pathways and longitudinal dynamics underlying these associations, particularly within Central and Eastern European contexts.

• Keywords
• COVID-19, Czech Republic, refugees, social determinants of health, vaccination hesitancy,
• MeSH
• COVID-19 * prevention & control   psychology   MeSH
• Adult MeSH
• Trust * psychology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Vaccination Hesitancy * psychology   statistics & numerical data   MeSH
• Politics MeSH
• Cross-Sectional Studies MeSH
• Surveys and Questionnaires MeSH
• SARS-CoV-2 MeSH
• Aged MeSH
• Social Media MeSH
• Socioeconomic Factors MeSH
• Vaccination * psychology   MeSH
• COVID-19 Vaccines * administration & dosage   MeSH
• Health Literacy * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic epidemiology   MeSH
• Names of Substances
• COVID-19 Vaccines * MeSH

ABSTRACTBackground: European health care workers recently experienced serious challenges to their mental health. Following the extremely stressful experience of the COVID-19 pandemic, the war in Ukraine caused a humanitarian influx of refugees in need of social and healthcare. We aimed to explore: (1) how working with refugees has affected the mental well-being of health care workers in the context of the COVID-19 pandemic, and (2) the nature of health care workers' emotional strain related to the refugee situation and the war in Ukraine.Methods: We used a combination of quantitative regression analyses and qualitative content analysis to assess data collected by an online questionnaire in 2022. The study included 1121 health care workers from the Czech arm of the international HEROES Study.Results: Quantitative findings did not indicate that working with Ukrainian refugees was reliably associated with a greater occurrence of symptoms of depression, anxiety, distress, or burnout. Qualitative analysis revealed five categories of emotional strain: impacts on working conditions, emotional reactions to refugees and the war, comparisons with the COVID-19 pandemic, and coping strategies.Conclusions: This study highlights the resilience of health care workers but also points to the need for ongoing support to address the complex emotional challenges they face during health crises.

Although we did not find a significant association between working with refugees and mental health issues, health professionals encountered emotionally challenging situations.Emotionally challenging situations involved reactions to the war and refugees, worsening working conditions, and higher subjective strain than during the COVID-19 pandemic.When comparing health workers caring for with refugees and COVID-19 patients, we found differences in their mental health issues.

• Keywords
• COVID-19 pandemic, Migración, Migration, Russian-Ukrainian war, emotional strain, guerra ruso-ucraniana, malestar psicológico, pandemia de COVID-19, psychological distress,
• MeSH
• Adaptation, Psychological MeSH
• COVID-19 * psychology   epidemiology   MeSH
• Depression psychology   epidemiology   MeSH
• Adult MeSH
• Mental Health * MeSH
• Middle Aged MeSH
• Humans MeSH
• Pandemics MeSH
• Burnout, Professional * psychology   epidemiology   MeSH
• Surveys and Questionnaires MeSH
• Stress, Psychological * psychology   MeSH
• SARS-CoV-2 MeSH
• Refugees * psychology   MeSH
• Anxiety psychology   epidemiology   MeSH
• Health Personnel * psychology   statistics & numerical data   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic epidemiology   MeSH
• Ukraine ethnology   MeSH

BACKGROUND: Plasma sulfur amino acids (SAAs), particularly cysteine, are associated with obesity. One proposed mechanism is the altered regulation of the stearoyl-CoA desaturase (SCD) enzyme. Changes in the SCD enzyme activity have been linked to obesity, as well as to plasma SAA concentrations. OBJECTIVE: This study aimed to investigate whether estimated SCD activity mediates the associations between plasma SAAs and measures of overall adiposity and specific fat depots. METHODS: We examined cross-sectional data from a subset of the Maastricht Study (n = 1129, 50.7% men, 56.7% with (pre)diabetes). Concentrations of methionine, total homocysteine, cystathionine, total cysteine (tCys), total glutathione (tGSH), and taurine were measured in fasting plasma. Outcomes included measures of overall, peripheral and central adiposity, and liver fat. SCD activity was estimated by ratios of serum fatty acids as SCD16 and SCD18 indices. The associations between plasma SAAs and measures of adiposity or liver fat were examined with multiple linear regression analysis. Multiple mediation analysis was used to investigate whether the significant associations were mediated by SCD16 and SCD18 indices. RESULTS: Plasma tCys was positively associated with all adiposity measures (β ranged from 0.15 to 0.30). SCD16 significantly mediated all associations (proportion mediated ranged from 5.1% to 9.7%). Inconsistent mediation effects were found for SCD18. Despite a significant inverse association of plasma tGSH with all adiposity measures (β ranged from -0.08 to -0.16), no significant mediation effect was found. CONCLUSIONS: Plasma tCys may promote excessive body fat accumulation via upregulation of SCD activity.

• Keywords
• Body fat depots, Obesity, Plasma sulfur amino acids, Stearoyl-CoA desaturase, Total cysteine,
• MeSH
• Adiposity * MeSH
• Cysteine * blood   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Obesity blood   MeSH
• Cross-Sectional Studies MeSH
• Stearoyl-CoA Desaturase * metabolism   blood   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Cysteine * MeSH
• Stearoyl-CoA Desaturase * MeSH

A prediction model based on the processing of FTIR spectra and partial least squares regression (PLS) was developed for the determination of thehydroperoxide number of diesel fuels. The sets of calibration and validation standards were composed of fresh and aged diesel fuels. The hydroperoxide number determined via the standard titration method ranged from 0 to 65 mg·kg-1. While the calibration standards were utilized for the model construction, the validation standards were used for its optimization and validation. Several preprocessing methods, together with various numbers of latent variables, were utilized to improve model prediction ability. The model with the lowest Root Mean Square Error of Prediction was developed using mean centering, variance scaling, second derivative, and smoothing methods. Both examined smoothing techniques, i.e., Savitzky-Golay and Gap-Segment derivative, provided similar results. The use of the commonly available and affordable FTIR method, allowing rapid analysis, proved to be cost effective alternative to highly erroneous and laborious titration methods utilizing toxic reagents. In general, the developed model showed good predictive ability and is a perfect solution for fast screening of oxidative aging level of conventional hydrocarbon-based fuels.

• Keywords
• FTIR spectroscopy, Hydroperoxide, Hydroperoxide number, Multivariate calibration, Partial least squares regression,
• Publication type
• Journal Article MeSH

To identify clinical features that predict the risk of meeting difficult-to-treat (D2T) rheumatoid arthritis (RA) definition in advance. This retrospective analysis included RA patients from the ATTRA registry who initiated biologic (b-) or targeted synthetic (ts-) disease-modifying anti-rheumatic drugs (DMARDs) between 2002 and 2023. Patients with D2T RA met the EULAR criteria, while controls achieved sustained remission, defined as a Simple Disease Activity Index (SDAI) < 3.3 and a Swollen Joint Count (SJC) ≤ 1, maintained across two consecutive visits 12 weeks apart. Patients were assessed at baseline and at one and two years before fulfilling the D2T RA definition. Predictive models were developed using machine learning techniques (lasso and ridge logistic regression, support vector machines, random forests, and XGBoost). Shapley additive explanation (SHAP) values were used to assess the contribution of individual variables to model predictions. Among 8,543 RA patients, 641 met the criteria for D2T RA, while 1,825 achieved remission. The machine learning models demonstrated an accuracy range of 0.606-0.747, with an area under the receiver operating characteristic curve (AUC) of 0.656-0.832 for predicting D2T RA. SHAP analysis highlighted key predictive variables, including disease activity measures (DAS28-ESR, CDAI, CRP), patient-reported outcomes (HAQ), and the duration of b/tsDMARD treatment. We identified clinical features predictive of D2T RA at baseline and up to one year before meeting the formal criteria. These findings provide valuable insights into early indicators of D2T RA progression and support the importance of earlier recognition and timely therapeutic intervention to improve long-term patient outcomes.

• Keywords
• Difficult-to-treat rheumatoid arthritis, Explainable artificial intelligence, Machine learning, Real-world data,
• MeSH
• Antirheumatic Agents * therapeutic use   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Registries MeSH
• Retrospective Studies MeSH
• Arthritis, Rheumatoid * drug therapy   diagnosis   MeSH
• Aged MeSH
• Machine Learning * MeSH
• Severity of Illness Index MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Antirheumatic Agents * MeSH

Infrared spectroscopy of macroscopic samples can be calibrated against reference analysis, such as lipid profiles acquired by gas chromatography, and serve as a fast, low-cost, quantitative analytical method. Calibration of infrared microspectroscopic images against reference data is in general not feasible, and thus spatially resolved quantitative analysis from infrared spectral data has not been possible so far. In this work, we present a deep learning-based calibration transfer method to adapt regression models established for macroscopic infrared spectroscopic data to apply to microscopic pixel spectra of hyperspectral IR images. The calibration transfer is accomplished by transferring microspectroscopic infrared spectra to the domain of macroscopic spectra, which enables the use of models obtained for bulk measurements. This allows us to perform quantitative chemical analysis in the imaging domain based on infrared microspectroscopic measurements. We validate the suggested microcalibration approach on microspectroscopic data of oleaginous filamentous fungi, which is calibrated toward lipid profiles obtained by gas chromatography and measurements of glucosamine content to perform quantitative infrared microspectroscopy.

• Publication type
• Journal Article MeSH

Donor kidney tissue based transcriptomics may represent new dimension for prediction of kidney transplant outcomes. In this prospective, single-center study, 276 kidneys from 174 deceased brain-death donors were assessed by microarrays to identify phenotypes of procurement biopsies. Molecular classifiers (extreme gradient boosting, logistic and Poisson regression) with 10-fold cross-validation were employed to categorize donors based on clinical variables (age, BMI, hypertension, ECD kidney) and histological scores (vascular fibrous intimal thickening, interstitial fibrosis, tubular atrophy, arteriolar hyaline thickening). Archetypal analysis and linear mixed model were applied to determine molecular phenotypes and their association with posttransplant 1-year eGFR in 234 donor kidneys. Three molecular archetypes were identified. The "ideal" archetype (median donor age 42 years, low KDRI, minimal chronic histological changes) was associated with the highest 1-year eGFR, while the "marginal" archetype (68 years, extensive chronic changes, high KDRI) with the lowest one. The "intermediate" archetype yielded better 1-year eGFR despite donor profiles similar to the marginal group. While KDRI predicted 1-year eGFR, adding molecular archetypes improved model performance (AIC 80.0 vs. 83.7;p<0.05). External validation in an independent dataset (n=174, GSE147451) confirmed predictive value of the model. Molecular profiling of procurement biopsies may help to identify donor kidneys with higher posttransplant eGFR.

• Keywords
• donor kidney quality, gene expression, kidney transplantation, microarray,
• Publication type
• Journal Article MeSH

BACKGROUND: Despite advances in therapeutic development, an anthracycline-cytarabine induction regimen remains the gold standard for acute myeloid leukaemia (AML) treatment. However, reliable predictive markers for assessing treatment sensitivity, adjusting therapy intensity, and guiding the use of experimental therapies are still lacking. This study aimed to develop a predictive model of AML chemoresistance. METHODS: Transcriptome sequencing and DNA methylation analysis of leukaemic blasts were performed to identify differentially expressed and methylated genes between responding (RES) and non-responding (non-RES) patients. A logistic regression nomogram model was developed using obtained data to predict complete remission (CR) and was further validated. RESULTS: Compared to RES patients, non-RES patients exhibited a significant overexpression of interferon-related DNA damage resistance signature (IRDS) genes at diagnosis. Based on the expression of three IRDS genes (IFIT5, IFI44L, IFI44), we developed the IRDS score, which demonstrated high predictive accuracy, with calculated probabilities of CR of 0.71 for RES patients and 0.31 for non-RES patients. Downregulation of histone and chromatin remodelling genes following therapy administration was a hallmark of a successful treatment response. Integrative analysis revealed 1108 genes with concordant changes in both gene expression and DNA methylation between RES and non-RES patients, including IRDS genes IFIT5 and IFI44L. CONCLUSIONS: The IRDS score-based model predicts AML chemoresistance with high accuracy and feasibility. It is quick, cost effective, and requires readily available biological material. This tool shows promise for guiding treatment decisions and identifying candidates for intensified or experimental therapies.

• Keywords
• Acute myeloid leukaemia, Chemoresistance, Gene expression signature, Interferon-stimulated genes, Predictive biomarker,
• Publication type
• Journal Article MeSH

OBJECTIVES: To explore the prevalence and distribution of ultrasound-detected lesions indicating structural damage at the enthesis (e.g., bone erosions, enthesophytes, and calcifications) in patients with spondyloarthritis (SpA), comparing those with axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA), and to investigate the demographic, clinical, and metabolic factors linked to these lesions. METHODS: A cross-sectional analysis was conducted using data from the DEUS study, a multicentre investigation involving 20 rheumatology centres and including 413 patients with SpA (224 with axSpA and 189 with PsA). All participants underwent standardized clinical and ultrasound assessment of the large lower limb entheses (quadriceps tendon, proximal and distal patellar tendons, Achilles tendon, and plantar fascia). Entheseal structural lesions were explored by ultrasound and classified according to OMERACT definitions. Bivariate analyses and multivariate logistic regression were used to assess associations between ultrasound lesions and SpA patients' characteristics. RESULTS: In SpA patients, enthesophytes were the most common lesion (78.7 %), followed by calcifications (43.6 %) and bone erosions (24.9 %). Enthesophytes were more prevalent in PsA (86.8 %) compared to axSpA (71.9 %) (p < 0.001), with no significant differences in erosions and calcifications. However, lesion distribution varied across different entheses. Multivariate analysis revealed that entheseal erosions were significantly associated with inflammatory markers, HLA-B27 positivity, clinical enthesitis, and longer disease duration. Enthesophytes were significantly linked to PsA, psoriasis, clinical enthesitis, and longer disease duration. Calcifications were positively associated with hypertension, metabolic syndrome, and obesity. All lesions were associated with biologic DMARD use. CONCLUSIONS: This study reveals a high prevalence of ultrasound-detected structural damage at the enthesis and identifies distinct SpA phenotypes based on these findings.

• Keywords
• Calcifications, Enthesitis, Enthesophytes, Erosions, OMERACT, Structural damage, Ultrasound,
• MeSH
• Achilles Tendon diagnostic imaging   MeSH
• Adult MeSH
• Enthesopathy * diagnostic imaging   MeSH
• Phenotype MeSH
• Middle Aged MeSH
• Humans MeSH
• Cross-Sectional Studies MeSH
• Arthritis, Psoriatic * diagnostic imaging   MeSH
• Spondylarthritis * diagnostic imaging   MeSH
• Ultrasonography MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

OBJECTIVES: This study aimed to examine the association between all-cause hearing disorder onset and dementia risk, owing to prior reports of inconsistencies and untested potential biases. DESIGN: National cohort study. SETTING AND PARTICIPANTS: All members of an Israeli nonprofit health maintenance organization aged 51-71 years without hearing disorder or dementia diagnoses at cohort entry were followed up to 17.2 years for incident dementia. At cohort entry, there were 102,067 participants (mean age 57.8, SD = 5.7 years; female: 53,242, 52.2%). METHODS: Hearing disorder was a time-varying covariate classified as present from the age of the first diagnosis onward, otherwise absent. Cox regression models were fit to quantify all-cause dementia risk with hazard ratios (HR) and their 95% confidence intervals (CIs) in the primary analysis, applying inverse probability weights, adjusted for 20 potential sources of confounding. RESULTS: During follow-up, incident all-cause hearing disorder onset was 50,769 (49.7%) and dementia 6612 (6.5%). Dementia was observed among 4506 (8.9%) individuals with a hearing disorder and 2106 (4.11%) without. In the primary analysis, hearing disorder onset was statistically significantly (P < .05) associated with an increase of all-cause dementia risk (adjusted HR, 1.91; 95% CI, 1.79-2.03). Of 15 complementary analyses, 10 were consistent with the primary analysis, 2 showed that hearing aid and/or assistive listening device use was associated with reduced dementia risk, and 3, although significant, showed moderate reverse causation. CONCLUSIONS AND IMPLICATIONS: In this study, hearing disorder onset was associated with increased dementia risk. Policymakers, patients, and clinicians may wish to monitor hearing disorders to consider possible preventive dementia measures with hearing aids and/or assistive listening devices.

• Keywords
• Dementia, epidemiology, hearing, reverse causality, risk,
• MeSH
• Dementia * epidemiology   etiology   MeSH
• Risk Assessment MeSH
• Cohort Studies MeSH
• Middle Aged MeSH
• Humans MeSH
• Hearing Disorders * epidemiology   complications   MeSH
• Proportional Hazards Models MeSH
• Risk Factors MeSH
• Aged MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Israel epidemiology   MeSH