Search Results (4,454)

Cognitive impairment is common in Post-COVID-19 Condition (PCC), yet full neuropsychological testing remains resource-intensive. Because eye movements are known to be altered in certain cognitive disorders, Eye-Tracking (ET) offers a fast, non-invasive complementary approach for large-scale screening. This study aimed to predict neuropsychological test scores of participants with PCC from ET metrics using machine and deep learning models. ET data was collected from 172 participants performing a battery of visual tasks designed to elicit smooth pursuit and fixational eye movements, as well as pupil responses to light. Cognitive performance was assessed through established neuropsychological tests. We applied regression and classification models (e.g., Random Forest, XGBoost, and deep neural networks) to predict neuropsychological performance. Models were trained using ET data alone and in combination with the Montreal Cognitive Assessment (MoCA) scores, a widely used neuropsychological test for global cognitive screening. Although predicting individual test scores was challenging, combining them into a global composite measure improved performance. Model sensitivity and specificity reached 88% and 34% using ET data alone, and 87% and 60% when integrating ET with MoCA. This last trained model outperformed the conventional MoCA, highlighting the potential of ET as a rapid screening support tool for cognitive assessment. Full article

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on six World Bank indicators—fixed broadband subscriptions, internet users, mobile subscriptions, patent applications, R&D expenditure, and secure internet servers—achieves a training R² of 0.804 and a test R² of 0.430 under temporal out-of-sample validation spanning the COVID-19 structural break. TreeSHAP decomposition identifies fixed broadband as the strongest predictor of model-estimated GDP per capita growth (mean |SHAP| = 0.948; bootstrap rank 1 in 78% of 50 resamples; Friedman Chi-square (5) = 168.16, p < 0.001), providing predictive support for Hypothesis H1. Innovation indicators, represented by patent applications and R&D expenditure, exceed the pre-specified materiality threshold, providing predictive support for H2, while SHAP dependence plots reveal pronounced non-linear threshold patterns consistent with S-curve diffusion theory, supporting H3. Temporal SHAP decomposition identifies three structural phases: broadband dominance (1995–2007), crisis-induced reconfiguration (2008–2013), and quality convergence (2014–2024). The framework reconciles contradictory findings from prior literature by visualizing the complete functional form of the broadband–growth relationship without imposing a parametric specification. Full article

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

Wastewater-based epidemiology has emerged as a valuable complementary tool for population-level monitoring. This study evaluated the early warning value of wastewater surveillance for monitoring SARS-CoV-2 and its correlation with COVID-19 infection trends. From May 2024 to December 2025, 526 wastewater samples were collected from five treatment plants. Spearman correlation and a quasi-Poisson generalized additive model (adjusting for wastewater temperature) were used to assess relationships between SARS-CoV-2 RNA concentration, the number of reported cases, and lag associations. Wastewater viral loads (copies/mL) significantly correlated with reported cases. Wastewater temperature was positively correlated with both viral concentrations and case numbers. A significant lagged association was observed for the N gene, with relative risk peaking at a 10-day lag. Although the ORF1ab gene was not significant for most lag periods, its temporal trend was consistent with that of the N gene. Wastewater surveillance of SARS-CoV-2, particularly targeting the N gene, can effectively predict COVID-19 infection dynamics with a 10-day lead time, thereby supporting wastewater surveillance as an early warning tool for public health monitoring. Full article

Traditional and COVID-19-specific severity scores are applied in intensive care units (ICUs) to guide decision-making and predict mortality. Since traditional severity scores (APACHE II, SAPS II, SAPS 3, and SOFA) were not originally designed for SARS-CoV-2, this study compared their performance with COVID-19–specific models (Shang-COVID and SEIMC), including a novel distinction between early (≤7 days) and late (>7 days) ICU mortality. Adult ICU COVID-19 patients from the first two pandemic waves in Portugal were included (n = 286). Six scores were calculated, and four outcomes assessed: hospital, ICU, early ICU, and late ICU mortality. Discrimination was assessed using ROC curves with AUCs, 95% CIs, and p-values. AUCs were compared using the Delong test (early vs. late ICU mortality and across scores within each wave) and the Hanley & McNeil test (between waves for each score). Traditional scores demonstrated robust mortality prediction. SEIMC performed well for hospital (AUC_wave1 = 0.808; AUC_wave2 = 0.724) and ICU mortality (AUC_wave1 = 0.805; AUC_wave2 = 0.706). SEIMC (AUC_wave1 = 0.786; AUC_wave2 = 0.800) and Shang-COVID (AUC_wave1 = 0.617; AUC_wave2 = 0.736) showed potential for early mortality prediction but require further validation and recalibration. Overall performance was superior during the first wave, likely reflecting differences in patient characteristics, viral variants, and health measures. Traditional severity scores demonstrated stable robust prediction of ICU and hospital mortality in COVID-19 cases. Disease-specific scores did not significantly outperform established models, though also showed good predictive ability in some contexts, particularly early ICU mortality. These findings highlight the need for continuous validation and recalibration of predictive tools as clinical contexts evolve. Full article

Evidence on hospitalised COVID-19 patients during the Omicron BA.5 wave from Eastern European, vaccine-heterogeneous cohorts remains limited. We conducted a retrospective single-centre cohort study of 395 consecutive adults admitted with laboratory-confirmed COVID-19 to a tertiary infectious-diseases unit in western Romania between 1 July and 31 October 2022. Median age was 72 years (IQR 65–81); 33.2% were unvaccinated, 42.8% had documented prior SARS-CoV-2 infection, and 41.3% were obese. Multivariable logistic regression identified independent predictors of in-hospital mortality and post-acute symptom burden. In-hospital mortality was 15.7% (62/395). Vaccination was independently associated with lower mortality (adjusted odds ratio [aOR] 0.55, 95% CI 0.30–0.99; p = 0.048), as was each 1% increase in admission SpO₂ (aOR 0.83, 95% CI 0.76–0.92; p < 0.001), whereas COPD independently increased mortality risk (aOR 2.42, 95% CI 1.15–5.10; p = 0.020). Interleukin-6 was the most discriminating admission biomarker for in-hospital mortality (AUROC 0.70). Bloodstream bacterial co-infection, detected in 22.5% of patients tested on clinical suspicion, was dominated by gut-derived organisms with case-fatality ≥30%. At discharge, 90.1% reported persistent symptoms, most commonly cognitive (24.6%). Prior SARS-CoV-2 infection independently predicted post-acute symptom burden (aOR 2.96, 95% CI 1.75–5.01; p < 0.001), with a specific cardiopulmonary signature. In this BA.5 cohort, vaccination remained protective; IL-6 was the most informative admission biomarker; bloodstream infections suggested gut translocation; and prior infection was an independent determinant of early post-acute symptom burden. Full article

Vitamin D has been implicated in immune modulation and susceptibility to respiratory infections, including COVID-19. However, data in asymptomatic pediatric populations, particularly those with household exposure, remain limited. This study aimed to investigate the association between serum vitamin D levels and hematological parameters with SARS-CoV-2 PCR positivity in asymptomatic children, and to evaluate their potential role in early risk stratification. This retrospective study included 127 asymptomatic children (aged 2–18 years) with confirmed household exposure to COVID-19. Participants were classified as PCR-positive (n = 74) or PCR-negative (n = 53). Serum 25(OH)D3 levels and hematological parameters were analyzed. Univariate and multivariable logistic regression analyses were performed to identify independent predictors. Receiver operating characteristic (ROC) curve analysis was used to assess discriminative performance, and a combined multimarker model was constructed. Serum vitamin D levels were significantly lower in PCR-positive children compared to PCR-negative children (17 ± 8 vs. 27 ± 11 ng/mL, p = 0.001). White blood cell (p = 0.002), platelet (p = 0.01), and neutrophil counts (p = 0.01) were significantly reduced, while basophil counts were higher in PCR-positive children (p = 0.02). In multivariable analysis, vitamin D (OR: 0.87, 95% CI: 0.82–0.93, p < 0.001), platelet (p = 0.02), neutrophil (p = 0.02), and basophil counts (p = 0.01) remained independent predictors. ROC analysis showed that vitamin D had moderate discriminative performance (AUC: 0.75, 95% CI: 0.67–0.83), while platelet (AUC: 0.64), neutrophil (AUC: 0.61), and basophil (AUC: 0.62) counts showed modest performance. The combined multimarker model demonstrated improved predictive ability (AUC: 0.80, 95% CI: 0.72–0.88), with sensitivity of 71.6% and specificity of 68.2%. Additionally, vitamin D deficiency was significantly more frequent in PCR-positive children (43% vs. 19%, p = 0.003). Conclusions: Lower vitamin D levels and associated hematological alterations are independently associated with SARS-CoV-2 PCR positivity in asymptomatic children. A combined biomarker approach may improve early risk stratification using simple and routinely available parameters. Further prospective studies are needed to validate these findings and clarify the role of vitamin D in preventive strategies. Full article

Background: Risk factors for severe COVID-19 and in-hospital mortality are well described, but it remains unclear whether the same factors predict mortality after hospital discharge. Distinguishing risk profiles across clinical phases may improve patient management and follow-up strategies. Methods: We conducted a retrospective observational cohort study of 595 adults hospitalized with PCR-confirmed SARS-CoV-2 infection in Portugal (September–November 2020). The primary outcome was all-cause mortality during hospitalization and up to 120 days post-discharge. Secondary outcomes included intensive care unit (ICU) admission, maximum disease severity (WHO Clinical Progression Scale), oxygen supplementation, and length of stay. Univariable and multivariable regression analyses were performed using logistic regression for binary outcomes and linear regression for continuous outcomes. Results: Overall mortality was 22.5%, rising from 14.1% in-hospital to 22.5% at 120-day follow-up (p < 0.001), with 37.3% of deaths occurring post-discharge. ICU admission was required in 17.6% of patients and was significantly associated with obesity (OR = 2.12, 95% CI: 1.39–3.23, p < 0.001) and male sex (OR = 1.78, 95% CI: 1.14–2.78, p = 0.010) in univariable analysis. In contrast, post-discharge mortality was associated with longer hospital stay (18.4 vs. 9.9 days, p < 0.001) and a higher prevalence of malignancy (28.0% vs. 13.1%, p = 0.032), but not with ICU admission. In multivariable logistic regression, oxygen supplementation was the strongest predictor of 120-day mortality (OR = 2.50, 95% CI: 1.38–4.51, p = 0.002). Only pulmonary diseases and obesity were independently associated with maximum disease severity. Conclusions: Risk factors for acute COVID-19 severity differ from those for post-discharge mortality. These findings support a phase-specific approach to risk stratification, suggesting that patients with obesity are at increased risk of early respiratory deterioration, while patients with malignancy may benefit from closer post-discharge follow-up regardless of ICU admission status. Full article

Introduction: Coronavirus disease 2019 (COVID-19) can cause persistent cardiovascular alterations, including autonomic dysfunction. Heart rate (HR) recovery (HRR) after exercise is a simple marker of autonomic modulation associated with functional capacity and clinical prognosis. Evaluating HRR during the six-minute walk test (6MWT) may help identify residual functional limitations in diverse patients. Objective: To compare pulmonary function, maximal inspiratory pressure (MIP), functional capacity, dyspnea, fatigue, and functional status in post-COVID-19 patients. Methods: This cross-sectional study included 75 adults (mean age: 47.6 ± 13.1 years; 54.7% male) who recovered from COVID-19 divided into 2 groups based on HRR 1 min after the 6MWT: delayed (≤12 beats/min); and non-delayed (>12 beats/min). Pulmonary function, MIP, exercise capacity (via 6MWT), dyspnea, muscle fatigue, and functional status were assessed. Results: Based on HRR 1 min after 6MWT, 27 (36%) participants were classified with abnormal HRR and 48 (64%) with normal HRR. There were statistical differences between the groups regarding demographic or clinical characteristics, pulmonary function, MIP, muscle fatigue, or functional status (p > 0.05). The delayed HRR group exhibited a smaller reduction in HR in first minute of recovery (ΔHR = 6 vs. 23 beats/min), higher baseline HR (p = 0.010), and greater dyspnea (p = 0.020). Furthermore, this group exhibited worse functional performance in the 6MWT, with shorter distance walked (437.33 vs. 494.27 m; p = 0.019) and a lower percentage of predicted distance (74.66 ± 12.98% vs. 82.94 ± 15.71%; p = 0.023) compared with the non-delayed HRR group. Conclusion: Delayed HRR post-COVID-19 was associated with poorer functional performance and greater dyspnea, regardless of pulmonary function. The blunted reduction in HRR after exertion suggests impaired cardiovascular autonomic modulation, possibly related to attenuated vagal reactivation, which may contribute to exercise intolerance observed in this population. Full article

The current paper examines how individual/personality factors are associated with the political legitimacy of government restrictions at the onset of the COVID-19 pandemic. A total of 1262 US-based participants completed an online survey comprising several scales (predictor factors), such as the Just World Scale, the Police Legitimacy Scale, and the Authoritarianism Scale measuring aggression, submission, and conventionalism. In addition, they completed scales measuring their Fear of COVID and Perceptions of Government (outcome factors). The results suggest that those who viewed the president or federal government as most responsible had lower legitimacy scores than those who reported their governor, state government, or local official or government to be responsible. Also, those who aligned with the Republican party had the lowest mean for fear of COVID, while the highest was in the “Other” political affiliation, followed by the Democrats, who had the second highest. It also turned out that whereas one’s relationships with those who have been hospitalized or died as a result of COVID and individual risk factors for COVID were not significant variables in predicting perceptions of the federal government’s handling of the pandemic, the most significant factors were Authoritarianism, Fear of COVID-19, (older) Age, Change in Federal Trust and Political Ideology. Fear of COVID-19 was the only significant factor predicting government legitimacy and individual decisions to engage in protection measures during the pandemic. Theoretical and practical implications are discussed. Full article

Hemoglobin A1c is a central biomarker for long-term glycemic control and a key predictor of diabetes-related complications. The COVID-19 pandemic disrupted routine healthcare delivery and introduced potential metabolic effects of SARS-CoV-2 infection, yet the long-term impact of COVID-19 on glycemic trajectories in individuals with diabetes remains unclear. In this retrospective study, we leveraged harmonized electronic health record data from the National Clinical Cohort Collaborative to evaluate changes in HbA1c before and after documented SARS-CoV-2 infection in adults with diabetes (n = 93,320). Patients were required to have repeated HbA1c measurements pre- and post-infection and stable exposure to key antihyperglycemic medications. A paired statistical analysis was used to identify individuals with statistically significant post-infection changes in HbA1c. We then developed and evaluated multiple supervised machine learning classifiers using an 80/20 train–test split and cross-validation to assess demographic, clinical, and structural factors associated with significant glycemic change. Most patients (71%) did not experience a statistically significant change in average HbA1c following COVID-19 infection, and among those who did, decreases were more common than increases. A random forest classifier achieved the best overall performance, and feature importance and SHAP analyses highlighted body mass index, insulin use, age, and socioeconomic proxies as key contributors. These findings suggest that while COVID-19 infection does not substantially alter long-term glycemic control for most patients with diabetes, individual-level clinical and structural factors influence post-infection glycemic variability. Full article

Background: Although several risk factors have been reported for long COVID (LC), reliable biomarkers for this illness remain lacking. Insulin-like growth factor (IGF)-I, a major mediator of growth hormones, plays an important role in metabolism, neuroprotection, and systemic homeostasis, and therefore may be useful in predicting the severity and prognosis of LC. Methods: This study included patients who visited a specialized clinic for long COVID between 2022 and 2025 during the Omicron period and had serum IGF-I measurements taken. IGF-I levels were expressed as age- and sex-adjusted standard deviation scores (IGF-I SD), and patients were classified into low (SD < −1.0), normal (−1.0 ≤ SD < 1.0), and high (SD ≥ 1.0) groups. Clinical characteristics, patient-reported outcomes, laboratory data, and follow-up duration were analyzed. Results: A total of 811 patients were included (median 42 years; 52.5% female). Compared with the normal group, the low-IGF-I group exhibited higher fatigue (FAS: 37.0 vs. 34.0; p < 0.05) and depressive (SDS: 50.0 vs. 49.0; p < 0.05) status. Multivariable linear regression analyses identified lower IGF-I SD as independently associated with higher scores of both FAS and SDS. IGF-I SD values showed negative correlations with ferritin (ρ = −0.125, p < 0.05) and TSH (ρ = −0.202, p < 0.01) and positive correlations with albumin (ρ = 0.227, p < 0.01) and FT4 (ρ = 0.165, p < 0.01). Among the 237 patients who completed follow-up, the median duration from the initial visit to recovery tended to be longer in the low-IGF-I group (221 days) compared with the normal (191 days) and high (109 days) groups, although these differences were not statistically significant overall. In patients aged < 50 years, the low-IGF-I group showed a relatively longer follow-up duration (p < 0.05). Furthermore, the low-IGF-I group had a longer time to recovery compared to the high-IGF-I group (p < 0.05), and this difference was more pronounced in patients under 50 years of age, with significant differences observed among the three IGF-I groups. Conclusions: Lower IGF-I levels in LC may be associated with greater fatigue and depressive symptoms and longer recovery time, particularly in younger patients. Further studies are needed to clarify the clinical significance of these findings. Full article

Background: Early identification of patients at risk of severe COVID-19 is critical for timely interventions. We evaluated a biomarker-based risk stratification model, the Composite COVID-19 Biomarker Risk (CCBR) score, integrating age and inflammatory biomarkers (IL-6, PAI-1, LDH, neutrophil-to-lymphocyte ratio [NLR], and ferritin) measured at hospital admission to support early clinical risk assessment. Methods: In this retrospective single-center study, 235 hospitalized COVID-19 patients were classified into non-severe (n = 106) and severe (n = 129) groups. Biomarkers were measured within 24 h of admission. The CCBR score (0–6 points) was constructed by assigning one point to each parameter exceeding predefined cut-off values derived from published clinical thresholds and confirmed using receiver operating characteristic (ROC) curve analysis (Youden’s index). Patients were stratified into three risk categories: low risk (0–1 points), moderate risk (2–3 points), and high risk (4–6 points). Associations between CCBR scores and clinical outcomes, including severe disease, acute respiratory failure, dyspnea, complications, and antibiotic use, were assessed using logistic regression and ROC analyses. Internal validation was performed using split-sample validation and bootstrap resampling. Results: CCBR scores were significantly higher among patients with severe disease (p > 0.001). Each 1-point increase in CCBR was associated with a 6.78-fold increase in the odds of severe disease (OR = 6.78, p < 0.001). ROC analysis demonstrated moderate to good discriminative performance for severe disease (AUC = 0.714) and acute respiratory failure (AUC = 0.751). Conclusions: The CCBR score represents a simple biomarker-based model integrating inflammatory and endothelial dysfunction markers for early stratification of COVID-19 severity. This approach may assist clinicians in identifying patients at higher risk of severe disease and acute respiratory failure early during hospitalization. Full article

The effectiveness of infection prevention depends on not only uptake but also the timing of adoption. Vaccination studies typically treat uptake as binary, overlooking when while investigating why individuals get vaccinated. Using the novel mRNA COVID-19 vaccines as a case study, the influences of risk perceptions and social norms on vaccination timing were examined through an Innovation Diffusion framework. An online survey was conducted in November 2021 to assess vaccination behaviors, attitudes, and peer expectations of 1710 U.S. residents (51.64% females, 31.23% minorities, with a relatively balanced distribution across age and income brackets). Participants were classified by vaccination timing and intentions as early adopters, early majority, late majority, or laggards for comparative analyses. One year after vaccine rollout, 64.3% had received at least one dose; 20.1% reported no intention to vaccinate, and this resistance persisted through May 2023 when the pandemic ended. Vaccine confidence and prior behavior (e.g., influenza vaccination) demonstrated strong gradients across adoption timing. Earlier uptake was associated with higher perceived vaccine importance, infection risk, and peer uptake, whereas age and education effects diminished over time. Perceived illness severity and disease knowledge showed inconsistent influences. Later adopters anticipated higher post-vaccination infection risk and greater peer non-vaccination, reinforcing hesitancy. Social norms (but not risk perception) mediated the relationship between confidence and timing; earlier adoption further predicted booster acceptance. These findings highlight the importance of trust, correcting efficacy misperceptions, and leveraging positive peer norms to promote timely vaccination and inform strategies for other infectious diseases. Full article

Background: The COVID-19 pandemic resulted in over 705 million infections and 7 million deaths, underscoring the importance of understanding disease behavior across diverse environments. As NASA, SpaceX, and ISRO prepare for more frequent missions, managing health risks for astronauts and space tourists is essential. Objective: This study reviews the literature on airborne infections in space, identifies research gaps, and establishes preparedness strategies for potential COVID-19 outbreaks during space missions. Methods: A systematic literature review was conducted to identify studies examining airborne infectious diseases in space. To compare these findings with Earth-based data, pathogen safety data sheets were used. A separate systematic review was conducted to explore similarities between COVID-19 and the identified airborne infectious diseases. A comparative approach was used to predict COVID-19’s potential behavior in microgravity. Existing guidelines for managing airborne diseases in space and on Earth were reviewed and compared to develop a set of preparedness recommendations for COVID-19 in space. Results: Nine airborne infectious diseases occurring in space were identified. Six tentative effects of COVID-19 in a microgravity environment were theorized in this study. We propose recommendations to improve current space travel health guidelines and address the identified risks. Conclusions: The results of this study will change the course of human space exploration by assisting in the protection of space travelers and guiding the development of new protocols that include comprehensive safety features. Full article