Search Results (50,683)

Background/Objectives: Long COVID (LC) has affected 7.2% of the population of the United States (US). Mental and physical health have been increasing in prevalence over the last few years. This study aimed to investigate the association between various characteristics and mental and physical health status among US adults with LC. Methods: The study was cross-sectional in design and used data from the 2023 Medical Expenditure Panel Survey (MEPS). We assessed predisposing, enabling, and need variables in US adults with MEPS-defined LC using multivariable logistic regression analysis. The data was weighted to produce nationally representative estimates. Results: It was determined that individuals with a low income level, a high degree of pain, and poor physical health were each associated with higher odds of poor mental health in US adults with LC. An age of 50–70+ was associated with lower odds of poor physical health in US adults with LC. Educational achievement up to and including high school, having a functional limitation, exercise participation, any pain, multiple comorbid conditions, and poor mental health were each associated with higher odds of poor physical health in US adults with LC. Conclusions: Several variables were associated with poor mental and physical health status among US adults with LC. Further research should be conducted to explore these variables in more detail and investigate possible interventions for healthcare providers. Full article

This scoping review represents the first of 2020–2025, peer-reviewed publications investigating the possible relationships among burnout, nutrition, and nutrition/food literacy during and following the COVID-19 pandemic. The main conceptual insight is that these relationships are addressed only incidentally in existing research. Searches on 9 January 2026 were of the keywords (burnout OR job stress) AND (nutrition OR diet OR eating pattern OR food intake) AND (nutrition literacy OR food labeling) AND (food literacy OR health literacy). An eight-database search produced 160 returns. The included reports from these searches were from Google Scholar alone (n = 6). The addition of four relevant reports from a previous search of “burnout AND nutrition AND (nutrition literacy OR food literacy)” increased the included studies to ten. Although 2020 was the lower date limit, publication of the results was between 2023 and 2025. The finding is that research conducted during this period reports co-occurring issues of burnout, nutrition, and nutrition/food literacy in specific populations. The primary discovery is that assessing the relationships among these terms was not the aim of the included studies. This lack of dedicated research on this topic presents an opportunity for burnout and nutrition researchers to investigate these relationships intentionally. Full article

Black women faced disproportionate social, emotional, and structural burdens during the dual pandemics of COVID-19 and racial injustice, yet their mental health experiences remain underexamined. This study explores how Black women understood, navigated, and coped with these intersecting crises. Seven Black women in North Carolina participated in a seven-session virtual photovoice study, including four photo discussion sessions. Participants generated four photo assignments, took photos, and discussed them using the SHOWeD framework. Recorded discussions were analyzed using Reflexive Thematic Analysis and Sort and Sift, Think and Shift, with participants supporting theme development. Narratives revealed multi-level mental health impacts: at the individual level, caregiving overload, compounded grief, and constant vigilance; at the interpersonal level, invisibility, racial misreading, and everyday racism that intensified distress and made help-seeking itself a labor of confronting providers’ biases; at the community level, trauma from witnessing racial violence and pandemic loss; and structural factors such as inequity, food insecurity, and policing as primary drivers of mental health strain. Participants employed healing as resistance through faith, nature, sister circles, and culturally concordant care. Black women’s mental health during the dual pandemics is inseparable from structural racism and gendered labor; healing is individual and collective, and crisis preparedness requires system-level change driven by Black communities. Full article

Background and Clinical Significance: Encephalitis is a rare neurological complication associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. In rare cases, focal neuroinflammation can manifest as a mass-like parenchymal lesion, creating profound diagnostic and treatment dilemmas by mimicking primary central nervous system neoplasms. Case Presentation: A 34-year-old female presented with cephalalgia, nausea, confusion, facial palsy, and a new onset of focal impaired awareness seizures (FIAS). Brain magnetic resonance imaging (MRI) revealed a prominent hyperintense lesion within the left temporal lobe with associated vasogenic edema and focal leptomeningeal enhancement highly suspicious of a low-grade glial neoplasm. Although nasopharyngeal RT-PCT was negative, the presence of serum anti-SARS-CoV-2 IgM and IgG suggested recent subclinical SARS-CoV-2 infection. To resolve diagnostic ambiguity and avoid empiric oncological overtreatment, a stereotactic brain biopsy was performed. Histopathology revealed acute neuroinflammation characterized by reactive gliosis, microglial hyperplasia, and perivascular lymphatic cuffing, with no evidence of neoplastic presence. Quantitative tissue RT-PCR confirmed the presence of SARS-CoV-2 (Ct33). Follow-up imaging demonstrated complete resolution of the abnormalities following conservative treatment with corticosteroids and antiepileptics, though mild clinical symptoms persisted for 12 months thereafter. Conclusions: Encephalitis presents a rare yet critical manifestation of SARS-CoV-2. Establishing definitive etiology remains challenging. Stereotactic biopsy is a valuable tool to guide appropriate treatment in cases of ambiguous imaging and clinical findings. Radiographic resolution may precede complete clinical recovery. Full article

Macroeconomic forecasting has become increasingly challenging in environments characterized by structural breaks, nonlinear dynamics, and elevated economic uncertainty. Traditional econometric forecasting models frequently experience substantial predictive deterioration during periods of financial crises, geopolitical instability, and rapidly evolving macroeconomic conditions due to their reliance on assumptions of parameter stability and linear economic relationships. In response to these limitations, this study proposes an integrated causal machine learning framework designed to improve macroeconomic forecasting performance under structural instability and uncertainty. The proposed framework combines structural break detection techniques, machine learning algorithms, causal inference methodologies, and Explainable Artificial Intelligence (XAI) tools within a unified empirical architecture. More specifically, the study integrates Bai–Perron structural break analysis, Markov-Switching regime identification, Double Machine Learning (DML), Causal Forest estimation procedures, and SHAP-based explainability techniques. The empirical analysis employs a U.S. macroeconomic time-series dataset covering major crisis episodes, including the 2008 Global Financial Crisis, the COVID-19 pandemic, and the 2022 inflation shock. The dataset combines inflation, monetary, financial, energy-market, and uncertainty indicators obtained from publicly available U.S. macroeconomic databases. The empirical findings demonstrate that causal machine learning models significantly outperform conventional econometric frameworks such as VAR and TVP-VAR models, as well as standard machine learning algorithms including Random Forest (RF), XGBoost, and LSTM networks. The Double Machine Learning framework generates the strongest forecasting performance across all forecasting horizons, economic regimes, and robustness specifications. The results further reveal that macroeconomic relationships are highly regime-dependent and strongly influenced by uncertainty indicators, financial volatility, oil price shocks, and monetary policy dynamics. Explainability analysis additionally shows that uncertainty measures and energy market variables become dominant drivers of inflation forecasts during crisis periods characterized by elevated instability. The study contributes to the growing literature on macroeconomic forecasting by bridging econometric forecasting theory, causal inference methodologies, machine learning techniques, and explainable artificial intelligence within a unified forecasting framework. The findings provide important implications for central banks, policymakers, and financial institutions seeking more adaptive, transparent, and robust forecasting systems under uncertain macroeconomic environments. Full article

Introduction: Community engagement offers an approach to improve our understanding of vaccine hesitancy, yet some worry that public engagement centered around controversial and politicized topics such as COVID-19 vaccines carries a risk of increasing concerns. Methods: Respondents who reported hesitance to vaccinate against COVID-19 in a December 2020 national panel survey were recruited to participate in three interactive and respectful virtual community engagement meetings. A second survey in September 2021 resampled respondents to ascertain changes over time. Of the 291 respondents to both survey waves who had been willing to participate in community meetings, 94 (32%) participated. Multivariate linear and logistic regressions were used to assess the potential impact of meeting participation on outcomes of interest. Results: At follow-up, participants had nearly double the odds of vaccinating against COVID-19 (adjusted Odds Ratio: 1.88; 95% Confidence Interval: 1.06–3.34), about one-third the odds of contracting COVID-19 disease (aOR: 0.37; 95% CI: 0.14–0.96), and increased trust in the Centers for Disease Control and Prevention (CDC) (adjusted Regression Coefficient: 4.16; 95% CI: 0.37–7.94), compared to non-participants. Most participants found the meetings unbiased (89%) and trustworthy (90%). Conclusions: The design of our community meetings to focus on learning from and supporting the decision-maker (versus only promoting “shots in arms”) improved health outcomes and increased trust. Similar meetings could improve how public health engages communities. Full article

Cancer survivors face an elevated risk of severe COVID-19 outcomes and were prioritized for vaccination, yet reported rates of vaccination refusal in this population vary widely. The objective of this scoping review was to map the available evidence on COVID-19 vaccination refusal among adult cancer survivors in the United States, to characterize the reported barriers and enablers, and to examine how differences in measurement account for variation in the reported estimates. PubMed, EBSCOhost, CINAHL, medRxiv, and Google Scholar were searched for studies published between 1 December 2020 and 30 April 2026 reporting primary data on COVID-19 vaccination refusal among U.S. adults with a current or previous cancer diagnosis. Findings were charted and summarized descriptively. Six sources comprising 3344 participants met the eligibility criteria. Reported refusal ranged from 7.3% to 39.0%, a more than fivefold difference, with a median of 14.7%. This variation corresponded closely to what each source measured: declination of an offered vaccine, likelihood of acceptance on a Likert scale, hesitancy assessed before vaccines were widely available, or observed non-receipt of any dose. Barriers included concerns about vaccine safety, efficacy, and interaction with cancer treatment, mistrust of science and government, and low perceived need; clinician recommendation was the most consistent enabler. Estimates of vaccination refusal in this population are not directly comparable and should be interpreted alongside the construct measured. Full article

Background/Objectives: Vaccination remains an important means of protection against influenza and COVID-19. Administering influenza and COVID-19 vaccines as a combined formulation may be advantageous. Methods: This phase 3, randomized study evaluated an investigational combination nucleoside-modified messenger RNA (modRNA) vaccine formulated with monovalent XBB.1.5-adapted BNT162b2 vaccine (BNT162b2) and an investigational influenza modRNA vaccine (hereafter investigational combination modRNA vaccine); we report safety, tolerability, and immunogenicity of the investigational combination modRNA vaccine in healthy 18- to 64-year-olds in two cohorts (2 and 3). Results: In Cohort 2, 3127 participants received the investigational combination modRNA vaccine and 1563 received the concomitant licensed quadrivalent influenza vaccine (QIV) with BNT162b2. In Cohort 3, 1189 participants received the investigational combination modRNA vaccine, 605 received QIV, 607 received the investigational influenza modRNA vaccine, and 1191 received BNT162b2. Prespecified noninferiority criteria of the geometric mean ratio of strain-specific hemagglutination inhibition (HAI) or SARS-CoV-2 Omicron XBB.1.5 neutralizing titers or differences in percentages of participants achieving HAI sero-conversion or Omicron XBB.1.5 seroresponse for investigational combination modRNA vaccine to concomitant QIV and BNT162b2 (or to QIV and BNT162b2 each administered alone in Cohort 3) were met for influenza A and SARS-CoV-2 Omicron XBB.1.5 strains but not for the influenza B strain. The investigational combination modRNA vaccine was well tolerated with an acceptable safety profile. Conclusions: The single-dose investigational combination influenza plus COVID-19 modRNA vaccine elicited robust immune responses against influenza A and SARS-CoV-2 with acceptable safety. Further work is required to optimize influenza B responses. The modRNA platform offers promise and potential advantages for vaccine development. ClinicalTrials.gov Identifier: NCT06178991 (approval date: 20 December 2023). Full article

MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression and play a fundamental role in host response to viral infections. SARS-CoV-2 has been shown to dysregulate host miRNA expression, potentially as a mechanism to modulate cellular pathways for its own replication or to evade immune responses. However, evidence from the literature related to the specific miRNA landscape in SARS-CoV-2 infection is conflicting and unclear. We employed an integrative multi-model approach using human nasal lung epithelial cell lines and pulmonary organoids to map miRNA expression dynamics after SARS-CoV-2 infection. Data obtained from the CALU-3 miRNAome were successively validated in other two cellular models (hAEC and hLORG), and from this comparative analysis, two miRNAs, miR-141-3p and miR-33a-5p, were found to be significantly dysregulated. These miRNAs are involved in critical pathways, including cytokine-mediated signalling, apoptotic processes and epithelial cell junction integrity. High-throughput miRNA profiling was followed by functional enrichment analysis of their predicted targets to delineate affected biological pathways. By highlighting candidate miRNAs and regulatory pathways that may contribute to disease pathogenesis, we identified robust, infection-associated hallmarks across cell models, establishing a foundation for future therapeutic strategies for COVID-19 based on the development of miRNA-guided approaches. Full article

Background/Objectives: Cutaneous squamous cell carcinoma (cSCC) is the second most common non-melanoma skin cancer (NMSC) and represents the leading cause of NMSC-related deaths. Despite its growing global burden, comprehensive epidemiological and clinicopathological data from Easter Europe remains limited. This study aimed to evaluate the epidemiological, clinical, histopathological, and surgical characteristics of cSCC diagnosed before, during and after the COVID-19 pandemic. Methods: We conducted a retrospective, descriptive observational study including 332 lesions diagnosed between January 2017 and December 2025 at the Clinical Pathology Department of the Mureș Clinical County Hospital. Demographic, epidemiological, topographic, histopathologic, surgical, and volumetric parameters were analyzed. Tumours were stratified into low-, high-, and very-high-risk categories according to the National Comprehensive Cancer Network (NCCN) criteria. Results: The cohort demonstrated a significant male predominance (n = 193 vs. n = 139; p = 0.0355), with females presenting a higher median age (77 vs. 75; p = 0.0489). Lesions were predominantly located in the head and neck region (n = 216; p < 0.0001), which was significantly associated with very-high-risk tumours (p = 0.0051). Low-risk tumours accounted for 62.35% of cases, while high-risk and very-high-risk lesions comprised 19.88% and 17.77%, respectively (p < 0.0001). Ulcerations were strongly associated with very-high-risk tumours (p < 0.0001). Poor differentiation was more frequent outside the head and neck region (p < 0.0001) and varied significantly across the pandemic periods (p = 0.0349). Tumoral and excision volumes were higher in very-high-risk (p = 0.0070; p = 0.0004) and ulcerated tumours (p < 0.001), with a peak in volume during the COVID-19 period (p < 0.0001). A decrease through the years of diagnosis was observed in tumoral volumes (r = −0.2295; p < 0.0001) and patients showed a weak positive correlation with diagnosis year (r = +0.13; p = 0.019). Conclusions: The study provides an epidemiological and clinicopathological characterization of cSCC within one of the largest Romanian cohorts to date. Tumour aggressiveness was primarily driven by histopathological and topographical features rather than demographic factors. While the COVID-19 pandemic did not induce persistent changes in tumour risk profiles or surgical outcomes, it influenced diagnosis timing and tumour burden. These findings highlight the importance of incorporating temporal and emerging systemic factors, such as pandemics, and infectious events, into future epidemiological models to improve preparedness, early detection, future treatment schemes, and risk stratification in cSCC. Full article

Background/Objectives: Music-based interventions have been used to alleviate chronic pain and psychological distress; however, their effectiveness varies across individuals, particularly in real-world settings. The COVID-19 pandemic has further highlighted the need for feasible, therapist-guided but self-directed interventions that can be implemented in daily life. This exploratory pilot study primarily evaluated the feasibility of therapist-guided, self-directed personalized music listening in daily life. Exploratory quantitative and qualitative outcomes were also assessed to inform the design of future controlled studies. Methods: Seven patients with fibromyalgia participated in a 4-week music-listening period and a 4-week non-intervention period. The intervention was conducted in participants’ daily living environments during the COVID-19 pandemic. A music therapist guided music selection and listening strategies, while participants implemented the intervention independently. Pain scores were recorded daily, and psychological measures (GHQ-12, JFIQ) and salivary β-endorphin levels were assessed at predefined times. Results: The therapist-guided, self-directed intervention was feasible in participants’ daily lives. Individual responses varied. Some participants reported psychological experiences, including autobiographical memory recall, increased motivation, and greater engagement in daily activities. No consistent effects on pain intensity or salivary β-endorphin levels were observed. Conclusions: This feasibility pilot study suggests that therapist-guided, self-directed personalized music listening is feasible in daily life. Although consistent quantitative improvements were not observed, some participants reported positive psychological experiences, including autobiographical memory recall, increased motivation, and greater engagement in daily activities. These preliminary findings support the feasibility of this approach and warrant larger, adequately controlled studies incorporating objective adherence monitoring and an appropriate washout period. Full article

Genome-wide association studies have identified numerous genetic variants statistically significantly associated with body mass index (BMI). However, the functional mechanisms underlying most associations between single nucleotide polymorphisms (SNPs) in non-coding regions and BMI remain poorly understood. Here, we implemented an integrative 7-criterion quantitative scoring system (gene localization, histone modifications, transcription factor binding sites (TFBS), SNP clouds, tissue expression patterns, evolutionary conservation, and COVID-19 associations) to prioritize putative functional loci among 94 BMI-associated SNPs. Six SNPs resided within long non-coding RNA (lncRNA) genes: rs2245368 (exonic, DTX2P1-UPK3BP1-PMS2P11), rs2033529 (exonic, LINC00951), rs2836754 (intronic, ETS2-AS1), rs2815752 (intronic, LINC02796), rs17203016 (intronic, MYOSLID-AS1), and rs7239883 (intronic, LINC00907). We prioritized them because they are located within lncRNA gene bodies and therefore showed stronger functional support compared to other variants which only had non-coding regulatory elements in their vicinity. Notably, rs1928295 exhibited strong GATA2 binding evidence, while rs13201877 had extensive transcription factor occupancy (64 factors). Multiple variants demonstrated putative regulatory potential through epigenomic evidence, including DNase I hypersensitivity and cell-type-specific chromatin accessibility. We show that most BMI risk alleles are not human-specific and are conserved across primates. Our findings suggest putative candidate non-coding regulatory elements in BMI and provide prioritized obesity-associated non-coding variants for functional validations. Full article

Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics. Full article

Digital transformation has fundamentally reshaped higher education, creating a need for adaptive systems capable of integrating technological infrastructure, learner capabilities, institutional readiness, and learning analytics into a unified decision-support environment. While existing studies predominantly examine online learning from pedagogical or behavioural perspectives, comparatively little attention has been devoted to configurable system architectures that support institutional monitoring and continuous optimization of digital learning ecosystems. This paper addresses this gap by proposing a configurable Digital Learning Capability Assessment Framework (DLCAF), a modular systems framework designed to assess, monitor, and optimize digital learning environments through the integration of infrastructure capabilities, digital literacy, learner motivation, technology acceptance, and institutional performance indicators. The framework employs a layered architecture comprising data acquisition, capability assessment, analytics, decision-support, and feedback modules, enabling flexible configuration according to institutional requirements and educational contexts. To demonstrate the applicability of the proposed framework, a survey involving 220 students from 27 study programs across Croatian higher education institutions was conducted during the COVID-19 digital transition. The empirical findings serve as an application case for validating the framework and illustrating how learner perceptions, technical constraints, institutional support, and digital readiness can be systematically incorporated into an adaptive decision-support process. The results indicate that technical infrastructure, learner motivation, digital competencies, communication quality, and institutional support collectively influence the effectiveness of digital learning environments. The proposed framework transforms these heterogeneous indicators into actionable institutional intelligence that supports evidence-based planning, continuous monitoring, and targeted intervention strategies. By repositioning digital learning evaluation as a systems engineering problem rather than solely an educational assessment exercise, this work contributes a reusable and extensible framework that can be deployed across diverse higher education environments. The architecture provides a foundation for future integration of artificial intelligence, learning analytics, predictive modelling, and adaptive recommendation mechanisms, supporting the development of intelligent digital learning ecosystems capable of continuous improvement and institutional decision support. Full article

Long COVID (LC) is a heterogeneous post-infectious syndrome characterized by persistent symptoms, yet the biological basis underlying its interindividual variability remains poorly understood. Given the clinical overlap between LC and myalgic encephalomyelitis (ME), and prior demonstration that haptoglobin (Hp) phenotypes modulate symptom severity in ME, we investigated whether Hp phenotypes similarly stratify post-exertional cognitive dysfunction in LC. In this longitudinal observational study, 44 individuals with LC and 20 short-course COVID controls, who recovered rapidly from SARS-CoV-2 infection without persistent symptoms or sequelae, underwent Hp phenotyping alongside metabolomic and physiological profiling before and after a standardized 90 min passive post-exertional challenge. Hp phenotypes identified clinically distinct LC subgroups. Compared with Hp1-1 individuals, Hp2 allele carriers exhibited greater fatigue, poorer physical function, and more severe post-exertional symptoms. Immediately following the challenge, Hp2-2 participants with LC showed significant cognitive decline, whereas Hp1-1 individuals demonstrated cognitive resilience and more favorable longitudinal cognitive trajectories. This differential susceptibility was accompanied by higher post-exertional cerebral fractional tissue oxygen extraction in the right hemisphere in Hp1-1 individuals and by distinct metabolic signatures, with Hp2 allele carriers exhibiting lower post-exertional plasma concentrations of citric acid, isethionate, and glucosamine. Lower metabolite levels were associated with poorer cognitive performance. These findings support Hp phenotypes as promising candidate biomarkers for biological stratification in Long COVID, pending validation in larger independent cohorts. Full article