Search Results (50,744)

Post-exertional malaise (PEM) and cognitive dysfunction (hereafter “cognitive dysfunction”, including the patient-reported syndrome often described as “brain fog”) are among the most disabling features of Long COVID; yet, approved disease-modifying treatments remain lacking. Emerging evidence implicates interacting disturbances in mitochondrial bioenergetics, redox regulation and neurovascular inflammation, although much of the supporting evidence remains indirect and derives from acute COVID-19, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), primary mitochondrial disease, inflammatory biology and mechanistic pharmacology rather than from direct Long COVID intervention trials. This hypothesis-generating narrative review develops a mechanism-based translational framework: that a pathway-targeted nutraceutical programme may modulate selected elements of these three axes, subject to prior demonstration of formulation quality, pharmacokinetic feasibility, target engagement and safety. Candidate modules comprise coenzyme Q10 and alpha-lipoic acid for bioenergetic/redox support; selenium, sulforaphane and resveratrol for Nrf2–thioredoxin-related redox regulation; and Boswellia serrata, luteolin and eicosapentaenoic acid for putative prostaglandin/resolution-pathway modulation. Sonlicromanol provides a conceptual mechanistic precedent for combined redox and prostaglandin-directed pharmacology, but it is not considered pharmacologically equivalent to an eight-agent nutraceutical combination. We summarise the mechanistic rationale, distinguish direct from indirect evidence, define qualitative evidence-grading criteria, outline safety and interaction considerations, and propose a staged translational research programme. This framework is intended to generate falsifiable hypotheses for future Long COVID studies, not to imply established clinical efficacy. Full article

The COVID-19 pandemic led to a dramatic increase in healthcare-associated infections and antimicrobial resistance, particularly in intensive care units (ICUs). The aim of this study was to provide a comprehensive genomic characterisation of all clinically significant opportunistic pathogens (OPs) isolated from patients and the hospital environment in COVID-19 ICUs, and to use these data to reconstruct transmission pathways, identify reservoirs, and assess the molecular mechanisms of antimicrobial resistance and virulence. Whole-genome sequencing (WGS) was performed on 175 isolates isolated from patients and the hospital environment (including personal protective equipment, PPE) between 2021 and 2023. The species collection included nine OP species. Bioinformatic analysis included multilocus sequence typing, core genome single-nucleotide polymorphism analysis, phylogenetic reconstruction, and in silico detection of resistance and virulence genes and plasmid replicons. High-risk multidrug-resistant (MDR) clones were identified among Klebsiella pneumoniae, Acinetobacter baumannii, Escherichia coli, and Staphylococcus aureus. Core genome SNP analysis confirmed direct transmission of K. pneumoniae between patients and healthcare worker medical gloves. PPE was identified as a major reservoir, accounting for 68.4% of environmental isolates. The study demonstrates the power of WGS for high-resolution epidemiological surveillance, confirms the critical role of contaminated PPE in nosocomial transmission, and highlights the dominance of internationally spreading MDR clones in COVID-19 ICUs. Full article

Background: Despite the increasing availability of tailored vaccines, vaccine-preventable respiratory diseases (VPRDs), including influenza, respiratory syncytial virus (RSV), COVID-19, and pneumococcal disease (PD), continue to impose a significant health and economic burden. This study assessed the epidemiological and economic burden of VPRD among adults aged ≥65 across four Nordic countries (Denmark, Sweden, Norway, and Finland). Methods: Data on incidence, hospital admissions, and mortality were obtained from national statistics (week 21, 2024–week 20, 2025) and supplemented with peer-reviewed literature. A static model was developed to quantify epidemiological and economic burden associated with VPRD during the 2024/2025 winter season. Results: During the 2024/2025 season, VPRD resulted in approximately 830,000 symptomatic cases, 60,300 hospital admissions, and 7750 deaths across the Nordic countries. COVID-19 showed the highest estimated incidence and numbers of hospital admissions, followed by influenza, PD, and RSV. Mortality was comparable between COVID-19 and influenza, followed by PD and RSV. Annual healthcare costs were approximately €1132 million, with productivity losses constituting an additional 7%. Conclusions: Despite national vaccination efforts, VPRDs remain a major health and economic burden in the Nordic countries. Strengthening immunization programs through improved vaccine uptake, broader coverage, and use of advanced vaccines may contribute to further reducing this burden. Full article

Recreational fisheries contribute significantly to global economies through tourism, employment, and ecosystem service valorization. However, a systematic understanding of their economic magnitude and regional disparities remains fragmented. This study conducts a systematic review of 112 peer-reviewed articles published between 1991 and 2024, synthesizing the economic evidence across three key domains: consumer behavior, resource management, and ecological interactions. Consistent with previous research, our systematic synthesis documents a pronounced imbalance in the existing evidence base: studies from high-income countries more frequently report established management frameworks (e.g., bag limits, catch-per-unit-effort monitoring) and robust economic valuation mechanisms (e.g., Willingness to Pay estimation), whereas low- and middle-income countries face significant barriers in infrastructure and resource quantification. We identify that non-market valuation methods, particularly the travel cost method and consumer surplus analysis, are critical for internalizing the environmental value of fisheries. Furthermore, while ecological restoration demonstrates clear positive feedback loops for local economies, the sector exhibits high vulnerability to external shocks, such as the COVID-19 pandemic. This review highlights the urgent need to bridge the data gap in developing regions and suggests that transferring successful adaptive management models from developed nations is essential for achieving equitable and sustainable growth in the global recreational fishery economy. Full article

Introduction: Long COVID-19 is a complex and multifaceted condition characterized by persistent and fluctuating symptoms extending beyond the acute phase of infection. Aim: To explore the health and support experiences of the Spanish population living with long COVID-19. Methodology: A qualitative study with an interpretative phenomenological approach was used. Semi-structured interviews were conducted over 10 months (2023–2024) across 17 Spanish provinces. The sample included 36 participants. Results: Participants reported persistent and fluctuating symptoms that severely limited daily functioning and generated anxiety, depression, and post-traumatic distress. Chronic fatigue and cognitive impairment affected social participation and work performance, frequently leading to absenteeism and job loss. Delayed diagnosis limited professional knowledge, and perceived lack of credibility contributed to dissatisfaction with healthcare services. Family members and patient associations were key sources of emotional and practical support. Conclusions: Long COVID-19 has a multidimensional impact that requires a comprehensive and multidisciplinary approach. Participants’ narratives reveal how underestimation of symptoms, delayed diagnosis, social stigma, limited healthcare and social support, additional family burdens, and disrupted work and social routines create complexity in care and challenge existing health and economic systems. Full article

Background/Objectives: Although SARS-CoV-2 infection is usually mild in children, some develop severe respiratory disease (RD) or multisystem inflammatory syndrome in children (MIS-C). We evaluated long-term respiratory symptoms, pulmonary function, and cardiopulmonary exercise performance in children recovering from SARS-CoV-2-related RD and MIS-C. Methods: This single-center prospective observational study included children hospitalized for SARS-CoV-2-related RD or MIS-C. Pulmonary function tests (PFTs) and cardiopulmonary exercise testing (CPET) were performed at a mean follow-up of 22.8 ± 9.8 months after hospitalization and compared between groups. Results: Forty-two children (25 RD, 17 MIS-C) were included. Persistent post-COVID symptoms were reported by 38.1% of participants and were more frequent in the RD group (56.0% vs. 11.8%; p < 0.01), with fatigue being the predominant symptom (p = 0.02). Pulmonary function and CPET parameters were comparable between groups, with no evidence of clinically significant ventilatory limitation or impaired aerobic capacity. VO₂peak did not differ between groups. Multivariable regression identified the breathing reserve index and OUES normalized to body weight as strong positive predictors of aerobic capacity in both groups. Despite differences in follow-up duration, stratified analyses using a 24-month cut-off showed comparable physiological findings. Conclusions: Children demonstrated preserved pulmonary function and exercise capacity nearly two years after hospitalization for severe SARS-CoV-2-related RD or MIS-C. Despite these favorable objective findings, subjective symptoms persisted in a substantial subset of patients, particularly those recovering from RD, suggesting that symptom resolution may lag behind objective physiological recovery. OUES normalized to body weight and the breathing reserve index may serve as complementary markers of aerobic capacity. Full article

Whereas existing approaches to financial regime detection calibrate thresholds to historical price-and-return statistics, we propose a framework built on immunisation: a particle filter whose observation model is calibrated to the distributional shape of market stress rather than specific historical episodes. The particle filter—with theoretical foundations in probabilistic robotics and autonomous driving, and adapted to financial markets—forms the inferential backbone. Its observation model stacks distributional shape descriptors—skewness, tail asymmetry, kurtosis, and the share of leading sector-eigenvalue energy in cross-asset return covariance—computed across a hierarchy of temporal windows and injected as structured distributional archetypes, replacing the random initialisation of Thrun and Burgard, and of Reisinger. Applied to S&P 500 across four distinct crises (Dotcom 2002, Lehman 2009, COVID-19 2020, and the 2022 inflation-driven bear market), the descriptors show pre-crisis discrimination, with effect sizes (Cohen’s d) of 2.9 or more for realised volatility, Bowley downside skewness, and tail-quantile features, and 1.3 for sector concentration (${\lambda }_1$ ratio). Out of sample (2015–2026), the pipeline confirms endogenous regime transitions with lead-time before the market trough. With the flexibility of particle filters, the richness of the observation model is the primary enabler of real-time regime detection. We frame the system as a distributional-shape monitor that detects regime transitions in real time, not a pre-peak forecaster: highly sensitive, it registers deformation as stress becomes measurable, with the attendant sensitivity–specificity trade-off. On the abrupt COVID-19 shock, it confirms the transition sixteen trading days before the trough, without claiming pre-peak detection; confirmation timing scales with each crisis’s own duration, from roughly two to three weeks for the fastest episodes to several months for the slowest. Full article

This study investigates whether Chinese sectoral stock returns respond heterogeneously to international oil price movements and whether such responses vary across crisis regimes. Using daily data from January 2015 to June 2026, we analyze ten major sectoral indices of the Shanghai Stock Exchange, Dubai crude oil returns, oil price volatility, and the US dollar–Chinese yuan exchange rate. Dubai crude oil is used as the benchmark because it reflects Asia-oriented crude oil pricing and China’s imported energy cost conditions. The empirical analysis proceeds in several steps. First, baseline regressions are estimated to examine the average effect of oil returns on sectoral stock returns while controlling for domestic market-wide movements and exchange rate changes. Second, market-adjusted sectoral returns are used to isolate genuine sector-specific oil transmission from common market shocks. Third, GARCH(1,1)-based oil volatility and crisis-period interaction terms are introduced to identify the uncertainty effect of oil price movements during the COVID-19 pandemic, the post-pandemic period, and the US–Iran/Middle East geopolitical conflict period. Finally, DCC-GARCH dynamic conditional correlations are used as a robustness check. The results show that Chinese sectoral stock returns do not respond uniformly to oil price movements. The timing-adjusted results provide little evidence that lagged Dubai oil returns systematically predict next-day sectoral returns. Nevertheless, oil-price uncertainty exhibits selective and regime-dependent effects, particularly for Energy, Materials, Consumer Staples, Consumer Discretionary, Industrials, and Utilities. Full article

The COVID-19 pandemic shifted pediatric infection epidemiology from bacterial toward viral predominance as public health measures relaxed. To determine how hospital antibiotic prescribing adapted, antimicrobial consumption was characterized and linked to underlying pathogen type. Antibiotic use was retrospectively analyzed among 567 children admitted with a positive microbiological finding to the pediatric ward of a tertiary hospital in Split, Croatia, during the first quarters of 2021–2024. Consumption was quantified using Days of Therapy (DOT), Length of Therapy (LOT), and Days of Antibiotic Spectrum Coverage (DASC) per 1000 patient-days (PD). Antibiotics were grouped by the WHO Access/Watch/Reserve (AWaRe) classification and linked to discharge diagnosis and pathogen type. The proportion of children receiving antibiotics rose from 60.5% (26/43) in 2021 to 71.4% (130/182) in 2024, with the cohort shifting toward younger infants and virally driven diagnoses. DOT, DASC, and DOT/LOT peaked in 2022 (DOT 2164/1000 PD, DOT/LOT 2.2) before settling near 1400/1000 PD, with mean per-day spectrum (DASC/DOT) declining modestly (7.7 to 6.8). Watch-group antibiotics dominated throughout (~80%), with Access contribution of ~20% and Reserve antibiotic use negligible. Ceftriaxone usage rose fivefold (150 to 755 DOT/1000 PD) to become the leading agent. Ceftriaxone and azithromycin were administered more often to children with viral than bacterial infections (respiratory syncytial virus being most common), whereas vancomycin and meropenem were tied to bacterial isolates. These findings identify a high-yield stewardship target and underscore the value of multi-metric, pathogen-aware surveillance. Full article

The current study examined whether individual differences in attachment orientation, mate-retention tactics (benefit-provisioning and cost-inflicting strategies), and COVID-19-related stressors (impact, uncertainty, and perceived risk) were associated with IPV victimisation across two time points, one month apart, in Brazil and the UK (N = 1416 at Wave 1; N = 353 at Wave 2). Linear mixed-effects models controlled for age, sex, ethnicity, and relationship length. IPV victimisation did not significantly change over time, and COVID-19-related variables were not associated with any form of IPV. Brazilian participants reported higher psychological IPV than UK participants, although no country differences emerged for physical or sexual IPV. Cost-inflicting mate-retention strategies were positively associated with all forms of IPV in both countries, with consistently stronger associations in the UK. Attachment avoidance was positively associated with all forms of IPV in Brazil but was negatively associated with psychological and sexual IPV in the UK. Attachment anxiety and benefit-provisioning strategies were associated with psychological IPV in Brazil only. These findings suggest that associations between attachment, mate-retention strategies, and IPV victimisation may vary across cultural contexts, underscoring the value of culturally sensitive approaches to IPV research and intervention. Full article

Background/Objectives: Comparisons of abdominal Surgical-Site Infection (SSI) rates during COVID-19 often treat recorded incidence as a direct measure of biological risk, although the pandemic altered operative volume and case mix, prevention practices, and post-discharge detection. This review examines when reported SSI changes are interpretable. Methods: We conducted a critical narrative review, informed by SANRA and critical interpretive synthesis, of MEDLINE/PubMed, Embase, Scopus, and Europe PMC (January 2018–25 June 2026). Comparative SSI studies formed the core evidence set, supported by contextual studies on emergency presentation, biological risk, prevention, and surveillance. Results: Studies reported lower, unchanged, non-monotonic, and higher SSI rates. These differences became more coherent when the recorded rate was separated into operative composition, true infection probability, and detection probability. No core study measured all domains required to isolate a biological pandemic effect. Evidence from acute pancreatitis and abdominal trauma showed that disease severity, care access, inflammatory status, and prognostic relationships varied with health-system context. Conclusions: We propose the Surgical-Site Infection Signal-Validity Framework (SSI-SVF). It classifies findings as probable true improvement, apparent improvement, masked deterioration, mixed deterioration, or indeterminate and requires joint assessment of operative composition, true infection risk, and detection probability before interpreting a calendar-period change as altered surgical safety. Full article

Financial contagion—the propagation of shocks across markets, sectors, and time—remains one of the central questions in empirical finance, and yet most computational approaches model it at the wrong granularity. Existing work captures contagion at the asset level by measuring realised return or volatility comovements between specific securities, which conflates a structural pattern with its surface manifestation. We argue that the genuine causal regularity in contagion is between event types (rate decisions, default announcements, regulatory actions) rather than between individual assets. This paper introduces TECG, a framework that learns time-stamped causal-temporal association edges—predictive dependencies rather than identified causal effects—between abstract financial event types from news text and uses them to forecast multi-step contagion cascades. Methodologically, TECG can be read as a neural impulse response function (IRF) for event sequences: where the canonical structural-VAR IRF traces the dynamic response of one continuous variable to a shock in another, TECG traces the conditional intensity with which one event type fires after a shock to another event type, at a learned and possibly multi-modal lag. The framework couples LLM-based event extraction with a multivariate neural Hawkes process whose intensity functions are parameterised by a temporal graph neural network. The resulting graph has interpretable, time-stamped edges of the form “event-type A triggers event-type B with lag distribution L and conditional intensity κ.” We evaluated TECG on an aggregated corpus of approximately 482,000 financial news items spanning 2007–2023 and report three findings. First, the learned edges recover associations consistent with known causal relationships in finance—central-bank announcements preceding sector-level earnings revisions, default events at one institution preceding due-diligence events at competitors—without supervision on these relationships. Second, edges learned from data up to 2018 transfer to held-out cascades from 2020 (COVID equity crash) and 2023 (US regional banking stress) with substantially better fidelity than baselines that ignore temporal structure or operate at the asset level. Third, the framework supports generalised impulse response analysis: given a hypothetical seed event, it produces a distribution over downstream event chains that an analyst can interrogate, time-stamp, and entity-resolve. We are explicit that the empirical validation is observational and that the causal interpretation rests on assumptions we discuss at length. All headline comparisons are supported by paired-bootstrap significance tests; the extraction front end is independently evaluated on a manually annotated benchmark, with an explicit error-propagation analysis; and additional comparisons against recent temporal point-process and temporal-graph baselines are reported. The framework’s principal limitation is that it does not separately identify endogenous fire-sale or leverage-spiral dynamics; we point to where these gaps could be closed. Full article

Rumors, misleading claims, and other factually risky information units may gain visibility during crises before verification processes are complete. Existing detection systems often address factual status or diffusion separately, whereas operational monitoring requires identifying units that are both factually risky and highly amplified by a forecast horizon. This paper proposes the Disinformation Diffusion Virality Risk Index (DDVRI), an explainable early-warning framework for estimating this joint risk from evidence observed during a prespecified early window. The formulation defines early histories, adjudicated factual labels, fold-specific amplification thresholds estimated without test outcomes, optional absolute amplification floors, block-structured early representations, calibrated risk scores, and capacity-constrained alerting rules. The proposed framework is evaluated using two open datasets, one focused on crisis rumors and another focused on COVID-19 health misinformation with social engagement information. The results support DDVRI as a probabilistic early-warning and prioritization methodology for rare cases that combine factual risk and high-amplification, rather than as a general fake-news classifier or an automatic moderation system. Full article

The mitigation measures adopted to reduce the spread of COVID-19 have not only impacted the transmission of respiratory infections but also other infectious diseases. In this study, we explored the effect of the pandemic on the epidemiology of Central Nervous System (CNS) infections by conducting a retrospective analysis of diagnostic data obtained from cerebrospinal fluids (CSFs) collected before, during, and after the pandemic from patients with acute-suspected neuro-infectious syndrome. The samples were analyzed using a meningitis/encephalitis molecular syndromic panel. A total of 4203 CSFs were split into three groups: pre-pandemic (August 2018–February 2020), pandemic (March 2020–March 2022) and post-pandemic (April 2022–March 2024). Each pathogen was statistically assessed individually, comparing one group vs. another. Results showed that viral pathogens were the most frequently detected across all groups; however, Streptococcus pneumoniae was the single most identified pathogen. Comparison of the pre-pandemic and pandemic groups by statistical analysis showed a significant reduction in neuro-infections caused by enterovirus, S. pneumoniae and N. meningitidis, while the other pathogens were not affected. In conclusion, a reduction in CNS infections caused by respiratory and close-contact-transmitted pathogens, including both viral and bacterial agents, was observed during the period when COVID-19 containment measures were in effect. Full article