Search Results (1,847)

Complete or partial loss of smell (anosmia), sometimes in association with distorted olfactory perceptions (parosmia), is a common neurological symptom affecting nearly 60% of patients suffering from post-acute neurological sequelae of COronaVIrus Disease of 2019 (COVID-19) syndrome, called long COVID. Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) may gain access from the nasal cavity to the brain (neurotropism), and the olfactory route has been proposed as a peripheral site of virus entry. COVID-19 is a risk factor for developing Alzheimer’s Disease (AD), an age-dependent and progressive neurodegenerative disorder characterized in affected patients by early olfaction dysfunction that precedes signs of cognitive decline associated with neurodegeneration in vulnerable brain regions of their limbic system. Here, we summarize the recent literature data supporting the causal correlation between the persistent olfactory deterioration following SARS-CoV-2 infection and the long-delayed manifestation of AD-like memory impairment. SARS-CoV-2 infection of the olfactory neuroepithelium is likely to trigger a pattern of detrimental events that, directly and/or indirectly, affect the anatomically interconnected hippocampal and cortical areas, thus resulting in tardive clinical dementia. We also delineate future advancement on pharmacological and rehabilitative treatments to improve the olfactory dysfunction in patients recovering even from the acute/mild phase of COVID-19. Collectively, the present review aims at highlighting the physiopathological nexus between COVID-19 anosmia and post-pandemic mental health to favor the development of best-targeted and more effective therapeutic strategies in the fight against the long-term neurological complications associated with SARS-CoV-2 infection. Full article

The COVID-19 pandemic has profoundly affected societies around the world. Although the emergency phase of coronavirus disease 2019 (COVID-19) has ended, the threat it poses remains persistent. This review aims to clarify the mechanisms of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection to support effective management of the disease. A central focus is the host cellular response to the viral infection, with particular emphasis on the role of cytokines. Cytokines play a dual role in antiviral defense: they contribute to the inhibition of viral replication and facilitate the clearance of pathogens, yet dysregulated cytokine responses can result in severe immunopathology. Interferons (type I, type II, and type III) and other cytokines are pivotal in activating intracellular antiviral mechanisms and in orchestrating the recruitment of immune cells through extracellular signaling. Effective immune responses to viral infections are governed not only by primary immune cells—such as dendritic cells, T lymphocytes, and B lymphocytes—but also by the local cytokine milieu shaped by infected and neighboring cells. Given the presence of endogenous inhibitors and autoantibodies in vivo, it is essential to evaluate the functional activity of cytokines in clinical samples. We propose a novel approach to quantify biologically active cytokine levels. Full article

Beyond their traditional role as short-lived antimicrobial cells, neutrophils are increasingly recognized as key regulators of adaptive immunity and tumor progression. This AI-assisted integrative review investigated the neutrophil–T-cell axis, particularly the role of Galectin-9 (Gal-9), across adult T-cell leukemia/lymphoma (ATL), Sézary syndrome (SS), coronavirus disease 2019 (COVID-19), and psoriasis. Leveraging AI tools (GPT-5 and Adobe Acrobat AI Assistant) for literature synthesis (2000–2025) and expert validation, we aimed to identify common immunological mechanisms. Across all conditions, neutrophils displayed persistent activation, elevated Gal-9 expression, and modulated T-cell interactions. In ATL and SS, neutrophilia correlated with poor survival and TCR signaling dysregulation, suggesting Gal-9-mediated immune modulation. In COVID-19 and psoriasis, neutrophil-derived Gal-9-linked innate hyperactivation to T-cell exhaustion and IL-17-driven inflammation. These findings define a recurring neutrophil–Gal-9 regulatory module connecting innate and adaptive immune responses. This study underscores the feasibility of combining AI-driven literature synthesis with expert review to identify unifying immunological mechanisms and therapeutic targets across malignancy and inflammation. Full article

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated not only with respiratory illness but also with profound vascular and coagulation disturbances. Long COVID (LC) is characterized by persistent symptoms such as fatigue, dyspnea, cognitive impairment, and palpitations. Mechanistically, SARS-CoV-2 induces direct endothelial injury, promotes a pro-inflammatory cytokine milieu, and activates platelets, leading to immunothrombosis and impaired fibrinolysis. Consequently, patients exhibit microthrombosis, elevated plasma D-dimer, fibrinogen dysregulation, and persistent hypercoagulability. Clinically, this translates into an increased risk of venous thromboembolism, including deep vein thrombosis and pulmonary embolism, as well as arterial thrombotic events such as myocardial infarction and stroke, which may persist months after acute infection. Understanding the interplay between endothelial injury, inflammation, and coagulation is crucial for risk stratification and the development of preventive and therapeutic strategies. We conducted a systematic narrative review of the literature, including human clinical and mechanistic studies identified through PubMed, Scopus and Web of Science up to 30 September 2025. This review synthesizes current evidence on vascular complications in LC, highlighting endothelial dysfunction as a central pathophysiological nexus linking the acute phase of SARS-CoV-2 infection with chronic LC manifestations. Full article

The coronavirus disease 2019 (COVID-19) pandemic remains a significant public health threat globally with significant socio-economic impacts. People living with human immunodeficiency virus (HIV) (PLWH) have a high risk of severe outcomes of COVID-19 due to immunosuppression. Clinical manifestation of COVID-19 in HIV patients largely remains unclear. We carried out a pilot study to investigate the clinical laboratory data and circulating cytokines in PLWH infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in Tatarstan, Russia. Serum samples were collected from three groups: PLWH with COVID-19 (PLWH/COVID-19), COVID-19-only, and uninfected controls. We found an increased risk of severe COVID-19 in PLWH/COVID-19 patients compared to COVID-19-only. Four fatal cases were in PLWH/COVID-19, while there was no fatality in COVID-19-only. Pro-inflammatory cytokines, such as IL-5, IL-6, IL-9, and IL-15, were elevated in PLWH/COVID-19 compered to COVID-19-only. These preliminary findings highlight the potential for more severe COVID-19 in PLWH/COVID-19 where pro-inflammatory cytokines could play pathogenic role. Full article

The impact of physical health conditions on coronavirus disease of 2019 (COVID-19) severity in World Trade Center disaster-exposed populations remains understudied. We examined the association of type, number and diagnosis time of pre-existing health conditions with COVID-19 severity, using the WTC Health Registry (WTCHR). We analyzed 3568 WTCHR enrollees with self-reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a 2021 follow-up survey. COVID-19 severity was measured by self-reported symptom duration (<2, 2–4, and >4 weeks) and hospitalization (hospitalized versus not). Pre-existing gastroesophageal reflux disease (GERD), respiratory conditions, cardiovascular conditions, and diabetes were self-reported and categorized into four groups (no diagnosis, post-9/11, pre-9/11, and undefinable). We used multinomial logistic regression and binary logistic regression to analyze the association of comorbidities with COVID-19 symptom duration and hospitalization, respectively, adjusting for post-traumatic stress disorder and demographic factors. Analysis was also conducted separately by enrollee type: rescue and recovery workers (RRW) vs. community members (non-RRW). Having all four health conditions post-9/11 was associated with longer symptom duration after SARS-CoV-2 infection (>4 weeks) among RRW (AOR: 2.66, 95% CI: 1.03–6.87). Reporting a post-9/11 respiratory condition was associated with an increased risk of being hospitalized among RRW and an increased risk of longer symptom duration (>4 weeks) among non-RRW. While post-9/11 diabetes was associated with an increased risk of longer symptom duration among RRW, post-9/11 GERD and pre-9/11 cardiovascular conditions were associated with an increased risk of longer symptom duration and being hospitalized among non-RRW, respectively. The impact of certain health conditions on COVID-19 severity varied across enrollee types and time of diagnosis. Given the lasting health impacts of 9/11-related exposures, targeted medical surveillance and proactive healthcare interventions are critical for mitigating the risk of severe COVID-19 illness in this population. Full article

Background/Objectives: Quantitative computed tomography (CT) metrics are widely used to assess pulmonary involvement and to predict short-term severity in coronavirus disease 2019 (COVID-19). However, it remains unclear whether baseline artificial intelligence (AI)-based quantitative high-resolution computed tomography (HRCT) metrics of pneumonia burden provide incremental prognostic value for in-hospital composite adverse outcomes beyond routine clinical factors, or whether these imaging-derived markers carry any exploratory signal for long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection among hospitalized patients. Most existing imaging studies have focused on diagnosis and acute-phase prognosis, leaving a specific knowledge gap regarding AI-based quantitative HRCT correlates of early deterioration and subsequent reinfection in this population. To evaluate whether combining deep learning-derived, quantitative, HRCT features and clinical factors improve prediction of in-hospital composite adverse events and to explore their association with long-term reinfection in patients with COVID-19 pneumonia. Methods: In this single-center retrospective study, we analyzed 236 reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients who underwent baseline HRCT. Median follow-up durations were 7.65 days for in-hospital outcomes and 611 days for long-term outcomes. A pre-trained, adaptive, artificial-intelligence-based, prototype model (Siemens Healthineers) was used for pneumonia analysis. Inflammatory lung lesions were automatically segmented, and multiple quantitative metrics were extracted, including opacity score, volume and percentage of opacities and high-attenuation opacities, and mean Hounsfield units (HU) of the total lung and opacity. Patients were stratified based on receiver operating characteristic (ROC)-derived optimal thresholds, and multivariable Cox regression was used to identify predictors of the composite adverse outcome (intensive care unit [ICU] admission or all-cause death) and SARS-CoV-2 reinfection, defined as a second RT-PCR-confirmed episode of COVID-19 occurring ≥90 days after initial infection. Results: The composite adverse outcome occurred in 38 of 236 patients (16.1%). Higher AI-derived opacity burden was significantly associated with poorer outcomes; for example, opacity score cut-off of 5.5 yielded an area under the ROC curve (AUC) of 0.71 (95% confidence interval [CI] 0.62–0.79), and similar performance was observed for the volume and percentage of opacities and high-attenuation opacities (AUCs up to 0.71; all p < 0.05). After adjustment for age and comorbidities, selected HRCT metrics—including opacity score, percentage of opacities, and mean HU of the total lung (cut-off −662.38 HU; AUC 0.64, 95% CI 0.54–0.74)—remained independently associated with adverse events. Individual predictors demonstrated modest discriminatory ability, with C-indices of 0.59 for age, 0.57 for chronic obstructive pulmonary disease (COPD), 0.62 for opacity score, 0.63 for percentage of opacities, and 0.63 for mean total-lung HU, whereas a combined model integrating clinical and imaging variables improved prediction performance (C-index = 0.68, 95% CI: 0.57–0.80). During long-term follow-up, RT-PCR–confirmed reinfection occurred in 18 of 193 patients (9.3%). Higher baseline CT-derived metrics—particularly opacity score and both volume and percentage of high-attenuation opacities (percentage cut-off = 4.94%, AUC 0.69, 95% CI 0.60–0.79)—showed exploratory associations with SARS-CoV-2 reinfection. However, this analysis was constrained by the very small number of events (n = 18) and wide confidence intervals, indicating substantial statistical uncertainty. In this context, individual predictors again showed only modest C-indices (e.g., 0.62 for procalcitonin [PCT], 0.66 for opacity score, 0.66 for the volume and 0.64 for the percentage of high-attenuation opacities), whereas the combined model achieved an apparent C-index of 0.73 (95% CI 0.64–0.83), suggesting moderate discrimination in this underpowered exploratory reinfection sample that requires confirmation in external cohorts. Conclusions: Fully automated, deep learning-derived, quantitative HRCT parameters provide useful prognostic information for early in-hospital deterioration beyond routine clinical factors and offer preliminary, hypothesis-generating insights into long-term reinfection risk. The reinfection-related findings, however, require external validation and should be interpreted with caution given the small number of events and limited precision. In both settings, combining AI-based imaging and clinical variables yields better risk stratification than either modality alone. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the critical need for scalable, timely, and unbiased methods to monitor disease transmission at the population level. Wastewater-based epidemiology (WBE) provides an effective method for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission by detecting viral RNA shed into the sewage system. Because it does not rely on individual testing, WBE can offer timely, cost-effective, and community-level insights into infection trends. In this study, we present a hierarchical Beta-Binomial model that integrates SARS-CoV-2 RNA concentration in wastewater with reported COVID-19 case counts to enhance the monitoring of community-level transmission dynamics. The model incorporates wastewater viral loads as a predictor and reported cases as the response, while adjusting for testing volume to account for biases introduced by fluctuations in testing practices. This approach enables reliable estimation of the effective reproduction number ($R_t$), even in the absence of consistent reporting of clinical data. Applied to twenty counties in California, our modeling framework demonstrates the potential of wastewater surveillance to inform public health decision making, particularly in locations with sparse clinical data. Full article

Saliva specimens are widely used for coronavirus disease 2019 (COVID-19) testing using RT-qPCR due to their advantages over nasopharyngeal swabs of being non-invasive and self-collectable. However, saliva collection can be time-consuming in individuals with reduced saliva secretion, including those with diabetes, diseases involving salivary glands such as Sjögren’s syndrome, and older adults. In this study, we evaluated the diagnostic performance of mouth rinse specimens, which can be easily collected even from individuals with reduced saliva secretion, as an alternative to saliva for RT-qPCR COVID-19 testing. Among the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive specimens analyzed, 88.2% were derived from patients possessing risk factors associated with reduced salivary secretion, including diabetes, use of medications such as anticholinergics or antihistamines, smoking, and older age. The analysis results of mouth rinse specimens demonstrated 96.7% overall agreement with those of saliva specimens, with a sensitivity of 94.1% and specificity of 100%; however, the viral load in the mouth rinse specimens was lower than that in saliva because of sample dilution. These findings suggest that mouth rinse specimens are a practical, versatile, and reliable alternative specimen for RT-qPCR COVID-19 testing. Full article

Background/Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can naturally infect a broad spectrum of animal species, with cats, minks, and ferrets being highly susceptible. There is a potential risk that infected animals could transmit viruses to humans. Moreover, SARS-CoV-2 continues to evolve via mutation and genetic recombination, resulting in the continuous emergence of new variants that have triggered a wave of reinfection. Therefore, safe and effective corona virus disease 2019 (COVID-19) vaccines for animals are still being sought. Methods: We generated three recombinant Modified vaccinia virus Ankara (MVAs) expressing the prefusion-stabilized S proteins, S6P, DS6P, and BA2S6P, targeting the full-length S protein genes of the ancestral, Delta, and Omicron BA.2 strains of SARS-CoV-2. Subsequently, the safety, immunogenicity, and protective efficacy of these MVA-based oral COVID-19 vaccine candidates were assessed in mice, minks, and cats. Results: These recombinant MVAs are safe in mice, minks, and cats. Oral or intramuscular vaccination with rMVA-S6P induced a robust SARS-CoV-2 neutralizing antibody (NA) response and conferred complete protection against the SARS-CoV-2 challenge in mice. Meanwhile, oral or intramuscular administration of these recombinant MVAs in combination induced a potent and durable NA response against homotypic SARS-CoV-2 pseudovirus in mice, minks, and cats, respectively. Conclusions: These findings suggest that the MVA-vectored vaccines are promising oral COVID-19 vaccine candidates for animals, and that the combined vaccination approach is an effective administration strategy for such vaccines. Full article

The coronavirus disease 2019 (COVID-19) pandemic has revealed a close and multifaceted relationship between viral infection, systemic inflammation, and cardiovascular health. Among the cardiac complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), atrial fibrillation (AF)—especially new-onset atrial fibrillation (NOAF)—has emerged as a major determinant of disease severity and prognosis. Clinical studies and meta-analyses show that 5–10% of hospitalized COVID-19 patients develop AF, with markedly higher rates in critically ill individuals. Both pre-existing and NOAF are independently associated with increased risks of intensive care admission, mechanical ventilation, thromboembolic events, and mortality. The underlying mechanisms involve a combination of cytokine-mediated inflammation, endothelial dysfunction, microvascular injury, and dysregulation of the renin–angiotensin–aldosterone system (RAAS). Viral downregulation of angiotensin-converting enzyme 2 (ACE2) receptors contributes to myocardial fibrosis, while hypoxia, oxidative stress, and autonomic imbalance further promote electrical remodeling and arrhythmogenesis. Post-infectious studies indicate that atrial structural changes and autonomic dysfunction may persist for months, predisposing survivors to recurrent arrhythmias. Technological advances in telecardiology and digital medicine have provided new tools for early detection and long-term monitoring. Wearable electroencephalography (ECG) devices, implantable loop recorders (ILRs), and artificial intelligence (AI)-based diagnostic algorithms enable continuous rhythm surveillance and individualized management, improving outcomes in post-COVID patients. This review summarizes current evidence on the epidemiology, pathophysiology, clinical implications, and monitoring strategies of AF in COVID-19. It underscores the importance of integrating telemedicine and AI-assisted diagnostics into cardiovascular care to mitigate the long-term arrhythmic and systemic consequences of SARS-CoV-2 infection. Full article

The coronavirus disease 2019 (COVID-19) pandemic in Tocantins, Brazil, exhibited distinct phases between 2020 and 2025, with high mortality concentrated in 2020–2021 and subsequent stabilization at residual levels. Using epidemiological data, statistical modeling, and genomic surveillance, we show that the crisis peaked in 2021, coinciding with the circulation of Gamma and Delta, when health system capacity was severely strained. From 2022 onwards, the spread of Omicron led to record incidence but proportionally low mortality, reflecting accumulated immunity, vaccination, and improved clinical management. Vaccination represented the turning point, reducing hospitalizations and deaths by over 90% and driving a clear decoupling between incidence and severity. Interrupted time-series and generalized additive model (GAM) analyses confirmed sustained reductions in transmission and severity associated with mass immunization. Genomic sequencing of 3941 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes identified 166 lineages and successive variant replacements, culminating in the predominance of LP.8.1.4 in 2025. To our knowledge, this is one of the few integrated, long-term analyses (2020–2025) combining epidemiological and genomic data, capturing the full succession of variants up to LP.8.1.4 and highlighting Tocantins as a strategic “variant corridor” linking Brazil’s North and Central-West regions. These findings underscore the dual role of vaccination and genomic surveillance in shaping the epidemic trajectory and the importance of sustaining both strategies to mitigate future health crises. Full article

In COVID-19 (coronavirus disease 2019), multi-organ complications depend on the immune system’s activity. α1-Acid glycoprotein (AGP) is a highly glycosylated positive acute-phase protein having multifaceted immunomodulatory and protective effects. We were interested in changes in serum AGP concentrations, expression of its glycans, and oxidation-reduction potential (ORP) between severe COVID-19 patients, convalescents, and healthy controls, and whether any of the analyzed parameters could serve as an additional diagnostic biomarker of severe COVID-19 and/or help monitor recovery. We were also interested in associations between the examined parameters. AGP concentrations were measured using an immunoturbidimetric method. The profile and degree of AGP glycosylation were analyzed using lectin-ELISA with lectins: sialo-specific from Sambucus nigra (SNA) and Maackia amurensis (MAA), fucose-specific from Lotus tetragonolobus (LTA) and Aleuria aurantia (AAL). The static and capacitive ORP (sORP and cORP, respectively) were measured using MiOXSYS C+^® device (Caerus Biotechnologies, Vilnius, Lithuania). Statistica13.3PL software was used for statistical analysis. AGP concentrations increased in COVID-19 patients, showing high clinical usefulness in distinguishing them from convalescents and controls. AGP α2,6-sialylation (reactivity with SNA) was reduced in COVID-19 vs. other study groups, while α2,3-sialylation (reactivity with MAA) was reduced in convalescents vs. controls. The expression of LTA-reactive fucose (Lewis^x structures, Le^x) was reduced in COVID-19 patients compared to controls and convalescents, but AGP reactivity with AAL did not differ between the study groups. The sORP was reduced, and the cORP was increased in COVID-19. The observed negative correlations between sORP and AGP levels may suggest the antioxidant effect of AGP during severe COVID-19. Higher levels of serum AGP in severe COVID-19, together with low expression of sialic acid α2,6-linked and Le^x structures, accompanied by reduced sORP, constitute a characteristic pattern of biomarker expression during severe COVID-19. The increased expression of SNA-reactive sialic acid and Le^x structures may reflect the recovery process after SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The observed negative correlations between AGP and sORP levels may suggest that serum AGP in COVID-19 also plays a role as an antioxidative molecule. Full article

Background: This cross-sectional study reports exploratory observations on respiratory symptom patterns in individuals with prior coronavirus disease 2019 (COVID-19), evaluating associations with exercise habits, number of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection episodes, vaccine doses received, body mass index (BMI), age, sex, and comorbidities. Methods: A total of 240 participants were assessed for age, sex, height, weight, BMI, comorbidities, SARS-CoV-2 infection episodes, vaccine doses received, and exercise habits; the self-reported duration of symptomatic periods was summarized descriptively and was not modeled as an exposure or outcome. Results: Compared with the first SARS-CoV-2 infection episode (reference), patients who experienced a second episode had higher odds of dyspnea (adjusted odds ratio; OR = 7.61; 95% confidence interval CI = 1.54–37.66). In univariate analysis, patients who received three vaccine doses had lower odds of dyspnea than those who received two doses (OR = 0.39; 95% CI = 0.16–0.98), but this association was not significant after adjustment (adjusted OR = 0.46; 95% CI = 0.13–1.63). After adjustment, patients who exercised had lower odds of secretion compared with those who did not (adjusted OR = 0.30; 95% CI = 0.12–0.73). Conclusions: These cross-sectional, hypothesis-generating observations suggest higher adjusted odds of dyspnea among individuals with repeat infection and lower adjusted odds of sputum among those reporting regular exercise; estimates are imprecise and subject to residual confounding due to unbalanced group sizes. Confirmation in larger, longitudinal cohorts is required. Full article