Search Results (1,319)

Background/Objectives: Lateral flow assays (LFAs) are widely used point-of-care diagnostic devices due to their low cost, portability, and ease of use. However, most LFAs provide only qualitative results, limiting their utility for applications requiring continuous biomarker monitoring. This study introduces DeepBand, a deep learning-enabled multi-stage framework designed to automate the continuous quantification of analyte concentrations from unstandardized smartphone-captured lateral flow assay (LFA) images. Methods: A publicly available dataset containing 672 COVID-19 LFA images corresponding to four analyte concentrations (0.0, 1.8, 3.7, and 7.4 ng) was analyzed. A multi-stage pipeline was developed consisting of: (1) YOLOv11-based object detection to isolate the LFA cartridge from background artifacts, (2) a custom computer vision algorithm to identify and crop the test and control bands, and (3) a custom convolutional neural network (CNN) trained as a supervised regression model to predict continuous analyte concentrations. Data augmentation, hyperparameter optimization, and 5-fold cross-validation were used to improve model robustness. Results: The YOLOv11 model achieved approximately 99% mAP50 and 93.96% mAP95 for cartridge detection. Initial CNN models exhibited systematic underprediction of higher concentrations due to target imbalance; replacing mean squared error with Huber loss substantially improved performance, resulting in a final 20% held-out test set RMSE of 0.0292 ng. Analysis of HSV image channels demonstrated that the saturation-channel test-to-control intensity ratio was strongly correlated with analyte concentration (r = 0.94), consistent with the Beer–Lambert law governing LFA signal formation. Channel ablation studies confirmed the saturation channel as the most informative feature, while saliency mapping showed that the model primarily focused on biologically relevant test and control line regions. Conclusions: The proposed deep learning-enabled workflow, DeepBand, successfully integrates object detection, image processing, and CNN-based regression to provide automated quantitative interpretation of LFA results from smartphone images. Furthermore, the observed agreement between model behavior and Beer–Lambert theory suggests that the network learns biologically meaningful signal characteristics, supporting its potential for quantitative point-of-care diagnostics and longitudinal disease monitoring. Full article

Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), exposed critical gaps in global preparedness for rapidly evolving viral pandemics. Though current antiviral treatments have shown some efficacy in managing COVID-19, substantial limitations, such as inefficient targeted drug delivery, aberrant host immune responses, and inadequate preparedness for future viral pandemics, were also evident. These challenges underscore the urgent need for target-specific, precise and efficient therapeutic strategies to modulate viral entry and immune dysregulation. Extracellular vesicles (EVs), cell-released cargo-carrying nanoscale vesicles, can both facilitate and inhibit viral infection depending on their cargo composition. Thus, endogenous EVs can promote or inhibit COVID-19 pathogenesis by modulating critical pathways, including angiotensin-converting enzyme 2 (ACE2)-mediated viral entry, transmembrane protease serine 2 (TMPRSS2)- dependent spike protein activation, nuclear factor kappa B (NF-κB)-driven inflammatory signaling, and NLRP3 inflammasome activation. In contrast, engineered EVs, such as ACE2-expressing EVs, mesenchymal stem cell-derived EVs and microRNA-enriched EVs, have therapeutic potential as they can facilitate antiviral defense through immune modulation, viral neutralization and suppression of cytokine storm. Moreover, EV-associated nucleic acids, proteins and lipids can act as biomarkers for disease detection and severity stratification. A deeper understanding of EV-virus mechanistic insights can enhance preparedness for similar viral diseases. In this review, we provide a comprehensive analysis of the molecular mechanisms underlying EV-virus interactions highlighting the therapeutic and diagnostic potential of EVs in tackling COVID-19 and future viral pandemics. Full article

Wastewater-based epidemiology (WBE) is a rapidly evolving discipline that employs the analysis of human excreted biomarkers in untreated, community wastewater to provide near real-time population-level health assessments. While its popularity to support public health decision-making and intervention grew during the COVID-19 pandemic, understanding its utility and acceptability in healthcare is currently underexplored. This study aimed to generate evidence to support the integration of WBE into the healthcare setting via an educational webinar with the following objectives: (i) assess change in perspectives of WBE via surveys, (ii) understand major applications of interest and applicability to healthcare, and (iii) capture perspectives on its utility in this context. Comparative findings indicated an overall increase in support of WBE (95% to 100%), a reduction in concerns regarding data accuracy (40% to 25%), and an increase in support for the use of WBE to influence public policy (65% to 75%). Follow-up, open-ended survey responses indicated the workshop was an effective method to educate on WBE and its wide variety of applications. Overall, this pilot study revealed there is merit in further investigating the utility of integrating WBE data into the healthcare setting, as its integration demonstrates high levels of potential acceptability in clinical practice. Future work that reaches a wider audience and tests specific educational interventions is needed to further confirm utility, determine best educational outreach methods, and explore relevant and useful applications in the healthcare setting. Full article

From a health perspective, the 21st century has witnessed the emergence and global impact of several major viral diseases, most notably coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2, which has been associated with a substantial burden of secondary fungal infections. Patients suffering from COVID-19 illnesses frequently developed fungal co-infections, which can worsen clinical outcomes and complicate therapeutic efforts. Hospitalized individuals with viral infections are particularly susceptible to invasive fungal pathogens, including Aspergillus, Candida, and Mucorales species. The co-pathogenesis between respiratory virus and fungi is complex, involving dynamic interactions among the pathogens and the host immune system. Opportunistic fungal infections were found to be more prevalent in COVID-19-infected individuals, who require mechanical ventilation, have diabetes, or exhibit neutropenia. This review aims to provide a comprehensive overview of fungal co-infections associated with COVID-19 disease, with a focus on their pathogenesis, biomarkers, diagnostic approaches, and potential treatment strategies. Overall, the available evidence indicates that viral-induced immune dysregulation, epithelial barrier damage, and clinical risk factors contribute to the development and severity of fungal co-infections in COVID-19 patients. Early recognition using reliable biomarkers and standardized diagnostic approaches, together with timely and pathogen-directed antifungal therapy, are essential for improving clinical outcomes. Full article

Severe COVID-19 is characterized by immune dysregulation, yet reliable biomarkers associated with adverse outcomes remain limited. Immunoglobulin G (IgG) N-glycosylation modulates antibody effector function and reflects the immune system’s inflammatory state. In this study, we investigated whether specific IgG glycosylation profiles are associated with COVID-19 severity, mortality, and clinical course in patients without known prior SARS-CoV-2 infection. Of the 806 admitted COVID-19 patients included in the study, 377 hospitalized patients were longitudinally monitored, and 2001 serum samples were analyzed. Significant alterations in IgG N-glycome composition were observed in patients with severe disease and those who died. The most prominent changes included increased agalactosylation and decreased mono- and digalactosylation, features associated with a more pro-inflammatory IgG glycosylation profile. Nosocomial infections were associated with a more pro-inflammatory IgG glycosylation profile. No statistically significant differences in IgG N-glycome composition were observed between vaccinated and unvaccinated patients, although the small number of vaccinated patients limited this analysis. The observed glycomic profiles likely reflect immune responses characteristic of the early phase of the COVID-19 pandemic. Whether similar glycosylation changes occur in other infectious or inflammatory diseases remains to be established. IgG glycosylation analysis holds promise for personalized risk stratification and early therapeutic intervention in COVID-19 and other severe infectious diseases. Full article

Toll-like receptors (TLRs) play an important role in innate immunity, mediating viral recognition, antiviral responses, and cytokine production. This study evaluated TLR7 and TLR9 expression levels in COVID-19 patients and their associations with COVID-19 infection and disease severity. A total of 134 adults were included: 94 COVID-19 patients and 40 controls. RT-PCR confirmed SARS-CoV-2 infection; TLR7 and TLR9 expression levels were determined by quantitative real-time PCR. Participants’ clinical characteristics and laboratory findings were evaluated. TLR7 and TLR9 expression levels were significantly higher in COVID-19 patients than in controls (both p < 0.001). Both markers were independently associated with COVID-19 infection by multivariate analysis (OR: 6996.516, p < 0.001; OR: 7.365, p = 0.004) and showed excellent discriminatory performance by ROC analysis (AUC: 0.973 and 0.937, both p < 0.001). Among COVID-19 patients, TLR9 expression and ALT were independently associated with moderate-to-critical disease (OR: 1.448, p = 0.030; OR: 1.092, p = 0.032). TLR9 expression and ALT showed modest discriminatory performance for distinguishing moderate-to-critical COVID-19 from mild disease (AUC: 0.623, p = 0.042; AUC: 0.641, p = 0.020, respectively), while the combined model demonstrated a slight improvement (AUC: 0.659, p = 0.008). TLR7 and TLR9 may represent potential biomarkers for distinguishing COVID-19 patients from controls, and TLR9 may provide additional information on disease severity. These findings need validation in larger, multicenter studies before clinical application. Full article

Background: Severe COVID-19 reflects a multi-layered host response with systemic inflammation, thrombo-inflammation, tissue damage, oxidative stress and altered antiviral interferon biology. We performed an integrated exploratory analysis of first-wave hospitalized patients to identify biomarker patterns associated with adverse clinical evolution during established admission. Methods: We analyzed 60 hospitalized COVID-19 patients and 18 healthy controls for soluble IFNAR2 (sIFNAR2) comparison. Biomarker samples were obtained during hospitalization, approximately seven days after symptom onset. Outcomes were final clinical status, severe respiratory involvement, post-sampling clinical worsening and death. Analyses included non-parametric testing, false-discovery-rate adjustment, effect-size estimation, exploratory ROC curves, parsimonious regression, penalized internal validation, composite scores and molecular-structure analyses. Results: Final status was favorable outcome in 31 patients, severe non-fatal disease in 22 and death in 7. sIFNAR2 was higher in patients than in healthy controls and highest among non-survivors, but did not distinguish favorably from severe non-fatal disease. The most consistent severity-associated signals were IL-6, D-dimer, total thiols, IL-10, LDH, ferritin, leukocytes and IL-1RA. D-dimer, IL-6 and ferritin yielded the largest exploratory univariable AUCs for severe respiratory involvement, whereas ferritin, IL-10, IL-1RA and sIFNAR2 predominated in event-limited mortality analyses, which were based on only seven deaths. Composite multi-axis scores and PLS-DA were tools requiring external validation. Conclusions: Biomarker patterns during admission were associated with adverse evolution. Conventional markers remained the most practical signals, while cytokines, redox markers and sIFNAR2 provided complementary biological information. sIFNAR2 should be interpreted as an exploratory complementary marker of the interferon receptor axis, mainly linked to mortality, not as a stand-alone clinical test or functional measure of IFNAR signaling. Full article

Platelet activation plays a central role in arterial thrombosis, thromboinflammation, and microvascular injury. Conventional platelet function tests evaluate platelet responsiveness to exogenous agonists ex vivo but do not directly reflect in vivo platelet activation. Although several soluble platelet-derived molecules, including platelet factor 4 (PF4), β-thromboglobulin (β-TG), soluble P-selectin, soluble CD40 ligand, glycocalicin, and soluble glycoprotein (GP) VI, have been investigated as in vivo platelet activation markers, their clinical use is limited by preanalytical instability, lack of platelet specificity, constitutive shedding, or uncertain disease specificity. Soluble C-type lectin-like receptor 2 (sCLEC-2) has recently emerged as a promising biomarker of in vivo platelet activation. CLEC-2 is expressed predominantly in platelets and megakaryocytes, and its soluble form is released from activated platelets as both a shed molecule and a microparticle-associated form. Compared with PF4 and β-TG, sCLEC-2 is less susceptible to artifactual release during routine blood collection, making it more suitable for clinical laboratory testing. Elevated sCLEC-2 levels have been reported in acute coronary syndrome, acute ischemic stroke, disseminated intravascular coagulation, thrombotic microangiopathy, antiphospholipid antibody syndrome, and coronavirus disease 2019 (COVID-19). In thrombocytopenic disorders, indices incorporating platelet count, such as the C2PAC index, sCLEC-2/D-dimer ratio, and sCLEC-2 × D-dimer/platelet count, may better reflect platelet activation and disease status than sCLEC-2 concentration alone. However, preanalytical standardization, assay harmonization, reference interval validation, and disease-specific cutoff values remain essential. This review summarizes the biological basis, assay systems, clinical evidence, and future perspectives of sCLEC-2 as an emerging laboratory marker of in vivo platelet activation. Full article

Backgroundand Objectives: Retinal microcirculation may serve as a surrogate marker of systemic vascular status and can be non-invasively assessed using optical coherence tomography angiography (OCTA). Emerging evidence suggests that retinal and choroidal microvascular alterations may occur following coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C), potentially reflecting systemic vascular and inflammatory processes. This review summarizes the current evidence regarding retinal and choroidal microvascular changes detected by OCTA in pediatric patients with COVID-19 and MIS-C. Materials and Methods: A structured literature search of the National Center for Biotechnology Information (NCBI) PubMed database was conducted from inception until April 2026. Original English-language studies evaluating retinal and choroidal microcirculation using OCTA in pediatric patients with COVID-19 or MIS-C were identified and reviewed. Results: Available studies suggest variable retinal and choroidal microvascular alterations following COVID-19 and MIS-C, including changes in vessel density, perfusion, and foveal avascular zone parameters. Several investigations reported reduced vessel density, particularly within the deep capillary plexus, whereas others demonstrated increased peripapillary vascular parameters or no significant differences during the observation period. Differences in patient characteristics, disease severity, timing of imaging, OCTA protocols, and study design likely contribute to the heterogeneity of the reported findings. Conclusions: OCTA represents a promising research tool for investigating retinal and choroidal microvascular alterations associated with pediatric COVID-19 and MIS-C. However, the available evidence remains limited by small observational studies, methodological heterogeneity and inconsistent findings. Larger prospective longitudinal studies using standardized imaging protocols are needed to determine the clinical significance and reproducibility of OCTA-derived parameters and to establish whether they may have potential as biomarkers of ocular or systemic vascular involvement. Full article

Background: COVID-19 is a multisystem disorder associated with inflammatory, metabolic, electrolyte, and endocrine disturbances. However, the clinical relevance of dysnatremia and acute hormonal alterations remains uncertain, particularly when assessed alongside established clinical risk factors. Methods: This prospective cohort study included 252 adults hospitalized with confirmed COVID-19. Clinical, laboratory, hormonal, and metabolic parameters were assessed at admission, and their associations with disease severity, intensive care unit (ICU) admission, and in-hospital mortality were analyzed. Results: The median age was 73 years, 50.4% of patients were female, and severe or critical disease was present in 81.7%. Dysnatremia and/or endocrine alterations were detected in 55.2% of patients, including hyponatremia in 16.7%, while thyroid-function alterations were observed in 40.9%. Dysnatremia was associated with higher glucose levels and inflammatory-cell changes. Several hormonal and metabolic parameters showed statistically significant univariate associations with adverse outcomes; however, their discriminative performance was weak, with AUC values below clinically meaningful thresholds. After adjustment for major clinical confounders, dysnatremia and endocrine alterations were not independent predictors of disease severity, ICU admission, or mortality. Conclusions: These findings suggest that electrolyte and endocrine abnormalities in hospitalized COVID-19 patients primarily reflect systemic stress physiology and acute disease burden rather than functioning as independent prognostic biomarkers. Full article

Coronavirus disease 2019 (COVID-19) is increasingly recognized as a multisystem disorder associated with persistent metabolic complications extending beyond the acute phase of infection. Accumulating evidence suggests that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may disrupt glucose homeostasis through mechanisms involving pancreatic β-cell dysfunction, insulin resistance, chronic inflammation, oxidative stress, mitochondrial dysfunction, and hypoxia-related signalling. This review summarizes current evidence regarding the molecular and cellular mechanisms linking SARS-CoV-2 infection to impaired insulin secretion and post-COVID metabolic disturbances. Particular emphasis is placed on the regulation of insulin secretion, β-cell compensation and failure, oxidative stress, inflammatory signalling, mitochondrial dysfunction, and the development of the post-COVID metabolic phenotype. Emerging evidence indicates that persistent metabolic abnormalities after COVID-19 may range from transient dysglycaemia to new-onset diabetes mellitus and metabolic syndrome. The review also discusses clinical implications, biomarkers, therapeutic perspectives, and unresolved questions regarding the reversibility of post-COVID β-cell dysfunction. A better understanding of the mechanisms underlying post-COVID metabolic dysfunction may improve risk stratification, facilitate early intervention, and support development of targeted therapeutic strategies aimed at preserving β-cell function and long-term metabolic health. Full article

Background/Objectives: Hypoxic–ischemic encephalopathy (HIE) remains a leading cause of neonatal death and long-term neurodisability. Cardiovascular dysfunction commonly accompanies HIE and influences neurological recovery, yet it is difficult to recognize because conventional clinical signs correlate poorly with the myocardial function. This review summarizes the current evidence on cardiac dysfunction in neonates with HIE, with particular attention to the diagnostic tools used to evaluate cardiovascular function and the clinical outcomes associated with myocardial injury. Methods: We conducted a narrative review informed by a systematic, reproducible search of four databases (PubMed, Embase, Cochrane Library, and Web of Science) from 2000 to 2026, using a Population–Concept–Context framework to identify studies of term and near-term neonates (≥35 weeks gestation) with HIE or perinatal asphyxia reporting a measure of cardiac dysfunction (electrocardiographic, biomarker, echocardiographic, or clinical hemodynamic). Records were screened and selected using Covidence systematic review software; eligible studies were original, primary-data reports with a minimum sample size of 20 neonates. Findings were synthesized narratively and grouped thematically by diagnostic modality and clinical outcome. Results: Eighty-one studies met the pre-specified eligibility criteria following a systematic multi-database search and structured screening process. As a subset of included studies are secondary analyses or overlapping cohorts, an aggregate patient count is not reported. Reported prevalence of cardiac dysfunction varied widely with the definition applied, reaching 70–90% when multimodal assessment was used. Blood pressure, heart rate, urine output, and lactate were inconsistently reliable indicators of myocardial function, and ejection fraction and fractional shortening often remained normal despite injury. Tissue Doppler imaging and speckle-tracking strain appeared to be more sensitive, with impaired left ventricular global longitudinal strain associated with brain injury. The right ventricle was more commonly and severely affected, with reduced TAPSE, RV fractional area change, and RV strain associated with death or MRI-defined injury. Each additional 24 h of inotropic support was associated with increased odds of adverse short- and long-term outcomes by 14% and 12%, respectively. Conclusions: Cardiac dysfunction is common and prognostically important but remains underrecognized by the routine clinical assessment and cardiovascular monitoring. Multimodal monitoring centered on TnECHO, near infra-red spectroscopy, and specific biomarkers may enable earlier, physiology-based management. 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

COVID-19, caused by the SARS-CoV-2 coronavirus, emerged as a global pandemic in 2020, with profound impacts on public health and a substantial burden on intensive care units (ICUs). Because critically ill patients are at increased risk of rapid clinical deterioration and death, there is a need for reliable early prognostic biomarkers to support risk stratification and optimize clinical management. Therefore, this study investigated whether clinical and laboratory parameters obtained at hospital admission could serve as prognostic biomarkers for endotracheal intubation and mortality in critically ill patients with COVID-19 admitted to ICUs. A retrospective analytical study was conducted using the medical records of 107 critically ill patients with confirmed COVID-19 admitted to ICUs. Patients were classified according to clinical outcome as survivors (n = 22) or non-survivors (n = 85). Clinical characteristics and laboratory biomarkers obtained at hospital admission were analyzed. D-dimer, lactate dehydrogenase (LDH), red cell distribution width (RDW), the AST/ALT ratio, and age were independently associated with mortality. In addition, D-dimer and LDH demonstrated moderate discriminatory performance for predicting the need for endotracheal intubation. These findings suggest that routinely available clinical and laboratory parameters obtained at hospital admission may serve as complementary prognostic biomarkers to support early risk stratification and clinical decision-making in critically ill patients with COVID-19. Nevertheless, prospective multicenter studies are warranted to externally validate these findings and further establish their clinical applicability. Full article

Patient-reported outcomes (PROs) are often regarded in clinical practice as less reliable than biomarkers because they are patient-dependent and not objectively measurable in the same way as circulating molecules or imaging findings. This view may be reductive. Biomarkers are frequently considered reliable because they are easily quantifiable, yet biological meaning does not depend solely on measurement. In precision and personalized medicine, the absolute level of a circulating hormone, cytokine, or metabolite is often insufficient unless interpreted in relation to how a given patient, at a given time, responds to that molecular signal. If biomarkers are considered less absolute and more context-dependent, the relevance of PROs becomes clearer. Standardized PROs capture the subjective impact of disease on the patient system in a reproducible and clinically interpretable manner. This is particularly relevant in Long COVID, where no validated diagnostic biomarker is currently available, and severity stratification cannot rely exclusively on objective biological measures. Among available tools, the Fatigue Assessment Scale (FAS) may be particularly useful for quantifying the fatigue burden in Long COVID, including both physical and mental components. Moreover, FAS scores have been shown to correlate with the number of symptoms reported by patients, suggesting that this scale may help stratify patients not only according to fatigue severity but also according to overall symptom burden. Greater standardization of clinical characterization through instruments such as the FAS may improve comparability across cohorts and facilitate interpretation of data from different clinical trials. Full article