Search Results (119)

Cardiomyopathies are traditionally classified by structural and genetic phenotypes, but emerging evidence highlights chronic myocardial inflammation as a pivotal driver of disease progression across different etiologies. This review synthesizes the current literature on the cellular and molecular inflammatory mechanisms underlying hypertrophic cardiomyopathy, Anderson–Fabry disease, cardiac amyloidosis, arrhythmogenic cardiomyopathy, and dilated cardiomyopathy. Across these distinct conditions, endogenous triggers such as metabolic substrates, misfolded amyloid fibrils, mechanical stress, or viral genomes act as damage-associated molecular patterns. These stimuli activate innate and adaptive immune cascades, notably the Toll-like receptors, the NF-κB pathway, and the NLRP3 inflammasome. This immune activation establishes a pro-inflammatory microenvironment that promotes fibroblast reprogramming, myocardial edema, and progressive fibrotic or fibro-fatty remodeling. Inflammation is an active, core pathophysiological mechanism rather than a passive secondary bystander in cardiomyopathies. Recognizing these shared immune pathways provides a framework for improved risk stratification and highlights the potential for targeted immunomodulatory therapies to alter disease trajectories. Full article

Background: Monoclonal antibodies (mAbs) initially played a major role in outpatient COVID-19 management by providing rapid passive immunity and reducing progression to severe disease. However, continuous SARS-CoV-2 evolution progressively compromised the effectiveness of several anti-spike products. This narrative review summarizes the trajectory of COVID-19 mAbs across three phases: early clinical efficacy, loss of efficacy due to immune escape, and future directions. Methods: We conducted a narrative review focusing on mechanisms of action, pivotal clinical trials, and real-world effectiveness of neutralizing anti-spike mAbs and host-directed immunomodulatory mAbs. Emphasis was placed on the impact of variants—especially Omicron—on susceptibility and clinical use, as well as on emerging next-generation platforms. Results: First-generation neutralizing mAbs substantially reduced the hospitalization rates during the Alpha and Delta waves, while immunomodulatory mAbs became standard options for the hyperinflammatory phase in hospitalized patients. With the emergence of Omicron and its sub-lineages, extensive immune escape led to marked reductions in neutralization for many earlier anti-spike agents and consequent restrictions in use. Later-generation approaches targeting more conserved epitopes provided temporary solutions but were also challenged by ongoing antigenic drift. Host-directed immunomodulators retained clinical relevance because their mechanism is independent of viral spike mutations. Conclusions: The clinical role of monoclonal antibodies in COVID-19 has been dynamic and increasingly constrained by viral evolution. Future strategies should prioritize broadly neutralizing antibodies targeting conserved epitopes, innovative delivery platforms, and integration with real-time surveillance to preserve clinical utility in the endemic phase and improve preparedness for future outbreaks. Full article

Protease inhibitors (PIs) remain important components of HIV-2 treatment, but resistance genotyping is frequently challenging in individuals with low or undetectable plasma viremia. Proviral DNA sequencing may provide access to archived viral variants and improve understanding of long-term resistance and clinical evolution. In this retrospective longitudinal study, 27 individuals with HIV-2, both ART-experienced and ART-naïve, followed at a hospital in Lisbon, were analyzed. The HIV-2 protease gene was amplified from peripheral blood mononuclear cell-derived proviral DNA, cloned, and sequenced (Sanger sequencing) at baseline and, for ART-treated participants, after eight years of follow-up. Resistance profiles were interpreted using the Stanford HIVdb, HIV-2EU, and Rega algorithms. Clinical data, including ART history, CD4 counts, and plasma viral load, were collected longitudinally. Amino acid diversity was assessed using Shannon entropy, and longitudinal CD4 dynamics were evaluated using mixed-effects models with time-varying ART exposure. Sensitivity analyses were performed using generalized estimating equations (GEE). A total of 222 clonal HIV-2 protease sequences clustered within group A. Major PI resistance mutations were detected in 21.4% of ART-experienced and 23.1% of ART-naïve individuals at baseline. Longitudinal resistance trajectories varied across participants, including persistence, apparent emergence, and non-detection of previously identified mutations. Mixed-effects modeling revealed substantial inter-individual variability in CD4 trajectories, with no statistically significant associations observed between CD4 evolution and ART status, time, or their interaction. GEE analyses yielded consistent results, supporting robustness across modeling frameworks. Entropy analysis identified localized sequence diversity changes restricted to a small number of protease residues, with positions 60 and 75 differing between groups at baseline and position 21 showing longitudinal variation among treated participants. This study demonstrates that proviral DNA sequencing captures archived HIV-2 protease diversity and reveals persistent and dynamic resistance patterns within the viral reservoir. While no population-level association between ART exposure and CD4 trajectory was observed, marked inter-individual variability highlights the complexity of longitudinal immune recovery in HIV-2 infection. These findings support the value of proviral sequencing as a complementary research tool for characterizing long-term viral evolution in settings where plasma-based genotyping is limited. Full article

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disorder arising from the convergence of genetic susceptibility, epigenetic remodeling, environmental exposures, and dysregulated immune networks. Although traditionally characterized by autoantibody production and immune complex mediated tissue injury, advances in genomics, systems immunology, and multi-omics profiling have revealed that lupus represents a multilayered failure of immune homeostasis driven by interconnected molecular circuits. Genetic variants enriched in regulatory immune enhancers establish a permissive transcriptional landscape that sensitizes innate nucleic acid sensing pathways and interferon signaling. Epigenetic remodeling further amplifies inflammatory transcriptional programs, while environmental triggers such as ultraviolet radiation and viral infection initiate bursts of nucleic acid release and immune activation. Defective apoptotic cell clearance, mediated in part by scavenger receptor dysfunction and complement abnormalities, increases the availability of immunogenic nucleic acids that engage pattern recognition receptors and drive chronic type I interferon production. This interferon-dominated environment rewires immune cell metabolism, alters differentiation trajectories of T and B lymphocytes, and sustains autoreactive immune circuits. Emerging multi-omics studies reveal distinct molecular endotypes defined by interferon signatures, metabolic states, and immune cell composition, highlighting the heterogeneity of disease mechanisms across patients. In this review, we integrate genetic, epigenetic, metabolic, and immunological insights to propose a systems-level model of lupus pathogenesis in which defective debris clearance, nucleic acid sensing, interferon amplification, and metabolic reprograming form a self-reinforcing pathogenic network. Understanding this integrated molecular architecture provides a foundation for biomarker-guided therapeutic strategies and precision medicine approaches aimed at disrupting the key nodes that sustain chronic autoimmunity in SLE. Full article

Cancer immunotherapy has transformed oncology by harnessing the immune system to recognize and eliminate malignant cells. However, many cancers exhibit limited or variable responses to this class of treatment due to insufficient antigen presentation and impaired interferon (IFN) signaling, creating an immunologically “cold” tumor microenvironment (TME) characterized by poor immune cell infiltration and treatment resistance. Viral mimicry has emerged as a therapeutic strategy to overcome these limitations by reactivating innate antiviral pathways within tumor cells. Viral mimicry occurs through the reactivation of endogenous retroviruses (ERVs) and other retrotransposons (e.g., LINE-1), which subsequently stimulate downstream nucleic acid sensing pathways. The resulting type I/III IFN responses restore antigen presentation and attract cytotoxic immune cells, sensitizing resistant tumors to immunotherapy. However, systemic stimulation of these pathways can trigger context-dependent inflammation and adaptive resistance, highlighting the need for temporal and spatial control. In this review, we examine the mechanistic foundation and clinical trajectory of viral mimicry, with an emphasis on its potential integration with established treatments and engineered immune cell platforms. By identifying the molecular and clinical gaps, viral mimicry can be harnessed to enhance tumor-specific immune activation and overcome treatment resistance in cancer immunotherapy. Full article

HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are central to modern antiretroviral therapy (ART) because of their high potency and durable effect on viral suppression. However, drug resistance mutations (DRMs) within HIV-1 IN emerge, which can compromise long-term treatment efficacy. Many distinct DRMs that arise under INSTI therapy have been extensively tabulated in public repositories and literature. However, the timelines over which they emerge, accumulate, and consolidate in patients have not been systematically integrated across clinical and experimental studies. In this review, we synthesize current evidence on the temporal evolution of DRMs within HIV-1 IN by examining mutational kinetic data from viruses derived from people living with HIV/AIDS (PLWH) and from in vitro selection experiments. We compare experimental timelines to recent computational predictions derived from Potts-based fitness landscapes coupled with kinetic Monte Carlo simulations and identify reproducible kinetic classes that distinguish fast-, intermediate-, and slow-emerging DRMs. Rapidly emerging DRMs such as E92Q and N155H typically appear early under drug pressure and often represent low-barrier adaptive responses, whereas the most clinically consequential mutations, such as Q148H/K/R, G140A/S, and E138K, arise only after extended therapy and generally require compensatory mutational backgrounds to persist. Although absolute emergence times vary substantially between in vivo and in vitro systems, consistent temporal trends across datasets support the existence of underlying epistatic constraints that shape drug resistance evolution. Understanding DRM timelines is clinically relevant because it provides a framework for interpreting resistance detected at virological failure, informs optimal timing of resistance testing, and may enable earlier identification of high-risk evolutionary trajectories before durable resistance is established. Full article

[Problem] Converting viral tourism popularity into long-term destination sustainability is a central governance challenge in the digital era. [Aim] This study aims to explicitly measure how emotional value mediates the transition from ephemeral online traffic to durable offline place attachment. [Methodology] Adopting a descriptive mixed-methods approach, data were collected through semi-structured interviews with 16 purposively selected participants (including tourists and locals) recruited via on-site intercepts and online snowball sampling. The inclusion criterion required active engagement with Harbin’s digital tourism discourse. Qualitative transcripts were coded using the NVivo 12 software and subsequently converted into panel data. Grey Panel Relational Clustering was then utilized to geometrically track tourist emotional trajectories. [Results] The analysis identified three structural tourist typologies—the Full-Link Empathy Type, Pragmatic Verification Type, and Traffic-Driven Co-conspirator Type—and revealed three corresponding synergistic paths driving online–offline integration: Virtual–Real Isomorphism, Complementarity, and Symbiosis. [Conclusions] The findings demonstrate that sustainable destination resilience depends fundamentally on the qualitative composition of emotional engagement across different tourist types, rather than sheer visitor volume. [Implications] This study contributes an empirically grounded, emotional value-driven framework to sustainable tourism theory, offering differentiated governance strategies for destinations navigating the volatility of platform-driven attention economies. Full article

Gene therapy relies on safe and efficient delivery systems, yet traditional viral vectors and synthetic polymers often fail to meet these requirements due to immunogenicity and biocompatibility concerns. This review highlights self-assembling short peptides as a highly programmable and biocompatible non-viral platform uniquely positioned to overcome these translational bottlenecks. To provide a comprehensive overview of next-generation gene delivery, we systematically trace the trajectory from fundamental chemistry to clinical applications. First, we elucidate the supramolecular interactions and mechanisms driving peptide–nucleic acid co-assembly. Second, we outline concrete design strategies, detailing how sequence engineering and environmental responsiveness dictate the formation of optimized nanomorphologies. Third, we critically analyze how these nanocarriers navigate critical physiological and intracellular barriers, with a specific focus on cellular uptake, endosomal escape, and cargo release. Finally, we demonstrate the platform’s versatility in emerging frontiers, particularly mRNA vaccines and CRISPR/Cas9 gene editing. We conclude by identifying current obstacles to clinical translation and proposing future directions centered on multifunctional integration and stimuli-responsive design. Full article

Alzheimer’s disease is a devastating progressive neurodegenerative disorder characterized by extracellular amyloid-beta plaques and intracellular tau tangles. Despite recent advancements in amyloid-beta-targeting immunotherapies, achieving safe and definitive disease control remains a profound clinical challenge. The CRISPR/Cas9 system has emerged as a powerful technology for precision neurogenetics, offering significant potential to address the fundamental questions behind Alzheimer’s disease. This comprehensive review delineates the trajectory of CRISPR applications in Alzheimer’s disease research and therapeutics. First, we explore the integration of CRISPR in engineering high-fidelity in vitro models, such as isogenic induced pluripotent stem cells and three-dimensional cerebral organoids, alongside advanced in vivo mammalian models. Second, we examine how these platforms facilitate unbiased high-throughput genetic screening to uncover molecular underpinnings regulating tau, lipid metabolism, and neuroinflammation. Third, we critically evaluate precision editing strategies targeting core risk genes (APP, MAPT, APOE, and TREM2), explicitly highlighting the severe physiopathological trade-offs between therapeutic efficacy and loss-of-function toxicity. Finally, we address the ultimate translational bottlenecks impeding clinical application. By dissecting the packaging limits of adeno-associated viral vectors and the physical barricade of the blood–brain barrier, we underscore the necessity of transitioning toward next-generation base editors and non-viral lipid nanoparticles to realize safe and efficacious in vivo clinical gene therapies against Alzheimer’s disease. Full article

Background: Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have generated major public health concerns worldwide. Young adults represent a critical group for viral transmission due to their high proportion of asymptomatic infections. Objective: To characterize the dynamics of SARS-CoV-2-specific antibodies in individuals aged 20–29 years from Bogotá, Colombia, across two longitudinal phases. Methods: Phase I assessed seroprevalence, seroconversion, spatial clustering, symptoms associated with seropositivity and antibody kinetics following natural infection. Phase II evaluated vaccine-induced antibodies, immune memory, and neutralizing capacity. Analyses included Functional Principal Component Analysis, survival analysis, clustering, and predictive modeling. Results: In Phase I, a seroprevalence of 15.59% (17/109 participants enrolled) was observed, while seroconversion among those who completed all six sampling points was 30.18% (16/53), with clusters of positive cases in different areas of Bogotá. The symptoms most associated with seropositivity included mucus hypersecretion, fever, and respiratory difficulty. Antibody responses were heterogeneous: naturally infected individuals generally showed high titers during the first 1–2 months, remaining detectable up to 4 months. The reduction in dimensionality suggested dominant humoral patterns, and clustering revealed two immune profiles differing in the risk of seroconversion. Predictive modeling indicated diverse antibody trajectories over 12 months. In Phase II (2024), three long-term immune memory clusters (low, medium, high) were observed; post-vaccination IgG titers were observed, although in most cases they lacked neutralizing activity. Conclusions: This longitudinal exploratory observational study provides an initial characterization of antibody dynamics in young adults, suggesting their potential epidemiological relevance and offering preliminary insights into post-infection and post-vaccination immunity. Full article

Respiratory syncytial virus (RSV), a member of the genus Orthopneumovirus, is an etiological agent in infant lower respiratory tract infections (LRTIs) producing substantial global morbidity. Here, secretoglobin (Scgb1a1)-derived progenitors play a primary role in triggering innate, inflammatory, and cell state transitions in response to RSV LRTIs. Whether RSV activation of innate signaling in this epithelial sentinel population leads to chronic airway disease is unknown. To understand the role of innate signaling in Scgb1a1-derived progenitors, a model of RSV post-viral disease (PVLD) was developed and studied in the presence or absence of RelA conditional knockout (CKO). Single-cell RNA sequencing (scRNA-seq) studies showed that RSV-PVLD induced a transition of atypical, differentiation-intermediate, alveolar type 2 (aAT2) cells characterized by tumor protein 63 (TRP63), aquaporin 3 (AQP3), and Itgβ4 expression, as well as changes in PDGFRβ mesenchyme. A single-cell trajectory analysis and lineage-tracing experiments using Scgb1a1 CreER^TM X mTmG mice demonstrated that the Scgb1a1+ populations were precursors to the aAT2 population. Mechanistically, we found that the formation of the aAT2 population was prevented by RelA CKO. A differential gene expression analysis revealed that RSV-PVLD coordinately upregulates nuclear receptor subfamily 1 group D (Nr1d1/2), clock and basic helix-loop-helix ARNT-like 1 (Bmal) genes both in the aAT2 cell and in its Pdgfrα+ mesenchymal niche in a RelA-dependent manner. A systematic analysis of intercellular epithelial–mesenchymal communication in the scRNA-seq data showed that the clock-dysregulated epithelial–mesenchymal niche produces aberrant ANGPTL4 expression. ANGPTL4 upregulation was confirmed by the measurement of both its mRNA and protein. Moreover, ANGPTL4 is biologically active in the BALF of RSV-PVLD mice, inhibiting lipoprotein lipase activity. We conclude that RSV-PVLD is mediated, at least in part, by RelA signaling in Scgb1a1-derived epithelial progenitors, dysregulating ANGPTL4 signaling in an epithelial–mesenchymal niche, resulting in persistence of atypical alveolar epithelial cells with dysregulated of clock gene expression. Full article

Simian foamy viruses (SFVs) are ancient retroviruses that co-evolve with nonhuman primates (NHPs), although genomic data from Asian and African monkeys are limited. We report the characterization of three new SFV colobine genomes from two Asian species (Trachypithecus francoisi (Tfr) and Pygathrix nemaeus (Pne)) and one African monkey (Colobus guereza, Cgu), obtained via metagenomics analysis of peripheral blood leukocyte tissue culture isolates. Genomic analyses found conserved structural, enzymatic, and auxiliary genes flanked by long terminal repeats, with all major transcriptional and structural motifs highly preserved. An in-frame Δtas mutation in tissue culture and ex vivo specimens was identified in the SFVpne genome, which may promote viral latency. Phylogenetic analyses revealed that these colobine SFVs have distinct evolutionary trajectories without clustering together, contradicting a strict virus–host co-evolution. We developed a new generic SFV PCR assay using these genomes with increased detection sensitivity for Colobinae SFVs and identified four new human infections with Cgu-derived SFV in the Democratic Republic of Congo. Our findings indicate that SFV evolution in colobine monkeys is shaped by host switching, cross-species transmission, and high viral diversity. Our study underscores the importance of broadening SFV genomic sampling to better understand viral evolution, zoonotic risk, and improved diagnostic capabilities. Full article

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) remains a major cause of both community-onset and hospital-acquired infections, yet longitudinal data from Latin American hospitals spanning the COVID-19 pandemic are scarce. We characterized temporal trends, seasonality, and the impact of the COVID-19 pandemic on MRSA prevalence and incidence density among clinical S. aureus isolates at a tertiary-care hospital in western Mexico over 9.5 years. Methods: We analyzed 6625 non-duplicate clinical S. aureus isolates (6609 with valid resistance data) from June 2016 to December 2025. Temporal trends were assessed using Mann–Kendall tests, Theil–Sen estimation, and binomial generalized linear models. Seasonality was evaluated through STL decomposition, generalized additive models, and Fourier analysis. An interrupted time series (ITS) model with GLS-AR(1) and Newey–West corrections compared three COVID-19 phases: pre-pandemic (2016–2020), high viral circulation (2020–2022), and post-peak stabilization (2022–2025). Exposure-adjusted incidence densities (per 1000 patient-days) were analyzed in parallel. Results: MRSA prevalence declined from 28.1% pre-pandemic to 14.0% post-peak (Mann–Kendall z = −9.03, p < 0.001; OR = 0.85 per year, 95% CI: 0.829–0.871). MRSA incidence density decreased by 50%, from 1.27 to 0.63 per 1000 patient-days, while aggregate S. aureus incidence density remained stable (z = −0.17, p = 0.868). The ITS joint Wald test confirmed a significant cumulative shift in MRSA trajectory post-pandemic (p = 0.019 counts; p = 0.012 incidence density), with a significant post-peak level drop (p = 0.008). S. aureus exhibited moderate seasonality peaking in May–July (GAM edf = 7.26, p < 0.001), whereas MRSA showed only marginal seasonal variation. Conclusions: MRSA declined markedly across the study period, with the steepest reduction following the Omicron peak. The decline persisted after adjustment for pandemic-related fluctuations in hospital volume, supporting periodic reassessment of empiric anti-MRSA prescribing policies in similar settings. Full article

Background: Direct-acting antiviral (DAA) therapy has transformed chronic hepatitis C virus (HCV) infection into a curable disease. Beyond viral eradication, increasing attention has been directed toward metabolic changes following sustained virological response (SVR), particularly alterations in lipid metabolism. This study aimed to assess the long-term evolution of lipid parameters after HCV cure in a real-world clinical cohort. Methods: We conducted a prospective, single-center observational study including 85 patients with chronic HCV infection who achieved SVR after DAA therapy. Lipid parameters, including total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides, were assessed at baseline and during post-SVR follow-up at 24, 48, and 96 weeks. Body mass index (BMI) and non-invasive fibrosis indices were also evaluated. Longitudinal changes were analyzed using mixed-effects models. Results: Total cholesterol increased from 157.7 ± 35.6 mg/dL at baseline to 179.6 ± 42.9 mg/dL at SVR 24 and further to 189.0 ± 40.3 mg/dL at SVR 48, stabilizing at 177.7 ± 38.3 mg/dL at SVR 96. LDL-C showed a similar trajectory from 94.6 ± 30.8 mg/dL at baseline to 107.5 ± 33.3 mg/dL at SVR 24, further raising to 115.7 ± 36.2 mg/dL at SVR48, and 111.8 ± 39.5 mg/dL at SVR 96. HDL-C showed minimal change, while triglycerides demonstrated greater interindividual variability without a consistent population-level trend. BMI remained stable over follow-up (26.6 ± 4.7 to 27.6 kg/m²). Linear mixed-effects models confirmed a significant effect of time after SVR on total cholesterol and LDL-C (p < 0.05). Conclusions: In this real-world cohort, HCV cure with DAA therapy was associated with sustained long-term changes in lipid metabolism, characterized by increases in total cholesterol and LDL-C independent of major weight changes. These findings support the importance of continued metabolic monitoring after SVR, particularly in patients with additional cardiometabolic risk factors. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article