Search Results (561)

Schizophrenia is a challenging multifactorial neuropsychiatric disease that involves interactions between genetic susceptibility and environmental insults. Increasing evidence implicates viral infections as significant environmental contributors, particularly during sensitive neurodevelopmental periods. This review synthesises current findings on the viral hypothesis of schizophrenia, encompassing a wide array of neurotropic viruses, including influenza viruses, herpesviruses (HSV-1 and 2, CMV, VZV, EBV, HHV-6 and 8), hepatitis B and C viruses, HIV, HERVs, HTLV, Zika virus, BoDV, coronaviruses (including SARS-CoV-2), and others. These pathogens can contribute to schizophrenia through mechanisms such as direct microinvasion, persistent central nervous system infection, immune-mediated neuroinflammation, molecular mimicry, and the disturbance of the blood–brain barrier. Prenatal exposure to viral infections can trigger maternal immune activation, resulting in cytokine-mediated alterations in the neurological development of the foetus that persist into adulthood. Genetic studies highlight the role of immune-related loci, including major histocompatibility complex polymorphisms, in modulating susceptibility to infection and neurodevelopmental outcomes. Clinical data also support the “mild encephalitis” hypothesis, suggesting that a subset of schizophrenia cases involve low-grade chronic neuroinflammation. Although antipsychotics have some immunomodulatory effects, adjunctive anti-inflammatory therapies show promise, particularly in treatment-resistant cases. Despite compelling associations, pathogen-specific links remain inconsistent, emphasising the need for longitudinal studies and integrative approaches such as viromics to unravel causal relationships. This review supports a “multi-hit” model in which viral infections interfere with hereditary and immunological susceptibilities, enhancing schizophrenia risk. Elucidating these virus–immune–brain interactions may facilitate the discovery of biomarkers, targeted prevention, and novel therapeutic strategies for schizophrenia. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

Adult entertainment work may be associated with increased vulnerability to sexually transmitted infections, particularly HIV. In Brazil, pre-exposure prophylaxis (PrEP) for HIV infection has been available through the Brazilian Unified Health System (SUS) since November 2017, representing a significant advancement in public sexual health policy. The objective of this study was to understand the individual and social determinants that promote PrEP use among adult entertainment workers. This was a cross-sectional, analytical, and quantitative study. A multivariate modeling approach was employed to identify factors independently associated with PrEP use. The study included 254 adult entertainment workers using oral PrEP through the SUS, predominantly young adults (141; 55.5%), SUS users (248; 97.6%), single (213; 83.9%), non-white (142; 55.9%), cisgender (148; 58.3%), and heterosexual (152; 59.8%). Factors independently associated with greater PrEP use included having adult entertainment as the main source of income (aPR: 2.69; 95% CI: 1.86–3.95), prior use of PEP (aPR: 2.49; 95% CI: 1.63–3.81), undergoing any type of health treatment (aPR: 1.56; 95% CI: 1.15–2.12), and having a history of STIs (aPR: 1.51; 95% CI: 1.10–2.08). Conclusion: PrEP use in this population was strongly influenced by structural and contextual factors, indicating that the availability of the technology alone does not ensure its effectiveness. Full article

While viral pathogens are often subdivided into neurotropic and non-neurotropic categories, systemic inflammation caused by non-neurotropic viruses still possesses the ability to alter the central nervous system (CNS). Studies of CNS disease induced by viral infection, whether neurotropic or not, are presented with a unique set of challenges. First, because brain biopsies are rarely necessary to diagnose viral-associated neurological disorders, antemortem tissue samples are not readily available for study and human pathological studies must rely on end-stage, postmortem evaluations. Second, in vitro models fail to fully capture the nuances of an intact immune system, necessitating the use of animal models to fully characterize pathogenesis and identify potential therapeutic approaches. Non-human primates (NHP) represent a particularly attractive animal model in that they overcome many of the limits posed by more distant species and most closely mirror human disease pathogenesis and susceptibility. Here, we review NHP infection models of viruses known to infect and/or replicate within cells of the CNS, including West Nile virus, the equine encephalitis viruses, Zika virus, and herpesviruses, as well as those known to alter the immune status of the brain in the absence of significant CNS penetrance, including human immunodeficiency virus (HIV) in the current era of combination antiretroviral therapy (cART) and the coronavirus of severe acute respiratory syndrome (SARS)-CoV−2. This review focuses on viruses with an established role in causing CNS disease, including encephalitis, meningitis, and myelitis and NHP models of viral infection that are directly translatable to the human condition through relevant routes of infection, comparable disease pathogenesis, and responses to therapeutic intervention. Full article

Background: With a high human immunodeficiency virus (HIV) adult prevalence, people living with HIV (PLHIV) in Botswana continue to experience a high burden of comorbid HIV and cancer. We sought to investigate the trends of acquired immunodeficiency syndrome (AIDS) defining cancers (ADCs), non-AIDS defining cancers (NADCs), and associated risk factors in PLHIV compared with those without HIV. Methods: We analyzed data from adults aged ≥18 years reported in Botswana National Cancer Registry and National Data Warehouse. The crude, age-standardized incidence rate (ASIR), standardized incidence ratios (SIRs) of cancers and time trends were computed. Risk factors were determined using the Cox-regression model. Results: Over a 30-year period, 27,726 cases of cancer were documented. Of these, 13,737 (49.5%) were PLHIV and 3505 (12.6%) were people without HIV and 10,484 (37.8%) had an unknown HIV status. Compared to the HIV-uninfected, the PLHIV had higher and increasing trends in the cancer incidence overall during the study period (from 44.2 to 1047.6 per 100,000; p-trend < 0.001) versus (from 1.4 to 27.2 per 100,000; p-trend < 0.001). The ASIRs also increased in PLHIV for overall ADCs, NADCs and other sub-types like cervical, lung, breast, and conjunctiva cancers (p-trend < 0.001). Further, PLHIV had elevated SIRs for cervical cancer, Kaposi sarcoma in males and some NADCs. The most common risk factors were HIV infection and female sex for ADCs incidence and advanced age and being HIV-uninfected for NADCs incidence. Conclusions: Increasing trends of ADCs and NADCs during ART expansion were observed among PLHIV compared to those without HIV highlighting a greater need for targeted effective prevention and screening strategies including the provision of access to timely HIV and cancer treatment. Full article

In this paper, we develop an HIV/AIDS epidemic model that incorporates a saturated incidence rate to reflect the limited transmission capacity and the impact of behavioral saturation in contact patterns. The model is formulated as a system of seven non-linear ordinary differential equations representing key population compartments. In addition to model formulation, we introduce an optimal control problem involving three control measures: educational campaigns, screening of unaware infected individuals, and antiretroviral treatment for aware infected individuals. We begin by establishing the positivity and boundedness of the model solutions under constant control inputs. The existence and local and global stability of both the disease-free and endemic equilibrium points are analyzed, depending on the effective reproduction number (${\mathcal{R}}_e$). Bifurcation analysis reveals that the model undergoes a forward bifurcation at ${\mathcal{R}}_e=1$. A local sensitivity analysis of ${\mathcal{R}}_e$ identifies the disease transmission rate as the most sensitive parameter. The optimal control problem is then formulated by incorporating the dynamics of infected subpopulations, control costs, and time-dependent controls. The existence of optimal control solutions is proven, and the necessary conditions for optimality are derived using Pontryagin’s Maximum Principle. Numerical simulations support the theoretical analysis and confirm the stability of the equilibrium points. The optimal control strategies, evaluated using the Incremental Cost-Effectiveness Ratio (ICER), indicate that implementing both screening and treatment (Strategy D) is the most cost-effective intervention. These results provide important insights for designing effective and economically sustainable HIV/AIDS intervention policies. Full article

Background: Vaccines represent one of the most affordable and efficient tools for controlling infectious diseases; however, the development of efficacious vaccines against complex pathogens remains a major challenge. Adjuvants play a relevant role in enhancing vaccine-induced immune responses. One such molecule is interferon-stimulated gene 15 (ISG15), a key modulator of antiviral immunity that acts both through ISGylation-dependent mechanisms and as a cytokine-like molecule. Methods: In this study, we assessed the immunostimulatory potential of ISG15 as an adjuvant in Modified Vaccinia virus Ankara (MVA)-based vaccine candidates targeting Zika virus (ZIKV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Early innate responses and immune cell infiltration were analyzed in immunized mice by flow cytometry and cytokine profiling. To elucidate the underlying mechanism of action of ISG15, in vitro co-infection studies were performed in macrophages. Finally, we evaluated the magnitude and functional quality of the elicited antigen-specific cellular immune responses in vivo. Results: Analysis of early innate responses revealed both platform- and variant-specific effects. ISG15AA preferentially promoted natural killer (NK) cell recruitment at the injection site, whereas ISG15GG enhanced myeloid cell infiltration in draining lymph nodes (DLNs), particularly when delivered via MVA. Moreover, in vitro co-infection of macrophages with MVA-based vaccine vectors and the ISG15AA mutant led to a marked increase in proinflammatory cytokine production, highlighting a dominant role for the extracellular, ISGylation-independent functions of ISG15 in shaping vaccine-induced immunity. Notably, co-infection of ISG15 with MVA-ZIKV and MVA-SARS-CoV-2 vaccine candidates enhanced the magnitude of antigen-specific immune responses in both vaccine models. Conclusions: ISG15, particularly in its ISGylation-deficient form, acts as a promising immunomodulatory adjuvant for viral vaccines, enhancing both innate and adaptive immune responses. Consistent with previous findings in the context of Human Immunodeficiency virus type 1 (HIV-1) vaccines, this study further supports the potential of ISG15 as an effective adjuvant for vaccines targeting viral infections such as ZIKV and SARS-CoV-2. Full article

Psychostimulants such as methamphetamine (Meth) induce high dopamine (DA) levels in the brain, which can modify immune cells expressing DA receptors. This is relevant in conditions of infection with the human immunodeficiency virus (HIV), overlapping with substance use. However, the effects of DA on HIV latency phenotypes are largely unknown. We used single-cell methods and gene network computational analysis to understand these relationships, using the U1 latent promonocyte model to identify signatures of latency and its reversal in the context of DA exposure. Our findings point to mechanisms by which high DA levels in the brains of substance users may impact HIV transcription and neuroinflammation. Our data indicate that latency is maintained along with the expression of histone linkers and components of chromatin organization, with increased metabolic pathways that may lead to pathways in neurodegeneration. DA exposure decreased latency signature genes, histone linkers, and protein-containing complex organization components, unleashing inflammatory pathways and HIV gene transcription. Overall, this work suggests that DA can induce latency reversal through mechanisms that can be harnessed to drive cells. The proposed methods developed here in cell lines can be used to identify latency signatures in other HIV infection systems. Full article

Cardiac glycosides (CGs), a class of plant- and animal-derived compounds historically used to treat heart failure, have garnered renewed interest for their diverse pharmacological properties beyond Na⁺/K⁺-ATPase (NKA) inhibition. Recent studies reveal that CGs modulate key signaling pathways—such as NF-κB, PI3K/Akt, JAK/STAT, and MAPK—affecting processes central to cancer, viral infections, immune regulation, and neurodegeneration. In cancer, CGs induce multiple forms of regulated cell death, including apoptosis, ferroptosis, pyroptosis, and immunogenic cell death, while also inhibiting angiogenesis, epithelial–mesenchymal transition, and cell cycle progression. They demonstrate broad-spectrum antiviral activity by disrupting viral entry, replication, and mRNA processing in viruses such as HSV, HIV, influenza, and SARS-CoV-2. Immunologically, CGs regulate Th17 differentiation via RORγ signaling, although both inhibitory and agonistic effects have been reported. In the nervous system, CGs modulate neuroinflammation, support synaptic plasticity, and improve cognitive function in models of Alzheimer’s disease, epilepsy, and multiple sclerosis. Despite their therapeutic potential, clinical translation is hindered by narrow therapeutic indices and systemic toxicity. Advances in drug design and nanocarrier-based delivery are critical to unlocking CGs’ full potential as multi-target agents for complex diseases. This review synthesizes the current knowledge on the emerging roles of CGs and highlights strategies for their safe and effective repurposing. Full article

There is a significant portion of the South African population with unknown HIV status, which slows down epidemic control despite the progress made in HIV testing. Machine learning (ML) has been effective in identifying individuals at higher risk of HIV infection, for whom testing is strongly recommended. However, there are insufficient predictive models to inform targeted HIV testing interventions in South Africa. By harnessing the power of supervised ML (SML) algorithms, this study aimed to identify the most consistent predictors of HIV testing in repeated adult population-based surveys in South Africa. The study employed four SML algorithms, namely, decision trees, random forest, support vector machines (SVM), and logistic regression, across the five cross-sectional cycles of the South African National HIV Prevalence, Incidence, and Behavior and Communication Survey (SABSSM) datasets. The Human Science Research Council (HSRC) conducted the SABSSM surveys and made the datasets available for this study. Each dataset was split into 80% training and 20% testing sets with a 5-fold cross-validation technique. The random forest outperformed the other models across all five datasets with the highest accuracy (80.98%), precision (81.51%), F₁-score (80.30%), area under the curve (AUC) (88.31%), and cross-validation average (79.10%) in the 2002 data. Random forest achieved the highest classification performance across all the dates, especially in the 2017 survey. SVM had a high recall (89.12% in 2005, 86.28% in 2008) but lower precision, leading to a suboptimal F₁-score in the initial analysis. We applied a soft margin to the SVM to improve its classification robustness and generalization, but the accuracy and precision were still low in most surveys, increasing the chances of misclassifying individuals who tested for HIV. Logistic regression performed well in terms of accuracy = 72.75, precision = 73.64, and AUC = 81.41 in 2002, and the F₁-score = 73.83 in 2017, but its performance was somewhat lower than that of the random forest. Decision trees demonstrated moderate accuracy (73.80% in 2002) but were prone to overfitting. The topmost consistent predictors of HIV testing are knowledge of HIV testing sites, being a female, being a younger adult, having high socioeconomic status, and being well-informed about HIV through digital platforms. Random forest’s ability to analyze complex datasets makes it a valuable tool for informing data-driven policy initiatives, such as raising awareness, engaging the media, improving employment outcomes, enhancing accessibility, and targeting high-risk individuals. By addressing the identified gaps in the existing healthcare framework, South Africa can enhance the efficacy of HIV testing and progress towards achieving the UNAIDS 2030 goal of eradicating AIDS. Full article

Inflammation is a crucial component of the immune response essential for host defense and tissue repair. However, when the immune response becomes dysregulated, it can contribute to the pathogenesis of chronic diseases. While acute inflammation is a short-lived, protective response, chronic inflammation is sustained over time and can lead to immune dysfunction, tissue damage, and disease progression. The chronic inflammation theory of disease suggests that persistent immune activation/inflammation underlies both infectious and non-infectious conditions and serves as a unifying mechanism across distinct pathological states. In this review article, we argue that human immunodeficiency virus (HIV) infection represents a prime model for studying chronic inflammation, and that despite effective viral suppression with antiretroviral therapy (ART), people living with HIV (PLWH) exhibit persistent immune activation, systemic inflammation, and an increased risk of cardiovascular, metabolic, and neurodegenerative diseases. Here, the interplay between microbial translocation, immune dysregulation, and metabolic reprogramming fuels a state of chronic inflammation that accelerates disease progression beyond HIV itself. Key factors such as T-cell exhaustion, persistent monocyte/macrophage activation, and immunometabolic dysfunction contribute to such a sustained inflammatory state. This review explores the molecular and cellular mechanisms driving chronic inflammation in HIV infection with a focus on immunometabolism and its implications for broader inflammatory diseases. By understanding such pathways, we can identify novel therapeutic targets to mitigate inflammation-driven disease progression not only in HIV but across a spectrum of chronic inflammatory conditions. Full article

HIV, primarily targeting CD4 cells, infiltrates the CNS through various mechanisms, including chemokine-mediated signaling and blood–brain barrier disruption, leading to neuroinflammation and neuronal dysfunction. Viral proteins such as gp120, Tat, and Vpr directly induce neurotoxicity, oxidative stress, and mitochondrial dysfunction, exacerbating cognitive deficits and motor impairments observed in HIV-associated neurocognitive disorders (HANDs). Host genetic factors, including CCR5 mutations and HLA alleles, influence susceptibility to HIV-related neurologic complications, shaping disease progression and treatment responses. Advanced molecular and bioinformatics techniques, from genome sequencing to structural modeling and network analysis, provide insights into viral pathogenesis and identify potential therapeutic targets. These findings underscore the future potential of precision medicine approaches tailored to individual genetic profiles to mitigate neurologic complications and improve outcomes in HIV-infected populations. This comprehensive review explores the intricate interplay between HIV infection and neurogenetics, focusing on how the virus impacts the central nervous system (CNS) and contributes to neurocognitive disorders. This report delves into how the virus influences genetic expression, neuroinflammation, and neurodegeneration, offering insights into molecular mechanisms behind HAND. Full article

Emerging but limited evidence suggests that HIV infection does not affect in-hospital COVID-19 mortality, regardless of the prevalence of HIV infection in most parts of sub-Saharan Africa, especially the southern Africa region, and Zambia, Lusaka District in particular, is not an exception. Therefore, this study aimed to determine the effect of HIV infection, demographics, and clinical factors on mortality among hospitalized COVID-19 patients at Levy Mwanawasa University Teaching Hospital (LMUTH). A cross-sectional study was conducted with a sample size of 698 adults admitted for COVID-19 at LMUTH from 18 March 2020 to 31 December 2021. For all statistical analysis of data, STATA statistical software, version 15 MP (College Station, TX 77845, USA) was used—ensuring that appropriate statistical techniques were applied to the data. Unadjusted and adjusted logistic regressions were conducted to model COVID-19 mortality among COVID-19 patients based on their HIV status while controlling for five predictor variables. Based on the results, the best predictors of in-hospital COVID-19 mortality were HIV status, number of comorbidities, age in years, smoking, and alcohol intake. The results suggest that COVID-19 mortality among those with HIV and those without HIV infection was different. People living with HIV infection had increased odds of COVID-19 mortality compared to those without HIV. The results further suggested that a unit increase in age was associated with increased odds of COVID-19 mortality. Furthermore, drinking alcohol and having two or more comorbidities increased the odds of COVID-19 mortality compared to not drinking alcohol, having no comorbidity, or having a comorbidity. This study, therefore, concludes that HIV infection has a significant effect on COVID-19 mortality among patients hospitalized at LMUTH and that the proportion of COVID-19 mortality in the HIV-infected group is relatively higher than in the uninfected group. Therefore, there is a need for close monitoring of COVID-19 patients with HIV infection. Full article

During 2022, AIDS claimed a life every minute and about 9.2 million HIV-infected people were not on treatment. In addition, a person living with HIV is estimated to be 20–30 times more susceptible to developing active tuberculosis. Every year, 130,000 infants are newly infected, with vertical transmission being the main cause of pediatric HIV infection. Thus, the development of an effective, safe, and accessible vaccine for neonates and/or adults is an urgent need to prevent or control HIV infection or progression to AIDS. An effective HIV vaccine should induce long-lasting mucosal immunity, broadly neutralizing antibodies, innate immunity, and robust stimulation of CD4+ and CD8+ T-cell responses. Recombinant BCG is a promising live-attenuated bacterial vaccine vector because of its capacity to stimulate T-cell immunity. As a slow-growing microorganism, it provides prolonged low-level antigenic exposure upon infecting macrophages and APCs, potentially stimulating both effector and memory T-cell responses. BCG is considered safe and is currently administered to 80% of infants in countries where it is part of the national immunization program. Additionally, BCG offers several benefits as a live vaccine vehicle since it is cost-effective, easy to mass-produce, and heat stable. It is also well-suited for newborns, as maternal antibodies do not interfere with its efficacy. Furthermore, BCG has a strong safety profile, having been administered to over three billion people as a TB vaccine. In this review, we provide an extensive summary of the literature relating to immunogenicity studies in animal models performed since 2011. Moreover, we provide a comprehensive analysis of the key factors influencing the design of recombinant BCG as a live vaccine vehicle: (i) expression vectors; (ii) selection of HIV immunogen; (iii) promoters to regulate gene expression; (iv) BCG strain and BCG codon optimization; (v) genetic plasmid stability; (vi) influence of preexisting immunity, route, and dose immunization; and (vii) safety profile. Full article

Over the past two decades, metabolic syndrome (MetS) and infections caused by multidrug-resistant (MDR) pathogens have emerged as converging global health challenges. Traditionally investigated as separate entities, accumulating evidence increasingly supports a bidirectional relationship between them, mediated by chronic inflammation, immune dysregulation, gut microbiota alterations, and antibiotic-driven expansion of the resistome. This narrative review examines the complex immunometabolic interplay linking MetS and MDR infections, focusing on molecular mechanisms, clinical implications, and prospective research directions. A systematic literature search was conducted using major databases, including PubMed and Scopus, targeting studies from the last 15 years that explore the interface between metabolic dysfunction and antimicrobial resistance. Particular attention is given to key immunometabolic pathways such as the IRS–PI3K–AKT–mTOR axis; the contribution of visceral adiposity and Toll-like receptor (TLR)-mediated inflammation; and the role of gut dysbiosis in augmenting both susceptibility to infections and metabolic derangements. Evidence is presented supporting the hypothesis that MetS increases host vulnerability to MDR pathogens, while chronic MDR infections may reciprocally induce systemic metabolic reprogramming. Viral infections with established metabolic sequelae (e.g., HIV, hepatitis C virus [HCV], and cytomegalovirus [CMV]) are also considered to broaden the conceptual framework. Although current data remain largely associative and fragmented, the emerging MetS–MDR syndemic model poses substantial challenges for translational research, antimicrobial stewardship, and personalized therapeutic strategies. Recognizing this reciprocal relationship is pivotal for refining infection risk stratification, optimizing treatment, and informing public health policies. Further investigations are warranted to elucidate the magnitude and directionality of this association and to identify predictive immunometabolic biomarkers that may guide targeted interventions in high-risk populations. Full article