Zainab H. Athab

Purpose Sex hormone-binding globulin (SHBG), which regulates androgen and estrogen bioavailability, has been linked to cognitive decline, but its relationship with temporal lobe changes—an area vulnerable in early Alzheimer’s disease (AD)—remains unclear. This study aimed to investigate whether plasma SHBG levels are associated with temporal lobe volume and cognitive performance across the cognitive spectrum from normal aging to AD. Methods Participants included individuals with AD (n = 85), mild cognitive impairment (MCI; n = 304), and cognitively normal controls (CN; n = 50). Cognitive performance was assessed using the ADAS-Cog 11, MMSE, and CDR-SB. Temporal lobe volumes were derived from MRI scans using tensor-based morphometry (TBM), and plasma SHBG levels were measured using a validated immunoassay. Multiple regression analyses adjusted for age, sex, education, handedness, and APOE ε4 status were conducted, followed by mediation analysis to test indirect effects through temporal lobe volume. Results After covariate adjustment, elevated plasma SHBG levels were significantly associated with reduced temporal lobe volume in the MCI group. Across both MCI and AD participants, greater temporal lobe volume correlated with better cognitive performance on all tests. Mediation analysis indicated that in MCI, the relationship between higher plasma SHBG and poorer cognitive outcomes was significantly mediated by reduced temporal lobe volume. Conclusion These findings suggest that elevated SHBG may contribute to early cognitive impairment in MCI through its impact on temporal lobe integrity, highlighting SHBG as a potential target in the prodromal stages of AD.

Background Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating innovative therapeutic strategies. Nanoparticles have emerged as a promising tool due to their unique properties, including tunable size, high surface area, drug-loading capacity, and the ability to be functionalized for targeted delivery. Their potential in early detection, precise drug delivery, and localized therapy positions them as a transformative approach in CVD management. Objectives This review explores the latest advancements in nanoparticle-based interventions for CVDs, focusing on their role in targeted drug delivery, diagnostic applications, and therapeutic efficacy. We highlight how engineered nanoparticles can improve specificity, reduce systemic side effects, and enhance treatment outcomes. Methods We examine preclinical and clinical studies on lipid-based, polymeric, and inorganic nanoparticles optimized for CVD treatment. Their surface modifications, drug-release mechanisms, and targeting capabilities are analyzed, with particular attention to their applications in atherosclerosis, myocardial repair, and inflammation modulation. Results Functionalized nanoparticles demonstrate significant potential in CVD therapy by enabling site-specific drug delivery to atherosclerotic plaques, damaged cardiac tissue, and inflamed vasculature. Chemical and natural-based nanoparticles show enhanced therapeutic precision, with improved outcomes in drug bioavailability and reduced off-target effects. Conclusion Nanotechnology is reshaping CVD treatment through advanced drug delivery systems that enhance precision and efficacy. By leveraging nanoparticle engineering, future therapies can achieve targeted, personalized interventions, offering new hope in combating CVDs. Graphical abstract

Macrophage migration inhibitory factor (MIF) is one of several pivotal cytokines that contribute to orchestrate immune regulation, inflammation, and cell survival, acting as both a mediator of host defense and a factor that viruses can exploit. This comprehensive review delineates MIF’s multifaceted roles across diverse viral families—including Retroviridae, Herpesviridae, Hepadnaviridae, Orthomyxoviridae, Flaviviridae, Paramyxoviridae, and Picornaviridae—highlighting the mechanisms by which viruses manipulate MIF signaling to promote replication, evade immune responses, and induce tissue damage. Viral strategies often involve upregulating MIF expression or leveraging its receptor pathways, such as CD74, CXCR2, and CXCR4, to enhance viral persistence, disrupt barrier integrity, and skew immune polarization toward pro-viral or immunosuppressive states. Conversely, MIF-driven inflammation can worsen pathogenic processes, including cytokine storms, neuroinvasion, and fibrosis, contributing to disease severity. Notably, pharmacologic inhibition of MIF has shown promise in preclinical models, reducing viral replication and mitigating tissue damage, thereby positioning MIF as one of the compelling targets for host-directed antiviral therapies. Understanding the complex, context-dependent functions of MIF in viral infections provides transformative insights for innovative treatment strategies that aim to disrupt virus-host interactions, control inflammation, and improve clinical outcomes in infectious diseases. Future research exploring MIF’s molecular interactions, genetic variations, and therapeutic modulation will be crucial for harnessing its potential for personalized, effective antiviral interventions.

Background Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. Objectives This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. Methods Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. Results A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. Conclusion ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.