Search Results (1,149)

Substance Use Disorder (SUD) constitutes a major and persistent global public health burden, accounting for approximately 600,000 deaths annually, largely driven by opioid use. Despite substantial advances in addiction neuroscience, currently approved therapeutic strategies remain limited in efficacy, as they predominantly target isolated neurobiological processes and fail to concurrently address core mechanisms such as glutamatergic hyperactivity, mesolimbic hypodopaminergic, and dysfunction of cortical and executive control networks. This mechanistic fragmentation contributes to persistently high relapse rates and underscores the need for integrative and multitarget therapeutic approaches. Within this context, ibogaine has re-emerged as a clinical candidate due to its distinctive multimodal neuropharmacological profile and its reported capacity to modulate multiple pathways implicated in addictive behaviours. However, the clinical translation of ibogaine remains substantially constrained by fragmented and heterogeneous evidence, the absence of regulatory frameworks in several jurisdictions, limited phytochemical validation and standardization of available formulations, and unresolved concerns regarding cardiac safety. This scoping review critically synthesizes the available preclinical and clinical literature on ibogaine in the treatment of SUD, with particular emphasis on reported effects on withdrawal symptoms and craving, dose–response relationships, and the occurrence of cardiac adverse events. By clarifying the current state of the evidence and delineating key translational constraints, this review defines the conditions under which ibogaine, an indole alkaloid isolated from Tabernanthe iboga Baill. (Apocynaceae), may warrant continued investigation. The hypothesis of a neurobiological “reset”, supported by emerging preclinical and clinical data, positions ibogaine as a compound of relevance in addiction research and highlights the need for rigorous pharmacological, toxicological, and regulatory evaluation to inform safer and more standardized clinical pathways. Full article

RNA viruses pose a persistent global threat due to their high mutation rates, zoonotic potential, and rapid adaptability. Emergence events have risen steadily, as demonstrated by major outbreaks caused by Influenza A, Ebola, Zika, and Chikungunya viruses, followed by the coronavirus epidemics of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV-1) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and culminating in the COVID-19 pandemic. These characteristics frequently compromise the durability of existing vaccines and antiviral therapies, highlighting the urgent need for new antiviral agents. Alkaloids, a structurally diverse class of nitrogen-containing natural compounds, have gained attention for their ability to interfere with multiple stages of the viral life cycle, including entry, replication, protein synthesis, and host immune modulation. To our knowledge, this review compiles all currently reported alkaloids with antiviral activity against RNA viruses and summarizes their proposed mechanisms of action, distinguishing evidence from in vitro, in vivo, and in silico studies. Quaternary alkaloids are discussed separately because their permanent ionic charge enables distinctive interactions with membranes and host pathways. Although many findings are promising, clinical translation remains limited by incomplete mechanistic validation, scarce in vivo data, suboptimal bioavailability, narrow therapeutic windows, and inconsistent experimental methodologies. To advance the field, future research should prioritize RT-qPCR–based antiviral evaluation to accurately quantify viral replication, incorporate mechanistic assays to clarify modes of action, apply structure–activity relationship (SAR) approaches for rational optimization, and expand in vivo pharmacokinetic and efficacy studies to assess therapeutic feasibility. Overall, alkaloids represent a promising yet underdeveloped reservoir for next-generation antiviral discovery against rapidly evolving RNA viruses. Full article

Background/Objectives: Diabetes is major metabolic disorder and rapidly increasing public health problem globally. The greatest way to reduce diabetic complications is adequate knowledge about the condition. Hence, the primary objectives of this study were to evaluate the psychometric properties of the Simplified Diabetes Knowledge Test—Arabic version (SDKT-A) among Iraqi insulin-dependent diabetic patients. Additionally, the secondary objectives were to assess the associated independent variables and the risk of atherosclerosis and cardiovascular risk event by using atherogenic indices and lipid ratios with the SDKT-A. Methods: A cross-sectional, descriptive study was conducted in primary healthcare clinics. The SDKT was translated into Arabic using forward–backward translation, reconciliation, and pilot testing. Thereafter, psychometric properties of the SDKT-A were evaluated depending on different criteria. Atherogenic indices of Castelli risk indices I and II (CRI-I and II), triglyceride/HDL ratio, non-HDL-C ratio, atherogenic coefficient (AC), and triglyceride–total cholesterol–body weight index (TCBI) were calculated using specific formulas. Results: The SDKT-A questionnaire showed acceptable readability and validity. Cronbach’s alpha test (95% confidence interval) was 0.662 (0.59–0.73). The Pearson correlation coefficient of reliability for test–retest was found to be 0.659. The item difficulty index for most items was between 0.237 and 0.877. The point biserial correlation values ranged from 0.028 to 0.535 with Ferguson’s sigma value equal to 0.962. The content validation results showed a significant content validity ratio (CVR) value for most of the questions, ranging from 0.8 to 1. The content validity index (CVI) value for SDKT-A was found to be 0.98, which showed good agreement between experts. In addition, the exploratory factor analysis with promax rotation identified four domains for the final 20 items of the SDKT-A that explained 41.83% of the scale total variance. The mean score of the SDKT-A was 11.09 ± 3.40. The total score of the SDKT-A was positively and significantly correlated with education level (r = 0.322, p < 0.01). In addition, the total scores of the SDKT-A were negatively and significantly correlated with glycemic control, age, CRI-I, CRI-II, triglyceride/HDL ratio, AC, non-HDL-C ratio, and TCBI. Furthermore, the glycemic control (HbA1c) was positively and significantly correlated with the preventive measures factor (r = 0.175, p < 0.05), and were negatively and significantly correlated with the lifestyle and modification factor (r = −0.169, p < 0.05), diet and monitoring factor (r = −0.158, p < 0.05), and awareness factor (r = −0.149, p < 0.05). Conclusions: This study showed acceptable psychometric properties for the SDKT-A, with low levels of knowledge of diabetic disease in the sample population. Finally, comprehensive and interactive educational programs regarding lifestyle and modification, diet, and monitoring and awareness in primary healthcare centers in Iraq are warranted. Full article

DNA nanotechnology offers an unprecedented level of structural programmability for organizing metallic nanoparticles into precisely defined architectures, providing a powerful platform for plasmonic biosensing. In particular, gold and silver nanoparticles assembled on DNA nanostructures enable nanometer-scale control over interparticle distance, orientation, and spatial symmetry, which directly govern collective plasmonic behaviors and optical signal transduction. This review summarizes recent advances in DNA nanostructure-mediated assembly of metal nanoparticles, with an emphasis on design principles and assembly strategies that enable static and dynamic control of nanoparticle organization. Representative examples are discussed to illustrate how well-defined plasmonic assemblies give rise to tunable optical responses, including localized surface plasmon resonance modulation, chiroptical signals, fluorescence enhancement or quenching, and surface-enhanced Raman scattering. The role of structural programmability and stimulus-responsive reconfiguration in translating molecular recognition events into amplified optical outputs is highlighted in the context of biosensing. Finally, current challenges and future perspectives are outlined, focusing on structural robustness, signal reproducibility, and integration toward practical and multiplexed biosensing platforms. Full article

Therapeutic inertia in lipid-lowering treatment remains a striking paradox of modern cardiovascular medicine: at a time when the causal role of LDL-cholesterol in atherosclerotic disease is unequivocal and potent therapies are widely available, a substantial proportion of high- and very-high-risk patients still fail to receive timely treatment intensification. Contemporary European and international data consistently show fewer than one in three patients in secondary prevention achieve guideline-recommended LDL-C targets, revealing a persistent and unacceptable gap between scientific evidence and clinical reality. This narrative review examines therapeutic inertia as a key explanatory framework for this gap, describing its epidemiology, mechanisms, and clinical consequences in secondary cardiovascular prevention. We summarize the main physician-, patient-, and system-level determinants and propose recurrent clinician “phenotypes” of inertia that may help explain why opportunities are missed even in the highest-risk patients. The consequences are profound: therapeutic inertia contributes to what we propose as the conceptual framework of an “avoidable atherosclerotic burden”, the cumulative vascular injury that accrues each period in which LDL-C remains above target, translating into higher rates of avoidable cardiovascular events, and increased healthcare costs. Emerging strategies such as upfront combination therapy, decision-support systems, structured lipid pathways, and the integration of artificial intelligence offer practical tools to shift lipid management from reactive to proactive care. Overcoming therapeutic inertia is therefore not merely a matter of improving process metrics, but a clinical and ethical imperative. Closing the gap between evidence and practice requires transforming optimal lipid management from an exception into a system-level default, ensuring that every patient receives the full benefit of therapies proven to save lives. This work proposes a novel characterization of clinician ‘phenotypes’ and the concept of ‘avoidable atherosclerotic burden’ as a framework to understand and address this gap. Full article

Synucleinopathies, including Parkinson’s disease (PD), are neurodegenerative disorders characterized by aberrant aggregation of α-synuclein (α-syn), a presynaptic protein with an intrinsic disorder nature. The transition of soluble monomers into oligomeric and fibrillar species represents a key molecular event driving neuronal dysfunction and neurodegeneration. Emerging evidence suggests that nutraceuticals, bioactive compounds derived from dietary sources, can modulate α-syn aggregation at multiple conformational stages. Polyphenols, alkaloids, ginsenosides, and food-derived peptides interfere with α-syn structure and assembly, suppressing the formation of toxic oligomer species and promoting the clearance of misfolded assemblies. Despite this potential, clinical translational of nutraceuticals is currently limited by poor systemic bioavailability and restricted central nervous system penetration due to blood–brain barrier constraints, which have largely confined research to preclinical studies. In this context, this review summarizes current knowledge of nutraceutical interventions targeting the conformational landscape of α-syn and highlighting both direct and indirect molecular mechanisms with involved in aggregation-prone species. Furthermore, we critically examine key challenges related to bioavailability and clinical translation, focusing on advanced delivery systems and precision-based approaches to enhance neuroprotective efficacy and support the potential of nutraceuticals as novel or adjunctive therapeutic strategies for PD. Full article

DEAD-box (DDX) RNA helicases are essential regulators of RNA metabolism and gene expression. Among them, DDX10 remains poorly characterized despite growing evidence supporting its involvement in human diseases. This review provides a comprehensive analysis of DDX10, from its structural and functional features to its emerging roles in solid tumors and hematologic malignancies. We discuss how DDX10, through its conserved domains, contributes to pre-rRNA processing, ribosome biogenesis, and cell proliferation, and explore potential links between DDX10 and processes such as liquid–liquid phase separation (LLPS) and epigenetic regulation, which may underlie its roles in cancer cell plasticity and stress response. We argue that the dysregulation of these fundamental cellular processes positions DDX10 as a focal point where aberrant RNA metabolism and altered molecular condensates converge to disrupt transcriptional homeostasis and drive oncogenic transformation. Aberrant DDX10 expression is a recurrent feature across multiple cancers, where it promotes tumor progression, therapy resistance, and poor prognosis. Moreover, DDX10 participates in oncogenic fusion events, most notably the NUP98::DDX10 fusion identified in a subset of acute myeloid leukemias, which drives leukemogenesis by disrupting transcriptional regulation and cellular differentiation. Given its tumor-associated expression and diverse biological functions, DDX10 is increasingly recognized as a potential diagnostic biomarker and a promising target for therapeutic strategies. By consolidating current knowledge under this unifying framework, this review highlights the multifaceted roles of DDX10 in cancer biology, advocating further research into its molecular functions and translational potential. Full article

Intravenous thrombolysis with recombinant tissue-type plasminogen activator (tPA) remains a keystone of acute ischemic stroke treatment but in a subset of patients is complicated by angioedema, a potentially life-threatening adverse event largely mediated by bradykinin signaling. The unpredictable and idiosyncratic nature of this reaction has long suggested an underlying genetic contribution, yet its molecular architecture has remained poorly characterized. We hypothesized that alteplase-associated angioedema represents a multigenic susceptibility phenotype, arising from the convergence of rare genetic variants across multiple interacting physiological systems rather than from a single causal variant. To explore this hypothesis, we performed clinical exome sequencing in a cohort of 11 patients who developed angioedema following alteplase administration. Rather than identifying a shared pathogenic variant, we observed distinct yet convergent patterns of genetic vulnerability, allowing patients to be grouped according to dominant, but overlapping, biological axes. These included alterations affecting bradykinin regulation (e.g., ACE, SERPING1, XPNPEP2), endothelial structure and hemostasis (e.g., VWF, COL4A1), neurovascular and calcium signaling (e.g., SCN10A, RYR1), and vascular repair or remodeling pathways (e.g., PSEN2, BRCA2). Notably, many of the identified variants were classified as Variant of Uncertain Significance (VUS) or likely benign significance in isolation. However, when considered within an integrated, pathway-based framework, these variants can be interpreted as capable of contributing cumulatively to system level fragility, a phenomenon best described as “contextual pathogenicity”. Under the acute biochemical and proteolytic stress imposed by thrombolysis, this reduced physiological reserve may allow otherwise compensated vulnerabilities to become clinically manifest. Together, these findings support a model in which severe alteplase-associated angioedema appears as an emergent property of interacting genetic networks, rather than a monogenic disorder. This systems level perspective underscores the limitations of gene centric interpretation for adverse drug reactions and highlights the potential value of pathway informed, multi-genic approaches to risk stratification. Such frameworks may ultimately contribute to safer, more personalized thrombolytic decision, while providing a conceptual foundation for future functional and translational studies. Full article

The concept of individual cellular intelligence reframes cells as dynamic entities endowed with sensory, reactive, adaptive, and memory-like capabilities, enabling them to navigate lifelong metabolic and extrinsic stressors. A likely vital component of this intelligence system is stress-responsive G protein-coupled receptor (GPCR) networks, interconnected by common signaling adaptors. These stress-regulating networks orchestrate the detection, processing, and experience retention of environmental cues, events, and stressors. These networks, along with other sensory mechanisms such as receptor-mediated signaling and DNA damage detection, allow cells to acknowledge and interpret stressors such as oxidative stress or nutrient scarcity. Reactive responses, including autophagy and apoptosis, mitigate immediate damage, while adaptive strategies, such as metabolic rewiring, receptor expression alteration and epigenetic modifications, enhance long-term survival. Cellular experiences that are effectively translated into ‘memories’, both transient and heritable, likely rely on GPCR-induced epigenetic and mitochondrial adaptations, enabling anticipation of future insults. Dysregulation of these processes and networks can drive pathological states, shaping resilience or susceptibility to chronic diseases like cancer, neurodegeneration, and metabolic disorders. Employing molecular evidence, here, we underscore the presence of an effective cellular intelligence, supported by multi-level sensory GPCR networks. The quality of this intelligence acts as a critical determinant of somatic health and a promising frontier for therapeutic innovation. Future research leveraging single-cell omics and systems biology may unravel the molecular underpinnings of these capabilities, offering new strategies to prevent or reverse stress-induced pathologies. Full article

Background: Artificial intelligence (AI) and machine learning (ML) are increasingly used in the diagnosis and management of bone metastases, spanning lesion detection, segmentation, prognostic modeling, fracture risk assessment, and surgical decision support. However, the literature is heterogeneous and rapidly evolving, making it difficult for clinicians to contextualize these developments. Methods: We performed a narrative review of the literature on AI/ML applications in bone metastasis management, focusing on studies that address clinically relevant problems such as detection and segmentation of metastatic lesions, prediction of skeletal-related events and survival, and support for reconstructive decision-making. We prioritized recent, peer-reviewed work that reports model performance and highlights opportunities for clinical translation. Results: Most published studies center on imaging-based diagnosis and lesion segmentation using radiomics and deep learning, with generally high internal performance but limited external validation. Emerging work explores prognostic models and biomechanically informed fracture risk estimation, yet these remain at an early proof-of-concept stage. Very few frameworks are integrated into routine workflows, and explainability, bias mitigation, and health-economic impacts are rarely evaluated. Conclusions: AI and ML tools have substantial potential to standardize imaging assessment, refine risk stratification, and ultimately support personalized management of bone metastases. Future research should focus on externally validated, multimodal models; development of AI-augmented alternatives to the Mirels score; federated multicenter collaboration; and routine incorporation of explainability and cost-effectiveness analyses. Full article

Background/Objectives: SA001, a mofetil-ester prodrug of rebamipide, was developed to enhance gastrointestinal absorption and systemic exposure, which was confirmed in a prior Phase 1 study. Given the limited efficacy of current symptomatic therapies for primary Sjögren’s syndrome (pSS), this trial aimed to assess whether the improved bioavailability of SA001 could translate into clinical benefits. Methods: This multicenter, randomized, double-blind, placebo-controlled Phase 2a study enrolled adults who met the 2016 ACR–EULAR criteria for pSS. The participants were randomly assigned to one of four groups: SA001 at 360, 720, or 1080 mg/day (administered twice daily for 8 weeks) or placebo. Exploratory ocular assessments included tear break-up time, ocular surface staining, the Schirmer test, and the Standard Patient Evaluation of Eye Dryness. Oral endpoints included unstimulated whole salivary flow and the Xerostomia Inventory. Anti-SSA(Ro) antibodies were assessed both quantitatively and qualitatively. Safety evaluations comprised adverse events (AEs), ophthalmic examinations, laboratory tests, and vital signs. The efficacy outcomes were exploratory, and this study was not powered to formally test efficacy hypotheses. Results: Twenty-eight women (mean age 58.54 ± 9.29 years; range 41–75 years) were enrolled in this study and randomly assigned to one of the study groups. SA001 showed no statistically significant improvements versus placebo in ocular or oral endpoints, and no consistent dose–response relationship was observed. The anti-SSA(Ro) findings did not differ meaningfully across the groups. SA001 was generally well-tolerated, with infrequent, mostly mild-to-moderate AEs; however, one serious AE occurred in the placebo group. No clinically relevant ophthalmic or laboratory safety signals were detected. Conclusions: Despite the fact that markedly increased systemic exposure has been demonstrated previously, SA001 did not improve the dryness outcomes in pSS. These findings suggest that systemic exposure alone may be insufficient in established glandular disease and highlight the need for tissue-exposure-driven strategies and biomarker-informed patient selection in future studies. Predefined primary efficacy endpoints and objective, gland-proximal measures of target engagement (e.g., standardized salivary gland ultrasonography and salivary or tear fluid biomarker assessments) may help to better interpret local pharmacodynamic activity and the likelihood of a clinically meaningful benefit. Full article

Chilli veinal mottle virus (ChiVMV) is an important potyvirus that poses a serious threat to crop production. In this study, small RNA sequencing and molecular cloning were used to obtain the complete genome sequence of a ChiVMV isolate identified in pepper plants in Sichuan (SC1 isolate). Molecular evolutionary and phylogenetic analysis of SC1 and 35 ChiVMV isolates revealed four clades of ChiVMV isolates. Recombination analysis found 23 recombinant events and 28 recombinants, with the SC1 isolate arising from the recombination of the PK isolate from Pakistan and the YNpe isolate from Yunnan, China. A full-length infectious cDNA clone of ChiVMV was constructed and demonstrated to be infectious in both Nicotiana benthamiana and pepper plants. Moreover, a Myc-tag was inserted after NIb, and the derived infectious clone of ChiVMV remained infectious, and NIb-Myc was readily expressed in infected host plants. These reverse genetic tools will promote the study of the function of ChiVMV-encoded proteins, especially the NIb protein, and facilitate basic and translational studies of ChiVMV. Full article

We developed OmicIntegrator, a broadly adaptable pipeline designed to standardize and integrate publicly available transcriptomic, proteomic, and phosphoproteomic datasets. We applied this workflow to Arabidopsis thaliana etiolated seedlings to identify protein kinases and phosphatases relevant to skotomorphogenic development, a phase during which seedlings rely on tightly regulated signaling networks to ensure survival in darkness. This meta-analysis provided a comprehensive view of gene and protein expression, revealing discrepancies between transcript and protein abundance, suggesting post-transcriptional and post-translational regulation. By integrating multiple datasets, OmicIntegrator reduces experimental bias and enables the detection of phosphorylation events that may be missed in single-condition studies. Distinct phosphorylation patterns were detected across different protein kinase families. Motif enrichment analysis showed a strong overrepresentation of RxxS motifs among phosphosites in protein phosphatases and microtubule-associated proteins, consistent with potential regulation by calcium-dependent protein kinases (CPKs). Across omics layers, CPK3 and CPK9 repeatedly emerged as prominent candidates, highlighting them as priorities for future functional studies in skotomorphogenesis. Overall, our results demonstrate the power of OmicIntegrator as a flexible framework to contextualize signaling landscapes and identify robust patterns and candidate genes and for generating testable hypotheses from integrated multi-omics data in plant developmental biology. Full article

Despite major advances in guideline-directed cardiovascular therapy, residual cardiovascular risk persists, partly driven by oxidative stress, chronic inflammation, endothelial dysfunction, and mitochondrial injury not fully addressed by current drugs. Translation of plant-based cardioprotectants is constrained by preparation-dependent variability in extract chemistry (plant part/cultivar/processing and extraction method), low and variable systemic exposure for key actives (notably curcuminoids and many polyphenols), and clinically relevant safety/interaction considerations (e.g., hepatotoxicity reports with concentrated green tea extracts and antiplatelet-related bleeding-risk considerations for some botanicals). We therefore provide a mechanism- and translation-oriented synthesis of evidence for cardioprotective botanicals, chosen for long-standing traditional use and scientific validation with reproducible experimental data and, where available, human studies, including Crataegus monogyna, Allium sativum, Olea europaea, Ginkgo biloba, Leonurus cardiaca, and Melissa officinalis. Across studies, polyphenols (especially flavonoids and phenolic acids) and organosulfur compounds are most consistently associated with cardioprotection, while terpene-derived constituents and secoiridoids contribute mechanistically in plant-specific settings (e.g., Ginkgo and Olea). Predominantly in experimental models, these agents engage redox-adaptive (Nrf2), mitochondrial (mPTP), endothelial, and inflammatory (NF-κB) pathways, with reported reductions in ischemia–reperfusion injury, oxidative damage, and apoptosis. Clinical evidence remains heterogeneous and is largely confined to short-term studies and surrogate outcomes (blood pressure, lipids, oxidative biomarkers, endothelial function), with scarce data on hard cardiovascular endpoints or event reduction. Priorities include standardized, chemotype-controlled formulations with PK/PD-guided dosing and adequately powered randomized trials that assess safety and herb–drug interactions. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article