Search Results (11,970)

This study investigates the synergistic therapeutic potential of Rivastigmine (RVG) and Epigallocatechin (EGC) in Alzheimer’s disease (AD), a multifactorial neurodegenerative disorder characterized by neuroinflammation, oxidative stress, and dysregulated signaling pathways. Conventional therapies primarily provide symptomatic relief and target limited pathways, highlighting the need for multi-target strategies with improved efficacy and safety. An integrated in silico approach combining pharmacokinetic evaluation, network pharmacology, molecular docking, and molecular dynamics simulations is used to determine the synergistic potential of RVG and EGC. Pharmacokinetic analysis indicates favorable drug-likeness and acceptable ADME/Tox profiles for both compounds. Network pharmacology identified 146 overlapping targets associated with AD, highlighting key hub genes including NFKB1, MAPK1, STAT1, PRKACA, GRB2, LYN, and PTPN11, which are involved in neuroinflammation, synaptic signaling, and neuronal survival. Functional enrichment analysis indicated significant involvement of MAPK/ERK signaling and immune-regulatory pathways. Importantly, the PD-1/PD-L1 signaling pathway is identified as a novel mechanism connecting neuroimmune modulation with intracellular kinase-driven neurodegeneration. Molecular docking studies showed strong binding affinities of RVG and EGC toward key AD-related targets, particularly MAPK1, supported by stable hydrogen bonding and interaction profiles. Molecular dynamics simulations confirmed stable protein-ligand interactions, with EGC contributing structural stability and RVG exhibiting adaptive flexibility within the binding pocket. These results suggest that the RVG-EGC combination exhibits synergistic potential by simultaneously modulating neuroinflammatory, oxidative stress, and kinase-mediated signaling pathways. The integration of PD-1/PD-L1 and MAPK/ERK signaling provides a novel mechanistic pathway for multi-target therapeutic intervention in AD. Full article

Lung, breast, colorectal and prostate cancer account for over 41% of global cancer incidence and 39% of mortality, yet durable control of advanced disease remains limited. Plant secondary metabolites are promising multitarget leads, but their polypharmacological mechanisms cannot be captured by single-target approaches, and the evidence across these four cancers has not been synthesised within a unified framework. This review provides an integrated comparative analysis of network-pharmacology studies of plant-derived anticancer compounds across the four cancers, cataloguing phytochemical profiles, identifying shared and cancer-specific targets, quantifying the concordance between computational predictions and experimental validation, and appraising the translational gap. A systematic search of biomedical databases (2016–2026) identified 101 peer-reviewed studies (40 breast, 33 colorectal, 24 lung, and 14 prostate) combining network pharmacology with experimental validation. AKT1, EGFR, TP53, STAT3, MAPK1/3, CASP3, and HSP90AA1 recurred as cross-cancer hub genes, with the phosphoinositide 3-kinase/AKT and mitogen-activated protein kinase pathways most frequently implicated. Cancer-specific signatures comprised the androgen receptor in prostate, the oestrogen receptor and human epidermal growth factor receptor 2 in breast, β-catenin/Wnt in colorectal, and the epidermal growth factor receptor/RAS axis with epithelial-to-mesenchymal transition effectors in lung cancer. Flavonoids, terpenoids, alkaloids, and polyphenols predominated. The persistent validation gap remains the principal barrier to translation. Full article

Huntington’s disease (HD) has traditionally been conceptualized as a neuron-centric disorder primarily attributed to cell-autonomous toxicity of mutant huntingtin (mHTT) in striatal medium spiny neurons. However, this framework inadequately explains the prolonged presymptomatic phase, selective network vulnerability, early non-motor manifestations, and limited success of neuron-targeted therapeutic interventions. Accumulating evidence from molecular biology, transcriptomics, neuroimaging, and preclinical therapeutics supports a reframing of HD as a disorder of neuroglial systems dysfunction. We synthesize data demonstrating that astrocytes, microglia, and oligodendrocyte lineage cells are not passive bystanders but play direct and interactive roles in HD pathogenesis through defined molecular mechanisms. Expression of mHTT in glial populations impairs synaptic homeostasis, metabolic coupling, immune resolution, and myelin integrity, generating self-amplifying pathological feedback loops that destabilize neural circuits long before overt neuronal death. Critically, we evaluate glial replacement therapy as a potential disease-modifying strategy. Preclinical studies demonstrate that transplantation of healthy human glial progenitor cells substantially ameliorates motor, cognitive, and neuropathological deficits in multiple HD models through oligodendroglial remyelination and lactate-mediated metabolic support, despite persistent neuronal mHTT expression. Effective HD therapy will likely require strategies that jointly target the genetic cause and the dysfunctional neuroglial microenvironment. By integrating systems neuroscience with glial biology and translational strategy, this review defines a neuroglial framework for HD that opens a plausible path toward meaningful disease modification and positions HD as a model disorder for glial-centric interventions in neurodegeneration. Full article

The increasing threat posed by wheat rust diseases caused by Puccinia spp. necessitates the development of resistance strategies that extend beyond conventional race-specific mechanisms. Although recent reviews (2023–2025) have emphasized gene discovery and genomic approaches, comparatively less attention has been given to the potential roles of metabolic regulation and micronutrient homeostasis in host–pathogen interactions. Here, we present a narrative synthesis of current evidence and propose a conceptual framework in which induced mutagenesis (ethyl methanesulfonate, EMS, and γ-irradiation) serves as a tool for investigating interactions among redox regulation, iron (Fe) homeostasis, and disease resistance. A key component of this framework is the proposed interplay between reactive oxygen species (ROS) signaling and Fe partitioning. Vacuolar iron transporters (VITs), ferritins, and associated transport networks regulate intracellular Fe distribution and may influence Fe availability at the host–pathogen interface, potentially affecting fungal development and host defense responses. This concept of “iron-withholding immunity” may operate alongside ROS-mediated defense processes, linking metabolism with immune function. Observations from mutant wheat populations are broadly consistent with the hypothesis that these processes may contribute to durable adult-plant resistance (APR), which is characterized by reduced disease development, coordinated defense responses, and relative stability across environments. In some studies, Fe-enriched mutant lines have been associated with enhanced expression of pathogenesis-related genes and the occurrence of combined APR and seedling-resistance phenotypes, suggesting possible links between micronutrient homeostasis and immunity. Integration of high-throughput phenotyping with genotype × environment × time (G × E × T) frameworks may further improve our understanding of quantitative resistance and disease-associated traits. Overall, this review highlights the potential importance of nutrient homeostasis, redox regulation, and susceptibility modulation as components of future research aimed at developing climate-resilient and nutritionally improved wheat cultivars. Full article

Background: Influenza viruses can cause mild to severe illnesses. The burden of disease varies widely depending on multiple factors, including the type and subtype of circulating viruses, timing of the season, flu vaccine efficacy and vaccination rates. Influenza viruses are also highly prone to genetic change and rapid spread due to modern human movement patterns, making influenza surveillance vital for public health awareness, guidance, policy, disease mitigation, and annual recommendations on vaccine composition. Methods: A network of three National Influenza Reference Centers (NIRCs) was established in the United States more than 10 years ago to support the Centers for Disease Control and Prevention’s (CDC) Influenza Division with its national influenza surveillance efforts. Located in California, New York, and Wisconsin, they are funded by CDC via a collaborative agreement with the Association of Public Health Laboratories (APHL). The role of the NIRCs is critical to national and global influenza surveillance, providing rapid information on circulating influenza strains from three arms of laboratory testing: (1) the virus isolation project (VIP), (2) next-generation sequencing (NGS), and (3) anti-viral drug resistance testing. Results: Here, we review the data generated in the VIP lab of the New York State (NYS) NIRC before, during, and after the COVID-19 pandemic and discuss its utility in an understanding of disease dynamics and viral evolution, as well as public health policy and decision making during this historic period in health care. Conclusion: Continued preparedness and surveillance are critical to mitigating the impact of evolving influenza viruses. Full article

Background/Objectives: Accurate prediction of craniofacial growth is essential for establishing orthodontic diagnoses and planning treatment. However, the ultimate extent of jaw growth remains largely judged subjectively based on clinical experience. In this study, we developed a convolutional neural network (CNN) model to predict chronological age from lateral cephalograms and to investigate whether artificial intelligence (AI) can autonomously learn growth-related morphological features. We also reevaluated which anatomical regions are most informative for predicting growth. Methods: We retrospectively analyzed 2116 cephalograms from patients 5–30 years old with malocclusion. After excluding patients with craniofacial syndromes or systemic diseases, 2014 images were used for training and 102 for testing. The mean age of the training dataset was 19.51 years (standard deviation [SD]: 4.71), whereas that of the test dataset was 18.20 years (SD: 7.33). In addition to the entire cephalograms, five regional datasets were generated (mandible, maxilla, cervical vertebrae, frontal region, and cranial base). All images were resized to 256 × 256 pixels and trained with a ResNet50-based CNN. Performance was evaluated using mean absolute error (MAE), Pearson’s correlation coefficient (r), and coefficient of determination (R²). To assess growth-related learning, test data were divided into growth (5–19 years) and post-growth (20–30 years) groups, and age trends were analyzed using a sliding window approach with an 8-year window. Results: The model trained on entire cephalograms achieved high accuracy in the younger group (MAE 1.16 years, r 0.952, R² 0.884), but performance declined markedly in the older group. Among the regional models, accuracy was highest for the mandible, followed by the cervical vertebrae and maxilla. Conclusions: The CNN model predicted chronological age with high accuracy, particularly in patients <20 years old, and may have captured age-associated craniofacial features related to growth. Prediction was most accurate in the mandible and cervical vertebrae—regions clinically used to assess growth. As a proof of concept, this approach may provide a foundation for future studies of craniofacial growth prediction through transfer learning, although further validation is required. Full article

Urgency urinary incontinence (UUI) and Alzheimer’s disease (AD) are highly comorbid conditions in women, but the underlying molecular mechanisms are largely unknown. Therefore, we used network enrichment analyses and an elaborate literature search to integrate the most significant genes from four genome-wide association studies (GWASs) and other genetic, expression and functional evidence into a molecular landscape of female UUI. This molecular landscape centers around local, i.e., bladder-based, processing of the AD-associated amyloid precursor protein (APP). To further elucidate how APP processing is implicated in the comorbidity between UUI and AD, we conducted polygenic risk score (PRS)-based analyses, which showed that genetic risk factors associated with AD and a decreased amyloid beta 42/40 blood level ratio (also) contribute to UUI susceptibility. In conclusion, APP processing constitutes a putative molecular link between UUI and AD, adding further weight to their clinical comorbidity and having implications for the treatment (and prevention) of both traits. Full article

Leaf color in tomato (Solanum lycopersicum L.) is a complex and highly informative trait that reflects pigment metabolism, chloroplast development, genetic regulation, hormonal signaling, and environmental influences. This review synthesizes current knowledge on the biological basis and diversity of tomato leaf coloration, with a particular focus on the roles of chlorophylls, carotenoids, anthocyanins, and flavonoids in generating distinct visual phenotypes. It further discusses the molecular and physiological mechanisms associated with key leaf color types, including dark green, pale green, chlorotic, purple, albino, and variegated leaves, and describes how these phenotypes develop through coordinated regulation of pigment biosynthesis, chloroplast biogenesis, and stress-responsive pathways. The review also summarizes the effects of environmental factors such as light, temperature, water availability, nutrient status, salinity, heavy metals, and biotic stress on leaf pigmentation through changes in photosynthetic efficiency and oxidative balance. In addition, hormonal regulation of leaf color is discussed with emphasis on the roles of abscisic acid (ABA), ethylene (ET), cytokinins (CKs), auxins, jasmonic acids (JA), and salicylic acid (SA) in regulating chlorophyll retention and senescence-associated color transitions. Importantly, leaf coloration functions not only as a morphological trait but also as a sensitive biomarker of plant physiological status, enabling early detection of nutrient deficiencies, abiotic stress, and disease. Recent advances in multi-omics approaches, imaging technologies, and machine learning have significantly improved the understanding of the regulatory networks controlling leaf pigmentation and their relationship with crop performance. However, important gaps remain in integrating molecular mechanisms with whole-plant and field-level responses. Future progress will depend on combining systems biology, high-throughput phenotyping, and predictive modeling to translate leaf color studies into practical applications for improving tomato productivity, stress resilience, and climate adaptation. Full article

The assessment of consciousness has been shaped largely by research on acquired disorders of consciousness after acute or chronic brain injury, but similar problems of unreliable behavioral expression increasingly arise in neurodegenerative disease. This translational overlap is especially relevant when preserved cognition, awareness, or intentionality cannot be reliably expressed because of severe motor impairment, fluctuating arousal, cognitive decline, aphasia, apraxia, or impaired cooperation. In neurodegenerative disease, degeneration of arousal systems, large-scale brain networks, cognition, and motor pathways may similarly make observable behavior an unreliable measure of awareness. The challenge is not only to determine if a patient responds, but also to ask if residual awareness, intentionality, or covert cognition can still be detected through physiological signals. This review discusses how contemporary modalities reshape this assessment. Electroencephalography has moved from a descriptive measure of background activity to a bedside tool capable of probing event-related responses, network organization, and cortical complexity. Magnetic resonance methods reveal altered connectivity within thalamocortical and default mode network systems, while functional near-infrared spectroscopy adds a portable hemodynamic approach that may be repeated at the bedside and integrated with active paradigms. Brain–computer interfaces provide a translational step by converting neural responses into evidence of command following or, in selected patients, into communication, and artificial intelligence strengthens these approaches by extracting clinically meaningful patterns from complex neural and hemodynamic data. Additionally, autonomic measures, including heart rate variability and baroreflex indices, are considered as auxiliary physiological context for arousal and engagement, and not as direct markers of awareness. Because the most mature evidence for covert awareness and cognitive-motor dissociation comes from acquired disorders of consciousness, this review treats brain injury literature as a methodological foundation instead of as directly interchangeable evidence for neurodegenerative disease. It then examines how these approaches may be adapted to neurodegenerative contexts, especially ALS, severe dementia, Lewy body disease with fluctuating cognition, and conditions in which communication or motor output becomes unreliable. Full article

Recent advances in generative artificial intelligence (AI) have shown significant promise for brain magnetic resonance imaging (MRI), enabling applications such as image synthesis, modality translation, reconstruction, super-resolution, segmentation, anomaly detection, and disease identification. This PRISMA-ScR-guided scoping review provides a structured synthesis of recent peer-reviewed studies on generative AI for brain MRI analysis published between January 2024 and March 2026. A total of 43 studies meeting predefined inclusion criteria were analyzed. We review major generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and transformer-based generative models, and summarize their applications across key neuroimaging tasks. We also provide an overview of the publicly available datasets commonly used for model development and evaluation. Beyond reporting performance, this review critically examines the current evidence with respect to reproducibility, external validation, data availability, evaluation validity, data leakage, hallucination and safety risks, and barriers to clinical translation. Although many studies report promising results on retrospective benchmark datasets, external validation, prospective evaluation, reader studies, and clinically oriented assessments remain relatively uncommon. Challenges related to generalization, dataset heterogeneity, computational requirements, privacy, and regulatory considerations continue to limit real-world deployment. Overall, the reviewed literature demonstrates that generative AI has substantial potential to improve brain MRI analysis through realistic data generation, enhanced image quality, and more informative feature representations. However, the current evidence primarily supports technical feasibility and methodological advances rather than established clinical utility. We conclude by identifying key research gaps and future research directions toward more robust, interpretable, reproducible, and clinically translatable generative AI frameworks for brain MRI analysis. Full article

Foodborne pathogens rely on colonization, biofilm formation, virulence expression, and environmental adaptation as fundamental biological drivers of food safety risk. Quorum sensing (QS), a cell-density-dependent microbial communication mechanism, coordinates the expression of these key phenotypes by integrating intraspecies, interspecies, and host-derived signals, making QS an attractive intervention target in food microbial control. Although QS research has advanced considerably in recent years, existing reviews have largely focused on individual bacterial species or specific classes of signal molecules. A systematic integration of how QS coordinately drives both food spoilage and pathogen virulence remains lacking. In this review, we conceptualize the QS network as a central regulatory hub connecting microbial signal perception to hazardous phenotype expression. We systematically examine the mechanistic roles of QS in food spoilage, biofilm formation, host colonization and invasion, and toxin production. We also summarize current QS-targeted intervention strategies, including inhibition of signal synthesis, enzymatic signal degradation, receptor antagonism, and indirect regulation via beneficial microorganisms. Building on the available evidence, we further analyze the key challenges limiting practical application: signal system specificity, ecological safety, industrial-scale feasibility, and microbial adaptability. Overall, QS-based strategies offer a non-bactericidal route for food microbial control, although substantial barriers remain for translation into complex food matrices. Reframing QS function and intervention from the perspective of food safety risk formation provides an analytical framework that bridges mechanistic understanding with practical application. This framework also establishes a theoretical foundation for developing next-generation food preservation and foodborne disease control strategies. Full article

Gastric cancer is a multifactorial disease characterized by complex interactions among transcriptional regulation, inflammation, metabolic adaptation, and angiogenesis. However, systemic molecular relationships linking these processes remain insufficiently understood. This study aimed to evaluate circulating levels of FOXA2 (Forkhead Box A2), Ang-1 (Angiopoietin-1), ApoE4 (Apolipoprotein E4), PEN-2 (Presenilin Enhancer-2), and NF-κB (Nuclear Factor kappa B) in gastric cancer patients and to explore their potential integrated role in disease biology. Serum samples were obtained from 40 patients with gastric cancer (20 preoperative and 20 postoperative) and 20 healthy controls. Protein levels were measured using enzyme-linked immunosorbent assay (ELISA), followed by statistical analysis. Serum levels of FOXA2, ApoE4, PEN-2, and NF-κB were significantly decreased in gastric cancer patients compared with healthy controls, whereas Ang-1 levels were significantly increased. No statistically significant differences were observed between preoperative and postoperative groups. These findings indicate coordinated dysregulation of transcriptional, inflammatory, metabolic, and angiogenic processes in gastric cancer. The identified FOXA2–NF-κB–PEN-2–ApoE4–Ang-1 axis may be considered an integrated circulating molecular profile reflecting tumor host interactions and may provide a potential foundation for future non-invasive biomarker development and translational research. This study proposes systems-level circulating molecular network model rather than isolated biomarker alterations. Full article

Background: Mirikizumab, a first-in-class interleukin-23p19 antagonist, was approved for ulcerative colitis (2023) and Crohn’s disease (2025). The US Food and Drug Administration (FDA) identified a hepatotoxicity signal during pre-approval review, mandating post-marketing surveillance. No independent pharmacovigilance analysis has been published. Aims: To characterise the post-marketing safety profile of mirikizumab using multi-database pharmacovigilance, with a focus on hepatotoxicity and IL-23 inhibitor class comparison. Methods: Disproportionality analysis of the FDA Adverse Event Reporting System (FAERS; Q4 2023–Q4 2025) and Japanese Adverse Drug Event Report database (JADER) was performed using four algorithms (reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, empirical Bayesian geometric mean). Signals of disproportionate reporting were defined by concordance of all four methods. Active comparator analysis against risankizumab, guselkumab and ustekinumab, Weibull time-to-onset modelling and hepatotoxicity case characterisation were conducted. Reporting followed READUS-PV guidelines. Results: We identified 564 mirikizumab reports in FAERS and 123 in JADER. Nine signals met all four criteria in FAERS, including spontaneous abortion (Reporting odds ratio (ROR) 10.16, 95% CI 5.16–20.02), pulmonary embolism (ROR 5.56, 2.93–10.56) and injection site reactions. Hepatotoxicity showed no disproportionate reporting in either FAERS (ROR 1.19, 0.74–1.92; n = 17) or JADER (ROR 0.24, 0.05–1.19; n = 1). Comparator analysis identified cytomegalovirus infection and interstitial lung disease as mirikizumab-specific versus the IL-23 class. Weibull analysis (β = 0.65) indicated early-onset adverse event clustering. Discussion: This first multi-database pharmacovigilance study of mirikizumab did not confirm the FDA-flagged hepatotoxicity signal. Potential signals warranting further investigation include thromboembolic events and pulmonary toxicity. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most prevalent chronic liver disease worldwide, imposing a severe public health burden. Its core pathological hallmark is excessive hepatic lipid accumulation driven by systemic metabolic dysregulation. Concomitant hepatocellular injury impairs hepatic lactate clearance, leading to aberrant lactate buildup in the liver microenvironment. However, the causal role of lactate in exacerbating liver lipid metabolism dysfunction and driving the progression of MASLD remains unclear. Methods: First, we performed a comprehensive bioinformatic analysis of publicly available transcriptomic datasets. Mining of the Gene Expression Omnibus (GEO) database showed that lactate dehydrogenase (LDH) expression was significantly upregulated in liver tissues from both metabolic dysfunction-associated fatty liver disease (MASLD) patients and MASLD mouse models. Next, network pharmacology approaches were employed to predict putative molecular targets that could mediate lactate’s biological effects. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these candidate targets were predominantly enriched in pathways governing fatty acid metabolism and long-chain fatty acid transport. Molecular docking and molecular dynamics simulations further suggested possible interactions and supported the prioritization of cluster of differentiation 36 (CD36) as candidate lipid metabolism regulators potentially involved in lactate-mediated effects. Finally, liver-specific Ldha knockdown mice (AAV8-TBG-shRNA) and free fatty acid-induced steatotic AML12 hepatocytes were used to investigate the functional relevance of these findings in vivo and in vitro. Results: Network pharmacology analyses preliminarily identified the PPAR signaling pathway as a candidate pathway potentially linking lactate to MASLD. Experimental results showed that exogenous lactate administration was associated with significantly increased lipid accumulation in steatotic AML12 hepatocytes and the livers of MASLD mice, manifested as elevated triglyceride levels and enhanced lipid droplet formation, accompanied by upregulated expression of PPARγ and CD36. Conversely, inhibiting endogenous lactate production or silencing PPARγ or CD36 attenuated this lipid-accumulation phenotype and significantly reduced intracellular triglyceride levels. Conclusions: In conclusion, these findings indicate that lactate exposure is associated with hepatic lipid accumulation and upregulation of the PPARγ/CD36 axis. Pharmacological inhibition or silencing of PPARγ or CD36 attenuates this phenotype, suggesting that this pathway may contribute to lactate-associated hepatic steatosis and potentially accelerate MASLD progression. Full article

Introduction: Hypertension (HTN) management is essential to improving cardiovascular and cerebrovascular morbidity and mortality in the older adult, yet optimal blood pressure (BP) targets remain uncertain. We aim to compare the incidence of major adverse cardiovascular events (MACE) among older adults presenting to the emergency department (ED) with systolic blood pressure (SBP) 140–159 mmHg and diastolic blood pressure (DBP) 90–99 mmHg versus those with SBP ≥ 160 mmHg and DBP ≥ 100 mmHg. Methods: Retrospective analysis was conducted using the Global Collaborative Network including adults aged ≥ 65 years presenting to the ED with essential HTN without end-stage renal disease. Cohort 1 (SBP ≥ 160 mmHg and DBP ≥ 100 mmHg) included older adults with more severely elevated BP, while Cohort 2 (SBP 140–159 mmHg and DBP 90–99 mmHg) included those with moderately elevated BP. The primary outcome was all-cause mortality over 5 years. Secondary outcomes included AKI, CHF, AMI, ischemic stroke, and hemorrhagic stroke. Propensity score matching was used to balance baseline characteristics. Results: After propensity score matching, 191,829 patients remained in each cohort. The mean age was 72.6 (±5.9) years; 51.8% were female, 10.5% had diabetes mellitus, and 5.65% were obese. Cohort 2 (SBP 140–159 mmHg) had lower risks across all primary and secondary outcomes compared with Cohort 1 (SBP ≥ 160 mmHg). The largest risk difference (RD) was observed for AKI (RD = 1.28%; 95% CI, 1.09–1.46%; p < 0.0001), followed by ischemic stroke (RD = 1.25%; 95% CI, 1.11–1.39%; p < 0.0001), CHF (RD = 1.11%; 95% CI, 0.93–1.29%; p < 0.0001), AMI (RD = 0.70%; 95% CI, 0.58–0.83%; p < 0.0001), all-cause mortality (RD = 0.33%; 95% CI, 0.15–0.51%; p < 0.0001), and hemorrhagic stroke (RD = 0.29%; 95% CI, 0.22–0.36%; p < 0.0001). All outcomes demonstrated consistently higher absolute risk in Cohort 1 compared with Cohort 2. Conclusion: Cohort 2 (SBP 140–159 mmHg) was associated with a statistically significant but small decrease in cardiovascular, cerebrovascular, and mortality outcomes, with all absolute RDs uniformly small (<1%) and minimal survival benefit over time. These small gains must be weighed against overtreatment risks, such as medication costs, hypotension, and falls, underscoring the need for individualized, risk-based HTN management in older adults. Full article