Abstract:

Polyphenols are a structurally diverse group of plant secondary metabolites widely recognized for their antioxidant, anti-inflammatory, antimicrobial, and chemoprotective properties, which have stimulated their extensive use in food, pharmaceutical, nutraceutical, and cosmetic products. However, their chemical heterogeneity, wide polarity range, and strong interactions with plant matrices pose major challenges for efficient extraction, separation, and reliable analytical characterization. This review provides a critical overview of contemporary strategies for the extraction, separation, and identification of polyphenols from plant-derived matrices. Conventional extraction methods, including maceration, Soxhlet extraction, and percolation, are discussed alongside modern green technologies such as ultrasound-assisted extraction, microwave-assisted extraction, pressurized liquid extraction, and supercritical fluid extraction. Particular emphasis is placed on environmentally friendly solvents, including ethanol, natural deep eutectic solvents, and ionic liquids, as sustainable alternatives that improve extraction efficiency while reducing environmental impact. The review further highlights chromatographic separation approaches—partition, adsorption, ion-exchange, size-exclusion, and affinity chromatography—and underlines the importance of hyphenated analytical platforms (LC–MS, LC–MS/MS, and LC–NMR) for comprehensive polyphenol profiling. Key analytical challenges, including matrix effects, compound instability, and limited availability of reference standards, are addressed, together with perspectives on industrial implementation, quality control, and standardization.

Abstract:

Queuing theory and the Erlang equation are directly applicable to small hospital departments such as maternity and pediatrics. Bed capacity tables can be easily generated linking annual births/admissions to the required available beds, using expected births/admissions and length of stay (LOS). Two bed calculators are provided. For example, in maternity the total bed days includes any admissions during pregnancy and after birth, i.e., excluding the time spent in the birthing unit. It is emphasized that bed days must be calculated using real time length of stay as opposed to the usual midnight figure. The bed occupancy margin is directly linked to size and not ‘efficiency’. Based on the Erlang B equation which links available beds, occupied beds and turn-away, a figure of 0.1% turn-away has been chosen as the minimum acceptable number of beds, i.e., only 1 in a thousand admissions suffer a delay before a bed can be found. Two bed calculators are provided which can be used for obstetric, maternity, midwife-led, birthing wards and neonatal unit bed capacity. Specific issues relating to neonatal critical care bed capacity are highlighted. The negative effects of turn-away are likely to be context specific, hence, critical care > theatres > birthing unit > maternity unit. The far greater uncertainty regarding future births is discussed along with the variable nature of seasonality in births. For pediatrics much of bed demand is also influenced by the trend in births. Suggestions are made for a pragmatic approach to bed planning. Evidence is presented which suggests that for maternity (and other relative short stay admissions) the majority of overhead/indirect costs and most staffing costs should be apportioned based on admissions, and not LOS. Apportionment based on LOS creates the spurious illusion that LOS is the major cost driver and that reducing LOS will immediately save costs. Several lines of evidence point to the minimum cost per patient in maternity (antenatal + postnatal) lying greater than 30 beds (plus associated labor/birthing beds), and the minimum economic size around 12 beds. Around 30 beds probably mark the point where it is possible to make small cost savings by reducing LOS. Allocating total organizational costs to individual units and then to patients is far less precise than is realized and can be done in different ways which all heavily rely on the steady-state assumption. The real world of daily arrivals, case mix and clinical severity is never in steady state. Below 20 to 30 beds Poisson statistical plus environment induced randomness in daily arrivals imply that staff costs become increasingly fixed irrespective of LOS. When bed availability is the bottleneck then reducing LOS may increase throughput per bed and increase income, however, is this for the benefit of the patient or for the benefit of the organization, and does it lead to higher unanticipated total costs including patient harm? Finally, a list of nine ‘never do this’ catastrophic pitfalls are given for doctors to identify dubious capacity advice from managers and external ‘experts’.

Abstract:

Background/Objectives: Considering the excretion pathways and gadolinium concentrations of gadolinium-based contrast agents (GBCAs), our institution has developed a tailored administration protocol for patients with renal impairment to facilitate more rapid elimination and minimal retention of gadolinium. This study aims to evaluate the 8-year clinical outcomes and safety of this institutional protocol. Methods: This single-center retrospective study included patients with renal impairment who underwent GBCA-enhanced MRI between January 2015 and December 2022. The protocol recommended specific GBCAs and adjusted doses based on chronic kidney disease (CKD) stage and serum bilirubin levels: gadoxetate disodium was used for normal serum bilirubin level due to its dual excretion pathway, while macrocyclic agents were used for those with elevated serum bilirubin levels. During the follow-up period, occurrence of nephrogenic systemic fibrosis (NSF) and evidence of gadolinium deposition in brain tissues were evaluated. Results: A total of 288 patients (age, 64.6 ± 11.7 years; male, 64.9%) underwent 716 GBCA-enhanced MRI examinations in accordance with the institutional protocol. The cohort included 62 patients with CKD stage 4 and 131 patients with CKD stage 5 or undergoing hemodialysis. In patients with CKD stage 4 and 5 and those undergoing hemodialysis, 597 examinations were performed using gadoxetate disodium, and 119 used macrocyclic agents. No cases of NSF or gadolinium deposition in brain tissues were identified over mean follow-up intervals of 27.5 and 27.8 months, respectively. Conclusions: The tailored GBCA administration protocol, considering the excretion pathways and gadolinium concentrations, appears to be safe with respect to NSF and gadolinium deposition in brain tissues for patients with renal impairment.

Abstract:

The epicuticle of Cataglyphis niger is endowed with hydrocarbons comprising both linear and branched alkanes. The linear alkanes create an impermeable layer that protects the ants from desiccation, whereas the branched alkanes have communicative roles. Studies of the biosynthesis of both classes of hydrocarbons revealed disparate pathways, which suggests an independent evolution. It is hypothesized that the driving force for the evolution of alkanes was acquiring means for attaining impermeability. Being more abundant in foragers linear alkanes have been secondarily coopted for signaling colony foraging intensity and accordingly adjusting task allocation. The evolution of branched alkanes is less clear and seems more complex. They are biosynthetically derived from branched fatty acid that may have been the roots of their evolution. Due to their bactericide activity branched fatty acids evolved as protective means. Secondarily, the biosynthesis of these acids was coopted for producing branched alkanes for communicative roles. Using branched alkanes as signals is adaptive due to their numerous isomers that convey large informational content. Moreover, being hydrophobic they blend within the linear alkane layer that covers the ants’ body surface. However, branched alkanes decrease the cuticular impermeability, so hypothetically their proportions are the result of a tradeoff steady state.

Abstract: This study aims to characterizing both the pre-existing conditions that increase susceptibility and the long-term, post-acute sequelae ("long flu") following influenza. A longitudinal cohort study was conducted using data from the FinnGen cohort of 429,209 individuals including 9,204 influenza cases. A disease-wide association study (DWAS) framework was employed, using Cox proportional hazards models adjusted for age and sex to analyze 110 influenza-comorbid clinical endpoints. Pre-existing conditions, most notably cardiovascular diseases such as heart failure, coronary atherosclerosis, atrial fibrillation, and stroke, were significantly associated with an increased likelihood of a subsequent influenza diagnosis. Following influenza, individuals had a substantially elevated risk for a durable, multi-system "long flu" syndrome. The most robust and persistent risks were for new-onset cardiovascular and neurological diseases. Risks for thromboembolic events, heart failure, atrial fibrillation, stroke, and myocardial infarction remained significantly elevated for one to five years following influenza. Similarly, influenza was associated with a long-term increased incidence of neurodegenerative disorders, including migraine (with and without aura), Alzheimer's disease, and dementia. These findings underscore the urgent need to intensify preventive strategies, particularly through targeted vaccination of at-risk individuals, and to develop integrated care pathways to manage the multi-organ sequelae of long flu.

Abstract:

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty.

Abstract:

Soybean aphid (SBA), Aphis glycines Matsumura (Hemiptera: Aphididae), and soybean cyst nematode (SCN), Heterodera glycines Ichinoe (Tylenchida: Heteroderidae), are major pests of soybean, Glycine max L. Merr., in the U.S. Midwest. This study examined three-way interactions among soybean, SBA, and SCN using demographic and transcriptomic analyses. SCN-resistant and SCN-susceptible cultivars were evaluated under three treatments (SBA, SCN, SCN+SBA) in a randomized complete block design with six replicates, repeated eight times in greenhouse cone-tainers. Plants were infested with 2,000 SCN eggs at planting or 15 SBA at the V2 stage. Aphid populations were counted at 5-, 15-, and 30-days post-infestation (dpi), and SCN eggs sampled at 30 dpi. SCN egg density increased significantly in the susceptible cultivar but remained unchanged in the resistant cultivar in the presence of SBA, while SBA populations declined under SCN infestation. RNA-seq identified 4,637 differentially expressed genes (DEGs) at 5 dpi and 19,032 DEGs at 30 dpi. Analyses focused on DEGs shared across treatments but discordantly expressed in resistant cultivars during SBA–SCN interactions. Weighted Gene Co-expression Network Analysis revealed seven and nine modules at 5 and 30 dpi, respectively. Enrichment analyses identified ‘Plant–Pathogen Interaction’ and ‘Cutin, Suberin, and Wax Biosynthesis’ at 5 dpi, and ‘Isoflavonoid Biosynthesis’ and ‘One-Carbon Pool by Folate’ at 30 dpi. Several DEGs overlapped with SCN resistance QTLs, identifying candidate genes for cross-resistance breeding.

Abstract: Diabetic retinopathy (DR) is a leading cause of vision impairment and permanent blindness worldwide, requiring an accurate and automated system to classify its multi-grade severity to ensure timely patient intervention. However, standard Convolutional Neural Networks (CNNs) often struggle to capture the fine, high-frequency microvascular patterns critical for diagnosis. This study proposes a Robust Intelligent CNN Model (RICNN) designed to improve multi-level DR classification by integrating Gabor-based feature extraction with deep learning. The model also incorporates SMOTE (Synthetic Minority Oversampling Technique) balancing and Adam optimization for efficient convergence. The proposed RICNN was evaluated on the Messidor dataset (1,200 images) across four severity levels: Mild, Moderate, Severe, and Proliferative DR. The results showed that RICNN achieved superior performance with 89% accuracy, 88.75% precision, 89% recall, and 89% F1-score. The model also demonstrated high robustness in identifying advanced stages, achieving AUCs of 97% for Severe DR and 99% for Proliferative DR. Comparative analysis confirms that texture-aware Gabor enhancement significantly outperforms Local Binary Pattern (LBP) and Color Histogram approaches. These findings indicate that the proposed RICNN provides a reliable and intelligent foundation for clinical decision support systems, potentially reducing diagnostic errors and preventing vision loss in high-risk populations.

Abstract: Background: Hoarding disorder (HD) has been classed alongside obsessive-compulsive disorder (OCD) for decades, yet its later age of onset, ego-syntonic saving, and limited response to OCD treatments imply a separate biology.Methods: We re-analysed the 2022 genome-wide association meta-analysis of hoarding symptoms with the same three-step pipeline recently applied on a larger 2025 OCD GWAS. The approach combined (1) MAGMA gene-based tests, (2) partitioned heritability by stratified LD-score regression and custom χ² enrichment, and (3) S-PrediXcan transcriptome-wide association in six brain regions. Identical annotation panels—two glutamatergic sets, two pruning sets, a monoaminergic control, and a housekeeping control—were applied to both disorders to allow direct comparison.Results: HD showed no single-variant genome-wide hits but did reveal pathway-level patterns distinct from OCD. Hoarding heritability concentrated in genes supporting adult synaptic plasticity and cellular metabolism, most notably the BDNF → TrkB → mTOR → CREB cascade and sigma-1/CYP homeostatic modules. The strongest nominal signals included predicted down-regulation of NTRK2 and enrichment of several mTOR components. Pruning pathways displayed modest, secondary enrichment. By contrast, OCD heritability was dominated by immune-mediated synaptic elimination, adhesion, and astrocytic support genes, with glutamatergic panels contributing little.Conclusions: The data argue against a single "pruning-driven" mechanism for compulsive disorders. Instead, they support a model in which HD arises mainly from impaired adult synaptic remodeling and metabolic resilience within reward and decision circuits, producing enduring attachment to possessions rather than ritualistic neutralisation. This plasticity framework matches the clinical picture of HD and suggests new treatment directions that enhance circuit flexibility—such as BDNF or mTOR agonism—rather than attempting to reverse developmental pruning defects. Replication in larger, deeply phenotyped hoarding cohorts is needed to confirm these findings and to refine therapeutic targets.

Abstract:

Background/Objectives: Breast cancer is a biologically complex malignancy whose high prevalence and therapeutic resistance represent a continuous challenge for global health. The Tumor Microenvironment (TME) is a crucial component in disease progression, and the Extracellular Matrix (ECM), particularly its 3D collagen architecture, is recognized for mediating interactions that influence invasion, metastasis, and pharmacological response. This review aims to critically synthesize recent evidence to elucidate the multifaceted role of collagen in the progression and modulation of therapeutic response in breast adenocarcinoma. Methods: A comprehensive literature review was conducted, analyzing studies addressing specific collagen subtypes, ECM stiffening (fibrosis), biomechanical signaling, and its impact on drug transport kinetics and immunomodulatory effects. Results: The results demonstrate that structural alterations of collagen not only orchestrate a pro-tumoral microenvironment, fostering aggressive phenotypes and immune evasion, but also create a physical barrier that compromises drug delivery efficiency and promotes metastatic dissemination. The synthesis of the data reinforces collagen as a potent prognostic biomarker and a promising therapeutic target for overcoming stroma-mediated resistance. Conclusions: Targeting the collagen-rich stroma and its 3D network is a critical frontier for therapeutic innovation. Developing adjuvant strategies to modulate the ECM has the potential to enhance clinical outcomes and optimize the distribution of antineoplastic agents, especially in patients with high degrees of tumor fibrosis.

Abstract:

The family of N,N-dimethylaminomethylferrocenes is one of the most important in ferrocene chemistry. They serve as precursors for a range of anti-malaria and anti-tumour medicinal compounds in addition to being key precursors for ferrocene ligands in the Lucite alpha process. A brief discussion on the importance of, and the synthesis of N,N-dimethylaminomethyl-substituted ferrocenes preludes the synthesis of the new ligand 1,1´,2,2´-tetrakis-(N,N-dimethylaminomethyl)ferrocene. The crystal structure of this compound is reported and a comparison is made with its disubstituted analogue, 1,2-bis-(N,N-dimethylaminomethyl)ferrocene. The tetrahedral nickel dichloride complexes of both these ligands have been crystallographically characterised. Finally, a pointer to future research in the area is given which includes a discussion of a new method to extract ferrocenylmethylamines from mixtures using additives and a new synthetic avenue from substituted cyclopentadiene itself.

Abstract: Traditional cosmological redshift is defined as unbounded wavelength stretching from zero to infinity, which is inconsistent with a photon‑energy interpretation and implies physically unreasonable energy loss or divergence. In earlier work, a photon‑energy–based redshift z_new=z/(1+z), naturally bounded between 0 and 1, was introduced and embedded in a Hubble–Hawking cosmological model with positive curvature and light‑speed cosmic rotation. Methods: Using the energy‑based redshift within this rotating Hubble–Hawking framework, direct analytic relations are derived connecting the cosmic scale factor, the Hubble parameter, the age of the universe, luminosity and comoving distances, galactic recession speeds, and a revised form of Hubble’s law with angular velocity equal to the Hubble parameter. The same redshift prescription is then applied to the Son et al. progenitor‑age–corrected Pantheon+ supernova sample to perform a purely kinematic re‑analysis of the expansion history. Results: The analytic relations indicate that the universe has been continuously decelerating since the Planck era, as steadily increasing baryonic mass slows an initially light‑speed expansion, and they predict a slow future decline of the 2.725 K cosmic microwave background temperature. In the re‑analysis of Pantheon+ with progenitor‑age bias removed and z_new=z/(1+z) adopted, the best‑fit solution shifts from mild deceleration to strong, continuous deceleration, incompatible with the late‑time acceleration required by flat ΛCDM; the supernova data no longer favor an accelerating universe but instead support a cosmos that has been decelerating throughout its post‑Planck evolution. Independently, the CMB temperature can be related to a Hubble–Hawking temperature via the geometric mean of the Hubble mass and the Planck mass, implying that the product of cosmic mass and the square of the cosmic temperature remains approximately constant, and yielding a current baryon acoustic bubble radius of 135.2 Mpc that can be used to refine the true expansion (or deceleration) rate. On galactic scales, an empirical “super gravity” relation with a mass limit for ordinary gravity of roughly 180 million solar masses reproduces both low‑dark‑matter and high‑dark‑matter galaxies by scaling effective dark mass as (baryon mass)1.5. Conclusions: Taken together, the energy‑based redshift, the nearly isotropic CMB sky, and the spinning black‑hole–like Hubble–Hawking universe form a single, self‑consistent narrative in which the present cosmos is nearly radially static, dominated by light‑speed rigid rotation and net deceleration rather than late‑time acceleration. In this picture, supernova distances, CMB isotropy, and BAO scales are unified by strong binding and rigid co‑rotation into a rotating universe with negligible true expansion. Planck’s nearly isotropic CMB sky, usually interpreted as evidence for a homogeneous FLRW universe with accelerating expansion, can instead be reinterpreted as evidence for a late, slow deceleration phase in a light‑speed rotating, positively curved Planck–Hubble–Hawking universe.

Abstract: Viral infections have been implicated in psychiatric outcomes through immune-mediated pathways. This 12-month prospective cohort study compared psychiatric symptoms and inflammatory cytokine profiles in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), hepatitis C virus (HCV), and tick-borne encephalitis virus (TBEV), and assessed their predictive value. 37 patients hospitalized with viral infections and 32 healthy controls were evaluated using psychiatric interviews and the Hospital Anxiety and Depression Scale (HADS). The study was divided into two stages. In Stage 1, during the acute infection, a psychiatric assessment was conducted and cytokine levels were measured in the patients’ blood. In Stage 2, one year later, the psychiatric as-sessment was repeated. No significant differences were found in psychiatric diagnosis rates or symptom severity between infection groups, regardless of viral type or neu-roinvasive capacity. Some cytokines (eg., IL-1β, TNF-α, IL-10, and sIL-2Rα) showed as-sociations with individual symptoms, but these were inconsistent and not predictive. Cluster analysis identified two distinct inflammatory profiles - one characterized by higher cytokine levels (predominantly in COVID-19 and TBEV cases) and the other by lower cytokine levels (mostly in HCV and controls). However, different cytokine profiles did not correspond to clinical outcomes. The results suggest that psychiatric sequelae after viral infections are not directly driven by specific cytokines or infection type but rather emerge from a complex interaction of immune, psychological, and environmental factors. Single cytokine measurement is insufficient and cannot be used as a tool for assessing the risk of developing psychiatric disorders. Future studies should focus on composite bi-omarkers and systems-based models such as neuroimmune-metabolic-oxidative path-ways (NIMETOX), Immune-Inflammatory Response System (IRS)/ Compensatory Im-mune Response System (CIRS)/ Oxidative & Nitrosative Stress (O&NS) for improved predictive accuracy.

Abstract: This research examines how rising pressures from global risks, including natural disasters, geopolitical conflicts, cybercrime, and government regulations, affect sustainable supply chains and logistics systems. These pressures are becoming more frequent, intense, and unpredictable. The Global Pressure Supply Chain Index, developed by the Federal Reserve Bank of New York, serves as a proxy for quantifying these pressures and as a comprehensive metric of stress within global supply chains and logistics systems driven by macroeconomic factors. Further investigation is warranted. Quantitative analyses indicate that systemic global risks have a significantly positive effect on these systems. However, additional analyses show that the influence of macroeconomic indicators on these systems remains generally low to moderate. Supplementary statistical tests demonstrate that, among external systemic risks, government trade regulations, cybercrimes, cyberattacks, the transportation index, and political conflicts are significant predictors of pressures on global supply chains and logistics. These factors serve as indicators for forecasting economic fluctuations, which lead to disruptions, delays, and costs in supply chains and logistics systems.

Abstract: Land degradation (LD) is a dominant threat of the decade, which is deteriorating arable lands globally. Therefore, this intensification of LD has stimulated global governing bodies and researchers to take the initiative against this dilemma through sustainable and eco-friendly approaches. Geographical mapping is critical for analyzing land formation, its types, and uses; data-based maps provide a detailed overview of land use. In this study, we have created simplified SRTM-based maps for Saudi Arabia related to soil types, soil thickness, and soil uses either as vegetation or for agricultural aspects using GIS tools. Results of these GIS analyses showed that the maximum area of the country is sandy, followed by loam and sandy loam. Meanwhile, the maximum soil thickness is either under 0-4 meters or 43-50 meters. This geological display of the country could be instrumental in assessing the soil types and what sort of inputs or steps can be taken to make each type of soil fertile. Moreover, we also mentioned the land degradation pathways impacting the country’s arable lands and explained the pathways that can help assess such land losses. Besides land loss pathways, we explained the most suitable mitigation strategies, including mulching, cover cropping, agroforestry, riparian buffer strips, agroforestry, terracing, and nutrient use efficiency. In this article, we also focused on the aims of the Saudi Green Initiative and the steps that are being taken by international governing bodies like UNDP, UNEP, FAO, and the World Bank to mitigate land degradation in the region. However, further studies are required to assess the intensity of these solutions at each soil type and thickness.

Abstract:

Ground‑mounted photovoltaics, including agrivoltaic concepts, are increasingly deployed on agricultural land. In practice, damaged modules from repowering modules are sometimes stored on‑site for prolonged periods, creating localized vegetation suppression and land‑stewardship concerns that are rarely quantified. We present two anonymized case studies from Czechia (nominal capacities of 0.861 and 1.109 MWp; commissioned 2010 and 2009; repowered 2022 and 2021), where cracked backsheets and/or broken front‑glass modules were stacked and stored directly on grasslands within PV parcels. Using GIS delineation on orthophotos supported by field photographs, we quantified the land area (19,560 and 22,100 m²), PV panel area (plan‑ view; 4,960 and 5,080 m²), and stored PV module area (plan‑ view storage footprint; 109 and 100 m²). Stored module counts were estimated from visible stacks (≈1800 and ≈2000 modules). Using a conservative mass range of 18–25 kg/module, the stored masses were ~32–45 t and ~36–50 t, respectively. Although the storage footprints constitute <1% of the land area, they create persistent “dead zones” on agricultural land and concentrate tens of tonnes of material directly on the soil. We discuss regulatory and economic barriers to timely removal in the context of circular‑economic goals and propose practical reporting indicators for repowering projects on agricultural land: A_store (m²), N_store (pcs), M_store (t), storage duration, condition class, and storage interface.

Abstract: High-dimensional survival analyses require calibrated risk and honest uncertainty, but standard elastic-net Cox models yield only point estimates. We develop a fully Bayesian elastic-net Cox (BEN–Cox) modelfor high-dimensional proportional hazards regression that places a hierarchical global–local shrinkage prior on coefficients and performs full Bayesian inference via Hamiltonian Monte Carlo. We represent the elastic–net penalty as a global–local Gaussian scale mixture with hyperpriors that learn the ℓ1/ℓ2 trade-off, enabling adaptive sparsity that preserves correlated gene groups and, using HMC on the Cox partial likelihood, yields full posteriors for hazard ratios and patient-level survival curves. Methodologically, we formalize a Bayesian analogue of the elastic-net grouping effect at the posterior mode and establish posterior contraction under sparsity for the Cox partial likelihood, supporting the stability of the resulting risk scores. On the METABRIC breast-cancer cohort (n = 1 , 903; 440 gene-level features from an Illumina array with ≈ 24,000 gene-level features (probes)), BEN–Cox achieves slightly lower prediction error, higher discrimination, and better global calibration than a tuned ridge Cox baseline on a held-out test set. Posterior summaries provide credible intervals for hazard ratios, identify a compact gene panel that remains biologically plausible. BEN–Cox provides a theory-backed, uncertainty-aware alternative to tuned penalised Cox models, improving calibration and yielding an interpretable sparse signature in correlated, high-dimensional survival data

Abstract: This paper seeks to analyze the philosophy of marriage in India as a construct based on three distinct and conflicting models: the contract, the institution, and the moral bond. The primary focus is to consider how the marriage contract, as a sacred Muslim Nikah and a secular civil agreement under the Special Marriage Act, 1954 and Hinduism as a sacred event and also, a civil agreement under the Hindu Marriage Act, 1955. The moral bond is represented by widows and modern-day “companionate” partnership. It concludes that Indian marriage is a struggle between all three models due to globalization, post-colonial feminist critiques of its patriarchal nature, and the individualization of Western ideals around partnership and friendship. The quintessential example of all three struggles is love-cum-arranged marriage.

Abstract: Alcohol-associated liver disease (ALD) is driven by complex interactions among hepatic lipid accumulation, oxidative stress, inflammation, cell death, and disruption of the gut–liver axis. Therapeutic strategies capable of targeting multiple interconnected pathogenic pathways remain limited. In this study, we investigated the protective potential of graphene oxide nanoparticles (GNPs) in a chronic ethanol-fed rat model of ALD. Male Wistar rats were subjected to ethanol feeding and intermittently treated with GNPs (10 mg/kg) by oral gavage. Hepatic injury was assessed by biochemical parameters, histology, lipid accumulation, gene and miRNA expression, protein analysis, and gut microbiome profiling. Ethanol feeding induced hepatic steatosis, oxidative stress, apoptotic and necroptotic signaling, intestinal barrier disruption, gut dysbiosis, and activation of hepatic inflammatory pathways. GNP treatment markedly attenuated ethanol-induced lipid accumulation, normalized liver morphology, and reduced biochemical markers of liver injury. These effects were accompanied by restoration of antioxidant defenses, including Nrf2 and HO-1, and suppression of CYP2E1 expression and cell death–associated markers. In parallel, GNPs preserved intestinal architecture, maintained tight junction gene expression, and suppressed intestinal inflammatory responses. Gut microbiome analysis revealed partial restoration of ethanol-induced dysbiosis, including recovery of beneficial postbiotic-associated bacterial taxa. Improved intestinal homeostasis was associated with attenuation of hepatic TLR4-associated inflammatory signalling and modulation of macrophage-associated markers. Furthermore, GNP treatment partially normalized ethanol-induced dysregulation of miRNAs implicated in lipid metabolism, inflammation, and oxidative stress. Collectively, these findings demonstrate that GNPs exert a coordinated protective effect against ethanol-induced liver injury by modulating multiple pathological processes along the gut–liver axis. This multi-targeted activity highlights the therapeutic potential of graphene oxide nanoparticles as an intervention strategy for early-stage alcohol-associated liver disease.

Abstract: Real-world data streams, such as news articles and social media posts, are dynamic and nonstationary, creating challenges for real-time structured representation via knowledge graphs, where relation extraction is a key component. Continual relation extraction (CRE) addresses this setting by incrementally learning new relations while preserving previously acquired knowledge. This work investigates the use of pretrained language models for CRE, focusing on large language models (LLMs) and the effectiveness of memory replay in mitigating forgetting. We evaluated decoder-only models and an encoder-decoder model on TACRED and FewRel in English. Our results show that memory replay is most beneficial for smaller instruction-tuned models (e.g., Flan-T5 Base) and base models such as Llama2-7B-hf. In contrast, the remaining instruction-tuned models in this work do not benefit from memory replay, yet some, like Mistral-7B, already achieve higher accuracies without it and surpass prior methods. We further observed that Llama models in this work are more prone to hallucinations. To the best of our knowledge, this work provides the first reproducible benchmarks for LLMs in CRE. It offers a novel analysis of knowledge retention and hallucination behavior—dimensions that have not been systematically studied in earlier research.