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.

Abstract: Sentiment classification struggles with complex semantic relationships using static text graphs. We introduce the Quantum-Enhanced Adaptive Graph Convolutional Network (QAGCN), a hybrid quantum-classical architecture for robust sentiment representation. QAGCN's core is a Quantum-Enhanced Graph Construction Module employing a Parameterized Quantum Circuit (PQC) to dynamically learn emotional association strengths between word pairs. This generates a task-adaptive adjacency matrix, which then feeds into classical GNN layers. Evaluations on benchmark datasets (Yelp, IMDB, Amazon, MC, RP) demonstrate QAGCN's superior or competitive accuracy against state-of-the-art classical graph models and the Quantum Graph Transformer. QAGCN notably improved performance on Amazon where prior quantum models struggled, underscoring its adaptive graph construction's efficacy. An ablation study confirms the critical contribution of PQC-driven adaptive graph learning. Our findings highlight the significant potential of quantum-enhanced adaptive graph learning for complex Natural Language Processing.

Abstract: E-Payment has become popular in mobile com-merce, can provide consumers with a convenient way to makepurchases electronically. Currently, however, all too many E-Payment systems are primarily focused on securing a consumer’sfinancial information and do little to prevent privacy leaks andAI-generated scams. This paper defines AEP-M, a novel AI-enhanced anonymous e-payment scheme developed for mobiledevices that uses TrustZone and divisible e-cash. Since mobiledevices have very limited processing power and each transactionmust be performed in real time, the proposed solution combinesan efficient divisible e-cash system with AI-powered anomalydetection techniques to improve both the security, privacy andfraud detection in mobile payments. In addition to enablingusers to divide a single withdrawal of an e-coin of a largeamount into multiple transactions without disclosing their iden-tity to either banks or merchants, AEP-M integrates AI-basedrisk assessment to identify suspicious spending behaviors torapidly mitigate fraud and continuously monitor transactions.By employing a combination of bit decomposition and pre-computation to minimize the computational overhead of thetransaction process, AEP-M provides the optimal performancein terms of minimizing the max number of exponentiationoperations required to perform the frequent online spendingprocess on elliptic curves. Finally, AEP-M also incorporates anARM TrustZone to protect a user’s financial data and importantprivate data; an SRAM PUF is used as a Root of Trust to deriveAI-powered keys and manage sensitive data, thereby increasingboth the security and reliability of the system. A prototype ofAEP-M was implemented and evaluated using the BN curve ata 128-bit security level. The experimental results demonstratedthat AEP-M is capable of improving the Security, Efficiency andFraud Detection capabilities of Mobile Digital Payments whilemaintaining User Privacy and Anonymity.

Abstract:

The selection of an optimal antifoam is critical for efficient fermentation, as industrial agents often have detrimental side effects like growth inhibition, while some can enhance productivity. This study presents a rational approach to developing and screening novel silicone-polyol antifoam emulsions. A key finding was the discovery of selective antibacterial activity in agent 3L10, which strongly inhibited Gram-positive bacteria (especially Corynebacterium glutamicum) but not Gram-negative strains. This specificity, likely mediated by interaction with the mycolic acid layer of C. glutamicum, highlights the necessity for strain-specific antifoam testing. A comprehensive evaluation protocol—combining chemical design, cytotoxicity screening across diverse microorganisms, determination of minimum effective concentrations (MEC), and validation in model bioreactor fermentations—was established. Through this process, agent 6T80 was identified as a promising candidate. It exhibited low MEC, high emulsion stability, no cytotoxicity, and did not impair growth or recombinant protein production in B. subtilis or P. putida fermentations. The study concludes that agent 6T80 is suitable for further application in processes involving Gram-negative and certain Gram-positive hosts, whereas agent 3L10 serves as a valuable tool for studying surfactant-membrane interactions. The developed methodology enables the targeted selection of highly efficient and biocompatible antifoams for specific biotechnological processes.

Abstract: Precise quantification of fine motor behavior is essential for understanding neural circuit function and evaluating therapeutic interventions in neurological disorders. While markerless pose estimation frameworks such as DeepLabCut (DLC) have transformed behavioral phenotyping, the choice of convolutional neural network (CNN) backbone significantly impacts tracking performance, particularly for tasks involving small distal joints and partial occlusions. in this paper, we present the first systematic comparison of nine CNN architectures implemented in DLC for lateral-view analysis of fine reaching movements in the Montoya Staircase test, a gold standard assay for skilled forelimb co-ordination in rodent models of stroke and neurodegenerative disease. Using a dataset of videos representing both control and M1-lesioned conditions, we rigorously evaluated models across six critical dimensions: spatial accuracy (RMSE, PCK@5px), mean average precision (mAP), occlusion robustness, inference speed and GPU memory usage. Our results reveal that multi-scale DLCRNet architectures substantially outperformed classical backbones, with DLCRNet_ms5 achieving the highest overall accuracy and DLCRNet_stride16_ms5 providing the best trade-off between precision and computational efficiency. These findings provide critical methodological guidance for neuroscience la-boratories and highlight the importance of architecture selection for rigorous quantification of fine motor behavior in preclinical research.

Abstract: Accurate detection of buried utilities and reliable characterization of shallow subsurface conditions are critical requirements in civil and industrial engineering projects, particularly in urban areas developed over conductive clay–marl formations. In such environments, commonly used electromagnetic techniques often fail due to severe signal attenuation, increasing uncertainty during excavation and infrastructure planning. This study presents a high-resolution engineering workflow based on Electrical Resistivity Tomography (ERT) for the simultaneous detection of buried stormwater and sewer pipes and the geotechnical characterization of shallow subsurface materials. The methodology was applied in an industrial area southwest of Pamplona (Navarra, Spain), where Eocene marls and clays dominate the geological setting. Three ERT pro-files, each 23.5 m long, were acquired using a pole–dipole array with a dense electrode spacing of 0.5 m, allowing decimetric-scale resolution and investigation depths of up to 7–8 m. Data were processed and inverted using both smooth (L2-norm) and robust (L1-norm) inversion schemes to evaluate their influence on anomaly detection and stratigraphic imaging. The resulting resistivity models clearly identified elongated conductive and resistive anomalies corresponding to known buried sewer and stormwater pipes, despite the highly conductive background. In addition, the ERT sections revealed lateral and vertical variations within the clay–marl sequence, including sandy and compact detrital facies of direct relevance for foundation design and excavation planning. Borehole data available in the study area corroborated the geophysical interpretation. A complementary Ground Penetrating Radar (GPR) survey confirmed the ineffectiveness of electromagnetic methods under the same conditions due to rapid signal attenuation. Rather than focusing solely on utility detection, the proposed approach frames ERT as a dual-purpose engineering tool capable of providing continuous subsurface infor-mation that bridges the gap between sparse borehole data and construction needs. The workflow presented here is transferable and scalable, offering a practical protocol for urban and industrial projects in conductive soils where conventional techniques are limited.

Abstract: A type II cadherin, Cadherin-19 (CDH19), plays a vital role in neural crest development. CDH19 regulates cell–cell junctions and migration by forming catenin-cytoskeleton complexes. Although anti-CDH19 monoclonal antibodies (mAbs) are used for specific applications such as Western blotting and immunohistochemistry (IHC), suitable anti-CDH19 mAbs for flow cytometry are limited. Here, novel anti-human CDH19 mAbs (Ca19Mabs) were developed through flow cytometry-based high-throughput screening. One clone, Ca19Mab-8 (IgG1, κ), specifically recognized CDH19-overexpressing Chinese hamster ovary-K1 cells but did not bind to other 21 CDHs (including both type I and type II) in flow cytometry. Additionally, Ca19Mab-8 recognized endogenous CDH19 in the human glioblastoma cell line LN229. The dissociation constant (KD) of Ca19Mab-8 for LN229/CDH19 was 9.0 × 10⁻⁹ M. Ca19Mab-8 can detect CDH19 in Western blotting and IHC. These findings suggest that Ca19Mab-8 is versatile for basic research and has potential applications in clinical diagnosis and tumor therapy.

Abstract: Background: Oncology medication costs are increasing internationally; patient attitudes towards these costs remain unclear. Methods: A three-part cross-sectional questionnaire was distributed to patients with breast cancer to determine their attitudes towards oncology medication costs and to ex-plore potential patient acceptable methods to reduce these costs. Results: 321 patients were eligible for inclusion and 180 fully completed the questionnaire (56.1% response rate). Overall, 67.8% (N = 122/180) of patients found the costs presented in the questionnaire to be unacceptable. 92.2% (N = 166/180), 87.8% (N = 158/180) and 68.9% (N = 124/180) of participants found the costs of pembrolizumab, palbociclib and trastuzumab respectively to be unacceptable. 72.8% (N = 131/180) of patients indicated that they would like to be better informed about the societal costs of their cancer treatment and 81.1% (N = 146/180) of patients believed that reducing the costs of cancer treatment to society is important. There was a statistically significant difference in patient desires to be better informed of societal drug costs between those with early-stage breast cancer and those with metastatic disease (75.8% vs 47.4%, χ2 = 6.923, p = 0.009). Conclusion: These findings indicate that many Irish patients with breast cancer find the societal costs of oncology medications to be unacceptable, and many patients have a de-sire to be better informed of these costs.

Abstract: 3-Amino-4-hydroxybenzoic acid (3,4-AHBA) is a non-proteinogenic aromatic compound that functions as a key biosynthetic precursor for diverse secondary metabolites with pharmaceutical and industrial value. Microbial production of 3,4-AHBA offers a sustain-able alternative to petroleum-based chemical synthesis; however, metabolic complexity and trade-offs between growth and product formation constrain rational strain design. Here, genome-scale metabolic (GSM) modeling and flux balance analysis (FBA) were in-tegrated with targeted genetic engineering to elucidate and enhance 3,4-AHBA production in Streptomyces thermoviolaceus. A genome-scale metabolic model was constructed and ex-panded by incorporating the nspH–nspI gene operon, which encodes the 3,4-AHBA bio-synthetic pathway. In silico FBA predicted substantial rewiring of central carbon metabo-lism, with carbon flux redirected from glycolysis and the tricarboxylic acid cycle toward aspartate-derived intermediates and 3,4-AHBA synthesis, accompanied by reduced bio-mass-associated flux. Guided by these predictions, an engineered strain (St::NspHI) was developed and experimentally evaluated. Consistent with model predictions, the engi-neered strain exhibited lower growth rates and glucose uptake than the wild type, reflect-ing a metabolic burden. Nevertheless, 3,4-AHBA production was achieved exclusively in the engineered strain. Comparison of simulated and experimental fluxes revealed overes-timation by FBA, likely due to secondary metabolism and incomplete genome annotation. Overall, GSM-guided design enables optimization of precursor production.