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• Pub. Date : 2025
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The continuous utilization of nuclear energy has led to the accumulation of spent nuclear fuel (SNF) containing uranium, transuranium, and fission products (FPs). Reprocessing and pyrochemical methods have shown the potential for SNF reuse, thereby reducing its environmental impact. Voloxidation, a pivotal step in SNF recycling, involves thermal treatment in an oxidizing atmosphere to enhance the reactivity. During voloxidation, Se-79, which is a FP with a long half-life, is released as SeO2 under oxidizing conditions, necessitating technologies to capture it. CaO pellets (CPs) were prepared to capture gaseous SeO2. The effects of operating conditions on SeO2 capture performance were investigated. The CP reacts strongly with SeO2 to form CaSeO3, exhibiting a high adsorption capacity of 17.5 mol kg􀀀 1 and >99 % efficiency at 700 ◦C. The mechanical strength and thermal stability assessments indicate suitability for practical applications. Depending on the flow rate, the CP required for SeO2 capture was estimated when processing 1 t of SNF, thereby contributing to the design of effective SeO2 capture systems for safe and sustainable nuclear waste management

• Book : 57(1)
• Pub. Date : 2025
• Page : pp.1-8
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ABSTRACTCitronellol (CT) is a naturally occurring lipophilic monoterpenoid which has shown anticancer effects in numerous cancerous cell lines. This study was, therefore, designed to examine CT's potential as an anticancer agent against glioblastoma (GBM). Network pharmacology analysis was employed to identify potential anticancer targets of CT. A comprehensive data mining was carried out to assess CT and GBM‐associated target genes. Protein–protein interaction network was constructed to identify hub genes and later GO and KEGG enrichment analysis was performed to elucidate the possible mechanism. Human glioblastoma cell line “SF767” was used to confirm in silico findings. MTT, crystal violet, and trypan blue assays were performed to assess the cytotoxic effects of various concentrations of CT. Subsequently, ELISA and qPCR were performed to analyze the effects of CT on proapoptotic and inflammatory mediators. In silico findings indicated that CT differentially regulated proapoptotic and inflammatory pathways by activating caspase‐3 and 8 and inhibiting nuclear factor‐kappa B (NF‐κB), tumor necrosis factor‐α, Janus kinase 2 (JAK2). Molecular docking also demonstrated strong binding affinities of CT with the above‐mentioned mediators when compared to 5‐fluorouracil or temozolomide. In SF767 cell line, CT displayed dose‐dependent cytotoxic and antioxidant effects, and upregulation of annexin‐V, caspase‐3, and 8 along with downregulation of inflammatory modulators. In a nutshell, it can be concluded from these findings that CT possesses robust anticancer activity which is mediated via differential regulation of caspase‐3, JAK2, and NF‐κB pathways.

• Book : 13(1)
• Pub. Date : 2025
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• Pub. Date : 2025
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• Pub. Date : 2025
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ABSTRACTAccurately predicting individual antidepressant treatment response could expedite the lengthy trial‐and‐error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning‐based methods that predict individual‐level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA‐MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4–12 weeks post‐initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel‐wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross‐validation methods (i.e., k‐fold and leave‐one‐site‐out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10‐fold cross‐validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non‐)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non‐)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non‐)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response‐related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

• Book : 46(1)
• Pub. Date : 2025
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• Pub. Date : 2025
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• Pub. Date : 2025
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• Pub. Date : 2025
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The disposal of radioactive waste within the UK is managed through a comprehensive regulatory framework.This framework requires radioactive waste to be sufficiently well characterized to ensure its disposal is compliant with the regulations and the acceptance criteria for any receiving facility. This is the responsibility of both the waste consignor and the receiving facility.Characterization of solid radioactive waste is extremely challenging. This is due to the wastes exhibiting a high degree of heterogeneity which leads to significant uncertainty. Understanding the total uncertainty requires combining the uncertainties associated with numerous stages of the characterization process.Experience suggests that whilst uncertainties are included in waste characterization, approaches are variable in quality. This makes it challenging to present an appropriate level of confidence in the waste characterization and the subsequent decisions made to stakeholders.This paper introduces the concept and principles of uncertainty and uncertainty management in waste characterization, summarizing challenges, and gaps in the current approach. It recommends a solution is sought to address shortfalls in the understanding of uncertainty; identify sources of uncertainty across the whole characterization lifecycle; and agree how specialists might combine these uncertainties and communicate them to stakeholders.

• Book : 57(1)
• Pub. Date : 2025
• Page : pp.1-7
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