Introduction: Allogeneic stem cell transplantation (allo-HCT) involves a long trajectory with high risk of complications. In person-centred care (PCC), patients’ needs, resources and the care relationship are central to the care process. Healthcare professionals’ (HCPs) ratings of PCC have not previously been investigated in this context. Objectives: The aim of this study was to investigate healthcare professionals’ ratings and views of person-centred care in allo-HCT care, and associations with individual characteristics and targeted PCC education. Design: Cross-sectional study, employing quantitative and qualitative methods. Methods: 85 HCPs at two Swedish allo-HCT centres participated (80% women; mean age: 44 years, range: 23-72 years). A survey was conducted using the PCC Assessment Tool (P-CAT), containing 13 items, a total scale (min 13-max 65) and two subscales (I: min 8-max 40; II: min 5-max 25). Additionally, HCPs’ written responses to four study-specific questions about PCC were collected. Results: The mean for P-CAT total scale was 45.31, (subscale I: 28.41; subscale II: 16.90). Higher ratings of PCC were reported for assessment of patients’ needs, discussion about how to provide PCC and patients’ care, while time to provide PCC, the care environment and how the organization prevents providing PCC were rated lower. Higher age and targeted PCC education were associated with higher PCC ratings. HCPs described PCC as the patient being seen as a capable individual with their own resources, with PCC increasing patient and family involvement—giving higher satisfaction and tailored care for patients. However, HCPs reported time as a barrier for PCC. Conclusion: HCPs’ ratings of PCC in this context are high regarding discussing and assessing patients’ needs, but there is room for improvement regarding organizational and environmental aspects. Targeted PCC education increases the level of PCC. HCPs’ views of PCC partly reflect the foundations of PCC—patient’s narrative, capability and involvement.

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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
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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.

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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.

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