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To understand the experiences, needs, and preferences for supportive care, among children and adolescents (0–19 years) diagnosed with cancer: a systematic review of qualitative studies.
Get PDFThis study aimed to understand the experiences, needs, and preferences for supportive care, among children and adolescents (0–19 years) diagnosed with cancer. A qualitative systematic review has been reported according to PRISMA guidelines. A comprehensive search was conducted across multiple databases (APA PsycINFO, CINAHL, and Medline) and citation searches. Studies were screened according to pre-determined inclusion and exclusion criteria. Methodological quality was evaluated. Findings were extracted in relation to the context of interest of experiences, needs, and preferences of supportive care. Each finding was accompanied by a qualitative verbatim illustration representing the participant's voice. 4449 publications were screened, and 44 studies were included. Cancer populations represented in the included studies included lymphoma, leukaemia, brain cancer, sarcomas, and neuroblastoma. Two overarching synthesised findings were identified as (1) coping, caring relationships, communication, and impact of the clinical environment, and (2) experiences of isolation, fear of the unknown, restricted information, and changing self. Children and adolescents articulated that cancer care would be enhanced by developing a sense of control over their body and healthcare, being involved in communication and shared decision-making, and ensuring the clinical environment is age-appropriate. Many experienced a sense of disconnection from the rest of the world (including peers, school, and experiences of prejudice and bullying), and a lack of tailored support and information were identified as key unmet care needs that require further intervention. Children and adolescent who are diagnosed with cancer are a unique and understudied group in oncological survivorship research, with the slowest progress in improvement of care over time. This review will facilitate the development of future interventions and promote the importance of tailored support for children and adolescents at all stages of the cancer journey. Children and adolescents continue to experience a range of difficulties despite routine contact with cancer healthcare professionals. Children and adolescents should be carefully assessed about their individual circumstances and preferences for support given the clear implications from this review that "one size" does not fit all
Emojicitation, a novel method to explore the hidden terrain of emotions in research. [Preprint]
Get PDFControversial topics often evoke strong emotions and expose deeply held values, yet emotions may be difficult for research participants to articulate, or they may be supressed or concealed, making them a hidden terrain difficult to explore in research. Creative research methods, like the emoji-citation proposed in this study, may help researchers to explore this hidden terrain. This paper outlines methodological considerations for using emoji in qualitative research and presents a novel analytical framework for rigorous interpretation of emoji-based data. Drawing on the use of emojicitation in a mixed methods study exploring midwives' perspectives on freebirth, this paper demonstrates how emoji can be effectively integrated into qualitative research on sensitive topics. Fourteen midwives participated in the qualitative phase, sharing their views on the choice to give birth attended in the UK. At enrolment, they expressed their views using only emoji. Their answers were used during the interview to elicit deeper discussion and emotional reflection on the topic of the study. Emoji in this study acted as emotional touchpoints, helping participants access and articulate their feelings, generating rich emotional data. Emoji brought an element of playfulness to research, enhancing social interaction and fostering participant's power in the co-construction of meaning. Emoji also enhanced the storytelling power of research outputs, helping audiences connect with participants' viewpoints. Emojicitation is a novel and evocative method for exploring emotional data in controversial topics. The emojianalysis framework presented here offers a robust and adaptable approach to analysing emoji data, showing promise for future qualitative research
Childbirth: from fear and uncertainty, to confidence and calm.
Get PDFIn this article, the authors explore attitudes towards pregnancy, particularly in terms of overcoming fear. They argue that "it's not just knowing what to do, it's believing you can do it"
Power transformer health index and life span assessment: a comprehensive review of conventional and machine learning based approaches.
Get PDFPower transformers play a critical role within the electrical power system, making their health assessment and the prediction of their remaining lifespan paramount for the purpose of ensuring efficient operation and facilitating effective maintenance planning. This paper undertakes a comprehensive examination of existent literature, with a primary focus on both conventional and cutting-edge techniques employed within this domain. The merits and demerits of recent methodologies and techniques are subjected to meticulous scrutiny and explication. Furthermore, this paper expounds upon intelligent fault diagnosis methodologies and delves into the most widely utilized intelligent algorithms for the assessment of transformer conditions. Diverse Artificial Intelligence (AI) approaches, including Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), are elucidated offering pragmatic solutions for enhancing the performance of transformer fault diagnosis. The amalgamation of multiple AI methodologies and the exploration of time-series analysis further contribute to the augmentation of diagnostic precision and the early detection of faults in transformers. By furnishing a comprehensive panorama of AI applications in the field of transformer fault diagnosis, this study lays the groundwork for future research endeavors and the progression of this critical area of study
Lebanon: fig holding and SDG#1 no poverty.
Get PDFPoverty reduction is pivotal for sustainable development and has been identified as the first sustainability goal (SDG#1) by the United Nations. Poverty has remained a key challenge for countries in the Arab world, which is characterised by persisting levels of poverty and significant income inequalities, where the top 10% of people account for 64% of all wealth. Arab states have undertaken diverse efforts to reduce poverty on national and regional levels to operationalise the 2030 Sustainability Agenda. Nevertheless, the achievements of objectives have been rendered difficult by numerous challenges such as ongoing regional conflicts, civil wars, political instabilities and refugee crises (mainly due to the Syrian war). Factors such as the COVID-19 pandemic and the recent Ukrainian war have also impacted food security and aggravated the poverty situation in the region. Insights regarding poverty in the region come from the Multidimensional Poverty Index (MPI), which is published by the United Nations Development Programme and measures factors of living conditions that affect family spending and poverty rates, such as health, education, and living standards (such as nutrition, child mortality, years of schooling, sanitation, electricity, drinking water, and assets, among other factors). The MPI showed that in the aftermath of COVID-19, the Arab region experienced a significant household income loss with an extreme poverty rate of 11.3% in 2023. The pandemic has pushed eight million additional people into extreme poverty, and a total of 48 million individuals in the Arab world are now living below the poverty line. Different surveys converge in their findings that between 70% to 85% of families in non-oil-producing countries in the region have to borrow money or rely on some form of aid to cover their monthly needs, which renders them vulnerable and likely to slip into poverty. The present case is set in Lebanon, located in the Arab region. It describes and illustrates how Fig Holding, a family-run enterprise, has used its capabilities and resources to engage people in need and help alleviate poverty's effects on families and individuals in adverse conditions created by the economic crisis, especially after the Beirut port explosion. The case also shows how Fig Holding has transferred its knowledge to other contexts, notably Armenia, which was confronted with a refugee influx due to the military offensive in Nagorno Karabach in September 2023. The present case examines actions and initiatives adopted by a family business to achieve community engagement and support impoverished people and individuals in need during a crisis
GASSM: global attention and state space model based end-to-end hyperspectral change detection.
Get PDFAs an essential task to identify anomalies and monitor changes over time, change detection enables detailed earth observation in remote sensing. By combining both the rich spectral information and spatial image, hyperspectral images (HSI) have offered unique and significant advantages for change detection. However, traditional hyperspectral change detection (HCD) methods, predominantly based on convolutional neural networks (CNNs), struggle with capturing long-range spatial-spectral dependencies due to their limited receptive fields. Whilst transformers based HCD methods are capable of modeling such dependencies, they often suffer from quadratic growth of the computational complexity. Considering the unique capabilities in offering robust long-range sequence modeling yet with linear computational complexity, the emerging Mamba model has provided a promising alternative. Accordingly, we propose a novel approach that integrates the global attention (GA) and state space model (SSM) to form our GASSM network for HCD. The SSM based Mamba block has been introduced to model global spatial-spectral features, followed by a fully connected layer to perform binary classification of detected changes. To the best of our knowledge, this is the first to explore using the Mamba and SSM for HCD. Comprehensive experiments on two publicly available datasets, compared with eight state-of-the-art benchmarks, have validated the efficacy and efficiency of our GASSM model, demonstrating its superiority of high accuracy and stability in HCD
FusDreamer: label-efficient remote sensing world model for multimodal data classification.
Get PDFWorld models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, i.e., hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer
Multi-compartmental risk assessment of heavy metal contamination in soil, plants and wastewater: a model from industrial Gazipur, Bangladesh.
Get PDFHeavy metal contamination in industrial-agricultural regions poses global challenges, yet comprehensive risk assessment models addressing both ecological and human health impacts are scarce. This study introduces a novel multi-compartmental risk assessment framework applied to the Saldha River region of Gazipur, Bangladesh, a rapidly industrialising area experiencing significant environmental stress. Here, we analysed eight heavy metals (Cr, Pb, Cu, Fe, Mn, Zn, Ni, and Cd) in soil, wastewater, and plant samples (spinach, wild rice, and nut grass) via atomic absorption spectrophotometry (AAS). Ecological risks were evaluated through contamination factor (CF), pollution load index (PLI), and geo-accumulation index (Igeo), while human health risks were assessed using hazard indices (HI). Results revealed severe Cd contamination (enrichment factor 2563.19), indicating substantial anthropogenic influence. Correlation analysis of wastewater samples showed strong associations between metal pairs, such as Cu–Zn(0.92), Cu-Fe (0.90) and Zn-Mn (0.87), indicating common industrial sources. Transfer factor (TF) analysis in plants demonstrated substantial variability in metal uptake, with Mn and Ni showing the highest bioavailability, increasing risks to local food chains. Human health risk assessments indicated hazard indices (HI) exceeding safety thresholds for both adults and children, underscoring the urgent need for mitigation strategies. This study offers a novel, integrative framework for assessing multi-source contamination and provides critical baseline data for future environmental policy development. The model is adaptable to industrial regions worldwide, such as textile hubs in Southeast Asia or metal processing zones in Europe and North America, offering new insights into contamination pathways and risk management
Lengthened partial repetitions elicit similar muscular adaptations as full range of motion repetitions during resistance training in trained individuals.
Get PDFResistance training using different ranges of motion may produce varying effects on muscular adaptations. The purpose of this study was to compare the effects of lengthened partial repetitions (LPs) versus full range of motion (ROM) resistance training (RT) on muscular adaptations. In this within-participant study, thirty healthy, resistance-trained participants had their upper extremities randomly assigned to either a lengthened partial or full ROM condition; all other training variables were equivalent between limbs. The RT intervention was an eight-week program targeting upper-body musculature. Training consisted of two training sessions per week, with four exercises per session and four sets per exercise. Muscle hypertrophy of the elbow flexors and elbow extensors was evaluated using B-mode ultrasonography at 45 and 55% of humeral length. Muscle strength-endurance was assessed using a 10-repetition-maximum test on the lat pulldown exercise, both with a partial and full ROM. Data analysis employed a Bayesian framework with inferences made from posterior distributions and the strength of evidence for the existence of a difference through Bayes factors. Both muscle thickness and unilateral lat pulldown 10-repetition-maximum improvements were similar between the two conditions. Results were consistent across outcomes with point estimates close to zero, and Bayes factors (0.16 to 0.3) generally providing "moderate" support for the null hypothesis of equal improvement across interventions. Trainees seeking to maximize muscle size should likely emphasize the stretched position, either by using a full ROM or LPs during upper-body resistance training. For muscle strength-endurance, our findings suggest that LPs and full ROM elicit similar adaptations
Time-evolved metrics for safety pharmacological assessments of small molecules and biologics.
No full textSafety of a small-molecule drug is oftentimes a more important criterion than efficacy in determining drug approval. Aspects of safety pharmacological and toxicological liabilities, often resulting from dose-dependent undesirable interaction with either the primary target of interest or secondary targets, have a huge role to play in determining 'first-in-human' dosage and phase I clinical trials. Given the open thermodynamic nature of the human subjects, it is mandatory that kinetics of drug-target and drug-off-target interactions govern the way selectivity margins are assessed, and dose is decided. However, lack of sufficient thrust on kinetics in guiding early drug discovery decisions has resulted in an overreliance on IC50 measure (a proxy for thermodynamic Ki) as a means of computing safety across the target of interest and potential off-targets. Moreover, based on established practises and known weight of evidence of targets with safety adverse events, the primary panel of secondary pharmacology targets are biased with greater preference for G-protein coupled receptors, transporters and ion-channels with a paucity of enzymes. This can pose unique challenges in assessing safety, especially for advancing and emergent modalities. In this perspective, the critical role kinetic margins should play in assessing safety is emphasised given the myriad assay conditions that can modulate the equilibrium thermodynamic measure as embodied in the proxy report of IC50. Further, it advocates selective and judicious expansion of primary safety panels with greater representation of enzymes and reduced redundancy in eventual read-outs based on potential for correlative output among the off-target classes assessed
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