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Sustainable Healthcare Practices: Pathways to a Carbon-Neutral Future for the Medical Industry
Get PDFThe healthcare sector is essential for public health but contributes substantially to environmental pollution and carbon emissions, largely through energy-intensive operations, extensive waste generation, and resource-heavy pharmaceutical production. As climate change intensifies, there is a growing imperative for healthcare to adopt carbon-neutral practices that align with global sustainability goals. This narrative review explores the pathways through which healthcare can transition toward carbon neutrality, focusing on energy-efficient hospital designs, eco-friendly medical supplies, sustainable waste management, and low-carbon pharmaceutical manufacturing. Energy-efficient hospital design utilizes renewable energy, sustainable architecture, and AI-driven energy optimization to lower operational emissions. Environmentally sustainable medical supplies reduce single-use plastics by incorporating biodegradable and reusable materials, as well as sustainable procurement practices. Waste management strategies, including waste segregation, recycling, and energy recovery systems, help reduce healthcare’s environmental footprint, while green chemistry and renewable energy integration in pharmaceutical manufacturing further mitigate emissions. Although financial, regulatory, and operational challenges remain, advances in green technology and increasing awareness provide new opportunities for healthcare organizations to adopt sustainable practices. By prioritizing both environmental responsibility and patient care, the healthcare sector can contribute significantly to global climate objectives. This review highlights the importance of collaboration, policy support, and investment in sustainable healthcare to ensure a resilient, low-carbon future
A rare case of dicephalic parapagus conjoined twins diagnosed in the third trimester
Get PDFDicephalic parapagus conjoined twins, a rarely occurring form of conjoined twinning has poor prognosis and remains a significant cause of perinatal deaths. Since majority of cases of conjoined twins are not compatible with life, early and reliable detection with diagnostic medical imaging remains crucial for adequate patient counselling, medical and surgical management. We present a case of dicephalic parapagus twin gestation with associated congenital anomalies detected for the first time with ultrasound in the third trimester in a 29-year-old pregnant woman. An initial first trimester ultrasound at 14 weeks gestational age was unremarkable.
Ultrasound fetal anatomical survey in the third trimester remains reliable for the detection of rare fetal anomalies such as dicephalic parapagus twins that might have been missed in earlier scans. Continuous efforts should therefore be made for the inclusion of detailed anatomical survey in the early third trimester; particularly in women with unremarkable earlier ultrasound scans and in those reporting late for obstetric care
A Smartphone Application Based on Dialectical Behavior Therapy Skills for Binge Eating Episodes: Study Protocol for a Randomized Controlled Trial
Get PDFBackground/Objectives: With the rapid progression of technology, applications have been proposed as a promising alternative to conventional psychotherapeutic treatment. Nonetheless, research on unguided self-help applications for binge eating remains scarce, with most existing studies utilizing cognitive behavioral therapy (CBT) principles. Therefore, this paper presents the protocol for a randomized controlled trial designed to evaluate the efficacy and acceptability of eMOTE, a standalone application designed specifically for women in Portugal who binge eat. eMOTE, adapted from dialectical behavior therapy (DBT), is unique in that it focuses on teaching emotion regulation skills while also integrating core CBT strategies. Methods: At least 68 females who self-report binge eating episodes will be randomized into an intervention group with access to eMOTE for eight weeks or a delayed waitlist, which will have access to eMOTE after the T1 assessment. Assessments will be conducted at baseline (T0), post-intervention (T1), and at 2-month follow-up (T2). The primary outcomes will include objective and subjective binge eating frequency and binge eating symptomatology, while secondary outcomes will assess global levels of ED psychopathology, shape concern, weight concern, eating concern, dietary restraint, compensatory behaviors, mindfulness, emotion regulation difficulties, intuitive eating, psychological distress, and body mass index. Conclusions: This study will contribute to the limited literature on the use of smartphone technology as an alternative to traditional psychotherapy. Furthermore, this standalone application will offer insights into the use of emotion regulation and food monitoring components designed for adult females experiencing binge eating episodes
The benefits and challenges of queering TESOL: a qualitative research synthesis
Get PDFThis study presents a qualitative research synthesis of 14 empirical studies published between 2017 and 2024, examining queer pedagogies within international TESOL contexts. By synthesising recent literature, this study addresses both the theoretical underpinnings of queer pedagogies, as well as the benefits and challenges related to their practical application in TESOL. Specifically, the characteristics of queer pedagogies identified are the integration of LGBTQ+ identities within the curriculum, challenging heteronormative assumptions, and creating an environment where learners can openly discuss LGBTQ + topics. The findings show that queer pedagogies can foster critical thinking, improve communicative skills and a greater sense of belonging among learners, highlighting their potential to facilitate second language acquisition. However, challenges arise due to entrenched societal beliefs, institutional reluctance and limited teacher training on LGBTQ+ issues. The thematic analysis reveals that while queer pedagogies promote diversity, inclusion and equity in language teaching, educators often face external pressures and insufficient resources, evidencing the need for targeted professional development to support the broader acceptance of queer pedagogies. This research contributes to TESOL scholarship by providing insights into how queer pedagogies can advance language education, including through their integration with other pedagogical approaches. Ultimately, it advocates for increased academic and pedagogical resources to support English language teachers in the implementation of queer pedagogies, and points towards the need for future studies to explore how these pedagogies are experienced by diverse learners across varied TESOL contexts
Brexanolone, zuranolone and related neurosteroid GABA A receptor positive allosteric modulators for postnatal depression
Get PDFExplainable AI for Parkinson's disease prediction: A machine learning approach with interpretable models.
Get PDFParkinson's Disease (PD) is a chronic, progressive neurological disorder with significant clinical and economic impacts globally. Early and accurate prediction remains challenging with traditional diagnostic methods due to subjectivity, delayed diagnosis, and variability. Machine Learning (ML) approaches offer potential solutions, yet their clinical adoption is hindered by limited interpretability. This study aimed to develop an interpretable ML model for early and accurate PD prediction using comprehensive multimodal datasets and Explainable Artificial Intelligence (XAI) techniques. The study applied five ML algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), XGBoost, and a stacked ensemble method to a publicly available dataset (n = 2105) from Kaggle. Data encompassed demographic, medical history, lifestyle, clinical symptoms, cognitive, and functional assessments with specific inclusion/exclusion criteria applied. Preprocessing involved normalization, Synthetic Minority Oversampling Technique (SMOTE), and Sequential Backward Elimination (SBE) for feature selection. Model performance was evaluated via accuracy, precision, recall, F1-score, and Area Under Curve (AUC). The best-performing model (RF with feature selection) was interpreted using SHAP and LIME methods. Random Forest combined with Backward Elimination Feature Selection achieved the highest predictive accuracy (93 %), precision (93 %), recall (93 %), F1-score (93 %), and AUC (0.97). SHAP and LIME analyses indicated UPDRS scores, cognitive impairment, functional assessment, and motor symptoms as primary predictors, enhancing clinical interpretability. The study demonstrated the effectiveness of an interpretable RF model for accurate PD prediction. Integration of ML and XAI significantly improves clinical decision-making, diagnosis timing, and personalized patient care. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.
Transforming youth engagement in disaster risk management and heritage conservation through adapting the concept of brain re-engineering and reimagination
Get PDFThe escalating intensity of global disasters and the growing vulnerability of cultural heritage require innovative solutions that actively engage youth as key stakeholders. Despite their potential for creativity, innovation, and technological proficiency, youth remain significantly underrepresented in disaster risk management (DRM) and heritage conservation efforts. This paper introduces the Brain Re-Engineering and Reimagination (BRECR) framework, adapted from its original use in agriculture, as a strategic approach to addressing these gaps and catalyzing youth-led initiatives. The BRECR framework comprises five pillars: perception change, ideation and enterprise, technological solutions, sustainability, and social equity in public policy. We argue that by reshaping societal narratives, fostering youth-driven innovation hubs, integrating advanced technologies, and emphasizing long-term capacity building, we demonstrate how BRECR can bridge critical gaps in policy, education, and practice. This paper proposes actionable strategies for enhancing youth engagement and empowerment, positioning them as leaders in building resilient communities and safeguarding cultural heritage. By reimagining youth as active changemakers, we can harness their creativity and technological expertise to establish inclusive, sustainable, and impactful solutions for future generations
Too Woke or Not Woke Enough? Racial Awareness in the Church of England
Get PDFThe Church of England has recently engaged again with issues of racism by setting up the Anti-Racism Taskforce in 2020, followed by the Archbishops’ Commission for Racial Justice in 2021. Both groups stressed the lack of progress in tackling racism in the Church and the need to raise awareness of racial injustice at all levels. This paper reports on the measurement of racial awareness among 3,167 clergy and lay people who took part in the Church 2024 survey. Eight items in the survey were used to create the racial awareness scale. Results suggested a mixed picture with a majority awareness that racial inequality is an important issue that needs to be addressed, a majority rejection of the idea that there may be local or institutionally embedded racism and enthusiasm for diversifying leadership but not for taking specific actions relating to historic slavery. Multiple regression analysis showed racial awareness was shaped by a complex mixture of individual, contextual and religious factors
Artificial intelligence for obesity management: A review of applications, opportunities, and challenges
Get PDFTraditional obesity management approaches, including dietary interventions, physical activity programmes, pharmacotherapy, and behavioural therapies, face significant limitations in scalability, personalisation, and long-term adherence rates. The emergence of artificial intelligence (AI) technologies, particularly machine learning and deep learning algorithms, has opened new frontiers for transforming obesity prevention, diagnosis, and management strategies. This comprehensive narrative review synthesises current evidence on AI applications in obesity management, examining technological innovations from predictive risk models to personalised digital therapeutics. The review explores AI-based diagnostic tools utilising computer vision for body composition analysis, predictive algorithms identifying high-risk individuals using electronic health records, personalised behavioural interventions powered by reinforcement learning, and remote monitoring systems integrating wearable technologies with intelligent data analytics. Furthermore, it investigates clinical effectiveness of AI-driven digital therapeutics platforms and examines AI integration within clinical decision support systems. The analysis reveals significant benefits including enhanced scalability for population-level interventions, improved personalisation through real-time data integration, increased precision in risk stratification, and potential cost-effectiveness through optimised resource allocation. However, substantial challenges remain, including data privacy and security concerns, algorithmic bias that may exacerbate health disparities, limited large-scale clinical validation, declining user engagement over time, and complex regulatory and ethical considerations. Addressing these challenges through multidisciplinary collaboration, robust validation studies, and ethical frameworks will be critical for successfully integrating AI technologies into routine obesity care and achieving equitable health outcomes across diverse populations