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Beyond Gestational Diabetes:Maternal and Offspring Health and Lifestyle 3 years Postnatally in a secondary analysis of the UPBEAT Trial Cohort
No full textBackground: Gestational diabetes (GDM) is associated with increased future obesity risk in affected mothers and children. Objective: We assessed if dietary behaviours learnt during a GDM pregnancy positively impact maternal and child health 3 years postpartum. Method: In a secondary analysis, we included women with obesity recruited to the UPBEAT randomised controlled trial with 3-year follow-up postnatally (n = 441). Maternal and offspring anthropometry and dietary data were recorded antenatally and at follow-up. Data were assessed using linear/logistic regression, adjusting for confounders. Results: Women with GDM (22%) had higher BMI (median 35.6 vs. 34.2 kg/m 2; p = 0.049) and energy intake (1738.2 vs. 1551.6 kcal/day; p = 0.005) at ~16 weeks' gestation compared to unaffected women, but lower gestational weight gain (4.5 kg vs. 6.6 kg; p < 0.001). However, at 3 years postpartum BMI was similar between groups (35.8 vs. 35.2 kg/m 2; p > 0.5). GDM-exposed infants had a higher birthweight (55.4 vs. 45.9th centile; p = 0.008) than unexposed infants and at 3 years of age were more likely to be overweight/obese (International Obesity Task Force, IOTF, standards; OR 2.32; 95% CI 1.38, 3.91) but with similar skinfold thicknesses and dietary patterns. Conclusion: Women with GDM demonstrated reduced gestational weight gain, and despite a higher BMI than women without GDM in early pregnancy, this difference was not evident at 3 years postpartum. However, while maternal and offspring dietary behaviours were comparable between groups, exposed offspring were at increased risk of overweight/obesity at 3 years of age.</p
Relationship between poverty and symptoms of depression and anxiety among adolescents in Nepal:Examining the mediating effect of external and internal resilience
No full textObjective This study aimed to examine the mediating effect of two types of resilience (internal, with factors such as problem solving and perseverance, and external resilience, with factors such as social support and sense of belonging) among adolescents affected by family poverty in Nepal in relation to symptoms of depression and anxiety. Methods Participants identified as living in deprived conditions ( n = 491) in a cross-sectional survey completed measures about poverty, depressive and anxiety symptoms, and external and internal resilience. Mediation analyses were performed using poverty scores as the predictor, internal and external resilience as mediators, and depression and anxiety scores as outcomes, tested through parallel and serial mediation models. Results External resilience emerged as the only pathway across both parallel and serial mediation models. External resilience showed significant indirect effects for both depression (β = 0.797, 95 % CI: 0.181–1.677) and anxiety (β = 0.557, 95 % CI: 0.101–1.237). No sequential mediation was found. Although the total association of poverty with depression and anxiety was not significant—likely due to participants' homogeneity in poverty—external resilience accounted for 54.33 % of the association with depression and 31.61 % with anxiety, contributing to total indirect effects of 66.79 % and 38.25 %, respectively. Conclusions This study expands the evidence on the mediating effect of external resilience in the association between poverty and depression and anxiety in Nepali adolescents. The mediating effect was stronger for depressive outcomes than for anxiety. Factors of external resilience are discussed as a critical target for mental health interventions among adolescents living in poverty.</p
Optimizing super-feature selection for machine learning-enhanced spectroscopic analysis in biomedical research
Get PDFPurposeMachine-learning-powered label-free infrared spectroscopic methods offer significant potential for diagnostic and biomedical applications. However, their applications have been limited by spectral noise, where critical features are often obscured by overlapping bands and data redundancy. Although various feature selection methods have been proposed, many suffer from limitations in consistency and interpretability. To address these challenges, we introduce a novel multi-model machine learning approach that integrates five distinct algorithms to identify a set of “super-features”—spectral features consistently deemed significant across all models.Principal resultsThis novel workflow outperforms traditional algorithms, achieving superior classification accuracy (>99%) in distinguishing infected from healthy cells, despite using fewer spectral features. To ensure robustness and generalizability, we developed a comprehensive validation strategy that includes independent classifier evaluations, label randomization, and unsupervised analyses. Importantly, the identified super-features accurately differentiated infection states across multiple time points and enhanced the biological interpretability of infection-associated biochemical changes.ConclusionsThese findings highlight the potential of advanced multi-model feature selection techniques to enhance the diagnostic power of spectroscopic data in biomedical research, offering high accuracy and valuable biological insights into infection progression
Transdiagnostic Neurocognitive Endophenotypes for Schizophrenia, Bipolar I Disorder and a Broad Psychosis/Bipolar I Disorder Phenotype:A Mega-Analysis of Twin and Sibling Data
Get PDFBackgroundPsychiatric research is increasingly embracing a paradigm shift from categorical diagnoses to neurobiologically meaningful dimensions that cross current diagnostic boundaries. This transposition calls for redefining endophenotypes to accommodate transdiagnostic vulnerabilities. We sought to identify shared and disorder-specific neurocognitive endophenotypes for schizophrenia, bipolar I disorder (BD-I) and a broad psychosis/BD-I phenotype in a mega-analysis of twin/sibling data.Study DesignWe performed genetic model fitting to intelligence (IQ) and computerised neurocognitive data derived from 1050 twins/siblings from three research centres in the UK, Denmark and the Netherlands, affected (n=257) or unaffected (n=793) by schizophrenia, other primary psychoses and BD-I. We examined the endophenotypic status of IQ, spatial working memory (SWM), visual recognition, sustained attention/rapid visual processing (RVP), mental flexibility, and spatial planning/problem solving (all validated as endophenotypes for schizophrenia in previous studies) in relation to schizophrenia, BD-I and the broad phenotype.Study ResultsAfter covarying for age, gender, education and research centre, IQ and SWM emerged as transdiagnostic endophenotypes, showing statistically significant heritabilities (h2 67-75% and 28-30%, respectively), phenotypic correlations (rph |0.14|-|0.25|) and genetic correlations (rg |0.18|-|0.42|) with all diagnostic phenotypes. Additionally, all remaining cognitive domains received validation as endophenotypes for the broad phenotype, and all, but RVP, for schizophrenia.ConclusionsIQ and SWM tap into transdiagnostic elements of the genetic vulnerabilities to psychosis and BD-I. Our findings add to emergent evidence which spurs cautious optimism that a psychiatric nosology based on aetiology rather than phenotypical classifications may be feasible in the future, enabling biotyping and novel approaches to treatment
A representative European Parliament? Members of European parliamentary party groups and the representation of citizens’ preferences.
Get PDFThe link between citizens' and representatives' preferences is central to representative democracy. Research on representation of citizens' preferences in the European Parliament (EP) has primarily concentrated on national political parties and candidates. We ask how well transnational EP party groups and members of the EP (MEPs) represent their voters on the left–right and EU-integration dimensions. We use data from four waves of the European Election Studies and surveys of MEPs. We show that MEPs in centrist parties tend to be closer to their voters on the left–right dimension than others, with EU positions making little difference to this. Our findings indicate the median voter tends to be more Eurosceptic than the median MEP across most centrist party groups whilst the opposite is true for the most Eurosceptic groups. These results have important implications for the study of representation and democracy in the EU
Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis
Get PDFBACKGROUND AND HYPOTHESIS: Digital remote monitoring (DRM) captures service users' health-related data remotely using devices such as smartphones and wearables. Data can be analyzed using advanced statistical methods (eg, machine learning) and shared with clinicians to aid assessment of people with psychosis' mental health, enabling timely intervention. Such methods show promise in detecting early signs of psychosis relapse. However, little is known about clinicians' views on the use of DRM for psychosis. This study explores multi-disciplinary staff perspectives on using DRM in practice.STUDY DESIGN: Fifty-nine mental health professionals were interviewed about their views on DRM in psychosis care. Interviews were analyzed using reflexive thematic analysis. Study Results: Five overarching themes were developed, each with subthemes: (1) the perceived value of digital remote monitoring; (2) clinicians' trust in digital remote monitoring (3 subthemes); (3) service user factors (2 subthemes); (4) the technology-service user-clinician interface (2 subthemes); and (5) organizational context (2 subthemes).CONCLUSIONS: Participants saw the value of using DRM to detect early signs of relapse and to encourage service user self-reflection on symptoms. However, the accuracy of data collected, the impact of remote monitoring on therapeutic relationships, data privacy, and workload, responsibility and resource implications were key concerns. Policies and guidelines outlining clinicians' roles in relation to DRM and comprehensive training on its use are essential to support its implementation in practice. Further evaluation regarding the impact of digital remote monitoring on service user outcomes, therapeutic relationships, clinical workflows, and service costs is needed.</p
Verifiably robust conformal prediction for probabilistic guarantees under adversarial attacks
Get PDFConformal Prediction (CP) is a popular uncertainty quantification method that provides distribution-free, statistically valid prediction sets, assuming that training and test data are exchangeable. In such a case, CP's prediction sets are guaranteed to cover the (unknown) true test output with a user-specified probability. Nevertheless, this guarantee is violated when the data is subjected to adversarial attacks, which often result in a significant loss of coverage. Recently, several approaches have been put forward to recover CP guarantees in this setting. These approaches leverage variations of randomised smoothing to produce conservative sets which account for the effect of the adversarial perturbations. They are, however, limited in that they only support ℓ2-bounded perturbations and classification tasks. This paper introduces VRCP (Verifiably Robust Conformal Prediction), a new framework that leverages recent neural network verification methods to recover coverage guarantees under adversarial attacks. We also demonstrate how VRCP can be used to mitigate poisoning attacks. Our VRCP method is the first to support perturbations bounded by arbitrary norms including ℓ1, ℓ2, and ℓ∞, as well as regression tasks. We evaluate and compare our approach on image classification tasks (CIFAR10, CIFAR100, and TinyImageNet) and regression tasks for deep reinforcement learning environments. In every case, VRCP achieves above nominal coverage and yields significantly more efficient and informative prediction regions than the SotA.</p
A qualitative exploration of women's experiences of food insecurity around pregnancy aligned with the socio-ecological model
No full textPregnancy, postpartum, and infancy are pivotal periods when food insecurity (FI), defined as inconsistent access to sufficient, safe, and nutritious food, can adversely impact maternal and child health. Despite high FI rates in the UK, little is known about women's experiences during this time. This study explores FI during pregnancy and postpartum amongst a multi-ethnic group of women. Between October 2023 and February 2024 semi-structured interviews were conducted with purposively sampled food-insecure individuals (>18 years, pregnant or postpartum <12 months, residing in South London with recourse to public funds). Demographics were analysed with SPSS, and interviews using Reflexive Thematic Analysis in NVivo. Findings were discussed in alignment with the socio-ecological model. Three key themes were generated from eleven interviews. 1) Societal systems failure and its nutritional impact, 2) System 'soothers' mitigating FI, and 3) Creating Coordinated Care. Theme one describes the structural drivers' influencing maternal food strategies, eating and feeding behaviours. Theme two explores factors protecting or inhibiting women access to support. Theme three discusses the benefits and opportunities for improved health and social care coordination to address FI during pregnancy. This study emphasizes how structural determinants exacerbate FI during pregnancy and postpartum, with unique structural drivers to this life course period worsening its impact. FI influences health eating despite resourceful cooking and food management strategies. Greater coordinated care is urgently needed to address FI, promote healthy diets, and improve access.</p
Thriving at Work:A Synthesis of Human Resource Management Perspectives and a Future Research Agenda
Get PDFThriving at work is a psychological state defined by dual experiences of vitality and learning. Existing research suggests that HRM practices can play a pivotal role in fostering employee thriving. In this perspective paper, we review the current literature on the relationship between HRM practices and employee thriving through five broad conceptual frameworks: (1) high-performance HRM systems, (2) development-oriented HRM, (3) purposeful and responsible HRM, (4) relational and inclusive HRM, and (5) multilevel contextual HRM. Beyond this review, we propose four key avenues for future research aimed at advancing our understanding of how HRM practices contribute to employee thriving. These research directions seek to explore the underlying mechanisms, contexts, and conditions that influence the effectiveness of HRM practices in promoting thriving, with a particular focus on sustaining employee thriving over time. Through these insights, we aim to provide a more nuanced understanding of how HRM can be strategically designed and implemented to support sustainable and regenerative thriving in dynamic work environments