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Role of pancreatic lipase inhibition in obesity treatment: mechanisms and challenges towards current insights and future directions
The worldwide health emergency of obesity is closely connected to how dietary fats are metabolized, whereas the process is significantly influenced by pancreatic lipase (PL), an enzyme critical for lipid hydrolysis into fatty acids. This narrative review employs a methodological approach utilizing literature searches of PubMed data up to March 2024. The search term criteria encompasses keywords related to the role, mechanism, challenges, and current and future treatments of pancreatic lipase in obesity with an overall references is 106. This paper offers a comprehensive explanation of the role of PL, underlining its significance in the digestive process and lipid imbalances that contribute to obesity and by extension, its impact on obesity development and progression. Additionally, it delves into the dual functionality of the pancreas, emphasizing its impact on metabolism and energy utilization which, when dysregulated, promotes obesity. A focal point of this review is the investigation into the efficacy, challenges, and adverse effects of current pancreatic lipase inhibitors, with orlistat being highlighted as a primary current drug delivery. By discussing advanced obesity treatments, including the exploration of novel anti-obesity medications that target specific biological pathways, this review underscores the complexity of obesity treatment and the necessity for a multifaceted approach. In conclusion, this paper emphasizing the importance of understanding the role of enzymes like pancreatic lipase mechanistic and adopting a multidisciplinary approach to treatment and side effects of current obesity drugs and explore new emerging therapeutic strategies for more effective obesity management.
Ethical considerations in AI for child health and recommendations for child-centered medical AI
There does not exist any previous comprehensive review on AI ethics in child health or any guidelines for management, unlike in adult medicine. This review describes ethical principles in AI for child health and provides recommendations for child-centered medical AI. We also introduce the Pediatrics EthicAl Recommendations List for AI (PEARL-AI) framework for clinicians and AI developers to ensure ethical AI enabled systems in healthcare for children.
Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes
Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.
Not all who integrate are academics: zooming in on extra-academic integrative expertise
Solving complex problems requires integrating knowledge and skills from various domains. The importance of cross-domain integration has motivated researchers to study integrative expertise: what knowledge and skills help achieve cross-domain integration? Much of the existing research focuses on the integrative expertise of academic researchers who perform inter- and transdisciplinary research. However, academics are not the only ones facilitating integration. In transdisciplinary research, where academics collaborate with professionals, stakeholders, and policymakers, these extra-academic actors can contribute significantly to cross-domain integration. Moreover, many complex problems are addressed entirely outside of universities. This paper contributes to a broader, more inclusive understanding of integrative expertise by drawing attention to the diversity of extra-academic integrative expertise, providing examples of what this expertise looks like in practice, and reflecting on differences with its academic counterpart. The contributions are based on a case study of integrative expertise in Oosterweel Link, a large urban development project in Antwerp, Belgium.
Enhancing children’s numeracy and executive functions via their explicit integration
Executive functions (EF) are crucial to regulating learning and are predictors of emerging mathematics. However, interventions that leverage EF to improve mathematics remain poorly understood. 193 four-year-olds (mean age = 3 years; 11 months pre-intervention; 111 female, 69% White) were assessed 5 months apart, with 103 children randomised to an integrated EF and mathematics intervention. Our pre-registered hypotheses proposed that the intervention would improve mathematics more than practice as usual. Multi-level modelling and network analyses were applied to the data. The intervention group improved more than the control group in overall numeracy, even when controlling for differences across settings in EF and mathematics-enhancing practices. EF and mathematics measures showed greater interconnectedness post-intervention. In addition, disadvantaged children in the intervention group made greater gains than in the control group. Our findings emphasise the need to consider EFs in their integration with co-developing functions, and in their educational and socio-economic context.
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