Bmc Med
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The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. ⋯ However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.
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Medication Treatment Satisfaction (M-TS) from the patients' perspective is important for comprehensively evaluating the effect of medicines. The extent to which current patient-reported outcome measures (PROMs) for M-TS are valid, reliable, responsive, and interpretable remains unclear. To assess the measurement properties of existing PROMs for M-TS and to highlight research gaps. ⋯ Most existing PROMs for M-TS require further exploration of measurement properties. Reporting guidelines are needed to enhance the reporting quality of the development and validation of PROMs for M-TS.
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Sex disparity between metabolic-obesity (defined by body mass index, BMI) phenotypes and obesity-related cancer (ORC) remains unknown. Considering BMI reflecting overall obesity but not fat distribution, we aimed to systematically assess the association of our newly proposed metabolic-anthropometric phenotypes with risk of overall and site-specific ORC by sex. ⋯ There was a significant sex disparity between metabolic-anthropometric phenotypes and ORC risk. We advised future ORC prevention and control worth taking body shape and sex disparity into account.
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The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. ⋯ Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.
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The combination of anti-programmed death 1 (PD-1) inhibitors and tyrosine kinase inhibitors is an effective treatment strategy in endometrial cancer. We aimed to explore the efficacy and safety of camrelizumab plus apatinib as an alternative therapeutic option in patients with previously treated endometrial cancer. ⋯ Camrelizumab plus apatinib showed promising antitumor activity with manageable toxicity in patients with advanced or recurrent endometrial cancer who had failed at least one prior systemic therapy. The findings of this study support further investigation of camrelizumab plus apatinib as an alternative therapeutic option, especially for patients with MSS/pMMR tumors.