Bmc Med
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Immune checkpoint inhibitors (ICIs) had modest advances in the treatment of extensive-stage small cell lung cancer (ES-SCLC) in clinical trials, but there is a lack of biomarkers for prognosis in clinical practice. ⋯ We constructed a novel prognostic model for ES-SCLC to predict survival employing baseline tumor burden, nutritional and inflammatory parameters, it is easily measured to screen high-risk patient populations.
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Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach. ⋯ Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research.
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Multicenter Study
A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.
The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging. ⋯ By leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.
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Multicenter Study Observational Study
Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.
Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalaninemia (HPA) in children. However, traditionally, adjusting the diet requires significant time from clinicians and parents. This study aims to investigate the development of a machine-learning model that predicts a range of dietary Phe intake tolerance for children with HPA over 10 years following diagnosis. ⋯ Our model integrates metabolic and genetic information to accurately predict age-specific Phe tolerance, aiding in the precision management of patients with HPA. This study provides a potential framework that could be applied to other inborn errors of metabolism.
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Neurogenic erectile dysfunction, characterized by neurological repair disorders and progressive corpus cavernosum fibrosis (CCF), is an unbearable disease with limited treatment success. IL-17A exhibits a complex role in tissue remodelling. Nevertheless, the precise role and underlying mechanisms of IL-17A in CCF under denervation remain unclear. ⋯ IL-17A assumes a pivotal role in denervated CCF by activating the mTORC2-ACACA signalling pathway, presenting itself as a potential therapeutic target for effectively overcoming CCF and erection rehabilitation in neurogenic ED.