We have previously shown that lithium can protect against the polyglutamine toxicity of the Huntington's disease mutation in cell models. Here, we demonstrate for the first time in vivo that lithium can protect against the toxicity caused by aggregate-prone proteins with either polyglutamine or polyalanine expansions in Drosophila. We also show that these protective effects can be partly accounted for by lithium acting through the Wnt/Wg pathway, as a GSK3beta-specific inhibitor and overexpression of dTCF also mediate protective effects. Our data suggest that lithium deserves serious consideration for further studies as a therapeutic for polyglutamine diseases, particularly as it is an established drug that has been used for several decades for chronic treatment of affective disorders.
Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning models. Methods: MR imaging data were analysed from a random sample of 5 patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.
Conclusion:Although breast cancer patients who receive post-mastectomy implant reconstruction are at increased risk of breast ALCL, the absolute risk remains extremely low. Furthermore, the risk does not appear to be increased compared to patients that receive implantation (for cosmesis or reconstruction) in previously reported literature.
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