An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

Background: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability.

-Butylidenephthalide Modulates Autophagy to Ameliorate Neuropathological Progress of Spinocerebellar Ataxia Type 3 through mTOR Pathway.

Spinocerebellar ataxia type 3 (SCA3), a hereditary and lethal neurodegenerative disease, is attributed to the abnormal accumulation of undegradable polyglutamine (polyQ), which is encoded by mutated ataxin-3 gene (). The toxic fragments processed from mutant can induce neuronal death, leading to the muscular incoordination of the human body. Some treatment strategies of SCA3 are preferentially focused on depleting the abnormal aggregates, which led to the discovery of small molecule -butylidenephthalide (-BP).

n-Butylidenephthalide exhibits protection against neurotoxicity through regulation of tryptophan 2, 3 dioxygenase in spinocerebellar ataxia type 3.

Spinocerebellar ataxia type 3 or Machado-Joseph disease (SCA3/MJD) is characterized by the repetition of a CAG codon in the ataxin-3 gene (ATXN3), which leads to the formation of an elongated mutant ATXN3 protein that can neither be denatured nor undergo proteolysis in the normal manner. This abnormal proteolysis leads to the accumulation of cleaved fragments, which have been identified as toxic and further they act as a seed for more aggregate formation, thereby increasing toxicity in neuronal cells. To date, there have been few studies or treatment strategies that have focused on controlling toxic fragment formation.