Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging.

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight into how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

Aggresomal sequestration and STUB1-mediated ubiquitylation during mammalian proteaphagy of inhibited proteasomes.

The 26S proteasome, a self-compartmentalized protease complex, plays a crucial role in protein quality control. Multiple levels of regulatory systems modulate proteasomal activity for substrate hydrolysis. However, the destruction mechanism of mammalian proteasomes is poorly understood. We found that inhibited proteasomes are sequestered into the insoluble aggresome via HDAC6- and dynein-mediated transport. These proteasomes colocalized with the autophagic receptor SQSTM1 and cleared through selective macroautophagy, linking aggresomal segregation to autophagic degradation. This proteaphagic pathway was counterbalanced with the recovery of proteasomal activity and was critical for reducing cellular proteasomal stress. Changes in associated proteins and polyubiquitylation on inhibited 26S proteasomes participated in the targeting mechanism to the aggresome and autophagosome. The STUB1 E3 Ub ligase specifically ubiquitylated purified human proteasomes in vitro, mainly via Lys63-linked chains. Genetic and chemical inhibition of STUB1 activity significantly impaired proteasome processing and reduced resistance to proteasomal stress. These data demonstrate that aggresomal sequestration is the crucial upstream event for proteasome quality control and overall protein homeostasis in mammals.

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease.

Parkinson's disease (PD) is a common neurodegenerative disease. However, therapeutic methods of PD are still limited due to complex pathophysiology in PD. Here, optical measurements of individual neurons from in vitro PD model using optical diffraction tomography (ODT) are presented. By measuring 3D refractive index distribution of neurons, morphological and biophysical alterations in in-vitro PD model are quantitatively investigated. It was found that neurons show apoptotic features in early PD progression. The present approach will open up new opportunities for quantitative investigation of the pathophysiology of various neurodegenerative diseases. © 2017 International Society for Advancement of Cytometry.

Glucose-Sensitive Hydrogel Optical Fibers Functionalized with Phenylboronic Acid.

Hydrogel optical fibers are utilized for continuous glucose sensing in real time. The hydrogel fibers consist of poly(acrylamide-co-poly(ethylene glycol) diacrylate) cores functionalized with phenylboronic acid. The complexation of the phenylboronic acid and cis-diol groups of glucose enables reversible changes of the hydrogel fiber diameter. The analyses of light propagation loss allow for quantitative glucose measurements within the physiological range.