Chromosomal instability (CIN) is a hallmark of many cancers and a major contributor to tumorigenesis. Centromere and kinetochore associated proteins such as the evolutionarily conserved centromeric histone H3 variant CENP-A, associate with centromeric DNA for centromere function and chromosomal stability. Stringent regulation of cellular CENP-A levels prevents its mislocalization in yeast and flies to maintain genome stability. CENP-A overexpression and mislocalization are observed in several cancers and reported to be associated with increased invasiveness and poor prognosis. We examined whether there is a direct relationship between mislocalization of overexpressed CENP-A and CIN using HeLa and chromosomally stable diploid RPE1 cell lines as model systems. Our results show that mislocalization of overexpressed CENP-A to chromosome arms leads to chromosome congression defects, lagging chromosomes, micronuclei formation and a delay in mitotic exit. CENP-A overexpressing cells showed altered localization of centromere and kinetochore associated proteins such as CENP-C, CENP-T and Nuf2 leading to weakened native kinetochores as shown by reduced interkinetochore distance and CIN. Importantly, our results show that mislocalization of CENP-A to chromosome arms is one of the major contributors for CIN as depletion of histone chaperone DAXX prevents CENP-A mislocalization and rescues the reduced interkinetochore distance and CIN phenotype in CENP-A overexpressing cells. In summary, our results establish that CENP-A overexpression and mislocalization result in a CIN phenotype in human cells. This study provides insights into how overexpression of CENP-A may contribute to CIN in cancers and underscore the importance of understanding the pathways that prevent CENP-A mislocalization for genome stability.

Objectives Delirium is highly prevalent in patients with advanced cancer. This study aimed to investigate delirium rates and potential associated factors such as mortality in patients admitted to an acute palliative care unit (APCU). Our second aim was to validate the Korean version of the Memorial Delirium Assessment Scale (K‐MDAS). Methods A total of 102 patients with advanced cancer, and who were admitted to the APCU, were assessed. Demographic data were collected alongside clinical diagnosis, Eastern Cooperative Oncology Group (ECOG) performance status, clinical symptoms according to the Edmonton Symptom Assessment System, history of smoking, alcohol use, hypnotic use, and daily dose of morphine were collected. The Confusion Assessment Method, the Delirium Rating Scale‐Revised 98, and the K‐MDAS were measured at admission and 1 week later. Results Twenty‐four patients (23.52%) were diagnosed with delirium, and associated factors were old age (P = 0.007), higher ECOG (P = 0.011), and drowsiness (P < 0.001). The presence of delirium was an independent predictor of 1‐month mortality; male gender, higher body mass index, and hypnotic use were also related to 1‐month mortality. The K‐MDAS had reliable internal consistency (α = 0.942) and showed sensitivity of 0.958 and specificity of 0.921 at the optimal cutoff score for diagnosing delirium of 9. Conclusions Delirium was prevalent in patients admitted to the APCU and was associated with 1‐month mortality. The K‐MDAS showed acceptable reliability and validity and can be used to screen for delirium in a palliative care setting.

Competing Interest Statement Dr. Dale reports that he was a Founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. He receives funding through research grants from GE Healthcare to UCSD. Dr. Rakow-Penner is a consultant for Human Longevity, Inc. and receives funding through research grants from GE Healthcare. The terms of these arrangements have been reviewed by and approved by UCSD in accordance with its conflict of interest policies. Dr. Igor Vidić is employed as a consultant for Cortechs Labs, Inc. Dr. Seibert reports personal honoraria in the past three years from Varian Medical Systems, Multimodal Imaging Services Corporation, and WebMD.

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. Methods:The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.Results: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI.The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were

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