Biology subjects

Abraham, A.

Publications and source records attributed to Abraham, A..

3 recordsLinked to original sources

bioRxiv2018

Association between Parkinson’s Disease subtypes and tests of physical function: the 360-degree turn test is most predictive

BACKGROUND AND PURPOSEPeople with Parkinsons disease (PD) present phenotypes that can be characterized as tremor-dominant (TD) or postural instability / gait difficulty (PIGD) subtypes. Differentiation of subtypes allows clinicians to predict the disease course and adjust treatment accordingly. We examined whether brief mobility and balance measures can discriminate PIGD from TD phenotypes.\n\nMETHODSWe performed a cross-sectional study with individuals with PD (N=104). Blinded raters assessed participants with the UPDRS or MDS-UPDRS, and potential predictor variables: 360-degree turn test, one-leg stance, backward perturbation test and tandem walk. Participant were classified as PIGD or TD based on the Unified Parkinsons Disease Rating Scale or the Movement Disorder Society revision (UPDRS or MDS-UPDRS) assessment results. Differences in study variables between subtype groups were assessed with univariate analyses. Receiver operating characteristic (ROC) curve analyses were performed to investigate the ability of candidate predictor variables to differentiate PD subtypes.\n\nRESULTSMean age and disease duration were 68{+/-}9 and 7{+/-}5 years, respectively, and Hoehn & Yahr Stages I-IV median (1st,3rd quartile) = II (II, III). No differences between subtypes were observed for tandem walk or reactive postural control. PIGD participants performed worse on number of steps (p<0.001) and time to complete (p=0.003) the 360-degree turn test and one-leg stance (p=0.006). ROC curves showed only the 360-degree turn test could discriminate PIGD from TD with high sensitivity.\n\nCONCLUSIONSThe 360-degree turn test requires minimal time to administer and may be useful in mild-moderate PD for distinguishing PIGD from TD subtypes.

neuroscience↗

bioRxiv2018

Functional Balance between TCF21-Slug defines phenotypic plasticity and sub-classes in high-grade serous ovarian cancer

Cellular plasticity and transitional phenotypes add to complexities of cancer metastasis initiated by single cell epithelial to mesenchymal transition or cooperative cell migration (CCM). We identified novel regulatory cross-talks between Tcf21 and Slug in mediating phenotypic and migration plasticity in high-grade serous ovarian adenocarcinoma. Live imaging discerned CCM as being achieved either through rapid cell proliferation or sheet migration. Transitional states were enriched over the rigid epithelial or mesenchymal phenotypes under conditions of environmental stresses. The Tcf21-Slug interplay identified in HGSC tumors through effective stratification of subtypes also contributed to class-switching in response to disease progression or therapy. Our study effectively provides a framework for understanding the relevance of cellular plasticity in situ as a function of two transcription factors.

cancer biology↗

bioRxiv2016

Predicting brain-age from multimodal imaging data captures cognitive impairment

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.\n\nHighlightsO_LIBrain-based age prediction is improved with multimodal neuroimaging data.\nC_LIO_LIParticipants with cognitive impairment show increased brain aging.\nC_LIO_LIAge prediction models are robust to motion and generalize to independent datasets from other sites.\nC_LI

neuroscience↗