Biology subjects

Schmid, R.

Publications and source records attributed to Schmid, R..

4 recordsLinked to original sources

bioRxiv2019

Volume and intramuscular fat content of upper extremity muscles in individuals with chronic hemiparetic stroke

Stroke survivors often experience upper extremity deficits that make activities of daily living (ADLs) like dressing, cooking and bathing difficult or impossible. Survivors experience paresis, the inability to efficiently and fully activate muscles, which combined with decreased use of the upper extremity, will lead to muscle atrophy and potentially an increase in intramuscular fat. Muscle atrophy has been linked to weakness post stroke and is an important contributor to upper extremity deficits. However, the extent of upper extremity atrophy post hemiparetic stroke is unknown and a better understanding of these changes is needed to inform the direction of intervention-based research. In this study, the volume of contractile tissue and intramuscular fat in the elbow and wrist flexors and extensors were quantified in the paretic and non-paretic upper limb using MRI and the Dixon technique for the first time. Total muscle volume (p[≤]0.0005) and contractile element volume (p[≤]0.0005) were significantly smaller in the paretic upper extremity, for all muscle groups studied. The average percent difference between limbs and across participants was 21.3% for muscle volume and 22.9% for contractile element volume. We also found that while the percent intramuscular fat was greater in the paretic limb compared to the non-paretic (p[≤]0.0005), however, the volume of intramuscular fat was not significantly different between upper limbs (p=0.231). The average volumes of intramuscular fat for the elbow flexors/extensors and wrist flexors/extensors were 28.1, 28.8 and 19.9, 8.8 cm3 in the paretic limb and 29.6, 27.7 and 19.7, 8.8 cm3 in the non-paretic limb. In short, these findings indicate a decrease in muscle volume and not an increase in intramuscular fat, which will contribute to the reduction in strength in the paretic upper limb.

bioengineering↗

bioRxiv2019

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

PurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.\n\nMethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm (XGBoost) was trained on 70% of the patients (N=28) and tested on 30% (N=17) to predict KRT81+ vs. KRT81-tumor subtypes. The average sensitivity, specificity and ROC-AUC value were calculated. Chemotherapy response was assessed stratified by subtype. Radiomic feature importance was ranked.\n\nResultsThe mean{+/-}STDEV sensitivity, specificity and ROC-AUC were 0.90{+/-}0.07, 0.92{+/-}0.11, and 0.93{+/-}0.07, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81-patients (7.0 vs. 22.6 months, HR 1.44, log-rank-test P=<0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 0.85, P=0.037) compared to KRT81-patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 0.88, P=0.027).\n\nConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for overall patient survival and response to chemotherapy.

bioinformatics↗

bioRxiv2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗

bioRxiv2019

Adapting the ELEAT: Early Life Exposure Assessment Tool) to Portugal - a pilot study to tackle gene-environment interactions in Autism Spectrum Disorder

BackgroundAutism Spectrum Disorder (ASD) is a pervasive and clinically heterogeneous neurodevelopmental disorder characterized by deficits in social communication and interaction skills, and repetitive and stereotyped behaviours. It is known that ASD has a strong genetics component, but heritability estimates of 50-80% suggest that modifiable non-genetic factors may play an important role in the onset of the disorder. Recently, pre-, peri and post-natal exposure to a variety of environmental factors has been implicated in ASD. Yet, the comprehensive assessment of environmental exposures in this pathology, using large population datasets, is still lacking. The objective of this study was to pilot an environmental exposure assessment tool in Portugal.\n\nMethodsTo examine environmental exposures in a population of Portuguese children with ASD, we translated, adapted and piloted the Early Life Exposure Assessment Tool (ELEAT). The ELEAT was originally developed to assess environmental factors in studies of neurodevelopmental disorders. It is a questionnaire filled by mothers of children with ASD, enquiring about Demographic Information, Maternal Conditions/Medical Interventions, Breastfeeding and Child Diet, Maternal Diet, Supplements, Lifestyle, Home and Environment, Environment, Occupation and Exposures. The ELEAT gathers information about environmental exposure along key phases for early neurodevelopment, from 3 months prior to conception, pregnancy, labor and delivery to the first year of life of the child. Two focus groups were realized, one with mothers of typically-developing children and another with mothers of children with ASD, in order to discuss the mothers opinion regarding the tool comprehensiveness and relevance.\n\nResultsThe large majority of mothers were sure about their answers for all modules, with a small fraction of the group reporting difficulties for the Occupations/Exposures module. Most mothers considered the ELEAT to be a little too long, but generally found that the instructions were clear and, most importantly, agreed that the questions were important.\n\nConclusionsIntegration of the pilot feedback will allow us to enhance the tool and optimize its usage in Portuguese-speaking communities, improving its capacity to assemble accurate environmental data from diverse cultural settings, and to be extended to larger population datasets. Combined with genetic and clinical data, the ELEAT will contribute to the identification of modifiable lifestyle and environmental risk factors for ASD. Such evidence may eventually provide the opportunity for disease prevention or reduced severity by mitigating exposure when genetic susceptibility is identified early in life.

developmental biology↗