Biomedical subjects

Simon Rogers

Publications and source records attributed to Simon Rogers.

4 recordsLinked to original sources

PubMed2025

Interrogating functional connectivity of in vitro neural glia tissue model modulated through integrative control of matrix stiffness and a neurotrophic factor.

Brain function emerges from intricate cellular communication within neural networks. Both In silico neuronal models and primary neuron cells have revealed that the branching architecture of individual neurons determines the bioelectrical signal propagation pattern and dynamics. However, whether stem cell-differentiated neurons can build functional connectivity regulated by neuronal morphology has yet to be determined. Here, we hypothesized that neurite length, branching, or both factors would regulate the functional connectivity of the stem cell-differentiated neural network. We examined this hypothesis by differentiating mouse cortical neural stem cells (NSCs) on Matrigel substrates with varying storage moduli, both with and without basic fibroblast growth factor (bFGF). Interestingly, with bFGF, Matrigel with a storage modulus (G') of 100 Pa drives NSCs to differentiate into neurons with more dendritic branches, while the gel with G' of 50 Pa led to the development of longer neurites with fewer branches. Notably, branch-rich neural networks exhibited an increased frequency of calcium transients. Using a MATLAB-based analysis pipeline incorporating graph theory, we constructed spatial and temporal calcium activity maps, revealing that branching complexity, more than neurite length, correlates with the density and strength of functional neural circuits. Overall, this study demonstrates that the dendritic branching of neurons, modulated with matrix stiffness and neurotrophic factors, is a key element in enhancing the electrophysiological functionality of the stem cell-differentiated neural network. This finding will have a significant impact on efforts to reconstruct functional neural tissue models, advancing both regenerative therapies and unexplored applications, including biological computing.

Animals↗

PubMed2003

Expression profiling targeting chromosomes for tumor classification and prediction of clinical behavior.

Tumors are associated with altered or deregulated gene products that affect critical cellular functions. Here we assess the use of a global expression profiling technique that identifies chromosome regions corresponding to differential gene expression, termed comparative expressed sequence hybridization (CESH). CESH analysis was performed on a total of 104 tumors with a diagnosis of rhabdomyosarcoma, leiomyosarcoma, prostate cancer, and favorable-histology Wilms tumors. Through the use of the chromosome regions identified as variables, support vector machine analysis was applied to assess classification potential, and feature selection (recursive feature elimination) was used to identify the best discriminatory regions. We demonstrate that the CESH profiles have characteristic patterns in tumor groups and were also able to distinguish subgroups of rhabdomyosarcoma. The overall CESH profiles in favorable-histology Wilms tumors were found to correlate with subsequent clinical behavior. Classification by use of CESH profiles was shown to be similar in performance to previous microarray expression studies and highlighted regions for further investigation. We conclude that analysis of chromosomal expression profiles can group, subgroup, and even predict clinical behavior of tumors to a level of performance similar to that of microarray analysis. CESH is independent of selecting sequences for interrogation and is a simple, rapid, and widely accessible approach to identify clinically useful differential expression.

Breast Neoplasms↗

PubMed2003

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.

Algorithms↗

PubMed2002

Health-related quality of life in patients with primary Sjögren's syndrome and xerostomia: a comparative study.

OBJECTIVE: To compare the health status of groups of Primary Sjögren's and Xerostomia patients, using the Medical Outcomes Short Form 36 (SF-36). The SF-36 is a generic measure, divided into eight domains, used in the assessment of health-related quality of life. PATIENTS AND METHODS: The SF-36 was given to 2 groups: Group 1 comprised 43 patients diagnosed with Primary Sjögren's Syndrome (1 degrees SS) and an unstimulated whole salivary flow rate (UFR) of <0.1 ml/min). Group 2 (n = 40) reported Xerostomia but had an UFR >0.2 ml/min. Sub groups of patients in Groups 1 and 2 were compared with community normative data, for the SF-36. RESULTS: There were trends to suggest lower SF36 scores for 1 degrees SS patients but there were no significant differences between the mean domain scores of Groups 1 and 2. 1 degrees SS and Xerostomia patients registered lower mean scores across all 8 domains, compared with normative community data. CONCLUSION: The SF-36 was unable to detect significant differences between subjects with 1 degrees SS and Xerostomia but a larger sample size is required to confirm these findings. The results of this limited study suggest that a disease-specific measure is required to assess the impact 1 degrees SS on health-related Quality of life (QOL).

Activities of Daily Living↗