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Biomedical subjects

Simon Rogers

Publications and source records attributed to Simon Rogers.

11 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↗
PubMed2006

Bisphosphonate-associated Osteonecrosis of the jaws: a guide for the general dental practitioner.

UNLABELLED: Bisphosphonates are used most commonly in the management of metastatic malignant disorders to bone and for osteoporosis. The number of patients on treatment is increasing. Although there is clear evidence of medical efficacy, there are increasing numbers of reports concerning bisphosphonate-associated osteonecrosis of the jaws. This side-effect poses significant problems and is very difficult to treat. The aim of this article is to outline the proposed pathogenesis, incidence, presenting signs and symptoms, and risk factors. Also, suggestions are made as to the most appropriate measures for prevention and management of bisphosphonate-associated jaw osteonecrosis. CLINICAL RELEVANCE: General dental practitioners have a key role to play in identifying patients at risk of developing bisphosphonate-associated osteonecrosis of the jaws, preventing occurrence of the condition and initiating early specialist referral.

Analgesics↗
PubMed2005

Facial appearance after operations for oral and oropharyngeal cancer: a comparison of casenotes and patient-completed questionnaire.

Disfigurement after operations for oral and oropharyngeal cancer is an important issue. Our aim was to review the casenotes of patients who reported distress in the appearance domain of the University of Washington Quality of Life Scale (UWQOL) and to find out what help they were given. Of 278 patients with previously untreated oral and oropharyngeal squamous cell carcinoma treated between 1995 and 1999, 114 (41%) were concerned about their appearance. Factors that correlated with these concerns included tumours more than 2cm in size, T2 or worse stage, free tissue reconstruction, segmental mandibular resection, and neck dissection. In only 7 of the 114 was there any mention of the patient's appearance in the clinic notes, of whom 4 were given help (2 scar revisions and 2 oral rehabilitations). This study suggests that patients' concerns about disfigurement are poorly recognised in routine clinical practice and supports the principle of using the UWQOL scale to discuss problems of appearance with patients.

Adaptation, Psychological↗
PubMed2005

A Bayesian regression approach to the inference of regulatory networks from gene expression data.

MOTIVATION: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections. RESULTS: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections.

Algorithms↗
PubMed2005

The relation between health-related quality of life, past medical history, and American Society of Anesthesiologists' ASA grade in patients having primary operations for oral and oropharyngeal cancer.

Pre-existing medical problems have the potential to affect postoperative survival, complications, and health-related quality of life (QoL). Our aim was to explore the relation between past medical history, American Society of Anesthesiologists' (ASA) score, health-related QoL, and survival. We collected data from 278 consecutive patients with previously untreated oral and oropharyngeal squamous cell carcinoma operated on primarily from 1995 to 1999 inclusive. Past medical history was recorded from the case notes, ASA grade from the anaesthetic record, and QoL was measured using the University of Washington Quality of Life Questionnaire (UW-QoL). Responses to questionnaires were received from (71%) at baseline (63%) at 6 months (73%) at 1 year, and (65%) 18 months or longer. Past medical history was associated with lower ASA scores. At baseline both history and ASA scores were related to the UW-QoL. Longitudinally patients in ASA grade 1 or with no past history scored better in these UW-QoL domains. Past history did not predict survival (P = 0.83), nor did the UW-QoL composite score (P = 0.30), whilst ASA was associated with crude survival (P = 0.003) and disease-specific survival (P = 0.03). When analyses were stratified for adjuvant radiotherapy, type of operation, size of tumour, and age then the relation to ASA was maintained and trends in the past history and UW-QoL remained not significant. ASA, which is often recorded as part of preoperative assessment, reflects both survival and health-related QoL, and is more useful than past history alone for predicting outcome.

Aged↗
PubMed2004

Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines.

Treatment of Wilms tumor has a high success rate, with some 85% of patients achieving long-term survival. However, late effects of treatment and management of relapse remain significant clinical problems. If accurate prognostic methods were available, effective risk-adapted therapies could be tailored to individual patients at diagnosis. Few molecular prognostic markers for Wilms tumor are currently defined, though previous studies have linked allele loss on 1p or 16q, genomic gain of 1q, and overexpression from 1q with an increased risk of relapse. To identify specific patterns of gene expression that are predictive of relapse, we used high-density (30 k) cDNA microarrays to analyze RNA samples from 27 favorable histology Wilms tumors taken from primary nephrectomies at the time of initial diagnosis. Thirteen of these tumors relapsed within 2 years. Genes differentially expressed between the relapsing and nonrelapsing tumor classes were identified by statistical scoring (t test). These genes encode proteins with diverse molecular functions, including transcription factors, developmental regulators, apoptotic factors, and signaling molecules. Use of a support vector machine classifier, feature selection, and test evaluation using cross-validation led to identification of a generalizable expression signature, a small subset of genes whose expression potentially can be used to predict tumor outcome in new samples. Similar methods were used to identify genes that are differentially expressed between tumors with and without genomic 1q gain. This set of discriminators was highly enriched in genes on 1q, indicating close agreement between data obtained from expression profiling with data from genomic copy number analyses.

Adolescent↗
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↗
PubMedDate not supplied

The latent process decomposition of cDNA microarray data sets.

We present a new computational technique (a software implementation, data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/) which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called Latent Process Decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important advantages over the standard use of dendrograms. First, the ability to objectively assess the optimal number of sample clusters. Second, the ability to represent samples and gene expression levels using a common set of latent variables (dendrograms cluster samples and gene expression values separately which amounts to two distinct reduced space representations). Third, in constrast to standard cluster models, observations are not assigned to a single cluster and, thus, for example, gene expression levels are modeled via combinations of the latent processes identified by the algorithm. We show this new method compares favorably with alternative cluster analysis methods. To illustrate its potential, we apply the proposed technique to several microarray data sets for cancer. For these data sets it successfully decomposes the data into known subtypes and indicates possible further taxonomic subdivision in addition to highlighting, in a wholly unsupervised manner, the importance of certain genes which are known to be medically significant. To illustrate its wider applicability, we also illustrate its performance on a microarray data set for yeast.

Algorithms↗