Mathematical Modelling of Optical Coherence Tomography

In this chapter a general mathematical model of Optical Coherence Tomography (OCT) is presented on the basis of the electromagnetic theory. OCT produces high resolution images of the inner structure of biological tissues. Images are obtained by measuring the time delay and the intensity of the backscattered light from the sample considering also the coherence properties of light. The scattering problem is considered for a weakly scattering medium located far enough from the detector. The inverse problem is to reconstruct the susceptibility of the medium given the measurements for different positions of the mirror. Different approaches are addressed depending on the different assumptions made about the optical properties of the sample. This procedure is applied to a full field OCT system and an extension to standard (time and frequency domain) OCT is briefly presented.Comment: 28 pages, 5 figures, book chapte

Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples

<p>Abstract</p> <p>Background</p> <p>Readthrough fusions across adjacent genes in the genome, or transcription-induced chimeras (TICs), have been estimated using expressed sequence tag (EST) libraries to involve 4-6% of all genes. Deep transcriptional sequencing (RNA-Seq) now makes it possible to study the occurrence and expression levels of TICs in individual samples across the genome.</p> <p>Methods</p> <p>We performed single-end RNA-Seq on three human prostate adenocarcinoma samples and their corresponding normal tissues, as well as brain and universal reference samples. We developed two bioinformatics methods to specifically identify TIC events: a targeted alignment method using artificial exon-exon junctions within 200,000 bp from adjacent genes, and genomic alignment allowing splicing within individual reads. We performed further experimental verification and characterization of selected TIC and fusion events using quantitative RT-PCR and comparative genomic hybridization microarrays.</p> <p>Results</p> <p>Targeted alignment against artificial exon-exon junctions yielded 339 distinct TIC events, including 32 gene pairs with multiple isoforms. The false discovery rate was estimated to be 1.5%. Spliced alignment to the genome was less sensitive, finding only 18% of those found by targeted alignment in 33-nt reads and 59% of those in 50-nt reads. However, spliced alignment revealed 30 cases of TICs with intervening exons, in addition to distant inversions, scrambled genes, and translocations. Our findings increase the catalog of observed TIC gene pairs by 66%.</p> <p>We verified 6 of 6 predicted TICs in all prostate samples, and 2 of 5 predicted novel distant gene fusions, both private events among 54 prostate tumor samples tested. Expression of TICs correlates with that of the upstream gene, which can explain the prostate-specific pattern of some TIC events and the restriction of the <it>SLC45A3-ELK4 </it>e4-e2 TIC to <it>ERG</it>-negative prostate samples, as confirmed in 20 matched prostate tumor and normal samples and 9 lung cancer cell lines.</p> <p>Conclusions</p> <p>Deep transcriptional sequencing and analysis with targeted and spliced alignment methods can effectively identify TIC events across the genome in individual tissues. Prostate and reference samples exhibit a wide range of TIC events, involving more genes than estimated previously using ESTs. Tissue specificity of TIC events is correlated with expression patterns of the upstream gene. Some TIC events, such as <it>MSMB-NCOA4</it>, may play functional roles in cancer.</p

Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre-and post-treatment prostatic biopsies from patients with advanced prostate cancer

Background: Although chemotherapy for prostate cancer (PCa) can improve patient survival, some tumours are chemo-resistant. Tumour molecular profiles may help identify the mechanisms of drug action and identify potential prognostic biomarkers. We performed in vivo transcriptome profiling of pre- and post-treatment prostatic biopsies from patients with advanced hormone-naive prostate cancer treated with docetaxel chemotherapy and androgen deprivation therapy (ADT) with an aim to identify the mechanisms of drug action and identify prognostic biomarkers. Methods: RNA sequencing (RNA-Seq) was performed on biopsies from four patients before and ~22 weeks after docetaxel and ADT initiation. Gene fusion products and differentially-regulated genes between treatment pairs were identified using TopHat and pathway enrichment analyses undertaken. Publically available datasets were interrogated to perform survival analyses on the gene signatures identified using cBioportal. Results: A number of genomic rearrangements were identified including the TMPRSS2/ERG fusion and 3 novel gene fusions involving the ETS family of transcription factors in patients, both pre and post chemotherapy. In total, gene expression analyses showed differential expression of at least 2 fold in 575 genes in post-chemotherapy biopsies. Of these, pathway analyses identified a panel of 7 genes (ADAM7, FAM72B, BUB1B, CCNB1, CCNB2, TTK, CDK1), including a cell cycle-related geneset, that were differentially-regulated following treatment with docetaxel and ADT. Using cBioportal to interrogate the MSKCC-Prostate Oncogenome Project dataset we observed a statistically-significant reduction in disease-free survival of patients with tumours exhibiting alterations in gene expression of the above panel of 7 genes (p = 0.015). Conclusions: Here we report on the first “real-time” in vivo RNA-Seq-based transcriptome analysis of clinical PCa from pre- and post-treatment TRUSS-guided biopsies of patients treated with docetaxel chemotherapy plus ADT. We identify a chemotherapy-driven PCa transcriptome profile which includes the down-regulation of important positive regulators of cell cycle progression. A 7 gene signature biomarker panel has also been identified in high-risk prostate cancer patients to be of prognostic value. Future prospective study is warranted to evaluate the clinical value of this panel

Late stage spinodal decompositon in an oligomeric blend of polystyrene / polybutadiene: a test of the scaling law for the structure-function

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An in vitro multi-parametric approach to measuring the effect of implant surface characteristics on cell behaviour

Orthopaedic implants are designed to promote biocompatibility and hence their integration with surrounding tissue. This involves influencing cell-implant interactions through changes in both surface topography and surface roughness. However, the large range of machining techniques used in implant manufacture and inconsistencies in the measurement techniques used for surface characterization make it difficult to measure the impact of surface characteristics on cell-implant interactions. Here, we describe a new in vitro multi-parametric approach that uses commercially available arrays of engineered surfaces that linearly increase in roughness, as measured by Ra, and that can be used to obtain quantitative measurements of cell attachment, differentiation and bone formation. Using this model, we demonstrate that cell attachment above 50% confluency occurs over a narrow range of roughness (Ra from 0.0125 νm to 6.3 νm) and that promotion of cell differentiation and bone development, while significantly influenced by surface topography, does not correlate directly with initial levels of cell attachment. These results compare well with published in vivo implant biocompatibility data indicating that this approach has the potential to offer a rapid, reliable and reproducible in vitro prediction of in vivo implant biocompatibility. © 2010 IOP Publishing Ltd.link_to_subscribed_fulltex