Methods and compositions for diagnosing conditions
A product, tissue technology, applied in the field of diagnosis of pathological conditions and composition, which can solve the problems of increasing treatment costs, economic and physical harm
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Embodiment 1
[0291] Example 1: Classification panels from analysis of clinical thyroid samples
[0292] Prospective clinical thyroid FNA samples (n=248) and postoperative thyroid tissue (n=220) were examined with the Affymetrix Human Exon 1.0ST microarray with the aim of identifying genes with significantly different mRNA expression between benign and malignant samples.
[0293] Intensity data from approximately 6.5 million probes were extracted, normalized and aggregated using Affymetrix software. A core probe set of approximately 280,000 was subsequently used in feature selection and classification. The models used include LIMMA (for feature selection) and SVM (for classification) (Smyth 2004). A combination of LIMMA and algorithms were used in several separate analyses, to identify the top genes used in each taxonomic group.
[0294] While the annotation and mapping of genes to transcript cluster identifiers (TCIDs) is continuously evolving, the nucleotide sequences of the probes and ...
Embodiment 2
[0300] Example 2: Molecular profiling of thyroid nodules
[0301] Subject notes a lump on their thyroid. The individual consults his family physician. The family physician decided to obtain a sample from the mass and perform molecular profiling on it. The physician uses the kit to obtain a sample by fine needle aspiration, performs an adequacy test, stores the sample in a liquid-based cytology solution, and sends it to the molecular profiling business. Optionally, the doctor may have cytology done by another party or a laboratory. If cytology results in an inconclusive diagnosis, the remainder of the sample is sent to the molecular profiling business or a third party. Molecular profiling companies split the samples, some for cytological analysis, and the remainder for mRNA extraction from the samples, analyze the quality and suitability of the extracted mRNA samples, and analyze the expression of a subset of the genes listed in Figure 4 Levels and alternative exon usage. ...
Embodiment 3
[0303] Example 3: Identification of Hurthle cell adenoma and Hurthle cell carcinoma in thyroid tissue
[0304] Postoperative thyroid tissue samples and clinical thyroid FNA biopsy samples were examined using the Affymetrix Human Exon 1.0ST microarray with the aim of identifying biomarkers whose mRNA expression was significantly different between benign and malignant samples. These biomarkers were then used to train a molecular classifier using the same cohort of postoperative tissue samples. Information learned during training of the algorithm using tissue samples, including but not limited to biomarker selection for each thyroid subtype, is combined with the next step of training the algorithm using clinical FNA samples such that biomarkers in FNA can be maintained The high-dimensional nature of molecular expression and use it to train optimized or next-generation molecular classifiers. By combining information learned from tissue and clinical FNA, we demonstrate that the mo...
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