Human embryo editing against disease is unsafe and unproven - despite rosy predictions.
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CRISPR-Cas9 genome editing↗
Biomedical subjects
Kevin J Mitchell
Publications and source records attributed to Kevin J Mitchell.
The transmembrane semaphorin Sema6A controls cerebellar granule cell migration.
The transmembrane semaphorin protein Sema6A is broadly expressed in the developing nervous system. Sema6A repels several classes of developing axons in vitro and contributes to thalamocortical axon guidance in vivo. Here we show that during cerebellum development, Sema6A is selectively expressed by postmitotic granule cells during their tangential migration in the deep external granule cell layer, but not during their radial migration. In Sema6A-deficient mice, many granule cells remain ectopic in the molecular layer where they differentiate and are contacted by mossy fibers. The analysis of ectopic granule cell morphology in Sema6a-/- mice, and of granule cell migration and neurite outgrowth in cerebellar explants, suggests that Sema6A controls the initiation of granule cell radial migration, probably through a modulation of nuclear and/or soma translocation. Finally, the analysis of mouse chimeras suggests that this function of Sema6A is primarily non-cell-autonomous.
Analysis of Variance↗
Automating tissue bank annotation from pathology reports - comparison to a gold standard expert annotation set.
Surgical pathology specimens are an important resource for medical research, particularly for cancer research. Although research studies would benefit from information derived from the surgical pathology reports, access to this information is limited by use of unstructured free-text in the reports. We have previously described a pipeline-based system for automated annotation of surgical pathology reports with UMLS concepts, which has been used to code over 450,000 surgical pathology reports at our institution. In addition to coding UMLS terms, it annotates values of several key variables, such as TNM stage and cancer grade. The object of this study was to evaluate the potential and limitations of automated extraction of these variables, by measuring the performance of the system against a true gold standard - manually encoded data entered by expert tissue annotators. We categorized and analyzed errors to determine the potential and limitations of information extraction from pathology reports for the purpose of automated biospecimen annotation.
Abstracting and Indexing↗
Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports.
We have developed a pipeline-based system for automated annotation of Surgical Pathology Reports with UMLS terms that builds on GATE--an open-source architecture for language engineering. The system includes a module for detecting and annotating negated concepts, which implements the NegEx algorithm--an algorithm originally described for use in discharge summaries and radiology reports. We describe the implementation of the system, and early evaluation of the Negation Tagger. Our results are encouraging. In the key Final Diagnosis section, with almost no modification of the algorithm or phrase lists, the system performs with precision of 0.84 and recall of 0.80 against a gold-standard corpus of negation annotations, created by modified Delphi technique by a panel of pathologists. Further work will focus on refining the Negation Tagger and UMLS Tagger and adding additional processing resources for annotating free-text pathology reports.
Algorithms↗
A knowledge-based approach to information extraction from surgical pathology reports.
We describe the development of a prototype system for knowledge-based information extraction from surgical pathology reports. The current system includes abstract problem solving methods and a frame-based knowledge representation of body parts, procedures, diseases, and findings for prostate and breast cases. The system currently extracts the organ, procedure, and diagnoses, and sets an agenda of goals for further processing. A potential advantage of this approach is the ability to increase specificity of information extraction.
Artificial Intelligence↗