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50,760 result(s) for "Jones, David"
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History in a Crisis — Lessons for Covid-19
The history of human responses to epidemics carries various lessons for our current situation. Two of the more disheartening ones are that stigmatization follows closely on the heels of pathogens and that outbreaks too often claim the lives of health care providers. The article is accompanied by a slide show.
Pilocytic astrocytoma: pathology, molecular mechanisms and markers
Pilocytic astrocytomas (PAs) were recognized as a discrete clinical entity over 70 years ago. They are relatively benign (WHO grade I) and have, as a group, a 10-year survival of over 90 %. Many require merely surgical removal and only very infrequently do they progress to more malignant gliomas. While most show classical morphology, they may present a spectrum of morphological patterns, and there are difficult cases that show similarities to other gliomas, some of which are malignant and require aggressive treatment. Until recently, almost nothing was known about the molecular mechanisms involved in their development. The use of high-throughput sequencing techniques interrogating the whole genome has shown that single abnormalities of the mitogen-activating protein kinase (MAPK) pathway are exclusively found in almost all cases, indicating that PA represents a one-pathway disease. The most common mechanism is a tandem duplication of a ≈2 Mb-fragment of #7q, giving rise to a fusion between two genes, resulting in a transforming fusion protein, consisting of the N-terminus of KIAA1549 and the kinase domain of BRAF. Additional infrequent fusion partners have been identified, along with other abnormalities of the MAP-K pathway, affecting tyrosine kinase growth factor receptors at the cell surface (e.g., FGFR1) as well as BRAF V600E, KRAS, and NF1 mutations among others. However, while the KIAA1549-BRAF fusion occurs in all areas, the incidence of the various other mutations identified differs in PAs that develop in different regions of the brain. Unfortunately, from a diagnostic standpoint, almost all mutations found have been reported in other brain tumor types, although some retain considerable utility. These molecular abnormalities will be reviewed, and the difficulties in their potential use in supporting a diagnosis of PA, when the histopathological findings are equivocal or in the choice of individualized therapy, will be discussed.
ATRX and IDH1-R132H immunohistochemistry with subsequent copy number analysis and IDH sequencing as a basis for an “integrated” diagnostic approach for adult astrocytoma, oligodendroglioma and glioblastoma
Diffuse gliomas are represented in the 2007 WHO classification as astrocytomas, oligoastrocytomas and oligodendrogliomas of grades II and III and glioblastomas WHO grade IV. Molecular data on these tumors have a major impact on prognosis and therapy of the patients. Consequently, the inclusion of molecular parameters in the WHO definition of brain tumors is being planned and has been forwarded as the “ISN-Haarlem” consensus. We, here, analyze markers of special interest including ATRX, IDH and 1p/19q codeletion in a series of 405 adult patients. Among the WHO 2007 classified tumors were 152 astrocytomas, 61 oligodendrogliomas, 63 oligoastrocytomas and 129 glioblastomas. Following the concepts of the “ISN-Haarlem”, we rediagnosed the series to obtain “integrated” diagnoses with 155 tumors being astrocytomas, 100 oligodendrogliomas and 150 glioblastomas. In a subset of 100 diffuse gliomas from the NOA-04 trial with long-term follow-up data available, the “integrated” diagnosis had a significantly greater prognostic power for overall and progression-free survival compared to WHO 2007. Based on the “integrated” diagnoses, loss of ATRX expression was close to being mutually exclusive to 1p/19q codeletion, with only 2 of 167 ATRX-negative tumors exhibiting 1p/19q codeletion. All but 4 of 141 patients with loss of ATRX expression and diffuse glioma carried either IDH1 or IDH2 mutations. Interestingly, the majority of glioblastoma patients with loss of ATRX expression but no IDH mutations exhibited an H3F3A mutation. Further, all patients with 1p/19 codeletion carried a mutation in IDH1 or IDH2 . We present an algorithm based on stepwise analysis with initial immunohistochemistry for ATRX and IDH1-R132H followed by 1p/19q analysis followed by IDH sequencing which reduces the number of molecular analyses and which has a far better association with patient outcome than WHO 2007.
Polymorphous low-grade neuroepithelial tumor of the young (PLNTY): an epileptogenic neoplasm with oligodendroglioma-like components, aberrant CD34 expression, and genetic alterations involving the MAP kinase pathway
Epileptogenic tumors affecting children and young adults are a morphologically diverse collection of neuroepithelial neoplasms that, as a group, exhibit varying levels of glial and/or neuronal differentiation. Recent advances in molecular profiling technology, including comprehensive DNA sequencing and methylation analysis, have enabled the application of more precise and biologically relevant classification schemes to these tumors. In this report, we describe a morphologically and molecularly distinct epileptogenic neoplasm, the polymorphous low-grade neuroepithelial tumor of the young (PLNTY), which likely accounts for a sizable portion of oligodendroglioma-like tumors affecting the pediatric population. Characteristic microscopic findings most notably include infiltrative growth, the invariable presence of oligodendroglioma-like cellular components, and intense immunolabeling for cluster of differentiation 34 (CD34). Moreover, integrative molecular profiling reveals a distinct DNA methylation signature for PLNTYs, along with frequent genetic abnormalities involving either B-Raf proto-oncogene ( BRAF ) or fibroblast growth factor receptors 2 and 3 ( FGFR2 , FGFR3 ). These findings suggest that PLNTY represents a distinct biological entity within the larger spectrum of pediatric, low-grade neuroepithelial tumors.
Setting the standards for machine learning in biology
Machine learning is a branch of artificial intelligence (AI) involving computer programs that are able to improve their own performance through experience (training). The diverse applications of new ‘deep learning’ approaches with neural networks are now expanding into the field of biology. But these applications to biological data require more scrutiny and caution to increase the standards of publishing and allow the AI revolution in biology to take off.