1,512 research outputs found
Neurofibromatosis type 2 (NF2): A clinical and molecular review
Neurofibromatosis type 2 (NF2) is a tumour-prone disorder characterised by the development of multiple schwannomas and meningiomas. Prevalence (initially estimated at 1: 200,000) is around 1 in 60,000. Affected individuals inevitably develop schwannomas, typically affecting both vestibular nerves and leading to hearing loss and deafness. The majority of patients present with hearing loss, which is usually unilateral at onset and may be accompanied or preceded by tinnitus. Vestibular schwannomas may also cause dizziness or imbalance as a first symptom. Nausea, vomiting or true vertigo are rare symptoms, except in late-stage disease. The other main tumours are schwannomas of the other cranial, spinal and peripheral nerves; meningiomas both intracranial (including optic nerve meningiomas) and intraspinal, and some low-grade central nervous system malignancies (ependymomas). Ophthalmic features are also prominent and include reduced visual acuity and cataract. About 70% of NF2 patients have skin tumours (intracutaneous plaque-like lesions or more deep-seated subcutaneous nodular tumours). Neurofibromatosis type 2 is a dominantly inherited tumour predisposition syndrome caused by mutations in the NF2 gene on chromosome 22. More than 50% of patients represent new mutations and as many as one-third are mosaic for the underlying disease-causing mutation. Although truncating mutations (nonsense and frameshifts) are the most frequent germline event and cause the most severe disease, single and multiple exon deletions are common. A strategy for detection of the latter is vital for a sensitive analysis. Diagnosis is based on clinical and neuroimaging studies. Presymptomatic genetic testing is an integral part of the management of NF2 families. Prenatal diagnosis and pre-implantation genetic diagnosis is possible. The main differential diagnosis of NF2 is schwannomatosis. NF2 represents a difficult management problem with most patients facing substantial morbidity and reduced life expectancy. Surgery remains the focus of current management although watchful waiting with careful surveillance and occasionally radiation treatment have a role. Prognosis is adversely affected by early age at onset, a higher number of meningiomas and having a truncating mutation. In the future, the development of tailored drug therapies aimed at the genetic level are likely to provide huge improvements for this devastating condition
Breast Cancer and <em>BRCA1</em> and <em>BRCA2</em> Pathogenic Variants
Breast cancer remains the most common female cancer worldwide. The majority will arise spontaneously, with almost a third having a heritable component. Approximately 5–10% of all breast cancers will have a strong inherited element with pathogenic variants in the BRCA1 and BRCA2 amongst the most studied breast cancer genes. An overview of breast cancer is provided with references to the clinical and pathological features in BRCA1 and BRCA2 related cancers. The roles of PARP inhibitors and immunotherapy are discussed. The management of healthy individuals harbouring a pathogenic variant in the two genes is reviewed and future directions considered
The effect of variable labels on deep learning models trained to predict breast density
Purpose: High breast density is associated with reduced efficacy of
mammographic screening and increased risk of developing breast cancer. Accurate
and reliable automated density estimates can be used for direct risk prediction
and passing density related information to further predictive models. Expert
reader assessments of density show a strong relationship to cancer risk but
also inter-reader variation. The effect of label variability on model
performance is important when considering how to utilise automated methods for
both research and clinical purposes. Methods: We utilise subsets of images with
density labels to train a deep transfer learning model which is used to assess
how label variability affects the mapping from representation to prediction. We
then create two end-to-end deep learning models which allow us to investigate
the effect of label variability on the model representation formed. Results: We
show that the trained mappings from representations to labels are altered
considerably by the variability of reader scores. Training on labels with
distribution variation removed causes the Spearman rank correlation
coefficients to rise from to either when
averaging across readers or when averaging across images.
However, when we train different models to investigate the representation
effect we see little difference, with Spearman rank correlation coefficients of
and showing no statistically significant
difference in the quality of the model representation with regard to density
prediction. Conclusions: We show that the mapping between representation and
mammographic density prediction is significantly affected by label variability.
However, the effect of the label variability on the model representation is
limited
Surfaces Meeting Porous Sets in Positive Measure
Let n>2 and X be a Banach space of dimension strictly greater than n. We show
there exists a directionally porous set P in X for which the set of C^1
surfaces of dimension n meeting P in positive measure is not meager. If X is
separable this leads to a decomposition of X into a countable union of
directionally porous sets and a set which is null on residually many C^1
surfaces of dimension n. This is of interest in the study of certain classes of
null sets used to investigate differentiability of Lipschitz functions on
Banach spaces
Basal Cell Carcinomas in Gorlin Syndrome: A Review of 202 Patients
Gorlin syndrome (Naevoid Basal Cell Carcinoma Syndrome) is a rare autosomal dominant syndrome caused by mutations in the PTCH gene with a birth incidence of approximately 1 in 19,000. Patients develop multiple basal cell carcinomas of the skin frequently in early life and also have a predisposition to additional malignancies such as medulloblastoma. Gorlin Syndrome patients also have developmental defects such as bifid ribs and other complications such as jaw keratocysts. We studied the incidence and frequency of basal cell carcinomas in 202 Gorlin syndrome patients from 62 families and compared this to their gender and mutation type. Our data suggests that the incidence of basal cell carcinomas is equal between males and females and the mutation type cannot be used to predict disease burden
Identification of small interfering RNA targeting Signal Transducer and Activator of Transcription 6: Characterisation and selection of candidates for pre-clinical development
The interleukin (IL)-13 pathway and its associated transcription factor, signal transducer and activator of transcription 6 (STAT6), have been clearly implicated in the pathogenesis of bronchial asthma. We have developed a system to effectively screen the STAT6 gene for targeting with small interfering (si) RNA molecules. By incorporating an in silico and in vitro screening system we were able to identify fourteen siRNA molecules suitable for pre-clinical drug development. Furthermore, we were able to demonstrate that modification of certain siRNAs, designed to improve in vivo longevity, was possible without significant loss of target knockdown efficacy and that the siRNA produced by our selection process did not induce demonstrable interferon responses. These data suggest that several STAT6-targeting siRNA suitable for pre-clinical development are available for potential use in the treatment of asthma
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