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

The robustness of the higher-order 2SLS and general k-class bias approximations to non-normal disturbances

In a seminal paper Nagar (1959) obtained first and second moment approximations for the k-class of estimators in a general static simultaneous equation model under the assumption that the structural disturbances were i.i.d and normally distributed. Later Mikhail (1972) obtained a higher-order bias approximation for 2SLS under the same assumptions as Nagar while Iglesias and Phillips (2010) obtained the higher order approximation for the general k-class of estimators. These approximations show that the higher order biases can be important especially in highly overidentified cases. In this paper we show that Mikhail.s higher order bias approximation for 2SLS continues to be valid under symmetric, but not necessarily normal, disturbances with an arbitrary degree of kurtosis but not when the disturbances are asymmetric. A modified approximation for the 2SLS bias is then obtained which includes the case of asymmetric disturbances. The results are then extended to the general k-class of estimators

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

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

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 $0.751\pm0.002$ to either $0.815\pm0.006$ when averaging across readers or $0.844\pm0.002$ 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 $0.846\pm0.006$ and $0.850\pm0.006$ 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

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