Size of third and fourth ventricle in obstructive and communicating acute hydrocephalus after aneurysmal subarachnoid hemorrhage

In patients with acute hydrocephalus after aneurysmal subarachnoid haemorrhage (SAH), lumbar drainage is possible if the obstruction is in the subarachnoid space (communicating hydrocephalus). In case of intraventricular obstruction (obstructive hydrocephalus), ventricular drainage is the only option. A small fourth ventricle is often considered a sign of obstructive hydrocephalus. We investigated whether the absolute or relative size of the fourth ventricle can indeed distinguish between these two types of hydrocephalus. On CT-scans of 76 consecutive patients with acute headache but normal CT and CSF, we measured the cross-sectional surface of the third and fourth ventricle to obtain normal planimetric values. Subsequently we performed the same measurements on 117 consecutive SAH patients with acute hydrocephalus. These patients were divided according to the distribution of blood on CT-scan into three groups: mainly intraventricular blood (n = 15), mainly subarachnoid blood (n = 54) and both intraventricular and subarachnoid blood (n = 48). The size of the fourth ventricle exceeded the upper limit of normal in 2 of the 6 (33%) patients with intraventricular blood but without haematocephalus, and in 15 of the 54 (28%) patients with mainly subarachnoid blood. The mean ratio between the third and fourth ventricle was 1.45 (SD 0.66) in patients with intraventricular blood and 1.42 (SD 0.91) in those with mainly subarachnoid blood. Neither fourth ventricular size nor the ratio between the third and fourth ventricles discriminates between the two groups. A small fourth ventricle does not necessarily accompany obstructive hydrocephalus and is therefore not a contraindication for lumbar drainage

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo

Nomograms to predict survival and the risk for developing local or distant recurrence in patients with rectal cancer treated with optional short-term radiotherapy

Surgical oncolog

Flint, procurement and use

The functional and technological analysis of prehistoric artefact

2010 SSO John Wayne Clinical Research Lecture: Rectal Cancer Outcome Improvements in Europe: Population-Based Outcome Registrations will Conquer the World

During the past two decades, rectal cancer treatment has improved considerably in Europe. Clinical trials played a crucial role in improving surgical techniques, (neo)adjuvant treatment schedules, imaging, and pathology. However, there is still a wide variation in outcome after rectal cancer. In most western health care systems, efforts are made to reduce hospital variation by focusing on selective referral and encouraging patients to seek care in high-volume hospitals. On the other hand, the expertise for diagnosis and treatment of common types of cancer should be preferably widespread and easily accessible for all patients. As an alternative to volume-based referral, hospitals and surgeons can improve their results by learning from their own outcome statistics and those from colleagues treating a similar patient group. Several European surgical (colo)rectal audits have led to improvements with a greater impact than any of the adjuvant therapies currently under study. However, differences remain between European countries, which cannot be easily explained. To generate the best care for colorectal cancer in the whole of Europe and to meet political and public demands for transparency, the European CanCer Organisation (ECCO) initiated an international, multidisciplinary, outcome-based quality improvement program: European Registration of Cancer Care (EURECCA). The goal is to create a multidisciplinary European registration structure for patient, tumor, and treatment characteristics linked to outcome registration. Clinical trials will always play a major role in improving rectal cancer treatment. To further improve outcomes and diminish variation, EURECCA will establish the basis for a strong, multidisciplinary, international audit structure that can be used as a template for similar projects worldwide

Erythematous nodes, urticarial rash and arthralgias in a large pedigree with NLRC4-related autoinflammatory disease, expansion of the phenotype

Autoinflammatory disorders (AID) are a heterogeneous group of diseases, characterized by an unprovoked innate immune response, resulting in recurrent or ongoing systemic inflammation and fever1-3. Inflammasomes are protein complexes with an essential role in pyroptosis and the caspase-1-mediated activation of the proinflammatory cytokines IL-1β, IL-17 and IL-18

CAPICE:a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.