Descrição de uma forma autossômica dominante de síndrome de Kabuki por mutação no gene MLL2

Aims: Although there are more than 400 cases of Kabuki syndrome described in the literature, it is believed that this syndrome is under-diagnosed. Most cases occur sporadically, despite cases with autosomal dominant familial transmission being described. Here we describe three cases identified in the same family. Cases description: A family (mother and two children) was diagnosed with Kabuki syndrome. The three patients show the typical characteristics (facial appearance, musculoskeletal abnormalities, cognitive impairment, growth retardation and peculiar dermatoglyphic pattern) associated with other anomalies described in the syndrome (congenital heart disease and increased susceptibility to infections). Genetic studies revealed a nonsense mutation c.14710 C > T (p.Arg4904X) in the MLL2 gene in the three members of the family. Conclusions: With the description of another case of familial Kabuki syndrome, the authors wish to illustrate the autosomal dominant inheritance with variable expressivity, which are present in this situation, and to alert to the need for a rigorous clinical and molecular evaluation of the affected patient’s relatives, allowing appropriate genetic counseling

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm

Learning to segment when experts disagree

Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [1] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS

“Ten Commandments” for the Appropriate use of Antibiotics by the Practicing Physician in an Outpatient Setting

A multi-national working group on antibiotic stewardship, from the International Society of Chemotherapy, put together ten recommendations to physicians prescribing antibiotics to outpatients. These recommendations are: (1) use antibiotics only when needed; teach the patient how to manage symptoms of non-bacterial infections; (2) select the adequate ATB; precise targeting is better than shotgun therapy; (3) consider pharmacokinetics and pharmacodynamics when selecting an ATB; use the shortest ATB course that has proven clinical efficacy; (4) encourage patients’ compliance; (5) use antibiotic combinations only in specific situations; (6) avoid low quality and sub-standard drugs; prevent prescription changes at the drugstore; (7) discourage self-prescription; (8) follow only evidence-based guidelines; beware those sponsored by drug companies; (9) rely (rationally) upon the clinical microbiology lab; and (10) prescribe ATB empirically – but intelligently; know local susceptibility trends, and also surveillance limitations

Vaccines for the Leishmaniases: Proposals for a Research Agenda

The International Symposium on Leishmaniasis Vaccines, held in Olinda, Brazil, on March 9–11, 2009, congregated international experts who conduct research on vaccines against the leishmaniases. The questions that were raised during that meeting and the ensuing discussions are compiled in this report and may assist in guiding a research agenda. A group to further discussion on issues raised in this policy platform has been set up at http://groups.google.com/group/leishvaccines-l