Claudin-19 mutations and clinical phenotype in Spanish patients with familial hypomagnesemia with hypercalciuria and nephrocalcinosis.

Familial hypomagnesemia with hypercalciuria and nephrocalcinosis is an autosomal recessive tubular disorder characterized by excessive renal magnesium and calcium excretion and chronic kidney failure. This rare disease is caused by mutations in the CLDN16 and CLDN19 genes. These genes encode the tight junction proteins claudin-16 and claudin-19, respectively, which regulate the paracellular ion reabsorption in the kidney. Patients with mutations in the CLDN19 gene also present severe visual impairment. Our goals in this study were to examine the clinical characteristics of a large cohort of Spanish patients with this disorder and to identify the disease causing mutations. We included a total of 31 patients belonging to 27 unrelated families and studied renal and ocular manifestations. We then analyzed by direct DNA sequencing the coding regions of CLDN16 and CLDN19 genes in these patients. Bioinformatic tools were used to predict the consequences of mutations. Clinical evaluation showed ocular defects in 87% of patients, including mainly myopia, nystagmus and macular colobomata. Twenty two percent of patients underwent renal transplantation and impaired renal function was observed in another 61% of patients. Results of the genetic analysis revealed CLDN19 mutations in all patients confirming the clinical diagnosis. The majority of patients exhibited the previously described p.G20D mutation. Haplotype analysis using three microsatellite markers showed a founder effect for this recurrent mutation in our cohort. We also identified four new pathogenic mutations in CLDN19, p.G122R, p.I41T, p.G75C and p.G75S. A strategy based on microsequencing was designed to facilitate the genetic diagnosis of this disease. Our data indicate that patients with CLDN19 mutations have a high risk of progression to chronic renal disease.

Nutrient patterns and their food sources in an International Study Setting: report from the EPIC study.

BACKGROUND Compared to food patterns, nutrient patterns have been rarely used particularly at international level. We studied, in the context of a multi-center study with heterogeneous data, the methodological challenges regarding pattern analyses. METHODOLOGY/PRINCIPAL FINDINGS We identified nutrient patterns from food frequency questionnaires (FFQ) in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study and used 24-hour dietary recall (24-HDR) data to validate and describe the nutrient patterns and their related food sources. Associations between lifestyle factors and the nutrient patterns were also examined. Principal component analysis (PCA) was applied on 23 nutrients derived from country-specific FFQ combining data from all EPIC centers (N = 477,312). Harmonized 24-HDRs available for a representative sample of the EPIC populations (N = 34,436) provided accurate mean group estimates of nutrients and foods by quintiles of pattern scores, presented graphically. An overall PCA combining all data captured a good proportion of the variance explained in each EPIC center. Four nutrient patterns were identified explaining 67% of the total variance: Principle component (PC) 1 was characterized by a high contribution of nutrients from plant food sources and a low contribution of nutrients from animal food sources; PC2 by a high contribution of micro-nutrients and proteins; PC3 was characterized by polyunsaturated fatty acids and vitamin D; PC4 was characterized by calcium, proteins, riboflavin, and phosphorus. The nutrients with high loadings on a particular pattern as derived from country-specific FFQ also showed high deviations in their mean EPIC intakes by quintiles of pattern scores when estimated from 24-HDR. Center and energy intake explained most of the variability in pattern scores. CONCLUSION/SIGNIFICANCE The use of 24-HDR enabled internal validation and facilitated the interpretation of the nutrient patterns derived from FFQs in term of food sources. These outcomes open research opportunities and perspectives of using nutrient patterns in future studies particularly at international level.

Mutation prevalence of cerebral cavernous malformation genes in Spanish patients.

OBJECTIVE To study the molecular genetic and clinical features of cerebral cavernous malformations (CCM) in a cohort of Spanish patients. METHODS We analyzed the CCM1, CCM2, and CCM3 genes by MLPA and direct sequencing of exons and intronic boundaries in 94 familial forms and 41 sporadic cases of CCM patients of Spanish extraction. When available, RNA studies were performed seeking for alternative or cryptic splicing. RESULTS A total of 26 pathogenic mutations, 22 of which predict truncated proteins, were identified in 29 familial forms and in three sporadic cases. The repertoire includes six novel non-sense and frameshift mutations in CCM1 and CCM3. We also found four missense mutations, one of them located at the third NPXY motif of CCM1 and another one that leads to cryptic splicing of CCM1 exon 6. We found four genomic deletions with the loss of the whole CCM2 gene in one patient and a partial loss of CCM1and CCM2 genes in three other patients. Four families had mutations in CCM3. The results include a high frequency of intronic variants, although most of them localize out of consensus splicing sequences. The main symptoms associated to clinical debut consisted of cerebral haemorrhage, migraines and epileptic seizures. The rare co-occurrence of CCM with Noonan and Chiari syndromes and delayed menarche is reported. CONCLUSIONS Analysis of CCM genes by sequencing and MLPA has detected mutations in almost 35% of a Spanish cohort (36% of familial cases and 10% of sporadic patients). The results include 13 new mutations of CCM genes and the main clinical symptoms that deserves consideration in molecular diagnosis and genetic counselling of cerebral cavernous malformations.

The expression level of BAALC-associated microRNA miR-3151 is an independent prognostic factor in younger patients with cytogenetic intermediate-risk acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease whose prognosis is mainly related to the biological risk conferred by cytogenetics and molecular profiling. In elderly patients (60 years) with normal karyotype AML miR-3151 have been identified as a prognostic factor. However, miR-3151 prognostic value has not been examined in younger AML patients. In the present work, we have studied miR-3151 alone and in combination with BAALC, its host gene, in a cohort of 181 younger intermediate-risk AML (IR-AML) patients. Patients with higher expression of miR-3151 had shorter overall survival (P=0.0025), shorter leukemia-free survival (P=0.026) and higher cumulative incidence of relapse (P=0.082). Moreover, in the multivariate analysis miR-3151 emerged as independent prognostic marker in both the overall series and within the unfavorable molecular prognostic category. Interestingly, the combined determination of both miR-3151 and BAALC improved this prognostic stratification, with patients with low levels of both parameters showing a better outcome compared with those patients harboring increased levels of one or both markers (P=0.003). In addition, we studied the microRNA expression profile associated with miR-3151 identifying a six-microRNA signature. In conclusion, the analysis of miR-3151 and BAALC expression may well contribute to an improved prognostic stratification of younger patients with IR-AML.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).