Jorge Oliveira

High Promoter Methylation Levels of APC Predict Poor Prognosis in Sextant Biopsies from Prostate Cancer Patients

Purpose: Prostate cancer is a highly prevalent malignancy and constitutes a major cause of cancer-related morbidity and mortality. Owing to the limitations of current clinical, serologic, and pathologic parameters in predicting disease progression, we sought to investigate the prognostic value of promoter methylation of a small panel of genes by quantitative methylation-specific PCR (QMSP) in prostate biopsies. Experimental Design: Promoter methylation levels of APC, CCND2, GSTP1, RARB2, and RASSF1A were determined by QMSP in a prospective series of 83 prostate cancer patients submitted to sextant biopsy. Clinicopathologic data [age, serum prostate-specific antigen (PSA), stage, and Gleason score] and time to progression and/or death from prostate cancer were correlated with methylation findings. Log-rank test and Cox regression model were used to identify which epigenetic markers were independent predictors of prognosis. Results: At a median follow-up time of 45 months, 15 (18%) patients died from prostate cancer, and 37 (45%) patients had recurrent disease. In univariate analysis, stage and hypermethylation of APC were significantly associated with worse disease–specific survival, whereas stage, Gleason score, high diagnostic serum PSA levels, and hypermethylation of APC, GSTP1, and RASSF1A were significantly associated with poor disease-free survival. However, in the final multivariate analysis, only clinical stage and high methylation of APC were significantly and independently associated with unfavorable prognosis, i.e., decreased disease-free and disease-specific survival. Conclusions: High-level APC promoter methylation is an independent predictor of poor prognosis in prostate biopsy samples and might provide relevant prognostic information for patient management.

TCF21 and PCDH17 methylation: An innovative panel of biomarkers for a simultaneous detection of urological cancers

The three main types of urological cancers are mostly curable by surgical resection, if early detected. We aimed to identify novel DNA methylation biomarkers common to these three urological cancers, potentially suitable for non-invasive testing. From a candidate list of markers created after gene expression assessment of pharmacologically treated cell lines and tissue samples, two genes were selected for further validation. Methylation levels of these genes were quantified in a total of 12 cancer cell lines and 318 clinical samples. PCDH17 and TCF21 methylation levels provided a sensitivity rate of 92% for bladder cancer, 67% for renal cell tumors and 96% for prostate cancer. Methylation levels were significantly different from those of cancer free individuals (n = 37) for all tumor types (p < 0.001), providing 83% sensitivity and 100% specificity for cancer detection. Although in urine samples the sensitivity was 60%, 32% and 26% for bladder, renal, and prostate tumors, respectively (39% overall), absolute specificity was retained. We identified novel and highly specific methylation markers common to the three main urological cancers. However, additional efforts are required to increase the assay’s sensitivity, enabling the simultaneous non-invasive screening of urological tumors in a single voided urine analysis.

Pro-oxidant effects of Ecstasy and its metabolites in mouse brain synaptosomes.

3,4‐Methylenedioxymethamphetamine (MDMA or ‘Ecstasy’) is a worldwide major drug of abuse known to elicit neurotoxic effects. The mechanisms underlying the neurotoxic effects of MDMA are not clear at present, but the metabolism of dopamine and 5‐HT by monoamine oxidase (MAO), as well as the hepatic biotransformation of MDMA into pro‐oxidant reactive metabolites is thought to contribute to its adverse effects.

Deregulated expression of selected histone methylases and demethylases in prostate carcinoma.

Prostate cancer (PCa), a leading cause of cancer-related morbidity and mortality, arises through the acquisition of genetic and epigenetic alterations. Deregulation of histone methyltransferases (HMTs) or demethylases (HDMs) has been associated with PCa development and progression. However, the precise influence of altered HMTs or HDMs expression and respective histone marks in PCa onset and progression remains largely unknown. To clarify the role of HMTs and HDMs in prostate carcinogenesis, expression levels of 37 HMTs and 20 HDMs were assessed in normal prostate and PCa tissue samples by RT-qPCR. SMYD3, SUV39H2, PRMT6, KDM5A, and KDM6A were upregulated, whereas KMT2A-E (MLL1-5) and KDM4B were downregulated in PCa, compared with normal prostate tissues. Remarkably, PRMT6 was the histone modifier that best discriminated normal from tumorous tissue samples. Interestingly, EZH2 and SMYD3 expression levels significantly correlated with less differentiated and more aggressive tumors. Remarkably, SMYD3 expression levels were of independent prognostic value for the prediction of disease-specific survival of PCa patients with clinically localized disease submitted to radical prostatectomy. We concluded that expression profiling of HMTs and HDMs, especially SMYD3, might be of clinical usefulness for the assessment of PCa patients and assist in pre-therapeutic decision-making.

Application of the end loaded split and single-leg bending tests to the mixed-mode fracture characterization of wood

Abstract This work describes the application of end loaded split and single-leg bending tests to the mixed-mode fracture characterization of wood. Experimental tests and numerical validation analyses were performed. A new data reduction scheme based on the crack equivalent concept is proposed. The method overcomes the difficulties inherent to these tests, such as crack length monitoring during propagation and influence of clamping conditions. The single-leg bending test is simpler to execute and provided accurate results. The obtained mixed-mode fracture energy is associated with the pure mode values and the obtained trend point to a linear fracture criterion as a candidate to describe the fracture behavior of the Pinus pinaster Ait. wood.

Exploring embedding matrices and the entropy gradient for the segmentation of heart sounds in real noisy environments.

In this paper we explore a novel feature for the segmentation of heart sounds: the entropy gradient. We are motivated by the fact that auscultations in real environments are highly contaminated by noise and results reinforce our suspicions that the entropy gradient is not only robust to such noise but maintains a high sensitivity to the S1 and S2 components of the signal. Our whole approach consists of three stages, out of which the last two are novel contributions to this field. The first stage consists of typical pre-processing and wavelet reconstruction to obtain the Shannon energy envelogram. On the second stage we use an embedding matrix to track the dynamics of the system, which is formed by delay vectors with higher dimension than the corresponding attractor. On the third stage, we use the eigenvalues and eigenvectors of the embedding matrix to estimate the entropy of the envelogram. Finite differences are used to estimate entropy gradients, in which standard peak picking approaches are used for heart sound segmentation. Experiments are performed on a public dataset of pediatric auscultations obtained in real environments and results show the promising potential of this novel feature for such noisy scenarios.

Coupled hidden Markov model for automatic ECG and PCG segmentation

Automatic and simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) segmentation is a good example of current challenges when designing multi-channel decision support systems for healthcare. In this paper, we implemented and tested a Montazeri coupled hidden Markov model (CHMM), where two HMMs cooperate to recreate the “true” state sequence. To evaluate its performance, we tested different settings (two fully connected and two partially connected channels) on a real dataset annotated by an expert. The fully connected model achieved 71% of positive predictability (P+) on the ECG channel and 67% of P+ on the PCG channel. The partially connected model achieved 90% of P+ on the ECG channel and 80% of P+ in the PCG channel. These results validate the potential of our approach for real world multichannel application systems.

LAMA2 gene mutation update: Toward a more comprehensive picture of the laminin-α2 variome and its related phenotypes

Congenital muscular dystrophy type 1A (MDC1A) is one of the main subtypes of early‐onset muscle disease, caused by disease‐associated variants in the laminin‐α2 (LAMA2) gene. MDC1A usually presents as a severe neonatal hypotonia and failure to thrive. Muscle weakness compromises normal motor development, leading to the inability to sit unsupported or to walk independently. The phenotype associated with LAMA2 defects has been expanded to include milder and atypical cases, being now collectively known as LAMA2‐related muscular dystrophies (LAMA2‐MD). Through an international multicenter collaborative effort, 61 new LAMA2 disease‐associated variants were identified in 86 patients, representing the largest number of patients and new disease‐causing variants in a single report. The collaborative variant collection was supported by the LOVD‐powered LAMA2 gene variant database (https://www.LOVD.nl/LAMA2), updated as part of this work. As of December 2017, the database contains 486 unique LAMA2 variants (309 disease‐associated), obtained from direct submissions and literature reports. Database content was systematically reviewed and further insights concerning LAMA2‐MD are presented. We focus on the impact of missense changes, especially the c.2461A > C (p.Thr821Pro) variant and its association with late‐onset LAMA2‐MD. Finally, we report diagnostically challenging cases, highlighting the relevance of modern genetic analysis in the characterization of clinically heterogeneous muscle diseases.

A multi-spot exploration of the topological structures of the reconstructed phase-space for the detection of cardiac murmurs

Acoustic heart signals are generated by a turbulence effect created when the heart valves snap shut, and therefore carrying significant information of the underlying functionality of the cardiovascular system. In this paper, we present a method for heart murmur classification divided into three major steps: a) features are extracted from the heart sound; b) features are selected using a Backward Feature Selection algorithm; c) signals are classified using a K-nearest neighbors classifier. A new set of fractal features are proposed, which are based on the distinct signatures of complexity and self-similarity registered on the normal and pathogenic cases. The experimental results show that fractal features are the most capable of describing the non-linear structure and the underlying dynamics of heart sounds among the all feature families tested. The classification results achieved for the mitral auscultation spot (88% of accurary) are in agreement with the current state of the art methods for heart murmur classification.

A39 Triad of neuronal vulnerability in huntington’s disease: huntingtin proteostasis, inclusion body formation and mitochondrial function

Introduction The preferential vulnerability of striatal medium spiny neurons is a pathological hallmark in Huntington’s Disease (HD). Striatal neurons have been proposed to differ from other less vulnerable neuronal populations, namely in their proteostasis of mutant huntingtin (mHtt) and in mitochondrial function. However, it remains unclear whether and how these differences interact to condition neuronal survival. Aims Assess how mHtt proteostasis and mitochondrial function change over time in striatal vs. cortical neurons, and whether they correlate with vulnerability. Methods Longitudinal survival analysis in neuronal HD models expressing mHtt was performed using fluorescence microscopy. NAD(P)H levels were measured using fluorescence lifetime imaging and oxygen consumption was measured using cell respirometry. Results In striatal and cortical neurons, mHtt inclusion body (IB) formation correlated with higher survival and lower risk of mitochondrial membrane potential (MMP) loss. Striatal neurons displayed higher risks of death and of MMP loss, despite having lower levels of mHtt than their cortical counterparts, which correlated with a lower risk of IB formation. The mHtt concentration threshold for IB formation was similar in striatal and cortical neurons. Striatal neurons presented lower levels of NAD(P)H and reduced maximal respiration capacity. Conclusion The higher vulnerability of striatal neurons in HD may be attributed to a decreased ability to cope with a soluble and toxic form of mHtt due to a combination of lower risk of IB formation and lower energetic resources. Acknowledgements Fundação para a Ciência e a Tecnologia P2020-PTDC/NEU-NMC/0412/2014, POCI-01-0145-FEDER- 016577, 3599-PPCDT; SFRH/BD/108733/2015; UID/QUI/50006/2013