Loneliness Mediates the Relationship Between Early Life Stress and Perceived Stress but not Hypothalamic-Pituitary-Adrenal Axis Functioning

Many authors have proposed that early life stress (ELS) provokes a dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and contributes negatively to the management of stress in adulthood. However, these associations have not always been observed, making it necessary to include new factors that could explain the different results found. In this regard, people with ELS experiences report less social support during adulthood, suggesting that loneliness could be a mediating factor. Thus, our aims were to investigate whether ELS was related to both perceived stress and diurnal HPA axis activity, and whether loneliness mediates these relationships, in a community sample (N=187, 18-55years old). Fourteen cortisol samples were collected on two non-consecutive days to obtain the overall diurnal cortisol, diurnal cortisol slope, and bedtime levels. Additionally, ELS was assessed with the Risky Families Questionnaire (RFQ) and the Recalled Childhood and Adolescence Perceived Stress (ReCAPS) measure. Results revealed that ELS was associated with perceived stress, but not HPA axis functioning, and loneliness mediated the relationship between ELS and perceived stress, but not between ELS and HPA axis functioning. Similar results were found for both ELS questionnaires, suggesting that the ReCAPS is an adequate tool. These results highlight the importance of loneliness in understanding the long-term effects of ELS, and they indicate different effects of ELS on subjective and physiological stress indicators

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Hydrogel-Assisted Antisense LNA Gapmer Delivery for In Situ Gene Silencing in Spinal Cord Injury

After spinal cord injury (SCI), nerve regeneration is severely hampered due to the establishment of a highly inhibitory microenvironment at the injury site, through the contribution of multiple factors. The potential of antisense oligonucleotides (AONs) to modify gene expression at different levels, allowing the regulation of cell survival and cell function, together with the availability of chemically modified nucleic acids with favorable biopharmaceutical properties, make AONs an attractive tool for novel SCI therapy developments. In this work, we explored the potential of locked nucleic acid (LNA)-modified AON gapmers in combination with a fibrin hydrogel bridging material to induce gene silencing in situ at a SCI lesion site. LNA gapmers were effectively developed against two promising gene targets aiming at enhancing axonal regeneration—RhoA and GSK3ß. The fibrin-matrix-assisted AON delivery system mediated potent RNA knockdown in vitro in a dorsal root ganglion explant culture system and in vivo at a SCI lesion site, achieving around 75% downregulation 5 days after hydrogel injection. Our results show that local implantation of a AON-gapmer-loaded hydrogel matrix mediated efficient gene silencing in the lesioned spinal cord and is an innovative platform that can potentially combine gene regulation with regenerative permissive substrates aiming at SCI therapeutics and nerve regeneration.This work was supported by Fundação para a Ciência e a Tecnologia ( FCT , Portugal) in the framework of the Harvard-Portugal Medical School Program ( HMSP-ICT/0020/2010 ); Project NORTE-01-0145-FEDER-000008 , supported by the Norte Portugal Regional Operational Programme (NORTE 2020) , under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) ; Fundo Europeu de Desenvolvimento Regional funds through COMPETE 2020 - Operational Program for Competitiveness and Internationalization (POCI) , Portugal 2020; by Portuguese funds through FCT/Ministério da Ciência, Tecnologia e Ensino Superior in the framework of the project “Institute for Research and Innovation in Health Sciences” ( POCI-01-0145-FEDER-007274 ); Marie Curie Actions of the European Community’s 7th Framework Program ( PIEF-GA-2011-300485 to P.M.D.M.); Santa Casa da Misericordia de Lisboa – Prémio Neurociências Mello e Castro , and FCT fellowship SFRH/BPD/108738/2015 (to P.M.D.M). Funding for open access charge: Project NORTE-01-0145-FEDER-000012 , financed by Norte Portugal Regional Operational Programme (NORTE 2020) , under the PORTUGAL 2020 Partnership Agreement, through the ERDF . We would like to acknowledge the support from Paula Magalhães and Tânia Meireles from the i3S Cell Culture and Genotyping Core Facility in real-time PCR experiments

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

<p>Abstract</p> <p>Background</p> <p>Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.</p> <p>Methods</p> <p>This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.</p> <p>Results</p> <p>The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated.</p> <p>Conclusions</p> <p>The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.</p

Endurance of methanogenic archaea in anaerobic bioreactors treating oleate-based wastewater

Methanogenic archaea are reported as very sensitive to lipids and long chain fatty acids (LCFA). Therefore, in conventional anaerobic processes, methane recovery during LCFA-rich wastewater treatment is usually low. By applying a start-up strategy, based on a sequence of step feeding and reaction cycles, an oleate-rich wastewater was efficiently treated at an organic loading rate of 21 kg COD m(-3) day(-1) (50 % as oleate), showing a methane recovery of 72 %. In the present work, the archaeal community developed in that reactor is investigated using a 16S rRNA gene approach. This is the first time that methanogens present in a bioreactor converting efficiently high loads of LCFA to methane are monitored. Denaturing gradient gel electrophoresis profiling showed that major changes on the archaeal community took place during the bioreactor start-up, where phases of continuous feeding were alternated with batch phases. After the start-up, a stable archaeal community (similarity higher than 84 %) was observed and maintained throughout the continuous operation. This community exhibited high LCFA tolerance and high acetoclastic and hydrogenotrophic activity. Cloning and sequencing results showed that Methanobacterium- and Methanosaeta-like microorganisms prevailed in the system and were able to tolerate and endure during prolonged exposure to high LCFA loads, despite the previously reported LCFA sensitivity of methanogens.This study has been financially supported by FEDER funds through the Operational Competitiveness Programme (COMPETE) and by national funds through the Portuguese Foundation for Science and Technology (FCT) in the frame of the projects FCOMP-01-0124-FEDER-007087 and FCOMP-01-0124-FEDER-014784. Financial support from FCT and the European Social Fund (ESF) through PhD grants SFRH/BD/48960/2008 and SFRH/BD/24256/2005 attributed to Andreia Salvador and Ana Julia Cavaleiro is also acknowledged

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest

Epidemiology of traumatic spinal cord injury in Galicia, Spain: trends over a 20-year period

[Abstract] Study design: Observational study with prospective and retrospective monitoring. Objective: To describe the epidemiological and demographic characteristics of traumatic spinal cord injury (TSCI), and to analyze its epidemiological changes. Setting: Unidad de Lesionados Medulares, Complejo Hospitalario Universitario A Coruña, in Galicia (Spain). Methods: The study included patients with TSCI who had been hospitalized between January 1995 and December 2014. Relevant data were extracted from the admissions registry and electronic health record. Results: A total of 1195 patients with TSCI were admitted over the specified period of time; 76.4% male and 23.6% female. Mean patient age at injury was 50.20 years. Causes of injury were falls (54.2%), traffic accidents (37%), sports/leisure-related accidents (3.5%) and other traumatic causes (5.3%). Mean patient age increased significantly over time (from 46.40 to 56.54 years), and the number of cases of TSCI related to traffic accidents decreased (from 44.5% to 23.7%), whereas those linked to falls increased (from 46.9% to 65.6%). The most commonly affected neurological level was the cervical level (54.9%), increasing in the case of levels C1–C4 over time, and the most frequent ASIA (American Spinal Injury Association) grade was A (44.3%). The crude annual incidence rate was 2.17/100 000 inhabitants, decreasing significantly over time at an annual percentage rate change of −1.4%. Conclusions: The incidence rate of TSCI tends to decline progressively. Mean patient age has increased over time and cervical levels C1–C4 are currently the most commonly affected ones. These epidemiological changes will eventually result in adjustments in the standard model of care for TSCI

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients