Vocal Folds Disorders Detection and Classification in Endoscopic Narrow-Band Images

The diagnosis of vocal folds (VF) diseases is error- prone due to the large variety of diseases that can affect them. VF lesions can be divided in nodular, e.g. nodules, polyps and cysts, and diffuse, e.g. hyperplastic laryngitis and carcinoma. By endoscopic examination, the clinician traditionally evaluates the presence of macroscopic formations and mucosal vessels alteration. Endoscopic narrow-band imaging (NBI) has recently started to be employed since it provides enhanced vessels contrast as compared to classical white-light endoscopy. This work presents a preliminary study on the development of an automatic diagnostic tool based on the assessment of vocal cords symmetry in NBI images. The objective is to identify possible protruding mass lesions on which subsequent vessels analysis may be performed. The method proposed here is based on the segmentation of the glottal area (GA) from the endoscopic images, based on which the right and the left portions of the vocal folds are detected and analyzed for the detection of protruding areas. The obtained information is then used to classify the VF edges as healthy or pathological. Results from the analysis of 22 endoscopic NBI images demonstrated that the proposed algorithm is robust and effective, providing a 100% success rate in the classification of VF edges as healthy or pathological. Such results support the investment in further research to expand and improve the algorithm presented here, potentially with the addition of vessels analysis to determine the pathological classification of detected protruding areas

Do beta-blockers worsen respiratory status for patients with COPD?

Patients with chronic obstructive pulmonary disease (COPD) who use cardioselective beta-blockers (beta1-blockers) do not experience a significant worsening of their short-term pulmonary status as measured by changes in forced expiratory volume in 1 second (FEV1), or by changes in patients' self-reported symptoms. If such harmful effects do exist, they are likely to be less clinically important than the substantial proven benefits of beta-blockade for patients with concomitant cardiovascular disease (strength of recommendation: A, based on a high-quality meta-analysis of controlled trials). Limited evidence suggests that most patients with congestive heart failure and COPD without reversible airflow obstruction tolerate carvedilol, which causes both nonselective beta- and alpha-adrenergic blockade (SOR: B, based on limited-quality cohort studies)

The introduction of capillary structures in 4D simulated vascular tree for ART 3.5D algorithm further validation

open6openBarra, Beatrice; El Hadji, Sara; De Momi, Elena; Ferrigno, Giancarlo; Cardinale, Francesco; Baselli, GiuseppeBarra, Beatrice; EL HADJI, Sara; DE MOMI, Elena; Ferrigno, Giancarlo; Cardinale, Francesco; Baselli, Giusepp

High-resolution models of solar granulation: the 2D case

Using grid refinement, we have simulated solar granulation in 2D. The refined region measures 1.97*2.58 Mm (vertical*horizontal). Grid spacing there is 1.82*2.84 km. The downflows exhibit strong Kelvin-Helmholtz instabilities. Below the photosphere, acoustic pulses are generated. They proceed laterally (in some cases distances of at least the size of our refined domain) and may be enhanced when transversing downflows) as well as upwards where, in the photosphere they contribute significantly to 'turbulence' (velocity gradients, etc.) The acoustic pulses are ubiquitous in that at any time several of them are seen in our high-resolution domain. Their possible contributions to p-mode excitation or heating of the chromosphere needs to be investigated

Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest\u2014crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier\u2014FIX\u2014cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment

High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

A Novel Approach of Groupwise fMRI-Guided Tractography Allowing to Characterize the Clinical Evolution of Alzheimer's Disease

Guiding diffusion tract-based anatomy by functional magnetic resonance imaging (fMRI), we aim to investigate the relationship between structural connectivity and functional activity in the human brain. To this purpose, we introduced a novel groupwise fMRI-guided tractographic approach, that was applied on a population ranging between prodromic and moderate stages of Alzheimer's disease (AD). The study comprised of 15 subjects affected by amnestic mild cognitive impairment (aMCI), 14 diagnosed with AD and 14 elderly healthy adults who were used as controls. By creating representative (ensemble) functionally guided tracts within each group of participants, our methodology highlighted the white matter fiber connections involved in verbal fluency functions for a specific population, and hypothesized on brain compensation mechanisms that potentially counteract or reduce cognitive impairment symptoms in prodromic AD. Our hope is that this fMRI-guided tractographic approach could have potential impact in various clinical studies, while investigating white/gray matter connectivity, in both health and disease

How random is your heart beat?

We measure the content of random uncorrelated noise in heart rate variability using a general method of noise level estimation using a coarse grained entropy. We show that usually - except for atrial fibrillation - the level of such noise is within 5 - 15% of the variance of the data and that the variability due to the linearly correlated processes is dominant in all cases analysed but atrial fibrillation. The nonlinear deterministic content of heart rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p

Exploring resting-state functional connectivity invariants across the lifespan in healthy people by means of a recently proposed graph theoretical model

In this paper we investigate the changes in the functional connectivity intensity, and some related properties, in healthy people, across the life span and at resting state. For the explicit computation of the functional connectivity we exploit a recently proposed model, that bases not only on the correlations data provided by the acquisition equipment, but also on different parameters, such as the anatomical distances between nodes and their degrees. The leading purpose of the paper is to show that the proposed approach is able to recover the main aspects of resting state condition known from the available literature, as well as to suggest new insights, perspectives and speculations from a neurobiological point of view. Our study involves 133 subjects, both males and females of different ages, with no evidence of neurological diseases or systemic disorders. First, we show how the model applies to the sample, where the subjects are grouped into 28 different groups (14 of males and 14 of females), according to their age. This leads to the construction of two graphs (one for males and one for females), that can be realistically interpreted as representative of the neural network during the resting state. Second, following the idea that the brain network is better understood by focusing on specific nodes having a kind of centrality, we refine the two output graphs by introducing a new metric that favours the selection of nodes having higher degrees. As a third step, we extensively comment and discuss the obtained results. In particular, it is remarkable that, despite a great overlapping exists between the outcomes concerning males and females, some intriguing differences appear. This motivates a deeper local investigation, which represents the fourth part of the paper, carried out through a thorough statistical analysis. As a result, we are enabled to support that, for two special age groups, a few links contribute in differentiating the behaviour of males and females. In addition, we performed an average-based comparison between the proposed model and the traditional statistical correlation-based approach, then discussing and commenting the main outlined discrepancies