Publications by authors named "Ciaccio E"
Physical and psychological symptoms and survey importance in celiac disease.
World J Gastrointest Endosc
December 2024
Celiac disease is an autoimmune condition that affects approximately 1% of the worldwide community. Originally thought to be confined mostly to the small intestine, resulting in villous atrophy and nutrient malabsorption, it has more recently been implicated in systemic manifestations as well, particularly when undiagnosed or left untreated. Herein, the physical and psychological symptoms of celiac disease are described and explored.
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- The study aimed to understand the mechanisms behind reentrant ventricular tachycardia (VT) and enhance the targeting of catheter ablation procedures in postinfarction patients.
- Researchers collected and analyzed electrogram data during both sinus rhythm and VT, finding distinct voltage gradients, with significantly lower mean voltage at the VT isthmus compared to its boundaries.
- The findings suggest that the isthmus has uniform slow conduction, which helps maintain the VT circuit, challenging previous assumptions about conducting channels.
The international Working Group of the Signal Summit is a consortium of experts in the field of cardiac electrophysiology dedicated to advancing knowledge on understanding and clinical application of signal recording and processing techniques. In 2023, the working group met in Reykjavik, Iceland, and laid the foundation for this manuscript. Atrial fibrillation (AF) is the most common arrhythmia in adults, with a rapidly increasing prevalence worldwide.
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- - This paper introduces a new framework for detecting Alzheimer's disease (AD) by analyzing EEG signals, utilizing a unique Lattice123 pattern inspired by the Shannon information entropy theorem for feature extraction.
- - By generating directed graphs and using kernel functions, the model creates six feature vectors for each EEG signal, applying multilevel discrete wavelet transform (MDWT) to capture detailed features in both frequency and spatial domains.
- - The model achieves over 98% classification accuracy and over 96% geometric mean, demonstrating its effectiveness in identifying subtle EEG signal changes related to AD, and is ready for validation with larger datasets.
Antrodiaetus is a lineage of mygalomorph spider (Mygalomorphae: Antrodiaetidae) that has persisted since the late Cretaceous and has a disjunct Holarctic distribution and strong morphological conservatism. These folding-door spiders possess a life history (i.e.
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- Catheter ablation for ventricular tachycardia (VT) currently uses isochronal late activation mapping (ILAM), which helps identify isthmus regions by categorizing activation times into a limited number of isochrones.
- This study evaluates whether the methods used in ILAM are the most effective by testing various numbers of isochrones and exploring continuous metrics that improve isthmus detection.
- Results showed that increasing the number of isochrones or using continuous metrics significantly enhanced the identification precision of isthmus regions, indicating that current practices can be optimized for better outcomes in VT ablation.
Article Synopsis
- * The study investigates 18 topics related to the psychological and brain effects of celiac disease, drawing from over 500 sources, with notable areas of research including quality of life, depression, and neurological disorders like ataxia and epilepsy.
- * A growing interest in this research area is evident since 1990, marked by more citations for recent studies, as well as increased manuscript lengths, indicating an evolving understanding of the psychological and neurological implications of celiac disease.
Article Synopsis
- This study investigates the role of left ventricular (LV) wall thickness, assessed via contrast-enhanced CT, in identifying areas of functional conduction block in patients with postinfarction reentrant ventricular tachycardia (VT).
- The researchers analyzed data from 6 patients, finding that regions of significant change in wall thickness (ΔT) were closely associated with boundaries where electrical conduction block occurred during VT.
- The results suggest that measuring the curvature of activation wavefronts based on LV wall thickness can aid in precisely locating VT isthmus areas in patients following a heart attack.
Article Synopsis
- The study investigates using a graph convolutional network (GCN) to identify critical isthmus areas in patients with scar-related reentrant atrial tachycardia (SRRAT) for effective ablation.
- Researchers collected electroanatomic maps from 29 SRRAT cases to create an optimal GCN model for predicting isthmus points based on key electrogram features.
- Results indicated that the GCN successfully predicted isthmus areas with a median distance of approximately 8 mm from actual areas, suggesting potential for improved identification of critical ablation targets in clinical practice.
Article Synopsis
- Sinus rhythm activation time is a key indicator for evaluating the substrate of heart tissue following an infarct, particularly in the context of ventricular tachycardia (VT).
- A study involving dogs post-infarction revealed that analyzing sinus rhythm and VT electrograms can reveal patterns that predict VT inducibility.
- The findings suggest that the sinus rhythm activation signature can help differentiate between conditions that support reentrant VT and those that do not, which is important for targeting treatment locations within the heart.
Background: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures.
View Article and Find Full Text PDFRole of activation signatures in re-entrant ventricular tachycardia circuits.
J Cardiovasc Electrophysiol
February 2024
Article Synopsis
- Researchers developed a method to quickly verify the location of the ventricular tachycardia (VT) isthmus using heart surface electrogram recordings in dogs with induced myocardial infarctions.
- They used a multielectrode array to collect electrograms and analyzed activation signals to differentiate between inner and outer VT circuit pathways, finding significant relationships in activation rates.
- The activation signal algorithm showed potential for accurately identifying the optimal ablation site to prevent VT by blocking electrical impulses in the isthmus.
Correlation relationships of the reentrant ventricular tachycardia circuit.
Comput Methods Programs Biomed
November 2023
Article Synopsis
- A study was conducted using 19 canine experiments to examine how reentrant ventricular tachycardia (VT) is initiated and sustained, focusing on mapping heart circuitry post-infarction.
- Findings revealed a correlation between the extrastimulus interval used to induce VT and the VT cycle length, suggesting that a specific boundary length (LIB) within the heart's circuitry plays a crucial role in VT dynamics.
- The research concluded that while shorter LIBs are closely related to VT cycle length, other circuit parameters like isthmus width and angle showed less relevance, potentially due to changes in the heart tissue's structure after an infarct.
Article Synopsis
- Celiac disease (CD) is an autoimmune disorder affecting the small intestine, and recent studies explore its potential connection to cardiovascular disease (CVD), although results have varied.
- A review of literature reveals that while some studies suggest people with CD may be at higher risk for certain cardiovascular issues like myocardial infarction, the relationship with stroke and other heart conditions remains unclear.
- Further investigation is essential to understand how adherence to a gluten-free diet influences CVD risk and to develop effective strategies for identifying and managing cardiovascular risk in individuals with CD.
Article Synopsis
- A brain tumor is an abnormal mass within the skull that can cause serious health issues by pressuring healthy brain tissue and varies in effects depending on its location.
- Malignant brain tumors can grow quickly, leading to higher mortality rates, making early detection crucial for effective treatment.
- This review analyzed 124 research articles from 2000 to 2022, focusing on the challenges faced by computer-aided diagnostic (CAD) systems and AI techniques in detecting brain tumors, as well as future research directions.
Article Synopsis
- The study investigates how mapping electrical activity during sinus rhythm can help identify areas of disrupted conduction in hearts affected by ischemic re-entrant ventricular tachycardia.
- Researchers constructed activation maps from electrograms of canines with postinfarction hearts, focusing on regions known as isthmus lateral boundaries (ILB).
- Results show significant differences in activation times at ILB locations compared to other circuit parts, suggesting that these areas may be linked to long-lasting changes in electrical properties due to heart tissue damage.
Article Synopsis
- Celiac Disease (CD) is a gluten intolerance affecting genetically susceptible individuals, posing public health concerns due to its high prevalence and low diagnosis rates.
- The study explores the use of Artificial Intelligence (AI) to analyze biopsy images of the small intestine, aiming to distinguish between normal individuals, CD patients, and those with Non-Celiac Duodenitis (NCD).
- The AI model, specifically a Support Vector Machine (SVM), showed outstanding accuracy of 98.53% for differentiating between normal and CD, and 98.55% for distinguishing normal from NCD, marking a significant advancement in automated biopsy image analysis.
Article Synopsis
- Schizophrenia is a serious and long-lasting mental illness, and this study aims to improve its detection using advanced EEG signal analysis techniques.
- The research introduces a new model called the carbon chain pattern (CCP) combined with an iterative tunable q-factor wavelet transform (ITQWT) to extract and analyze features from EEG signals effectively.
- The model achieved impressive detection accuracies of 95.84% with a single EEG channel and 99.20% using a voting method across multiple channels, showcasing its potential for accurate schizophrenia diagnosis.
Purpose: To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images.
Method: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data.
Article Synopsis
- ADHD is a prevalent neurodevelopmental disorder, and this research involves analyzing over 4000 noisy EEG signals to distinguish between ADHD and healthy individuals.
- A new EEG classification model, named TMP19, combines various techniques including the Tunable Q Wavelet Transform (TQWT) and neighborhood component analysis (NCA) to extract, select, and classify informative features from the EEG data.
- The model achieved impressive classification accuracies of 95.57% and 77.93% using different validation methods, showcasing the effectiveness of this approach in differentiating between ADHD and non-ADHD individuals.
Article Synopsis
- - Atrial fibrillation (AF) is the most common heart rhythm disorder, and there are new methods for assessing it, focusing on complex fractionated atrial electrograms (CFAEs) to differentiate between two types of AF: paroxysmal (ParAF) and persistent (PerAF).
- - Previous studies have not accurately measured the quality and stability of CFAE signals over time and in different locations, which can lead to unreliable assessments and hinder AF treatment.
- - This research utilizes specific nonlinear indices (like dominant frequency and sample entropy) to evaluate CFAE stability and develop a simple classification model that effectively distinguishes between ParAF and PerAF, achieving better performance through techniques like correlation matrix filters and Random Forests.
Article Synopsis
- Researchers are focusing on using machine learning for cough-based disease detection, particularly for diagnosing multiple conditions like Covid-19, heart failure, and asthma.
- A new dataset of cough sounds from 642 subjects was created to train a model that can automatically identify these diseases.
- The model, named DKPNet41, demonstrated impressive accuracy of 99.39% in classifying cough sounds into the specified categories, showcasing its potential as an effective diagnostic tool.
Article Synopsis
- Adults with celiac disease (CD) face challenges in dating, with a study showing that 44.3% have dated while managing their condition, and many feel it significantly affects their dating life.
- The online survey, completed by 538 participants, indicated that females and younger individuals are especially impacted by CD-related dating concerns, including anxiety about explaining dietary restrictions.
- The findings reveal that CD leads to hesitance around dating and intimacy, contributing to lower quality of life and riskier eating behaviors during social interactions.
Postoperative pain and persisting fatigue represent critical concerns for patients receiving lung transplantation. The purpose of this study was to illustrate the trajectory of symptoms in a patient who presented with a posttransplant musculoskeletal syndrome after double redo-lung transplantation and attended therapeutic sessions of global postural re-education during the symptomatic phase. A 32-year-old woman with interstitial lung disease underwent double lung transplantation.
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- * Left ventricular hypertrophy on heart ultrasound (US) is a key diagnostic indicator for HCM, but interpretation can vary due to human error.
- * Researchers developed an automated diagnostic tool using a pretrained network to analyze US images, achieving 100% accuracy in distinguishing HCM patients from healthy subjects with a specific integrated index threshold.