Notes

dST-Tiso Period of time, the sunday paper Electrocardiographic Gun involving Ventricular Arrhythmia Inducibility in People who have Ajmaline-Induced Brugada Type I Structure.
In this work, the computational simulation of thermal gradients related to internal lesions according to the phenomenon of pathological angiogenesis is proposed, this is based on the finite element method, and using a three¬dimensional geometric model adjusted to suit the real female anatomy. The simulation of the thermal distribution was based on the bioheating equation; it was carried out using the COMSOL Multiphysics® software. As a result, the simulation of both internal and superficial thermal distributions associated to lesions smaller than 1 cm and located inside the simulated breast tissue were obtained. An increase in temperature on the surface of the breast of 0.1 ° C was observed for a lesion of 5 mm in diameter and 15 mm in deep. A qualitative validation of the model was carried out by contrasting the simulation of anomalies of 10 mm in diameter at different depths (10, 15 and 20 mm) proposed in the literature, with the simulation of the model proposed here, obtaining the same behavior for the three cases.Clinical Relevance- The 3D computational tool adjusted to suit the anatomy of the real female breast allows obtaining the temperature distribution inside and on the surface of the tissue in healthy cases and with abnormalities associated with temperature elevations. It is an important characteristic of the model when the behavior of the parameters inside the tissue needs to be analyzed.Bone tissue is constantly changed adapting to its mechanical environment and capable of repairing itself. Ultra-sound has recently been used as a diagnostic technique to assess bone conditions. To optimize the experimental model as best as possible computational simulation techniques have been focused on clinical applications in bone. This study aims to analyze by finite element method the propagation of ultrasound waves along the cortical bone. The wave propagation phenomenon is well studied and described by the Helmholtz equation. The first part of the work analytically solves the Helmholtz equation, and later the COMSOL Multiphysics software is used. It was established a cylindrical geometry as the bone sample. The software analyzes with "Pressure Acoustic, Frequency Domain" module. An extremely fine mesh is used for the solution in order not to lose information. According to the analytical solution, the results show the behavior of the acoustic pressure waves throughout the samples. In addition, attenuation coefficients are calculated for biological materials such as bone and muscle. Simulation methods allow to analyze adjustable parameters in the development of new devices. Thus, optimizing resources and allowing the researcher to better understanding the problem to be solved.Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three predominant subtypes, persistent inattention (ADHD-I), hyperactivity-impulsivity (ADHD-HI) and a combination of both (ADHD-C). Identifying reliable features to distinguish different subtypes is significant for clinical individualized treatment. In this work, we conducted a two-stage electroencephalogram (EEG) microstate analysis on 54 healthy controls and 107 ADHD children, including 54 ADHD-Is and 53 ADHD-Cs, aiming to examine the dynamic temporal alterations in ADHDs compared to healthy controls (HCs), as well as different EEG signatures between ADHD subtypes. Results demonstrated that the dynamics of resting-state EEG microstates, particularly centering on salience (state C) and frontal-parietal network (state D), were significantly aberrant in ADHDs. Specifically, the occurrence and coverage of state C were decreased in ADHDs (p=0.002; p=0.0015), while the duration and contriding a new window for better diagnosis of ADHD.The type of the atherosclerotic plaque has significant clinical meaning since plaque vulnerability depends on its type. In this work, we present a computational approach which predicts the development of new plaques in coronary arteries. More specifically, we employ a multi-level model which simulates the blood fluid dynamics, the lipoprotein transport and their accumulation in the arterial wall and the triggering of inflammation using convection-diffusion-reaction equations and in the final level, we estimate the plaque volume which causes the arterial wall thickening. The novelty of this work relies on the conceptual approach that using the information from 94 patients with computed tomography coronary angiography (CTCA) imaging at two time points we identify the correlation of the computational results with the real plaque components detected in CTCA. In the next step, we use these correlations to generate two types of de-novo plaques calcified and non-calcified. Evaluation of the model's performance is achieved using eleven patients, who present de-novo plaques at the follow-up imaging. The results demonstrate that the computationally generated plaques are associated significantly with the real plaques indicating that the proposed approach could be used for the prediction of specific plaque type formation.Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein interactions can be an exceptionally time-consuming and expensive process. Alternatively, this process can be simulated using modern deep learning methods that have the potential of utilising vast quantities of data to reduce the cost and time required to provide accurate predictions. We seek to leverage a set of BERT-style models that have been pre-trained on vast quantities of both protein and drug data. The encodings produced by each model are then utilised as node representations for a graph convolutional neural network, which in turn are used to model the interactions without the need to simultaneously fine-tune both protein and drug BERT models to the task. We evaluate the performance of our approach on two drug-target interaction datasets that were previously used as benchmarks in recent work.Our results significantly improve upon a vanilla BERT baseline approach as well as the former state-of-the-art methods for each task dataset. Our approach builds upon past work in two key areas; firstly, we take full advantage of two large pre-trained BERT models that provide improved representations of task-relevant properties of both drugs and proteins. Secondly, inspired by work in natural language processing that investigates how linguistic structure is represented in such models, we perform interpretability analyses that allow us to locate functionally-relevant areas of interest within each drug and protein. By modelling the drug-target interactions as a graph as opposed to a set of isolated interactions, we demonstrate the benefits of combining large pre-trained models and a graph neural network to make state-of-the-art predictions on drug-target binding affinity.Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field. However, there are still challenges to overcome with regards to modelling the properties of a protein, especially when labelled resources are scarce. Developing interpretable deep learning models is an essential criterion, as proteomics research requires methods to understand the functional properties of proteins. The ability to derive quality information from both the model and the data will play a vital role in the advancement of proteomics research. In this paper, we seek to leverage a BERT model that has been pre-trained on a vast quantity of proteomic data, to model a collection of regression tasks using only a minimal amount of data. We adopt a triplet network structure to fine-tune the BERT model for each dataset and evaluate its performance on a set of downstream task predictions plasma membrane localisation, thermostability, peak absorption wavelength, and enantioselectivity. Our results significantly improve upon the original BERT baseline as well as the previous state-of-the-art models for each task, demonstrating the benefits of using a triplet network for refining such a large pre-trained model on a limited dataset. As a form of white-box deep learning, we also visualise how the model attends to specific parts of the protein and how the model detects critical modifications that change its overall function.Transcranial direct current stimulation (tDCS) delivers weak current into the brain to modulate neural activities. Many methods have been proposed to determine electrode positions and stimulation intensities. Proteasome inhibition Due to the trade-off between intensity and focality, it is actually a multi-objective optimization problem that has a set of optimal solutions. However, traditional methods can produce only one solution at each time, and many parameters need to be determined by experience. In this study, we proposed the nondominated sorting genetic algorithm II (NSGA-II) to solve the current optimization problem of multi-electrode tDCS. We also compared the representative solutions with LCMV solutions. The result shows that a group of solutions close to the optimal front can be obtained just in only one run without any prior knowledge.The modeling of biosensors is useful in the design stage. The main device simulator, like Silvaco, has poor software resources for bio-receptors simulations. The modeling is challenging due to the high complexity of the living matter. It requires complementary knowledge from biochemistry, biosensors technology and electronic devices, like FET - Field Effect Transistors. This paper presents an analytical model for the product concentrations versus the time for enzymatic FET based on zero, one or two-order reaction. The mathematical model is confronted with an experimental model tested on a glucose biosensor that uses glucose-oxidase receptor enzyme. The biosensor response time was 36 seconds for enzyme loading of 2μmol/l.Clinical Relevance- The analytical model proposed in this paper represents an analytical tool in the design stage, for any biosensor used in clinical practices. Their modeling is missing.The COVID-19 pandemic has placed an extreme healthcare burden across the global community, and new population-based analyses are needed to identify successful mitigation and treatment efforts. The objective of this study was to design a computational algorithm to estimate the time-delay between a peak infection and associated death rate, and to estimate a measurement of the daily case-fatality ratio (D-CFR). Daily infection and death rates from January 22, 2020 through April 15, 2021 for the United States (US) were downloaded from the US Center for Disease Control COVID-19 website. A Savitzky-Golay filter estimated the moving time average of each data sequence with 5 different window-sizes. A locally-designed inflection point identification algorithm with a variable length line-fitting sub-routine identified peak infection and death rates, and quantified the time-delay between a peak infection and subsequent death rate. Although filter window-size did not affect the time-delay calculation (p = 0.99), there was a significant effect of fitting-line length (p less then 0.001). A significant effect of time-delay length was found among three infection outbreaks (p less then 0.001), and there was a significant difference between time-delay lengths (p less then 0.01). A maximum D-CFR of approximately 7% occurred during the first infection outbreak; however, starting approximately 2.5 months after the first peak, a significant negative linear trend (p less then 0.001) in the D-CFR continued until the end of the analyzed data. In conclusion, this research demonstrated a new method to quantify the time-delay between peak daily COVID-19 infection and death rates, and a new metric to approximate the continuous case-fatality ratio for the ongoing pandemic.
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