This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential ...phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.
The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of ...applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.
Abstract Background Individuals with subjective memory impairment (SMI) report worsening of memory without impairment in cognitive tests. Despite normal cognitive performance, they may be at higher ...risk of cognitive decline compared with individuals without SMI. Methods We used a discriminative function (a support vector machine) trained on an independent data set of 226 healthy control subjects and 191 patients with probable Alzheimer's disease (AD) dementia to characterize the baseline gray matter patterns of 24 individuals with SMI and 53 control subjects. We tested for associations of these gray matter patterns with SMI presence, cognitive performance at baseline, and cognitive decline at follow-up. Results Individuals with SMI showed greater similarity to an AD gray matter pattern compared with control subjects without SMI. In addition, episodic memory decline was associated with an AD gray matter pattern in the SMI group. Conclusions Our results indicate a link between the gray matter atrophy pattern of patients with AD and the presence of SMI. Furthermore, multivariate pattern recognition approaches seem to be a sensitive method for identifying subtle brain changes that correspond to future memory decline in SMI.
Hypochlorous acid (HClO) has been gradually recognized as a significant reactive oxygen species (ROS) owing to its stability in organisms. ROS concentration in cancerous cells is approximately ten ...times that in normal cells and excessive HClO is closely associated with tissue damage. Herein, a new turn-on fluorescent probe (MOBT-Cl) based on ESIPT for sensing HClO has been synthesized. MOBT-Cl possessed a large Stokes shift (130 nm), a rapid response time (around 10 s), and ultra-sensitivity (110-folds, LOD = 0.14 nM). Moreover, MOBT-Cl was successfully applied to imaging HClO in cells, zebrafish, and mice. Importantly, MOBT-Cl effectively distinguished cancerous cells from normal cells and monitored HClO levels in APAP-treated cells and zebrafish as well as diagnosed APAP-induced liver injury. The proposed probe held great potential for investigating the precise role of HClO in multiple physiological processes.
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•An ultra-sensitivity ESIPT fluorescent probe (MOBT-Cl) for sensing HClO had been designed.•MOBT-Cl can distinguish cancerous cells from normal cells by monitoring HClO levels.•MOBT-Cl was used for the estimation of HClO fluxes in APAP-treated cells, zebrafish, and mice.•MOBT-Cl can diagnose HClO-mediated hepatotoxicity and evaluate the detoxification of hepatoprotective drugs.
Schizophrenia is a disease that affects approximately 1% of the population. Its early accurate diagnosis is of vital importance to apply adequate therapy as soon as possible. We present a Statistical ...Discriminant Diagnosing (SDD) system that discriminates between healthy controls and subjects and that supports diagnosis by a medical professional. The system works with {feature, electrode} EEG pairs which are selected based on the statistical significance of the p‐values computed over the brain P3b wave. A bank of evoked potential pre‐processed and filtered EEG signals is recorded during an auditory odd‐ball (AOD) task and serves as input to the SDD system. These EEG signals comprise 20 features and 17 electrodes, both in time (t) and frequency (f) domain. The relevance of the Parieto‐Temporal region is shown, allowing us to identify highly discriminant {feature, electrode} pairs in the detection of schizophrenia, resulting lower p‐values in both Right and Left Hemispheres, as well as in Parieto‐Temporal EEG signals. See for instance, the {PSE, P4} pair, with p‐value = 0.00003 for (parametric) t Student and p‐value = 0.00019 for (nonparametric) U Mann‐Whitney tests, both under the 15 Hz cutoff frequency of a low pass EEG preprocessing filter. The relevance of this pair is in agreement with previously published related results. The proposed SDD system may provide the human expert (psychiatrist) with an objective complimentary information to help in the early diagnosis of schizophrenia.
Early accurate diagnosis of schizophrenia is of vital importance to apply adequate therapy as soon as possible. We present a Statistical Discriminant Diagnosing (SDD) system that discriminates between healthy controls and subjects and that supports diagnosis by a medical professional. The system works with {feature, electrode} EEG pairs which are selected based on the statistical significance of the p‐values computed over the brain P3b wave. The proposed SDD system may provide the human expert (psychiatrist) with an objective complimentary information to help in the early diagnosis of schizophrenia.
Capsule endoscopy, also known as wireless capsule endoscopy or video capsule endoscopy, is a noninvasive procedure that uses a swallowed capsule-shaped miniature camera for direct visual and ...diagnostic evaluation of gastrointestinal (GI) disease. Although originally intended as a tool to examine the small intestine, which is mostly beyond the reach of conventional endoscopy, capsule endoscopy is now also being used to examine the entire length of the GI tract.
To summarize current literature evidence on the role of computed tomography (CT) scan in the diagnosis and assessment of coronavirus disease 2019 (COVID-19) pneumonia.
Recent guidelines on the use of ...CT scans in COVID-19 vary between countries. However, the consensus is that it should not be used as the first line; a notion supported by the WHO. Currently, several investigations are being used including reverse transcription PCR testing, chest radiographs, and ultrasound scans, and CT scans. They are ideally performed later during the disease process as the sensitivity and specificity are highest by that time. Typical COVID-19 features on CT scans vary but include vascular enlargement, ground-glass opacities, and ground glass opacification together with consolidation.
Since COVID-19 was declared as a global pandemic, there was a push towards identifying appropriate diagnostic tests that are both reliable and effective. There is a general agreement that CT scans have a high sensitivity but low specificity in diagnosing COVID-19. However, the quality of available studies is not optimal, so this must always be interpreted with the clinical context in mind. Clinicians must aim to weigh up the practicalities and drawbacks of CT scans when considering their use for a patient. The ease and speed of use of CT scans must be balanced with their high radiation doses, and infection control considerations.
Reliable serologic tests are needed for diagnosis and surveillance of Zika virus infection. We evaluated the Euroimmun and Dia.Pro serologic tests for detection of Zika virus IgM and IgG by using a ...panel of 199 samples from a region endemic for flaviviruses. Kinetics of Zika virus antibodies were monitored from 300 sequential specimens sampled over a period of 10 months after infection. We observed suboptimal performance; sensitivity for Zika virus IgM was low, especially in the Euroimmun assay (49%), whereas IgM could be detected for months with the Dia.pro assay. The specificity of the Zika virus IgG assays was also low, especially that of Dia.Pro (62%); findings were strongly influenced by the epidemiologic context. These results highlight the complexity of serologic diagnosis of Zika virus infection in regions endemic for flaviviruses. Accurate analysis of the performance of assays is required to adapt and interpret algorithms.
Objectives
The aim of this research is to add to the current understanding of the latent factor structure of personality disorders by performing a review of the existing literature (Study 1) and a ...factor analytical study on the factor structure and the relationship between self‐reported Axis I and Axis II psychopathology (Study 2).
Design
The current research (Study 2) is cross‐sectional and multicenter.
Results
We found support for the assumption that the borderline personality disorder is a multidimensional construct. Second, we found evidence for a single‐factor structure of the narcissistic, dependent as well as the avoidant personality disorder. Third, we found support for the current Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) distinction between Axis I and Axis II, Axis I psychopathology being explained by the factor neuroticism and Axis II disorders to be further subdivided into the higher order factors of internalizing and externalizing pathology.
Conclusions
An adaptation to the current DSM‐IV borderline personality criteria should be made, while various findings show that the borderline construct is multidimensional. Second, deletion of the dependent and narcissistic personality in the DSM‐V might be unjust. Third, Axis I psychopathology can be explained by the factor neuroticism, and Axis II disorders should be further subdivided into the higher order factors of internalizing and externalizing pathology.
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•A smart coating is composed of MPDA-loaded containers and thermal-responsive resin.•The coating can hierarchically report coating damage and interfacial corrosion.•NIR irradiation ...attributes to the coating crack closure and Phen release.•Localized corrosion on substrates can be inhibited by released corrosion probes.
For conventional anticorrosion coatings, it is challenging to simultaneously report and repair two failure stages involving coating damage and interfacial metal corrosion. Thus, it leads to difficulties for continuous detection of coating service status, and may result in premature failure of metal-based equipment. Herein, we present a novel photothermal responsive composite epoxy system with hierarchical damage reporting and healing capabilities, which is enabled by mesoporous polydopamine nano-spheres loaded with crystal violet lactone (CVL) and phenanthroline (Phen). In our design, the coating damage is indicated by the immediately intensified fluorescent and can be rapidly closed in 90 s of NIR irradiation. Furthermore, the on-demand released corrosion probes (Phen) are able to chelate with Fe2+ ions generated from localized corrosion, producing distinct visual red color to report corrosion pits and resisting corrosion developing process. The proposed smart coatings provide a concept for achieving hierarchically self-reporting and self-repairing function of general coating defects and substrate corrosion, thus having certain potential to be applied in smart anticorrosion applications.
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