Major Depressive Disorder and Chronic Fatigue Syndrome Show Characteristic Heart Rate Variability Profiles Reflecting Autonomic Dysregulations: Differentiation by Linear Discriminant Analysis.

Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild

Due to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where images are captured under non-ideal conditions with large variations in illumination, occlusion, pose, and resolution. These variations increase within-class variability and between-class similarity, which degrades the discriminative power of the features extracted from the periocular trait. Despite the remarkable success of convolutional neural network (CNN) training, CNN requires a huge volume of data, which is not available for periocular recognition. In addition, the focus is on reducing the loss between the actual class and the predicted class but not on learning the discriminative features. To address these problems, in this paper we used a pre-trained CNN model as a backbone and introduced an effective deep CNN periocular recognition model, called linear discriminant analysis CNN (LDA-CNN), where an LDA layer was incorporated after the last convolution layer of the backbone model. The LDA layer enforced the model to learn features so that the within-class variation was small, and the between-class separation was large. Finally, a new fully connected (FC) layer with softmax activation was added after the LDA layer, and it was fine-tuned in an end-to-end manner. Our proposed model was extensively evaluated using the following four benchmark unconstrained periocular datasets: UFPR, UBIRIS.v2, VISOB, and UBIPr. The experimental results indicated that LDA-CNN outperformed the state-of-the-art methods for periocular recognition in unconstrained environments. To interpret the performance, we visualized the discriminative power of the features extracted from different layers of the LDA-CNN model using the t-distributed Stochastic Neighboring Embedding (t-SNE) visualization technique. Moreover, we conducted cross-condition experiments (cross-light, cross-sensor, cross-eye, cross-pose, and cross-database) that proved the ability of the proposed model to generalize well to different unconstrained conditions. [ FROM AUTHOR]

Randomized placebo-controlled trial of oral tannin supplementation on COVID-19 symptoms, gut dysbiosis and cytokine response.

The clinical study aim was to investigate whether a tannin-based dietary supplementation could improve the efficacy of standard-of-care treatment of hospitalized COVID-19 patients by restoring gut microbiota function. Adverse events and immunomodulation post-tannin supplementation were also investigated. A total of 124 patients receiving standard-of-care treatment were randomized to oral tannin-based supplement or placebo for a total of 14 days. Longitudinal blood and stool samples were collected for cytokine and 16S rDNA microbiome profiling, and results were compared with 53 healthy controls. Although oral tannin supplementation did not result in clinical improvement or significant gut microbiome shifts after 14-days, a reduction in the inflammatory state was evident and significantly correlated with microbiota modulation. Among cytokines measured, MIP-1α was significantly decreased with tannin treatment (p=0.03) where it correlated positively with IL-1ß and TNF- α, and negatively with stool Bifidobacterium abundance.

SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy.

The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 µl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.

CLASSIFICATION OF STUDENTS’ ACHIEVEMENT VIA MACHINE LEARNING BY USING SYSTEM LOGS IN LEARNING MANAGEMENT SYSTEM

During emergency remote teaching (ERT) process, factors affecting the achievement of students have changed. The purposes of this study are to determine the variables that affect the classification of students according to their course achievements in ERT during the pandemic process and to examine the classification performance of machine learning techniques. For these purposes, the logs from the learning management system were used. In the study, analyzes were carried out with various machine learning techniques and their performances were compared. As a result of the study, it was observed that Fisher’s Linear Discriminant Analysis was the best technique in classification according to F measure performance criteria. As another result, the most effective variable, in classifying students, is the average number of days logged into the system per month and week. It has been observed that total activity duration (min), total number of weeks and total number of page views during the semester are less influential factors. Accordingly, it could be suggested to check the monthly and weekly follow-up of the lectures instead of the total follow-ups per semester. In addition, students’ interaction patterns can be monitored with course tracking systems. © 2022. Turkish Online Journal of Distance Education. All Rights Reserved.

Binary Logistic Regression and Linear Discriminant Analyses in Evaluating Laboratory Factors Associated with COVID-19: A Comparison of Two Statistical Methods

Introduction: Few studies have been conducted to construct a reliable predictive model for the differential diagnosis of severe and non-severe Coronavirus disease-2019 (COVID-19) in the early stages of the disease. This study aimed to compare the accuracy of linear discriminate analysis (LDA) and binary logistic regression (BLR), as two empirical correlations, in predicting COVID-19 severity using single laboratory data and calculated indexes such as the neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Materials and Methods: We investigated 109 patients with confirmed COVID-19 pneumonia. Epidemiological, demographic, clinical, laboratory, and outcome data were obtained, and the patients were classified into two groups: mild group (42 patients) and severe group (67 patients). Results: A comparison of the clinical data in the severe and non-severe groups showed significant differences in SpO(2) and respiratory rate. In addition, significant difference in NLR, SII, white blood cell count, neutrophil count, mean corpuscular volume and mean corpuscular hemoglobin, lymphocyte count, erythrocyte sedimentation rate, lactate dehydrogenase, and blood urea nitrogen was found between both groups. Moreover, there was a small difference between the LDA and LR models, and LDA was more appropriate for a smaller sample size. Conclusion: Our predictive models could help clinicians to identify patients at risk of severe COVID-19 Such prediction can be performed by a simple blood test. LDA and BLR can be used to effectively classify patients with severe and non-severe COVID-19, even with violation of the normality assumption.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Potential Mucosal Irritation Discrimination of Surface Disinfectants Employed against SARS-CoV-2 by Limacus flavus Slug Mucosal Irritation Assay.

Preventive measures have proven to be the most effective strategy to counteract the spread of the SARS-CoV-2 virus. Among these, disinfection is strongly suggested by international health organizations' official guidelines. As a consequence, the increase of disinfectants handling is going to expose people to the risk of eyes, mouth, nose, and mucous membranes accidental irritation. To assess mucosal irritation, previous studies employed the snail Arion lusitanicus as the mucosal model in Slug Mucosal Irritation (SMI) assay. The obtained results confirmed snails as a suitable experimental model for their anatomical characteristics superimposable to the human mucosae and the different easily observed readouts. Another terrestrial gastropod, Limacus flavus, also known as " Yellow slug ", due to its larger size and greater longevity, has already been proposed as an SMI assay alternative model. In this study, for the first time, in addition to the standard parameters recorded in the SMI test, the production of yellow pigment in response to irritants, unique to the snail L. flavus, was evaluated. Our results showed that this species would be a promising model for mucosal irritation studies. The study conducted testing among all those chemical solutions most commonly recommended against the SARS-CoV-2 virus.

Red cell distribution width (RDW): a prognostic indicator of severe COVID-19.

BACKGROUND: The global mortality rate for coronavirus disease 2019 (COVID-19) is 3.68%, but the mortality rate for critically ill patients is as high as 50%. Therefore, the exploration of prognostic predictors for patients with COVID-19 is vital for prompt clinical intervention. Our study aims to explore the predictive value of hematological parameters in the prognosis of patients with severe COVID-19. METHODS: Ninety-eight patients who were diagnosed with COVID-19 at Jingzhou Central Hospital and Central Hospital of Wuhan, Hubei Province, were included in this study. RESULTS: The median age of the patients was 59 [28-80] years; the median age of patients with a good prognosis was 56 [28-79] years, and the median age of patients with a poor outcome was 67 [35-80] years. The patients in the poor outcome group were older than the patients in the good outcome group (P<0.05). The comparison of hematological parameters showed that lymphocyte count (Lym#), red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) were significantly lower in the poor outcome group than in the good outcome group (P<0.05). Further, the red cell volume distribution width-CV (RDW-CV) and red cell volume distribution width-SD (RDW-SD) were significantly higher in the poor outcome group than in the good outcome group (P<0.0001). Receiver operating characteristic (ROC) curves showed RDW-SD, with an area under the ROC curve (AUC) of 0.870 [95% confidence interval (CI) 0.796-0.943], was the most significant single parameter for predicting the prognosis of severe patients. When the cut-off value was 42.15, the sensitivity and specificity of RDW-SD for predicting the prognosis of severe patients were 73.1% and 80.2%, respectively. Reticulocyte (RET) channel results showed the RET level was significantly higher in critical patients than in moderate patients and severe patients (P<0.05), which may be one cause of the elevated RDW in patients with a poor outcome. CONCLUSIONS: In this study, the hematological parameters of COVID-19 patients were statistically analyzed. RDW was found to be a prognostic predictor for patients with severe COVID-19, and the increase in RET may contribute to elevated RDW.

Raman spectroscopy-based detection of RNA viruses in saliva: A preliminary report.

Several non-invasive Raman spectroscopy-based assays have been reported for rapid and sensitive detection of pathogens. We developed a novel statistical model for the detection of RNA viruses in saliva, based on an unbiased selection of a set of 65 Raman spectral features that mostly attribute to the RNA moieties, with a prediction accuracy of 91.6% (92.5% sensitivity and 88.8% specificity). Furthermore, to minimize variability and automate the downstream analysis of the Raman spectra, we developed a GUI-based analytical tool "RNA Virus Detector (RVD)." This conceptual framework to detect RNA viruses in saliva could form the basis for field application of Raman Spectroscopy in managing viral outbreaks, such as the ongoing COVID-19 pandemic. (http://www.actrec.gov.in/pi-webpages/AmitDutt/RVD/RVD.html).

Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters.

BACKGROUND: The third fatal coronavirus is the novel coronavirus (SARS-CoV-2) that causes novel coronavirus pneumonia (COVID-19) which first broke out in December 2019. Patients will develop rapidly if there is no any intervention, so the risk identification of severe patients is critical. The aim of this study was to investigate the characteristics and rules of hematology changes in patients with COVID-19, and to explore the possibility differentiating moderate and severe patients using conventional hematology parameters or combined parameters. METHODS: The clinical data of 45 moderate and severe type patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms, and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between moderate and severe cases were analyzed, and the combination parameters with the best diagnostic performance were selected using the linear discriminant analysis (LDA) method. RESULTS: Of the 45 patients with the novel 2019 corona virus (COVID-19) (35 moderate and 10 severe cases), 23 were male and 22 were female, with ages ranging from 16 to 62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, white blood cell count (WBC), neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red blood cell distribution width-coefficient of variation (RDW-CV), and red cell volume distribution width-standard deviation (RDW-SD) parameters in the severe group were significantly higher than those in the moderate group (P<0.05); meanwhile, lymphocyte count (Lym#), eosinophil count (Eos#), high fluorescent cell percentage (HFC%), red blood cell count (RBC), hemoglobin (HGB), and hematocrit (HCT) parameters in the severe group were significantly lower than those in the moderate group (P<0.05). For NLR parameter, it's area under the curve (AUC), cutoff, sensitivity and specificity were 0.890, 13.39, 83.3% and 82.4% respectively; meanwhile, for PLR parameter, it's AUC, cutoff, sensitivity and specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameters of NLR and RDW-SD had the best diagnostic efficiency (AUC =0.938), and when the cutoff value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the combined parameter NLR&RDW-CV (AUC =0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively. CONCLUSIONS: The combined NLR and RDW-SD parameter is the best hematology index. It may help clinicians to predict the severity of COVID-19 patients and can be used as a useful indicator to help prevent and control the epidemic.