Association of automated carotid IMT measurement and HbA1c in Japanese patients with coronary artery disease

AIMS: The purpose of this study was to evaluate whether carotid IMT (cIMT) identified using automated software is associated with HbA1c in Japanese patients with coronary artery disease. METHODS: 370 consecutive patients (males 218; median age 69 years±11) who underwent carotid-US and first coronary angiography were prospectively analyzed. After ultrasonographic examinations were performed, the plaque score (PS) was calculated and automated IMT analysis was obtained with a dedicated algorithm. Pearson correlation analysis was performed to calculate the association between automated IMT, PS and HbA1c. RESULTS: The mean value of cIMT was 1.00±0.47mm for the right carotid and 1.04±0.49mm for the left carotid; the average bilateral value was 1.02±0.43mm. No significant difference of cIMT was detected between men and women. We found a direct correlation between cIMT values and HbA1c (p=0.0007) whereas the plaque score did not correlate with the HbA1c values (p>0.05) CONCLUSION: The results of our study confirm that automated cIMT values and levels of HbA1c in Japanese patients with coronary artery disease are correlated whereas the plaque score does not show a statistically significant correlation

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation

Background and Objective: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE. Methods: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures. Results: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data. Conclusions: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100; specificity, 99.02; accuracy, 99.51). Xception achieved an AUC of 0.994 (sensitivity, 98.04; specificity, 100; accuracy, 99.02). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21; specificity, 83.33; accuracy, 86.27). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments. © 2020 Elsevier Lt

RECOMED: A comprehensive pharmaceutical recommendation system

Objectives To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals. Methods A comprehensive pharmaceutical recommendation system was designed based on the features of people, diseases, and medicines extracted from two major drug databases and the created datasets of patients and drug information. Then, the recommendation was given based on recommender system algorithms using patient and caregiver ratings and the knowledge obtained from drug specifications and interactions. Sentiment analysis was employed by natural language processing approaches in pre-processing, along with neural network-based methods and recommender system algorithms for modelling the system. Patient conditions and medicine features were used to make two models based on matrix factorization. Then, we used drug interaction criteria to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs using data from 2304 patients as a training set and 660 patients as our validation set. We used knowledge from drug information and combined the model's outcome into a knowledge-based system with the rules obtained from constraints on taking medicine. Results Our recommendation system can recommend an acceptable combination of medicines similar to the existing prescriptions available in real life. Compared with conventional matrix factorization, our proposed model improves the accuracy, sensitivity, and hit rate by 26 %, 34 %, and 40 %, respectively. In addition, it improves the accuracy, sensitivity, and hit rate by an average of 31 %, 29 %, and 28 % compared to other machine learning methods. We have open-sourced our implementation in Python. Conclusion Compared to conventional machine learning approaches, we obtained average accuracy, sensitivity, and hit rates of 31 %, 29 %, and 28 %, respectively. Compared to conventional matrix factorisation our proposed method improved the accuracy, sensitivity, and hit rate by 26 %, 34 %, and 40 %, respectively. However, it is acknowledged that this is not the same as clinical accuracy or sensitivity, and more accurate results can be obtained by gathering larger datasets

An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients

Rapid Recovery of Tigers Panthera Tigris in Parsa Wildlife Reserve, Nepal

Information on density and abundance of globally threatened species such as tigers Panthera tigris is essential for effective conservation as well as to evaluate the success of conservation programmes. We monitored tigers in Parsa Widlife Reserve, Nepal, using camera traps, in 2013, 2014 and 2016. Once believed to be a sink for tigers from adjacent Chitwan National Park, Parsa now provides a new hope for tigers. Spatially explicit capture–recapture analysis over 3 survey years revealed an increase in tiger density from 0.78 to 1.38 individuals per 100 km2 from 2013 to 2016. The tiger abundance was estimated to be seven (6–13), 11 (10–16) and 17 (17–20) in 2013, 2014 and 2016, respectively. Resettlement of communities from the core area, reduced anthropogenic pressure, and improved security have made Parsa Wildlife Reserve a suitable habitat for tigers. Tiger abundance increased considerably within a 5 km radius of the evacuated village sites, from two in 2013 to eight in 2014 and 10 in 2016. Population turnover has remained moderate (< 30% per year), with persistence of individuals in multiple years. Dispersing tigers from Chitwan's source population accounted for a large portion (c. 40%) of the tigers detected in Parsa. Conservation efforts along with annual monitoring should be continued in Parsa to sustain the increase and monitor the persistence of tigers. The Chitwan–Parsa complex should be managed as a single ecological unit for conserving the Endangered tiger and other wide-ranging species.Global Challenges (FSW

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19. © 2020 Wiley Periodicals LL

Estudo eletrocardiográfico de éguas da raça Crioula

A eletrocardiografia constitui ferramenta indispensável no diagnóstico de arritmias e distúrbios de condução elétrica do coração de equinos, bem como na determinação do prognóstico de cardiopatias, do desempenho atlético, da eficiência do treinamento, além de sugerir distúrbios eletrolíticos. No entanto, as variáveis eletrocardiográficas em equinos podem sofrer influência de diversos fatores como a idade, sexo, raça e constituição morfofuncional, dentre outas, tornando-se necessário conhecer as características de normalidade para as diferentes raças e fases do desenvolvimento. Descendentes dos cavalos da Península Ibérica, a raça Crioula foi trazida ao continente americano há mais de quatro séculos, resultando em características físicas e de resistência únicas, dada por sua seleção natural. Desta forma, objetivou-se com o presente trabalho avaliar e comparar os aspectos eletrocardiográficos de fêmeas da raça Crioula, em diferentes idades, bem como avaliar possíveis alterações eletrocardiográficas secundárias a prenhes. Para tanto, 84 éguas hígidas (34 prenhes e 50 não prenhes) da raça Crioula foram submetidas à avaliação eletrocardiográfica digital na derivação ápice-base, e os registros eletrocardiográficos subdivididos quanto à idade em G1 (até 4 anos), G2 (5 a 9 anos), G3 (acima de 10 anos). Não foram observadas arritmias cardíacas fisiológicas ou patológicas e distúrbios de condução elétrica do coração nas 84 éguas. Houve predomino de taquicardia sinusal, ondas P bífidas, complexos QRS do tipo rS e ondas T bifásicas em todos os grupos. Apenas a duração média do complexo QRS foi superior no grupo G1 (110,65±8,49) quando comparadas aos grupos G2 (101,98±10,02) e G3 (100,92±10,72). As variáveis autonômicas mensuradas (ITV, NNmédio e SDNN) foram inferiores nas éguas prenhes em relação às não prenhes, sugerindo maior participação do sistema nervoso autônomo simpático e ou menor participação parassimpática. Conclui-se, portanto, que a idade influenciou apenas na duração do complexo QRS , e que a prenhes foi capaz de diminuir as variáveis de variabilidade da frequência cardíaca no domínio do tempo e, possivelmente, influenciar na avaliação eletrocardiográfica das éguas Crioulas aqui testadas

Punica granatum L. protects mice against hexavalent chromium-induced genotoxicity

This study investigated the chemoprotective effects of Punica granatum L. (Punicaceae) fruits alcoholic extract (PGE) on mice exposed to hexavalent chromium [Cr(VI)]. Animals were pretreated with PGE (25, 50 or 75 mg/kg/day) for 10 days and subsequently exposed to a sub-lethal dose of Cr(VI) (30 mg/kg). The frequency of micronucleated polychromatic erythrocytes in the bone marrow was investigated and the Cr(VI) levels were measured in the kidneys, liver and plasm. For the survival analysis, mice were previously treated with PGE for 10 days and exposed to a single lethal dose of Cr(VI) (50 mg/kg). Exposure to a sub-lethal dose of Cr(VI) induced a significant increase in the frequency of micronucleated cells. However, the prophylactic treatment with PGE led to a reduction of 44.5% (25 mg/kg), 86.3% (50 mg/kg) and 64.2% (75 mg/kg) in the incidence of micronuclei. In addition, the 50 mg/kg dose of PGE produced a higher chemoprotective effect, since the survival rate was 90%, when compared to that of the non-treated group. In these animals, reduced amounts of chromium were detected in the biological materials, in comparison with the other groups. Taken together, the results demonstrated that PGE exerts a protective effect against Cr(VI)-induced genotoxicity