ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.Comment: 9 pages, 6 figure

A novel approach based on CatBoost and explainable artificial intelligence for diagnosis of COVID-19 cases using patients' symptoms

The COVID-19 virus, which was discovered in December 2019 in the city of Wuhan, China and quickly spread throughout the world, continues to be an important threat to the health of the world. Despite all the strategies used to deal with the spread of COVID-19, more contrivances are still needed to deal with its consequences. In this research, the clinical characteristics of people have been used as input data to diagnose a person with COVID-19, which is the result of collecting information from similar studies. Also, various algorithms including support vector machine, logistic regression, k nearest neighbor (k=9), simple bayes, random forest, LightGBM, XgBoost and CatBoost have been used, among which the CatBoost algorithm, with a sensitivity of 97.97%, accuracy 97.72% and 96.96% accuracy showed the best results. In this algorithm, the trial and error method has been used to adjust hyperparameters as accurately as possible to achieve the desired results, and SHAP is used to interpret the results and determine the impact of features on the output

Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry

International audienceThe inter-subject variability poses a challenge in cross-subject Brain-Computer Interface learning and classification. As a matter of fact, in cross-subject learning not all available subjects may improve the performance on a test subject. In order to address this problem we propose a subject selection algorithm and we investigate the use of this algorithm in the Riemannian geometry classification framework. We demonstrate that this new approach can significantly improve cross-suject learning without the need of any labeled data from test subjects

Can laboratory tests at the time of admission guide us to the prognosis of patients with COVID-19?

Introduction: To enhance the COVID-19 patients’ care and to optimize utilizing medical resources during the pandemic, relevant biomarkers are needed for prediction of the disease’s progression, the current study was aimed to determine the factors that effect on mortality of COVID-19 patients who admitted in Baharloo hospital in Iran. Methods: in the current retrospective study, 56 patients who were died because of COVID-19 infection were randomly selected from those who were admitted to Baharloo hospital. One patient who was diagnosed with COVID-19 and had recovered from it matched with each non-survived patient in the term of age. Laboratory tests of all these patients at the time of admission were recorded and compared. All analyses performed using spss version 22 by considering α:0.05 as a significant level. Results: There was no statistical difference in the age and gender distribution between the two groups (p>0.05). The prevalence of diabetes among survived patients was 37.5% and among non-survived patients was 26.8% and there was no statistical difference between two groups about this comorbidity (p:0.22). Also, there was no statistical difference in the prevalence of hypertension and coronary heart diseases between two groups (p>0.05). Lymphocyte percentage, Blood oxygen level, and platelet (PLT) count was significantly higher in patients who had recovered (P<0.05).         Conclusions: LDH level, Lymphocyte percentage, PLT count, and blood Oxygen saturation have associations with severe forms of COVID-19 infection and can be used as predictors to assess the patients who are suspected of infection with COVID-19 at the time of admission

Clinical characteristics and outcomes of COVID-19 patients with a history of cardiovascular disease

New emerging severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) primarily affects the lungs, but the virus may cause cardiovascular disease (CVD), and a history of CVD is usually associated with comorbidities, which could increase the severity of infections. In this study, we collected demographic and clinical characteristics data from 123 patients with a history of CVD, who were confirmed to have SARS-CoV-2 infection by polymerase chain reaction (PCR) test in Razi Hospital, Rasht, Iran, from March 2021 to June 2021. Chi-Square and Fisher's Exact test with a significance level of P less than 0.05 was performed. All statistical analysis was performed with SPSS software version 26.0. Among the studied patients, 99 patients were discharged and 24 of them died. 62 (50.4%) of the study population were female and 61 (49.6%) were male, and there is no significant association between gender and the outcome of patients (P = 0.159). The total mean age of patients was 68.35±12.41. Statistical analysis has represented a significant relation of death outcomes in CVD patients with age 60 years and older (P = 0.001), in comparison with patients younger than 60 years. In this present study, no significant relation between underlying disease and mortality rate was reported, but in COVID-19 patients with a history of CVD and age upper than 60 years, death outcome was more probable

Diagnostic Accuracy of Ultrasonography in Diagnosis of Metatarsal Bone Fracture; a Cross Sectional Study

Introduction: Metatarsus is one of the most common sites in the sole of foot bones fractures. The aim of this study was to determine the diagnostic accuracy of ultrasound in diagnosis of metatarsal bone fractures following foot trauma. Methods: This cross-sectional study was carried out on patients with blunt foot trauma admitted to emergency department of a hospital in Mashhad, Iran from January to September 2016. All patients were evaluated with bedside ultrasound for the presence of first to fifth metatarsal fractures and screening performance characteristics of ultrasonography in detection of metatarsal fractures were calculated considering foot radiography as the reference test. Results: The study was conducted on 102 patients with a mean age of 35.14±14.32 years (56.8% male). The most common signs of trauma in physical examination were pain and tenderness (100%), swelling (96.1%), ecchymosis (14.7%) and deformity (1.9%). Sensitivity, specificity, and positive and negative likelihood ratio of ultrasonography in detection of metatarsal bone fracture were 96.7% (95% CI: 0.83-0.99), 84.5% (95% CI: 0.73-0.92), 73.1% (95% CI: 0.57-0.85), and 98.3% (95% CI: 0.91-0.99), respectively. The overall accuracy of ultrasonography was 0.906 (95% CI: 0.844 – 0.969) based on area under the receiver operating characteristic (ROC) curve. Conclusion: Considering the excellent diagnostic accuracy, ultrasonography can be used as an alternative means in diagnosis of metatarsal bone fractures

Association of Interleukin 10 And Transforming Growth Factor β Gene Polymorphisms with Chronic Idiopathic Urticaria

Transforming growth factor β (TGF-β) and interleukin 10 (IL-10) are two anti-inflammatory cytokines that are implicated in the pathogenesis of urticaria. The goal of this study was to examine the possible association of polymorphisms of TGF-β and IL-10 genes with susceptibility to chronic idiopathic urticaria (CIU). This study was conducted on 90 patients with CIU. Polymerase chain reaction (PCR) was done to determine the genotype at 5 polymorphic sites; TGF-β (codon10C/T and codon25G/C) and IL-10 (-1082G/A, -819C/T, and -592C/A). The C allele at codon 25 of TGF-β was more prevalent in CIU patients compared to controls (OR = 9.5, 95% CI = 5.4-16.8, P<0.001). Genotypes of CT and CG at 10 and 25 codons of TGF-β gene, respectively, and AG, CT, and CA for loci of -1082, -819, and -592 of IL-10 gene were significantly higher in CIU patients (P<0.001). In haplotype analysis, frequency of TGF-β haplotypes differed between patients with CIU and controls; CC haplotype was overrepresented, while CG and TG haplotypes were underrepresented (P<0.001). These results suggest that TGF-β and IL-10 genetic variability could contribute to susceptibility to CIU. Additionally, patients with CIU seem to have genotypes leading to high production of TGF-β and IL-10.</p

Inter-professional relationships issues among iranian nurses and physicians: A qualitative study

Introduction: Nurse–physician inter-professional relationship is an important issue in health care system that can affect job satisfaction and patient care quality. The present study explores the major issues of nurse–physician inter-professional relationships in Iran. Materials and Methods: In this in-depth qualitative content analysis study conducted in 2014, 12 participants (5 physicians and 7 nurses) were recruited from two educational hospitals. The data were collected from deep, open, and unstructured interviews, and analyzed based on content analysis. Results: The participants in this study included 12 individuals, 6 females and 6 males, with the age ranging 27–48 years and tenure ranging 4–17 years. Four themes were identified, namely, divergent attitudes, uneven distribution of power, mutual trust destructors, and prudence imposed on nurses. Conclusions: The results revealed some major inter-professional issues and challenges in nurse–physician relationships, some of which are context-specific whereas others should be regarded as universal. It is through a deep knowledge of these issues that nurses and physicians can establish better collaborative inter-professional relationships

Efficacy of Experimental Hydrofluoric Acid (HF) on Bond Strength and Microleakage of Composite-Porcelain Interface

Background and Aims: The aim of this study was to evaluate the quality of an experimental hydrofluoric acid (HF) for preparation of porcelain and to compare it with two commercial hydrofluoric acids in Iranian trademark. Materials and Methods: A- Evaluation of etch pattern of experimental HF using scanning electron microscope (SEM): 6 feldespathic discs were divided into 3 groups. Each group was etched with related HF (experimental, Ultradent and Kimia) for 1 minute. SEM images were recorded at 3 magnifications. B- Bond strength test: 18 feldespathic discs were considered for each acidic group. Then the porcelain surfaces were etched and bonded to composite with unfilled resin. Consequently, the microshear test was done. C- Microleakage test: 54 discs were divided into 3 groups (n=18). Then the porcelain surfaces were etched and bonded to composite with unfilled resin and finally observed under stereomicroscope. The data were analyzed with one-way ANOVA and Smirnov tests. Results: SEM analysis showed no difference between groups in terms of etch pattern. Microshear bond strength values for experimental, Kimia, and Ultradent HF were 28.53 (±4.92), 28.21 (±6.61), and 26.14 (±7.61) MPa, respectively. There was no significant difference between the bond strength of test groups (P<0.05). Furthermore, no significant difference was found between the microleakage of test groups (P>0.05). Conclusion: Quality of experimental HF in terms of etch pattern, microshear bond strength and microleakage of composite/porcelain interface was similar to that of two commercial hydrofluoric acids