A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students' Cardiac Signal and MSY.

The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.

Relation of salivary MMP-8 with oral submucous fibrosis and oral squamous cell carcinoma: a cross sectional analytical study.

OBJECTIVES: We aim to evaluate salivary matrix metalloproteinases (MMP-8) levels in oral submucous fibrosis (OSF) and oral squamous cell carcinoma (OSCC) for the purpose of diagnosis at the early stage via non-invasive method. SETTING: The study was multicentre, carried out at a tertiary care hospital in Karachi, Pakistan. PARTICIPANTS: A total 60 participants of any age, sex and ethnicity were randomly selected for the purpose of this study. Patients demonstrating clinical evidence of OSF and biopsy-proven cases of OSCC were included. Patients with indeterminate histopathological report, immunodeficiency, autoimmune disorder, chronic medical and periodontal disease (periodontal depth greater than 5 mm) and individuals with interincisal mouth opening greater than 35 mm were excluded from the study. INTERVENTIONS: Salivary MMP-8 levels were observed in OSF, healthy and OSCC groups by using ELISA. One way analysis of variance was applied to establish whether MMP-8 levels of disease-free individuals and patients suffering from OSF and OSCC differed from each other. RESULTS: Statistically significant difference in salivary MMP-8 expression in diseased and control group was observed. MMP-8 levels in OSCC (0.64 ng/mL) and OSF (0.66 ng/mL) were underexpressed as compared with healthy participants (7.9 ng/mL). CONCLUSION: MMP-8 levels were underexpressed in OSCC and OSF patients as compared with controls, which imply that MMP-8 level has an inverse relation with OSCC and OSF.

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal.

Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F 1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

Correlation of histopathological patterns of OSCC patients with tumor site and habits.

INTRODUCTION: Oral cancer is considered a major global public health problem. The causes of OSCC are tobacco, alcohol, viral infections such as EBV, HPV, and herpes simplex virus, poor oral hygiene (including sharp teeth and decay), ill-fitting denture, ultraviolet (UV) exposure, nutrition, and genetic predisposition. The etiology of oral cancer varies in different populations due to area-specific etiological factors. OBJECTIVE: Finding a correlation of histopathological pattern to the tumor site and habits as an outcome of OSCC. METHODS: This cross-sectional study was conducted in Karachi, Pakistan. A total of 100 known cases of an oral squamous cell carcinoma were diagnosed with the help of biopsy reports and were examined for histopathologic features, site of the lesion, and risk habits. RESULTS: 48 years was the mean age at the time of diagnosis with a distribution of 61% men and 39% women. The frequently affected site was buccal mucosa and the prime risk habit was gutka followed by betel quid. Histologically, the degree of differentiation shows that moderately differentiated OSCC was most commonly present, while the most prevalent histopathological pattern was spindle cell carcinoma. The statistical relation between lesion site and tobacco habits was found to be significant with a p value (p = 0.01). CONCLUSION: Rates of oral squamous cell carcinoma are higher in males than females with a mean age at the time of diagnosis being less than 50 years. Frequently placing gutka in the buccal vestibule against buccal mucosa is responsible to make buccal mucosa the most common tumor site. This study provides baseline information regarding habits.

Deciphering Possible Association of Risk Factors for Dental Caries in Pakistani Population.

Obesity is a state of abnormal accumulation of fat in adipose tissues of the body to the level that one's health is adversely compromised. Tripathi et al. state (according to WHO) that obesity is now considered the fifth leading cause of mortality in the world. Caries is a multifactorial disease and one of the major oral health issues of the modern era affecting people around the globe. Rise in dental caries has been observed in developing countries as a result of factors including increased intake of carbohydrates. The present study aims for assessing the association of DMFT with BMI, age, and gender. This study was conducted in the dental OPD of the Dow University of Health Sciences, Karachi, from February 2016 till January 2017. A custom-made interview-based questionnaire was used to assess BMI, DMFT, and sociodemographics. The sample size was kept at 385. Age was reported as a strong predictor (R2 0.641) of DMFT followed by BMI and gender as the weakest predictors. Age and BMI had statistically significant association with DMFT scores, which shows that diet patterns may affect general health. High caloric intake over long periods affects BMI and also oral health.