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The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning. [ABSTRACT FROM AUTHOR]

Myocardial fibrosis is the excessive deposition of extracellular matrix (ECM) proteins in the cardiac interstitium leading to pathological conditions of the heart. The objective is to understand the pathophysiology of cardiac fibrosis and the quest for serum biomarkers that will assist in early diagnosis before the occurrence of major cardiac events. There are many serum biomarkers that get elevated highlighting ECM remodeling during cardiac fibrosis. Lysyl oxidase like -2 is one such ECM protein, plays a crucial role in the up-regulation of TGF - β, the transformation of cardiac fibroblast to myoblast, the migration of collagen, and cross-linking of collagen and elastin. However, assessment of lysyl oxidase like-2 (LOXL-2) in different pathologically driven cardiac fibrosis is limited. Also, none of the serum biomarkers has proved to be the most accurate diagnostic tool for assessing fibrosis independently; hence, meticulous, less invasive, and cost-effective serum biomarkers need to be scrutinized. Hence lysyl oxidase Like-2 (LOXL-2) in combination with other serum biomarkers like PICP/PINP/TIMP-1/ST-2, or Galectin-3 can be combined to assess the presence of fibrosis in the heart. This review includes the journal, articles, and research paper on cardiac fibrosis which was published in the last 10-15 years to highlight the huge gap in the treatment of cardiac fibrosis and the need for a new combination of biomarkers with better prognostic and diagnostic value. [ABSTRACT FROM AUTHOR]

Social media is an online platform with millions of users and is utilized to spread news, information, world events, discuss ideas, etc. During the COVID-19 pandemic, information and ideas are shared by users both officially and by citizens. Here, the detection of useful content from social media is a challenging task. Hence, natural language processing (NLP) and deep learning are widely utilized for the analysis of the emotions of people during the COVID-19 pandemic. Hence, this research introduces a deep learning mechanism for identifying the sentiment of the people by considering the online Twitter data regarding COVID-19. The intelligent lead-based BiLSTM is utilized to analyze people's sentiments. Here, the loss of the classifier while learning the data is eliminated through the incorporation of the intelligent lead optimization. Hence, the loss is reduced, and a more accurate analysis is obtained. The intelligent lead optimization is devised by considering the role of the informer in identifying the enemy base to safeguard the territory from attack along with the Monarch's knowledge. The performance of the intelligent lead-based BiLSTM for the sentiment analysis is assessed using the metrics like accuracy, sensitivity, and specificity and obtained the values of 96.11, 99.22, and 95.35%, respectively, which are 14.24, 10.45, and 26.57% enhanced performance compared to the baseline KNN technique. [ABSTRACT FROM AUTHOR]

The aim of the study was to identify the work-related ergonomic hazards experienced by the rag pickers. Multi-stage random sampling technique was used for selecting the sample. Tools used to conduct the study comprised of general information, rating scale for assessing intensity of discomfort, and Nordic Musculoskeletal Questionnaire. The statistical analysis of the quantitative variables was performed using SPSS software program, v20.0. Research revealed that rag pickers experienced severe pain in lower back (65%), followed by upper back (52.5%), feet (47%), and hands (43%). Older rag pickers, between 55 and 65 age groups, reported more discomfort or pain in backache and knee (μ = 2.00), as compared to other age groups. This may be due to the reason that with age, bones become weak and the strength of the muscles also reduces, and since they are involved in the work. Rag pickers experience many work-related ergonomic pro- blems, and these problems are associated with various risk factors such as working experiences and age. Frequent bending, standing, and carrying heavy loads on the back increased these problems. To lighten the load of ergonomic problems, pre- ventive and curative approaches are strongly suggested. Ergonomics and safe practices must also be established to reduce work-related vulnerabilities. [ABSTRACT FROM AUTHOR]

Background: Farming has been considered a physically demanding and associated with high risk occupation for musculoskeletal disorders. Agricultural workers have to adapt different postures while working in the fields, e.g. standing, bending, sitting, kneeling, raising hand, etc. They work for long duration in awkward postures, due to which they have to face a lot of postural discomforts. They go through a lot of physical hazards, and sometimes they have to face severe pain. Aim: The present study was conducted to find out the musculoskeletal disorders and postural discomfort among the agricultural workers while working in the fields. Materials and Methods: Present study was conducted on 150 agricultural workers. The sample was randomly selected in the Balramau area of Faizabad district. A self-structured interview schedule and the postural discomfort scale developed by Corlett and Bishop in (1976) were used for data collection. Result: The study revealed that maximum pain was felt in the upper back, upper arms, mid-back, lower arms, lower back, and buttocks. Female agricultural workers felt more pain as compared to male agricultural workers. Conclusion: Agricultural workers face lot of pain in different body parts. Proper training program and information about musculoskeletal disorders symptoms should be given to agricultural workers so that they can get help in identifying the symptoms. Training programmers to agricultural workers in adopting right postures to minimise the stress also need to be conducted. [ABSTRACT FROM AUTHOR]

Background: COVID-19 pandemic has caused havoc and many deaths around the world. Coronavirus is highly contagious and spreads like wildfire in the community. The nurses and other frontline health-care workers (HCWs) bore the brunt of this pandemic with maximum effect because they all worked with infected patients. Direct exposure has caused stress, anxiety, and physical cum mental discomfort among them. Objective: This study aimed to assess stress among nurses and family members related to COVID-19 outbreak. Methodology: In this study, mixed method of prospective approach was used. The study was conducted virtually using social media platforms by online questionnaire. It included 150 participants and information was collected on demographic data, change in relationship with family, spouse, children, self-concept, and perceived stress among themselves and their family members. Results: All the nurses were involved in the direct care of COVID-infected patients. Many participants (47%) became anxious and worried about themselves. More than half (56.7%) respondents had no change in relationship, while 40.7% experienced change in relationship with their family. Majority of the participants (88%) had long travel hours and accommodation issues. Although the nurses working for COVID patients were stressed, frightened, and anxious, most of them took pride in their work as a contribution toward the nation during this current pandemic. Conclusion: This study demonstrated that frontline HCWs were at increased risk of mental health consequences such as stress, anxiety, and frustration. Their children developed behavioral changes such as agitation and aggressiveness. Many nurses got more family support than before for their contribution during COVID-19 pandemic. [ABSTRACT FROM AUTHOR]