A controlled clinical trial to evaluate the efficacy of Shalaparnyadi Kwatha in the management of Vataja Grahani

Background: Current mechanized life, irregular dietary patterns, irregularity in daily practices, junk food indulgence, stressful life, over usage of pesticides and chemicals leads to various gastro intestinal disorders. These factors hamper the digestive capacity of individuals and develop the disease Grahani. Shalaparnyadi Kwatha mentioned in treatment of Vataja Grahani in Sharangadhara Samhita is taken up for study in the management of Vataja Grahani. Objective: To evaluate the efficacy of Shalapanryadi Kwatha in Vataja Grahani. Method:  The study was a double arm open labelled controlled clinical trial with pre and post-test study design. There were total of 41 subjects involved in the study and were divided into two groups - Group A (trial group) with 21 subjects and Group B (controlled group) with 20 subjects. Group A was administered with Shalaparnyadhi Kwatha and Panchamooladya Choorna and Group B was administered with Panchamooladya Choorna for 30 consecutive days. Result: A controlled clinical study was conducted on subjects of Vataja Grahani with Shalaparnyadi Kwatha and Panchamooladya Choorna in trial group and Panchamooladya Choorna in control group both the interventions were effective in management of Vataja Grahani. Based on the mean value and statistically significant difference between the groups, trial group showed better result than control group in Amayukta Mala Pravrutti and Udara Shoola. Conclusion: It can be concluded from the results that added effect of Shalaparnyadi Kwatha with Panchamooladya Choorna is more effective than Panchamooladya Choorna alone

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Implications and Types of Artefacts in Oral Histopathology Tissue Processing

There is a scarcity of information regarding the occurrence and appearance of artefacts in the literature studies observed till date. This study aims to provide a more a comprehensive approach on identifying the different types of artefacts and also attempts to provide with description regarding their relation to the parent slide and the remedies in order to prevent misinterpretation. This study aimed to prepare a comprehensive report of the commonly occurring artefacts in archival collection of pathology laboratory

The Coronavirus Pandemic: Associations of College Students\u27 Financial Situations and Optimism with Mental & Physical Health

The coronavirus pandemic has led to a turbulent environment, putting college students and their families in unprecedented situations. The rise in unemployment and concerns about the overall economy may be impacting student finances. Increased depression and anxiety are common responses to such stressful situations. However, certain psychosocial factors, such as optimism, may be a valuable resource for coping with stress. Individuals who are more versus less optimistic tend to show less distress and have better physical functioning. Thus, the purpose of this study was to examine how college students’ financial situation during the coronavirus pandemic is related to mental and physical health, as well as how optimism moderates this relationship. We hypothesized that worse financial situations would be associated with higher levels of depression, anxiety, and physical symptoms, but that optimism would buffer against worse outcomes. To investigate these hypotheses, students at a private university in Southern California were recruited through their university email addresses to complete an online questionnaire in the spring of 2020. Nearly 300 students self-reported their financial situation, depression, anxiety, physical symptoms (e.g., nausea, headaches), and optimism. Linear regression models tested associations. Results indicated that, as expected, a worsening financial situation and an increase in worry about paying for school were significantly associated with higher levels of depression, anxiety, and physical symptoms (ps \u3c 0.05). By contrast, greater optimism was associated with lower levels of depression, anxiety, and physical symptoms (ps \u3c 0.05). However, the effect of financial situation on students’ mental and physical health did not depend on optimism (ps \u3e 0.05). This may be because students in this study had lower optimism scores relative to pre-pandemic cohorts, suggesting they struggled to be optimistic during the pandemic. Further investigation on how financial situations and optimism relate to mental and physical health is crucial to not only improve the quality of life for college students, but to also help in creating and implementing effective mental and physical health interventions

Sign Language Recognition using Machine Learning

Deaf and dumb people communicate with others and within their own groups by using sign language. Beginning with the acquisition of sign gestures, computer recognition of sign language continues until text or speech is produced. There are two types of sign gestures: static and dynamic. Both gesture recognition systems, though static gesture recognition is easier to use than dynamic gesture recognition, are crucial to the human race. In this survey, the steps for sign language recognition are detailed. Examined are the data collection, preprocessing, transformation, feature extraction, classification, and outcomes. There were also some recommendations for furthering this field of study

EVALUATION OF ANTIPYRETIC ACTIVITY OF ETHANOLIC EXTRACT OF WEDELIA TRILOBATA

The aim of present study was to investigate antipyretic activity of ethanolic extract of leaves of Wedelia trilobata in yeast induced pyrexia in wistar albino rats. In which pyrexia was induced by an intraperitonial injection of 20% brewer’s yeast (10 ml/kg b.wt.). The body temperature of rats were measured before the injection of yeast and injected ethanolic extract of leaves of Wedelia trilobata (100 mg/kg b.wt.) and (200 mg/kg b.wt.) and followed by treatment with paracetamol (150 mg/kg b.wt.). The body temperature of experimental animals were recorded in the time interval of 0 hr, 1 hr, 2 hr and 3 hr with help of digital clinical thermometer which is placed in rectum in the depth of 2 cm and recorded body temperature values shown that the leaves extract of of Wedelia trilobata possess antipyretic activity

EVALUATION OF IN VITRO ANTIUROLITHIATIC ACTIVITY OF CHLORIS BARBATA

Objective: The present study was undertaken to evaluate the in vitro antiurolithiatic activity of the medicinal plant Chloris barbata.Methods: The crude plant extract was prepared by Soxhlet extraction method.Results: Both Ethanolic and Aqueous extracts showed their maximum efficiencies in the dissolution of calcium oxalate crystals. Ethanolic extract was even more efficient than Aqueous extract in the dissolution of calcium oxalate crystals. Our results have clearly indicated that the Aqueous and Ethanolic leaf extracts of Chlorisbarbata were quite promising for further studies in this regard. In this study, Neeri was used as standard drug.Conclusion: This study has given primary evidence for Chloris barbata as the plant which possess antiurolithiatic property

The Impacts of Social Support and Loneliness on the Physical Health and Coping Styles of College Students during COVID-19

Since the beginning of the COVID-19 pandemic, there has been an increased mental health risk among college students. Recent studies have suggested that this concerning phenomenon can be attributed to social isolation and loneliness caused by preventive measures including social distancing. Being socially isolated can also have harmful effects on one’s physical health, such as a weakened cardiovascular system. Furthermore, existing literature reported that social support can promote more active coping strategies, which is associated with better psychological adjustment. Nevertheless, there hasn’t been any research on the influence of social factors and loneliness both on students’ health and their coping styles during the pandemic. The purpose of the present study is to investigate how loneliness and perceived social support are associated with the physical health and coping styles of college students during COVID-19. As for the coping measure, the study will look specifically at two types of coping strategies: active coping and self-distraction. The variables were measured through an online survey administered across five different time points in 2020 with students enrolled in Chapman University. The study will focus on the first two waves of the survey, which took place in May and July of 2020. Social support and loneliness in May will be used to predict physical health and coping styles in July. It is hypothesized that students who reported higher levels of perceived social support would show better physical health and use active coping more than self-distraction. It is also predicted that those who feel higher levels of loneliness would report poorer physical health and engage more in self-distraction than in active coping. This study may contribute to the necessary endeavor to improve the physical and psychological wellbeing of college students during the global health crisis by promoting higher social support and alleviating the sense of loneliness