Teresa Fitzpatrick presents a connection between oppressed women and the plant of Wisteria in gothic fiction in her article “Wisteria: A Female Eco-Gothic Metaphor in American Fiction Through the Ages.” The connection between women and nature is common in literature as women are usually compared to the beauty or ferocity of flowers, rivers, or natural phenomena in general. The connection extends to the architecture and sort of plants that appear in gothic literature. Gothic novels have routinely been connected to the secrets and life stories of women who cannot have the liberty to live or share them outside their homes. A fearful house with a prisoned person, ghost, or a secret is mostly normally found in gothic writing. Women have exce

Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal

Cerium (III), Neodymium (III) and Samarium (III) Complexes existent a wide range of implementation that stretch from their play in the medicinal and pharmaceutical area because of their major significant pharmacological characteristic such as antifungal, anti-cancer, anti-bacterial ,anti-human immunodeficiency virus ,antineoplastic, anti-inflammation,inhibition corrosion,in some industrial (polymers, Azo dye).It is likely to open avenuesto research among various disciplines such as physics, electronics, chemistry and materials science by these complexes that contain exquisitely designed organic molecules.This paper reviews the definition, importance and various applications of Cerium (III), Neodymium (III) and Samarium (III) Complexes anddi

Objectives: The demand for orthodontic treatment is nowadays increasing significantly for aesthetic improvement and to correct various kinds of malocclusion, yet the prolonged treatment time remains the main obstacle. This review aimed to demonstrate various orthodontic techniques and highlight the evidence-based successful approaches used for acceleration of orthodontic tooth movement. Materials and Methods: Data and sources of information pertaining to accelerated orthodontic tooth movement premised on English-written articles were searched using electronic databases including Google Scholar, Scopus, PubMed and MEDLINE. Results: This review demonstrated the availability of different surgical and non-surgical methods to enhance tooth movem

Objective: To review and identify the major drivers for COVID-19 vaccine acceptance. Methods: A scoping review of studies of COVID-19 vaccine perceptions and barriers to using the COVID-19 vaccines. Two search engines, including PubMed and Google Scholar, were purposefully searched. Results: Eight studies from different countries were reviewed to categorize factors influencing people's acceptance of COVID-19 according to the Health Belief Model (HBM). Perceived susceptibility, and severity of the disease (COVID-19), in addition to perceived benefits of COVID-19 vaccination and "cues to action", can enhance vaccination acceptance. In contrast, perceived barriers to the COVID-19 vaccine can increase people's hesitancy to be vaccinated

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Synthetic anti-TB drugs are being used to treat tuberculosis (TB) as they are effective, however, they are accompanied by many side effects. The disease has remained largely uncured till date. The use of plant extracts or phytochemicals along with the anti-TB drugs is a very attractive strategy to make the treatment more effective as phytochemicals have no side-effects, are much less toxic than synthetic anti-TB drugs, are safe to use and most importantly, do not produce resistant strains as opposed to synthetic anti-TB drugs. Approximately 420,000 plant species have been identified globally and among them only a few have been explored for their therapeutic potential. Traditional medicine in different parts of the world has employed crud

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin