Volume 13 | Issue 2 |

 Abstract

Acne is a common skin disorder which is common in adolescents and adults. It is a common cutaneous inflammatory disorder of the pilosebaceous unit. It manifests primarily on the face, although it can also affect the upper arms, trunk, and back. Due to hormonal changes 99.5% of teenage boys and 83% of teenage girls are affected by acne which may continue throughout adolescence. Acne causing bacteria are Propionibacterium acne and Staphylococcus epidermidis. Herbal formulations are receiving more concentration in public because of their high-quality properties and less side effects. The idea that natural medicines are safer and have fewer negative effects than synthetic ones make them more acceptable. there have been a lot of topical and systematic treatments for Acne at use synthetic components. Because synthetic medications have a high risk of side effects and can be used in both internal and exterior therapy, Natural treatments are thought to be a safer and more effective option for treating acne.

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 Keywords

Herbal, Acne, Natural medicines, Pilosebaceous, Cutaneous Inflammatory

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Plant-based anticancer drugs have played a crucial role in cancer treatment for many years. According to Ayurveda, various cancer phases are intractable, chronic inflammatory disorders. Natural products have high potential for the prevention and treatment of different cancers. More importantly, anticancer naturopathic have been modified to gain first-line, second line, and third-line targeting in tumor tissues. This review will explore 25 plant-based anticancer drugs, their mechanisms of action, marketed products, side effects, and relevant references .

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 Keywords

Ayurveda, Naturopathic, and Anticancer , Tumor tissues, Intractable

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Sentiment analysis has evolved beyond textual data to incorporate multi-modal approaches, integrating textual, visual, and auditory cues for a more comprehensive understanding of emotions. This study proposes an Improved Multi-Modal Deep Learning Model for sentiment analysis, leveraging advanced feature fusion techniques and optimized neural architectures. The model combines transformer-based text encoders, convolutional neural networks for image processing, and deep audio embeddings to enhance sentiment prediction accuracy. A hybrid attention mechanism is employed to extract and weigh the most relevant features across modalities. Extensive experiments on benchmark datasets demonstrate the model's superiority over existing approaches in terms of accuracy, robustness, and generalization. This research contributes to the growing field of affective computing, offering a novel framework for real-world sentiment analysis applications such as social media monitoring and human-computer interaction.

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 Keywords

Sentiment Analysis, Deep Learning, Natural Language Processing (NLP), Multi Modal, Text Classification

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 Abstract

"Concept of grace", in Milton's Paradise Lost is an important theme which will answer various questions that sway in our mind. Twenty first century is challenging us with its problems and difficulties in understanding humanity. Individualism and selfish attitude of misusing human freedom is rampant today. War between Russia and Ukraine, Israel and Palestine, hype in unemployment and depletion of natural resources are worsening happiness of humanity. Hence, studying about the work of God's grace with humanity is relevant. This paper focuses mainly the concept of 'grace' in Milton's Paradise Lost. Over the years, the term grace has come to represent distinct things with philosophical and theological implications. This significant work offers a highly innovative 17th century reconstruction of the idea of predestination which predicts modern theological developments and throws crucial information on history. The study analyses Satan's politics of envy, Eve's desire to explore and Adam's weakness in flesh against 'the eternal grace' of God that is always present to save man. This paper studies also the presence of 'salvific grace' that is very much prevalent in many circumstances with special reference to Paradise Lost.

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 Keywords

Milton, Paradise Lost, Grace, Salvific Grace

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This research provides a comprehensive overview of micro-influencer marketing towards sustainability. As digital communication is rising through social media platforms, there is a more significant interaction among individuals globally. As global environmental concerns and sustainable development goals (SDGs) become increasingly critical the transition to eco-friendly menstrual products is essential. This study focuses on micro-influencers' impact on female consumer behaviour in purchasing sustainable sanitary pads. With their genuine and close-knit connections to niche audiences, micro-influencers significantly promote these products and spread awareness among consumers to foster consumer trust. We conducted a survey of 70 female respondents aged between 18 to 45 years who purchased a sustainable sanitary pad from the recommendation of the micro-influencers. The study revealed that micro-influencers positively impact female consumer behaviour; they trust the reviews and opinions given by the micro-influencers and follow them on social media. The research also examined the platforms through which the respondent first heard about sustainable sanitary pads, the factors influencing the consumer decision, such as environmental impact, comfort, and health benefits, and the challenges consumers face when adopting ecofriendly sanitary pads. This study can also serve as a significant reference for policymakers striving to accomplish the SDGs by 2030. Menstrual hygiene is directly linked to several SDGs, including promoting good health and well-being (SDG 3), advancing gender equality (SDG 5), sustainable consumption and production (SDG 12), and climate change (SDG 13). The results revealed that social media, especially Instagram, plays a dominating role in creating awareness of these pads. They highlighted the effectiveness of micro-influencer marketing in promoting sustainable pads. The insights gained offer valuable implications for marketers, sustainability advocates, and policymakers seeking to leverage micro-influencer marketing to foster sustainable menstrual hygiene practices and support global sustainability objectives.

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 Keywords

Micro-influencers, consumer behaviour, health benefits, menstrual hygiene, SDG'S Sanitary pads, Eco-friendly product

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The research initiative titled "Development, Formulation, and Evaluation of Natural Miracle Balm" explores the formulation of an innovative topical product that leverages the therapeutic properties of natural ingredients. This investigation seeks to create a multifunctional balm that is not only safe and effective but also environmentally sustainable, catering to the increasing consumer demand for natural skincare solutions. With advancements in our understanding of pain mechanisms, there has been a notable transition towards the development of novel drug delivery methods that provide tailored treatments while maintaining analgesic effectiveness. Acute pain functions as a warning signal, whereas chronic pain presents a multifaceted challenge that necessitates the careful selection of highly bioavailable analgesic medications for prolonged use. Topical therapies are designed to meet these challenges by facilitating the gradual release of active compounds, ensuring stable plasma concentrations, and providing a favorable safety profile. This review emphasizes recent findings related to topical formulations for pain management. The study presents a formulation for an herbal balm along with its preparation methodology. This formulation includes extracts from organically certified herbs, organic essential oils, and organic beeswax. The extraction process employs hydro distillation and solvent extraction techniques. The essential oils incorporated in the formulation act as analgesics.

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natural pain relief balm, Vitex Negundo oil, ginger essential oil.

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 Abstract

Nowadays in Today's world People using smartphones have increased quickly.So smart phones are used for various purposes also it can be used efficiently for personal security or different protection purposes. The dangerous incidents have alert us for first priority our safety issues and host of new apps have been developed to provide security systems to women from their smartphones.This abstract is about an Android based Application for Women Safety. This application is used whenever it needs a rise. It has many special features such as the single click the app identifies the location of the person. In this application it has voice features in case of emergency if we can't pick the phone. This feature of the application is to send the message to whoever has downloaded this application. Timing features are also set. It can also call for an emergency if required.

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Women Safety Application

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 Abstract

Predictive analytics powered by artificial intelligence (AI) is transforming preventive healthcare through early disease detection, risk assessment, and timely intervention. Traditionally, healthcare systems have responded reactively to disease evolution and treatment, with symptoms being observed at the last moment; thus, most of the time, it has increased healthcare expenses for a poor outcome for the patient. Conversely, predictive models powered by AI capitalize on large datasets such as electronic health records (EHRs), medical imaging, genetic profiles, and real-time data from wearable devices to reasonably predict any possible health risk prior to the clinical manifestation of the symptoms (Jiang et al., 2017). This proactive strategy is especially beneficial for dealing with chronic diseases, including diabetes, cardiovascular events, and neurodegenerative disorders, whereby early detection and intervention enormously decrease mortality rates and improve the quality of life for patients (Topol, 2019). Machine learning (ML) and deep learning techniques are the mainstay of predictive analytics, recognizing patterns in complex medical data that could be inconspicuous to human clinicians. For example, convolutional neural networks (CNN) have found enormous success in analyzing medical images for early-stage cancer detections and have outperformed the traditional methods with respect to sensitivity and specificity (Ardila et al., 2019). Along similar lines, in cardiology, neural networks have been used to analyze RNNs by predicting high-risk sudden cardiac arrest patients on ECG readings whereby preventive measures can easily be taken within the appropriate time (Attia et al., 2019). AI-driven prediction tools have an extended application in hospitals to predict patient deterioration, helping in better resource allocation and a reduction in unplanned hospital readmissions (Rajkomar et al., 2018). Predictive healthcare provided by AI enjoys morbidity and mortality improvements at levels higher than individual patient care. In other words, massive AI models coupled with big data land in epidemiological surveillance, as illustrated during the COVID-19 pandemic. So AI algorithms were used to analyze vast amounts of mobility data, social media posts, and clinical reports just for the purpose of tracking infection patterns, predicting outbreak hotspots, and assisting in planning vaccine distribution (Rahmani et al., 2021). AI predictive analytics is now beginning to influence precision medicine, in which treatments are customized to a patient's unique genetic and physiological characteristics rather than employing a one-size-fits-all approach (Shen et al., 2020). Despite AI's great promise, there are barriers in most areas to universal acceptance of predictive analytics in aid of AI. The ethical and regulatory questions that arise concerning privacy and algorithmic bias also address issues related transparency in AI's decision-making (Morley et al., 2020). Furthermore, a heavy investment is needed to put AI into existing healthcare workflows, which would cover healthcare infrastructure, training of clinicians to use these tools, and ongoing collaboration between data scientists and medical professionals (Esteva et al., 2019). Getting around these problems is going to be fundamental for AI to fulfill its promise in predictive health care. The paper looks at AI predictive analytics in preventive health care in a broad sense, addressing its methods, uses, benefits, and restrictions. We hope, through case studies and recent advancements, to show the role of AI in actually transforming global health care from the current reactive model toward a preventive framework with better patient health outcomes and lesser burden on health care systems around the world.

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 Keywords

Artificial Intelligence, Predictive Analytics, Preventive Health Care, Machine Learning, Deep Learning, Electronic Health Records, Disease Prediction, Health Care Innovations, AI Ethics, Data Privacy, Precision Medicine.

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 Abstract

Pond sediments are an integral part of the pond ecosystem .Geochemical studies of sediments to evaluate the concentration of heavy metals are necessary as it helps to assess the eco toxic potential of the pond sediments. The occurrence of elevated concentration of trace metals in sediments found at the bottom of the water column can be a good indicator of man induced pollution rather than natural environment of the sediment by geological weathering. The presence of heavy metals in aquatic ecosystems is the result of two main sources of contamination; natural processes and natural occurring deposits and anthropogenic activities. In the fresh water environment, toxic metals are potentially accumulated in sediments and subsequently transferred to man through the food chain. Sediments were collected from five ponds during pre monsoon 2017 (Feb,Mar,Apr,May) and monsoon 2016(Jun,Jul,Aug,Sep).The heavy metals like copper(Cu),iron(Fe),manganese(Mn) concentrations were found out. From the results the amount of heavy metals and hence contamination level of sediment samples were analysed.

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 Keywords

Heavy metals, Sediments, wastes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Polycystic ovarian syndrome (PCOS) is the most common endocrine pathology in females of reproductive age worldwide. Stein and Leventhal initially described it in 1935. The prevalence ranges between 5% and 15% depending on the diagnostic criteria applied. It is widely accepted among specialty society guidelines that the diagnosis of PCOS must be based on the presence of at least two of the following three criteria: chronic anovulation, hyperandrogenism (clinical or biological), and polycystic ovaries. It is a diagnosis of exclusion, and disorders that mimic clinical features of PCOS must be excluded. These include thyroid disease, hyperprolactinemia, and non-classical congenital adrenal hyperplasia. Selected patients may need more extensive workup if clinical features suggest other causes. Despite its high prevalence, PCOS is underdiagnosed and frequently takes more than one visit or different physicians to get identified, and these usually occur in more than a one- year timeframe. It is a very frustrating process for the patient. Delay in diagnosis can lead to the progression of comorbidities making it more difficult to implement lifestyle intervention, which is critical for the improvement of features of PCOS and quality of life.

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 Keywords

EFFICACY OF HOMEOPATHIC MANAGEMENT IN PCOD

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