Realm Of Medicine Research Articles

Cardiovascular Disease Prediction Using Machine Learning

Heart disease-related deaths have become a big issue in today's world, with one person dying from the disease every minute. It considers both male and female groups, and the ratio varies by location. It is also used for the 25-69 age group. This isn't to say that people of all ages will be affected by heart disease. This condition could start in the early stages of life, and predicting the source and sickness is currently a huge challenge. Heart disease is one of the world's most fatal problems, one that cannot be seen with the naked eye and manifests itself as soon as it reaches its limits. As a result, precise diagnosis at the right moment is necessary. Every day, the health-care business generates massive amounts of patient and illness-related data. Researchers and practitioners, on the other hand, do not make appropriate use of this data. Despite its lack of knowledge, the healthcare business now has a wealth of data. In data mining and machine learning, there are a variety of approaches and tools for extracting usable information from databases and using that information to make more accurate diagnoses and decisions. So, in order to detect such disorders in time for adequate treatment, a reliable, precise, and feasible approach is required. In the realm of medicine, machine learning algorithms and approaches have been used to process enormous data sets. Researchers employ a variety of data mining and machine learning approaches to analyse large data sets and aid in the accurate prediction of cardiac illnesses. This research compares and contrasts the Nave Bayes, Help Vector Machine, Random Forest, and supervised learning models to find the most successful algorithm. When compared to other algorithms, Random Forest has95.08 per cent more precision.

Extraction, Phytochemical Screening and HPTLC Analysis of Fenugreek Gum for Analysis of Different Phytoconstituents

Fenugreek gum is a natural polysaccharide that has been explored for various biomedical applications. The present study investigated the phytoconstituent profile of fenugreek gum, revealing it as a potent source of diverse bioactive compounds. The analysis encompassed steroids, fats and oils, alkaloids, proteins, saponins, tannins, phenolics, flavonoids, and carbohydrates. Extraction using methanol and aqueous solvents demonstrated the highest concentration of phytoconstituents, underscoring the efficacy of these solvents in isolating bioactive components from fenugreek. Further exploration involved the determination of Rf values and percentage area in chloroform, petroleum ether, and chloroform extracts, providing a comprehensive identification of the mixture’s components. The results suggested a rich and varied chemical composition in fenugreek gum, offering potential applications in diverse fields owing to the presence of steroids for physiological effects, fats and oils for nutritional benefits, alkaloids with potential pharmacological properties, proteins for cellular functions, saponins for their soap-like properties, and tannins and phenolics as antioxidants. Additionally, the presence of flavonoids and carbohydrates adds to its nutritional and medicinal value. The study’s findings contribute valuable insights into the phytochemical composition of fenugreek gum, shedding light on its potential applications in the realms of medicine, nutrition, and industry. The identification of optimal extraction methods and the characterization of phytoconstituents lay the foundation for future research and utilization of fenugreek gum as a versatile and valuable natural resource. Overall, this investigation underscores the significance of fenugreek gum as a rich repository of bioactive compounds with diverse potential applications across various domains.

In-depth Historical Analysis of Healthcare Screening Systems

Purpose: The management facilitated by Triage Systems raises numerous inquiries for both healthcare professionals and users, rendering it an increasingly pertinent concern. In Portugal, emergency services cater to millions of individuals annually. The dynamic shifts in care are abundant and contingent on the specific nature of the healthcare required. Change, therefore, stands as a perpetual element in the realm of medicine, with alterations unfolding at an accelerating pace and growing complexity. Within the realm of emergency services, Triage serves as the inaugural stage in the healthcare delivery process. It necessitates a meticulous and rigorous approach, a task reserved for adept professionals who are adequately prepared for responsibility. The intricate nature of Triage underscores its pivotal role, setting the tone for the subsequent stages of healthcare provision. As the landscape of healthcare continues to evolve, the effectiveness and precision of Triage become even more crucial, emphasizing the need for ongoing training and adaptability among healthcare professionals.
 Design/Method/Approach: The methodology of the research consists of a thorough literature review to compare different screening systems in healthcare, to better understand the current situation.
 Findings: The findings indicate that the Screening System is both valid and beneficial. Nevertheless, ongoing adjustments in service management are required to enhance its effectiveness and alignment.
 Theoretical Implications: This paper explores how healthcare screening systems have evolved over time, tracing their roots from historical practices to modern technologies. It identifies paradigm shifts in healthcare thinking, policy and technology that have influenced the development of screening systems.
 Practical Implications: The screening system is a vital process mandated for all individuals seeking assistance in the emergency department, serving to ascertain the severity of their clinical condition. The primary goal of this assessment is to delve into the historical evolution of Screening Systems in Healthcare. Presently, in Portugal, the Manchester Sorting System stands out as the predominant and widely utilized approach.
 Originality/Value: The study provides a comprehensive comparative analysis of healthcare screening systems.
 Research Limitations/Future Research: Future studies could perform other types of analyses. Namely, qualitative ones in which health professionals gave opinions.
 Paper Type: Conceptual
 JEL Classification: I1, M1

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Fabrication of Au-Ag Bimetallic Nanoparticles Using Pulsed Laser Ablation for Medical Applications: A Review.

In recent years, the synthesis of Au-Ag bimetallic nanoparticles has garnered immense attention due to their potential applications in diverse fields, particularly in the realm of medicine and healthcare. The development of efficient synthesis methods is crucial in harnessing their unique properties for medical applications. Among the synthesis methods, pulsed laser ablation in a liquid environment has emerged as a robust and versatile method for precisely tailoring the synthesis of bimetallic nanoparticles. This manuscript provides an overview of the fundamentals of the pulsed laser ablation in a liquid method, elucidating the critical factors involved. It comprehensively explores the pivotal factors influencing Au-Ag bimetallic nanoparticle synthesis, delving into the material composition, laser parameters, and environmental conditions. Furthermore, this review highlights the promising strides made in antibacterial, photothermal, and diagnostic applications. Despite the remarkable progress, the manuscript also outlines the existing limitations and challenges in this advanced synthesis technique. By providing a thorough examination of the current state of research, this review aims to pave the way for future innovations in the field, driving the development of novel, safe, and effective medical technologies based on Au-Ag bimetallic nanoparticles.

An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification

Technological advancements have had a profound impact on various aspects of human existence. The realm of medicine is one major area where technology has made important advances. We will talk specifically about the role technology has had in treating brain tumors, a serious and widespread condition. A large number of people pass away from brain tumors every year. Patients with BTs have a worse likelihood of survival when they receive subpar care and a false diagnosis. The most widely employed technique for detecting brain tumors is magnetic resonance imaging (MRI). Moreover, MRI is extensively utilized in medical imaging and image processing to identify variations in various regions of the body. A convolutional neural network (CNN)-based model was developed in this study to classify brain tumor. Using nine pre-trained CNN models (efficientnetb0, mobilenetv2, nasnetlarge, resnet50, resnet10, googlenet, vgg16, vgg19, and shufflenet), deep features were extracted from the acquired images. Then use a Support Vector Machines (SVM) classifier to classify the deep features. The classification accuracy results obtained from the various kernel functions, namely linear, gaussian, cubic, and quadratic—was then compared. The deep features retrieved from the efficientnetb0 model allowed accurate classification of brain tumors. The classification accuracy achieved using the Gaussian kernel function of SVM was recorded at an impressive 99.78%.

Meta-Analysis and Systematic Review in Medical Research: Methods, Applications, and Implications

Systematic reviews and meta-analyses are vital methodologies in medical research that consolidate and assess evidence pertaining to specific research questions. Systematic reviews adopt a meticulous approach to pinpoint, evaluate, and integrate all pertinent studies on a given topic. Conversely, meta-analyses employ statistical methods to amalgamate results from several studies, yielding a combined estimate of intervention effects or identifying patterns across research. Both methodologies hold paramount significance in medicine, aiding researchers, clinicians, and policymakers in comprehending the comprehensive evidence on a subject. They underpin evidence-based decision-making, reconcile conflicting results, augment statistical power, spotlight research voids, steer policy and clinical guidelines, optimize resource use, and bolster patient comprehension. This paper delves into the paramountcy of systematic reviews and meta-analysis in the realm of medicine. It underscores their myriad advantages and their pivotal role in enriching medical knowledge. Contemporary, real-world examples are furnished to elucidate these points. In essence, this paper offers an insightful exposition on the cruciality of systematic reviews and meta-analysis in medicine, underscoring their role in anchoring medical practice in top-tier evidence and ensuring the zenith of patient care.

Immunomodulatory Potential of Fungal Extracellular Vesicles: Insights for Therapeutic Applications.

Extracellular vesicles (EVs) are membranous vesicular organelles that perform a variety of biological functions including cell communication across different biological kingdoms. EVs of mammals and, to a lesser extent, bacteria have been deeply studied over the years, whereas investigations of fungal EVs are still in their infancy. Fungi, encompassing both yeast and filamentous forms, are increasingly recognized for their production of extracellular vesicles (EVs) containing a wealth of proteins, lipids, and nucleic acids. These EVs play pivotal roles in orchestrating fungal communities, bolstering pathogenicity, and mediating interactions with the environment. Fungal EVs have emerged as promising candidates for innovative applications, not only in the management of mycoses but also as carriers for therapeutic molecules. Yet, numerous questions persist regarding fungal EVs, including their mechanisms of generation, release, cargo regulation, and discharge. This comprehensive review delves into the present state of knowledge regarding fungal EVs and provides fresh insights into the most recent hypotheses on the mechanisms driving their immunomodulatory properties. Furthermore, we explore the considerable potential of fungal EVs in the realms of medicine and biotechnology. In the foreseeable future, engineered fungal cells may serve as vehicles for tailoring cargo- and antigen-specific EVs, positioning them as invaluable biotechnological tools for diverse medical applications, such as vaccines and drug delivery.

A dynamic annealing learning for PLSOM neural networks: Applications in medicine and applied sciences

In recent years, the field of unsupervised learning in neural networks has witnessed significant advancements. This innovative learning technique holds great promise for applications in diverse domains, with particular significance in the realms of medicine and applied sciences such as medical image analysis, drug discovery, predictive analytics, and pattern recognition. The neighborhood function plays a crucial role in the Improved Parameter-Less Self-Organizing Map (PLSOM2) algorithm by governing the rate of change in the vicinity of the winning neuron. During learning iterations, the neighborhood size is dynamically adjusted to encompass the activated neighboring neurons relative to the winning neuron. The training process begins with a larger neighborhood radius, promoting rough ordering, and gradually refines the process by reducing the radius for fine-tuning. This dynamic neighborhood size significantly influences the final training outcome of the PLSOM2 algorithm. However, one of the major bottlenecks of the PLSOM2 algorithm has been the slow ordering time and the challenge of determining an optimal neighborhood size. These issues often lead to topological defects during training, such as kinks or warps in the output maps. Merely increasing processing time has proven insufficient to overcome these challenges. In this paper, we propose a novel dynamic neighborhood function designed to accelerate the convergence process of the PLSOM2 algorithm, achieving the best shape and adaptation of the neighborhood width. The study demonstrates that by improving the neighborhood function of the PLSOM2 algorithm, map distortion can be effectively suppressed. Importantly, this enhancement enables the algorithm to handle network size, neighborhood size, and the large dimensional output space adeptly. It adaptively decreases the neighborhood size over time, ensuring convergence while appropriately managing network growth and avoiding twisting and misconfiguration. To assess the effectiveness of the proposed method, we conducted an extensive set of experiments across eight real-world benchmark datasets. Notably, the outcomes of these experiments are presented showcases the results of paired t-tests, highlighting the consistency and robustness of the proposed algorithm's performance. Despite non-significant p-values in many cases, the algorithm consistently excels across various datasets, underlining its practical significance. On the other hand, presents the results of One-way Analysis of Variance (ANOVA) tests. These results further reinforce the consistency of the performance of algorithm, as indicated by p-values exceeding the common significance threshold of 0.05. The combined findings from both tables provide strong statistical evidence of the proposed algorithm's robustness and effectiveness across diverse datasets. The proposed research introduces a dynamic neighborhood function that not only improves the PLSOM2 algorithm's convergence but also enhances its adaptability and topological preservation. These enhancements, supported by the statistical tests results, underscore the algorithm's practical significance and its suitability for real-world applications, where statistical significance may not always capture the full extent of its capabilities.

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

Integrated Medical Curriculum in Pakistani Educational Environment its Merits and Demerits

The majority of medical school curricula have been merged both horizontally and vertically, with clinical and fundamental sciences integrating vertically. This has contributed to a more complete education. The Flexnerian curriculum is being phased out to make room for the merging of basic sciences and clinical sciences, which are now taught concurrently across the curriculum. This modification was made to allow for integration. We have developed an alternate approach of integration in which the teaching of the sciences is displayed on the vertical axis and the defined learning outcomes are depicted on the horizontal axis; this strategy is then adopted throughout all courses. We believe that it is not sufficient to merely integrate the basic and clinical sciences in the field of medicine; rather, it is vital to lay an emphasis on the importance of humanism in addition to the public health sciences. In other words, we believe that merely integrating the basic and clinical sciences in the realm of medicine is insufficient. Integration of the basic and clinical sciences, humanism, and health population into the curriculum's vertical axis is essential not only in the early years of the programme, but throughout its entirety. This involves the use of problem- or case-based active teaching strategies in small groups. Place of Study: Rai Medical College Sargodha Study Duration: January 2022 to June 2022 Study Design: Empirical research Conclusion: In conclusion, an integrated medical curriculum has both its positive and negative aspects to consider. An integrated curriculum, on the other hand, can offer medical students with a thorough grasp of the human body, as well as improve critical thinking and foster interdisciplinary learning, provided that it is done successfully. Keywords: integrated curriculum types, sciences integration, curricular architecture and development

Guidance for Adolescents in Kota Atas Sabang Neighborhood Regarding the Dangers of Drug Abuse

Substances classified as narcotics play a crucial role in the realm of medicine and scientific advancement. However, the misuse of narcotics poses significant dangers, adversely affecting individuals and, ultimately, societies, nations, and states. Adolescents are increasingly susceptible to such misuse due to their developmental phase, characterized by experimentation that may lead to addiction. Their vulnerability is exacerbated by a lack of awareness regarding the perils of narcotics. This thesis aims to assess the extent of guidance provided by parents, religious figures, and the Sabang Police in addressing and guiding adolescents on the dangers of narcotics in the Kota Atas Sabang neighborhood. Additionally, it seeks to identify the factors contributing to adolescent narcotic abuse and to explore the efforts undertaken by law enforcement, educators, religious leaders, and community figures in preventing and addressing narcotics misuse among adolescents. The research methodology is descriptive, employing a combination of literature review and field research through observation, interviews, and questionnaires. The study includes 80 students from Sabang Public High School, three teachers, religious figures, and parents. The findings suggest that the guidance provided to adolescents regarding the dangers of narcotics is inadequate, influenced by a lack of guidance activities and attention from parents, religious figures, the Sabang Police, and educators. Consequently, it is recommended that community members actively involved in guidance activities intensify their efforts to enhance the effectiveness of these programs

An expedient synthesis of C3-arylidene-oxindole derivatives using calcite nanoflowers as an efficient heterogeneous catalyst

α-β unsaturated heterocyclic compounds such as C3-arylidene-oxindoles, with five-member rings containing nitrogen, have an important role in the realm of medicine. This study aims to synthesize the C3-arylindene-oxindoles derivative compounds using calcite nanoflowers (CaCO3 NFs) as a heterogeneous catalyst for the first time. These CaCO3 NFs prepared by the thermal decomposition method, which is an active and reusable catalyst for stereospecific Knoevenagel condensation reaction between 2-oxindole and aromatic aldehyde under different solvent conditions like water, ethyl alcohol, and 50% aqueous ethyl alcohol. This catalytic method is employed with a wide range of aromatic aldehydes to produce high yields of C3-arylidene-oxindoles (93-99%), with stereo-specifically E-isomers (100%) for 50% alcohol and alcohol as a solvent.

A review on glaucoma detection using deep learning algorithms

Glaucoma is a progressive eye condition that causes loss of vision and cannot be restored once it has occurred. The diagnosis of glaucoma is an important issue that has to be resolved in the realm of medicine. There have only been a limited number of research efforts aimed at detecting glaucoma in its earlier stages. Nevertheless, the performance of glaucoma illness identification utilizing the various approaches that are now available was ineffective. In addition to this, the time commitment involved in traditional methods of glaucoma illness identification was significantly higher. Finding glaucoma in color fundus photos is a difficult undertaking that calls both knowledge as well as years of practice. Deep learning techniques have rapidly emerged as a method of choice for the analysis of medical pictures. In this study, we took advantage of the application of various Convolutional Neural Networks (CNN) schemes to demonstrate the influence on performance of relevant factors such as the size of the data set, architecture, and the use of transfer learning versus newly defined architectures at an early stage with higher accuracy and a lower amount of time. Specifically, we compared the two types of architectures in terms of their ability to learn from previous examples. Deep learning techniques have rapidly emerged as a method of choice for the analysis of medical pictures. This study examines the primary deep learning ideas that are applicable to medical image analysis and provides a summary of the contributions to the area. We take a look at the application of deep learning to various image processing tasks such as classification, object identification, segmentation, and registration.

Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images

In the realm of medicine, value, resource use and final care are determined by good technological advancement. However, there are crucial components that must be present for a disease to be diagnosed. The monitoring of illness progression traditionally relies primarily on a subjective human judgment and is neither precise nor timely. One important aspect that utilizes data at various disease progression phases is to maintain routine disease surveillance. The Feed Forward Neural Network based Brain Tumor Diagnosis in Magnetic Resonance Images is provided in this paper as an automatic brain cancer diagnosis and grade classification method. It is highly helpful to have accurate information about the disease in order to classify it and make decisions. The suggested brain tumor diagnosis system can diagnose the condition and provide a reliable foundation for appropriate regulation, as opposed to manual approaches. Finally, the evaluated outcomes of the suggested model investigate numerous Magnetic Resonance Images of healthy and disease and demonstrate that, the proposed method has the highest accuracy.

Rheuminations

I’ll be honest…it has been 35+ years since I started Medical School [MBBS]. And in the realm of Medicine at large, and Immunology/Rheumatology in specific, we are still struggling to find a ‘cure’ for chronic systemic inflammatory immune-mediated diseases also known as ‘autoimmune rheumatic diseases (ARD)’. Rheumatology is still in its cradle having gotten the recognition as a subspecialty of Medicine only in 1972. I have seen the field evolve in terms of understanding the orchestrated ‘play’ of the immune cells along with cytokines etc. over the past 3 decades, all of which has led to the concept and birth of ‘biologic’ disease modifying anti-rheumatic drugs [b-DMARDs]. The first b-DMARD to get approved by the Food and Drugs Administration [FDA] was etanercept, a TNF-alpha receptor fusion protein in 1998. Infliximab, a chimeric monoclonal antibody against TNF soon followed in 1999. Ever since then, there has been a flurry of b-DMARDs including 3 more in the same family of TNF-antagonists, 2 in the Interleukin [IL]-6 antagonist class, 1 blocker of the second co-stimulatory T-cell signaling: CTLA-4Ig, 3 IL-1 antagonists, B-cell depleting chimeric monoclonal antibody directed against CD-20 etc. Also, 3 oral Janus-kinase inhibitors have joined the ‘gang’ and are called targeted synthetic DMARDs. I still remember the pre-b-DMARD era when rip roaring rheumatoid arthritis was still around, and with my Ustaad Saheb (Mentor), all what we had to offer pharmaceutically were the conventional synthetic [cs] DMARDs including methotrexate, hydroxychloroquine, sulfasalazine, leflunomide and [the younger generation can hold their breath] cyclosporine, d-penicillamine, chlorambucil…GOLD injections and even cyclophosphamide! Corticosteroids have been around since 1950! The 1st and “so far” […well, I/You might be next one!] the only Nobel Prize winning revelation in the realm of Rheumatology......................................

Argumentation approaches for explanaible AI in medical informatics

• Use of logic approaches, taking advantage of their characteristics of explainability and expressiveness in order to design ethical, explainable and justified medical systems. • Use of argumentation theory in Medical Informatics by overviewing existing approaches in the literature. • Argumentation Systems for Medical Informatics. Artificial Intelligence algorithms are powerful in performing accurate predictions, but they are often considered black boxes as they do not provide any explanation about how outputs are derived from inputs or why a decision is taken. Therefore, urgent is the need of a completely transparent and eXplainable Artificial Intelligence (XAI) as also recognized by the explicit inclusion of the right to explanation in the General Data Protection Regulation (GDPR). There has been much study on diagnosis, decision support, and interpretability, and there is significant interest in the development of Explainable AI in the realm of medicine. Interpretability in the medical field is not just an intectual curiosity, but a key factor. Medical choices impact the life of patients, and include risk and responsibility for the clinicians. This proposal investigates the benefit of using logic approaches for eXplainable AI by evidencing how their natural characteristics of explainability and expressiveness help in the design of ethical, explainable and justified intelligent systems. More specifically, the paper focuses on a detailed topic related to the use of argumentation theory in Medical Informatics by overviewing existing approaches in the literature. The overview categorizes approaches on the basis of the specific purpose the argumentation is used for, into the following categories: Argumentation for Medical Decision Making, Argumentation for Medical Explanations and Argumentation for Medical Dialogues .

Engineered nanomaterials and human exposure: An overview

Nanosilver is the most well-known name in the broad family of nanomaterials. These nanoparticles are found across a huge range of commodities, particularly cosmetics and food packaging. It is also well-known in the realm of medicine. New nanomaterials are typically employed in medicine, and they are often combined with other traditional medications such as cytostatic agents or antibiotics. However, in some situations, these enormous nanotechnology goods have come into contact with humans unwittingly. These ultrafine materials reach the human body via cutaneous, inhalation, and ocular pathways. That is why people should be exposed to nanomaterials. And that exposure is professional, consumer, and environmental in nature throughout its full life cycle, usage, and disposal. Unfortunately, there are currently hardly any workplace exposure limits available for engineered nanomaterials (ENMs), and the mechanism of impact of ENMs is not well understood also. As a result, there is a need to develop a system that can test all of the major features of ENMs that trigger biological responses in humans. For the comparison of nanoparticle behaviour, new types of exposure models may be required, which can track one particle individually, its shape, surface area, and other surface features such as nanomaterial conversion or the ability to become reactive, and so on. Life cycle assessment can be used to build conceptual models of exposure to engineered nanomaterials. It's critical to keep precise records of ENM floating in the air and assess its true nature. And it necessitates a large number of parameters as well as a variety of ENM materials that are so far on the market, as well as the concept of its (ENM) ability to participate in any biological process.

Green synthesis of nano-propolis and nanoparticles (Se and Ag) from ethanolic extract of propolis, their biochemical characterization: A review

Abstract Propolis is a natural bee product with phenolic components and flavonoid content. As propolis is hydrophobic, it is poorly absorbed by the body, querying the use of other technologies. This review focuses on the biosynthesis, characterization, and evaluation of some biological activities of nanoparticles (AgNPs and SeNPs). The nanoparticles were generated utilizing bee propolis extract, taking into account the benefits of green nanoparticle synthesis. Due to the smaller size, nano-propolis is more easily absorbed by the body. Nano-propolis has the potential to improve efficacy in the realms of medicine and biology. Green chemistry approach to nanoparticle synthesis offers several advantages, including process scaling, economic feasibility, and a safe technique to make nanoparticles. Bioreduced AgNPs can be employed as a therapeutic agent to treat a variety of human ailments. After deeply studying and reviewing different research studies, it was evaluated that the natural nanoparticles have the potential to be effective in the treatment of bacterial and fungal infections.

An Overview of Ondine’s Curse and its Diagnostic Options in the Medical Field

One of the most captivating mythical tales in the realm of medicine is Ondine’s curse. Ondine was an eternal water spirit who became human after falling in love, marrying, and having a child with a man. When she saw her husband sleeping with another woman, she cursed him to stay awake to control his breathing, according to one version of the story. The unusual illness marked by loss of autonomic breath control while voluntary breathing remains intact was cleverly called “Ondine’s curse” during the nineteenth century. Nowadays, Ondine’s curse is most commonly connected with congenital central hypoventilation syndrome; nevertheless, it is also used to describe a variety of respiratory illnesses in the medical literature.