Application of mobile cloud computing in emergency health care

Mobile applications in emergency health care help maintain patient confidentiality and manage patient records, data storage. Compiles and analyzes care of better quality care. new implementations come with new goals and technologies like using mobile application with cloud computing system and reducing the responding time to safe the patient life and give the patient best health care professional service transition to using of mobile application in emergency healthcare, this paper will present (MCCEH) mobile cloud computing in emergency health care model, mainly reducing the wasting time in emergency health care, The process starting once the accident occurred and the patient run the application, mobile application will detect the patient location and allow him to book nearest medical center or specialist in some emergency cases once the patient did the booking will send help request to medical center this process will include an online pre-register patient in the medical center to save time of patient registration, MCCEH model allows the patients to review the previous feedback and experiences of each specialist or medical center and allows doctors to be able to stay in contact with their patients more often and by communication through mobiles applications and share messages and photos of the accident or emergency case itself

Dermatological diagnosis by mobile application

Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application  has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.

Image analysis model for skin disease detection: framework

Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image and to analyze this image by applying filter to remove noise or unwanted things, convert the image to grey to help in the processing and get the useful information. This help to give evidence for any type of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid side effects

Developing a Thermally Stable Ester-Based Drilling Fluid for Offshore Drilling Operations by Using Aluminum Oxide Nanorods

Funding: This work was supported by an Institutional Links grant, ID 352343681, under the Newton–Mosharafa Fund partnership. The grant is funded by the UK Department for Business, Energy and Industrial Strategy, and Science, Technology & Innovation Funding Authority (STDF) and delivered by the British Council. For further information, please visit www.newtonfund.ac.uk. Science, Technology & Innovation Funding Authority (STDF) support was under the grant No. (30894). Acknowledgments: The authors would like to acknowledge the School of Engineering at the University of Aberdeen for providing the required facilities to complete this research. In addition, the authors would like to thank the support from MI-SWACO in providing commercial emulsifiers and consumables used in this project.Peer reviewedPublisher PD

Application of mobile cloud computing in emergency health care

Mobile applications in emergency health care help maintain patient confidentiality and manage patient records, data storage. Compiles and analyzes care of better quality care. new implementations come with new goals and technologies like using mobile application with cloud computing system and reducing the responding time to safe the patient life and give the patient best health care professional service transition to using of mobile application in emergency healthcare, this paper will present (MCCEH) mobile cloud computing in emergency health care model, mainly reducing the wasting time in emergency health care, The process starting once the accident occurred and the patient run the application, mobile application will detect the patient location and allow him to book nearest medical center or specialist in some emergency cases once the patient did the booking will send help request to medical center this process will include an online pre-register patient in the medical center to save time of patient registration, MCCEH model allows the patients to review the previous feedback and experiences of each specialist or medical center and allows doctors to be able to stay in contact with their patients more often and by communication through mobiles applications and share messages and photos of the accident or emergency case itself

Vitamins D, C, and E in the prevention of type 2 diabetes mellitus: modulation of inflammation and oxidative stress

The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide, and certain population subgroups are especially vulnerable to the disease. To reduce T2DM risk and progression at the population level, preventative strategies are needed that can be implemented on a population-wide scale with minimal cost and effort. Chronic low-grade inflammation resulting from oxidative stress and imbalances in the innate immune system has been associated with obesity, metabolic syndrome, and insulin resistance – critical stages in the development and progression of T2DM. Therefore, inflammation may play a causal role in the pathogenesis of T2DM, and reducing it via modulation of oxidative stress and the innate immune response could lead to a status of improved insulin sensitivity and delayed disease onset. Dietary supplementation with anti-inflammatory and antioxidant nutritional factors, such as micronutrients, might present a novel strategy toward the prevention and control of T2DM at the population level. This review examines current knowledge linking oxidation, inflammatory signaling pathways, and vitamin supplementation or intake to the risk of T2DM. The concept that micronutrients, via attenuation of inflammation, could be employed as a novel preventive measure for T2DM is evaluated in the context of its relevance to public health

Systematic review on ai-blockchain based e-healthcare records management systems

Electronic health records (EHRs) are digitally saved health records that provide information about a person's health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions

Comprehensive Evidence-Based Assessment and Prioritization of Potential Antidiabetic Medicinal Plants: A Case Study from Canadian Eastern James Bay Cree Traditional Medicine

Canadian Aboriginals, like others globally, suffer from disproportionately high rates of diabetes. A comprehensive evidence-based approach was therefore developed to study potential antidiabetic medicinal plants stemming from Canadian Aboriginal Traditional Medicine to provide culturally adapted complementary and alternative treatment options. Key elements of pathophysiology of diabetes and of related contemporary drug therapy are presented to highlight relevant cellular and molecular targets for medicinal plants. Potential antidiabetic plants were identified using a novel ethnobotanical method based on a set of diabetes symptoms. The most promising species were screened for primary (glucose-lowering) and secondary (toxicity, drug interactions, complications) antidiabetic activity by using a comprehensive platform of in vitro cell-based and cell-free bioassays. The most active species were studied further for their mechanism of action and their active principles identified though bioassay-guided fractionation. Biological activity of key species was confirmed in animal models of diabetes. These in vitro and in vivo findings are the basis for evidence-based prioritization of antidiabetic plants. In parallel, plants were also prioritized by Cree Elders and healers according to their Traditional Medicine paradigm. This case study highlights the convergence of modern science and Traditional Medicine while providing a model that can be adapted to other Aboriginal realities worldwide

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting