IoT-based disease prediction using machine learning.

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Current Landscape of Therapeutics for the Management of Hypertension - A Review.

Hypertension is a critical health problem. It is also the primary reason for coronary heart disease, stroke, and renal vascular disease. The use of herbal drugs in the management of any disease is increasing. They are considered the best immune booster to fight against several types of diseases. To date, the demand for herbal drugs has been increasing because of their excellent properties. This review highlights antihypertensive drugs, polyphenols, and synbiotics for managing hypertension. Evidence is mounting in favour of more aggressive blood pressure control with reduced adverse effects, especially for specific patient populations. This review aimed to present contemporary viewpoints and novel treatment options, including cutting-edge technological applications and emerging interventional and pharmaceutical therapies, as well as key concerns arising from several years of research and epidemiological observations related to the management of hypertension.

HPV pathogenesis, various types of vaccines, safety concern, prophylactic and therapeutic applications to control cervical cancer, and future perspective.

Over 98% of cervical cancers (CC) are caused by regular infections with "high risk" genotype of the human papilloma virus (HPV). However, this is not always the causative factor. Therefore, production of HPV vaccinations represents a significant chance to minimize the risk of CC. Phase III studies for a number of preventative HPV vaccines based on L1-virus-like particle (VLPs) have just been completed and the preliminary results are very convincing. However, there are a lot of practical concerns that need to be resolved before the use of these vaccinations. These vaccines were challenged with obvious queries such as protection time, subject receiving vaccines, time of vaccination, and how to include them into ongoing screening programs. Although these vaccines were 90% effective at preventing HPV infection as these offered only modest advantages for the removal of pre-existing infections. New advancements in the creation of therapeutic vaccinations have been explored for further improvement and post-vaccination surveillance. Therapeutic vaccines attempted to boost cell-mediated immunities and these are detrimental to the infected cell as opposed to neutralizing antibodies (different from prophylactic vaccines).

Dermatological disease prediction and diagnosis system using deep learning.

The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. However, the cost of such a diagnosis is currently limited and prohibitively high and restricted to developed areas. The present paper develops a holistic, critical, and important skin disease prediction system that utilizes machine learning and deep learning algorithms to accurately identify up to 20 different skin diseases with a high F1 score and efficiency. Deep learning algorithms like Xception, Inception-v3, Resnet50, DenseNet121, and Inception-ResNet-v2 were employed to accurately classify diseases based on the images. The training and testing have been performed on an enlarged dataset, and classification was performed for 20 diseases. The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision.