The prospective outcome of the monkeypox outbreak in 2022 and characterization of monkeypox disease immunobiology

A new threat to global health re-emerged with monkeypox’s advent in early 2022. As of November 10, 2022, nearly 80,000 confirmed cases had been reported worldwide, with most of them coming from places where the disease is not common. There were 53 fatalities, with 40 occurring in areas that had never before recorded monkeypox and the remaining 13 appearing in the regions that had previously reported the disease. Preliminary genetic data suggest that the 2022 monkeypox virus is part of the West African clade; the virus can be transmitted from person to person through direct interaction with lesions during sexual activity. It is still unknown if monkeypox can be transmitted via sexual contact or, more particularly, through infected body fluids. This most recent epidemic’s reservoir host, or principal carrier, is still a mystery. Rodents found in Africa can be the possible intermediate host. Instead, the CDC has confirmed that there are currently no particular treatments for monkeypox virus infection in 2022; however, antivirals already in the market that are successful against smallpox may mitigate the spread of monkeypox. To protect against the disease, the JYNNEOS (Imvamune or Imvanex) smallpox vaccine can be given. The spread of monkeypox can be slowed through measures such as post-exposure immunization, contact tracing, and improved case diagnosis and isolation. Final Thoughts: The latest monkeypox epidemic is a new hazard during the COVID-19 epidemic. The prevailing condition of the monkeypox epidemic along with coinfection with COVID-19 could pose a serious condition for clinicians that could lead to the global epidemic community in the form of coinfection

Real time surveillance for low resolution and limited data scenarios: An image set classification approach

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set are then projected onto the gallery subspaces. The residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We extensively evaluated the proposed technique using both low resolution and noisy images and with less gallery data to assess the suitability of our technique for the tasks of surveillance and video-based face recognition. The experiments show that the proposed technique achieves superior classification accuracy and has a faster execution time compared with existing techniques, especially under the challenging conditions of low resolution and a limited amount of gallery and test data