Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected.  In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system

Strange distributionally chaotic triangular maps III

In the class of triangular maps of the square we consider the strongest notion of distributional chaos, DC1, originally introduced by Schweizer and Sm\uedtal [Trans Amer Math Soc 1994;344:737\u2013854] for continuous maps of the interval. We show that a map is DC1 if F has a periodic orbit with period 60 2n, for any n 0. Consequently, a map in is DC1 if it has a homoclinic trajectory. This result is important since in general systems like , positive topological entropy itself does not imply DC1. It contributes to the solution of a long-standing open problem of A. N. Sharkovsky concerning classification of triangular maps of the square

An Adjuvanted Inactivated SARS-CoV-2 Microparticulate Vaccine Delivered Using Microneedles Induces a Robust Immune Response in Vaccinated Mice

SARS-CoV-2, the causal agent of COVID-19, is a contagious respiratory virus that frequently mutates, giving rise to variant strains and leading to reduced vaccine efficacy against the variants. Frequent vaccination against the emerging variants may be necessary; thus, an efficient vaccination system is needed. A microneedle (MN) vaccine delivery system is non-invasive, patient-friendly, and can be self-administered. Here, we tested the immune response produced by an adjuvanted inactivated SARS-CoV-2 microparticulate vaccine administered via the transdermal route using a dissolving MN. The inactivated SARS-CoV-2 vaccine antigen and adjuvants (Alhydrogel® and AddaVax™) were encapsulated in poly(lactic-co-glycolic acid) (PLGA) polymer matrices. The resulting MP were approximately 910 nm in size, with a high percentage yield and percent encapsulation efficiency of 90.4%. In vitro, the vaccine MP was non-cytotoxic and increased the immunostimulatory activity measured as nitric oxide release from dendritic cells. The adjuvant MP potentiated the immune response of the vaccine MP in vitro. In vivo, the adjuvanted SARS-CoV-2 MP vaccine induced high levels of IgM, IgG, IgA, IgG1, and IgG2a antibodies and CD4+ and CD8+ T-cell responses in immunized mice. In conclusion, the adjuvanted inactivated SARS-CoV-2 MP vaccine delivered using MN induced a robust immune response in vaccinated mice