Tetrahydrocurcumin (THC) is a major metabolite of curcumin and plays an important role in curcumin-induced biological effects. THC is a promising preventive and chemotherapeutic agent for cancer. A series of new pyrazole derivatives of THC have been synthesized as potent anticancer agents. Direct condensation of THC with various substituted hydrazines leads to new pyrazole derivatives of THC (1–18). The prepared compounds have been evaluated via in vitro MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay for their cell proliferation-inhibitory activity against human lung adenocarcinoma (A549), human cervical carcinoma (HeLa) and human breast carcinoma (MCF-7) cells. Most derivatives show significantly higher anticancer activity against all three tested cancer cell lines than the parent compound THC. Several compounds (7, 8, 12, 13 and 15) display promising anticancer activity against MCF-7 cell line with IC₅₀ values ranging from 5.8 to 9.3 µM. The most active compound (8) is substituted with 4-bromophenyl group at the pyrazole ring and inhibits the growth of all three tested cancer cell lines with an IC₅₀ values of (8.0 µM, A549), (9.8 µM, HeLa) and (5.8 µM, MCF-7). The obtained compounds can be a good starting point for the development of new lead molecules in the fight against cancer. Graphical Abstract

The world faces multiple public health emergencies simultaneously, such as COVID-19 and Monkeypox (mpox). mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.

Influenza virus is the main cause of an infectious disease called influenza affecting the respiratory system including the throat, nose and lungs.