17β-HSD10 is a mitochondrial enzyme that catalyzes the steroidal oxidation of a hydroxy group to a keto group and, thus, is involved in maintaining steroid homeostasis. The druggability of 17β-HSD10 is related to potential treatment for neurodegenerative diseases, for example, Alzheimer's disease or cancer. Herein, steroidal derivatives with an acidic hemiester substituent at position C-3 on the skeleton were designed, synthesized, and evaluated by using pure recombinant 17β-HSD10 converting 17β-estradiol to estrone. Compounds (IC = 6.95 ± 0.35 μM) and (IC = 5.59 ± 0.25 μM) were identified as the most potent inhibitors from the series. Compound inhibited 17β-HSD10 activity regardless of the substrate. It was found not cytotoxic toward the HEK-293 cell line and able to inhibit 17β-HSD10 activity also in the cellular environment. Together, these findings support steroidal compounds as promising candidates for further development as 17β-HSD10 inhibitors.
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http://dx.doi.org/10.1021/acsomega.3c10148 | DOI Listing |
Emerg Microbes Infect
January 2025
HIV/AIDS Unit, National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy.
The first evidence that Orthopoxvirus induced the expansion and the recall of effector innate Vδ2T-cells was described in a macaque model. Although, an engagement of αβ T-cells specific response in patients infected with human monkeypox (Mpox) was demonstrated, little is known about the role of γδ T-cells during Mpox infection. IFN-γ-producing γδ T-cells in the resistance to poxviruses may a key role in inducing a protective type 1 memory immunity.
View Article and Find Full Text PDFHistol Histopathol
January 2025
Neuropharmacology Division, Department of Pharmacology, ISF College of Pharmacy, Moga, Punjab, India.
Autism spectrum disorder (ASD) is a globally recognized neurodevelopmental condition characterized by repetitive and restrictive behavior, persistent deficits in social interaction and communication, mental disturbances, etc., affecting approximately 1 in 100 children worldwide. A combination of genetic and environmental factors is involved in the etiopathogenesis of the disease, but specific biomarkers have not yet been identified.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Key Laboratory for Photonic and Electronic Bandgap Materials, Ministry of Education, College of Chemistry and Chemical Engineering, Harbin Normal University, Harbin 150025, China.
Tryptophan participates in important life activities and is involved in various metabolic processes. The indole and aromatic binuclear ring structure in tryptophan can engage in diverse interactions, including π-π, π-alkyl, hydrogen bonding, cation-π, and CH-π interactions with other side chains and protein targets. These interactions offer extensive opportunities for drug development.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Department of Battery and Chemical Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Republic of Korea.
Designing and constructing hierarchically structured materials with heterogeneous compositions is the key to developing an effective catalyst for overall water-splitting applications. Herein, we report the fabrication of hollow-structured selenium-doped nickel-cobalt hybrids on carbon paper as a self-supported electrode (denoted as Se-Ni|Co/CP, where Ni|Co hybrids consist of nickel-cobalt alloy-incorporated nickel-cobalt oxide). The procedure involves direct growth of zeolitic imidazolate framework-67 (ZIF-67) on bimetal-based nickel-cobalt hydroxide (NiCoOH) electrodeposited on CP, followed by selenous etching and pyrolysis to obtain the final Se-Ni|Co/CP electrocatalytic system.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
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