Resveratrol (3,4',5 tri-hydroxystilbene), a natural plant polyphenol, has gained interest as a non-toxic agent capable of inducing tumor cell death in a variety of cancer types. However, therapeutic application of these beneficial effects remains very limited due to its short biological half-life, labile properties, rapid metabolism and elimination. Different studies were undertaken to obtain synthetic analogs of resveratrol with major bioavailability and anticancer activity. We have synthesized a series 3-chloro-azetidin-2-one derivatives, in which an azetidinone nucleus connects two aromatic rings. Aim of the present study was to investigate the effects of these new 3-chloro-azetidin-2-one resveratrol derivatives on human breast cancer cell lines proliferation. Our results indicate that some azetidin-based resveratrol derivatives may become new potent alternative tools for the treatment of human breast cancer.
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http://dx.doi.org/10.1016/j.bmcl.2013.09.054 | DOI Listing |
J Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
Cien Saude Colet
January 2025
Universidade Federal do Ceará. R. Alexandre Baraúna 1115, Rodolfo Teófilo. 60430-160 Fortaleza CE Brasil.
Mammography is one of the main methods available for breast cancer screening in Brazil. However, differences in timely access and performance of the exam can be highlighted based on social determinants of health, considered relevant due to their influence on the health situation of a population. Thus, the present study aimed to identify the social determinants of health associated with access to and performance of mammography in Brazilian women.
View Article and Find Full Text PDFCien Saude Colet
January 2025
Escola de Enfermagem Aurora Afonso Costa, Universidade Federal Fluminense. R. Dr. Celestino 74, Centro. 24020-091 Niterói RJ Brasil.
The aim is to unveil the useful value of breastfeeding for lactating women in a prison environment, based on Max Scheler's axiological perspective. This work was a qualitative, developed in a prison unit in Rio de Janeiro, where seven lactating women were interviewed. The phenomenological interview was used for data collection, while Laurence Bardin's content analysis was used for data analysis and treatment.
View Article and Find Full Text PDFCien Saude Colet
January 2025
Instituto René Rachou, Fundação Oswaldo Cruz (Fiocruz Minas). Av. Augusto de Lima 1715, Barro Preto. 30190-002 Belo Horizonte MG Brasil.
This article aims to identify the relationship between material deprivation and mortality from breast, cervical, and prostate neoplasms in the Brazilian adult population and the relationship between ethnicity/skin color and material deprivation. This cross-sectional ecological study calculated the mean mortality rate per 100,000 inhabitants, and deaths were standardized by age and gender and redistributed per to ill-defined causes, stratified by age group and ethnicity/skin color. We applied the Negative Binomial model, containing the interaction between ethnicity/skin color and the Brazilian Deprivation Index (IBP).
View Article and Find Full Text PDFBrief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
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