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Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets

Ng HW, Doughty SW, Luo H, Ye H, Ge W, Tong W, et al.

Chem Res Toxicol, 2015 Dec 21;28(12):2343-51.
PMID: 26524122 DOI: 10.1021/acs.chemrestox.5b00358

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.
Enhancing nursing competence in China: The interplay of resilience, mindfulness and social support

Yan X, Jia X, Feng L, Ge W, Kong B, Xia M, et al.

Nurse Educ Pract, 2024 Aug 02;79:104087.
PMID: 39142120 DOI: 10.1016/j.nepr.2024.104087

OBJECTIVES: To address the nursing crisis, it is imperative to comprehend the factors that influence nursing competencies, which are crucial for the delivery of quality patient care.
BACKGROUND: Facing demographic shifts and increasingly complex healthcare demands, China's nursing sector struggles with workforce shortages and the need to enhance core competencies. This research explores the interplay of social support, psychological resilience, mindfulness and nursing competencies in various hospital environments in China.
METHODS: Through a cross-sectional survey, 941 nurses across tertiary, secondary and private healthcare settings completed self-assessment questionnaires. The analysis included multiple linear regression and comparative methods to assess how psychological resilience, mindfulness and social support have an impact on nursing competencies.
RESULTS: Findings revealed a strong relationship between psychological resilience and nursing competencies, with resilience being a key predictor. Mindfulness and social support also significantly contributed to competency levels. Nurses in tertiary hospitals showed greater competencies than those in secondary or private facilities.
CONCLUSION: Enhancing nursing competencies requires targeted interventions focusing on professional development and supportive workplace cultures. Incorporating psychological resilience, social support and mindfulness into nurse training is crucial for improving practice and policy.
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.

Autophagy, 2021 Jan;17(1):1-382.
PMID: 33634751 DOI: 10.1080/15548627.2020.1797280

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

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