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Computational Methods for Predicting Chemical Reactivity of Covalent Compounds.

J Chem Inf Model

January 2025

Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities.

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Circadian Rhythm Disruption Exacerbates Autoimmune Uveitis: The Essential Role of PER1 in Treg Cell Metabolic Support for Stability and Function.

Adv Sci (Weinh)

January 2025

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.

Circadian rhythm plays a critical role in the progression of autoimmune diseases. While our previous study demonstrated the therapeutic effects of melatonin in experimental autoimmune uveitis, the involvement of circadian rhythm remained unclear. Using a light-induced circadian rhythm disruption model, we showed that disrupted circadian rhythms exacerbate autoimmune uveitis by impairing the stability and function of Treg cells.

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Background: Shenfu injection (SFI), derived from a traditional Chinese medicine (TCM) prescription, is an effective drug for the treatment of sepsis-induced myocardial injury (SIMI) with good efficacy, but its exact therapeutic mechanism remains unclear.

Methods: SwissTargetPrediction and GeneCards database were used to obtain relevant targets for SFI and SIMI. STRING 11.

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Flexible and modular latent transition analysis-A tutorial using R.

PLoS One

January 2025

National Institute of Public Health, University of Southern Denmark, Copenhagen K, Denmark.

Latent transition analysis (LTA) is a useful statistical modelling approach for describe transitions between latent classes over time. LTA may be characterized in terms of prevalence at each time point and through transition probabilities over time. Investigating predictors of these transitions is often of key interest.

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With the popularity of circular economy around the world, transactions in the second-hand sailboat market are extremely active. Determining pricing strategies and exploring their regional effects is a blank area of existing research and has important practical and statistical significance. Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data.

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