Publications by authors named "D P Shen"

Antibiotic resistance genes (ARGs) are emerging as significant environmental contaminants, posing potential health risks worldwide. Intensive livestock farming, particularly swine production, is a primary contributor to the escalation of ARG pollution. In this study, we employed metagenomic sequencing and quantitative polymerase chain reaction to analyze the composition of microorganisms and ARGs across four vectors in a typical swine fattening facility: dung, soil, airborne particulate matter (PM), and fodder.

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Bisphenol A (BPA), extensively utilized in the manufacture of epoxy resins and polycarbonate plastics, is prevalent in the environment. Its exposure has been associated with an increased risk of hepatic lesions; however, the underlying mechanisms and the spectrum of its effects remain poorly understood. This study investigates the role of the Keap1-Nrf2 signaling pathway in regulating BPA-induced hepatotoxicity in vivo using a rat model.

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Asphaltenes, as the most complex and strongly polar component among the four components of asphalt, have a significant impact on the macroscopic physicochemical properties of asphalt. Currently, the vast variety of molecular structures used to characterize asphaltenes increases the construction complexity of asphalt molecular models. To construct a more realistic asphalt molecular model and reduce the construction difficulty, this investigation obtains the molecular morphology, molecular polarity, and infrared spectrum indicators of 21 asphaltene molecules through quantum chemical calculations.

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Antibiotic resistance has become a critical health crisis globally. Traditional strategies using antibiotics can lead to drug-resistance, while inorganic antimicrobial agents can cause severe systemic toxicity. Here, we have developed a dual-antibiotic hydrogel delivery system (PDA-Ag@Levo/CMCS), which can achieve controlled release of clinical antibiotics levofloxacin (Levo) and classic nanoscale antibiotic silver nanoparticles (AgNPs), effectively eliminating drug-resistant .

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Article Synopsis
  • The study assesses a novel MRI radiomics stacking ensemble learning model for predicting outcomes of high-intensity focused ultrasound (HIFU) ablation in patients with uterine fibroids.
  • Data from 360 patients is analyzed, utilizing deep learning for automated segmentation and various statistical methods for feature selection to enhance predictive accuracy.
  • Results showed that among the models tested, the Multilayer Perceptron (MLP) performed the best with an area under the curve (AUC) of 0.858, indicating strong potential for preoperative prognosis using MRI features.
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