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Filename: controllers/Detail.php
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Function: insertAPISummary
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Colorectal cancer (CRC) is a leading cause of cancer death, and therefore demands special attention. Novel recent approaches for the chemoprevention of CRC focus on selective targeting of key pathways. We review the study by Zhang and colleagues, evaluating a selective approach targeting APC-deficient premalignant cells using retinoid-based therapy and TNF-related apoptosis-inducing ligand (TRAIL). This study demonstrates that induction of TRAIL-mediated death signaling contributes to the chemopreventive value of all-trans-retinyl acetate (RAc) by sensitizing premalignant adenoma cells for apoptosis without affecting normal cells. We discuss these important findings, raise few points that deserve consideration, and may further contribute to the development of RAc-based combination therapies with improved efficacy. The authors clearly demonstrate a synergistic interaction between TRAIL, RAc and APC, which leads to the specific cell death of premalignant target cells. The study adds to the growing body of literature related to CRC chemoprevention, and provides solid data supporting a potentially selective approach for preventing CRC using RAc and TRAIL.
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http://dx.doi.org/10.1586/era.10.139 | DOI Listing |
Langmuir
December 2024
School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.
To address the limitations of carbon nitride in photocatalysis, we propose constructing a three-dimensional interwoven SiC/g-CN composite structure. Utilizing the strong microwave-thermal conversion characteristics of SiC whiskers, localized "hot spots" are generated, which induce rapid thermal gradients, promoting rapid polymerization of urea and in situ formation of the interwoven network. This unique structure strengthens the interaction between these two components, creates multiple electron transport pathways, enhances CO adsorption, and effectively improves charge separation while reducing photogenerated carrier recombination.
View Article and Find Full Text PDFChem Asian J
December 2024
Tianjin University, School of Chemical Engineering and Technology, 92# Weijin Road, 300072, Tianjin, CHINA.
The reverse water gas shift (RWGS) reaction provides a convenient approach to convert CO2 to CO, which facilitates to achieve the goals of carbon peaking and carbon neutrality. Herein, the Cu/CeO2 catalyst prepared by a co-precipitation method using a mixture of Na2CO3 and NaOH at pH of 10 (sample Cu/CeO2-10) achieved an intrinsic reaction rate of 428.4 mmol•gcat-1•h-1 with 100% CO selectivity at 400 °C and CO2/H2 ratio of 1:4, which is much higher than Cu/CeO2 prepared by impregnation and other methods.
View Article and Find Full Text PDFJ Cell Mol Med
December 2024
Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
Increasingly, emerging research evidence has demonstrated that nonalcoholic fatty liver disease (NAFLD) is a disease closely associated with systemic inflammation. However, the specific upstream inflammatory factors engaged in the pathogenesis of NAFLD remain unclear. Our study aimed to identify the inflammatory regulators causally associated with NAFLD pathogenesis through Mendelian randomisation.
View Article and Find Full Text PDFOchsner J
January 2024
The University of Queensland Medical School, Ochsner Clinical School, New Orleans, LA.
Despite the substantial expenditures on health care in the United States, persistent underperformance in health system metrics necessitates innovative approaches to address complex patient needs. The MedVantage Clinic in New Orleans, Louisiana, offers a regionally tailored, value-based primary care model targeting patients with high social and medical needs. This study provides an evaluation of the efficacy of the MedVantage Clinic in improving the cost of care and service utilization for this population.
View Article and Find Full Text PDFVirus Evol
November 2024
Department of Biology, University of Maryland, College Park, MD 20742, USA.
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days.
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