Publications by authors named "K Tinka Tan"

The third most prevalent type of cancer in the world, colorectal cancer, poses a significant treatment challenge due to the nonspecific distribution, low efficacy, and high systemic toxicity associated with chemotherapy. To overcome these limitations, a targeted drug delivery system with a high cytotoxicity against cancer cells while maintaining a minimal systemic side effects represents a promising therapeutic approach. Therefore, the aim of this study was to develop an efficient gold nanocarrier for the targeted delivery of the anticancer agent everolimus to Caco-2 cells.

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Background: To study the relationship between the monocyte/high-density lipoprotein cholesterol ratio (MHR) and neutrophil-to-lymphocyte ratio (NLR) and coronary artery stenosis in Non-st-elevation acute coronary syndromes (NSTE-ACS) patients of different genders.

Methods: A total of 253 control and 800 NSTE-ACS patients were included, and clinic data (29 items) were also collected. NSTE-ACS patients were divided into low-risk (0-23) and high-risk (≥ 23) groups based on the Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) score.

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Opioid dependence can occur in 6% to 10% of patients undergoing breast reconstruction. With the expansion of interdisciplinary initiatives to decrease opioid use after surgery, an updated look at the incidence of and risk factors for prolonged opioid dependence after free flap breast reconstruction is essential. We retrospectively identified all cases of free flap breast reconstruction completed at our institution from 2017 to 2020.

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Dengue, caused by the dengue virus (DENV), poses a significant global health challenge. Effective vaccines and treatments for dengue are lacking due to gaps in understanding its pathogenesis and mechanisms in severe cases. This study investigates the role of immunoglobulin E (IgE) in dengue, focusing on its potential association with virus neutralization and antibody-dependent enhancement (ADE) in DENV replication.

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Inland waters face multiple threats from human activities and natural factors, leading to frequent water quality issues, particularly the significant challenge of eutrophication. Hyperspectral remote sensing provides rich spectral information, enabling timely and accurate assessment of water quality status and trends. To address the challenge of inaccurate water quality mapping, we propose a novel deep learning framework for multi-parameter estimation from hyperspectral imagery.

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