228 research outputs found
Effects of Coptis extract combined with chemotherapeutic agents on ROS production, multidrug resistance, and cell growth in A549 human lung cancer cells
Background: Non–small cell lung cancer is associated with high expression of multidrug resistance (MDR) proteins and low production of reactive oxygen species (ROS). Coptis extract (COP), a Chinese medicinal herb, and its major constituent, berberine (BER), have anticancer properties. This study aims to investigate the effects of COP and BER combined with chemotherapeutic agents, including fluorouracil (5-FU), camptothecin (CPT), and paclitaxel (TAX), on cell proliferation, ROS production, and MDR in A549 human non-small cell lung cancer cells. Methods: A549 cells were treated with different doses of COP and BER, combined with 5-FU, CPT, and TAX. Cell viability was measured by an XTT (2,3-bis-(2-methoxy-4- nitro-5-sulfophenyl)-2 H-tetrazolium-5-carboxanilide) assay. Intracellular ROS levels were determined by measuring the oxidative conversion of cell permeable 2′,7′-dichlorofluorescein diacetate to fluorescent dichlorofluorescein. MDR of A549 cells was assessed by rhodamine 123 retention assay. Results: Both COP and BER significantly inhibited A549 cell growth in a dose-dependent manner. Combinations of COP or BER with chemotherapeutic agents (5-FU, CPT, and TAX) exhibited a stronger inhibitory effect on A549 cell growth. In addition, COP and BER increased ROS production and reduced MDR in A549 cells. Conclusion: As potential adjuvants to chemotherapy for non–small cell lung cancer, COP and BER increase ROS production, reduce MDR, and enhance the inhibitory effects of chemotherapeutic agents on A549 cell growth
Catch the Butterfly: Peeking into the Terms and Conflicts among SPDX Licenses
The widespread adoption of third-party libraries (TPLs) in software
development has accelerated the creation of modern software. However, this
convenience comes with potential legal risks. Developers may inadvertently
violate the licenses of TPLs, leading to legal issues. While existing studies
have explored software licenses and potential incompatibilities, these studies
often focus on a limited set of licenses or rely on low-quality license data,
which may affect their conclusions. To address this gap, there is a need for a
high-quality license dataset that encompasses a broad range of mainstream
licenses to help developers navigate the complex landscape of software
licenses, avoid potential legal pitfalls, and guide solutions for managing
license compliance and compatibility in software development. To this end, we
conduct the first work to understand the mainstream software licenses based on
term granularity and obtain a high-quality dataset of 453 SPDX licenses with
well-labeled terms and conflicts. Specifically, we first conduct a differential
analysis of the mainstream platforms to understand the terms and attitudes of
each license. Next, we propose a standardized set of license terms to capture
and label existing mainstream licenses with high quality. Moreover, we include
copyleft conflicts and conclude the three major types of license conflicts
among the 453 SPDX licenses. Based on these, we carry out two empirical studies
to reveal the concerns and threats from the perspectives of both licensors and
licensees. One study provides an in-depth analysis of the similarities,
differences, and conflicts among SPDX licenses, revisits the usage and
conflicts of licenses in the NPM ecosystem, and draws conclusions that differ
from previous work. Our studies reveal some insightful findings and disclose
relevant analytical data, which set the stage for further research.Comment: 10 pages, 6 figures, accepted by SANER202
Progressive Conservative Adaptation for Evolving Target Domains
Conventional domain adaptation typically transfers knowledge from a source
domain to a stationary target domain. However, in many real-world cases, target
data usually emerge sequentially and have continuously evolving distributions.
Restoring and adapting to such target data results in escalating computational
and resource consumption over time. Hence, it is vital to devise algorithms to
address the evolving domain adaptation (EDA) problem, \emph{i.e.,} adapting
models to evolving target domains without access to historic target domains. To
achieve this goal, we propose a simple yet effective approach, termed
progressive conservative adaptation (PCAda). To manage new target data that
diverges from previous distributions, we fine-tune the classifier head based on
the progressively updated class prototypes. Moreover, as adjusting to the most
recent target domain can interfere with the features learned from previous
target domains, we develop a conservative sparse attention mechanism. This
mechanism restricts feature adaptation within essential dimensions, thus easing
the inference related to historical knowledge. The proposed PCAda is
implemented with a meta-learning framework, which achieves the fast adaptation
of the classifier with the help of the progressively updated class prototypes
in the inner loop and learns a generalized feature without severely interfering
with the historic knowledge via the conservative sparse attention in the outer
loop. Experiments on Rotated MNIST, Caltran, and Portraits datasets demonstrate
the effectiveness of our method.Comment: 7 pages, 5 figure
Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity
Electronic Health Record (EHR) data frequently exhibits sparse
characteristics, posing challenges for predictive modeling. Current direct
imputation such as matrix imputation approaches hinge on referencing analogous
rows or columns to complete raw missing data and do not differentiate between
imputed and actual values. As a result, models may inadvertently incorporate
irrelevant or deceptive information with respect to the prediction objective,
thereby compromising the efficacy of downstream performance. While some methods
strive to recalibrate or augment EHR embeddings after direct imputation, they
often mistakenly prioritize imputed features. This misprioritization can
introduce biases or inaccuracies into the model. To tackle these issues, our
work resorts to indirect imputation, where we leverage prototype
representations from similar patients to obtain a denser embedding. Recognizing
the limitation that missing features are typically treated the same as present
ones when measuring similar patients, our approach designs a feature confidence
learner module. This module is sensitive to the missing feature status,
enabling the model to better judge the reliability of each feature. Moreover,
we propose a novel patient similarity metric that takes feature confidence into
account, ensuring that evaluations are not based merely on potentially
inaccurate imputed values. Consequently, our work captures dense prototype
patient representations with feature-missing-aware calibration process.
Comprehensive experiments demonstrate that designed model surpasses established
EHR-focused models with a statistically significant improvement on MIMIC-III
and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code
is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure
the reproducibility
Cucurbitacin B inhibits proliferation, induces G2/M cycle arrest and autophagy without affecting apoptosis but enhances MTT reduction in PC12 cells
In the present study, the effect of cucurbitacin B (a natural product with anti-cancer effect) was studied on PC12 cells. It significantly reduced the cell number, changed cell morphology and inhibited colony formation while MTT results showed increased cell viability. Cucurbitacin B treatment increased activity of succinode hydrogenase. No alteration in the integrity of mem-brane, the release of lactic dehydrogenase, the mitochondrial membrane potential, and the expression of apoptotic proteins suggested that cucurbitacin B did not induce apoptosis. The cell cycle was remarkably arrested at G2/M phase. Furthermore, cucurbitacin B induced autophagy as evidence by accumulation of autophagic vacuoles and the increase of LC3II. In addition, cucurbitacin B up-regulated the expression of p-beclin-1, p-ULK1, p-Wee1, p21 and down-regulated p-mTOR, p-p70S6K, CDC25C, CDK1, Cyclin B1. In conclusion, cucurbitacin B inhibited PC12 proliferation but caused MTT pitfall. Cucurbitacin B induced G2/M cell cycle arrest, autophagy, but not the apoptosis in PC12 cells
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Omega-3 Polyunsaturated Fatty Acids Protect Neural Progenitor Cells against Oxidative Injury
The omega-3 polyunsaturated fatty acids (ω-3 PUFAs), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), derived mainly from fish oil, play important roles in brain development and neuroplasticity. Here, we reported that application of ω-3 PUFAs significantly protected mouse neural progenitor cells (NPCs) against H2O2-induced oxidative injury. We also isolated NPCs from transgenic mice expressing the Caenorhabditis elegans fat-1 gene. The fat-1 gene, which is absent in mammals, can add a double bond into an unsaturated fatty acid hydrocarbon chain and convert ω-6 to ω-3 fatty acids. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining showed that a marked decrease in apoptotic cells was found in fat-1 NPCs after oxidative injury with H2O2 as compared with wild-type NPCs. Quantitative RT-PCR and Western blot analysis demonstrated a much higher expression of nuclear factor erythroid 2-related factor 2 (Nrf2), a master transcriptional factor for antioxidant genes, in fat-1 NPCs. The results of the study provide evidence that ω-3 PUFAs resist oxidative injury to NPCs
Using Two-stage Network to Segment Kidneys and Kidney Tumors
There are many new cases of kidney cancer each year, and surgery is the most common treatment. To assist doctors in surgical planning, an accurate and automatic kidney and tumor segmentation method is helpful in the clinical practice. In this paper, we propose a deep learning framework for the segmentation of kidneys and tumors in abdominal CT images. The key idea is using a two-stage strategy. First, for each case, we use a 3d U-shape convolution network to get the localization of each kidney. Then using next 3d U-shape convolution network we obtain the precise segmentation results of each kidney. Finally, merge the results to obtain the complete segmentation. Also, we try some tricks to improve the performance
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