Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
421
result(s) for
"Zhang, Liyi"
Sort by:
PERFECT trial results: Combining neoadjuvant chemoradiotherapy with atezolizumab is feasible in resectable esophageal adenocarcinoma
Previous studies have shown that CROSS (carboplatin, paclitaxel and concurrent radiotherapy)‐based neoadjuvant chemoradiotherapy can extend the median survival of EAC patients with surgery alone from 27.1 months to 43.2 months. 4 However, approximately 50% of all patients with resectable EAC experience disease progression resulting in death after <5 years of clinical stability. Expression level of programmed death‐ligand 1 (PD‐L1), composition of tumor immune microenvironment and microbiome, and tumor mutational burden are known factors that affect the clinical efficacy of ICI. 6 Both preclinical and clinical evidence indicate that traditional tumor therapies can change the tumor immune microenvironment of EAC. 7 For example, taxane‐based chemotherapy can evoke T cell response 8 and radiotherapy can elevate PD‐L1 expression in EAC. 9 However, the feasibility and efficacy of neoadjuvant chemoradiotherapy plus ICI for resectable EAC remains to be determined. Compared to the responders of neoadjuvant chemoradiotherapy, the responders in the PERFECT trial had a numerically longer overall survival and progression‐free survival, but the patients treated with neoadjuvant chemoradiation combined with atezolizumab had similar overall survival and progression‐free survival as those who were treated with neoadjuvant chemoradiotherapy alone. [...]the six‐gene IFNγ‐signature was associated with the treatment response to neoadjuvant ICI therapy.
Journal Article
Circular RNA: The main regulator of energy metabolic reprogramming in cancer cells
The alterations in cancer‐associated metabolic enzymes and metabolites have exerted far‐reaching influence on cancer genetics/epigenetics, cancer development, and therapeutic resistance. [...]cancer‐associated energy metabolism pathways are potential targets for cancer therapy. [...]with the colon cancer cell lines mentioned above, circACC1 exogenous expression resulted in decreased lipid accumulation. [...]lipidomic analyses showed that the constituent metabolite levels were significantly increased after circACC1 silencing. [...]bevacizumab can induce AMPK activation leading to glucose depletion and ATP depletion in cancers. [...]the role of anti‐cancer therapy in cancer metabolism reprogramming needs to be considered when treating cancers.
Journal Article
A high-quality apple genome assembly reveals the association of a retrotransposon and red fruit colour
A complete and accurate genome sequence provides a fundamental tool for functional genomics and DNA-informed breeding. Here, we assemble a high-quality genome (contig N50 of 6.99 Mb) of the apple anther-derived homozygous line HFTH1, including 22 telomere sequences, using a combination of PacBio single-molecule real-time (SMRT) sequencing, chromosome conformation capture (Hi-C) sequencing, and optical mapping. In comparison to the Golden Delicious reference genome, we identify 18,047 deletions, 12,101 insertions and 14 large inversions. We reveal that these extensive genomic variations are largely attributable to activity of transposable elements. Interestingly, we find that a long terminal repeat (LTR) retrotransposon insertion upstream of
MdMYB1
, a core transcriptional activator of anthocyanin biosynthesis, is associated with red-skinned phenotype. This finding provides insights into the molecular mechanisms underlying red fruit coloration, and highlights the utility of this high-quality genome assembly in deciphering agriculturally important trait in apple.
Existing apple genome assemblies all derive from Golden Delicious. Here, the authors combine different sequencing technologies to assemble a high quality genome of an anther-derived homozygous genotype HFTH1 and find the association of a retrotransposon and red fruit colour.
Journal Article
Most Significant Impact on Consumer Engagement: An Analytical Framework for the Multimodal Content of Short Video Advertisements
The increasing popularity of short videos has presented sellers with fresh opportunities to craft video advertisements that incorporate diverse modal information, with each modality potentially having a different influence on consumer engagement. Understanding which information is most important in attracting consumers can provide theoretical support to researchers. However, the dimensionality of the multimodal features of short video advertisements is often higher than the available data, posing specific difficulties in data analysis. Therefore, designing a multimodal analysis framework is needed to comprehensively extract and reduce the dimensionality of the different modal features of short video advertisements, thus analyzing which modal features are more important for consumer engagement. In this study, we chose TikTok as the research subject, and employed deep learning and machine learning techniques to extract features from short video advertisements, encompassing visual, acoustic, title, and speech text features. Subsequently, we introduced a method based on mixed-regularization sparse representation to select variables. Ultimately, we utilized multiblock partial least squares regression to regress the selected variables alongside additional scalar variables to calculate the block importance. The empirical analysis results indicate that visual and speech text features are the key factors influencing consumer engagement, providing theoretical support for subsequent research and offering practical insights for marketers.
Journal Article
Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples
Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result. Aiming at this critical problem, we propose a novel model of spectral-spatial feature extraction and weighted fusion guided by superpixels. It aims to thoroughly “squeeze” and utilize the untapped spectral and spatial features contained in hyperspectral images from multiple angles and stages. Firstly, with the guidance of superpixels, we represent the hyperspectral image in the form of latent features and use the multi-band priority criterion to select the final discriminant features. Secondly, we design a pixel-based CNN and a two-scale superpixel-based GCN classification framework for weighted feature fusion. Compared with several excellent band selection methods, the superb performance of our feature extraction module is verified. In addition, under the condition of only five training samples for each class, we conducted comparative experiments with several of the state-of-the-art classification methods and verified the excellent performance of our method on three widely used data sets.
Journal Article
Novel stimulation strategy with autologous tumor cells to generate T cell receptor‐engineered T cells in esophageal squamous cell carcinoma
Adoptive cellular therapy (ACT, also known as cellular immunotherapy) is a form of immunotherapy that uses the autologous tumor‐cognate lymphocytes to eliminate the tumor. Since genetically‐modified T cells have been shown to have significant antitumor effects in certain hematological malignancies, this has led to an upsurge in research on ACT for solid tumors. Autologous tumor cells (ATCs), which express various tumor antigens, may be the current best stimulants for activating tumor‐reactive T cells. [...]previous studies have found that CD137, a specific biomarker of activated T cells, could be used to isolate and enrich tumor‐reactive T cells. In addition to ESCC, this treatment strategy may be applicable to other cancers, especially to cancer patients without prior knowledge of specific epitopes.
Journal Article
Upregulation of programmed cell death ligand 1 promotes resistance response in non‐small‐cell lung cancer patients treated with neo‐adjuvant chemotherapy
To assess the association of the programmed cell death ligand 1 (PD‐L1) with cisplatin‐based neo‐adjuvant chemotherapy (NAC) response, we investigated the level of PD‐L1 and found increased PD‐L1 expression in chemo‐resistant tumors compared with chemo‐sensitive tumors according to RNA‐Seq analysis. In a cohort of 92 patients with NAC, the positive staining of PD‐L1 was correlated with TNM stage, lower sensitive‐response rates and shorter overall survival rates. In another 30 paired tumor specimens pre‐ and post‐chemotherapy, the patients with high PD‐L1 expression post‐chemotherapy had a worse outcome and higher stable disease rate. CD8+ tumor‐infiltrating lymphocytes were found to be related to chemosensitive response and better prognosis and negative PD‐L1 expression. Furthermore, in two patient‐derived xenograft models and cell lines A549 and PC‐9, cisplatin upregulated PD‐L1 expression, and the enhancement of PD‐L1 in cancer cell lines was in a drug dose‐dependent manner. Moreover, the depletion of PD‐L1 significantly reduced cisplatin resistance. When phosphatidylinositol 3‐kinase/protein kinase B signaling was inhibited by corresponding inhibitors, PD‐L1 expression was downregulated and apoptosis was upregulated in the cisplatin‐treated cancer cells. These results suggest that the upregulation of PD‐L1 promotes a resistance response in lung cancer cells that might be through activation of the phosphatidylinositol 3‐kinase/protein kinase B pathway and suppression of tumor‐infiltrating lymphocytes. The high expression of PD‐L1 after NAC could be an indication of therapeutic resistance and poor prognosis in patients with non‐small‐cell lung cancer. We explored PD‐L1 was induced in NSCLC patients with chemo‐resistance. PD‐L1 expression was associated with the poor prognosis for NSCLC patients. PD‐L1 expression changed before and after NAC for NSCLC tissues.
Journal Article
E2112—Does a negative phase III trial of endocrine therapy plus histone deacetylase inhibitor in hormone receptor‐positive advanced breast cancer represent a death knell?
According to the Global cancer statistics, breast cancer tops the list of the most common cancers in 2020. Epigenetic modifications that can alter gene expression are one of the main causes of breast cancer progression and endocrine therapy resistance. 7 Previous studies have shown that histone deacetylase (HDAC) inhibitors, an epigenetic modifier, can reverse endocrine therapy resistance. 8 Entinostat is a selective class I HDAC inhibitor undergoing clinical trials for the treatment of multiple solid tumors. [...]other strengths include a strong supportive preclinical/clinical rationale, coprimary objectives of progression-free and overall survival, and a large cohort of patients recruited via the National Cancer Institute National Clinical Trials Network.
Journal Article
Consistency regularization for few shot multivariate time series forecasting
Multivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. Adequate training data are crucial for ensuring the model’s generalizability. However, obtaining a sufficient amount of high-quality labeled data is often a challenge in practical applications. To address this problem, we propose an algorithm that combines time-frequency mining with consistency regularization for multivariate time-series forecasting. First, we increase the amount of data by employing weak perturbation techniques, expanding the original data space. Additionally, we ensure that the model maintains stable predictions under variations in the input data consistency regularization. This approach provides the model with richer training samples, enabling it to learn and understand more comprehensively the different variations patterns and features in the data. Second, we used two complementary dependency extractors to adaptively capture interactions between variables from different levels of frequency patterns. This improves the model’s ability to perceive and process different frequency information. Finally, we validate the generalization and effectiveness of the proposed method on five real-world datasets. Extensive experimental results demonstrate that our method outperforms existing methods in terms of performance.
Journal Article