Publications by authors named "Siwen Yin"

Article Synopsis
  • The study aimed to create a machine learning model using CT radiomics to predict HER2 status in bladder cancer before surgery, validated across multiple centers.* -
  • It involved 207 patients in a retrospective analysis, using various ML techniques, with the random forest model showing the best predictive performance (AUC of 0.815 and accuracy of 75.5%).* -
  • The model highlights texture features from CT images as key predictors and offers a noninvasive method that could enhance clinical decision-making for bladder cancer treatment.*
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Article Synopsis
  • * Utilizing CT images, the researchers created a ResNet 50 model that generated a DL computed risk score (DLCR) which successfully stratified patients by risk levels and outperformed other predictive models, including the Rad-Score and traditional clinical models.
  • * With data from 707 patients, the DLCR demonstrated effective risk assessment across various subgroups and identified patients at higher risk of recurrence, highlighting its potential for improving clinical decision-making prior to surgery.
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Article Synopsis
  • An interactive, non-invasive AI system was developed to predict the malignancy risk associated with cystic renal lesions (CRLs) by utilizing a combination of 3D segmentation and classification models.
  • The study analyzed data from 715 patients and used advanced techniques like a 3D-ResNet50 network and gated recurrent unit (GRU) for feature extraction and classification.
  • Results showed outstanding performance in distinguishing benign from malignant CRLs, achieving high accuracy and sensitivity, thus enhancing clinical decision-making in diagnosing these lesions.
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Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC.

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Coactivator-associated arginine methyltransferase 1 (CARM1), a type I protein arginine methyltransferase (PRMT), has been widely reported to catalyze arginine methylation of histone and non-histone substrates, which is closely associated with the occurrence and progression of cancer. Recently, accumulating studies have demonstrated the oncogenic role of CARM1 in many types of human cancers. More importantly, CARM1 has been emerging as an attractive therapeutic target for discovery of new candidate anti-tumor drugs.

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The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed serious threats to global health and economy and calls for the development of safe treatments and effective vaccines. The receptor-binding domain in the spike protein (S) of SARS-CoV-2 is responsible for its binding to angiotensin-converting enzyme 2 (ACE2) receptor. It contains multiple dominant neutralizing epitopes and serves as an important antigen for the development of COVID-19 vaccines.

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Serine/threonine protein phosphatase 2A (PP2A) is a protein that has a wide range of biological functions. As prostate cancer progresses from hormone-sensitive prostate cancer to castration-resistant prostate cancer (CRPC), the expression level of PP2A has been found to decrease. The present study aimed to determine the roles that PP2A may play in prostate cancer and its association with the downstream factor, X-linked inhibitor of apoptosis (XIAP).

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The clear cell renal cell carcinoma (ccRCC) is not only a malignant disease but also an energy metabolic disease, we aimed to identify a novel prognostic model based on glycolysis-related long non-coding RNA (lncRNAs) and explore its mechanisms. With the use of Pearson correlation analysis between the glycolysis-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas (TCGA) dataset, we identified three glycolysis-related lncRNAs and successfully constructed a prognostic model based on their expression. The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated by univariate and multivariate Cox analyses, Kaplan-Meier survival analysis, and principal component analysis (PCA).

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Background: Aberrant autophagy and preternatural elevated glycolysis are prevalent in bladder cancer (BLCA) and are both related to malignant progression. However, the regulatory relationship between autophagy and glycolytic metabolism remains largely unknown. We imitated starvation conditions in the tumour microenvironment and found significantly increased levels of autophagy and aerobic glycolysis, which both regulated the progression of BLCA cells.

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Sphingosine‑1‑phosphate (S1P) serves an important role in various physiological and pathophysiological processes, including the regulation of cell apoptosis, proliferation and survival. Sphingosine kinase 1 (SPHK1) is a lipid kinase that phosphorylates sphingosine to generate S1P. S1P has been proven to be positively correlated with chemotherapy resistance in breast cancer, colorectal carcinoma and non‑small cell lung cancer.

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