Publications by authors named "Yaolin He"
Accurate preoperative grading of prostate cancer is crucial for assisted diagnosis. Multi-parametric magnetic resonance imaging (MRI) is a commonly used non-invasive approach, however, the interpretation of MRI images is still subject to significant subjectivity due to variations in physicians' expertise and experience. To achieve accurate, non-invasive, and efficient grading of prostate cancer, this paper proposes a deep learning method that adaptively fuses dual-view MRI images.
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- A study involving 758 lung adenocarcinoma patients analyzed their computed tomography images and EGFR gene tests to develop a machine learning model for predicting EGFR mutation status.
- Radiomic features were extracted from the imaging data and combined with clinical features, using methods like PCA and LASSO to optimize the feature selection process.
- Four different classifiers were used for validation, with the random forest and LGBM models showing the highest accuracy (AUC mean of 0.91 and 0.94, respectively), indicating that the selected features effectively predicted EGFR mutations in the patient group.
Goldnanoclusters (GNCs) have become a promising nanomaterial for bioimaging because of their unique optical properties and biocompatibility. In this study, lycosin-I peptide, which possesses a highly selective anticancer activity by affecting the permeability of cancer cell membrane, was firstly modified for constructing fluorescent GNCs (LGNCs) for bioimaging of tumor cells. The obtained LGNCs exhibited strong near-infrared (NIR) fluorescence, which can be further enhanced by the peptide-induced aggregation and selectively stained three cancerous cell lines over normal cell lines with low intrinsic toxicity.
View Article and Find Full Text PDFMicroRNAs (miRNAs/miRs) are highly conserved single-stranded small non-coding RNAs, which are involved in the physiological and pathological processes of breast cancer, and affect the prognosis of patients with breast cancer. The present study used the Gene Expression Omnibus (GEO)2R tool to detect miR-100 expression in breast cancer tissues obtained from GEO breast cancer-related datasets. Bioinformatics analysis revealed that miR-100 expression was downregulated in different stages, grades and lymph node metastasis stages of breast cancer, and patients with high miR-100 expression had a more favorable prognosis.
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