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1.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 41(3): 544-551, 2024 Jun 25.
Artigo em Zh | MEDLINE | ID: mdl-38932541

RESUMO

Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.


Assuntos
Algoritmos , Diagnóstico por Computador , Neoplasias Cutâneas , Humanos , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/classificação , Diagnóstico por Computador/métodos , Pele/patologia , Interpretação de Imagem Assistida por Computador/métodos
2.
Cells ; 10(12)2021 12 14.
Artigo em Inglês | MEDLINE | ID: mdl-34944031

RESUMO

Lamins are intermediate filaments that play a crucial role in sensing mechanical strain in the nucleus of cells. ß-catenin and megakaryoblastic leukemia-1 (MKL1) are critical signaling molecules that need to be translocated to the nucleus for their transcription in response to mechanical strain that induces osteogenesis. However, the exact molecular mechanism behind the translocation of these molecules has not been fully investigated. This study used 10% cyclic strain to induce osteogenesis in the murine osteoblast precursor cell line (MC3T3). The translocation of ß-catenin and MKL1 was studied by performing knockdown and overexpression of lamin A/C (LMNA). Cyclic strain increased the expression of osteogenic markers such as alkaline phosphatase (ALP), runt-related transcription factor 2 (RUNX2), and enhanced ALP staining after seven days of incubation. Resultantly, MKL1 and ß-catenin were translocated in the nucleus from the cytoplasm during the stress-induced osteogenic process. Knockdown of LMNA decreased the accumulation of MKL1 and ß-catenin in the nucleus, whereas overexpression of LMNA increased the translocation of these molecules. In conclusion, our study indicates that both MKL1 and ß-catenin molecules are dependent on the expression of LMNA during strain-induced osteogenesis.


Assuntos
Lamina Tipo A/metabolismo , Osteogênese , Estresse Mecânico , beta Catenina/metabolismo , Animais , Linhagem Celular , Subunidade alfa 1 de Fator de Ligação ao Core/metabolismo , Fluorescência , Humanos , Camundongos , Transativadores
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