RESUMEN
Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.
Asunto(s)
Nefropatías Diabéticas , Redes Neurales de la Computación , Síndrome de Sjögren , Espectrometría Raman , Humanos , Espectrometría Raman/métodos , Nefropatías Diabéticas/diagnóstico , Síndrome de Sjögren/diagnóstico , FemeninoRESUMEN
Metastasis, a major cause of cancer-related mortality worldwide, frequently occurs early in the diagnosis of lung adenocarcinoma (LUAD). However, the precise molecular mechanisms governing the aggressive metastatic behavior of LUAD remain incompletely understood. In this study, we present compelling evidence indicating that the long noncoding RNA linc01703 is significantly downregulated in metastatic lung cancer cells. Intriguingly, in vivo experiments revealed that Linc01703 exerted a profound inhibitory effect on lung cancer metastasis without discernible impact on the in vitro proliferation or invasion capacities of LUAD cells. Mechanistically, Linc01703 enhanced the interaction between Rab27a, SYTL1, and CD81, consequently promoting the secretion of CD81+ exosomes. These exosomes, in turn, suppressed the infiltration of immune cells within the tumor microenvironment, thereby impeding LUAD metastasis. Importantly, our analysis of lung cancer tissues revealed a correlation between reduced CD81 expression and an unfavorable patient prognosis. Collectively, our findings suggest that Linc01703 functions as a metastasis suppressor by facilitating the secretion of CD81+ exosomes through the formation of the Rab27a/SYTL1/CD81 complex.