BackgroundPancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.MethodsIn this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.ResultsOur analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.ConclusionOur study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.
The electronic, optical and vibrational properties of B 3 N 3 H 6 have been calculated by means of rstprinciples density functional theory (DFT) calculations within the generalized gradient approximation (GGA) and the local density approximation (LDA). The calculated structural parameters of B 3 N 3 H 6 are in good agreement with experimental work. With the band structure and density of states (DOS), we have analyzed the optical properties including the complex dielectric function, refractive index, absorption, conductivity, loss function and re ectivity. By the contrast, it is found that on the (001) component and (100) component have obvious optical anisotropy. Moreover, the vibrational properties have been obtained and analyzed.
First-principles study of cubic HfO2 with O and Hf vacancies was carried out with the plane-wave ultrasoft pseudopotential technique within the generalized gradient approximation (GGA). The crystal structure of cubic HfO2 with O and Hf vacancies was optimized. The band structure, electronic-state density, complex dielectric function, refractive index, extinction coefficient, complex conductivity function, loss function, absorption coefficient and optical reflectivity in pure, O vacancy and Hf vacancy cubic HfO2 were calculated. Compared to the pure cubic HfO2, we find that the O vacancy and Hf vacancy cubic HfO2 are some changes in electronic structure. The Fermi surface extends into the conduction band of the O vacancy cubic HfO2 and the Fermi surface extends into the valence band of the Hf vacancy cubic HfO2.
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