Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening.

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Study the interaction between juglone and calf thymus DNA by spectroscopic and molecular docking techniques.

Juglone (Jug) is one of the main active substances of Cortex Juglandis Mandshuricae in a folk anti-cancer prescription. Previously, there were few studies on its interaction with DNA and mechanism of action. The present paper studied, the mechanism of action between Jug and calf thymus DNA (ctDNA) by fluorescence spectroscopy, together with ethidium bromide (EB) fluorescence probe, UV-vis absorption spectroscopy, salt effect and ctDNA melting point (Tm) experiment, resonance scattering spectroscopy and molecular docking under the simulated human physiological conditions. The experimental findings indicated that Jug quiescently quenched the fluorescence of EB-ctDNA system, characteristic absorption peak intensity of ctDNA presented a decolorization effect after the interaction of ctDNA and Jug, the interaction with ctDNA enhanced of Jug resonance scattering peak and generated new resonance scattering peak, the salt exerted less effect on the interaction between Jug and ctDNA molecules, and the interaction with Jug increased the Tm value of ctDNA by 5.0 °C The binding constant (KA) between Jug and ctDNA was 2.12 × 105 L/mol (310 K) and the number of binding sites (n) was about 1. The interaction between Jug and ctDNA was an entropically driven spontaneous and endothermic process. The results of molecular docking further showed that the naphthoquinone plane was embedded in the region between the two TA bases in the ctDNA groove, and the 5'-hydroxyl and 4-naphthoquinone groups extended to the outside of the ctDNA double helix.

Structural prediction of ultrahard semi-titanium boride (Ti2B) using the frozen-phonon method.

The dynamical stability, crystal structure, and mechanical properties of semi-titanium boride (Ti2B) are extensively studied using the pseudopotential plane-wave method within first-principles calculations. Soft transverse acoustic phonon modes are identified in the material, which has been assumed for a long time to have an Al2Cu-type structure with a I4/mcm space group. Using the frozen-phonon method, a tetragonal I4/m structure is uncovered to be energetically more stable and have similar diffraction patterns compared to the previously assumed Al2Cu-type structure. Phonon dispersion and elastic constant calculations reveal the dynamical and mechanical stability of the I4/m structure of Ti2B at ambient pressure. The calculated high elastic constants, modulus, and ideal strength show that Ti2B possesses outstanding mechanical properties.