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Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future. [ABSTRACT FROM AUTHOR]

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor–acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen–antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π − π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21–1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection. [ABSTRACT FROM AUTHOR]

Herein, a novel, convenient, and highly selective electrochemical sensor for determination of nitrite based on a polythiophene-derivative film-modified glassy carbon electrode (GCE) was established. In this work, 2,5-di-thiophen-3-yl-thiazolo[5,4-d]thiazole (DTT), a novel thiophene derivative, was synthesized and used to form an original and excellent polymer film (PolyDTTF) on GCE through one-step electropolymerization for the first time. The modified electrodes were characterized by electron microscopy (SEM), Fourier transform infra-red spectroscopy (FT-IR), UV-visible spectra, Raman spectroscopy, and electrochemical technologies, in which the electrochemical sensor based on PolyDTTF was successfully constructed and demonstrated a significant electrocatalytic effect on nitrite. The influence of pH value, electrodeposition scanning times, scanning speed, and potential on the electrochemical behavior of nitrite were investigated in detail. Furthermore, the nitrite sensor exhibits excellent responses proportional to nitrite concentrations (R2 = 0.9972) over a concentration range of 5.5 × 10−9 ~ 3.5 × 10−5 M with a detection limit (LOD) of 2 nM, and has extremely good anti-interference ability for nitrite detection. This proposed sensor can be used to detect nitrite in actual samples, opening the possibility for applications in the food industry and environmental analysis. [ABSTRACT FROM AUTHOR]

• A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples of cervical carcinoma. • We used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade squamous intraepithelial Lesion, High-grade squamous intraepithelial Lesion, well differentiated squamous cell carcinoma, moderately differentiated squamous cell carcinoma, poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. • The experimental results indicate that the model established in this study is easy to operate and has high accuracy. • It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer. Cervical cancer is one of the most common malignant tumors among women, and its pathological change is a relatively slow process. If it can be detected in time and treated properly, it can effectively reduce the incidence rate and mortality rate of cervical cancer, so the early screening of cervical cancer is particularly critical and significant. In this paper, we used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade Squamous Intraepithelial Lesion, High-grade Squamous Intraepithelial Lesion, Well differentiated squamous cell carcinoma, Moderately differentiated squamous cell carcinoma, Poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples. The attention mechanism Efficient Channel Attention Networks module and Squeeze-and-Excitation Networks module were combined with the established one-dimensional convolutional hierarchical network model, and the results showed that the combined model had better diagnostic performance. The average accuracy, F1, and AUC of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model after 5-fold cross validations could reach 96.49%±2.12%, 0.97±0.03, and 0.98±0.02, respectively, which were 1.58%, 0.0140, and 0.008 higher than those of hierarchical network. The recall rate of the Principal Component Analysis-Efficient Channel Attention-hierarchical network model was as high as 96.78%±2.85%, which is 1.47% higher than hierarchical network. Compared with the classification results of traditional CNN and ResNet for seven types of cervical cancer staging, the accuracy of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model is 3.33% and 11.05% higher, respectively. The experimental results indicate that the model established in this study is easy to operate and has high accuracy. It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer. [ABSTRACT FROM AUTHOR]

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. [ABSTRACT FROM AUTHOR]