Indocyanine green (ICG) is a favorable fluorescence nanoprobe for its strong NIR-I fluorescence emission and good photothermal capabilities. However, the stability and tumor targeting ability of ICG is poor, which limits its further applications. To further improve the photothermal and therapeutic efficiency of ICG, bovine serum albumin (BSA) was utilized to encapsulate the ICG and the chemotherapeutic drug doxorubicin (DOX) was loaded to form the BSA@ICG-DOX theranostic nanoplatform. Methods: In this study, ICG-loaded BSA nanoparticles (NPs) and the BSA@ICG-DOX NPs were fabricated using reprecipitation methods. Next, the tumour inhibition ability and biocompatibility of the NPs were evaluated. A subcutaneous xenografted nude mice model was established and imaging guided synergetic therapy was performed with the assistance of BSA@ICG-DOX NPs under 808 nm laser irradiation. Results: The BSA@ICG NPs exhibited strong NIR-I fluorescence emission, excellent photothermal properties, biocompatibility, and tumor targeting ability. To further improve the therapeutic efficiency, the chemotherapeutic drug doxorubicin (DOX) was loaded into the BSA@ICG NPs to form the BSA@ICG-DOX theranostic nanoplatform. The BSA@ICG-DOX NPs were spherical with an average size of ~194.7 nm. The NPs had high encapsulation efficiency (DOX: 19.96% and ICG: 60.57%), and drug loading content (DOX: 0.95% and ICG: 3.03%). Next, excellent NIR-I fluorescence and low toxicity of the BSA@ICG-DOX NPs were verified. Targeted NIR-I fluorescence images were obtained after intravenous injection of the NPs into the subcutaneous cervical tumors of the mice. Conclusion:To improve the anti-tumor efficiency of the ICG@BSA NPs, the chemotherapeutic drug DOX was loaded into the BSA@ICG NPs. The NIR excitation/emission and targeted BSA@ICG-DOX NPs enables high-performance diagnosis and chemo/photothermal therapy of subcutaneous cervical tumors, providing a promising approach for further biomedical applications.

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Cervical biopsy (biopsy) is an important part of the diagnosis of cervical cancer. The artificial classification of biopsy images in diagnosis is difficult and depends on the clinical experience of pathologists. However, the classification accuracy of computerized biopsy tissue images with similar lesions is low, and the problem of incomplete experimental objects needs to be addressed. This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM). Using the LASSO algorithm for feature selection, the average optimization time was reduced by 35.87 seconds while ensuring the accuracy of the classification, and then serial fusion was performed. The EL-SVM classifier was used to identify and classify 468 biopsy tissue images, and the receiver operating characteristic (ROC) curve and error curve were used to evaluate the generalization ability of the classifier. Experiments show that the normalcervical cancer classification accuracy reached 99.64%, the normal-low-grade squamous intraepithelial lesion (LSIL) classification accuracy was 84.25%, the normal-high-grade squamous intraepithelial lesion (HSIL) classification accuracy was 87.40%, the LSIL-HSIL classification accuracy was 76.34%, the LSILcervical cancer classification accuracy was 91.88%, and the HSIL-cervical cancer classification accuracy was 81.54%.

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d’Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.

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