HPV58 E7 Protein Expression Profile in Cervical Cancer and CIN with Immunohistochemistry

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

Characteristics of Vaginal Microbiome in Reproductive-Age Females with HPV Infection in Xinjiang, China

OBJECTIVE

We investigated the characteristics of vaginal microbiome in reproductive-age females with HPV infection in Xinjiang, China.

METHODS

A total of 135 females of reproductive age were enrolled. There were 43 healthy HPV-negative females in control group (N group), 58 HPV-positive females in nonlesion group (P1 group), and 34 HPV-positive females in low-grade squamous intraepithelial lesion group (P2 group). DNA was extracted from the vaginal secretions, and V3-V4 regions of bacterial 16S rDNA were amplified and sequenced by NovaSeq. QIIME2 and R software were used to perform diversity analysis of bacteria. PICRUSt2 was used to predict the function of the vaginal microbiota.

RESULTS

Lactobacillus was the main genus of vaginal microbiota in asymptomatic reproductive-age females with or without HPV in Xinjiang. The diversity of vaginal microbiota in the P1 group was significantly higher than that in the N group, and the proportion of Gardnerella increased significantly. The vaginal microbiota structure of the P2 group was different from the N group, characterized by the decrease of Lactobacillus crispatus and the increase of Shuttleworthia. The function of the inordinate microbiome may play a role in accelerating HPV replication and integration.

CONCLUSION

The structure of vaginal microbiota alters under persistent HPV infection in asymptomatic females of reproductive age in Xinjiang. The Gardnerella increase is associated with increased susceptibility to HPV infection, and Lactobacillus iners predominance and Shuttleworthia presence may be a signature of HPV infection with low-grade squamous intraepithelial lesion.