Lu Sun

Ultrasonic Elastography Combined with Human Papilloma Virus Detection Based on Intelligent Denoising Algorithm in Diagnosis of Cervical Intraepithelial Neoplasia

The aim of this research was to study the application of ultrasonic elastography combined with human papilloma virus (HPV) detection based on bilateral filter intelligent denoising algorithm in the diagnosis of cervical intraepithelial neoplasia (CIN) and provide a theoretical basis for clinical diagnosis and treatment of CIN. In this study, 100 patients with cervical lesions were selected as research objects and randomly divided into control group and experimental group, with 50 cases in each group. Patients in control group and experimental group were diagnosed by ultrasonic elastography combined with HPV detection. The experimental group used the optimized image map of bilateral filter intelligent denoising algorithm for denoising and optimization, while the control group did not use optimization, and the differences between them were analyzed and compared. The diagnostic effects of the two groups were compared. As a result, the three accuracy rates of the experimental group were 95%, 95%, and 98%, respectively; the three sensitivity rates were 96%, 92%, and 94%, respectively; and the three specificity rates were 99%, 97%, and 98%, respectively. In the control group, the three accuracy rates were 84%, 86%, and 84%, respectively; the three sensitivity rates were 88%, 84%, and 86%, respectively; and the three specificity rates were 81%, 83%, and 88%, respectively. The accuracy, sensitivity, and specificity of experiment group were significantly higher than those of control group, and the difference was statistically significant ( P < 0.05 ). In summary, the bilateral filter intelligent denoising algorithm has a good denoising effect on the ultrasonic elastography. The ultrasonic image processed by the algorithm combined with HPV detection has a better diagnosis of CIN.

Prediction of Immunotherapy Response and Prognostic Outcomes for Patients With Ovarian Cancer Using PANoptosis‐Related Genes

BackgroundOvarian cancer (OC) is a lethal malignancy often diagnosed at a late stage with frequent recurrence and immunotherapy resistance. PANoptosis is a novel programmed cell death regulating tumors and immunity. We constructed a prognostic model based on PANoptosis‐related genes (PRGs) and evaluated its value for predicting immunotherapy response and survival in OC.MethodsPRGs linked to OC prognosis were identified from public databases, followed by using the STRING database to develop a protein–protein interaction (PPI) network. The LASSO and multivariate Cox regression analyses were used to construct a risk model, and its predictive value was verified by survival analysis, receiver operator characteristic (ROC) curve, and nomogram. Next, we analyzed the immune microenvironment by combining CIBERSORT, MCP‐counter, and ssGSEA algorithms and assessed the response of patients in different risk groups to immunotherapy using TIDE with immune phenotype score (IPS) methods. GSEA was performed to evaluate the activation status of biological pathways between patients in different risk groups. Finally, we verified the expression and potential biological functions of the key genes using quantitative reverse transcription‐PCR (qRT‐PCR), CCK‐8, scratch, and transwell assays.ResultsA PANoptosis‐related risk model for OC was constructed based on eight genes (PIK3CG, CAMK2A, CD38, NFKB1, PSMA4, PSMA8, PSMB1, and STAT4). The model could accurately evaluate the prognostic outcomes for OC patients, showing a high stability across different datasets. High‐risk patients had lower immune cell infiltration, elevated TIDE, and reduced IPS, which suggested weaker immunotherapy responsiveness and therefore a worse prognosis. In addition, pathway analysis showed that the high‐risk group was mainly enriched in tumor progression–related pathways. In vitro, PIK3CG, CAMK2A, NFKB1, PSMA4, and PSMB1 were upregulated in OC cell lines, and knockdown of PIK3CG notably suppressed the proliferative, migratory, and invasive capabilities of OC cells.ConclusionThe PRG model established in this study may contribute to the assessment of immunotherapeutic response and prognosis for OC patients.