Cervical cancer represents a major health problem among females due to its increased mortality rate. The conventional therapies are very aggressive and unsatisfactory when it comes to survival rate, especially in terminal stages, which requires the development of new treatment alternatives. With the use of nanotechnology, various chemotherapeutic drugs can be transported via nanocarriers directly to cervical cancerous cells, thus skipping the hepatic first-pass effect and decreasing the rate of chemotherapy side effects. This review comprises various drug delivery systems that were applied in cervical cancer, such as lipid-based nanocarriers, polymeric and dendrimeric nanoparticles, carbon-based nanoparticles, metallic nanoparticles, inorganic nanoparticles, micellar nanocarriers, and protein and polysaccharide nanoparticles. Nanoparticles have a great therapeutic potential by increasing the pharmacological activity, drug solubility, and bioavailability. Through their mechanisms, they highly increase the toxicity in the targeted cervical tumor cells or tissues by linking to specific ligands. In addition, a nondifferentiable model is proposed through holographic implementation in the dynamics of drug delivery dynamics. As any hologram functions as a deep learning process, the artificial intelligence can be proposed as a new analyzing method in cervical cancer.

Background/Objectives: This study evaluated the diagnostic accuracy of cervical cytology, CINtec^® (p16/Ki-67 dual staining), and PD-L1 immunohistochemistry, individually and in combination with high-risk HPV (HR-HPV) testing, for identifying histologically confirmed cervical lesions ranging from CIN1 to invasive carcinoma. Methods: We conducted a prospective cross-sectional study including 114 patients who underwent cervical cytology, CINtec^®, PD-L1 staining, HPV genotyping, and histopathologic confirmation at a tertiary clinical center between September 2024 and September 2025. Sensitivity, specificity, PPV, NPV, and ROC performance were calculated for each test across lesion categories. Multivariable logistic regression models incorporating HR-HPV status were used to assess added predictive value. Results: All tests showed poor performance for CIN1 (cytology AUC 0.488; CINtec^® 0.374; PD-L1 0.366). Diagnostic accuracy improved markedly with lesion severity. For CIN3, CINtec^® demonstrated the highest discriminative ability (AUC 0.826), with cytology and PD-L1 also performing well (AUC 0.820 and 0.753). Cytology achieved the strongest ROC performance for CIN2+ (AUC 0.937), CIN3+ (0.913), and invasive carcinoma (0.887). PD-L1 consistently showed lower accuracy across categories. Cytology + HR-HPV demonstrated the highest AUC across all lesion categories. Conclusions: Cytology and CINtec^® exhibited strong diagnostic accuracy for high-grade lesions, while PD-L1 showed limited utility as an independent screening marker. Combining cytology with HR-HPV testing enhanced predictive performance across all lesion categories. These findings support the continued use of cytology-based triage and highlight CINtec^® as a valuable adjunct for high-grade disease detection. Because this study used a high-prevalence referral cohort, specificity may be overestimated and not representative of population-based screening.

Background/Objectives: The cervicovaginal microbiome has emerged as a critical determinant of cervical health. In this study, we aimed to characterize the cervicovaginal microbiome across a spectrum of cervical health states and to identify community-level features that distinguish invasive disease from precursor states. Methods: We analyzed cervicovaginal samples of 86 patients with normal epithelium, low-grade (LSIL) and high-grade (HSIL) intraepithelial lesions, and cervical carcinoma (CCU) and available HPV genotyping. Vaginal samples were subjected to full-length 16S rRNA gene sequencing and genus-level taxonomic profiles were generated using ONT-supported workflows. Microbiome diversity and composition were assessed using Aitchison-based beta-diversity, non-parametric testing, and PERMANOVA. Differential abundance was evaluated using ANCOM-BC2 with false discovery rate correction. Disease-associated community shifts were quantified using log-ratio indices and co-occurrence network analysis. Results: Microbial diversity increased with disease severity, with cervical cancer showing the highest alpha diversity and distinct community composition. Normal samples were uniformly dominated by Lactobacillus, whereas LSIL and HSIL exhibited transitional communities with partial loss of lactobacillar dominance and increasing representation of anaerobic taxa. Cervical cancer was associated with depletion of Lactobacillus and expansion of anaerobic consortia. A Lactobacillus-to-anaerobe log-ratio declined monotonically with disease severity and robustly discriminated invasive cancer from precursor states. Microbial co-occurrence networks became progressively more structured with disease severity, transitioning to dense anaerobic networks in cervical cancer. Conclusions: Cervicovaginal microbiome signatures reflect cervical disease stage and may complement existing screening and risk stratification strategies.

Background/Objectives: Emerging evidence links vaginal microbiome dysbiosis with HPV persistence and CIN progression, but whether microbiome markers provide incremental prognostic value beyond molecular triage assays remains unclear. This study aimed to evaluate whether Lactobacillus depletion and Shannon diversity improve prediction of biopsy-confirmed CIN2+ and CIN3+ outcomes beyond CINtec and HPV-16 genotyping. Methods: This was a secondary analysis of a cross-sectional study including 82 women undergoing cervical screening or follow-up for abnormal cytology. Associations with CIN2+, CIN3+, and CINtec positivity were estimated using modified Poisson regression. Multiplicative interaction between HPV-16 and Lactobacillus depletion was formally tested. Incremental discriminative performance was assessed using area under the receiver operating characteristic curve (AUC), DeLong’s test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: HPV-16 was the strongest predictor of CIN2+ (PR = 6.08, p < 0.001) and CIN3+ (PR = 5.53, p = 0.001). A significant sub-multiplicative interaction indicated that Lactobacillus depletion carried its strongest prognostic signal in HPV-16-negative women (CIN3+: PR_interaction = 0.04, p = 0.003). Adding microbiome markers to CINtec + HPV-16 significantly improved CIN2+ discrimination (ΔAUC = 0.034, p = 0.031), driven by correct downward reclassification of non-events (NRI_non-events = 0.833). When added to HPV-16 + age, IDI for CIN2+ reached 0.092 (p = 0.004). Conclusions: Vaginal microbiome markers, particularly Lactobacillus depletion, provide statistically significant incremental discriminative value for CIN2+ beyond CINtec p16/Ki-67 dual staining and HPV-16 genotyping. Microbiome-based triage may be most impactful in HPV-16-negative women.