Search Results (1,004)

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways. Full article

Background: Cervical cancer, largely attributable to persistent infection with high-risk human papillomavirus (HPV), remains a major public health burden worldwide, including in Kazakhstan, where limited screening coverage and low public awareness contribute to substantial incidence and mortality. This study evaluated the cost-effectiveness and epidemiological impact of a nationwide HPV vaccination programme for 10-year-old girls in Kazakhstan using the quadrivalent Gardasil-4 vaccine. Methods: A 10-year modelling analysis (2025–2035) was conducted using the World Health Organization (WHO)-endorsed Universal Vaccination Impact and Cost-Effectiveness Assessment (UNIVAC) tool adapted to Kazakhstan-specific epidemiological and economic parameters. Vaccination coverage was projected at 98.0% for the first dose and 96.5% for the second dose. Incremental cost-effectiveness ratios (ICERs) and disability-adjusted life years (DALYs) averted were estimated from governmental and societal perspectives. Sensitivity analyses assessed uncertainty in vaccine coverage, vaccine costs, and epidemiological inputs. Results: Over the 10-year period, the vaccination programme was projected to reduce HPV-related disease cases by 68.2% (from 112,198 to 35,628) and deaths by 68.3% (from 15,921 to 5056), while averting 67,445 DALYs. The ICER was estimated at US$ 533 per DALY averted from the governmental perspective and US$ 1169 from the societal perspective. Projected healthcare cost savings reached US$ 42.8 million, driven largely by reductions in 21,748 hospitalisations and 13,706 outpatient visits. These findings remained robust in probabilistic sensitivity analysis, with the probability of cost-effectiveness increasing as the willingness-to-pay threshold rose. Conclusions: UNIVAC-based modelling suggests that introduction of a national HPV vaccination programme for 10-year-old girls in Kazakhstan using Gardasil-4 could substantially reduce cervical cancer burden and related mortality while generating considerable healthcare savings. These findings support the cost-effectiveness of nationwide HPV vaccination in Kazakhstan. Full article

Cervical cancer, primarily caused by human papillomavirus (HPV) infection, remains a major cause of cancer-related mortality among women worldwide, making early detection through cytological screening essential. However, manual analysis of cytology images is time-consuming and subject to variability, while recent deep learning approaches, particularly transformer-based architectures, often require high computational resources, limiting their use in resource-constrained settings. In this study, we propose ECAViTNet, a lightweight hybrid CNN–Transformer architecture for cervical cytology image classification that balances accuracy and efficiency. The model integrates Efficient Channel Attention modules for adaptive feature recalibration, residual connections for stable optimization, MobileViT blocks to capture local and global dependencies, and gated multi-scale fusion mechanisms to enhance feature representation, along with progressive downsampling and skip connections to preserve fine-grained details. The proposed approach was evaluated on the SIPaKMeD dataset, achieving a test accuracy of 96.42% with only 982,491 parameters and a macro-average F1-score of 0.96 and a weighted-average F1-score of 0.96, while maintaining balanced class-wise performance and reduced computational cost compared to recent methods. These results demonstrate that ECAViTNet is an effective and efficient solution for automated cervical cytology classification, with strong potential for deployment in mobile health systems and low-resource clinical environments. Full article

Cervical cancer screening with high-risk human papillomavirus (HR-HPV) testing requires effective triage of HPV-positive women. Koilocytosis is a classic cytopathic effect of HPV infection, but its clinical significance in low-grade squamous intraepithelial lesions (LSILs) remains unclear. We retrospectively evaluated 157 HPV-positive women with LSIL cytology and follow-up data, including 140 women with concurrent biopsy results. Koilocytes were identified in 93/157 cases (59.2%) and were less frequent in HPV16/18-positive cases. Cervical intraepithelial neoplasia ≥ grade 2 (CIN2+) was detected in 9/84 koilocyte-positive cases (10.7%) and 16/56 koilocyte-negative cases (28.6%), whereas non-CIN findings were more common in koilocyte-positive cases. Koilocyte-positive cases also showed a longer time to regression from LSIL to negative for intraepithelial lesions or malignancy. These findings suggest that koilocytosis mainly reflects productive HPV infection and has limited utility for predicting CIN2+ in HPV-based screening triage. Excluding koilocytosis-driven low-grade cytological changes from triage positivity criteria may improve specificity and positive predictive value, supporting higher triage thresholds. Full article

Background: Human papillomavirus (HPV) is a serious infection which is primarily transmitted through sexual intercourse. Almost 100% of cervical cancers are caused by HPV. Limited awareness of HPV leads to delayed cancer diagnoses, significantly increasing mortality and morbidity rates. Aim: The purpose of this study was to develop strategies to increase awareness of human papillomavirus screening and vaccination among women in Limpopo Province, South Africa. Setting: This study was carried out in the Vhembe District of the Thulamela Municipality of Limpopo Province. Methods: The E-Delphi method was used, and the researcher used a quantitative approach. A total population of 15 nursing managers was part of the study. Questionnaires were used to collect data. Data were analysed using the statistical package for the social sciences version 26. Results: In Round 1, 8 (53.3%) of the 15 participants strongly supported the strategy of updating women with the most recently revised HPV screening guidelines. In Round 2, consensus was achieved, with 14 (93.3%) of the participants strongly agreeing that the development of teaching programmes in healthcare facilities is necessary. This indicates a strong convergence of expert opinion on the importance of structured educational interventions to support the implementation of the strategy. The consensus in this study was defined as ≥70% agreement between participants on each item. Conclusions: The lack of awareness of HPV is concerning because early detection and treatment can prevent serious health problems. The study used the E-Delphi method to assess the effectiveness of strategies to increase awareness of HPV screening and vaccination in women. Contribution: Health policy initiatives may improve public awareness of HPV and vaccination, especially by focusing on educating nurses, which could improve women’s awareness and encourage HPV screening and vaccination. Full article

Background: HPV16 is the most prevalent high-risk genotype associated with cervical cancer, yet the genetic variability and immune potential of the replication protein E1 are less characterized in asymptomatic infections. We assessed HPV16 E1 diversity and predicted conserved T-cell epitopes. Methods: We conducted a descriptive cross-sectional study of cervical samples from women undergoing HPV screening. HPV was detected with universal primers and then HPV16-specific PCR. Thirty HPV16-positive samples underwent full-length E1 amplification and nanopore amplicon sequencing. Variability and phylogeny were analyzed with Clustal Omega and MEGA (maximum likelihood). MHC class I and II epitopes were predicted with the IEDB using HLA alleles representative of South American populations and evaluated for conservation, toxicity, allergenicity, and population coverage. Results: Mutations were detected in 14/30 samples, while 16 sequences matched the reference (GenBank: NC_001526.3). European lineages (A1–A3) predominated, with one sequence in the Asian-American lineage D. Seven highly conserved MHC I epitopes and 37 conserved MHC II epitopes were identified. Epitopes mapped to multiple regions across the E1 sequence. Predicted global coverage was 94.38% for MHC I, 83.75% for MHC II, and 99.09% combined. Conclusions: HPV16 E1 is highly conserved and contains candidate T-cell targets with broad predicted coverage, supporting evaluation for future vaccine or immunotherapy strategies. Full article

This narrative review examines current evidence on the management of cervical intraepithelial neoplasia (CIN) during pregnancy, a clinical scenario requiring a balance between maternal oncologic safety and fetal well-being. A qualitative synthesis was conducted using major databases (PubMed, Google Scholar, Cochrane Library, and ScienceDirect), focusing on studies published between 2019 and 2025, supplemented by key earlier publications. A total of 37 studies and international guidelines were included. Available evidence suggests that pregnancy does not substantially alter the natural history of human papillomavirus infection. Although CIN is generally associated with low progression rates and notable postpartum regression, findings remain heterogeneous and largely based on retrospective data. Colposcopy is essential for excluding invasive disease, while invasive procedures are reserved for suspected malignancy. Most guidelines support conservative management with surveillance during pregnancy and postpartum reassessment. HPV testing offers higher sensitivity, whereas cytology remains widely used due to its specificity. The impact of delivery mode remains inconclusive. Overall, current recommendations rely on limited evidence and should be interpreted with caution. Further prospective studies are needed to refine pregnancy-specific management strategies. Full article

Background: Proprioceptive input from cervical muscles plays a vital role in postural control and coordinated movement. A defect in cervical proprioception, known as joint position error (JPE), is often associated with neck pain. However, the presence of JPE in asymptomatic individuals with varying severities of text neck or forward head posture (FHP) remains underexplored. This study aimed to investigate the presence and correlation of JPE in healthy female university students with different levels of text neck severity. Methods: A cross-sectional study was conducted on 68 female students aged 18–25 years. Participants were categorized into four groups (normal posture, mild, moderate, and severe text neck) through visual observational assessment. JPE was measured in sitting and standing positions using three tools: an inclinometer, a smartphone-based goniometer, and a laser beam. Correlational and comparative analyses were conducted across all groups and measurement tools. Results: The results demonstrated the presence of JPE in all groups, regardless of text neck severity, with no statistically significant differences between them. Additionally, correlation analysis showed no or weak non-significant relationships between JPE and text neck severity across all measurement tools. Conclusions: Cervical JPE may be present in young adults regardless of their text neck posture, and no significant correlation was found between the severity of text neck and proprioceptive deficit. These findings suggest that text neck alone may not be a predictive factor for impaired cervical proprioception in asymptomatic individuals. Early screening remains important, but further research is needed to understand contributing factors and long-term implications. Full article

Background: Cervical cancer remains a major public health problem, particularly in low- and middle-income countries where screening coverage is suboptimal. Human papillomavirus (HPV) self-sampling has emerged as a promising strategy to improve participation in cervical cancer screening programs. This study aimed to evaluate attitudes, preferences, and acceptability regarding HPV self-sampling among Turkish women. Methods: This cross-sectional study was conducted at a tertiary care center in Türkiye between January and June 2025. A total of 302 women aged 30–65 years who had previously undergone clinician-collected cervical sampling were included. Participants completed a structured questionnaire assessing their preferences, concerns, and attitudes toward HPV self-sampling. Statistical analyses were performed using SPSS version 29. Results: Overall, 73.6% of participants supported the inclusion of HPV self-sampling in the national screening program. However, 49% preferred clinician-collected sampling, while 41.7% preferred self-sampling. Approximately 34.4% reported anxiety related to performing self-sampling. Higher educational level was significantly associated with increased acceptance (p = 0.002), whereas age was not significantly associated. Conclusions: HPV self-sampling appears to be an acceptable alternative among previously screened women and may help address certain barriers to participation. Further studies are needed to evaluate its real-world implementation and potential role in supporting participation in broader populations. Full article

Asymptomatic pharyngeal Neisseria meningitidis (Nm) carriage is seen in >30% of sexually transmitted infection (STI) clinic attendees. With increasing reports of Nm urethritis and STI-related invasive disease outbreaks, longitudinal assessment and genomic characterization of Nm among patients at an urban STI clinic population in Columbus, Ohio, was undertaken. This study determined the genomic basis of oropharyngeal, urogenital and rectal Nm isolates carried by patients presenting for care. Cultures using media selective for Neisseria spp., Nm-specific PCR screening of colonies with oxidase-positive Gram-negative diplococci, PCR-based genogrouping and whole-genome sequencing of confirmed Nm were performed. Overall, genomic data of 453 oropharyngeal, 10 urethral, 5 rectal and 1 cervical Nm isolate were obtained between January 2018 and December 2019. Among oropharyngeal Nm isolates, genogrouping identified 37.7% as cnl (capsule null locus), 28% B, 13.5% E, 10.8% Z, 2.6% C and 2.6% Y. However, the cps locus was inactivated in ≥80% of isolates with specified genogroups. The major clonal complexes (ccs) were cc53, cc32, cc41/44, cc1157, cc198 and cc4821. Two oropharyngeal, one rectal and three urethral isolates belonged to the ST-11 Nm urethritis clade (NmUC). Group Y ST-1466 Nm, recently linked to global urogenital and systemic infections, was also identified in the oropharynx. Full article

Background/Objectives: The rapid expansion of targeted therapies has reshaped oncology by exploiting ligand-receptor interactions (LRI) to improve treatment specificity and patient outcomes. However, the data describing these ligands remain fragmented across multiple sources, limiting accessibility for researchers and clinicians. To address this gap, we developed the OncoSolidDB, the first curated and oncology-focused bioinformatics database dedicated to ligands associated with solid malignancies. Methods: OncoSolidDB integrates and harmonizes data from reliable repositories, including ChEMBL, DrugBank and the Anti-Cancer Fund, consolidating curated structural, chemical, pharmacological, and clinical annotations along with standardized identifiers. Results: The database currently encompasses 243 ligands across 15 major solid tumor types including breast, lung, colorectal, melanoma, prostate, gastric, ovarian, cervical, bladder, esophageal, head and neck, thyroid, pancreatic, renal and liver cancer (Hepatocellular Carcinoma, HCC). Each entry is annotated by standardized identifiers (DrugBank, ChEMBL), approval year, chemical structures (SMILES strings, 2D images), and downloadable protein structure files (PDB format). Temporal coverage spans 1953–2025, enabling exploration of historical trends in oncology drug approvals. The database content is suitable for bioinformatics analysis, molecular docking, virtual screening, ligand-based modeling, and drug repurposing studies. Outputs are available through a freely accessible web interface that supports search browsing by cancer type. Conclusions: By consolidating oncology-specific ligand data into a single, structured platform, OncoSolidDB offers a valuable resource for advancing drug discovery, repurposing strategies, and the rational design of next-generation targeted therapies for solid tumors. OncoSolidDB is accessible via our Bioinformatics Research PortalEinstein. Full article

Background: Artificial intelligence (AI) is reshaping oncology at every stage of the cancer care pathway, from population-level screening through molecular diagnosis, treatment planning, and post-treatment surveillance. Despite an exponential growth in AI oncology publications exceeding 5000 peer-reviewed studies annually, a critical and persistent gap separates demonstrated algorithmic performance from genuine patient benefit. Most published evidence derives from retrospective, single-institution studies conducted in curated dataset environments that systematically differ from real-world clinical deployment conditions. This comprehensive review examines the translational maturity of AI applications across 18 major malignancies, providing an evidence-stratified, cross-cancer assessment of where AI has fulfilled, approaches, or remains far from fulfilling its transformative potential in oncological care. Methods: A structured narrative review was conducted across PubMed/MEDLINE, Embase, IEEE Xplore, and the Cochrane Library, supplemented by regulatory grey literature including FDA 510(k) decision summaries, CE Technical Files, and ClinicalTrials.gov. Search terms combined cancer site-specific terminology with AI methodology terms and translational outcome descriptors. Studies were only included if they applied an AI or machine learning methodology to a defined clinical oncological task, reported a clearly specified performance evaluation, and involved human subjects or human-derived clinical data. Evidence quality was assessed using QUADAS-2, PROBAST, and Cochrane RoB 2. A five-tier translational readiness framework, grounded in the NIH T0–T4 translational spectrum and CONSORT-AI/SPIRIT-AI guidelines, was applied a priori to enable cross-cancer comparison. A rigorous distinction was maintained between diagnostic accuracy and clinical utility, defined as demonstrated impact on clinical decision-making or patient-centered outcomes. Results: Across all 18 malignancies, AI development varied profoundly by cancer type. Breast cancer and prostate cancer (Tier 1) represent the most mature AI ecosystems, with multiple FDA-cleared tools for mammographic screening and digital pathology achieving prospective multi-institutional validation; however, randomized evidence demonstrating reduced cancer-specific mortality remains absent. Lung, hepatocellular, and melanoma AI (Tier 2) have achieved regulatory milestones but face documented performance disparities across demographic subgroups, including DermaSensor’s 20.7% specificity in primary care settings and HCC model failures in non-viral disease etiologies. Colorectal, glioma, pancreatic, and ovarian cancers (Tier 3) exhibit technical maturity without clinical clarity: colorectal CADe systems increase adenoma detection but meta-analyses of 18,232 patients across 21 RCTs fail to demonstrate improvement in advanced neoplasia detection or cancer incidence reduction. A full study-level presentation of pooled estimates, confidence intervals, and heterogeneity statistics for each cited randomized evidence base across all cancer types would extend beyond the intended scope and format of this cross-cancer narrative review. Gastric, esophageal, cervical, bladder, head and neck, and endometrial cancers (Tier 4) demonstrate promising single-institutional or geographically restricted results without multi-institutional external validation, particularly notable for cervical cancer AI’s transformative potential in low- and middle-income countries constrained by absent regulatory frameworks. Hematologic malignancies, sarcoma, and pediatric solid tumors (Tier 5) face structural barriers, workflow incompatibility in hematopathology, extreme rarity in sarcoma (>70 subtypes, <15,000 US cases annually), and irreducible ethical constraints in pediatric data governance, that cannot be resolved through algorithmic refinement alone. Conclusions: Oncological AI has not yet fulfilled its clinical promise. Across all five translational tiers, a single finding is consistent: diagnostic accuracy is not a surrogate for patient benefit. AI tools with high sensitivity and specificity have repeatedly failed to demonstrate equivalent reductions in cancer-specific mortality, overdiagnosis, or procedural harm under real-world outcome scrutiny. Simultaneously, documented performance disparities across races, ethnicity, disease etiology, and geographic setting reveal that current AI systems risk amplifying the very health inequities they are positioned to resolve. Bridging this translational gap requires three coordinated systemic shifts: regulatory frameworks mandating post-market outcome surveillance as a condition of clinical clearance; prospective trial designs measuring patient-centered endpoints rather than diagnostic concordance alone; and sustained infrastructure investment in federated data governance, demographically inclusive training datasets, and LMIC-accessible regulatory pathways. AI holds genuine potential to reduce cancer mortality on a global scale—but only if held to the evidentiary and equity standards that the stakes of oncological care demand. Full article

Background/Objectives: Forward head posture (FHP) has been associated with alterations in cervical sensorimotor function; however, its relationship with trunk muscle activation during dynamic movement tasks remains incompletely understood. This study examined whether individuals with FHP demonstrate differences in the timing and magnitude of trunk muscle activation during a rapid lower-limb movement task. Methods: One hundred asymptomatic young adults (18–25 years) were classified as having normal head posture (NHP; craniovertebral angle (CVA) > 55°) or forward head posture (FHP; CVA < 50°) using PostureScreen^® Mobile. Surface electromyography (EMG) was recorded bilaterally from the external oblique (EO), lumbar multifidus (MF), and the transversus abdominis/internal oblique region (TrA/IO) during ten externally cued right-leg raises. EMG amplitude and onset latency relative to rectus femoris activation were extracted for each muscle. Two multivariate analyses of variance (MANOVA) assessed overall group differences in EMG amplitudes and onset latencies. Significant multivariate effects were followed by univariate ANOVAs. Group × Side mixed-model ANOVAs evaluated side-to-side activation patterns. Pearson correlation and regression analyses examined correlations between craniovertebral angle (CVA) and EMG variables. Results: MANOVA revealed significant overall differences between the FHP and NHP groups for EMG amplitude (Wilks’ λ = 0.20, F(6, 93) = 62.14, p < 0.001, η²p = 0.80) and onset latency (Wilks’ λ = 0.10, F(6, 93) = 133.73, p < 0.001, η²p = 0.90). Follow-up ANOVAs showed significant differences for all EMG variables (all p < 0.001), with large effect sizes (Cohen’s d = 1.0–3.2). Mixed-model ANOVAs demonstrated significant Group × Side interactions (all p < 0.05). CVA showed significant moderate to strong correlations with EMG amplitude and onset latency measures (r = 0.43–0.79). Conclusions: FHP was associated with later trunk muscle activation and altered EMG activation patterns during the leg-raise task, including reduced activity recorded from the TrA/IO region and increased EO activation. These findings suggest FHP is associated with different trunk neuromuscular activation strategies during dynamic tasks. Despite consistent CVA–EMG associations, extreme multicollinearity limits the interpretation of CVA as an independent predictor. Full article

Background: Accurate colposcopy assessment is essential for detection of cervical precancerous lesions. However, the diagnostic performance depends heavily on the examiner’s experience and subjective interpretation. Recent advances in artificial intelligence offer new opportunities for automated image analysis. In particular, deep learning models have shown promise for colposcopy image analysis intended for precancer screening. However, their limited interpretability restricts their translational value in clinical gynecology. This study leverages a custom dataset of expert-annotated digital colposcopic images to quantify the diagnostic strengths of these architectures. Methods: A comparative analysis was provided of the well-established convolutional neural network YOLOv11 and the modern transformer-based RF-DETR architecture, both of which were trained for the segmentation of 10 distinct classes. Target objects included anatomical structures, medical instruments and colposcopic findings. Results: Our results demonstrate that the YOLO architecture provides better performance for anatomical structures, whereas the RF-DERT reaches higher scores for colposcopy findings. These findings demonstrate transformer architecture superiority in more nuanced segmentation of clinically relevant findings, providing an interesting framework for computer-aided decision support systems in colposcopy. Conclusions: Both YOLOv11 and RF-DETR enable effective segmentation of colposcopic images, with performance dependent on class size and characteristics. Best results were achieved for large anatomical structures, while small and underrepresented findings remained challenging due to class imbalance. YOLO offers greater stability and efficiency, whereas RF-DETR performs better on more complex cases. Limitations include data imbalance, variable image quality, and annotation inconsistencies; future work should address these issues to improve generalization. Full article

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. Full article