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PubMed2026

Enhancing Self Care Among Oral Cancer Survivors Using a Digital Approach: The Empowered Survivor Trial.

BACKGROUND: Oral and oropharyngeal cancer survivors experience debilitating physical and psychosocial challenges. Little knowledge exists on the efficacy of interventions to enhance self-efficacy in managing these challenges, increase survivorship preparedness, and improve health-related quality of life (HRQoL). METHODS: Individuals (n = 643) diagnosed with oral or oropharyngeal cancer diagnosed within past 3 years were randomized to a digital intervention, Empowered Survivor (ES) or a Generic Online Intervention (GO). Primary (self-efficacy, preparedness, HRQoL) and secondary outcomes (self-care activities) were measured at Baseline, 2-months, and 6-months. RESULTS: Participants assigned to ES reported greater self-efficacy and increased self-care activities of oral self-exams, swallowing, and mobility exercises than those assigned to GO (self-efficacy: 2 months, p = 0.003, 6-months, p < 0.001; self-care activities). CONCLUSIONS: The ES enhanced self-efficacy and increased self-care activities. Further examinations of survivorship preparedness and HRQoL in oral and oropharyngeal cancer are warranted. TRIAL REGISTRATION: Registered on clinicaltrials. gov as NCT04713449.

Humans↗

PubMed2026

Beyond risk factors: A capacity framework for cancer survivorship research.

Cancer survivorship research has identified numerous biological, behavioral, psychosocial, health care, and structural factors that influence recovery. However, these factors are typically studied as separate determinants rather than interacting influences. This commentary proposes available survivorship capacity as a unifying framework that explains how these diverse determinants collectively shape recovery and survivorship outcomes. Concepts from geroscience, health care delivery, rehabilitation, occupational therapy, and human factors science were synthesized to develop a conceptual framework of available survivorship capacity. The framework conceptualizes recovery as a function of the capacity remaining after competing health care and life demands draw upon survivors' finite physical, cognitive, emotional, social, financial, temporal, and health care resources. It generates testable propositions for measurement, intervention research, health care delivery, and implementation science while positioning available survivorship capacity as a common mechanism linking diverse determinants of recovery and identifying actionable targets for intervention. Available capacity offers a unifying conceptual framework for understanding heterogeneity in survivorship outcomes and intervention effectiveness while generating a research agenda for future survivorship science. Measuring and strengthening survivors' available capacity, while reducing unnecessary demands, may improve engagement in care, health behaviors, and long-term recovery.

Humans↗

PubMed2026

How prevalent is fear of cancer recurrence beyond 5 years: a systematic review of validated assessments across tumour types.

PURPOSE: Fear of cancer recurrence (FCR) is an established challenge for cancer survivors. Research however has largely focussed early in treatment, with varying assessments and often single tumour sites. This systematic review set out to determine prevalence of FCR in survivors beyond 5 years across all tumour types. METHOD: We designed a search strategy to identify publications assessing FCR in survivors beyond 5 years with sample size greater than 50, using validated measures. Applying PRISMA methodology and with defined inclusion and exclusion criteria two authors independently assessed the studies for eligibility. Data extraction recorded number of participants, tumour type, study design, FCR tool, time points for assessment and reported prevalence. Risk of bias was assessed to address quality. RESULTS: Ten papers were included, reporting FCR from 5 years to beyond 20 years. Validated tools employed were FCRI-SF and FOP-Q-SF. Sample sizes ranged from 64 to 5983 participants, with a heterogeneous mix of tumour types and age. Only two studies reported longitudinal measurements. Prevalence of FCR above defined threshold ranged from 13 to 33.9% for those studies with acceptable risk of bias reporting distinct cohorts beyond 5 years. CONCLUSION: The limited evidence suggests that clinically relevant FCR persists in some survivors at 5 years. Our review demonstrates the challenge of heterogeneous patient populations in FCR research emphasising the need for improved consensus on measurements and more prospective longitudinal research representing a comprehensive variety of tumour types. We address the clinical implications of persistent FCR and the need to implement effective interventions.

Humans↗

PubMed2026

Mining Stored-Specimen Studies for Information about Cancer Natural History.

The advent of new multicancer early detection tests and publication of early diagnostic results have generated expectations of clinical benefit from multicancer screening. The clinical benefit of a cancer screening test depends critically on disease natural history, which is typically learned from prospective screening studies. Retrospective studies of stored blood specimens are important in learning about a test's preclinical diagnostic performance but have rarely been used to infer natural history. The extent to which these studies might be harnessed to also learn natural history is discussed in the context of an article in this issue that infers the combined natural history of a range of cancers targeted by a multicancer early detection test using a case-control subsample of specimens from a large cohort study. The critical question concerns the identifiability of key transition rates in multistate models of natural history alongside state-specific sensitivities. The article suggests that these parameters are estimable within a Bayesian framework that leverages prior information about test sensitivity from diagnostic studies. We offer a heuristic discussion of identifiability in this setting and encourage formal study to determine the extent to which models with varying degrees of complexity may be learned from stored-specimen studies. See related article by Dai et al., p. 1535.

Humans↗

PubMed2026

A Qualitative Analysis of Cancer Survivors' Experience in a Time-Restricted Eating vs Control Clinical Trial to Address Cancer-Related Fatigue.

PURPOSE: To describe cancer survivors' lived experiences in a clinical trial that tested an individualized nutrition counseling with or without time-restricted eating to address cancer-related fatigue. METHODS: The Fatigue REDuction After cancer study was a two-arm, randomized controlled trial. Participants were adult cancer survivors who were 2 months to 2 years post-treatment. All participants received individualized nutrition counseling; those in the time-restricted eating group self-selected a consistent 10-hour eating window for 12 weeks. After the study, semi-structured exit interviews were conducted to gauge participants' experiences in the trial. Interviews were transcribed and two independent coders thematically analyzed the interviews using inductive and deductive coding. NVivo software was used for data organization and analysis. RESULTS: Participants (n = 24; TRE = 11; Control = 13) were 55 ± 13 years old, 75% were female, and they had a variety of cancer types. The majority of participants found that being in the study helped them to set and achieve lifestyle goals and would therefore recommend the study to others. Participants in the time-restricted eating group noted that time-restricted eating helped them set a better routine, providing a positive sense of control. However, some noted difficulty switching to a 14-hour fasting schedule, as it can interfere with their regular routine or employment schedules. Many participants noted they were happy that cancer-related fatigue was gaining more attention, hoping to find solutions for persistent cancer-related fatigue. CONCLUSION: The majority of participants found the study useful and, regardless of their group assignment or the intervention's impact on their fatigue, found the study helped them to gain better control of their dietary habits.

Humans↗

PubMed2026

Outcomes at rapid diagnostic centres and the association between non-specific symptoms and cancer: A systematic review and meta-analyses of up to 21,392 patients.

INTRODUCTION: Cancer remains a leading cause of mortality and poses a significant public health challenge. Several non-specific symptoms (NSSs) often indicate non-serious disease but can also accompany malignancy even in the absence of organ-specific signs. Therefore, the aim of the study was to comprehensively delineate the association between the most common NSSs (weight loss, fatigue, pain and nausea/appetite loss) and cancer or non-cancer diagnoses. METHODS: Database searches of PubMed and Embase were conducted applying search criteria to identify studies that investigated common NSSs in cancer patients diagnosed through rapid diagnostic centres (RDCs). The quality of the included studies was assessed using a modified Newcastle-Ottawa Scale (NOS). For each symptom, pooled relative risks (RRs) with 95% confidence intervals were derived using random-effects meta-analysis. RESULTS: Eleven studies met the inclusion criteria. All studies were considered to be of high methodological quality. The most frequent disease locations for cancer entities included hematologic, lung and lower gastrointestinal. Together with miscellaneous, rheumatic, and musculoskeletal conditions, these were the most common for non-cancer diagnoses. Nausea/appetite loss showed a statistically significant association with cancer (RR=1.20, 95%-CI 1.07-1.35). Pain showed a non-significant association (RR=1.07, 95%-CI 0.75-1.53) with substantial between-study heterogeneity, and weight loss showed a non-significant inverse trend (RR=0.92, 95%-CI 0.84-1.02). Fatigue showed no association with cancer (RR=1.00, 95%-CI 0.85-1.18). DISCUSSION/CONCLUSION: NSSs may be valuable for cancer risk assessment, but the associations remain modest. The complexity of patients' clinical presentations suggests that additional factors likely influence the cancer risk. Future research should examine symptom combinations and, where data allow, perform subgroup analyses.

Humans↗

PubMed2026

The Effect of Pain Catastrophizing on Acupuncture Treatment for Chronic Pain in Cancer Survivors.

CONTEXT: Pain catastrophizing (PC) predicts worse pain outcomes in cancer survivors. However, little is known whether PC influences pain outcomes of nonpharmacological treatments such as acupuncture. OBJECTIVES: This study aimed to assess the impact of PC on acupuncture efficacy for chronic pain in cancer survivors. METHODS: This secondary analysis of PEACE trial used two-sample t-test and Pearson's chi-squared test to analyze the pain outcomes of cancer survivors who received electroacupuncture (EA) or battlefield acupuncture (BFA). PC was measured using Pain Catastrophizing Scale (PCS). The Brief Pain Inventory (BPI) was used to measure pain severity and interference at the primary endpoint (week 12). RESULTS: Among 266 participants, 41 (15.41%) had a high baseline PC. Among those receiving EA, high PC patients had greater reductions in pain severity (-3.9 vs. -2.1, P = 0.006) and pain interference (-3.8 vs. -2.6, P = 0.04) than low PC. PC was not associated with pain outcomes in BFA group (P > 0.05 for both severity and interference). Among patients with high PC, a greater proportion were responders in the EA group than those in BFA group (83.3% vs. 43.5%, P = 0.009). Among low PC patients, there was no significant difference in the proportion of responders between the EA and BFA groups (66.1% vs. 64.5%, P = 0.8). CONCLUSION: We found that cancer survivors with high baseline PC had greater pain reductions with EA than BFA and compared to low PC patients. These findings suggest that EA may serve as a targeted treatment option for vulnerable patients with high PC and further support precision pain management.

Humans↗

PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗

PubMed2026

Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process.

ObjectiveThis work establishes a methodological framework for sources of pilot distraction and interruptions in a structured model that can be used as a tool for cockpit design/procedure assessment.BackgroundPilots must complete complex tasks, and distractions can impair performance and lead to errors that can cause aircraft accidents. Although various cockpit distractors are examined individually, there is no integrated approach.MethodDistraction and interruption sources were identified through a literature review and confirmed/extended by interviews with airline pilots. Associated weights were determined through pairwise comparisons, yielding a hierarchical model using the Analytic Hierarchy Process.Results26 sources of pilot distraction and interruptions were quantified and categorized into four categories: communication, head-down time, responding to abnormal conditions & unexpected situations, and searching for traffic.ConclusionA taxonomic structure for assessment is achieved with the top 5 sources identified as communications, technical interruptions, experience in type, environmental factors, operational irregularities, and airspace high terrain, accounting for 63.07%.ApplicationThe structured system is a flexible assessment scale that provides a taxonomic framework for airline risk management, supports future research, and cockpit design efforts.

Humans↗

PubMed2026

The Childhood Cancer and Leukemia International Consortium (CLIC): Expanding global collaboration in pediatric cancer etiology research.

Childhood cancers are rare, but incidence has risen modestly in countries with robust registration, partly reflecting improved diagnosis. In high-income countries, cancer is the leading cause of disease-related death in children. Marked inequities in incidence, survival, and research capacity underscore the need for large-scale collaboration to identify environmental, genetic, and contextual determinants of risk. The Childhood Cancer and Leukemia International Consortium (CLIC) was established in 2007 to study the etiology of childhood leukemia and later expanded in 2019 to include other childhood cancers, principally solid tumors. CLIC pools harmonized, individual-level data from case-control and cohort studies, obtained through interviews, record linkage (insurance claims, registries), or geographic information systems, and integrates germline genomic data where available. Membership has grown from 13 studies in 9 countries to 57 studies in 21 countries; recruitment spans the early 1960s to the present and encompasses approximately 150,000 cases across all tumor types and 300,000 controls with clinical, demographic, and exposure data, centralized via harmonized data dictionaries at the Data Coordination Center, established in 2014 at the International Agency for Research on Cancer, and supported by a secure analysis platform. Pooled analyses across diverse populations have implicated parental age, prenatal vitamin or folic acid use, mode of delivery, fetal growth, selected congenital anomalies, occupational or household exposures (e.g., pesticides), paternal smoking, and markers of early-life immune modulation (e.g., breastfeeding, daycare attendance) in leukemia risk, informing carcinogen evaluation and prevention. The integration of genetic ancestry and germline susceptibility data is clarifying ancestry-related differences in leukemia biology and outcomes, while confirming risk loci with population-specific effects. CLIC is now adding polygenic risk scores and exposomic data to refine etiologic subtyping and identify modifiable pathways, while broadening representation from underserved regions through partnership-building and capacity-strengthening.

Humans↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Targeting TP53 in triple-negative breast cancer: Molecular pathogenesis, therapeutic implications, and emerging pharmacological strategies.

Triple-negative breast cancer (TNBC) remains a highly aggressive and therapeutically challenging subtype, defined by the absence of oestrogen, progesterone, and HER2 expression. Tumour Protein 53 (TP53) mutations represent the most frequent genetic alteration, occurring in over 80% of cases and driving tumour initiation, progression, and therapeutic resistance. Mutant p53 proteins not only lose canonical tumour-suppressive functions but also often acquire gain-of-function (GOF) oncogenic properties that promote metastasis, genomic instability, and resistance to mechanisms like ferroptosis. This review examines the biological role of TP53 in TNBC pathogenesis and evaluates emerging pharmacological strategies aimed at targeting these vulnerabilities. Key approaches include the pharmacological reactivation of mutant p53 using small molecules such as APR-246, COTI-2, and the mutation-specific reactivator rezatapopt (PC14586), which has shown significant clinical tumour reduction in Y220C-mutant patients. Other strategies involve targeted protein degradation, the exploitation of synthetic lethal interactions (e.g., Chk1 or Aurora kinase B inhibition), and the use of natural products like cryptolepine or piperine derivatives. Recent clinical evidence further highlights the potential of combining epigenetic agents like decitabine with chemotherapy in TP53-mutant populations. Integrating TP53 mutation status into biomarker-driven treatment paradigms is a pivotal step toward achieving precision oncology and improving clinical outcomes for patients with TNBC.

Precision oncology↗

PubMed2026

Antibody-drug conjugates against multidrug-resistant cancers: Biomarker-guided patient selection, payload engineering, linker chemistry, and bystander effects.

Antibody-drug conjugates (ADCs) are one of the most significant advancements in modern cancer therapeutics. Combining the target selectivity of monoclonal antibodies with the cytotoxic potential of payloads, ADCs effectively kill cancer cells and offer hope to patients with even refractory cancer types. Beyond simply increasing the number of therapeutic options available for cancer patients, ADCs have become a powerful frontline agent in overcoming multidrug resistance (MDR). As one of the most challenging obstacles to effective cancer care, MDR is mediated by ATP-binding cassette (ABC) transporter-mediated drug efflux, target-based mutations, and dysregulated apoptosis. The clinical success of ADCs specifically engineered to overcome MDR, including in heterogeneous tumors and cancer cells that exhibit bypass signaling, is well established. This is especially evident with trastuzumab deruxtecan (T-DXd) in HER2-low, HER2-positive, and HER2-mutant cancers; sacituzumab govitecan (SG) in TROP2-expressing triple-negative breast cancer (TNBC) and urothelial carcinoma; and enfortumab vedotin in Nectin-4-positive bladder cancer. By overcoming MDR, ADCs have enabled more effective treatment algorithms across multiple malignancies. Most importantly, the clinical application of ADCs has become inextricably linked to cancer genomics. HER2 testing has evolved from a two-tiered system to a continuous spectrum including HER2-ultralow, HER2-low, HER2-positive, and ERBB2-mutant categories. Each of these categories exhibits different eligibility guidelines for ADC patient selection. As cancer cells continue to evolve and develop resistance to even ADCs through mutations and variants, researchers and clinicians have used pharmacogenomics to predict ADC response and resistance. To define the genomic architecture of ADC-resistant tumor subpopulations, single-cell transcriptomic studies and liquid biopsy approaches are being used to enable real-time examination of the tumor genome during ADC therapy, thereby optimizing treatment and circumventing resistance driven by emerging mutations and variants. This review provides a comprehensive analysis of the molecular structure of ADCs, the pharmacological principles underlying their potent cytotoxic activity against MDR cancer cells, the genomic and transcriptomic biomarkers that guide ADC patient selection, and the emerging resistance mechanisms that will shape the next generation of promising ADC development.

Humans↗

PubMed2026

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans↗

PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗

PubMed2026

HPV Testing Versus Cytology for Cervical Cancer Screening Among Women 50 Years and Older: Evidence From the HPV FOCAL Randomized Controlled Trial.

Evidence on the comparative effectiveness of HPV testing versus cytology specifically in women aged ≥ 50 years who are approaching screening cessation remains limited. This analysis included 6471 women aged ≥ 50 at baseline screening in the HPV FOCAL randomized clinical trial. Women were randomly allocated to receive cytology (Control Group, n = 3248, 50.19%) or HPV testing (Intervention Group, n = 3223, 49.81%) at baseline, with co-testing at 48-month exit. We calculated incidence rates and risk ratios for CIN2+ detection over follow-up and compared missed lesions at exit by screening method. At the 48-month exit, CIN2+ detection was lower among HPV baseline-negative women than among those in the cytology group (1.61/1000 [95% CI, 0.52-3.76] vs. 3.15/1000 [95% CI, 1.51-5.78]; risk ratio, 0.51 [95% CI, 0.11-0.91]), reflecting higher baseline detection with HPV testing and fewer prevalent lesions at exit. Even with cytology re-screening at 2 years, 50% of CIN2+ cases were missed compared to 30% with HPV testing. After adjusting for age, education, smoking status, and lifetime sexual partners, the hazard ratio for CIN2+ comparing HPV to cytology was 0.44 (95% CI, 0.22-0.88). Among women aged ≥ 50 years, HPV primary screening was more effective than cytology at detecting CIN2+ lesions and was associated with a continued lower subsequent risk following a negative HPV test, supporting its use in cervical cancer screening programs in this age cohort.

Aged↗

PubMed2026

Comprehensive analysis suggests CRIF1 is a potential target in breast cancer associated with prognosis and immune infiltration.

BACKGROUND: CRIF1 is a multifunctional factor that regulates cell biological processes such as the cell cycle, cell proliferation, and energy metabolism, and it is a new molecule that contributes to the poor prognosis of many malignancies. However, its involvement in breast cancer development is not fully known. MATERIALS AND METHODS: To investigate the relationship between CRIF1 expression, prognosis, and clinical characteristics using The Cancer Genome Atlas (TCGA-BRCA). The relationship between CRIF1 expression and the immunological microenvironment was investigated using CIBERSORT, ESTIMATE. Breast tissue and CRIF1 expression were validated by IHC. A tiny interfering plasmid was designed to transiently transfect breast cancer cell lines, and proliferation-related functional tests were carried out. The effect of sh CRIF1 on tumor formation was confirmed using a subcutaneous tumor experiment in naked mice. RESULTS: We discovered that CRIF1 was highly elevated in breast cancer tissues and associated with a poor prognosis. CRIF1 stimulates breast cancer cell proliferation, migration, and invasion. Knockdown decreased PI3K/AKT/mTOR signaling, which boosted autophagy activity. Immune infiltration research revealed that patients with high CRIF1 expression had higher CD8+ T cell expression but reduced macrophage M2 expression. CONCLUSION: Upregulation of CRIF1 in breast cancer cells enhances malignant behavior, which may be mediated by PI3K/AKT/mTOR signaling and is linked to cellular autophagy.

Humans↗

PubMed2026

AI Health message intervention: The role of message customization and message source in breast cancer screening among women of color.

OBJECTIVES: To examine the effectiveness of breast cancer screening messages with varying levels of customization (generic, targeted, and tailored) and to compare AI-generated versus human-generated messages. METHODS: A between-subjects experimental design with a control condition was employed. Message content followed a standardized structure and varied by level of customization: generic, targeted (demographic-based), and tailored (perceived susceptibility- and barrier-based). Messages were developed by either the authors or GenAI (ChatGPT-4o). A total of 391 participants recruited via Prolific were randomly assigned to five groups (generic, targeted-human, targeted-AI, tailored-human, and tailored-AI). Self-efficacy, behavioral intentions, attitudes, and message believability were measured using different scales. RESULTS: Customized (tailoring and targeting) health messages performed comparably to generic messages in shaping positive health outcomes. GenAI-generated messages also produced outcomes comparable to those of human-generated messages under standardized conditions. Significant negative indirect effects through message believability for the human-tailored condition was found relative to the generic condition. CONCLUSIONS: GenAI may be a useful tool for developing and customizing scalable health messages. Its effectiveness depends not only on customization but also on maintaining message quality, including readability, clarity, coherence, naturalness, and credibility. PRACTICAL IMPLICATIONS: GenAI may support health practitioners in developing customized and scalable breast cancer messages. However, professional review remains necessary to ensure that the message is culturally appropriate, responsive to patient concerns, and suitable for use alongside patient-provider communication.

Humans↗