Global mammographic asymmetry (GA) is generally considered benign, and its association with subsequent breast cancer risk is unclear. We examined whether GA on screening mammography predicts short-term and long-term breast cancer and whether this varies by Breast Imaging Reporting and Data System (BI-RADS) breast density. In this retrospective cohort study using the Korean National Health Insurance Service screening programme, we included women aged ≥40 years who underwent screening mammography in 2009-2010 and had no prior breast cancer. GA and BI-RADS density were recorded on baseline mammograms; incident invasive breast cancer through December 31, 2019 was ascertained from insurance claims. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for breast cancer associated with GA overall and by BI-RADS density and follow-up interval (<1, 1-2 and ≥2 years), adjusting for demographic, reproductive and lifestyle factors. Among 5,475,113 women, GA was present in 4.0%. Overall, GA was associated with a modestly increased breast cancer risk (aHR 1.15; 95% CI 1.11-1.19), strongest within 1 year of screening (aHR 1.90; 95% CI 1.70-2.12). In women with BI-RADS 1 breasts, GA doubled overall risk (aHR 2.03) and quadrupled short-term risk (<1 year: aHR 4.14), whereas in BI-RADS 4 breasts GA did not increase overall risk (aHR 0.94). GA is uncommon but identifies women at substantially elevated short-term breast cancer risk, particularly those with non-dense breasts, and has limited long-term prognostic value in extremely dense breasts. These findings support consideration of short-interval follow-up or supplemental imaging when GA is reported in non-dense breasts.

Computational approaches are transforming nutrition science by integrating data from wearables, digital health platforms, and multiomics technologies to unravel complex diet-health interactions. Traditional statistical models cannot adequately capture the temporal, nonlinear, and individual variability inherent in such data. Computational nutrition, integrating data science, machine learning, and systems modeling, has therefore emerged as a distinct and rapidly developing field. Landmark studies have demonstrated its potential to improve dietary assessment, predict metabolic responses, and design personalized interventions. From an early-career perspective, however, the rise of computational nutrition also exposes structural and educational gaps. Early-career researchers often encounter fragmented training, limited mentorship, and restricted access to interoperable data and computational infrastructure. Empowering early-career researchers through integrated curricula, equitable data access, and recognition of interdisciplinary contributions will be essential for ensuring that computational nutrition evolves into a transparent, reproducible, and inclusive discipline capable of advancing both personalized and population-level nutrition.

Epilepsy is the commonest chronic neurological disorder of childhood. In France, pregnancy-prevention rules and generic policies aim to improve safety and affordability of anti-seizure medications (ASM), but their paediatric impact is unknown. Tracking these patterns is key to assess policy effectiveness and to ensure safe, equitable, and sustainable access to treatment. We aimed to characterise nationwide temporal trends in paediatric ASM use, costs, generic uptake, and sex differences in France from 2014 to 2023. We conducted a retrospective drug-utilisation study using open-access OpenMedic files from the French National Health Data System. All dispensing of 24 ASMs to individuals <18 years during 2014-23 were retrieved by product code. Annual users, boxes, and costs were summarised; temporal trends used Spearman tests; multivariable logistic regression modelled sex differences; LASSO selected determinants of generic dispensing. Over the decade, 2,015,504 children consumed 15,748,141 ASM boxes, costing €274.4 million. Annual users increased 24% (from 174,889 to 216,607). Third-generation agents rose 70% (from 98,659 users per year to 168,290), whereas first- and second-generation agents fell 48% (from 1579 to 823) and 28% (from 87,929 to 63,038). Valproate use fell 37% overall (from 58,845 to 37,014) and 62% in girls (from 23,480 to 8975); lamotrigine and levetiracetam rose 49% (from 29,404 to 43,847) and 77% (from 37,290 to 65,940), respectively. Generics accounted for 11.2% of dispensing in 2014 (191,551 boxes) vs 20.2% in 2023 (455,934 boxes). Prescriptions by private psychiatrists and use of gabapentin, pregabalin, lamotrigine, and levetiracetam independently predicted generic uptake. ASM with available generic substitution would have cut 2023 spending by 8% (€3.88 million). Paediatric ASM practice in France is rapidly aligning with safer, newer ASM and sex-specific risk mitigation, yet generic penetration lacks. Targeted substitution strategies could release funds for innovative therapies without compromising seizure control. None.

The aims of this study were: ① to evaluate the diagnostic efficacy of six mainstream TI-RADS (Thyroid Imaging Reporting and Data System) classification systems (C-TIRADS, ACR-TIRADS, etc.) in the Northwestern Chinese population; and ② to identify risk factors for malignant thyroid nodules (TNs) using logistic regression based on clinical and ultrasound features, construct a quantifiable scoring Nomogram model, enable rapid and objective risk assessment, and assist in clinical decision-making. A total of 2,047 patients with TNs (1,433 malignant and 614 benign) were enrolled from January 2018 to January 2024 at Shaanxi Provincial People's Hospital. The nodules were divided into a training group (1,435 nodules) and a validation group (612 nodules) in a 7:3 ratio. Twelve characteristics were collected, including age, nodule size, margin, calcification, and the presence of suspicious lymph nodes. Independent risk factors were identified through univariate and multivariate logistic regression analyses to construct a Nomogram model. The model's performance was evaluated using receiver operating characteristic (ROC) curves, accuracy, and other metrics, and compared with the six traditional TI-RADS systems. Ten independent risk factors were identified, including age, nodule size, and irregular margins. In the validation group, the Nomogram model achieved an accuracy of 78.4%, a sensitivity of 81.6%, a specificity of 71.7%, and an area under the ROC curve (AUC) of 0.849. The sensitivities of the six TI-RADS systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE) for distinguishing benign and malignant nodules were 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; specificities were 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively; accuracies were 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; and AUCs were 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively, with no statistically significant differences among them. The Nomogram model significantly outperformed the traditional systems in measures such as AUC, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), Positive Likelihood Ratio (PLR), and Negative Likelihood Ratio (NLR) (P < 0.001). The six traditional TI-RADS systems demonstrate similar but overall limited diagnostic efficacy in the Northwestern Chinese population. The Nomogram model, by integrating multidimensional features and applying a quantitative scoring approach, improves the accuracy and objectivity of malignancy risk assessment. Compared to traditional models, it offers better clinical utility, supports optimized decision-making, and helps reduce unnecessary invasive procedures.

Multiparametric magnetic resonance imaging (mp-MRI) has emerged as a promising technique for evaluating the extent of bladder cancer, particularly for assessing muscle invasion. The Vesical Imaging Reporting and Data System (VI-RADS), introduced in 2018, aims to standardize the interpretation of mp-MRI for staging bladder tumors. This study investigates the diagnostic accuracy of VI-RADS in predicting detrusor muscle involvement preoperatively. To assess the diagnostic performance of VI-RADS scoring in determining muscle invasion in bladder cancer patients prior to surgical intervention. This prospective study enrolled 62 patients between June 2021 and April 2023 who presented with bladder masses. Each underwent mp-MRI followed by surgical treatment, including either TURBT or radical cystectomy. Two radiologists independently evaluated images using VI-RADS scoring. The results were compared with final histopathological findings to determine diagnostic accuracy. VI-RADS scores of 4 and 5 were observed in 10 (16%) and 19 (30.6%) patients respectively; scores of 3, 2, and 1 were assigned to 3 (4.8%), 25 (40.3%), and 5 (8.0%) patients, respectively. Histopathological evaluation confirmed muscle invasion in 29 patients (46.77%) and non-muscle-invasive disease in 33 (53.22%). The mp-MRI demonstrated a sensitivity of 93.10%, specificity of 93.94%, and an overall accuracy of 93.54% in detecting muscle invasion. Mp-MRI interpreted using the VI-RADS system offers more than 90% sensitivity and specificity for detecting muscle invasion in bladder cancer. VI-RADS may serves as a valuable tool for preoperative staging, potentially optimizing treatment planning and reducing unnecessary restaging TURBT.

Prostate magnetic resonance imaging (MRI) has become a crucial tool in diagnosing and managing prostate cancer, mainly by helping to avoid unnecessary biopsies and enhancing the detection of clinically significant disease. However, its clinical usefulness is often limited by wide variation in how images are acquired, interpreted, and reported worldwide. This inconsistency affects diagnostic accuracy and patient outcomes. In response, the Quality Improvement Subgroup of the European Society of Urogenital Radiology (ESUR) Prostate MRI Working Group has created a practical, three-step quality-improvement framework aimed at standardising and improving prostate MRI practices. This framework consists of: Step 1: 'Build it right', establishing a foundation of technical excellence through adherence to the Prostate Imaging Reporting and Data System (PI-RADS) technical standards, objective quality assessment using the Prostate Imaging Quality (PI-QUAL) score, and systematic artefact reduction. Step 2: 'See it right', emphasising interpretive excellence via structured training, institutional quality assurance metrics, and multidisciplinary collaboration. Step 3: 'Improve and innovate', promoting continual refinement through emerging technologies such as AI-driven assessment, deep learning reconstruction, and remote supervision. By incorporating this structured approach into daily practice, this framework aims to ensure that prostate MRI consistently fulfils its promise of accurate, reproducible, and patient-centred care. A coordinated effort towards international implementation, benchmarking, and outcome-based validation represents the next critical step to maximise global impact. KEY POINTS: Question Wide variation in prostate MRI acquisition, image quality, and reporting undermines diagnostic accuracy. A structured roadmap is needed to ensure consistent quality and reproducible practice. Findings The ESUR Prostate MRI Working Group outlines a three-step framework-'Build it right', 'See it right', 'Improve and innovate'-to standardise acquisition, interpretation, and quality assurance. Clinical relevance Applying this roadmap in clinical practice aims to enhance diagnostic confidence and promote consistent, high-quality prostate cancer care across diverse healthcare settings.

To systematically map Irish national health datasets and policy frameworks relevant to children and young people (0-24years) and appraise their readiness for quality improvement, equity monitoring, and interoperable reuse. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI)-guided scoping review will synthesise peer-reviewed and grey literature. Datasets will be benchmarked using World Health Organization Data Quality Review (DQR) domains, Findable, Accessible, Interoperable, Reusable (FAIR) principles, European Medicines Agency registry guidance and PROGRESS-Plus equity stratifiers. Outputs will be synthesised into structured matrices (national catalogue, key quality indicators and availability-variability layer) and an equity heat map. The review will characterise heterogeneity in coverage, coding, governance, equity stratification and linkage-readiness across perinatal, hospital, registry, surveillance and community datasets, identifying priority gaps for standardisation. Within the context of healthcare quality assurance, the synthesis will enable evidence-informed benchmarking across clinical domains, from perinatal outcomes to chronic disease management. The integration of DQR and FAIR appraisals will allow Irish health agencies to identify datasets that meet international standards of reliability, completeness and accessibility. Simultaneously, mapping PROGRESS-Plus variables will reveal where data gaps perpetuate inequities, informing targeted data-collection reforms. The resultant framework will provide a replicable model for how nations can align data governance with the continuous quality-improvement cycle central to the International Journal of Pharmaceutical Quality Assurance's mission - linking structure (data quality), process (data use) and outcomes (policy and patient benefit). This review will generate a decision-ready catalogue of Irish paediatric and young people's health datasets, highlighting strengths, gaps and opportunities for improvement. By appraising data quality, equity stratifiers and linkage readiness, it will provide actionable recommendations for standardisation and governance. Policymakers can use the outputs to align datasets with international best practice, clinicians can advocate for inclusion of outcome and patient-reported measures and researchers can identify priority areas for secondary analysis and linkage studies - supporting safer, fairer, and more effective child health services in Ireland. Strengthening child health data systems has direct societal benefits by enabling more equitable, transparent and evidence-based policy. By mapping available datasets and assessing equity stratifiers, this review will highlight gaps in capturing determinants such as ethnicity, deprivation and disability. Addressing these gaps will allow more accurate monitoring of health inequalities and ensure that vulnerable groups are not overlooked in service planning. The outputs will support a culture of accountability, inform public debate on data use and contribute to building a learning health system that promotes fairness, inclusivity and trust in healthcare for children and young people. This protocol delivers the first integrated, decision-ready framework to benchmark paediatric data ecosystems against international quality, equity and stewardship standards, enabling learning health systems and policy-relevant data governance in Ireland and comparable Organisation for Economic Co-operation and Development settings.

Persistent organic pollutants in India: Multi-compartment trends, source fingerprinting, and emerging environmental challenges (2000-2025).

PERSoN4: A Multiparametric Ultrasound Model to Improve CEUS LI-RADS for HCC.

Few therapies improve cardiovascular outcomes for people with kidney failure. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) reduce cardiovascular risk in patients with non-dialysis-dependent chronic kidney disease, but the cardiovascular benefits in patients with kidney failure remain uncertain. The objective of this study was to compare cardiovascular outcomes among patients with kidney failure and type 2 diabetes newly initiated on GLP-1 RA versus other antiglycemic agents. We analyzed electronic health records, Medicare claims, and Part D data from the United States Renal Data System (2011-2021) to identify new users of GLP-1 RA (n=3,629), dipeptidyl peptidase-4 inhibitors (DPP4i; n=21,369), and sulfonylureas (n=32,296) among patients with type 2 diabetes receiving maintenance dialysis. For the primary analysis, we performed 1:1 propensity score matching of GLP-1 RA to DPP4i initiators using 61 covariates. A prespecified secondary analysis compared propensity score-matched initiators of GLP-1 RA and sulfonylureas. The primary outcome was a modified major adverse cardiovascular events (MACE) composite of myocardial infarction, stroke, or all-cause mortality. Secondary outcomes included the individual components of the primary outcome and hospitalizations for heart failure. Cause-specific Cox models were used to estimate hazard ratios (HRs). Among 3,284 matched pairs of GLP-1 RA and DPP4i initiators, GLP-1 RA use was associated with lower risks of MACE (HR 0.87; 95% CI 0.78-0.97), all-cause mortality (HR 0.83, 95% CI 0.74-0.94), and heart failure hospitalization (HR 0.90, 95% CI 0.83-0.99) over up to 2 years of follow-up. Among 2,792 matched pairs, GLP-1 RA and sulfonylurea initiators, GLP-1 RA was associated with lower risks of MACE (HR 0.83; 95% CI 0.74-0.93) and all-cause mortality (HR 0.80; 95% CI 0.69-0.91). Among patients with type 2 diabetes receiving maintenance dialysis, GLP-1 RA initiation was associated with lower risk of cardiovascular events and all-cause mortality compared to other commonly prescribed antiglycemic agents.

Federally Qualified Health Centers (FQHCs) play a critical role in delivering preventive care, including cancer screening, to underserved populations in the United States. Despite widespread adoption of screening, disparities persist, particularly among socioeconomically disadvantaged patients. How neighborhood-level social risk contributes to variation in cancer screening across FQHCs remains unclear. To examine the association between average patient social risk-measured using patients' ZIP code-level overall and domain-specific Social Vulnerability Index (SVI)-and cancer screening rates across FQHCs. Retrospective cross-sectional study using 2022 Uniform Data System (UDS) data. 1,312 FQHCs with complete data, serving 29.8 million patients across 50 states and Washington, DC. FQHC-level social risk was calculated as the patient volume-weighted average SVI score across all patient ZIP codes served by the FQHC. Outcomes were FQHC-level breast, cervical, and colorectal cancer screening rates as defined by 2022 UDS clinical quality measure specifications. Linear regression models adjusted for FQHC characteristics, including patient volume, demographics, chronic condition prevalence, government funding, rurality, and Medicaid expansion status. Compared with FQHCs serving the least vulnerable communities, those serving the most socially vulnerable neighborhoods had cancer screening rates that were 7.8 percentage points (pp) lower for cervical cancer, 10.9pp lower for breast cancer, and 15.3pp lower for colorectal cancer (all p < 0.001). Disparities were largest for colorectal cancer and were most pronounced for the socioeconomic status SVI subdomain. Differences were larger in non-Medicaid expansion states and among larger FQHCs. Findings were consistent across sensitivity analyses. Substantial disparities in cancer screening persist across FQHCs and are closely linked to the socioeconomic characteristics of the communities they serve. Targeted interventions, sustained funding, and tailored resource allocation for FQHCs serving socially vulnerable populations may help improve screening uptake, reduce preventable disparities, and advance national cancer prevention goals.

Low-dose computed tomography (LDCT) lung cancer screening is recognized as a "teachable moment" for smoking cessation. However, evidence of its impact on smoking behavior is inconsistent, with limited research on incidental smoking-related findings, such as coronary artery calcification (CAC) or emphysema, and on sustained abstinence among former smokers. We comprehensively evaluated the effects of LDCT findings and physician counseling on smoking behaviors in both current and former smokers. A retrospective cohort study was conducted on 763 individuals who underwent national lung cancer screening at the Korean National Cancer Center between 2019 and 2021. Smoking status was assessed at a 2-year follow-up. Multivariable logistic regression was used to assess the association of CT findings and physician counseling with smoking cessation initiation (current smokers) and abstinence maintenance (former smokers). Among current smokers, higher Lung Imaging Reporting and Data System (Lung-RADS) categories (aOR = 18.84), moderate-to-severe CAC (aOR = 2.18), and emphysema (aOR = 1.95) were significantly associated with increased odds of quitting. Similarly, these findings significantly predicted sustained abstinence in former smokers, with CAC showing a particularly strong effect (aOR = 5.30). Physician counseling independently increased the odds of cessation in current smokers (odds ratio = 2.04) and abstinence maintenance in former smokers (odds ratio = 5.38). The effect of counseling did not significantly differ by the severity of low-dose computed tomography (LDCT) findings. Incidental smoking-related LDCT findings, particularly CAC and emphysema, are potent predictors of smoking behavior change. Physician counseling further enhances cessation outcomes, underscoring the value of integrating comprehensive counseling based on all relevant radiological findings into screening protocols. This study demonstrates that incidental smoking-related Low-dose computed tomography findings, such as coronary artery calcification and emphysema, strongly influence both smoking cessation in current smokers and sustained abstinence in former smokers, independent of malignant findings. Physician counseling significantly enhances these behavioral changes regardless of CT result severity. By highlighting the additive value of integrating structured smoking cessation counseling with interpretation of all salient radiological findings, the results support expanding lung cancer screening programs beyond cancer detection to serve as effective interventions for tobacco control, ultimately improving long-term health outcomes.

Research on error correction method of observation data system based on adaptive multi-model

Capillary electrophoresis (CE) often provides superior separation of macromolecules such as monoclonal antibodies (mAbs), a major biopharmaceutical class, compared with liquid chromatography. However, electropherograms frequently exhibit complex baselines and peak shapes that are not reliably handled by integration algorithms designed for chromatographic data, and manual integration is often required. Many concepts have been proposed to improve peak integration, ranging from incremental algorithmic refinements and signal-to-noise (S/N)-based approaches to artificial intelligence (AI)-driven strategies, but objective performance comparisons are not possible without shared reference data sets and agreed peak limits. To address this gap, we initiated a multinational collaboration involving industrial and academic laboratories to create a comprehensive reference data set for CE peak integration. A total of 227 challenging and practically relevant electropherograms were collected from diverse applications, converted to a standardized format, and independently integrated by multiple experts. Using dedicated software tools and a structured consensus process, mutually accepted reference integration limits were established for each data set. These reference electropherograms, together with the underlying integration rules, are now made available to the scientific community. Analysis of the reference data set identified general principles for reliable peak integration, including the importance of standardized zoom levels and consistent handling of small peaks near the noise level. The data set provides a common foundation for benchmarking commercial chromatography data systems (CDS) and for developing and validating new algorithmic and AI-based integration methods. We expect this work to speed up the development of practical, automated integration strategies for CE and that these core concepts can be applied to other separation techniques.

ABSTRACT Research Question/Issue Do college application fees contribute to gender disparities in higher education, particularly at STEM‐focused institutions in decentralised admissions systems? Research Findings/Insights Using U.S. data from 2001–2020, we find that a 1% increase in application fees is associated with a 1.77 percentage‐point decline in the female share of applicants at STEM‐focused institutions, with larger effects at public universities. No comparable effect is observed at non‐STEM institutions. Additional analysis indicates that application fees primarily alter the composition of applicants rather than overall application volumes, with male applications responding more strongly than female applications. Practitioner/Policy Implications Although application fees are typically viewed as administratively neutral, our results suggest they can disproportionately deter women from applying to STEM‐focused institutions. In decentralised systems common across the Asia‐Pacific region, reviewing fee structures or introducing targeted fee waivers may help promote greater gender equity in STEM participation. Methods Used We use institution‐level panel data from the Integrated Postsecondary Education Data System (IPEDS) and estimate fixed‐effects models exploiting within‐institution variation in application fees.

Breast cancer remains a major global health challenge, and conventional B-mode ultrasound frequently encounters limitations in reliably distinguishing benign from malignant lesions, particularly within intermediate Breast Imaging-Reporting and Data System (BI-RADS) categories. These uncertainties often contribute to unnecessary biopsies. Shear wave elastography (SWE), a quantitative method for assessing tissue stiffness, may improve diagnostic confidence by providing objective elasticity thresholds. This study aimed to evaluate the diagnostic accuracy of SWE in routine clinical practice and to determine optimal stiffness cut-off values for differentiating benign from malignant breast lesions. In this prospective single-center study, 73 women (mean age 48.6 ± 15.6 years) with BI-RADS 3-5 breast lesions underwent SWE examination prior to biopsy. Stiffness measurements (kPa and m/s) were obtained for each lesion. Histopathology served as the reference standard. Diagnostic performance was assessed, and optimal thresholds were determined using receiver-operating characteristic (ROC) analysis. Intra- and interobserver reproducibility were evaluated. Malignant lesions demonstrated significantly higher stiffness values (mean 56.7 kPa) compared with benign lesions (mean 18.7 kPa). SWE exhibited excellent diagnostic accuracy with an AUC of 0.932. The optimal cut-off value for distinguishing malignant from benign lesions was 40.03 kPa (sensitivity 88%, specificity 95%), corresponding to 3.6 m/s. Stiffness measurements showed high reproducibility across observers. SWE accurately differentiates benign from malignant breast lesions, improving specificity, and potentially reducing unnecessary biopsies. Standardization is needed to support widespread clinical adoption.

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies.

The Scythrididae Rebel, 1901, comprises small and morphologically homogeneous microlepidoptera whose identification relies mainly on genitalia examination. In Italy, this group has been more extensively investigated than other microlepidoptera families, but molecular data remain scarce. This study aims to improve the knowledge of Scythrididae diversity in southern Italy through DNA barcoding. Twenty-nine specimens collected in Calabria and Basilicata underwent DNA barcoding, yielding 28 sequences of 602–658 bp, which clustered into 10 Barcode Index Numbers (BINs), corresponding to 10 species. Three BINs are newly created in the Barcode of Life Data System (BOLD), enriching the global reference library. Except for Scythris aspromontis Jäckh, 1978, described for the study area, the remaining species represent faunistic novelties at different scales: Scythris dissitella (Zeller, 1847) is new for continental Italy and eight additional species are new for southern Italy or Calabria and Basilicata regions. Moreover, a significant genetic distance was observed between our sequence of Scythris cf. tabidella (Herrich-Schäffer, 1855) and sequences of tabidella available on BOLD, which will be addressed in detail in the discussions. The results highlight the underexplored diversity of Scythrididae in the Mediterranean region and confirm the effectiveness of integrative taxonomy combining DNA barcoding and morphology. This study provides the first molecular contribution to the Scythrididae fauna of southern Italy and contributes to improving the taxonomic knowledge of a group that remains insufficiently investigated in the Mediterranean region.

Background/Objectives: Early detection of prostate cancer (PCa) enables timely therapeutic intervention and improved clinical outcomes. Screening strategies are increasingly individualized and now incorporate multiparametric MRI findings, reported using the Prostate Imaging Reporting and Data System (PI-RADS), to refine biopsy decision-making. PROSTest is a novel machine learning (ML)-enhanced, 30-gene mRNA liquid biopsy assay developed to detect PCa from whole blood. In this prospective study (NCT06872619), we evaluated whether PROSTest could function as a pre-biopsy triage tool to inform biopsy decisions while preserving sensitivity for clinically significant prostate cancer (csPCa). Methods: Of 121 men evaluated, 111 (91.7%) completed the full diagnostic work-up-including PSA testing, PROSTest analysis, and PI-RADS assessment-and subsequently underwent image-guided biopsy. Peripheral blood samples for PROSTest were collected prior to biopsy. RNA-stabilized samples underwent RNA isolation followed by reverse transcription and quantitative PCR. Gene expression data were processed using a proprietary machine learning algorithm to generate a continuous range from 0 to 100. A clinically validated cut-off ≥ 50 was applied to produce a binary (positive/negative) result. The diagnostic accuracy of PROSTest was assessed against histology-confirmed prostate cancer. Results: The median age of participants was 69 years (47-83 years) and the median PSA was 7.5 ng/mL (IQR: 5.8-11.4 ng/mL); most patients (104 of 111; 93.7%) had a PI-RADS score of three to five. PCa was diagnosed in 97 men (87.4%) including eight in ISUP Grade Group (GG) 1, 46 in GG2, 33 in GG3, three in GG4 and seven in GG5. PROSTest was positive in 102/111 (91.9%). Among men with biopsy-confirmed PCa, diagnostic accuracy was 99% (93/94). Of the 17 men without histologic evidence of disease, eight (47%) were PROSTest-negative. The overall accuracy was 91% (84.1-95.6%) with an NPV of 89% (51.6-98.4%). Among the nine patients with positive PROSTest but negative biopsy, PI-RADS scores were 4 (n = 6), 3 (n = 1), and 2 (n = 2). Conclusions: PROSTest demonstrated an overall accuracy of 91% (95% CI: 84.1-95.6%) with an NPV of 89%. Among men without a detectable prostate cancer on biopsy, 47% (8/17) were PROSTest-negative. These results suggest that PROSTest may serve as a useful pre-biopsy triage assay.

Online social networks (OSNs) produce large volumes of user-generated data, enabling personalized services but also exposing users to significant privacy risks, a lack of transparency and frequent security breaches. Existing blockchain- and machine learning–based privacy-preservation methods struggle with high computational costs, limited scalability and weak malicious-node detection. To address these gaps, this work proposes a Blockchain-Driven Privacy Preservation Scheme with Progressive Graph Convolutional Networks (BPPS-SPD-PGCN) for secure and efficient protection of personal data in OSNs. The framework integrates Adaptive Two-Stage Unscented Kalman Filtering for data preprocessing, PGCN for malicious-node detection, ARPO for optimizing PGCN weights and Fair Proof-of-Reputation blockchain for secure access control. Two smart contracts (RG-SH and RG-ST) further enhance data confidentiality and storage integrity. Using the Epinions dataset, the proposed technique was evaluated through Accuracy, Precision, Recall, F1-score and Computational Time. The system achieved 99.04% Accuracy, 92.34% Precision, 99.14% Recall and 99.93% F1-score, outperforming PPB-OSN-GCN, HCS-PSC-SVM and BDI-ISPP-CNN. Overall, BPPS-SPD-PGCN provides a more robust, precise and secure privacy-preservation solution for OSNs, offering significant improvements over existing approaches.