Search Results (14,890)

Background/Objectives: Preoperative prognostic assessment is essential for optimizing treatment strategies in pancreatic ductal adenocarcinoma (PDAC). This study aimed to evaluate and compare the prognostic value of the Heidelberg Pancreatic Prognostic (HELPP) score and the C-reactive protein–albumin–lymphocyte (CALLY) index in patients with resectable PDAC. Methods: We retrospectively analyzed clinical and laboratory data of 109 patients with resectable PDAC who underwent curative-intent surgery and adjuvant therapy. Patients were stratified based on preoperative HELPP and CALLY scores. Overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan–Meier analysis, while independent prognostic factors were determined through multivariate Cox regression. Results: Kaplan–Meier survival analyses demonstrated that a HELPP score > 3 and a low CALLY index (≤1.029) were significantly associated with worse OS and DFS (log-rank p < 0.05). In multivariate analysis, the HELPP score was identified as an independent predictor of survival, whereas the CALLY index, although associated with survival in univariate analysis, did not reach statistical significance. In ROC analysis, both models exhibited acceptable discrimination, with the HELPP score achieving superior AUC values in predicting 1-year OS compared to the CALLY index. Conclusions: The HELPP score demonstrated independent prognostic value in multivariate analysis and may serve as a robust preoperative tool in resectable PDAC. The CALLY index, although not independently significant in multivariate analysis, showed strong prognostic separation in Kaplan–Meier survival analyses and may still aid in preoperative risk stratification, particularly where access to comprehensive scoring systems is limited. Full article

Artificial intelligence (AI) is increasingly transforming higher education, evolving from simple personalization tools into a wide range of applications that support teaching, learning, and assessment. This study examines how university instructors in Lebanon perceive and adopt AI in their academic practices, drawing on evidence from two private institutions: Notre Dame University–Louaize (NDU) and the Holy Spirit University of Kaslik (USEK). The study also proposes practical directions for effective institutional implementation. Using a cross-sectional design and convenience sampling, data were collected from 133 faculty members. Although 73.7% of participants reported moderate to high familiarity with AI, their actual classroom use of such tools remained limited. Adoption was primarily centered on chatbots (69.2%) and translation tools (54.9%), while more advanced technologies, such as adaptive learning systems and AI-based tutoring platforms, were seldom utilized (under 7%). Additionally, participants identified efficiency (69.2%), increased student engagement (44.4%), and personalized learning opportunities (42.9%) as the main benefits of AI integration. In contrast, they reported insufficient training (46.6%), restricted access to resources (45.9%), and concerns about the accuracy of AI-generated outputs (29.3%) as major barriers. Moreover, statistical analysis indicated a strong positive relationship between familiarity with AI and frequency of adoption, with no significant differences across gender, age, or academic qualifications. Overall, the results suggest that faculty members in Lebanese higher education currently view AI primarily as a helpful tool for improving efficiency rather than as a transformative pedagogical innovation. To advance integration, higher education institutions should prioritize targeted professional development, ensure equitable access to AI tools, and establish transparent ethical and governance frameworks. Full article

Fuels constitute one of the most strategically significant categories of goods in the global economy. In many countries, including Poland, fuel prices are determined not only by global market dynamics but also by domestic fiscal instruments such as excise taxes, value-added tax (VAT), and fuel surcharges. The primary objective of this study is therefore to assess the extent to which tax burdens and profit margins shape diesel prices in Poland, thereby providing a deeper understanding of the market’s sensitivity to fiscal interventions and the pricing strategies adopted by fuel companies. The analysis draws on weekly data for the period 2006–2025, encompassing crude oil prices, wholesale and retail diesel prices, and relevant tax components (VAT, excise tax, and fuel surcharges). Methodologically, the study employs the Bai–Perron breakpoint test alongside correlation and comparative methods. The findings indicate that changes in indirect taxation and the fuel surcharge in Poland were predominantly upward and incremental, exerting only limited immediate effects on wholesale and retail fuel prices. This pattern was particularly evident outside of periods of acute geopolitical shocks, such as the 2022 war in Ukraine, when government interventions aimed to mitigate sudden price surges. Moreover, analysis of PKN Orlen’s margin dynamics shows that the company remained consistently profitable, with the highest processing margins observed following the reduction of the VAT rate, highlighting the interplay between fiscal policy and corporate pricing behavior. An exception occurred in 2022, when political involvement led to negative retail margins despite a reduction in VAT, a policy decision intended to mitigate sharp increases in fuel prices. The evidence suggests that petrochemical companies have greater capacity to affect prices through adjustments to wholesale margins than to retail margins. The study also underscores the critical role of fiscal policy in protecting households from fuel price volatility. It also demonstrates that carefully designed adjustments to taxation and other fiscal instruments can meaningfully influence market outcomes and corporate profitability, thereby highlighting their importance in broader economic stabilization efforts. Full article

Background and Objectives: Hospital length of stay (LOS) after breast cancer surgery is a key indicator of postoperative recovery, healthcare quality, and hospital resource utilization. Traditional statistical approaches have identified general correlates of LOS but remain limited in predictive accuracy, particularly in heterogeneous real-world surgical populations. Machine learning (ML) models may offer improved performance by capturing nonlinear interactions among clinical, pathological, and operative factors. This study aimed to evaluate ML algorithms for LOS prediction and to identify determinants of prolonged hospitalization in a contemporary breast cancer cohort. Materials and Methods: We conducted a retrospective cross-sectional study of 198 consecutive breast cancer patients who underwent surgery between January 2022 and December 2023 at a single tertiary care center. Clinical, pathological, and surgical data were extracted from electronic medical records. Three regression models—multiple linear regression, Random Forest, and Gradient Boosting—were trained to predict continuous LOS, and three classification models were applied to prolonged LOS (≥10 days). Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and area under the curve (AUC). Feature importance was analyzed for the best-performing model. Results: The median LOS was 7 days (IQR 5–10), ranging from 1 to 26 days. Breast-conserving surgery showed the shortest LOS (median 3 days), while mastectomy with immediate reconstruction resulted in the longest stays (median 8 days). Random Forest regression achieved the lowest prediction error (MAE 2.31 days; RMSE 2.82; R² = 0.37), outperforming Gradient Boosting and substantially surpassing linear regression (MAE 8.63 days; R² = –8.17). Key predictors included age, surgical complexity, reconstruction modality, BMI, implant capacity, and tumor burden. Classification models yielded modest AUCs (0.545–0.589) with low sensitivity, indicating limited discriminative performance for dichotomized LOS outcomes. Conclusions: Machine-learning models, particularly Random Forest, substantially improve LOS prediction compared with classical regression and provide clinically meaningful insights into the drivers of hospitalization after breast cancer surgery. Continuous LOS modeling is more informative than binary thresholds. These findings support integrating ML-based tools into perioperative planning, resource allocation, and patient counseling in breast surgical care. Full article

As China’s industrialization progresses, the transformation of site properties across various regions has become increasingly common. Concurrently, with the relocation and market exit of some enterprises, the land occupied by the original factory sites has been developed for other uses. This study provides a comprehensive evaluation of soil and groundwater contamination levels and the associated ecological and health risks in abandoned industrial lands. The investigation focused on analyzing heavy metal and polycyclic aromatic hydrocarbon (PAH) contamination using various assessment methods, including the single-factor pollution index, Nemerow composite pollution index, and potential ecological risk index. These methods were used to assess the contamination levels of 11 heavy metals in both soil and groundwater. Additionally, health risk assessments for PAHs were conducted using the Incremental Lifetime Cancer Risk (ILCR) and Carcinogenic Risk (CR) models, considering both direct and indirect exposure pathways. The results indicated that the average concentration of each heavy metal in the soil did not exceed the screening thresholds, with all Nemerow index values falling below 1, suggesting that the site is not significantly polluted. Ecological risk assessment further revealed that most heavy metals posed minor risks, while some localized areas showed slight enrichment. Health risk assessments for PAHs indicated that, although the risks for both adults and children were within acceptable limits, the ingestion pathway for children showed a slightly higher risk compared to adults. The groundwater quality met Class IV standards, indicating no significant pollution. These findings provide data support and reference for future land-use planning, environmental management, and remediation strategies for abandoned industrial sites. Full article

Background/Objectives The leopard (Panthera pardus), an apex predator listed in CITES Appendix I and classified as Vulnerable by the IUCN, is undergoing severe population declines driven by habitat loss, human–wildlife conflict, and illegal trade. Rapid and reliable species and individual identification is critical for conservation and forensic applications, particularly when analyzing highly processed or degraded seized wildlife products, where morphological identification is often impossible. We aimed to develop and validate a robust multiplex quantitative real-time PCR (qPCR) assay combined with a short tandem repeat (STR) system for the species-specific detection and individual identification of P. pardus. Methods The qPCR assay (Ppar Qplex) was designed to target a mitochondrial Cytochrome b (Cyt b) fragment for species confirmation, a nuclear marker (PLP) for general Feliformia detection and quantification, and an artificial internal positive control (IPC) to monitor PCR inhibition. The assay’s performance was validated for robustness, specificity, sensitivity, repeatability, and reproducibility, utilizing DNA extracted from 30 P. pardus individuals (hair and feces) and tested against 18 related Feliformia species and two outgroups. Individual identification was achieved using a set of 18 STR loci and a sex determination system adapted from previously published Panthera panels. Results Validation demonstrated high specificity for the Ppar Qplex: mitochondrial amplification occurred exclusively in P. pardus samples. The nuclear marker consistently amplified across all 18 tested Feliformia species but not the outgroups. The assay showed high analytical sensitivity, successfully detecting DNA at concentrations as low as 1 pg/µL, with consistent results confirmed across different sample types, replicates, and independent users. Furthermore, the STR multiplex successfully generated 30 unique individual profiles using the 18 polymorphic loci and the sex determination system. Conclusions The combined qPCR assay and STR system provide a fast, sensitive, and highly specific molecular framework for rapid leopard detection, quantification, and individual identification from a wide range of sample types. These tools strengthen forensic capacity to combat wildlife crime and provide critical data to support evidence-based conservation management of P. pardus. P. pardus, an apex predator listed in CITES Appendix I and classified as Vulnerable by the IUCN, is undergoing severe population declines driven by habitat loss, human–wildlife conflict, and illegal trade. Rapid and reliable identification of seized specimens is therefore critical for conservation and forensic applications, mainly when products are highly processed. We developed and validated a multiplex quantitative real-time PCR (qPCR) assay targeting the mitochondrial gene Cytochrome b (Cyt b) for species-specific detection. The assay was tested on verified leopard individuals and validated across 18 Feliformia and two outgroup species (Homo sapiens, Canis lupus familiaris). Analytical performance was assessed through robustness, specificity, sensitivity, repeatability, and reproducibility. Mitochondrial amplification occurred exclusively in leopard samples, while nuclear markers amplified consistently across Feliformia but not in outgroup species. The assay’s limit of DNA detection is 1 pg/µL and produces consistent results across replicates, tested types of samples (hair, feces), and independent users, with internal controls confirming the absence of inhibition. In addition, we present the results of successful individual identification using the set of 18 STR loci and the sex determination system. The developed qPCR and STR systems provide a fast, sensitive, and specific solution for leopard detection and quantification, reinforcing forensic efforts against wildlife crime and supporting conservation of P. pardus. Full article

Background: Childhood undernutrition and overnutrition continue to be major public health challenges in South Africa. There is limited evidence on how socio-economic factors and dietary behaviors influence nutritional outcomes among school-aged children, particularly in rural areas such as Thulamela Municipality. Objective: This study aimed to examine the socio-demographic determinants, dietary patterns, and nutritional status among school-aged children in Thulamela Municipality, Limpopo Province, South Africa. Methods: A cross-sectional survey was conducted with 347 children aged 8–12 years. Simple random sampling was used to select eight villages from a total of 227 within the municipality. A snowball sampling method was used to recruit eligible children. Data on socio-demographic characteristics, including the child’s sex, parental education level, marital status, and employment status, were collected. Additionally, their dietary habits and meal frequency patterns were collected using structured questionnaires. Anthropometric measurements including height, weight, and BMI-for-age were obtained following WHO growth standards. Associations between variables were assessed using chi-square tests, with p-values < 0.05 considered statistically significant. Results: The prevalence of severe and moderate stunting was 20.5% and 21.0%, respectively. Overweight conditions and obesity affected 32.6% and 16.2% of participants, respectively. Parental education (p = 0.027), marital status (p = 0.001), and household income (p = 0.043) showed significant associations with height-for-age and BMI-for-age Z-scores. Additionally, regular breakfast consumption and the frequent intake of vegetables and dairy products were positively associated with improved nutritional outcomes (p < 0.05). Conclusions: The nutritional profile of school-aged children in Thulamela Municipality reflects a double burden of malnutrition, with concurrent high rates of stunting, overweight conditions, and obesity. Interventions that promote balanced diets and address socio-economic disparities are crucial for improving child growth and overall health. Socio-economic factors, including parental education, marital status, and household income, were significantly associated with children’s height-for-age and BMI-for-age. Furthermore, the regular consumption of breakfast, vegetables, and dairy products was associated with better nutritional outcomes, highlighting the influence of both dietary behaviors and socio-demographic determinants on child growth and health. Implementing nutrition education programs within schools that emphasize the value of balanced diets and highlighting the significance of eating breakfast regularly and incorporating vegetables and dairy products into daily meals is important. These programs should include both children and their caregivers to support regular healthy eating behaviors at home and in school. Additionally, schools should carry out regular growth monitoring and nutritional assessments to identify early indications of undernutrition or overnutrition, enabling prompt referrals and interventions for children who may be at risk. Full article

Background/Objectives: Viral hepatitis remains a significant global cause of chronic liver disease, highlighting the importance of effective vaccination strategies. This review assesses recent evidence on vaccine safety and effectiveness. Methods: A comprehensive search of PubMed, Embase, Web of Science, and Scopus identified English-language studies published from January 2000 to September 2025. Eligible studies evaluated vaccination for hepatitis A, B, C, or E, as well as vaccine responses in individuals with chronic liver disease or HIV infection. Of 5254 records screened, 166 studies met the inclusion criteria. Results: Hepatitis A vaccines demonstrated excellent safety, 95–100% short-term seroprotection, and durable immunity for both inactivated and live-attenuated formulations, with population-level reductions in disease incidence. Hepatitis B vaccines showed consistently strong immunogenicity across age groups, with over 90% seroprotection from recombinant and CpG-adjuvanted formulations. Effective prevention of mother-to-child transmission required maternal antiviral therapy, timely birth-dose vaccination, hepatitis B immunoglobulin (HBIG) administration, and post-vaccination serologic testing. Long-term data demonstrated immune persistence for up to 35 years and significant reductions in liver cancer following neonatal HBV vaccination. Limited studies in hepatitis C populations showed impaired responses, partially improved with higher or booster doses. Hepatitis E vaccines showed excellent safety and over 99% seroconversion. In non-viral liver disease and post-transplant populations, vaccine responses were reduced but remained clinically meaningful, especially with adjuvanted or higher-dose HBV vaccines. Among HIV-infected individuals, HAV vaccination was generally effective, while enhanced HBV regimens markedly improved seroprotection. Conclusions: Hepatitis A, B, and E vaccines are safe, immunogenic, and effective, with neonatal hepatitis B vaccination critical for preventing maternal transmission. No licensed HCV vaccine exists, and therapeutic HCV vaccines show limited efficacy. Optimized and targeted vaccination strategies are needed for individuals with chronic liver disease, HIV infection, HCV infection, transplant recipients, and other immunocompromised populations to maximize public health impact. Full article

This study identifies key determinants shaping countries’ Sustainable Development Goals performance and develops classification models for predicting country group membership based on the SDG Index. The research addresses the urgent need to optimize development policies amid limited resources and the approaching 2030 Agenda deadline. Using data from 154 countries (2024), the analysis reveals that key SDG determinants are fundamentally method-dependent: discriminant analysis identified Goals 10, 6, 15, and 5 as most influential for differentiating countries by SDGI level, while Random Forest identified Goals 4, 9, and 2 as the most important predictors. This divergence reflects fundamentally different analytical perspectives—linear contributions to group separation versus complex nonlinear interactions and synergies between goals—with critical policy implications for prioritization strategies. Correlation analysis demonstrates that sustainable development dynamics operate differently across development stages: high-development countries show strongest associations with technological advancement and institutional capacity, while low-development countries exhibit compensation effects where basic infrastructure provision occurs alongside lagging human capital development. The discriminant model achieved 94.08% overall accuracy with perfect classification for extreme SDGI categories, while the Random Forest model provides complementary insights into interactive pathways. The scientific contribution lies in demonstrating that perceived variable importance depends on analytical framework rather than representing objective reality, and in providing validated classification tools for rapid assessment in data-limited contexts. These findings offer actionable guidance for evidence-based resource allocation and policy prioritization in the critical final years of SDG implementation. Full article

The oral microbiome has become an emerging focus of oral cancer research, with growing evidence linking microbial communities to disease development, progression, and prognosis. However, there is limited consensus on optimal sampling strategies, storage methods, and analytical approaches. This narrative review critically evaluates current strategies for sampling, preservation, DNA extraction, sequencing, and data analysis in oral microbiome research related to oral cancer. We compared commonly used sampling methods, including saliva, oral rinse, swab, brush, and tissue biopsy, and reviewed preservation conditions, extraction kits, sequencing platforms, and analytical pipelines reported in recent oral microbiome studies. Sampling approaches affect microbial yield and site specificity. Saliva and oral rinse samples are convenient and noninvasive but may dilute lesion-specific microbial signals, whereas lesion-directed swabbing or brushing yields greater microbial biomass and biological relevance. Preservation media and storage temperature significantly influence microbial stability, and DNA extraction methods vary in their ability to remove host DNA. Although 16S rRNA gene sequencing remains the most common approach, shotgun metagenomics offers higher resolution and function insights but is still limited by clinical applicability. Differences in data pre- and post-processing models and normalization strategies further contribute to inconsistent microbial profiles. Given that oral mucosal sites differ markedly in structure and microenvironment, careful consideration is required to ensure that collected samples accurately represent the biological question being addressed. Methodological consistency across all workflow stages—from collection to analysis—is essential to generate reproducible, high-quality data and to enable reliable translation of oral microbiome research into clinical applications for cancer detection and risk assessment. Together, these insights provide a framework to guide future study design and support the development of clinically applicable microbiome-based biomarkers. Full article

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article

Satellite precipitation products serve as valuable global data sources for hydrological modeling, yet their applicability across different hydrological models remains insufficiently explored. The distributed physics-informed deep learning model (DPDL), as a representative of emerging differentiable, physics-based hydrological models, requires a systematic evaluation of the suitability of multi-source precipitation products within its modeling framework. This study focuses on the Xiangjiang River Basin in southern China, where both a DPDL model and a Soil and Water Assessment Tool (SWAT) model were constructed. In addition, two model training strategies were designed: S1 (fixed parameters) and S2 (product-specific recalibration). Multiple precipitation products were used to drive both hydrological models, and their streamflow simulation performance was evaluated under different training schemes to analyze the compatibility between precipitation products and hydrological modeling frameworks. The results show that: (1) In the Xiangjiang River Basin of southern China, GSMaP demonstrated the best overall performance with a Critical Success Index of 0.70 and a correlation coefficient (Corr) of 0.79; IMERG-F showed acceptable accuracy with a Corr of 0.75 but had a relatively high false alarm rate (FAR) of 0.32; while CMORPH exhibited the most significant systematic underestimation with a relative bias (RBIAS) of −8.48%. (2) The DPDL model more effectively captured watershed hydrological dynamics, achieving a validation period correlation coefficient of 0.82 and a Nash–Sutcliffe efficiency (NSE) of 0.79, outperforming the SWAT model. However, the DPDL model showed a higher RBIAS of +16.69% during the validation period, along with greater overestimation fluctuations during dry periods, revealing inherent limitations of differentiable hydrological models when training samples are limited. (3) The S2 strategy (product-specific recalibration) improved the streamflow simulation accuracy for most precipitation products, with the maximum increase in the NSE coefficient reaching 15.8%. (4) The hydrological utility of satellite products is jointly determined by model architecture and training strategy. For the DPDL model, IMERG-F demonstrated the best overall robustness, while GSMaP achieved the highest accuracy under the S2 strategy. This study aims to provide theoretical support for optimizing differentiable hydrological modeling and to offer new perspectives for evaluating the hydrological utility of satellite precipitation products. Full article

The western corn rootworm (Diabrotica virgifera virgifera LeConte) remains one of the most damaging pests of maize across Europe, including Romania. Reliable integrated pest management relies on monitoring systems capable of capturing adult flight activity under field conditions. This study presents a comparative field evaluation of three monitoring approaches: Virgiwit yellow sticky panels (YSP), pheromone-based CSALOMON^® KLP+ traps, and the automated iScout^® digital monitoring system. Monitoring was conducted at weekly intervals over an eight-week period (20 July–15 September 2025) in four maize fields in western Romania. Capture data were analyzed descriptively to assess relative trap performance and to explore associations with selected meteorological variables. KLP+ traps consistently recorded the highest numbers of adults, while YSP traps reproduced the main seasonal flight patterns. The iScout^® system captured fewer individuals but provided continuous temporal information on adult activity. Correlation analyses indicated generally weak and inconsistent relationships between trap captures and short-term weather variables, reflecting the limitations imposed by weekly manual sampling and site-specific variability. Overall, the results highlight the complementary strengths and limitations of manual and automated monitoring tools and support their exploratory use for characterizing seasonal flight activity and temporal population patterns of Diabrotica virgifera virgifera under field conditions. Further multi-year and device-specific validation is required before automated systems can be fully integrated into operational pest management frameworks. Full article

Background: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are prevalent neurodevelopmental conditions in childhood. Beyond biological factors, social and environmental conditions influence developmental experiences and pathways to diagnosis. Nationally representative studies examining multiple social determinants in relation to ADHD, ASD, and comorbidity across recent years remain limited. Methods: We analyzed pooled cross-sectional data from six cycles (2018–2023) of the U.S. National Survey of Children’s Health, including 205,480 children aged 3–17 years. Parent-reported, clinician-diagnosed current ADHD and ASD were the primary outcomes; comorbid ADHD and ASD were examined secondarily. Social determinants included household income relative to the federal poverty level, parental education, health insurance type, food insecurity, and caregiver-reported neighborhood safety. Survey-weighted prevalence estimates and logistic regression models accounted for the complex sampling design and adjusted for demographic, family, regional, and temporal factors. Results: The weighted prevalence of ADHD was 9.7% and ASD was 2.9%; 1.1% of children had comorbid ADHD and ASD. Lower household income, food insecurity, unsafe neighborhood conditions, and lower parental education were associated with higher adjusted odds of both conditions. Boys had substantially higher odds of ADHD and ASD. After adjustment, non-Hispanic Black and Hispanic children had lower odds of ASD than non-Hispanic White children, consistent with differential identification rather than lower underlying prevalence. Comorbidity was concentrated among socially disadvantaged children. Conclusions: ADHD and ASD are socially patterned across U.S. children. Integrating developmental screening with assessment of social risks may support more equitable identification and intervention. Full article

Fisheries management in the Mediterranean Sea faces persistent challenges due to the prevalence of data-poor and data-limited stocks, small-scale multi-species fisheries, and limited long-term monitoring programs. Effective assessment methodologies are critical to ensuring sustainable exploitation, yet traditional data-rich stock assessment models remain infeasible for many Mediterranean fisheries. This review provides a comprehensive synthesis of current methodologies developed and applied to assess data-poor fisheries in the Mediterranean context. We examine catch-only approaches, length-based methods, empirical indicators, and multi-indicator frameworks increasingly adopted by the General Fisheries Commission for the Mediterranean (GFCM) and the EU’s Data Collection Framework (DCF). Special attention is given to case studies from the western, central, and eastern Mediterranean that demonstrate the opportunities and limitations of these approaches. We further explore emerging tools, including integrated modeling frameworks, simulation-based harvest control rules, and participatory approaches involving fishers’ local knowledge, to highlight innovations suited to mixed, small-scale Mediterranean fisheries. The review concludes by identifying key gaps in data collection, assessment capacity, and institutional coordination, and proposes a roadmap for improving data-poor fisheries management under Mediterranean-specific ecological, socio-economic, and governance constraints. By consolidating methodological advances and practical lessons, this review aims to provide a reference framework for researchers, managers, and policymakers seeking to design robust, adaptive strategies for sustainable fisheries management in data-limited Mediterranean contexts. Full article