Search Results (1,583)

Background: HER2-positive breast cancer patients receiving chemotherapy and targeted therapy (including anthracyclines and trastuzumab) face an elevated risk of cardiotoxicity, which can lead to long-term cardiovascular complications. Identifying predictive biomarkers is essential for early intervention. Circulating microRNAs (miRNAs), known regulators of gene expression and cardiovascular function, have emerged as potential indicators of cardiotoxicity. This study aims to evaluate the differential expression of circulating miRNAs in HER2-positive breast cancer patients undergoing chemotherapy and to assess their prognostic ability for therapy-induced cardiotoxicity using machine learning models. Methods: Forty-seven patients were assessed for cardiac toxicity at baseline and every 3 months, up to 15 months. Blood samples were collected at baseline. MiRNA expression profiling for 84 microRNAs was performed using the miRCURY LNA miRNA PCR Panel. Differential expression was calculated via the 2−∆∆Ct method. The five most upregulated and five most downregulated miRNAs were further assessed using univariate logistic regression and receiver operating characteristic (ROC) analysis. Five machine learning models (Decision Tree, Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), k-Nearest Neighbors (KNN)) were developed to classify cardiotoxicity based on miRNA expression. Results: Forty-five miRNAs showed significant differential expression between cardiac toxic and non-toxic groups. ROC analysis identified hsa-miR-155-5p (AUC 0.76, p = 0.006) and hsa-miR-124-3p (AUC 0.75, p = 0.007) as the strongest predictors. kNN, SVM, and RF models demonstrated high prognostic accuracy. The decision tree model identified hsa-miR-17-5p and hsa-miR-185-5p as key classifiers. SVM and RF highlighted additional miRNAs associated with cardiotoxicity (SVM: hsa-miR-143-3p, hsa-miR-133b, hsa-miR-145-5p, hsa-miR-185-5p, hsa-miR-199a-5p, RF: hsa-miR-185-5p, hsa-miR-145-5p, hsa-miR-17-5p, hsa-miR-144-3p, and hsa-miR-133a-3p). Performance metrics revealed that SVM, kNN, and RF models outperformed the decision tree in overall prognostic accuracy. Pathway enrichment analysis of top-ranked miRNAs demonstrated significant involvement in apoptosis, p53, MAPK, and focal adhesion pathways, all known to be implicated in chemotherapy-induced cardiac stress and remodeling. Conclusions: Circulating miRNAs show promise as biomarkers for predicting cardiotoxicity in breast cancer patients. Machine learning approaches may enhance miRNA-based risk stratification, enabling personalized monitoring and early cardioprotective interventions. Full article

Superparamagnetic iron oxide nanoparticles (SPIONs) are commonly produced through wet-chemical methods that require high temperature and pressure and involve multiple synthesis steps. Our research group has developed an innovative, sustainable, and patented one-step aqueous synthesis operating at ambient temperature and pressure, enabling the direct production of SPIONs in suspension. In this work, we investigated the extension of this method to obtain polymer-coated SPIONs for biomedical imaging applications. Two water-soluble and biocompatible polymers—poly(ethylene glycol) (PEG) and poly(vinyl alcohol) (PVA)—were selected and prepared into twelve samples varying in polymer concentration and iron precursor molarity. Each formulation was characterized and compared to bare SPIONs synthesized with the same approach using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and alternating gradient magnetometry (AGM). The results confirm that the one-step method yields polymer-coated nanoparticles with a cubic spinel magnetite core. PEG produced spherical, monodisperse particles (10–30 nm) exhibiting superparamagnetic behavior but lower magnetization values (1–5 emu/g). In contrast, PVA-coated nanoparticles showed a morphology dependent on polymer concentration and reagent molarity, while maintaining an average size of ~10 nm and superparamagnetic behavior, with magnetization comparable to bare SPIONs (25–50 emu/g). A preliminary MRI evaluation of a selected PVA-coated sample revealed relaxivity values of r₁ = 0.12 mM⁻¹ s⁻¹ and r₂ = 6.44 mM⁻¹ s⁻¹, supporting the potential of this synthesis route for imaging-oriented nanomaterials. Full article

This study aimed to (1) analyse home advantage (HA) and home winning percentage (HW), and (2) examine the impact of team level on HA and HW across major European men’s handball leagues from 2021–2022 to 2024–2025. Match data from 6028 games across seven elite leagues—ASOBAL (Spain), Starligue (France), Bundesliga (Germany), Herre Handbold (Denmark), NB I (Hungary), Superliga (Poland), and Andebol I (Portugal)—were analysed, involving 423 team-seasons. Teams were grouped into three competitive levels using hierarchical clustering: high (HLT), medium (MLT), and low (LLT). Differences between leagues were significant for HA, with the Portuguese league showing the lowest values and falling below those of Denmark and Hungary, while the remaining competitions presented comparable results. Team level displayed a clear gradient, with LLT showing the greatest HA and HLT the smallest. Interaction effects were particularly evident for LLT, which recorded reduced HA in Portugal and France compared with Spain, Denmark, and Hungary. For HW, Portugal again recorded the lowest value, and the pattern across team levels was consistent (high > medium > low). Overall, the findings show that the local performance advantage in men’s elite handball is shaped by both competitive level and league-specific contexts, reflecting structural, organisational, and cultural characteristics of each competition. Full article

The accurate extraction of damage information around airport runways is crucial for the rapid development of subsequent damage effect assessment work and the timely formulation of the ensuing operational plan. However, the presence of dark interference areas such as trees and shadows in the background, as well as the increased irregularity at the edge of the crater due to the proximity to the crater, pose challenges to the accurate extraction of the crater area in high entropy images. In this paper, we present a multi-feature fusion-based two-stage method for airport crater extraction from remote sensing images. In stage I, we designed an edge arc segment grouping and matching strategy based on the shape characteristics of craters for preliminary detection. In stage II, we established a crater model based on the regional distribution characteristics of craters and used the marked point processing method for crater detection. In addition, during the step of calculating the magnitude of the edge gradient, we proposed a near-region search strategy, which enhanced the ability of the proposed method to accurately extract craters with irregular shapes. In the test images, the proposed method accurately extracts craters located around and within the runways. Among them, the average recall R and precision P of the proposed method for extracting all craters around the airport runways reached 89% and 87%, respectively, and the average recall R and precision P of the proposed method for extracting craters inside the runways reached 94% and 92%, respectively. Meanwhile, the results of comparative tests showed that our method outperformed other representative algorithms in terms of both crater extraction recall and extraction precision. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Desert plants develop unique functional traits and resource utilization strategies under environmental stress, among which, water and nitrogen utilization strategies are the key resource utilization strategies for desert plants. Research on plant water and nitrogen utilization and leaf functional traits has rarely involved high-altitude desert shrubs. The synergistic or trade-off relationship between water and nitrogen utilization in desert shrubs remains unclear, and the variation patterns of leaf functional trait combinations related to water and nitrogen utilization along environmental gradients urgently need to be studied. This study takes the typical desert shrubs in the eastern part of the Qaidam Basin on the Qinghai–Tibet Plateau in China as the research object, selects the stable carbon and nitrogen isotopes (δ¹³C, δ¹⁵N) of plant leaves to characterize the water use efficiency (WUE) and nitrogen use strategy (NUE) of plants, explores the main leaf functional traits related to water and nitrogen utilization, and analyzes the relationship between leaf functional traits and environmental factors. The results show that the resource utilization traits of desert shrubs can be divided into two groups: water and carbon utilization centered on δ¹³C and nutrient utilization centered on δ¹⁵N. There are synergistic or trade-off relationships among plant functional traits. There is a trade-off relationship between water and nitrogen utilization in plants. The leaf functional traits related to water and nitrogen utilization in plants form a “water and nitrogen utilization leaf economic spectrum” along the gradients of temperature, drought, salinity, and nutrients. In conclusion, desert plants adapt to the environment of high cold, drought, high salt content, and limited nutrients by adjusting the relevant leaf functional traits. This study combines the stable carbon and nitrogen isotopes of plant leaves with the combined characteristics of leaf functional traits under different environmental gradients, providing a new perspective for understanding the water and nitrogen utilization strategies of high-altitude desert shrubs and their adaptation mechanisms to arid environments. Full article

Background: Rabies is among the oldest known zoonotic viral diseases and is caused by members of the Lyssavirus genus. The prototype species, Lyssavirus rabies, effectively evades the host immune response, allowing the infection to progress unnoticed until the onset of clinical signs. At this stage, the disease is irreversible and invariably fatal, with definitive diagnosis possible only post-mortem. Given the advances in modern proteomics, this study aimed to identify potential protein biomarkers for antemortem diagnosis of rabies in dogs, which are the principal reservoir hosts of the rabies virus. Methods: Two hundred and thirty-one samples (brain tissues (BT), cerebrospinal fluids (CSF), and serum (SR) samples) were collected from apparently healthy dogs brought for slaughter for human consumption in South-East and North-Central Nigeria. All the BT were subjected to a direct fluorescent antibody test to confirm the presence of lyssavirus antigen, and 8.7% (n = 20) were positive. Protein extraction, quantification, reduction, and alkylation were followed by on-bead (HILIC) cleanup and tryptic digestion. The resulting peptides from each sample were injected into the Evosep One LC system, coupled to the timsTOF HT MS, using the standard dia-PASEF short gradient data acquisition method. Data was processed using Spectronaut^TM (v19). An unpaired t-test was performed to compare identified protein groups (proteins and their isoforms) between the rabies-infected and uninfected BT, CSF, and SR samples. Results: The study yielded 54 significantly differentially abundant proteins for the BT group, 299 for the CSF group, and 280 for the SR group. Forty-five overlapping differentially abundant proteins were identified between CSF and SR, one between BT and CSF, and two between BT and SR; none were found that overlapped all three groups. Within the BT group, 33 proteins showed increased abundance, while 21 showed decreased abundance in the rabies-positive samples. In the CSF group, 159 proteins had increased abundance and 140 had decreased abundance in the rabies-positive samples. For the SR group, 215 proteins showed increased abundance, and 65 showed decreased abundance in the rabies-positive samples. Functional enrichment analysis revealed that pathways associated with CSF, spinocerebellar ataxia, and neurodegeneration were among the significant findings. Conclusion: This study identified canonical proteins in CSF and SR that serve as candidate biomarkers for rabies infection, offering insights into neuronal dysfunction and potential tools for early diagnosis. Full article

To address the technical bottleneck of the coordinated improvement of high-temperature rutting resistance, low-temperature cracking resistance and environmental protection performance of road asphalt, and to address the existing problems in the research of sepiolite modified asphalt, such as the ambiguous microscopic mechanism of action, the lack of quantitative relationship between dosage and performance, and the unclear adaptability of modification processes, this study employed high-purity sepiolite as a modifier. After optimizing its microstructure through organic and surface modification, the sepiolite with the best compatibility with asphalt was selected. Four dosage gradients of 2%, 4%, 6%, and 8% were designed. Rheological tests were conducted to investigate the effects of sepiolite on the rutting resistance at high temperature, the cracking resistance at low temperature, and the fatigue durability of asphalt. Gas chromatography–mass spectrometry (GC–MS) was used to analyze changes in the organic components of asphalt fumes, while Fourier-transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) were applied to reveal the microscopic interaction mechanisms and smoke-suppression principles. Results show that pristine sepiolite exhibits the best compatibility with asphalt. Although modified sepiolite shows a 43–45% increase in specific surface area, the overall high–low temperature coordination of the modified asphalt decreases by 10–15%. The sepiolite dosage has a significant influence on asphalt performance: when the dosage is 4–6%, the rutting factor of asphalt increases by 25–30%, indicating the best high-temperature deformation resistance; at 4%, the asphalt creep stiffness decreases by over 15%, minimizing the low-temperature cracking risk; and at 2–4%, the fatigue life extends by 9–13%, with the most notable improvement at 2%. In terms of smoke suppression, the porous structure of sepiolite adsorbs 3–5% of the light volatile components in asphalt, while its metal oxides inhibit the release of aliphatic and aromatic hydrocarbons, reducing toxic fume emissions by 12–18%. Microscopically, the interaction between sepiolite and asphalt is dominated by physical adsorption without chemical functional group recombination. The fibrous network of sepiolite enhances the structural stability of asphalt, while the adsorption of small and medium molecular components optimizes the molecular weight distribution, achieving a dual effect of performance enhancement and smoke suppression. Full article

Background/Objectives: Acute heart failure (AHF) is a heterogeneous syndrome with phenotype-dependent prognosis. NT-proBNP is the reference biomarker, but standard echocardiographic measures (TAPSE, RV–RA gradient, LVOT VTI) offer only partial prognostic insight. The Virtue Index, defined as (RV–RA gradient)/(TAPSE × LVOT VTI), was introduced to integrate right–left ventricular interaction. This study evaluated its clinical and prognostic performance in AHF and its behavior across ejection-fraction phenotypes. Methods: We retrospectively analyzed 222 patients with AHF; complete data for Virtue calculation were available in 168 (99 HFrEF, 69 HFpEF) patients. HFmrEF patients were excluded from subgroup prognostic analyses. Correlation with NT-proBNP was assessed using Spearman testing with bootstrap intervals, and in-hospital mortality prediction was evaluated using ROC analysis with DeLong comparisons. Results: In HFpEF, the Virtue Index correlated moderately with NT-proBNP (ρ = 0.38, p = 0.002) and showed fair prognostic discrimination (AUC 0.704), similar to the RV–RA gradient (0.724) and higher than TAPSE or LVOT VTI. In HFrEF, correlation was weak (ρ = 0.19, p = 0.06) and predictive accuracy was modest (AUC 0.584), while LVOT VTI performed best (AUC 0.700). NT-proBNP outperformed all echocardiographic parameters in both groups. Conclusions: The Virtue Index reflects integrated hemodynamics and shows phenotype-dependent prognostic value in AHF, being more informative in HFpEF than in HFrEF. Although NT-proBNP remained superior, Virtue may complement biomarker-based risk assessment by offering a rapid, bedside estimate of short-term mortality risk. Full article

Background: Health disparities research increasingly relies on complex survey data to understand survival differences between population subgroups. While Peters–Belson decomposition provides a principled framework for distinguishing disparities explained by measured covariates from unexplained residual differences, traditional approaches face challenges with complex data patterns and model validation for counterfactual estimation. Objective: To develop validated Peters–Belson decomposition methods for survival analysis that integrate ensemble machine learning with transfer learning while ensuring logical validity of counterfactual estimates through comprehensive model validation. Methods: We extend the traditional Peters–Belson framework through ensemble machine learning that combines Cox proportional hazards models, cross-validated random survival forests, and regularized gradient boosting approaches. Our framework incorporates a transfer learning component via principal component analysis (PCA) to discover shared latent factors between majority and minority groups. We note that this “transfer learning” differs from the standard machine learning definition (pre-trained models or domain adaptation); here, we use the term in its statistical sense to describe the transfer of covariate structure information from the pooled population to identify group-level latent factors. We develop a comprehensive validation framework that ensures Peters–Belson logical bounds compliance, preventing mathematical violations in counterfactual estimates. The approach is evaluated through simulation studies across five realistic health disparity scenarios using stratified complex survey designs. Results: Simulation studies demonstrate that validated ensemble methods achieve superior performance compared to individual models (proportion explained: 0.352 vs. 0.310 for individual Cox, 0.325 for individual random forests), with validation framework reducing logical violations from 34.7% to 2.1% of cases. Transfer learning provides additional 16.1% average improvement in explanation of unexplained disparity when significant unmeasured confounding exists, with 90.1% overall validation success rate. The validation framework ensures explanation proportions remain within realistic bounds while maintaining computational efficiency with 31% overhead for validation procedures. Conclusions: Validated ensemble machine learning provides substantial advantages for Peters–Belson decomposition when combined with proper model validation. Transfer learning offers conditional benefits for capturing unmeasured group-level factors while preventing mathematical violations common in standard approaches. The framework demonstrates that realistic health disparity patterns show 25–35% of differences explained by measured factors, providing actionable targets for reducing health inequities. Full article

This paper presents a comprehensive review of forced convective heat-transfer phenomena in fluids, emphasizing the influence of fluid properties, tube geometries, and flow orientations under varying Prandtl numbers. Key governing parameters—including velocity, viscosity, thermal conductivity, density, specific heat, surface area, and flow regime (laminar or turbulent)—are expressed through dimensionless groups such as the Nusselt (Nu), Reynolds (Re), and Prandtl (Pr) numbers. The review encompasses heat-transfer characteristics of low-, medium-, and high-Prandtl-number fluids flowing through circular, square, triangular, and elliptical tubes in both horizontal and vertical orientations, aiming to critically evaluate the effectiveness and trends reported in previous studies. Where applicable, symmetry correlations—based on equivalent thermal and hydrodynamic behaviour along geometrically symmetric boundaries—were considered to interpret flow uniformity and heat-transfer distribution across cross-sectional profiles. Analysis reveals that over 84% of the reviewed studies emphasize on horizontal configurations and 55% on circular geometries, with medium-Prandtl-number fluids dominating experimental investigations. While these studies provide valuable insights, significant research gaps remain. Limited attention has been given to vertical orientations, where buoyancy effects may alter flow behaviour due to temperature and pressure gradients arising from variations in fluid density and viscosity, to non-circular geometries that enhance boundary-layer disruption, and to extreme-Prandtl-number fluids such as liquid metals and heavy oils, which are vital in advanced industrial applications. Bridging these gaps presents opportunities to design and optimize diverse engineering systems requiring efficient convective heat transfer. Practical examples include coolant flow in nuclear reactors, heat dissipation in high-performance CPUs, and high-speed airflow over automotive radiators. This review therefore underscores the need for future research extending forced-convection studies beyond conventional configurations, with particular emphasis on vertical orientations, complex geometries, and underexplored Prandtl-number regimes. Full article

Background/Objectives: Depression has increased substantially in Korea following the COVID-19 pandemic, with prevalence reaching 7.3% in 2023, the highest level in a decade, raising urgent concerns about widening mental health disparities. Although physical activity (PA) is associated with reduced depressive symptoms, nationally representative post-pandemic evidence from Korean adults remains limited. This study descriptively examined patterns of PA participation and depressive symptoms across key sociodemographic groups using 2023 Korea Community Health Survey (KCHS) data. Methods: We analyzed cross-sectional data from 228,249 adults aged ≥19 years in the 2023 KCHS. Depressive symptoms were measured using nine PHQ-9 items (1–4 on Likert scale). PA was assessed as the number of days per week (0–7) of moderate (MPA) and vigorous (VPA) physical activity according to KCHS operational definitions. All analyses incorporated complex survey features (strata, clusters, weights). Group differences were examined using design-corrected t-tests and ANOVA. Results: Women, adults aged 60 years or older, bereaved individuals, and those with lower educational attainment reported higher depressive symptom levels (p < 0.001). PA participation was higher among men, younger adults, married individuals, and those with higher education. Depressive symptom scores decreased with increasing PA frequency, with the lowest levels observed among adults active 5–7 days per week. Although mean differences were modest (0.02–0.12 points on the four-point scale; η² < 0.06), these steady population-level gradients provide meaningful baseline information for understanding post-pandemic mental health patterns in Korea. Conclusions: Although individual-level differences were small (η² < 0.06), the population-level gradients are important for monitoring mental health disparities in post-pandemic Korea. Women, older adults, bereaved individuals, and lower-education groups represent key high-burden populations. Future studies should employ longitudinal designs, objective PA measures, and confounder-adjusted models to clarify mechanisms and directionality, and evaluate the effectiveness of community-based PA interventions. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

Bidens pilosa L. is a cosmopolitan and invasive weed that strongly impacts agricultural systems in tropical regions. In Veracruz, Mexico, its presence extends mainly across mid-elevation zones where coffee, maize, and sugarcane are cultivated. This study characterized the bioclimatic and edaphic determinants of B. pilosa distribution using 581 georeferenced occurrences combined with 19 bioclimatic variables, elevation, and soil data. A Maxent model revealed the highest habitat suitability (0.65–1.0) in the central mountainous region between 800 and 1500 m.a.s.l., particularly under temperate–humid climates (Cfa, Cfb) and Acrisol–Leptosol soils. Principal component and redundancy analyses showed that annual precipitation (BIO12), precipitation of the driest month (BIO14), and temperature seasonality (BIO4) explained 74.7% of the total environmental variance. Cluster analysis identified four distinct ecological groups, confirming broad ecological plasticity. These findings indicate that B. pilosa is not randomly distributed but structured along climatic and soil gradients, with precipitation and elevation as major determinants of its ecological niche. Understanding these relationships provides a quantitative framework for predicting its expansion under future climate scenarios and for designing targeted management strategies in tropical agroecosystems. Full article

Soil biodiversity is crucial for maintaining biological soil resilience, understood as a temporal property and as the ability of soils to uphold or recover their ecological functions under stress thanks to the diversity and complementarity of their biological communities. To evaluate this property, we developed the Biological Soil Resilience Index (BSR), conceived as an evolution of the QBS-ar approach by integrating additional key bioindicators—entomopathogenic nematodes, entomopathogenic fungi, and earthworms—together with microarthropod eco-morphological adaptation scores. This multi-taxon framework provides a more comprehensive assessment of soil biological conditions than single-group indices and is specifically designed to be applied repeatedly over time to detect resilience trajectories. The Biodiversity Soil Resilience (BSR) Index was applied across nine sites subject to low, medium, and high anthropogenic disturbance, spanning urban, industrial, and airport environments. Results revealed not a resilience gradient but a clear disturbance gradient: low-impact sites achieved the highest BSR values (52–59), reflecting diverse and functionally complementary assemblages; medium-impact sites maintained moderate BSR value (27–42), but displayed imbalances among faunal groups; and high-impact sites showed the lowest values, including a critically low score at C_HI (17.86), where entomopathogens were absent and earthworm populations reduced. Entomopathogenic organisms proved particularly sensitive, disappearing entirely under severe disturbance. The BSR was sensitive to environmental gradients and effective in distinguishing ecologically meaningful differences among soil communities. Because it can be repeatedly applied over time, BSR provides the basis for monitoring long-term resilience dynamics, detecting early warning signals, and support timely mitigation or restoration measures. Overall, the study highlights the pivotal role of biodiversity in sustaining soil resilience and supports the BSR Index as a simple yet integrative tool for soil health assessment and for future resilience monitoring in disturbed landscapes. Full article