Search Results (1,166)

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Cross-market risk transmission in U.S. energy markets has become increasingly complex as fossil fuel prices, electricity markets, and clean energy financial exposure respond differently to stress episodes. Identifying whether dynamic spillover information contains forward-looking diagnostic value is therefore important for energy market risk monitoring. This study examines a daily six-market U.S. energy return panel covering WTI crude oil, Henry Hub natural gas, Brent crude oil, RBOB gasoline, PJM West electricity, and CELS clean-energy equity exposure from 2016 to 2025. We first estimate time-varying total, directional, and net connectedness using a TVP-VAR-DY framework and then transform the resulting connectedness measures into spillover-based features for supervised high-DSV20-state classification. The results show that energy-market connectedness is clearly time-varying, with crude oil benchmarks occupying central positions and market-level net spillover roles changing across market conditions. Under the retained label-80 Random Forest specification, connectedness-based features provide moderate diagnostic value for identifying future high-DSV20 states. Net WTI, Net Henry Hub, and Net CELS are the most informative spillover-role variables. Additional validation checks indicate that the evidence is best interpreted as support for diagnostic risk monitoring rather than as a high-accuracy forecasting system. The findings highlight the usefulness of dynamic connectedness measures as transparent inputs for energy-market risk assessment. Full article

[¹⁸F]-PSMA PET/CT is a high-impact modality for the staging and restaging of prostate cancer, but its wide anatomic coverage and tracer biology generate frequent incidental findings on both PET and the accompanying low-dose CT (LDCT). This narrative review is restricted in scope to fluorine-18 PSMA tracers because tracer-specific biodistribution and pitfall profiles shape what is perceived as incidentaloma: how confidently lesions can be categorized, and how often borderline findings trigger downstream testing, particularly for skeletal foci with [¹⁸F]-PSMA-1007. Specifically, [¹⁸F]-PSMA-1007 shows substantially higher rates of focal unspecific bone uptake than [⁶⁸Ga]-PSMA-11—reported in multicenter studies as affecting up to 40–50% of patients—which directly inflates the pool of potential incidentalomas and creates a tracer-specific false-positive problem with no parallel in gallium-68 practice. Additionally, [¹⁸F]-DCFPyL has different urinary clearance kinetics that affect bladder and ureteral uptake patterns, altering what qualifies as physiologic versus incidental in the pelvis. These differences mean that the threshold for Category B versus C classification—and the appropriate cascade-resistant language—must be tuned to the specific tracer in use. A framework built on [⁶⁸Ga]-PSMA-11 data would systematically underestimate bone pitfall frequency in [¹⁸F]-PSMA-1007 practice and could therefore paradoxically increase rather than reduce cascades if applied uncritically across tracers. These biodistribution differences have direct and concrete consequences for reporting behaviour and downstream management. In [¹⁸F]-PSMA-1007 practice, a focal bone uptake without a CT correlate in a mechanically plausible location—such as an anterior rib or vertebral endplate—should trigger Category B language in the report conclusion: the finding is documented in the body with explicit safety netting (“most consistent with unspecific uptake; no routine workup unless interval growth, new pain, or aggressive CT morphology”), and no referral to bone scintigraphy or MRI is generated. Without tracer-specific awareness, the same finding would typically prompt a reflex bone scan or whole-body MRI referral, delaying definitive prostate cancer management by weeks and adding imaging costs without diagnostic gain. By contrast, in [⁶⁸Ga]-PSMA-11 practice, an equivalent focal bone uptake without a CT correlate carries a higher prior probability of true metastatic disease given the lower background rate of unspecific uptake and should more often be reported at Category B with a lower threshold for escalation or more cautious language. For [¹⁸F]-DCFPyL, the higher urinary activity in the pelvis means that ureteral segments can mimic lymph node disease; recognizing this as a physiologic variant (Category C) rather than an equivocal nodal finding (Category B) avoids unnecessary pelvic MRI referrals that would otherwise be triggered by an uncontextualized report. In practical terms, the tracer-specific calibration of the overlay therefore changes not only the category assigned but also the specific safety-netting language and the escalation trigger, which directly modifies the downstream management pathway for each affected finding type. The scanned population—predominantly older men with a high prevalence of degenerative, inflammatory, and vascular abnormalities—creates substantial background noise that can drive low-value diagnostic cascades if incidental findings are communicated without actionability context. We integrate society-endorsed frameworks (EANM/SNMMI procedure guideline 2.0; E-PSMA; PSMA-RADS; and PROMISE/miTNM with miPSMA score) and propose a cascade-aware overlay for incidental findings that can be appended to existing PSMA reporting standards rather than replacing them. The A/B/C actionability overlay is a structured expert-consensus framework informed by existing evidence-based guidelines for specific finding types and by tracer-specific cohort data; it has not yet been prospectively validated as a standalone tool, and its current level of evidence is therefore analogous to a structured expert recommendation rather than an evidence-based clinical guideline. We operationalize a three-tier actionability scheme across PET- and CT-dominant findings, provide cascade-resistant language for conclusions, and clarify why SUVmax-only “probability scales” for lymph nodes are not recommended in routine reports. Three practical tables summarize PET incidental findings, lymph node reporting frameworks, and LDCT incidental findings, and two structured report templates are provided (concise and extended), with the extended version explicitly labelling actionability tiers and escalation triggers. Finally, we outline concrete AI use cases for standardization and triage while emphasizing governance to avoid the amplification of false positives and paradoxical growth of cascades. Full article

Nickel ferrite (NiFe₂O₄) is one of the most studied inverse-spinel ferrites because it combines moderate saturation magnetization, comparatively high electrical resistivity, chemical stability, and broad synthesis flexibility. Yet the literature shows that the measured structure and magnetism of NiFe₂O₄ are not intrinsic constants; they evolve strongly with dimensionality, size, thickness, strain state, cation distribution, surface spin disorder, and synthesis pathway. This review develops a unified theoretical and literature-based interpretation of how dimensionality reshapes the structural and magnetic behavior of NiFe₂O₄ across bulk ceramics, nanoparticles, one-dimensional nanostructures, polycrystalline thin films, and ultrathin epitaxial films. The review is anchored in the two uploaded nickel ferrite attachments and expanded using internet-sourced journal literature on spinel inversion, surface effects, mechanochemical synthesis, sputtered and pulsed laser deposited thin films, and epitaxial ultrathin-film anomalies. The central novelty of this article is the formulation of a dimensionality-dependent framework in which the observed magnetic response is governed by a competition among three coupled factors: (i) the cation-distribution function, which controls the A–B superexchange balance and therefore the net ferrimagnetic moment; (ii) the microstructural coherence function, which measures how crystallinity, strain, defects, and anti-phase boundaries preserve or degrade exchange continuity; and (iii) the surface/interface spin-order parameter, which quantifies the loss or reconfiguration of magnetic order at free surfaces and buried interfaces. Within this framework, bulk NiFe₂O₄ behaves as a near-equilibrium inverse spinel with relatively stable magnetization, whereas nanoscale NiFe₂O₄ experiences strong spin canting and finite-size suppression due to the growing fraction of disordered surface spins. Thin films introduce a distinct regime in which strain, texture, anti-phase boundaries, substrate mismatch, and growth kinetics determine both anisotropy and magnetization. In ultrathin epitaxial films, off-equilibrium cation redistribution and interface-controlled electronic reconstruction may even generate magnetization values far above bulk expectations. The review also compares major synthesis routes—solid-state reaction, sol–gel, co-precipitation, hydrothermal growth, reactive milling, combustion, pulsed laser deposition, and radio-frequency sputtering—and explains why each route biases the final dimensionality-dependent properties differently. A set of word-style equations is provided to formalize spinel inversion, finite-size suppression, anisotropy scaling, coercivity trends, and superparamagnetic crossover. Beyond summarizing the field, the review proposes a regime map linking dimensionality to characteristic structural defects and magnetic signatures, and it identifies unresolved questions concerning the true origin of enhanced magnetization in ultrathin NiFe₂O₄, the interplay between anti-phase boundaries and strain, and the distinction between intrinsic inversion changes and extrinsic substrate artifacts. The resulting article offers a submission-ready, originality-focused review that positions dimensionality as the master variable governing structure–magnetism correlations in nickel ferrite. Full article

Microbial inactivation is essential for extending the shelf life of raw milk. Radio frequency (RF) thermal pasteurization has emerged as a promising technology for small-scale dairy processing. This study aimed to determine optimal RF temperature–time conditions, evaluate their effects on milk quality across milk from different species of cows, and assess economic feasibility. Raw milk from Holstein Friesian, Jersey, and Brown Swiss cows was treated using a dielectric heating system (40.68 MHz) at 72–92 °C for 20–100 s. The results were compared with conventional low-temperature long-time (LTLT) pasteurization of untreated milk. The optimal condition was 92 °C for 50 s, reducing the aerobic plate count from 5.80 to 0.69 log CFU/mL (a 5.11 log reduction), with no detection of Staphylococcus aureus, Bacillus cereus, and Escherichia coli. RF treatment did not significantly affect milk composition (p > 0.05), and color changes remained within acceptable limits. Milk stored at 4 °C maintained quality and safety for up to 28 days. Economic analysis indicated a net present value of USD 134,721.78, a benefit–cost ratio of 3.25, and a payback period of 6.8 months, confirming economic feasibility. These findings demonstrate that RF pasteurization can improve processing efficiency and support sustainable dairy production. Full article

Accurate prediction of dry matter intake (DMI) is essential for ration formulation, nutrient supply, and evaluation of production efficiency in lactating dairy cows. Several DMI prediction models are currently used, but most comparative studies have emphasized overall accuracy rather than whether model bias changes across biologically relevant production contexts. The objective of this study was to evaluate the context-dependent bias of widely used DMI prediction models in lactating dairy cows across classes of energy-corrected milk (ECM) and lactation stage. A literature-derived database was assembled from 135 studies consisting of 436 treatments from 6985 Holstein cows, reporting observed DMI and the variables required to implement five prediction models and evaluate their prediction error (PE): NRC2001, the Cornell Net Carbohydrate and Protein System (CNCPS), NASEM2021, Agroscope2021, and GfE2023. PE was calculated as predicted DMI minus observed DMI, such that positive values indicated overprediction and negative values indicated underprediction. Observations were classified according to ECM and days in milk (DIM). Mixed models were fitted separately for the ECM class and the lactation-stage class, with the study fitted as a random effect. PE differed among models, and the pattern of bias depended on both the ECM and the lactation-stage classes. The interaction between the ECM class and the model was significant, indicating that productive level modified model bias. The interaction between lactation-stage class and model was also significant and more pronounced, indicating marked changes in model bias across lactation stages. Across classes, NASEM2021 generally remained closest to zero, whereas GfE2023 and CNCPS showed more negative PE values in most contexts. Agroscope2021 showed a more context-sensitive pattern, and NRC2001 remained comparatively moderate across several classes. These findings indicate that the evaluation of DMI prediction models based only on global mean bias may conceal an important biological structure in PE. Context-specific evaluation, particularly across the lactation stage, may provide a more informative basis for selecting DMI prediction models for research and practical ration formulation. Full article

Compound extreme climate events have become increasingly frequent under climate change and may alter terrestrial carbon cycling through different atmospheric and soil pathways. Focusing on the Dongting Lake Eco-Economic Zone, this study identified three types of compound extreme events during 2003–2024: atmospheric compound hot–dry events (ACHDs), soil compound hot–dry events (SCHDs), and drought-to-rewetting events (DRWs). We then examined their associations with monthly anomalies of net primary production (NPP), heterotrophic respiration (RH), and net ecosystem exchange (NEE) under different land cover backgrounds. The results showed that ACHDs and SCHDs both increased significantly, whereas DRWs exhibited a slight decreasing trend and a more scattered spatial distribution. During the same period, regional NPP increased significantly, RH decreased slightly, and NEE became more negative, indicating an overall strengthening of net carbon uptake. Different event types were associated with contrasting carbon flux response pathways: ACHDs were mainly associated with reduced NPP and slightly increased RH, thereby shifting NEE toward more positive values and weakening regional net carbon uptake, whereas SCHDs and DRWs were more strongly associated with reduced RH and more negative NEE. In addition, the event–carbon relationships differed among land cover types, with cropland, built-up land, and sparsely vegetated surfaces showing higher sensitivity to ACHDs, whereas the responses to SCHDs and DRWs varied markedly among forest, grassland, wetland, and open water classes. These results highlight that compound atmospheric and soil extremes influence regional carbon cycling through distinct component-specific pathways, and that land use background is an important factor associated with differences in carbon flux sensitivity in humid lake–floodplain systems. Full article

Anaerobic digestion is a viable pathway to mitigate environmental impacts from swine manure in tropical regions while contributing to circular economy strategies. However, no standardized or integrated framework currently exists that simultaneously quantifies the closure of energy, material, carbon, nutrient, and water loops at the farm scale. This research presents the techno-economic design and environmental assessment of a covered, mechanically agitated lagoon biodigester for a 10,000-head swine fattening module located in Matanzas, Cuba. The system is sized by integrating hydraulic, thermal, and structural parameters, and its economic viability is assessed through Net Present Value (NPV = $1.09 million), Internal Rate of Return (IRR = 32%), and a payback period of approximately three years. A comparative screening-level life cycle assessment shows that biogas-based electricity generation substantially reduces impacts on climate change, air quality, and fossil fuel scarcity compared with conventional diesel-based generation, with trade-offs in eutrophication and ecotoxicity. As a key methodological contribution, five quantitative circular economy indicators are proposed and calculated: the Energy Self-Sufficiency Ratio (ESSR = 1.71), the Waste Valorization Index (WVI = 0.91), the Decarbonization Index (DCI = 6.7), the Fertilizer Substitution Rate (FSR = 16.3 t N year⁻¹), and the Water Closure Factor (WCF = 1.30). These indicators show that the system achieves a 71% net energy surplus, valorizes over 90% of the input mass, avoids 6.7 times more emissions than it generates, replaces synthetic fertilizers, and returns more water than it consumes. The findings provide quantitative evidence that the convergence of mesophilic operation without auxiliary heating, high carbon intensity of the power grid, and availability of agricultural land enhances circularity performance in tropical covered lagoon bioreactors, and the proposed integrated indicator framework, aligned with ISO 59020:2024, provides a reproducible and transferable methodological basis for the comparative assessment of anaerobic digestion systems for livestock waste. Full article

Affected by global climate change and complex environmental factors, the frequency and intensity of forest fires have been rising. Accurate early detection is crucial for disaster mitigation. Traditional methods (e.g., manual monitoring) suffer from low efficiency or limited coverage, while deep learning methods (e.g., YOLO (You Only Look Once), Faster RCNN (Region-based Convolutional Neural Networks)) perform well but are sensitive to degraded images (haze, low light), reducing accuracy. To address blurred smoke features and attenuated flame brightness in degraded images, this paper proposes CoDeF-Net (Collaborative Detection Framework Network), a collaborative detection framework integrating Retinex-BCE (Retinex-based Bright Channel Enhancement) image enhancement with YOLOv11 (You Only Look Once version 11) to improve robustness. Experiments on 1757 real forest fire images show that Retinex-BCE achieves an FSIMC (Full-Reference Image Quality Assessment Metric based on Structural Similarity and Contrast) index of 0.9611 and an LOE (Loss of Edge) value of 254.78, preserving image structure. CoDeF-Net reaches AP@0.5 (Average Precision at Intersection over Union threshold 0.5) of 87.9% (3.8% higher than original YOLOv11), with low missed detection of small flames and enhanced stability in extreme scenarios, providing a feasible solution for forest fire monitoring under degraded images. Full article

Patients confined to bed for extended periods must frequently change sleeping posture to prevent pressure ulcers, which are difficult and undesirable to treat. In this study, three IMU sensors were placed on 108 bedridden patients to collect data for five different postures, resulting in 1,800,000 data points per sensor. These were converted into Eulerx, Eulery, and Eulerz values. The goal was to detect the sleep posture using a single IMU sensor. Four cases were defined: Case 1 used only IMU1, Case 2 only IMU2, Case 3 only IMU3, and Case 4 combined all three. Euler signals were converted into RGB images, framing the problem as an image classification task. A total of 2000 images were used, with 400 for training and 100 for testing in each case. A U-Net model was applied, achieving high IoU scores: 98.00%, 99.56%, 99.72%, and 98.20% respectively. Accuracy scores were 98.98%, 99.78%, 99.86%, and 99.09%, confirming U-Net’s effectiveness. Full article

Intelligence tests predict academic achievement, but their use in educational decision-making remains contested. We develop a decision-analytic framework, centered on a staged decision architecture, to determine when, for whom, and for which educational decisions intelligence testing adds value beyond grades, achievement measures, and contextual evidence. Drawing on psychometric scholarship, a generative account of achievement, and illustrative decision scenarios, we distinguish incremental validity from decision utility. Incremental validity refers to the predictive gain obtained by adding cognitive measures, whereas decision utility refers to the net benefit of using those measures once base rates, capacity constraints, error costs, fairness, and legitimacy are considered. We use the framework to identify conditions in which intelligence testing is expected to be most informative, especially educational transitions, contexts with uneven opportunity, and discrepancy-focused decisions such as underachievement or twice-exceptionality. We also specify minimum conditions for responsible use, including intended use, construct representation, reliability or precision, measurement comparability, predictive bias checks, and monitoring of distributional impact. We conclude that intelligence testing should be used conditionally and sequentially, with achievement and contextual indicators used first and cognitive assessment added only when it is likely to change the decision. Full article

Background: Heart failure (HF) remains a major early complication following myocardial infarction (MI), contributing significantly to morbidity and adverse clinical outcomes. Reliable early risk stratification is essential for optimizing post-MI management. This study aimed to evaluate the prognostic performance and incremental value of the HALP (Hemoglobin–Albumin–Lymphocyte–Platelet) score and the AHEAD score in predicting 1-month HF after MI. Methods: This retrospective cohort study included 3205 consecutive patients with MI. The primary endpoint was the development of HF within one month. Three multivariable logistic regression models were constructed: a baseline clinical model (Model 1), a HALP-integrated model (Model 2), and an AHEAD-integrated model (Model 3), with component variables excluded to avoid collinearity. Model performance was assessed using odds ratios (ORs), 95% confidence intervals (CIs), and discrimination metrics (AUC). Incremental predictive value was further evaluated using net reclassification improvement (NRI). Internal validation was performed using bootstrapping and 5-fold cross-validation. A predefined subgroup analysis was conducted in patients with preserved ejection fraction (EF ≥ 40%), excluding EF from the models. Results: In the full cohort, all models demonstrated high discriminative ability for 1-month HF (AUC range: 0.950–0.954), with minimal differences between models. The AHEAD-based model showed the highest point estimate (AUC = 0.954, 95% CI: 0.944–0.963), but ROC curves were largely overlapping. Despite limited changes in AUC, the AHEAD score provided moderate improvement in risk reclassification (NRI = 0.287), whereas the HALP score showed minimal incremental value (NRI = 0.152) and was not independently associated with HF in multivariable analysis. In the EF ≥ 40 subgroup, HF incidence was lower (1.9%), and model performance was attenuated but remained robust (AUC range: 0.839–0.882), with the AHEAD score retaining strong independent predictive value. Peak CKMB and creatinine were consistently associated with increased HF risk. Although the odds ratio for CKMB appeared close to unity, this reflects unit scaling, and clinically meaningful increases corresponded to substantial risk increments. A clear dose–response relationship between AHEAD score and HF probability was observed. Conclusions: While both HALP and AHEAD scores are associated with post-MI HF risk, only the AHEAD score provides consistent independent and incremental prognostic value beyond established clinical predictors. Its simplicity and ability to capture comorbidity burden make it a practical adjunct for early risk stratification, particularly in patients with preserved EF. However, given the minimal differences in discrimination metrics and lack of external validation, these findings should be interpreted cautiously and considered hypothesis-generating. Full article

Chronic exposure to fine particulate matter (PM_2.5) derived from wood combustion represents a major environmental health burden, particularly during pregnancy. However, the impact of pregestational and gestational (PM_2.5) exposure on the maternal great vasculature remains largely unexplored. This study evaluates the effects of wood smoke-derived (PM_2.5) on the structural architecture of the maternal aortic arch and associated hemodynamic changes during pregnancy in second-generation Sprague–Dawley rats. Animals were allocated into four groups (n = 12) according to filtered (FA) or non-filtered air (NFA) exposure during pregestational and gestational periods: FA/FA, FA/NFA, NFA/FA, and NFA/NFA. Morphometric analysis revealed significant reductions in tunica media (p = 0.0251) and adventitia thickness (p = 0.0014) in exposed groups, without changes in integrated optical density, suggesting alterations in elastic matrix organization without evidence of net mass loss. Histological analysis supported exposure-dependent structural heterogeneity, including elastic lamellae fragmentation and extracellular matrix disorganization. Each exposed group exhibited a distinct systolic blood pressure trajectory across gestation, with FA/NFA reaching the highest values at day 18 (151.0 ± 17.0 mmHg) and NFA/FA displaying sustained elevations despite gestational low-exposure conditions. Principal component analysis (49.2% explained variance) revealed a structured multivariate distribution of vascular and hemodynamic variables across exposure conditions, consistent with an exposure-window-dependent pattern. These findings suggest that (PM_2.5) exposure is associated with coordinated structural and hemodynamic changes in the aortic arch and support the hypothesis that the pregestational period may represent a window of increased susceptibility. Full article

Objectives: This study aimed to evaluate the diagnostic performance of peripapillary retinal nerve fiber layer (RNFL) thickness measurements corrected by artificial intelligence (AI) compared to original uncorrected values for glaucoma detection in highly myopic patients. Methods: This cross-sectional diagnostic accuracy study included 57 eyes from highly myopic patients (31 with glaucoma, 26 without glaucoma). Peripapillary RNFL parameters were obtained using Spectralis optical coherence tomography (OCT). A deep learning algorithm (MGU-Net) was employed to automatically segment retinal layers and compensate for scan tilt in elongated eyes, producing AI-corrected measurements. RNFL thickness values were extracted for six sectoral parameters (ST, SN, N, IN, T, IT) and global. Diagnostic performance was assessed using area under the ROC curve (AUC) and compared between corrected and uncorrected values. Multivariable logistic regression models were also developed using stepwise selection. Results: AI-corrected values were significantly lower than original measurements in all sectors (p < 0.001), with mean differences ranging from 15 to 35 µm. In glaucomatous eyes, significant thinning was observed in the global (p = 0.049) and inferior nasal (IN) sector (p = 0.037) among corrected values. The highest AUCs were found in IN (0.69), IT (0.67), and global (0.66) for corrected values, and in IT (0.63), T (0.59), and global (0.63) for uncorrected data. A model combining ST, T, and IT AI-corrected values achieved an AUC of 0.79. Conclusions: AI-corrected RNFL thickness measurements improve consistency and enhance diagnostic performance in highly myopic glaucoma patients. Correction algorithms may reduce false positives and help reveal glaucomatous damage otherwise obscured by myopic anatomical changes. Full article