Search Results (543)

Total anomalous pulmonary venous connection (TAPVC) is a severe congenital heart disease, yet its prenatal detection rate remains suboptimal. To support prenatal ultrasound screening of TAPVC, the post-left atrium space (PLAS) index and the left-atrial posterior-space-to-diagonal (LAPSD) ratio measured in the four-chamber view (4CV) have been proposed as useful biometric parameters. In this study, we developed a novel approach that integrates automated 4CV extraction (AE) from fetal cardiac ultrasound videos with automated measurement of these indices. The heart, crux, and descending aorta were segmented using DeepLabv3+, UNet3+, and SegFormer. The screening performance of the AE-based methods was comparable to that of manual 4CV extraction, as demonstrated by similar mean areas under the receiver operating characteristic curve (AUCs). In a clinical comparison study, the mean AUC values for residents, fellows, experts, AE-DeepLabv3+, AE-UNet3+, and AE-SegFormer were 0.784, 0.801, 0.996, 0.903, 0.928, and 0.940, respectively, for the PLAS index and 0.797, 0.801, 0.996, 0.919, 0.916, and 0.940, respectively, for the LAPSD ratio. Although experts demonstrated the best overall performance, the fully automated methods consistently outperformed both the residents and fellows. This approach may support less experienced examiners, improve screening accuracy, streamline clinical workflows, and ultimately enhance the prenatal detection of TAPVC. Full article

While handwritten signatures are a staple of biometric authentication, conventional verification models typically rely on Euclidean space assumptions, restricting the capture of complex, intrinsic signature structures. To address this, offline signature verification has increasingly modeled signatures as points on the Symmetric Positive Definite (SPD) manifold. Nevertheless, selecting an appropriate metric on this manifold for a given problem remains a significant challenge, typically relying on heuristic trial-and-error processes. To solve this, our primary contribution is a novel, end-to-end Riemannian framework featuring the Collective Background Knowledge (CBK) mechanism. CBK establishes synthetic writers as Riemannian centers, utilizing a learnable $\alpha \beta$-Log-Determinant divergence to adaptively discover the optimal local geometry from data. Instead of computing the direct distance between two SPD signature representations, we evaluate them relationally by measuring how each signature diverges from the shared CBK reference centers. These individual deviations form unique relational profiles for each signature, which are then compared using the dichotomy transform to create a dissimilarity vector. By jointly optimizing the CBK parameters under an SPD metric-learning approach, our model separates effectively similar and dissimilar pairs of signatures. Evaluated across five datasets under challenging blind intra- and cross-lingual conditions, our geometry-aware framework demonstrates robust generalization and competitive performance. Full article

Background/Objectives: The Geri-Fit^® program, recognized by the National Council on Aging, is known to improve strength in older adults, yet it lacks robust evidence for clinical outcomes. The current study was performed to assess the change in clinical outcomes in addition to patient-reported change in mobility and general well-being Methods: A total of 227 adults aged 60 and older were recruited from clinics and community sites across Galveston and Harris counties and participated in 45 min classes twice weekly for 12 weeks, led by trained Geri-Fit^® instructors. A mixed-methods approach includes pre- and post-collection of biometric measures of Hemoglobin A1c, total cholesterol, weight, and waist circumference. Participants also completed mid- and post-program surveys, reporting changes in health behaviors, psychosocial outcomes, and physical changes, and provided qualitative feedback. Results: 44% of participants lost weight, nearly half reduced their waist circumference, 43.5% improved their Hemoglobin A1c, and total cholesterol decreased significantly (from 167.77 to 155.04 mg/dL; p = 0.02). Self-reported outcomes indicated that almost 100% of participants showed improvement or maintenance in mobility, strength, physical activity, and well-being. Conclusions: These findings suggest that Geri-Fit^® is associated with favorable clinical outcomes and improved functional health, supporting its potential as a community-based intervention to enhance physical activity, improve self-management, or reduce the risk of chronic disease among older adults. Full article

Background/Objectives: Intermediate vision has become increasingly important after cataract surgery because many daily activities require functional visual performance beyond distance vision alone. Enhanced monofocal intraocular lenses may improve intermediate visual function while maintaining good distance visual acuity. The aim of this study was to compare binocular distance and intermediate visual outcomes after bilateral implantation of Clareon and TECNIS Eyhance intraocular lenses. Methods: This was a single-center, non-randomized comparative observational study with assessment of postoperative visual outcomes. Eighty-six patients who had previously undergone uncomplicated bilateral age-related cataract surgery with implantation of the same IOL model in both eyes were included. Forty-two patients received Clareon IOLs and forty-four patients received TECNIS Eyhance IOLs. Postoperative assessment was performed at least 12 weeks after surgery of the second eye. Binocular corrected distance visual acuity (CDVA) and distance-corrected intermediate visual acuity (DCIVA) were measured using ETDRS charts at 4 m and 66 cm. Results: Baseline biometric and clinical parameters were comparable between groups. Mean postoperative DCIVA was 0.23 ± 0.09 logMAR in the Clareon group and 0.21 ± 0.08 logMAR in the Eyhance group. The mean between-group difference, calculated as Clareon minus Eyhance, was 0.02 logMAR, with a 95% confidence interval from −0.02 to 0.06 logMAR. Mean binocular CDVA was 0.02 ± 0.02 logMAR in the Clareon group and 0.02 ± 0.03 logMAR in the Eyhance group, with a 95% confidence interval for the between-group difference from −0.01 to 0.01 logMAR. Mean postoperative manifest refraction spherical equivalent was −0.27 ± 0.42 D in the Clareon group and −0.29 ± 0.37 D in the Eyhance group. Conclusions: Both Clareon and TECNIS Eyhance IOLs provided good binocular distance and intermediate visual acuity after bilateral implantation. Intermediate visual performance after Clareon implantation was comparable to that achieved with TECNIS Eyhance, while distance visual acuity remained similarly high in both groups. These findings suggest that both IOL models may represent useful options for patients undergoing cataract surgery who expect good distance vision and functional intermediate visual performance. Full article

Introduction: Fish and humans share evolutionarily conserved pathways regulating lipid metabolism. However, the effects of pharmaceuticals on lipid homeostasis in fish remain poorly understood, particularly regarding mechanistic lipid dysregulation and its implications for fish physiology and environmental toxicology. While hypolipidemic drugs such as statins have been shown to modulate lipid metabolism in teleosts, other lipid-lowering agents, including cholesterol absorption inhibitors, remain largely unexplored. Additionally, synthetic hormones have been shown to interfere with lipid regulation, although their effects—particularly those of progestins—remain poorly characterized. Objective: This study aimed to explore the mechanistic lipid disruptions induced by potential hypo- and hyperlipidemic modulating pharmaceuticals in brown trout juveniles exposed to subchronic pharmacological conditions. Methodology: Juvenile brown trout were exposed via intramuscular injection every 72 h for 28 days and allocated into six experimental groups (n = 12 per group): control (C; 0.7% NaCl), solvent control (SC; 0.7% NaCl, 0.9% ethanol, 0.1% dimethyl sulfoxide), atorvastatin (ATV; 0.3 µg·g⁻¹), ezetimibe (EZB; 0.3 µg·g⁻¹), 17α-ethinylestradiol (EE2; 2 µg·g⁻¹), and levonorgestrel (LNG; 0.1 µg·g⁻¹). All concentrations represented pharmacological doses. On day 28, the fish were euthanized and sampled. Endpoints included biometric measurements, blood lipid profiling, serum biochemistry, and hepatic lipid accumulation. Results: ATV fish displayed greater body length, whereas EE2 increased liver weight and hepatosomatic index. EE2 reduced high-density lipoproteins and increased low-density lipoproteins, while atorvastatin reduced low-density lipoproteins. EE2 exposure also increased albumin levels and decreased glucose concentrations. Furthermore, EE2 significantly enhanced hepatic lipid deposition. Conclusions: The hyperlipidemic effects of EE2 were the most pronounced, whereas ATV produced the strongest hypolipidemic responses, consistent with its known effects in humans, and also influenced biometry. These findings provide a robust foundation for understanding how pharmaceuticals influence lipid metabolism and related physiological processes such as growth in fish, with relevance for both fish physiology research and environmental toxicology. Full article

In this article, the authors analyzed all known Pamphagidae taxa from the Iberian Peninsula belonging to the genera Eumigus, Ocnerodes, and Kurtharzia, studying and taking five measurements from each of the numerous specimens studied in various European museums. This allowedus, in addition to the traditional morphological approach, to undertake a comparative biometric study through Principal Component Analysis. The results not only confirmed the validity of the described taxa, but also allowed three taxa previously considered subspecies to be elevated to a species rank. Overall, this article lists and analyzes eight species of Eumigus, five of Ocnerodes, and two of Kurtharzia, for a total of 15 species. Ocnerodes brunnerii cyanipes Bolívar, 1902 is synonymous with O. brunnerii (Bolívar, 1876). At the end of the article, the authors carry out some biogeographical considerations. Full article

Background/Objectives: This study aimed to evaluate the biomechanical characteristics of the cornea to assess their diagnostic accuracy in distinguishing normal eyes from those suspected keratoconus eyes. Methods: In this cross-sectional study, corneal elevation and curvature radius were measured in 217 participants using Pentacam. Average values were obtained based on the best-fit sphere (BFS) and the enhanced best-fit sphere (EBFS). The biomechanical characteristics of the cornea were assessed using the Corvis ST device. Receiver operating characteristic curve analysis was performed to determine the diagnostic accuracy. Results: The radii of the BFS in the anterior and posterior corneas were significantly larger in the normal group compared to the suspected keratoconus group. Conversely, EBFS elevation values in both the anterior and posterior corneas were lower in the normal group. The velocity at which the cornea was first flattened had the highest diagnostic accuracy for identifying suspected keratoconus. Conclusions: Eyes with suspected keratoconus had a significantly smaller corneal radius on both the anterior and posterior surfaces compared with normal eyes. In addition, due to the increased deformability and reduced resistance to a given force, these parameters serve as valuable biometric indicators for distinguishing suspect eyes from normal eyes. Full article

Background/Objectives: Anthropometric measurements provide essential normative datasets that form the foundation for clinical practice and forensic identification. The human ear is a highly informative structure due to its complex morphology and individual specificity, making it a valuable tool for biometric systems. This study aimed to estimate biological sex based on auricular morphometric measurements, develop a logistic regression model for this purpose, and validate its performance using ROC analysis. Materials and Methods: This cross-sectional study included 120 adult participants (60 males, 60 females). Standardized digital photographs were analyzed in ImageJ to record 22 linear and 6 angular measurements using established anatomical landmarks. LASSO logistic regression was employed for variable selection and model shrinkage. The final model’s discriminative performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer–Lemeshow test, and the Brier score. Results: A comparative analysis revealed that most linear and angular measurements showed significant sexual dimorphism. Almost all linear dimensions (A1–A22) were significantly larger in males (p < 0.001). Auricular width (A2) and width at the level of the tragus (A3) emerged as the most robust indicators, demonstrating “very large” effect sizes. Conversely, the angle between the preauricular line and the vertical plane (A28) was significantly greater in females, providing a unique inverse relationship for sex estimation. A parsimonious 5-predictor model (incorporating A2, A3, A5, A10, and A28) achieved exceptional discriminative performance with an AUC of 0.980. Conclusions: Auricular morphometry is a highly effective tool for sex estimation. The findings confirm significant sexual dimorphism in the external ear, particularly in linear dimensions. The developed model may serve as a preliminary morphometric reference for future automated biometric recognition studies, although no artificial intelligence-based classification model was developed in the present study. Full article

Background/Objectives: Standard mechanical compression applied during screening mammography is a primary barrier that reduces patient compliance. Current guidelines attempt to standardize compression based solely on the one-dimensional “breast thickness” measured by the device. This study aimed to investigate the effects of three-axis anatomical breast dimensions, applied compression force, menstrual cycle phases, and BI-RADS breast density patterns on pain scores during mammography within a comprehensive biomechanical model. Methods: This retrospective cohort study included 443 female patients who underwent routine screening or diagnostic mammography. Patients with a history of breast implants, lactation, or prior breast surgery that could alter tissue biomechanics were excluded. Maximum pain scores (1–10 on a Visual Analog Scale [VAS]) were recorded. Transverse, anteroposterior, and superoinferior breast biometric measurements for each patient were calculated using advanced radiological workstations. Data were analyzed using One-Way ANOVA and Multiple Linear Regression (OLS) models. Results: The mean age of the participants was 49.7 ± 9.4 years, the mean applied compression force was 62.4 ± 10.3 N, and the mean pain score was 2.03 ± 2.12 (range: 1–10). The multiple linear regression analysis was statistically significant overall (F = 2.516, p = 0.015). Having a BI-RADS Type D (extremely dense) breast pattern was identified as the strongest independent factor associated with an increased pain score (p = 0.082, coefficient = 1.219). Age showed a trend toward a negative effect on pain (p = 0.072), while compression force showed a trend toward a positive effect (p = 0.067). Conversely, breast thickness (p = 0.231) and the three-dimensional mean breast size index (p = 0.568) demonstrated no independent predictive power. The menstrual cycle phase did not reach independent significance in the multivariate regression model (p = 0.117); however, non-parametric univariate analysis revealed a significant difference in pain across hormonal groups (Kruskal–Wallis H = 10.04, p = 0.039), with actively menstruating and luteal-phase women reporting higher pain than menopausal women. Conclusions: The pain experienced during mammography depends on the internal fibroglandular architecture (elasticity and stiffness) of the tissue rather than its external volumetric dimensions. Notably, neither device-measured breast thickness nor manually calculated three-dimensional breast dimensions independently predicted pain, challenging the widespread assumption that breast size drives mammographic discomfort. “One-size-fits-all” or thickness-based compression strategies should be abandoned in routine practice. Instead, “personalized compression” protocols that prioritize patient comfort without compromising image quality should be developed, particularly for younger patients and those with BI-RADS Type D, and to a lesser extent Type C, density patterns. Full article

The two-year research involving laboratory and field studies supported by Geant4 computer simulation is aimed at determining the optimal parameters of 1 MeV accelerated electrons and 80 keV X-ray pre-planting irradiation of wheat seeds in order to find the optimal dose range which increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. During the laboratory studies we measured the germination rate and biometric properties of plants, as well as the type, number, and average diameter of fungi found in the irradiated and non-irradiated seeds after irradiation with electrons and X-rays with the dose range 2–1000 Gy. Following the laboratory studies showing that the doses exceeding 30 $\mathrm{Gy}$ decreased the germination rate of wheat, field studies evaluated the impact of pre-planting irradiation with the doses in the range of 5–30 Gy on the wheat productivity and the rate of fungal diseases in wheat plants grown from irradiated and non-irradiated seeds. It has been found that the dose range 5–15 Gy is more preferable for pre-planting wheat irradiation, both for e-beam and X-rays, since it increases the crop yield while making wheat plants more resistant to fungal diseases caused by species of the genus Septoria. The X-ray dose of 15 $\mathrm{Gy}$ is found to be the most effective since it increased the yield up to 40% and also suppressed the Septoria glume blotch up to 40%. Since seed irradiation requires a particularly delicate approach given that the goal of irradiation is not only to reduce the rate of fungal diseases in the plants but also to increase the crop yield without detriment to the soil and the plant itself, consistency of dose uniformity across the seeds during pre-planting irradiation ensures the high reliability and repeatability of the irradiation effect. Our approach to irradiation planning with the use of Geant4 computer simulation allows us to precisely estimate the dose distribution in individual seeds and the distribution of radiation-chemical yield of radicals occurring as result of radiolysis in order to predict the effect of pre-planting irradiation and select the optimal irradiation parameters for maximizing the yield and crop quality. Full article

The following narrative review discusses the use of deep learning and 3D modeling in facial reconstruction from skeletal remains, focusing on accuracy, algorithmic bias, and evidential reliability. Forensic facial reconstruction (FFR) is a multidisciplinary field combining anthropology, medicine, and visual sciences to approximate the facial appearance of unidentified individuals from skeletal remains. Traditional manual methods, based on anatomical knowledge and facial soft tissue thickness (FSTT) measurements, are limited by subjectivity, labor intensity, and inter-expert variability. This narrative review summarizes contemporary AI-assisted approaches, with emphasis on convolutional neural networks (CNNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, which enable probabilistic prediction of facial morphology while accounting for demographic variables such as sex, age, and population ancestry. Key challenges affecting reconstruction accuracy—including dataset limitations, population-specific variability, and algorithmic bias—are discussed, alongside quantitative validation methods and concerns regarding model transparency. Legal and ethical considerations, such as privacy, biometric data protection, and the need for explainable AI (XAI) frameworks, are highlighted. Future perspectives include hybrid expert–AI workflows, the development of globally representative datasets, and the integration of multimodal data sources, including DNA phenotyping, 3D morphometrics, and biomechanical modeling. These advances aim to create standardized, interpretable, and biologically informed frameworks that enable AI to support expert judgment and enhance the reliability of forensic facial reconstructions. Full article

Accurate, non-invasive body weight estimation is essential for management and performance monitoring in beef cattle systems, yet conventional scales and manual measurements require animal handling, infrastructure, and labor. This study presents an integrated pipeline that segments cattle from overhead depth images and predicts body weight from extracted image features. The approach uses a Light Attention Encoder–Decoder (LAED) segmentation model combining depthwise separable convolutions, Gaussian Context Transformer (GCT) attention, a multi-scale dilated bottleneck, and dual heads for region and boundary prediction. Depth videos were collected using an overhead Intel RealSense D435 RGB-D camera from 60 beef heifers. To reduce animal-level leakage, leave-one-animal-out cross-validation was used for segmentation. LAED + GCT achieved 96.91% Dice (95% confidence interval (CI): 96.56–97.21%) and 94.22% IoU (95% CI: 93.58–94.77%), while operating at 33.08 frames per second. For weight prediction, biometric traits and deep features were evaluated using random forest, support vector regression, and fully connected neural networks. The best primary-metric body-weight model used biometric traits with support vector regression, achieving MAPE = 6.75%, pooled $R^2$ = 0.68, MAE = 23.92 kg, and RMSE = 31.79 kg. Among FCNN models trained independently within each cattle-level fold, the best result used ResNet50 features and achieved MAPE = 7.76%, a pooled $R^2$ = 0.56, an MAE = 27.60 kg, and an RMSE = 37.07 kg. The mean signed prediction bias for the biometric-SVR model was −1.04 kg, using predicted minus observed body weight, with a bootstrap 95% confidence interval of −9.63 to 7.41 kg. These results support the promise of overhead depth imaging for non-invasive cattle body segmentation and weight estimation, while larger external validation remains necessary. Full article

Metal accumulation in cacao (Theobroma cacao L.) cultivation represents an important agronomic and food-safety concern, particularly in acidic tropical soils where cadmium (Cd) and other trace metals can become bioavailable and translocate to plant tissues. Green magnetic nanomaterials offer a potential strategy for reducing metal mobility in agricultural substrates, but their performance depends on surface chemistry, dose, and plant genotype. In this study, we synthesized and evaluated MCRES, defined here as a maghemite–Citrus reticulata extract system, a biofunctionalized γ-Fe₂O₃-based nanosystem prepared by coupling iron oxide nanoparticles (NPs) with a 3% (w/v) Citrus reticulata peel extract. The objective was to determine whether citrus-mediated biofunctionalization could produce a scalable magnetic nanoamendment capable of modifying Cd and naturally occurring Ni partitioning in cacao seedlings. MCRES was recovered magnetically and dried, yielding 8.44 g of product from 10 g of precursor. Rietveld analysis performed in X ray diffractograms confirmed phase-pure cubic γ-Fe₂O₃ with a lattice parameter of 0.8332 nm, a crystallite size of 11.3(1) nm, and satisfactory refinement quality (χ² ≈ 1.34). Transmission electron microscope images showed quasi-spherical NPs with a log-normal size distribution centered at 7.5 nm. Magnetic measurements showed superparamagnetic-like behavior at 300 K, high saturation magnetization values of 62 emu g⁻¹ at 300 K and 71 emu g⁻¹ at 5 K, and elevated effective anisotropy values obtained from the Law of Approach to Saturation fitting. MCRES was applied at 0, 1, 2, 4, and 6 g pot⁻¹ to cacao seedlings containing Cd-amended Ultisol with naturally occurring Ni. Plant responses were genotype and dose dependent: TSH-1188 genotype showed limited dose sensitivity for most biometric variables, whereas ICS-95 genotype showed significant dose effects, with maximum growth at the 2 g pot⁻¹ treatment. Metal-partitioning results indicated that Cd remained comparatively mobile toward shoots, whereas Ni was preferentially retained in roots. In TSH-1188 genotype, the Ni translocation factor decreased from 3.07 in the control to 0.85–1.00 at higher MCRES doses. Compared with previous work on non-biofunctionalized nanomaghemite, these results suggest that citrus-mediated biofunctionalization produces a distinct Cd/Ni partitioning response. Overall, MCRES is recommended as a promising nursery-scale green nanoamendment for reducing metal mobility in cacao cultivation, but its agronomic use should be optimized according to genotype and dose. Future work should include side-by-side comparisons with unfunctionalized γ-Fe₂O₃, Citrus reticulata extract alone, and non-contaminated controls under field conditions to validate its long-term effectiveness and environmental safety. Full article

Background/Objectives: Corneal endothelial damage remains a key concern following phacoemulsification. This study aimed to quantify early postoperative changes in endothelial cell density metrics after cataract surgery in a Romanian population and to identify preoperative and intraoperative predictors of endothelial cell loss at 1 week and 1 month postoperatively. Methods: We conducted a retrospective observational study of 137 eyes that underwent standard phacoemulsification with intraocular lens implantation at Ocularcare Ophthalmology Hospital in Bucharest, Romania. Preoperative data included age, sex, and biometric parameters: anterior chamber depth (ACD), axial length (AL), and central corneal thickness (CCT). Corneal endothelium was assessed by specular microscopy preoperatively and at 1 week and 1 month postoperatively, with measurements of endothelial cell density (CD), number of analyzed cells (No), average cell size (ACS), minimum and maximum cell size (MinCS, MaxCS), and cell size variability (SD). Intraoperative parameters included average ultrasound energy (AVE) and actual phacoemulsification time (APT). Associations between demographic, biometric, and surgical variables and postoperative endothelial changes were analyzed using univariable and multivariable regression models. Results: In 137 eyes, mean CD decreased from 2401.99 ± 342.57 cells/mm² preoperatively to 2144.38 ± 449.92 at 1 week and 2053.15 ± 471.13 at 1 month. CCT increased from 534.64 ± 38.01 µm to 548.70 ± 41.34 µm at 1 week and remained higher than baseline at 1 month (545.67 ± 42.91 µm). Endothelial remodeling was reflected by significant increases in ACS, MaxCS, and SD, while No and MinCS showed no significant change. In adjusted models, lower postoperative CD was independently associated with shallower ACD and lower baseline CD at both timepoints, whereas higher AVE was associated with lower postoperative CD at 1 week but not at 1 month; sex was not independently associated with postoperative CD. Conclusions: In this Romanian cohort, phacoemulsification was associated with significant early endothelial cell loss measurable within the first postoperative month. The magnitude of CD reduction was influenced by both baseline patient ocular characteristics (ACD) and intraoperative phacoemulsification parameters, particularly ultrasound energy (AVE). These findings support the incorporation of preoperative biometric assessment and intraoperative ultrasound minimization strategies to reduce endothelial risk. Full article

Virtual reality (VR) technology provides a high sense of immersion and presence to users and can enhance the engagement and performance of learning. However, the VR learning environment introduces more complex audio–visual stimuli than the traditional multimedia learning environment. These excessive stimuli cause negative effects such as distraction and cognitive overload. To minimize these negative impacts and improve the learning environment, we must evaluate learners’ cognitive states under the VR environment. Cognitive states can be evaluated subjectively (e.g., through questionnaires) or objectively (e.g., using biometric signals). Subjective and objective methods must be used simultaneously, and correlations between them must be analyzed for quantifying objective measures. The accurate detection of cognitive states is challenging for traditional statistical analysis methods, necessitating the exploration of artificial intelligence (AI) techniques that can classify cognitive states. This study develops a VR learning experience evaluation system based on eye-tracking data. Cognitive states during VR learning are classified as cognitive overload, immersion, and distraction. Correlations between each cognitive state and eye-tracking metrics are evaluated, and the possibility of cognitive-state quantification is discussed. An LSTM-based model developed in this study classified cognitive states from eye-tracking data with moderate accuracy (75.60%) under a subject-independent validation setting. Full article