Search Results (442)

Objective: To investigate the association between prenatal ultrasonographic markers of macrossomia and C-peptide, a neonatal hyperinsulinemia marker, in pregnancies complicated by gestational diabetes mellitus (GDM), with a focus on fetal adipose tissue thickness, liver length, and interventricular septal thickness. Methods: This prospective cohort study included 223 pregnant women followed from 28 to 36 weeks of gestation in two referral centers in Brazil. The GDM group and matched controls underwent serial ultrasound assessments of fetal biometry, including thigh, abdominal, and subscapular skinfolds, fetal liver length, and interventricular septum thickness. Neonatal hyperinsulinemia was assessed using umbilical cord C-peptide levels. Statistical analyses included t-tests, chi-square tests, correlation analyses, and multivariate logistic regression. Results: Fetuses of mothers with GDM exhibited significantly greater abdominal [t(221) = −3.999, p < 0.01] and subscapular [t(221) = −2.502, p = 0.02] skinfolds, liver length [t(221) = −3.785, p < 0.01], and interventricular septum [t(221) = −4.781, p < 0.01] thickness. However, umbilical cord C-peptide levels did not differ significantly between groups [t(189) = −1.724, p = 0.09]. Only weak correlations were found between fetal ultrasound markers and C-peptide levels. Among all parameters, subcutaneous tissue thickness showed the highest (ρ = 0.30), though still limited, predictive value. Conclusions: Fetuses of mothers with GDM demonstrated increased measures of liver length, subscapular adiposity, and interventricular septal thickness compared to controls. However, these prenatal biometric markers showed weak correlations with neonatal C-peptide levels. Full article

The proliferation of biometric technology across various domains including user identification, financial services, healthcare, security, law enforcement, and border control introduces convenience in user identity verification while necessitating robust protection mechanisms for sensitive biometric data. While chaos-based encryption systems offer promising solutions, many existing chaos-based encryption schemes exhibit inherent shortcomings including deterministic randomness and constrained key spaces, often failing to balance security robustness with computational efficiency. To address this, we propose a novel dual-layer cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system for protecting biometric templates and facilitating secure feature matching operations. The framework implements a two-tier encryption mechanism where each layer independently utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring template-specific encryption patterns that enhance resistance against chosen-plaintext attacks. The framework performs dimensional normalization of input biometric templates, followed by image pixel shuffling to permutate pixel positions before applying dual-key encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates remain encrypted in storage, with decryption occurring only during authentication processes, ensuring continuous security while enabling biometric verification. The proposed system’s framework demonstrates exceptional randomness properties, validated through comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of ${10}^{320}$, negligible correlation coefficients (<${10}^{-2}$), and robust differential attack resistance with an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA Face and Iris databases, demonstrates practical computational efficiency, achieving average encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis against other state-of-the-art encryption schemes verifies the effectiveness and reliability of the proposed scheme and demonstrates our framework’s superior performance in both security metrics and computational efficiency. Our findings contribute to the advancement of biometric template protection methodologies, offering a balanced performance between security robustness and operational efficiency required in real-world deployment scenarios. Full article

Greenhouses have positively impacted plant production by allowing the cultivation of different crops per year. However, the accumulation of agricultural plastics, potentially contaminated with agrochemicals, raises environmental concerns. This work evaluates the combined effect of Bordeaux mixture and low-density polyethylene (LDPE) microplastics (<5 mm) on the growth of lettuce (Lactuca sativa L.) and soil microbial communities. Different levels of Bordeaux mixture (0, 100 and 500 mg kg⁻¹), equivalent to Cu(II) concentrations (0, 17 and 83 mg kg⁻¹), LDPE microplastics (0, 1% and 5%) and their combination were selected. After 28 days of growth, biometric and photosynthetic parameters, Cu uptake, and soil microbial responses were evaluated. Plant germination and growth were not significantly affected by the combination of Cu and plastics. However, individual Cu treatments influenced root and shoot length and biomass. Chlorophyll and carotenoid concentrations increased with Cu addition, although the differences were not statistically significant. Phospholipid fatty acid (PLFA) analysis revealed a reduction in microbial biomass at the highest Cu dose, whereas LDPE alone showed limited effects and may reduce Cu bioavailability. These results suggest that even at the highest concentration added, Cu can act as a plant nutrient, while the combination of Cu–plastics showed varying effects on plant growth and soil microbial communities. Full article

The cigarette butt (CB) recycling process yields several byproducts, including cleaned filters, solid debris (mainly paper and tobacco), and wastewater. This study aimed to assess, for the first time, the long-term suitability of these recycled byproducts for turfgrass cultivation. Under controlled conditions, Paspalum vaginatum Swartz was grown in sand–peat substrate, either unmodified (control) or amended with small pieces of uncleaned CBs or solid byproducts from CB recycling at concentrations of 25% or 50% (v/v). In additional tests, turfgrass grown in unmodified substrate received wastewater instead of tap water once or twice weekly. Over 7 weeks, physiological and biometric parameters were assessed. Plants grown with solid debris showed traits comparable to the control. Those grown with intact CBs or cleaned filters had similar biomass and coverage as the control but accumulated more carotenoids and antioxidants. Wastewater significantly enhanced plant growth when applied once weekly, while becoming toxic when applied twice, reducing biomass and coverage. After scalping, turfgrass recovered well across all treatments, and in some cases biomass improved. Overall, recycled CB byproducts, particularly wastewater used at optimal concentrations, can be a sustainable resource for promoting turfgrass growth. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The development of biometric techniques in domestic animals has greatly advanced scientific practices in wildlife research. The association between seminal characteristics and body and testicular biometry enables the selection of suitable breeders, though appropriate measurement techniques are required. The present study assessed differences among conventional methods and formulas for estimating testicular parameters. Testicular length, width, and thickness were measured using three methods in 13 adult male domestic cats. Testicular area, volume, and weight were estimated, from which the gonadosomatic index (GSI) was calculated. Sperm were collected using an alpha-2 adrenergic agonist and urethral catheterization, and characterized in terms of volume, vigor, total motility, progressive motility, concentration, plasma membrane integrity, and morphology. The three methods were consistent in terms of testicular area, volume, weight, and GSI. Moderate positive correlations were observed for testicular weight (r = 0.61, p < 0.05) and GSI (r = 0.58, p < 0.05). Testicular parameters showed strong positive correlations among each other (r > 0.80, p < 0.05). We observed a moderate positive correlation between head length and progressive motility (r = 0.65, p < 0.05). In conclusion, all testicular measurement and estimation techniques showed comparable performance. Therefore, testicular biometry is useful for selecting breeding males in feline conservation programs, wherein larger body biometrics are related to improved seminal and reproductive parameters. Full article

The water-soluble extract from vermicompost, also known as vermicompost tea (VT), has attracted interest in sustainable production research due to its potential to increase crop yields. However, information regarding the influence of this bioinput on strawberry cultivation remains limited. This study aimed to evaluate the effects of different VT solution concentrations on the mass fruit, physiology, and fruit quality of the hybrid strawberry cultivar ‘Portola’. The experiment was conducted in a greenhouse, with foliar and substrate applications of VT solutions at varying concentrations (0%, 2%, 4%, 6% and 8%) over 150 days. Evaluations included the chemical composition of the VT, as well as the physiological and agronomic parameters of the strawberry plants, such as gas exchange, biometric data, the physicochemical quality of the fruit and the nutritional composition. Significant differences in gas exchange parameters, particularly intercellular CO₂ concentration and stomatal conductance, were observed at the final growth stage. Of the quality and compositional parameters of the strawberries, only the soluble solids/titratable acidity (SS/TA) ratio was affected. The various VT dilutions induced physiological alterations in the strawberry plants, with energy being allocated towards mass fruit at the expense of fruit quality, specifically in terms of the SS/TA ratio. Full article

This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios. Full article

Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article

Ornamental fish shipped by road or rail may spend days in transit without food, leading to a reduction in somatic growth after transportation and during acclimatization. In the present study, a time-series (0, 2, 4, 6, 8, 10, and 12 days) experiment was conducted to investigate the growth recovery of male Siamese fighting fish (Betta splendens, 1.56 ± 0.02 g body weight, n = 15 per group) transported by road for two days. Biometric changes, nesting activity, skin pigmentation, digestive enzyme activity, muscle quality, and whole-body composition, were compared across all fish groups. The recovery in growth, as indicated by final body weight, increased with post-transportation time (p < 0.05), causing a significant reversal of weight loss with a proportionally stable condition factor from day 8 until the end of observation (p > 0.05). During this time period, the fish exhibited similar bubble-nest building activity to the control group that was not transported (p > 0.05). Color parameters, digestive enzyme activities, muscle quality, and whole-body composition of fish 8 days after shipping were comparable to the control fish group (p > 0.05). Our findings indicate that an 8-day recovery time is an appropriate protocol for Siamese fighting fish acclimatization following overland shipping. Full article

The loss of hectares of forest areas has become a global issue that has worsened over recent years due to unsustainable human activities. In a context of limited availability of productive land, it is urgent to adopt efficient strategies to recover the affected natural areas. Actions based on a circular economy, such as the use of organic chemical matrices recovered from water waste treatment plant waste, have proven to be effective. In this regard, the addition of plant growth-promoting bacteria (PGPB), such as Bacillus pretiosus and Pseudomonas agronomica, can contribute to the chemical treatment, favoring the recovery of soils, accelerating the recovery of vegetation cover, and inducing an increase in biodiversity. In this research, the effect of bio-fertigation under controlled laboratory conditions in Quercus pyrenaica is evaluated. After a thirty-six-week trial, the biometric and nutritional parameters of the plants were harvested and measured, and the diversity and composition of the metagenomes of their rhizospheres were evaluated. As well, the cenoantibiogram and the metabolic diversity were measured. The results showed that the use of these biofertilizers increased the variables related to plant production, quality of plant composition as an indirect means of their resilience, as well as an increase in rhizospheric microbial diversity and a reduction in their MIC resistance to the most widely used antibiotics. For all these reasons, the use of the biofertilizer result of the combination of WWTP waste, Bacillus pretiosus, and Pseudomonas agronomica is postulated as an environmentally friendly strategy that can contribute to the recovery of potential oak forest areas. Full article

Background/Objectives: Developmental dysplasia of the hip (DDH) affects 1–3% of newborns and requires early detection for optimal outcomes. DDH involves abnormal acetabular–femoral congruence between the acetabulum and femoral head, resulting from either shallow acetabular development or delayed femoral ossification of the femoral head. We evaluated the ossification nucleus of the femoral head (ONFH) to determine prevalence, radiographic timing, and associations with perinatal factors. Methods: We analyzed 100 pelvic radiographs of infants between 90 and 150 days of age. Participants were selected by convenience sampling, based on inclusion criteria. We identified the presence of ONFH and measured biometric parameters, morphology, and anatomical location. Sociodemographic and perinatal data were collected from the participating infants. Results: The prevalence of ONFH was 33%, and the mean age at visualization was 104 days. The presence of ONFH was correlated with birth weight (p = 0.011), discharge weight (p = 0.005), and weight at 1 month (p = 0.034). In our study, female sex (p = 0.004) was associated with a 4.966-fold higher odds of ONFH prevalence compared to males. Conclusions: This study provides relevant evidence regarding the prevalence, morphology, and characteristics of ONFH. Few studies report this information on ONFH in different populations. The optimal timing for radiographic visualization of ONFH in infants remains undefined, but the appearance of the ONFH was concentrated around 104 days of life. The novel association between weight and ONFH provides new insights into DDH. This provides new insights for DDH screening. This association warrants further research for the early detection of DDH. Full article

Biophilic design has emerged as a multidimensional response to growing concerns about health, well-being, and ecological balance in the built environment. Despite its rising prominence, research on the topic remains fragmented across building typologies, user groups, and geographic contexts. This study presents a comprehensive review of the biophilic design literature, employing a hybrid methodology combining structured content analysis and bibliometric mapping. All peer-reviewed studies indexed in the Web of Science and Scopus were manually screened for architectural relevance and systematically coded. A total of 435 studies were analysed to identify key trends, thematic patterns, and research gaps in the biophilic design discipline. This review categorises the literature by methodological strategies, building typologies, spatial scales, population groups, and specific biophilic design parameters. It also examines geographic and cultural dimensions, including climate responsiveness, heritage buildings, policy frameworks, theory development, pedagogy, and COVID-19-related research. The findings show a strong emphasis on institutional contexts, particularly workplaces, schools, and healthcare, and a reliance on perception-based methods such as surveys and experiments. In contrast, advanced tools like artificial intelligence, simulation, and VR are notably underused. Few studies engage with neuroarchitecture or neuroscience-informed approaches, despite growing recognition of how spatial design can influence cognitive and emotional responses. Experimental and biometric methods remain scarce among the few relevant contributions, revealing a missed opportunity to connect biophilic strategies with empirical evidence. Regarding biophilic parameters, greenery, daylight, and sensory experience are the most studied parameters, while psychological parameters remain underexplored. Cultural and climate-specific considerations appear in relatively few studies, and many fail to define a user group or building typology. This review highlights the need for more inclusive, context-responsive, and methodologically diverse research. By bridging macro-scale bibliometric patterns with fine-grained thematic insights, this study provides a replicable review model and valuable reference for advancing biophilic design as an evidence-based, adaptable, and human-centred approach to sustainable architecture. Full article

The white snook (Centropomus viridis) is an emerging aquaculture species with high market acceptance, exhibiting catadromous and protandric hermaphroditic characteristics in adulthood. This study aimed to preliminarily characterize certain hematological and biochemical parameters, as well as blood cell morphology, for identifying possible variations between sexes maintained under aquaculture recirculating system (RAS) conditions. The white snook broodstock was anesthetized with clove oil, and biometric values, as well as sex classification, were measured. Then, blood samples were collected from 14 females (7132 ± 1610 g) and 20 males (2200 ± 0.963 g) via caudal vessel puncture to analyze selected hematological parameters, blood biochemistry, and cellular morphology. Fulton’s condition factor (K) showed no differences between sexes, indicating a healthy fish status. Females showed significantly higher serum cholesterol, glucose, and triglyceride levels than males. Also, hematocrit (HCT) and mean corpuscular volume (MCV) were elevated in females. No sex-related differences were observed in red or white cell counts or in blood cell dimensions. Morphological characterization identified erythrocytes, thrombocytes, and three types of leukocytes: lymphocytes (small and large lymphocytes), neutrophils, and monocytes, with no eosinophils or basophils detected in either sex. These findings provide fundamental reference values for the hematological and biochemical profiles of C. viridis broodstock in captivity and highlight sex-specific differences relevant for reproductive and health monitoring. However, it should be considered that the sample size used to establish reference ranges for the species is small, so it is recommended to implement a monitoring plan for this and other broodstocks of this emerging species. Full article

Due to the increasing number of vehicles and the aging population, the vulnerability to sudden medical emergencies among drivers is a growing problem. Events such as heart attack, stroke, and loss of consciousness can occur without warning and endanger everyone on the road. Modern vehicles, equipped with electronic systems, can support real-time driver’s health monitoring through early detection technologies. The existing Driver Monitoring Systems (DMS) in our cars assess behavioral states such as drowsiness and distraction. In the future, DMS will include biometric sensors to monitor vital signs such as heart rate and respiration. By finding predictors of sudden illnesses (SI), such a system will provide valuable time for the driver to react before the strike of a medical event. In this paper, we present our vision for DMS operation with physiological monitoring capabilities. A brief overview of sensor’s types and their locations in the vehicle interior used in the research studies for monitoring the corresponding physiological parameters is presented. A comparative analysis of the advantages and disadvantages of the sensing methods used for physiological monitoring of the driver in real driving scenarios is made. Full article