Search Results (4,620)

Background/Objectives: Frequency-to-place mismatch between cochlear implant (CI) electrodes and cochlear tonotopy has been suggested to affect postoperative speech perception. This study aimed to examine the associations between frequency-to-place mismatch and speech perception outcomes across multiple linguistic levels in patients with CI and to assess how these associations change over time using postoperative computed tomography. Methods: This retrospective cohort study included 44 postlingually deafened adults who underwent unilateral cochlear implantation with a Flex28 electrode by a single surgeon at a tertiary care hospital. Speech perception was assessed using CI-2004, a Japanese speech perception test consisting of monosyllables, consonants, words, and sentences, in quiet settings at 3, 6, and 12 months after CI activation. Partial correlation analyses between frequency-to-place mismatch and postoperative speech perception scores were performed in 35 of the 44 patients, controlling for age and mean preoperative pure-tone thresholds. Results: Negative associations were observed between frequency-to-place mismatch and CI-2004 scores, particularly for monosyllable and consonant perception in uncorrected analyses. After correction for multiple comparisons, only consonant perception at 3 months after CI activation remained significant (r = −0.52, p = 0.002). Similar patterns were observed for other speech measures and at later time points, although these did not remain significant after correction. Conclusions: Frequency-to-place mismatch was associated with postoperative speech perception outcomes, particularly those involving phoneme-level recognition. After correction for multiple comparisons, only consonant perception at 3 months after CI activation remained significant. Full article

Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation. Full article

Background: The metamorphopsia questionnaire (MeMoQ) is an established patient-reported outcome measure (PROM) in the context of macular disease. However, its performance has not been proved in those being treated for various macular conditions with intravitreal anti-vascular endothelial growth factor (Anti-VEGF). The objective was to eliminate misfitting items, enhance measurement precision, and ensure optimal response categorisation. Methods: Rasch analysis was performed iteratively on 2286 responses from patients with macular diseases being treated with Anti-VEGF to optimise the MeMoQ. Fit statistics, reliability indices, person and item separation measures, and principal component analysis (PCA) of residuals were assessed to determine the optimal model. This study was conducted in an outpatient clinic specialising in retinal diseases in Hywel Dda University Health Board. Results: Misfitting items were removed in successive iterations, leading to optimised category probability curves and stable fit statistics for the MeMoQ. The resulting model for all responses included two final items, with person separation remaining inadequate reducing from 1.23 to 1.12 and reliability from 0.60 to 0.56. Category probability curves demonstrated good ordering of response variables with Andrich thresholds separated by >1.2 logits. In the subgroups of neovascular age-related macular degeneration and diabetic macular oedema person separation remained below two and reliability remained low. Conclusions: Rasch analysis demonstrated that the MeMoQ was not a valid or reliable PROM in this patient population. Therefore, the MeMoQ may not provide a reliable index of patient’s perception and visual experience when undergoing Anti-VEGF treatment. Full article

Background/Objectives: The IUCN endangered spotted pond turtle (Geoclemys hamiltonii) demonstrates markedly reduced resistance to Aeromonas hydrophila-induced infections compared to the red-eared slider (Trachemys scripta). This study investigates the immunological basis for this disparity by analyzing infection outcomes and splenic transcriptomes of both species post-pathogen challenge. Methods: In a preliminary experiment, 32 turtles (16 G. hamiltonii and 16 T. scripta) were exposed to A. hydrophila. Results: G. hamiltonii developed skin ulcer syndrome at a significantly higher incidence (81.25%) than T. scripta (12.5%) (p < 0.05). Comparative transcriptomic analysis identified 19 differentially expressed immune-related genes, with qPCR validation across five tissues (heart, liver, spleen, intestine, blood) revealing pronounced interspecies differences in IL18, STAT1, IFIH1, and CD28 expression. Notably, IL18 and its downstream effector STAT1 were robustly upregulated in T. scripta but were considerably lower in G. hamiltonii, correlating with delayed IFN-γ pathway activation and impaired epidermal barrier repair. Concurrently, CD28 upregulation in T. scripta facilitated rapid T-cell-mediated pathogen clearance, whereas its delayed induction in G. hamiltonii hindered adaptive immunity. These findings implicate dysregulated innate (IL18/STAT1) and adaptive (CD28) immune pathways as key determinants of G. hamiltonii’s susceptibility to bacterial infection. Conclusions: Despite the critical conservation status of G. hamiltonii, the immunological basis underlying its heightened susceptibility to bacterial infections remains largely unexplored; this study addresses this gap by comparing the splenic transcriptomes of G. hamiltonii and T. scripta following A. hydrophila challenge, identifying the dysregulated IL18-STAT1 Immune Axis and CD28-mediated adaptive immunity as key determinants, thereby providing actionable immune targets for conservation breeding and susceptibility screening in this endangered species. Full article

Background: The recent U.S. Food and Drug Administration authorization of AirPods Pro as over-the-counter hearing aids (HAs) has increased interest in consumer devices as potential alternatives to traditional amplification; however, their electroacoustic performance relative to clinically fitted HAs remains unclear. The purpose of this study was to compare the electroacoustic characteristics and real-ear measures of AirPods Pro 2nd generation (APP2), AirPods Pro 3rd generation (APP3), and a traditional receiver-in-the-canal HA across mild flat, mild-to-moderate sloping, and moderate flat hearing loss configurations. Methods: Outcome measures included 2cc coupler output curves, saturation sound pressure level for a 90 dB input (SSPL90), real-ear speech mapping, maximum power output (MPO), and real-ear-to-coupler differences. Results: Coupler-based electroacoustic measures showed that APP2 and APP3 produced output comparable to the traditional HA (within 7 dB). SSPL90 outputs were similar for APP2 and APP3, whereas the HA demonstrated profile-dependent increases. In contrast, real-ear measurements demonstrated that both APP2 and APP3 consistently produced less output relative to the HA that was fitted to NAL-NL2 targets, with the largest deviations observed for moderate hearing loss and at higher frequencies (up to 14 dB). Across all configurations, MPO was consistently highest for the HA, with both AirPods devices exhibiting reduced maximum output, especially in speech-critical frequency regions. Real-ear-to-coupler difference findings indicated reduced acoustic coupling for APP3 relative to APP2 and the HA, contributing to reduced in-ear amplification despite comparable coupler outputs. Conclusions: While AirPods Pro may offer benefit for mild hearing loss or moderate high-frequency hearing loss, they do not provide output comparable to prescriptively fitted HAs. These findings underscore the continued importance of clinical verification and prescription-based fitting of hearing assistive technology for achieving appropriate audibility across hearing loss configurations. Full article

The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six YOLO models, trained from scratch and fine-tuned, alongside a Faster R-CNN model, for automated kernel detection and counting from manually harvested field corn ear images. Model performance was assessed for predicting the yield and harvest index (HI) of field corn under varying nitrogen and irrigation rates. Results show that models trained with fine-tuning consistently outperform those trained from scratch in both accuracy and computational speed. Among all tested YOLO models, YOLOv11x achieved the highest performance, with a precision of 0.978, a recall of 0.968, a latency of 4.8 ms, and a prediction coefficient of determination (R²_pred) of 0.858 for the test set and 0.890 for cross-year datasets. The YOLOv8x model ranked second, whereas YOLOv10x was the worst-performing model. Compared to YOLO, Faster R-CNN performed poorly. Yield and HI predictions using YOLOv11x achieved R² values of 0.881 and 0.758, respectively, and captured treatment effects. Overall, the findings demonstrate that YOLO-based architecture is highly effective for detecting kernels and predicting yield in precision agriculture applications. Full article

Fusarium ear rot (FER), caused by F. verticillioides, is a devastating disease in maize, leading to substantial yield losses and mycotoxin contamination. Therefore, revealing the molecular mechanisms underlying FER resistance is essential for crop breeding. Here, we performed integrated transcriptomic and metabolomic analyses on two maize inbred lines with contrasting FER resistance: the resistant line ZL30-12 (ZL30) and the susceptible line 92C0468U (92C). Following F. verticillioides inoculation, ZL30 exhibited sustained inhibition of fungal colonization and fumonisin accumulation, whereas 92C showed progressive disease development and elevated fumonisin levels. Both transcriptomic and metabolomic analyses converged on the phenylpropanoid pathway, with DEGs enriched in phenylpropanoid metabolism and DAMs enriched in phenylpropanoid biosynthesis, highlighting its central role in resistance. Further integrative analysis revealed that the lignin biosynthetic process, a key branch of phenylpropanoid metabolism, plays an important role in resistance. Several key DEGs (ZmPAL, ZmHCT, peroxidases, and ZmCOMT) and DAMs (sinapic acid, sinapaldehyde, coniferin, cinnamic acid, and caffeic acid) were differentially regulated between the two lines. Correlation analysis revealed a significant correlation between ZmCOMT expression and sinapic acid accumulation. RT-qPCR validation confirmed the expression patterns of key lignin-associated genes. The elevated activation of lignin biosynthesis in ZL30, via time-dependent induction of key genes (ZmPAL, ZmHCT, and peroxidases), suggests an increase in lignin accumulation, which likely reinforces cell wall integrity and restricts fungal invasion, thereby contributing to FER resistance. Collectively, these findings provide insights into the molecular mechanisms of FER resistance and identify key lignin-associated genes as promising targets for maize breeding. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Animal welfare monitoring is essential in pig production. On-farm animal welfare (AW) assessments may provide a comprehensive overview but are resource-intensive. Abattoir-based assessments allow pigs from multiple farms to be inspected in a single facility. However, data on the relationship between these assessments remain limited, especially for heavy pigs (160–170 kg). This study investigates these associations in Italian heavy pig production. At the abattoir, 18,333 pig carcasses from 185 batches across 86 farms were scored for tail, skin (cranial and caudal) and ear lesions. On-farm AW assessments (management, structures and animal-based measures) were obtained from the national surveillance system (ClassyFarm). Tail lesion scores were higher in pigs with intact tails, whereas ear scores showed the opposite trend, suggesting a substitution effect between tail and ear biting. This indicates that tail docking is insufficient to fully prevent abnormal behaviours. Higher skin and ear scores were associated with suboptimal management, but tail scores were not, likely due to the multifactorial nature of tail biting. Herd size had no significant effect on welfare indicators. These results highlight the complexity of assessing AW and the importance of combining abattoir and farm data to obtain a more integrated monitoring system. Full article

Mammals and reptiles possess a metanephric kidney as the terminal renal organ for homeostasis of solutes and waste products. The development of the metanephric kidney has primarily been studied in mammalian model systems. Little is known about the conservation of metanephric kidney formation in non-mammalian species such as reptiles. Uniquely, reptiles maintain kidney progenitor cell populations throughout life and continually develop new nephrons, the functional unit of the kidney. The red-eared slider turtle, Trachemys scripta elegans, was utilized to investigate the conservation of reptilian metanephric kidney development. The nephron progenitor cell (NPC) marker, Six2, was detected in whole-mount turtle kidneys in a similar pattern to mammals. However, there were differences in progenitor cell niche morphology where turtle NPC populations formed distinct elongated rows instead of the rosette-like morphology found in the mouse. The pattern of NPC populations in the embryonic turtle kidney was maintained in the adult turtle. Whole-genome bisulfite sequencing was performed on cortical tissue containing the NPC populations from adult turtle kidneys and compared to those of adult mice. Significant conservation of gene methylation was detected in adult cortical tissue between the two species, although unique signatures were detected in turtle samples related to DNA repair and β-catenin signaling. This suggests a high level of conservation of metanephric kidney development at the genetic level. Full article

Plant architecture-related traits are key agronomic traits affecting crop growth and yield. To unravel the genetic architecture of plant height (PH), ear height (EH), tassel length (TL), and tassel primary branch number (TPBN), 379 DH lines derived from 21 maize hybrids were used for genome-wide association study (GWAS) and genomic selection (GS) analyses. Although plant architecture-related traits were significantly influenced by genotype and genotype-by-environment interactions, moderate to high broad-sense heritability was observed for PH (81.3%), EH (79.6%), TL (86.4%), and TPBN (82.5%). Using six different models for GWAS, seven unique SNPs on chromosomes 1, 2, and 3 were identified for PH, 92 unique SNPs located on chromosomes 1 to 9 were identified for EH, three unique SNPs on chromosome 6 were detected for TL, and 18 unique SNPs located on chromosomes 1, 4, 5, 8, and 10 were identified for TPBN at the p-value threshold of 7.42 × 10⁻⁶. A few hotspot genomic regions conferring plant architecture-related traits were identified, located in bins 2.07, 4.07, 8.03, 6.01, and 10.00. A total of 144 putative candidate genes were identified, which were enriched in endocytosis and lipid biosynthetic process, electron carrier activity, chloroplast stroma, and plastid stroma. The prediction accuracy evaluated through 5-fold cross-validation was 0.44 for PH, 0.43 for EH, 0.31 for TL, and 0.30 for TPBN. When the training population size (TPS) reached 60–70% or marker density (MD) reached 3000, the prediction accuracy tends to stabilize, indicating that the optimum size of TPS and MD were 60–70% and 3000 for GS, respectively. The highest prediction accuracy evaluated by using 30–5000 significant SNPs corresponding to the lowest p-value was 0.70 for PH, 0.85 for EH, 0.58 for TL, and 0.75 for TPBN, with an increase in accuracy of 59.1% to 150.0%. These results demonstrate that integrating GS with a subset of highly significant SNPs can substantially enhance prediction efficiency, thereby facilitating the selection of superior genotypes and accelerating the breeding of maize varieties with optimized plant architecture. This study has further elucidated the genetic basis of maize architecture-related traits and provided valuable information on how to implement GS to breed novel maize varieties with optimized plant types. Full article

Methicillin-resistant S. aureus (MRSA) is listed by the World Health Organization as a priority pathogen posing a major worldwide threat to public health. Two lineages of MRSA predominate as causes of human infections in the U.S.: USA300 and USA100. Although they are most often grouped together as MRSA, these two lineages differ in pathogenetic mechanisms in important ways. The epidemic spread of these two dominant lineages has been problematic because of the multidrug-resistant profile of USA100 and the virulence of USA300, as well as their ability to adapt to both community and hospital environments. In this perspective, we examine what is currently known about their distinctive biology and the consequent differences in infections caused by these two main MRSA epidemic clones. The purpose of this perspective is to provide critical insights to the clinical microbiology community to stimulate further research to inform the design of new prevention and management strategies for MRSA. Full article

Biometric systems are widely used in security and forensic applications. Conventionally, contact-based footprint scanners require physical contact, which presents significant limitations. These devices raise hygiene concerns and are impractical in field identification conditions, such as forensic investigations or disaster victim identification, where quick and non-invasive methods are essential. To address these challenges, a contactless footprint acquisition and identification system was developed using image processing techniques and a Convolutional Neural Network (CNN) based on the Visual Geometry Group–16 layer architecture. The system employs a Raspberry Pi 4, a Logitech C922 camera, and a ring light to capture footprints without direct surface contact. Captured images are processed with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve contrast and mean thresholding to generate binary images for clearer feature extraction. System performance was evaluated using a multiclass confusion matrix. The CNN correctly classified 158 of 160 test images, achieving an accuracy of 98.75%. This result demonstrates higher accuracy than earlier studies that used older CNN models, such as Alex Krizhevsky’s Network and LeCun’s Network-5, which performed with fewer subjects and lower accuracy rates. The developed system shows potential for biometric security, forensic investigations, and disaster response, where contactless and reliable identification is required. Future research can expand the dataset with more diverse footprints, test performance under varied conditions, and extend the approach to other contactless biometrics such as palmprints or ears. Full article

To cope with the increasing pressure from diseases and pests under climate change, the effect of 6 maize sowing dates on the plant health of an ultra-early maize variety (Pyroxenia, FAO 130) was analyzed in studies conducted from 2016 to 2018. The assessment of the response of the ultra-early variety to climate change will contribute to the identification of its predisposition to cultivation in terms of health recognition. The extent of plant damage caused by the frit fly (Oscinella frit L.), the European corn borer (Ostrinia nubilalis Hbn.), and the cereal leaf beetle (Oulema melanopus L.), as well as the severity of plant infection by Fusarium ear rot (Fusarium spp.) and maize smut (Ustilago maydis (D.C.) Corda), was assessed. Air temperature, precipitation, and the length of the growing period at individual sowing dates were also analyzed. The lowest level of insect damage and the highest level of disease infection were recorded in the final year of the study (2018), which was dry and had higher mean air temperature. Precipitation and temperature during the sowing dates ranged between 110.5 and 146.1 mm and 17.5 and 19.9 °C, respectively. The optimal sowing date for reducing maize losses caused by insect pests and diseases was found to be the earliest time points, i.e., between April 12 and 26. Full article

Background: Unanticipated difficult airways remain a leading cause of anesthesia-related morbidity and mortality, with traditional bedside predictors demonstrating limited sensitivity. Point-of-Care Ultrasound (POCUS) has emerged as a non-invasive adjunct offering real-time visualization and quantitative measurement of airway anatomy. This narrative review, structured according to the Scale for the Assessment of Narrative Review Articles (SANRA), synthesizes current evidence on POCUS as an adjunct for airway evaluation. We explore the sonoanatomy of the upper airway, the utility of ultrasound in predicting difficult laryngoscopy and intubation, its critical role in emergency front-of-neck access, and the verification of endotracheal tube placement. Furthermore, we discuss the integration of Artificial Intelligence (AI) in image interpretation and the necessity of standardized training curricula. Methods: We systematically searched PubMed/MEDLINE, Scopus, and Web of Science for English-language peer-reviewed studies addressing sonographic airway assessment, including sonoanatomy, prediction of difficult laryngoscopy/intubation, guidance for emergency FONA and endotracheal tube confirmation. Results: POCUS enhances visualization of critical anatomical structures, may improve anatomical assessment and risk stratification when combined with clinical assessment, and it may provide real-time guidance during emergency procedures. Integration of AI has shown promising diagnostic performance, primarily based on surrogate outcomes. Conclusions: Airway ultrasound may represent a shift toward personalized, safer airway management. However, standardized training protocols and validation in diverse clinical settings remain essential. Future research should focus on developing evidence-based algorithms integrating POCUS into airway management guidelines. Full article