Search Results (261)

The research investigates the psychopathological mechanisms through which perceived leader favoritism (PLF) can affect counterproductive work behavior (CWB) through the mediating effects of employee jealousy toward their coworkers and the specialized form of envy known as malicious envy. Integrating Social Comparison Theory with Affective Events Theory, the study proposes that perceived favoritism triggers unfavorable social comparisons, which in turn evoke negative emotional states that manifest in dysfunctional workplace behaviors. The research used a quantitative study design to gather information from 436 employees working in hotels. The researchers used structural equation modeling (SEM) to analyze the research data while they also conducted reliability and validity assessments. The study shows that perceived leader favoritism leads to higher organizational counterproductive work behavior (CWB-O) and higher interpersonal counterproductive work behavior (CWB-I) among employees. The study found that PLF leads to higher employee jealousy levels which result in higher malicious envy levels that drive CWB behavior. The research found that jealousy and malicious envy function as central psychological pathways which connect favoritism to workplace deviance. This study offers a new contribution to the existing body of knowledge about leadership by extending its scope to investigate how subtle favoritism practices lead to harmful results while explaining the emotional mechanisms which cause counterproductive behavior. The research develops an integrated framework which helps people comprehend how perceived leader favoritism affects emotional responses and behavioral patterns within modern workplaces. Full article

Background: Despite normal lung imaging and preserved pulmonary and cardiac function, many patients with long COVID continue to experience persistent respiratory symptoms. This study aimed to quantify the pulmonary blood and airway volumes in patients with long COVID compared with healthy controls and to evaluate the associations of COVID-19 vaccination with these imaging parameters. Methods: Patients with long COVID presenting with persistent respiratory symptoms despite normal laboratory findings, pulmonary function tests, chest radiography, and chest computed tomography (CT) were prospectively enrolled. CT datasets were analyzed using functional respiratory imaging (FRI), incorporating the three-dimensional reconstruction and automated segmentation of the lungs, airways, and pulmonary vasculature. Quantitative imaging parameters were compared with those of historical healthy controls from the COPDGene study, matched for age, sex, body mass index, comorbidities, and pulmonary function test parameters. Results: Thirty patients with long COVID (mean 221 ± 128 days after confirmed SARS-CoV-2 infection) and 30 matched healthy controls were included. Compared with controls, patients with long COVID demonstrated significantly lower pulmonary blood volumes in small and large pulmonary vessels, together with significantly reduced intrapulmonary airway volumes. Within the long COVID cohort, full COVID-19 vaccination was associated with a significantly greater small-vessel pulmonary blood volume and lobar airway volume compared with non-vaccinated individuals. Conclusions: These findings indicate that patients with long COVID exhibit persistent reductions in pulmonary blood and airway volumes despite normal conventional imaging and pulmonary function tests, suggesting the presence of subtle microvascular and small-airway abnormalities that may contribute to ongoing respiratory symptoms. The association between full COVID-19 vaccination and higher small-vessel pulmonary blood and lobar airway volumes suggests a potential protective effect on pulmonary structure and function; however, these findings require confirmation in larger, prospective controlled studies. Full article

Despite the rapid advancement of Emotion Recognition in Conversation (ERC), prevailing systems that primarily integrate language and speech exhibit substantial performance disparities on underrepresented emotion classes (e.g., Fear, Disgust). This study investigates whether fine-grained non-verbal visual modalities (facial action units, hand gestures, and body pose) can effectively mitigate these biases. We propose a multi-stream fusion architecture combining language, speech, and engineered pose-aware visual features, trained with class-imbalance-aware objectives. Experiments on MELD demonstrate that hybrid pose augmentation improves F1 on the least frequent classes: Fear +9.19%, Disgust +6.07%, Sadness +6.46%. We achieve an overall weighted F1 of 68.79%, competitive with recent state-of-the-art systems while uniquely targeting minority-class debiasing. These results establish fine-grained body language as a critical debiasing signal, recovering accuracy on the subtle expressions that text and speech alone fail to capture. Full article

Background: In recent years, aesthetic medicine has progressively prioritized conservative treatment volumes, subtle enhancements, and skin quality improvement over volumization. This study evaluates patient-reported psychosocial outcomes, satisfaction, and regret rates following minimally invasive aesthetic procedures using validated instruments. Methods: A review was conducted in accordance with PRISMA guidelines. PubMed, Scopus, and Cochrane Library were searched for studies reporting outcomes using validated PROMs (FACE-Q, GAIS, Decision Regret Scale, BDDQ, DCQ and COPS) with a minimum follow-up of 6 months. Studies evaluating facial botulinum neurotoxins, hyaluronic acid fillers, and biostimulation treatments were included. Given the heterogeneity in study design and outcome reporting, findings were synthesized narratively with descriptive comparisons across treatment modalities. Results: Increased treatment intensity was not consistently associated with greater satisfaction, and higher dose or volume often corresponded to more dissatisfaction and regret. The evidence for this pattern was stronger for botulinum toxin and hyaluronic acid fillers; weaker for biostimulators and preliminary for post-GLP-1 facial changes. Conservative approaches were associated with favorable PROMs, and unrealistic expectations, body dysmorphic disorder, and insufficient pre-treatment counseling were the principal predictors of poor outcomes. Conclusions: Patient satisfaction was not proportional to treatment intensity, particularly when results were perceived as unnatural or overcorrected. This relationship appears associative rather than causal, as patients receiving more intense treatment may differ in expectations, history, and psychological profile. A PROM-driven, patient-tailored approach seems more likely to produce durable satisfaction and minimize regret. Full article

Detecting localized morphological anomalies in three-dimensional point clouds is difficult because geometric deviations are entangled with rigid pose variation, residual registration error, sampling noise, and normal inter-subject variability. This challenge is particularly relevant in translational neuroimaging, where abnormal shape changes may be subtle and abnormal annotations are scarce. We propose an unsupervised framework that formulates 3D anomaly detection as a two-stage factorization problem, termed AdvFlow3D-AD. First, Fast Global Registration, followed by multi-scale Iterative Closest Point refinement, establishes a common geometric reference frame and reduces rigid-body nuisance variation. Second, an adversarially regularized normalizing flow models the residual distribution of aligned normal coordinates, enabling localized anomaly scores based on distance from the learned normal latent support. Percentile calibration on normal data then defines interpretable point-level and object-level operating points without requiring abnormal samples during training. We evaluate AdvFlow3D-AD on the Real3D-AD and Anomaly ShapeNet3D datasets, achieving a point-level area under the receiver operating characteristic curve (AUROC) of 0.747 on Real3D-AD and an object-level AUROC of 0.816 on Anomaly ShapeNet3D. We further present an exploratory neurodevelopmental brain-shape case study involving pediatric perinatal-asphyxia cases. The resulting anomaly maps showed qualitative spatial correspondence with anatomically plausible hippocampal and cerebellar regions under neuroradiological review. These results suggest that separating geometric nuisance variation from residual morphology can support interpretable anomaly localization when abnormal labels are limited. Full article

Accurate automated interpretation of upper-extremity musculoskeletal radiographs remains challenging because fracture appearance varies across anatomical regions and can be subtle under class imbalance. This study proposes a two-stage deep learning framework for MURA-based X-ray analysis, aiming to improve body-part recognition and body-part-wise abnormality detection. Multiple architectures were first compared for seven-class body-part classification, after which the selected hybrid Xception-Swin model was fine-tuned for abnormality detection within each anatomical subset. The framework combines Xception-derived local structural features with Swin Transformer contextual features using attention-based fusion, and performance was evaluated using accuracy, F1-score, AUC-ROC, Cohen’s kappa, calibration, component-level ablation, post hoc explainability, and zero-shot FracAtlas validation. For body-part classification, the model achieved accuracy = 0.9643, macro F1 = 0.9574, AUC-ROC = 0.9963, and kappa = 0.9579. For abnormality detection, accuracy ranged from 0.7289 to 0.8538, F1 from 0.7191 to 0.8508, AUC from 0.7693 to 0.9080, and kappa from 0.4449 to 0.7071. Ablation on hand and humerus radiographs showed the highest macro F1 with Hybrid Attention, while FracAtlas validation yielded AUC = 0.8247 and kappa = 0.5812. The results support complementary CNN-Transformer fusion and indicate preliminary cross-dataset generalizability. Implementation resources are available at Zenodo. Full article

Background: Liver cirrhosis is frequently associated with cardiovascular abnormalities collectively referred to as cirrhotic cardiomyopathy, characterized by impaired cardiac reserve and subclinical myocardial dysfunction despite preserved conventional systolic function. Advanced myocardial deformation imaging techniques, including two-dimensional speckle-tracking echocardiography (2D-STE) and cardiac magnetic resonance feature tracking (CMR-FT), may allow earlier detection of subtle ventricular impairment. We performed a systematic review and meta-analysis to comprehensively evaluate conventional and deformation-derived indices of biventricular systolic function in cirrhotic patients. Methods: PubMed, Scopus, and EMBASE databases were systematically searched for observational studies evaluating myocardial systolic function in adult cirrhotic patients using 2D-STE and/or CMR-FT. Comparative meta-analyses between cirrhotic patients and controls were performed using standardized mean differences (SMDs) with 95% confidence intervals (CIs). Separate analyses were conducted for left ventricular ejection fraction (LVEF), left ventricular global longitudinal strain (LV-GLS), left ventricular global circumferential strain (LV-GCS), left ventricular global radial strain (LV-GRS), right ventricular ejection fraction (RVEF), and right ventricular global longitudinal strain (RV-GLS). Weighted pooled descriptive analyses of clinical, laboratory, echocardiographic, and CMR findings were additionally performed. Results: Twenty studies including 1553 cirrhotic patients and 498 controls were included, whereas 14 studies were eligible for quantitative meta-analysis. Conventional LVEF remained globally preserved and showed no significant overall difference between cirrhotic patients and controls, although CMR-based studies demonstrated mildly higher LVEF values in cirrhosis. Meta-analysis revealed no significant overall differences in LV-GLS, LV-GCS, LV-GRS, or RVEF, whereas RV-GLS was significantly reduced in cirrhotic patients. Substantial heterogeneity was observed across most deformation analyses. Meta-regression demonstrated significant associations between LV-GLS variability and age, body mass index, MELD score, diabetes prevalence, heart rate, systolic blood pressure, and software vendor. Descriptive pooled analyses demonstrated larger cardiac chamber dimensions, increased filling pressures, mildly increased pulmonary pressures, and increased extracellular volume fraction values in cirrhotic populations. Conclusions: Patients with liver cirrhosis exhibit preserved conventional systolic function despite evidence of subtle myocardial mechanical abnormalities, particularly involving right ventricular longitudinal mechanics and diastolic function. Advanced deformation imaging with 2D-STE and CMR-FT may improve early detection of subclinical cardiac involvement in cirrhotic cardiomyopathy. Full article

Protein–protein and protein–peptide interactions are fundamental to biological processes, making the accurate prediction of their binding affinity crucial for drug design and mutational analysis. Here, we develop HyBind-NN, a multimodal graph neural network that integrates protein language models (PLMs) with 3D structural and dynamic datasets to predict protein–protein and protein–peptide affinity. First, we demonstrate that combining ESM-2 sequence embeddings with precise 3D Voronoi spatial geometry enables accurate affinity predictions across diverse structural datasets. Next, we show that the inherent limitations of static rigid-body structures can be mitigated through a multi-task learning framework. By utilizing residue-level root mean square fluctuations (RMSF) derived from molecular dynamics (MD) as an auxiliary training target, the model implicitly learns to capture the conformational entropy of flexible peptides without requiring computationally expensive MD simulations during inference. In our benchmarking study, we observe that this multimodal architecture outperforms both purely sequence-based and strictly structural state-of-the-art algorithms, achieving a mean absolute error of 0.89 for pK_D (1.12 kcal/mol for ∆G) on the independent benchmark. Finally, we confirmed through ablation analysis that while the PLM provides the dominant predictive signal, geometric representations and dynamic regularization are crucial for resolving subtle conformational rearrangements. This study highlights the synergistic potential of combining PLMs with physics-aware architectures and demonstrates their application towards the robust prediction of intermolecular binding affinity. Full article

While functional mobility is known to deteriorate with age, the specific mechanisms driving this decline, along with potential differences between men and women, are not fully established. Therefore, the present study applied a moderated mediation framework to investigate whether lower limb strength and body mass index (BMI) mediate the relationship between aging and functional mobility, and to explore if sex acts as a moderator in these relationships. A cross-sectional design was used to evaluate 408 independent older adults (mean age: 71.9 ± 3.9 years; 273 females and 135 males). Assessments included the Timed Up and Go (TUG) for functional mobility, the 30 s Chair Stand for leg strength, alongside BMI calculations. Direct and indirect pathways were subsequently evaluated using a moderated mediation analysis. Lower limb strength and BMI significantly predicted functional mobility in both sexes (p < 0.05), with no significant moderation in the indirect pathways. Regarding conditional effects, advancing age predicted poorer functional mobility in females (p = 0.008) but not in males (p = 0.141), independent of the mediators. Strength and BMI appear to be correlates of functional mobility. However, since the age-by-sex interaction term was not statistically significant, the potential sex-specific direct effect of age must be interpreted with caution. While these findings hint at possible subtle differences in aging trajectories, future research is needed to confirm whether interventions for older women require addressing factors beyond strength and body composition. Full article

This study evaluated the effects of partial replacement of fishmeal with 11% defatted Hermetia illucens meal (corresponding to approximately 35% replacement of the fishmeal-derived animal protein fraction) on growth performance, fillet proximate composition, and instrumental sensory responses of gilthead seabream (Sparus aurata) reared under commercial offshore farming conditions. A total of 60,000 fish were distributed into four sea cages and fed either a control diet (FM) or an insect-based diet (HIM) for 181 days. No significant differences were observed between dietary treatments in final body weight, weight gain, specific growth rate, feed conversion ratio, protein efficiency ratio, or somatic indices, indicating that insect meal inclusion did not impair productive performance under farm-scale conditions. Fillet proximate composition was largely preserved. Fillet sensory characteristics were assessed using an integrated artificial sensing platform including an electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue) coupled with multivariate analysis. E-eye and E-nose analyses showed no clear discrimination between dietary groups, indicating that dietary insect meal inclusion had limited effects on fillet visual appearance and volatile compound profiles. In contrast, E-tongue analysis revealed a clear separation between treatments, suggesting selective modulation of taste-related attributes associated with dietary inclusion of insect meal. Overall, the results demonstrate that defatted H. illucens meal can be incorporated into practical seabream diets under commercial farming conditions without compromising productive performance or major fillet quality traits. Furthermore, this study provides farm-scale evidence that artificial sensing technologies can effectively detect subtle diet-related changes in sensory characteristics, particularly those associated with taste perception. Full article

School peace, or Schulfrieden, is often portrayed in German education as a neutral condition enabling learning. Yet this study interrogates how peace itself becomes a tool of governance, selectively policing who can safely occupy the classroom. Examining the Berlin Senate’s October 2023 directive, which banned symbols showing solidarity with Palestine, we show that school peace is less about conflict resolution than about shaping affective hierarchies. Fear, anticipation, and symbolic association circulate to mark some bodies as threats while leaving others unexamined. Through the lens of Sara Ahmed’s affective economies and Zembylas’s affective ideology, we argue that the directive transforms political expression into a site of emotional correction, preemptively disciplining marginalized students and rendering their solidarity politically suspect. Peace, in this framing, is primarily rule and order: it secures institutional comfort while curtailing engagement with global injustice. The classroom becomes a laboratory of anticipatory governance, where ethical awareness is unevenly distributed, and dissent is contained before it emerges. By tracing how school peace operates affectively, the study reveals the subtle mechanics by which liberal education reproduces racialized hierarchies under the guise of neutrality. Full article

Background/Objectives: To explore potential associations between pediatric obesity and retinal and anterior segment ocular structures using OCT and ocular biometry. This study was designed as an exploratory, hypothesis-generating analysis without a pre-specified primary endpoint; all findings should be interpreted accordingly. Methods: This retrospective cross-sectional study included 52 children (104 eyes): 27 obese children (body mass index (BMI) percentile ≥95%) and 25 healthy controls (BMI percentile 5–85%). Optical coherence tomography (OCT) and ocular biometry were used to assess retinal nerve fiber layer (RNFL), ganglion cell complex (GCC), focal loss volume (FLV), global loss volume (GLV), Early Treatment Macular Map 5 (EMM5), corneal parameters, axial length (AL), anterior chamber depth (ACD), and white-to-white corneal diameter (WTOW). Group comparisons and cluster-robust bootstrap regression adjusted for inter-eye dependency, age, and sex; Bonferroni correction was applied. Results: Obese children showed nominally higher GCC average thickness, RNFL, and EMM5 values and shallower ACD; however, no parameter survived Bonferroni correction. ACD showed the most internally consistent exploratory pattern (unadjusted p = 0.006; adjusted p = 0.018; Bonferroni p = 0.249); however, this finding did not survive Bonferroni correction and should not be interpreted as a confirmed association. Other corneal and biometric parameters were not significantly different. Conclusions: Pediatric obesity may be associated with subtle ocular structural variations, but all findings are exploratory and hypothesis-generating. Larger prospective, pre-registered studies are needed to determine whether pediatric obesity is associated with structural ocular changes. Full article

Background: Radiology underpins diagnosis and treatment across pediatrics, yet most artificial intelligence (AI) tools are developed for adults and validated on adult datasets only. Of more than 200 AI systems cleared by the United States (U.S.) Food and Drug Administration (FDA), only about 3% include pediatric validation. Because children differ from adults in anatomy, physiology, pathology, epidemiology, and imaging protocols, adult-trained models often perform sub-optimally in pediatric settings. Methods: A narrative review of peer-reviewed literature from 2000 to 2025 was conducted using PubMed, MEDLINE, Google Scholar, and Scopus. Studies involving AI applications in pediatric X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and point-of-care ultrasound with quantitative performance metrics were included. Findings were synthesized by imaging modality, clinical task, and differences between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: AI demonstrated strong performance across multiple pediatric imaging tasks. In X-ray interpretation, AI detected fractures with area under the curve (AUC) values up to 0.96 (sensitivity, 90.8%; specificity, 88.7%). Pneumonia classification achieved 76.5% accuracy, and foreign body aspiration detection showed 95.3% specificity in HICs. In ultrasound, AI improved junior sonographers’ detection of intussusception (AUC 0.857 to 0.966) and reduced scan time by more than 50%. AI-assisted bone age estimation achieved a mean error of 0.39 years. In echocardiography, AI-derived ejection fraction showed excellent agreement with experts’ interclass correlation coefficient (ICC 0.983), and AI support improved atrioventricular septal defect detection (84.4% to 86.5%). In MRI, the use of AI enhanced lesion detection and supported quantitative analysis. Deep-learning models trained on routine T1- and T2-weighted sequences predicted liver stiffness across multi-site datasets, while advanced neuroimaging pipelines improved the identification of subtle epileptogenic lesions that are often missed on conventional pediatric MRI. However, adult-trained models showed limited generalizability to children. Still, excluding children under the age of two years improved the reading accuracy of pediatric chest X-rays (CXRs) by adult-trained models from 88% to 97%. AI faces challenges beyond the development of age-specific models. Substantial heterogeneity, limited pediatric-specific datasets, and unresolved medicolegal responsibility further restrict adoption worldwide. Challenges are amplified in LMICs, where unstable electricity, limited radiology resources, weak digital infrastructure, and scarce pediatric providers limit implementation. Additionally, many large language models underperform and lack inclusive algorithms suitable for pediatric radiology in many LMICs. Conclusions: AI can enhance diagnostic accuracy, efficiency, and access to pediatric imaging, particularly in resource-limited settings, through task-shifting and decision support. However, it cannot replace pediatric radiologists as of today. Safe adoption requires pediatric-specific model development, standardized validation metrics, diverse datasets that include LMIC populations, stronger digital infrastructure, robust radiologist training in AI capabilities, and the establishment of clear guidelines and medicolegal policies. Full article

Semaphorin 3A (Sema3A), a known axon chemorepulsive protein during development, is localised in perineuronal nets (PNNs) in the adult brain. PNNs are condensed aggregates of extracellular matrix molecules surrounding specific types of neurons, which regulate neuroplasticity and memory. However, the role of PNN-associated Sema3A in these processes remains unclear. To address this topic, we investigated the contribution of Sema3A to cerebellum-dependent learning and memory in adult mice using the eyeblink conditioning (EBC) paradigm. We interfered with Sema3A signalling by employing: (i) a molecular approach, in which secreted Sema3A receptors (neuropilin-1 bodies) were expressed in the anterior interposed nuclei (AIN) via viral vector injection; and (ii) a genetic approach, using mutant mice with impaired Sema3A signalling (K108N mice). Mice expressing neuropilin-1 bodies showed reduced EBC performance at the beginning of the memory retention phase. However, increased inflammation was found in the AIN of these mice, challenging the interpretation of these findings. K108N mice showed enhanced EBC performance at the beginning of the memory retention phase. No synaptic structural changes were detected in the AIN of K108N mice at the end of the EBC paradigm. Based on our findings in K108N mice, constitutively altered Sema3A signalling is associated with subtle improvement in cerebellar memory. Full article

Four new species of Chromadorea (Nematoda) from marine benthic habitats in Chinese sea areas are described and illustrated. Halichoanolaimus parvulus sp. nov. is characterized by a comparatively smaller body; amphideal fovea comprising 4.5–5 turns; spicules arched, middle portion broad, progressively attenuating distally; gubernaculum slender, comprising two detached lateral elements becoming narrower toward the distal end; a short precloacal seta present; tail conico-cylindrical with three-fifths cylindrical portion in males. Cobbionema sinica sp. nov. is characterized by a pharynx with a distinct anterior bulb and an enlarged posterior portion; cephalic setae measuring 6 µm in length; amphideal fovea consisting of 4–4.5 turns; spicules 2.1–2.5 cloacal body diameters long, arrow-shaped; gubernaculum rod-like, parallel to the distal part of spicules, tail conico-cylindrical, terminating in an enlarged end and a distinct spinneret; precloacal supplement absent. Linhystera longispicula sp. nov. is characterized by cuticle with subtle transverse striations; six lateral labial sensory setae and four cephalic sensory setae are setiform, collectively forming a single circle; buccal cavity minute and slit-like, amphideal fovea circular far from the anterior end; curved, stout spicules reach a length exceeding two times the cloacal body diameter; gubernaculum is absent; conico-cylindrical tail without terminal setae. Linhystera nanhaiensis sp. nov. also has the common characteristics of the genus. It has relatively large body size with long conico-cylindrical tail; three terminal setae present; spicules resembling ox horns, reaching 1.2 times the cloacal body diameter, gubernaculum absent. This work contributes to the study of nematode diversity in China. Full article