Search Results (505)

Background/Objectives: Placental vascular malperfusion, on both the maternal (MVM) and fetal (FVM) side, is a key mechanism linking hypertensive disorders of pregnancy, fetal growth restriction (FGR), stillbirth, preterm neonatal death and neonatal encephalopathy. Nevertheless, clinical use and medico-legal interpretation of placental findings remain inconsistent. To summarize recent evidence on the relationship between placental vascular malperfusion, perinatal mortality and neonatal brain injury, integrating standardized placental pathology with Doppler and angiogenic biomarkers, and to outline the main medico-legal implications. Methods: A PubMed search using the string “((placenta OR placental pathology) AND (stillbirth OR fetal death) AND (maternal vascular malperfusion OR fetal vascular malperfusion))” yielded 118 records. After excluding reviews, meta-analyses, case reports (except one illustrative SARS-CoV-2 placentitis case), non-human studies and papers without original histopathology, 33 studies were included: observational cohorts and case–control studies with standardized placental assessment, autopsy series, biomarker/Doppler cohorts, mechanistic work, one randomized trial protocol and a small number of focused clinical commentaries. Results: Across these studies, MVM emerges as the dominant placental lesion in pre-eclampsia, FGR and a large proportion of stillbirths, especially in early-onset disease and in association with maternal hypertension. FVM is strongly linked to stillbirth and term neonatal encephalopathy, and specific combinations of MVM, FVM and inflammatory lesions correspond to distinct patterns of brain injury. Large population-based cohorts confirm that maternal hypertensive disorders and placental malperfusion are major upstream causes of intrauterine hypoxia and preterm neonatal death. Doppler velocimetry and angiogenic biomarkers (PlGF, sFlt-1 and their ratio) are strongly associated with an increased likelihood of underlying MVM and adverse neonatal outcomes, although their predictive performance remains probabilistic and context-dependent rather than diagnostic. Mechanistic studies suggest roles for placental genomic instability and altered decidual immunity in defective placentation. Conclusions: Maternal and fetal vascular malperfusion represent converging pathways to FGR, stillbirth, preterm neonatal death and neonatal encephalopathy. Routine, standardized placental examination, interpreted together with Doppler and biomarker data, substantially improves causal attribution and timing of injury, with direct consequences for counselling, prevention and medico-legal assessment. Full article

Colorectal cancer (CRC) remains a leading cause of cancer morbidity and mortality worldwide, with nearly one quarter of patients presenting with metastatic disease at diagnosis. The epidermal growth factor receptor (EGFR) plays a central role in CRC pathogenesis through activation of downstream RAS/RAF/MAPK and PI3K/AKT/mTOR signaling pathways, and has become a major therapeutic target. Anti-EGFR monoclonal antibodies, cetuximab and panitumumab, have demonstrated survival benefit in selected patients, particularly those with left-sided, RAS wild-type tumors. However, primary and acquired resistance limit their efficacy, underscoring the need for predictive biomarkers and novel strategies. This review synthesizes current knowledge of EGFR biology, therapeutic integration, and biomarker development, including RAS and BRAF mutations, MSI status, HER2 amplification, EGFR ligands (AREG/EREG), consensus molecular subtypes, and liquid biopsy applications. We also discuss mechanisms of resistance such as pathway reactivation, receptor mutations, and epithelial-to-mesenchymal transition, alongside emerging approaches, including combination regimens, ctDNA-guided rechallenge, and genotype-specific inhibitors. Collectively, these insights highlight the evolving landscape of precision oncology in CRC and the importance of molecular stratification to optimize EGFR-targeted therapy and overcome resistance. Full article

To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article

Human–robot interaction (HRI) takes place in dynamic environments where both humans and robots act as active agents, making the system inherently unpredictable. Abrupt movements can originate from either side and include human reflexes, fatigue, or unexpected reactions, as well as robot malfunctions, control errors, or task changes. These unpredictable events generate significant risks for both interaction fluency and safety, affecting not only the physical domain (e.g., collisions, excessive forces) but also cognitive aspects such as trust and predictability. Although different application areas present domain-specific challenges, a comprehensive overview of abrupt movements in HRI is still lacking, especially in the industrial scenario. This review aims to consolidate current knowledge regarding how abrupt phenomena are analyzed, prevented, and mitigated across various contexts and to offer new insights for researchers. In detail, after describing the literature search and the screening process, the review categorizes abrupt events, highlights key methodological approaches, and identifies gaps and future directions. By providing a structured synthesis of existing strategies, this work guides researchers in developing safer and more adaptive HRI frameworks capable of handling unpredictability. Full article

Thermoelectric (TE) materials offer a promising route for direct thermal-to-electrical energy conversion via the Seebeck effect. Among them, GeTe exhibits superior performance in the mid-temperature range (500–800 K), whereas (Bi,Sb)₂Te₃ is widely regarded as the benchmark material for near low-temperature applications (< 450 K). To improve TE efficiency over a wider temperature range, segmented GeTe/(Bi,Sb)₂Te₃-based single-leg TE devices were developed. Specifically, based on nanocomposite technology, B₄C and SiC nanoparticles were, respectively, introduced into GeTe and (Bi,Sb)₂Te₃, achieving optimization of electrical conductivity alongside reduction in thermal conductivity, thereby enhancing the thermoelectric figure of merit (ZT). Finite element simulations were used to optimize the geometric structure of the segmented device, determining the ideal ratio of GeTe to (Bi,Sb)₂Te₃. The simulations predicted a maximum conversion efficiency (η_max) of 16.9% when the ratio of GeTe to (Bi,Sb)₂Te₃ was 0.24, with a power density of 18.5 mW/mm². Experimentally, the fabricated segmented device attained a peak conversion efficiency of 7.14% and a power density of 12.5 mW/mm² under a hot-side temperature of 773 K. These findings confirm that strategic segmentation, combined with nanoscale phonon scattering engineering, substantially improves overall TE device performance across broad temperature range, underscoring its potential for high-efficiency thermoelectric energy conversion systems. Full article

The interplay between TP53 alterations and Wnt/β-catenin signaling in colorectal cancer (CRC) remains unclear regarding mismatch repair (MMR) status, tumor budding (TB), poorly differentiated cluster (PDC), and prognosis. We analyzed 146 resected CRC cases, quantifying p53, Wnt3, and β-CTN indices and assessing MMR by PMS2 and MSH6 immunohistochemistry. p53 overexpression was associated with younger patients, left-sided tumors, nodal metastasis, and advanced stage, whereas wild-type tumors showed more mucinous differentiation. Deficient MMR was enriched among wild-type p53 cases. Principal component analysis identified distinct axes defined by p53, Wnt3, and β-CTN. Despite comparable Wnt3 levels, nuclear β-CTN accumulation was enhanced in tumors with aberrant (overexpression or null) p53 tumors, with increased TB and PDC indices. Low nuclear β-CTN independently predicted recurrence in stage I–III disease and worse overall survival in proficient MMR tumors (HR 3.07 and 2.52; p = 0.03 for both). A composite score integrating p53 binary status (aberrant vs. wild) with Wnt3 and whole β-CTN indices predicted survival beyond stage; each 1-point increase conferred a 2.56- and 1.77-fold higher risk of cancer-specific and overall mortality (p = 0.004 and 0.04). These findings suggest that p53 dysfunction is associated with alterations in Wnt/β-CTN signaling and that integrating signaling markers with staging may improve prognostic assessment in colorectal cancer. Full article

The Yingxian Wooden Pagoda, a structure with a history spanning a thousand years, currently faces significant wind-induced safety risks. To understand the aerodynamic mechanism behind this issue, this study uses Computational Fluid Dynamics (CFD) with the Realizable k-ε turbulence model to perform high-fidelity transient simulations at wind speeds from 10 to 30 m per second. The results show that the highest positive pressure occurs on the sides of the windward face, while a large low-pressure vortex zone forms on the leeward side. The simulations include both the Kármán vortex street and the measurement of three-dimensional vortex-induced forces, marking a major advancement. A key finding is the synchronized period (ratio ≈ 1) of the along-wind and cross-wind forces, which differs from streamlined cylinders and is due to the pagoda’s unique octagonal shape. The force amplitudes increase exponentially with wind speed, while the average drag and lift have a quadratic relationship. Additionally, a new shape-specific correction factor of 0.875 is introduced to adjust the classical Strouhal formula, which greatly improves prediction accuracy for this type of ancient structure. This study offers both a theoretical foundation and a practical “digital wind tunnel” method for assessing wind-induced risks and supporting the safety monitoring of historic timber structures. Full article

Background and Objective: Positive surgical margins (PSMs) remain a major challenge during radical prostatectomy, particularly in patients with high-risk prostate cancer (HR-PCa), where extracapsular extension, multifocal disease, and aggressive tumor biology substantially increase the likelihood of incomplete resection. In this setting, PSMs are strongly associated with early biochemical recurrence and frequently prompt adjuvant or salvage treatments, potentially exposing patients to overtreatment and added morbidity. Materials and Methods: To review and critically appraise established and emerging intraoperative technologies for surgical margin assessment during radical prostatectomy, with a specific focus on their potential role and relevance in patients with HR-PCa. Evidence Acquisition: A non-systematic literature review was performed using Pubmed, MEDLINE, Web of Science, and Google Scholar, focusing on preoperative, intraoperative ex vivo, and intraoperative in vivo technologies for margin assessment. Emphasis was placed on techniques with potential applicability to HR-PCa, where real-time intraoperative decision-making is particularly consequential. Evidence Synthesis: Preoperative tools, including multiparametric MRI, PSMA-PET imaging, and predictive nomograms, aid surgical planning but show limited sensitivity for microscopic extracapsular extension, especially in high-risk disease. Intraoperative frozen section analysis reduces positive surgical margin rates while enabling selective nerve-sparing (defined as a side-specific, risk-adapted preservation strategy); however, its widespread adoption is constrained by substantial logistical and resource requirements, and robust oncological outcome data in high-risk populations remain limited. Novel ex vivo approaches, such as fluorescence confocal microscopy and specimen-based PSMA PET/CT imaging, offer rapid whole-gland or targeted margin assessment with reduced dependency on dedicated pathology workflows. In parallel, emerging in vivo technologies, particularly PSMA-targeted near-infrared-fluorescence-guided surgery, enable real-time detection of residual tumor and facilitate selective re-resection, representing a biology-driven approach that may be especially suited to HR-PCa. Conclusions: In high-risk prostate cancer, intraoperative margin assessment technologies may extend beyond functional preservation and play a central role in optimizing oncological radicality and multimodal treatment sequencing. While NeuroSAFE remains the reference standard, PSMA-based ex vivo and in vivo technologies are particularly promising in HR-PCa due to their ability to integrate tumor biology into surgical decision-making. Prospective studies focusing on high-risk-specific oncological and patient-reported outcomes are needed before widespread clinical implementation. Full article

Moral disengagement (MD) typically peaks during adolescence. While the Dark Triad (DT) traits—Machiavellianism, psychopathy, and narcissism—are broadly linked to MD, the specific prospective pathways through which individual DT components predict distinct MD strategies remain unclear, particularly with respect to gender-specific variations in these influences among adolescents. To systematically investigate these temporal associations, this study employed Cross-Lagged Panel Network (CLPN) modeling on a sample of 1410 Chinese adolescents (M_age = 16.95, SD = 0.75) surveyed across three waves at three-month intervals. Results revealed a hierarchical pattern of DT influence: Machiavellianism exerted the strongest predictive effect on the MD system, followed by psychopathy, while narcissism showed negligible or even negative effects. Among MD strategies, euphemistic labelling, advantageous comparison and displacement of responsibility were the most responsive to DT traits. Bridge centrality analysis confirmed Machiavellianism as the primary cross-domain connector linking DT traits to MD. Weak but significant reciprocal effects were observed: MD slightly fed back onto later Machiavellianism and psychopathy, supporting a partially bidirectional process. Gender-separated networks revealed divergent pathways: Machiavellianism served as the key DT-MD bridge for males, whereas psychopathy fulfilled this role for females. These findings refine the understanding of the “dark side” of moral development by highlighting mechanism-specific MD vulnerabilities and demonstrating that the primary socio-cognitive pathway to MD is gender-contingent, thereby advancing developmental models of MD. Full article

Background: In this study, we aimed to compare metabolomic profiles, biodistribution, and detoxification patterns of the novel SN-38 derivative NMe with irinotecan (IR), and to identify NMe-specific metabolites to evaluate its preclinical pharmacokinetic advantages. Methods: In vivo ADME studies were conducted for NMe, a 9-aminomethyl SN-38 derivative, and IR following a single intraperitoneal dose of 40 mg/kg in mice. Additionally, ADMET properties were predicted using ADMETlab and SwissADME tools for comparison. Levels of NMe and irinotecan absorbed into plasma, distributed to tissues, and metabolized were monitored in liver, lung, spleen, kidney, and stool samples at 15, 30, and 60 min post-administration. Tissue extracts were analysed using high-performance liquid chromatography (HPLC), liquid chromatography–electrospray ionization quadrupole time-of-flight-tandem mass spectrometry (LC-ESI-QTOF-MS), and nuclear magnetic resonance (NMR) techniques after lyophilization and reconstitution. We compared the metabolomic profiles of irinotecan and NMe. Results: We identified and confirmed NMe-specific metabolites, including 9-CH₂-S-cysteine conjugate, 9-CH₂OH, and NMe-formyl. Notably, novel irinotecan metabolites (IR-OH and IR-ΔE) were detected in small amounts in kidney samples. In some cases, two literature-known photodegradation products of irinotecan were present. NMe was found to quickly metabolize with different distribution to tissues, significantly greater to kidney and liver. Two SN-38 glucuronides, SN-38G(α) and SN-38G(β), were detected corresponding to α- and β-anomers. Where it was possible, NMe, IR and SN-38 were quantified using external calibration curves. In IR group, controlled and prolonged release of SN-38 was confirmed in all samples, yet SN-38G was observed in minority only in plasma, kidney, or lungs. In NMe groups, great relative amounts of SN-38 and SN-38G were detected. Greater content of SN-38G in NMe group than in irinotecan is expected to contribute to modulation and alleviation of some side effects in irinotecan-involved therapies, such as gastrointestinal toxicities (GIT). Conclusions: NMe shows a distinct metabolic profile characterized by rapid biotransformation, higher systemic glucuronidation of SN-38, and formation of unique metabolites, suggesting a potentially wider therapeutic window and reduced toxicity compared with IR. Full article

Real-world traffic is highly dynamic, with pedestrians exhibiting unpredictable movements. Pedestrians’ poses are essential cues for predicting their actions, enabling vehicles to respond proactively and reduce accident risks. In autonomous driving, the distance between vehicles and pedestrians is critical, making 3D human pose estimation crucial. In this context, pedestrian pose estimation has been actively studied, and recently, light detection and ranging (LiDAR) sensors have attracted attention due to their accurate 3D depth information and privacy benefits. However, existing LiDAR-based 3D pose estimation methods mainly process 3D data directly, requiring high computational cost and memory. In this paper, we propose a lightweight LiDAR-based 3D human pose estimation method specifically designed for deployment in autonomous driving systems. Unlike conventional 3D direct processing methods, our approach strategically reduces computational complexity by projecting point clouds into 2D depth images and leveraging a lightweight MoveNet, followed by efficient 3D lifting. Furthermore, we introduce a self-occlusion correction algorithm to improve robustness under side-view and bending poses, where depth-based projections often suffer from distortion. Experimental results on benchmark datasets demonstrate that the proposed method achieves competitive pose estimation accuracy while substantially improving efficiency, highlighting its practicality and scalability for real-time autonomous vehicle applications. Full article

Background/Objectives: The aim of this study was to identify predictors of temporal window failure (TWF) in transcranial color-coded duplex sonography (TCCS) based on demographical and computed tomography (CT)-based parameters such as temporal bone thickness (TBT), and to define sex-specific thresholds for predicting TWF. Methods: We retrospectively analyzed a series of adult patients who underwent cranial CT and TCCS. Bitemporal TBT was measured in nine standardized regions on CT, and mean TBT per side was calculated. Temporal bone window (TBW) quality was graded with two semiquantitative TCCS scores assessing the visibility of the middle cerebral artery (MCA), contralateral temporal bone, mesencephalon, and ipsilateral sphenoid bone. Associations between TBT, sex, age, and TBW visibility were analyzed by correlation, chi-square tests, and logistic regression. Results: 200 patients (102 men, 98 women; mean age 68 ± 16 years) were enrolled. Mean TBT was 3.1 ± 0.7 mm (right) and 3.2 ± 0.7 mm (left). TBT correlated weakly with age (r = 0.15–0.18, p < 0.05) and was higher in women (p < 0.05). Age and sex influenced TBW visibility (p < 0.05) with small effect sizes. Increased TBT strongly predicted poor TBW (β ≈ –1.7, p < 0.001). Optimal TBT cut-offs predicting adequate TBW were 3.8 mm (men) and 3.3 mm (women), maximizing specificity (men: 0.95, women: 0.85) and negative predictive value (men: 0.87, women: 0.66). Conclusions: Advanced age and female sex were both associated with TWF. CT-assessed TBT represents a robust predictor of TCCS feasibility. Implementation of sex-specific TBT threshold values may facilitate patient pre-selection and improve procedural efficiency in neurosonographic diagnostics. Full article

Portable near-infrared (NIR) spectroscopy devices offer the advantages of rapid, non-destructive, and versatile coal quality analysis. However, in complex mining environments, variations in the probe–sample distance can cause significant spectral distortions, resulting in severe distribution shifts between the source and target domains and thus limiting model generalization. In practical industrial scenarios, target-domain data are often unavailable, making conventional domain adaptation methods that rely on target samples difficult to apply. To address this challenge, this paper proposes a target-free multi-source domain adaptation framework tailored for portable device distance-shift scenarios to achieve robust prediction of coal air-dried moisture (Mad). Under a multi-source joint learning strategy, the framework aligns cross-domain features through adversarial training and distribution matching, while a spectroscopy-specific data augmentation strategy is designed to simulate realistic measurement disturbances such as noise perturbation, baseline drift, and wavelength shift, thereby enhancing the model’s robustness from the source side. In addition, a Mad-aware triplet loss function is introduced to establish a balanced constraint between task consistency and domain invariance, effectively improving cross-domain generalization capability. Experimental results on multi-distance NIR datasets show that the proposed method significantly outperforms representative comparison algorithms in terms of $R^2$, RMSE, and MAE, verifying that the framework effectively mitigates the effects of probe–sample distance shifts under target-free conditions and achieves high-precision coal moisture prediction. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

Background/Objectives: Orthodontic mini-implants have become indispensable in modern orthodontics due to their ability to provide absolute anchorage, independent of patient compliance. Our research aims to illustrate the versatility of mini-implants in addressing diverse biomechanical challenges across different planes of tooth movement (sagittal, transverse, and vertical) based on a retrospective clinical analysis. Methods: A retrospective analysis of orthodontic treatments performed with mini-implants (Dual Top and JS systems) was conducted, focusing on predefined biomechanical objectives and outcomes. The analysis encompassed distinct biomechanical applications, including incisor retraction and space closure using sequential direct and indirect anchorage; transverse and vertical correction of adult open bite through mini-implant–assisted rapid palatal expansion (MARPE) and molar intrusion; deep bite correction via simultaneous upper and lower incisor intrusion; and unilateral molar distalization using palatal skeletal anchorage. Results: Mini-implants provided stable, reproducible anchorage in all cases, enabling complex three-dimensional tooth movements with minimal side effects. Sequential reuse of the same mini-implants for both indirect and direct anchorage reduced treatment invasiveness and enhanced anchorage efficiency. Combined skeletal expansion and posterior intrusion allowed improved transverse and vertical control in adult open-bite presentations. Pure incisor intrusion was achieved without molar extrusion or incisor proclination, while unilateral molar distalization was effectively managed using palatal skeletal anchorage. Across all cases, mini-implants enhanced treatment efficiency, reduced the need for auxiliary appliances, and ensured predictable outcomes. Conclusions: Orthodontic mini-implants represent a highly versatile and minimally invasive anchorage system adaptable to a broad range of biomechanical situations. Their ability to provide stable, reusable, and site-specific anchorage supports efficient correction of complex malocclusions and reinforces their pivotal role in contemporary orthodontic practice. Full article