Search Results (22,371)

Background: Amnestic mild cognitive impairment (aMCI) is considered a transitional state between normal aging and dementia, often without visible abnormalities on standard brain magnetic resonance (MR) images. The aim of the study was to analyze both microstructural and functional brain abnormalities using advanced MR techniques. Methods: The study included 27 patients with aMCI and an age-matched control group (CG) of 25 healthy subjects. All MR studies were performed on a 3T MR scanner (Philips, Ingenia) with a 32-channel head and neck coil using volumetric 3D T1 sequences, followed by a resting-state functional MRI (rs-fMRI) sequence. Volumetric analysis was performed using the Destrieux atlas to assess potential structural differences between groups. Seed-to-voxel functional connectivity analyses were conducted using the bilateral hippocampi and both anterior and posterior divisions of the parahippocampal gyri as seed regions. Results: Compared to healthy controls, reduced cortical thickness was observed in aMCI subjects in the temporal regions, frontal and orbitofrontal areas, limbic areas, parietal and sensorimotor cortices, as well as occipito-temporal regions. Additionally, significantly increased functional connectivity was observed between bilateral medial temporal lobe (MTL) regions and the right thalamus. Conclusions: Cortical thinning in various brain regions along with the increased functional connectivity between the MTL regions and the right thalamus may reflect potential compensatory mechanisms in response to initial subtle degenerative changes, emphasizing the importance of using both functional and structural imaging to detect early changes in aMCI patients. Full article

Background: Small-cell lung cancer (SCLC) represents an aggressive malignancy associated with a poor prognosis, underscoring the critical demand for enhanced monitoring methodologies. Circulating tumor DNA (ctDNA) constitutes a promising non-invasive biomarker; however, reports employing highly sensitive tumor-informed assays in SCLC remain scarce. This investigation aimed to assess the clinical utility of a personalized ctDNA monitoring strategy for predicting therapeutic outcomes and resistance in SCLC patients. Methods: This prospective observational study enrolled patients diagnosed with unresectable SCLC. Whole exome sequencing was conducted on baseline tumor specimens to design customized 16-plex multiplex polymerase chain reaction (PCR) panels. Serial blood samples were obtained at baseline, at six-week intervals during treatment, and upon disease progression. Detection of ctDNA-based minimal residual disease (MRD) was performed using a tumor-informed assay (Huajianwei^® bespoke MRD) with ultra-deep sequencing. Results: Among seven evaluable patients, the baseline ctDNA-MRD positivity rate was 100%. A significant positive correlation was observed between the baseline ctDNA levels and radiographic tumor burden (r = 0.821, 95% confidence interval [CI] 0.179–0.973, p = 0.034). Longitudinal analysis indicated that patients exhibiting an early decline in MRD levels demonstrated a non-significant trend toward superior progression-free survival (PFS) compared to those with an MRD increase. Though this between-group difference did not reach conventional statistical significance, it represented a trend-level finding (p = 0.0665, hazard ratio [HR] = 0.24, 95% CI: 0.02–3.19), with no definitive prognostic association confirmed in this pilot cohort. Notably, an elevation in MRD preceded radiographic progression by as much as 135 days in certain instances. Conclusions: This study shows that dynamic tumor-informed ctDNA-based MRD monitoring reflects tumor burden changes and may correlate with clinical outcomes in SCLC, supporting its potential to guide personalized treatment and facilitate earlier therapeutic interventions compared to conventional imaging techniques. Prospective multicenter validation is needed to confirm its clinical utility. Full article

Background: Congenital muscular torticollis (CMT) is usually managed conservatively during infancy, whereas surgical treatment is considered for persistent deformity in older children. However, evidence remains limited regarding the outcomes of distal resection combined with proximal release of the sternocleidomastoid muscle in children presenting beyond infancy. This study aimed to evaluate the functional and cosmetic outcomes of this combined approach in patients aged 3 years and older. Methods: This retrospective single-surgeon series included 37 patients with CMT aged 3 to 14 years who underwent distal resection combined with proximal release of the sternocleidomastoid muscle between 2002 and 2024. Preoperative and postoperative assessments were performed using the clinical outcome framework originally described by Lee et al., goniometric measurement of cervical rotation and lateral flexion, and clinical evaluation of head tilt, facial asymmetry, scar appearance, lateral band formation, and sternocleidomastoid V-column contour. Patients were also analyzed according to age at surgery, as 3–10 years and 11–14 years. Results: The mean age at surgery was 4.7 years, and the mean follow-up duration was 3.4 years. Significant postoperative improvement was observed in all major functional outcomes. Mean cervical rotation improved from 54.2 ± 8.6° to 87.9 ± 3.4°, and mean lateral flexion improved from 24.1 ± 6.8° to 44.5 ± 3.2° (both p < 0.001). Preoperative functional assessment scores averaged 6.8 ± 1.4, whereas postoperative total outcome scores averaged 14.2 ± 0.9. At final follow-up, no patient had residual head tilt. Mild residual facial asymmetry persisted in 3 patients (8.1%). Overall, postoperative outcomes were rated as excellent in 33 patients (89.2%) and good in 4 patients (10.8%). A slight partial loss of the sternocleidomastoid V-column contour was observed in 34 patients (91.9%), although this finding was not documented as a major cosmetic concern in the available clinical records. Hypertrophic scarring developed in 1 patient (2.7%). No lateral band formation, recurrence, revision surgery, infection, or hematoma was observed. Conclusions: Distal resection combined with proximal release provided favorable functional and cosmetic outcomes in children older than 3 years with CMT. The technique was associated with marked improvement in cervical motion, correction of head tilt, low complication rates, and a high proportion of excellent or good results. Full article

Background: The estimation of the postmortem interval (PMI) remains a complex challenge in forensic medicine. While macroscopic, biochemical, and molecular methods are well-documented, postmortem functional approaches at the organ level are largely underexplored. This pilot study investigated the feasibility of utilizing an isolated ex vivo kidney perfusion model to assess residual postmortem renal function—specifically glomerular filtration and tubular solute handling—as a potential chronological marker for PMI. Methods: Sixteen adult New Zealand rabbits were euthanized and randomly assigned to four postmortem interval groups (1, 5, 10, and 15 h). An unoxygenated, room-temperature crystalloid perfusion system was established to mimic natural postmortem decay. Initially, 32 kidneys were perfused; two were excluded due to anuria, resulting in 30 successfully analyzed kidneys. To strictly eliminate pseudoreplication bias, bilateral functional data were mathematically aggregated at the subject level, establishing the individual rabbit (n = 16) as the statistical unit. Results: Following statistical adjustment at the subject level, none of the measured functional parameters exhibited statistically significant chronological variation across the postmortem intervals (all p > 0.05; statistical significance defined as p < 0.05). Glomerular filtration was profoundly depressed across all groups, with adjusted inulin clearance ranging between 0.0031 and 0.0086 mL/min/g (peaking nonsignificantly at 10 h). Furthermore, active tubular reabsorption was virtually nonexistent; calculated reabsorbed loads for evaluated solutes, particularly potassium and sodium, yielded predominantly negative values. This phenomenon indicates a complete absence of physiological active reabsorption, reflecting instead a massive passive leakage of intracellular electrolytes into the tubular fluid due to cellular autolysis. Conclusions: Within this specific experimental setup, the isolated kidney perfusion model failed to demonstrate reproducible, time-dependent renal function useful for PMI estimation. These findings indirectly suggest that, unlike the prolonged supravital physiological resilience observed in skeletal muscle, highly metabolically active renal tissue rapidly loses its complex functional capacity following somatic death. Future studies exploring supravital renal function should consider targeting the immediate early postmortem period (0–1 h) or integrating advanced organ preservation techniques to unmask residual cellular capabilities. Full article

Aircraft wake vortices pose significant threats to following aircraft during takeoff and landing phases. Coherent Doppler lidar provides an effective remote sensing technique for wake vortex monitoring through radial velocity measurements. However, reliable identification of wake vortices from lidar observations remains challenging due to noise and the complex multi-scale evolution of vortex structures. In this study, we propose a physics-guided multi-scale deep network (HMNet) for aircraft wake vortex identification. First, we propose a denoising module (DE) to suppress noise in radial velocity fields. Subsequently, we design a hybrid multi-scale backbone network containing a hybrid multi-scale feature extraction module (HMFE) to capture vortex structures at different spatial scales. Furthermore, we propose a feature gradient guidance module (FGGM) to incorporate physically meaningful gradient cues and enhance vortex-sensitive features. HMNet is evaluated and tested on 1401 radial velocity field data samples collected on the runway at Shenzhen Bao’an Airport. The experimental results show that HMNet achieves 97.15% accuracy, 95.83% recall, and 96.84% F1 score. Compared with the baseline VGG16 and Random Forest, HMNet improves accuracy by 6.18% and 11.88%, respectively. These results demonstrate that HMNet provides an effective solution for lidar-based wake vortex identification and can support the development of intelligent air traffic management. Full article

Effective preparation for STEM subject exams requires not only content knowledge but also structured and efficient learning strategies. This observational study examined the association between the implementation of a structured learning framework (PIPES system) and student performance in extended-level mathematics and physics exams in Poland. The PIPES system integrates five evidence-based components: planning and goal setting, interleaved practice, the Pomodoro technique, exam question type categorization, and independent problem-solving with mentor support. The study evaluated the PIPES system as an integrated instructional framework rather than as a set of isolated components. Data from over 900 high school students across multiple cohorts (2020–2025) were analysed. Students who fully implemented the PIPES system achieved substantially higher average exam scores than national averages and comparison groups. For example, in the 2024/2025 school year, participants achieved 82% in mathematics compared to 33% nationally (d = 1.88), and 85% in physics compared to 52% (d = 1.14). Large score improvements were also observed between mock and final exams, particularly among initially lower-performing students. While the non-randomised design precludes causal inference, the magnitude and consistency of the observed associations suggest that the PIPES system may represent a promising structured approach to STEM exam preparation. Full article

Background: Chronic reducible scapholunate instability (SLI) remains a challenging condition, with multiple surgical options described, often associated with soft tissue disruption and postoperative stiffness. We describe a mini-invasive dorsal capsulodesis technique aimed at restoring carpal alignment while minimizing surgical morbidity, and we report preliminary clinical and radiographic outcomes. Methods: This study includes a retrospective analysis of the first 10 consecutive patients treated with this technique who had a minimum follow-up of 3 years. All patients presented with chronic, reducible scapholunate instability. Clinical outcomes were assessed using the Visual Analog Scale (VAS) for pain and the Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) score. Radiographic evaluation was performed to assess maintenance of scapholunate alignment and progression to degenerative changes. Results: At a mean follow-up of approximately 4 years, patients showed a substantial reduction in pain (mean VAS from 8 preoperatively to 2 postoperatively) and improvement in function (mean QuickDASH from 74.6 to 16.5). Radiographic evaluation demonstrated maintenance of carpal alignment in all cases, with no progression to scapholunate advanced collapse (SLAC) observed. Wrist range of motion improved postoperatively, with extension reaching approximately 80° and flexion 70°, without significant functional limitations. No major complications or reoperations were recorded. Patient satisfaction was high, with 9 patients reporting being extremely satisfied and 1 satisfied. Conclusions: This mini-invasive dorsal capsulodesis appears to be a feasible and tissue-sparing option for selected cases of chronic reducible scapholunate instability. In this preliminary series, the technique was associated with favorable clinical and radiographic outcomes at mid-term follow-up. Further studies with larger cohorts and comparative designs are needed to confirm these findings. Full article

Objectives: This study aims to investigate the correlations between changes in skin temperature and surface electromyography (sEMG) parameters during fatigue induced by varying exercise intensities. The study uses infrared thermal imaging and sEMG to explore whether skin temperature fluctuations can indicate muscle fatigue states. Methods: Two static contraction fatigue tests were administered on the right biceps brachii muscle group of 30 healthy male subjects at 30% and 70% MVC (Maximum Voluntary Contraction) intensity levels. Tests were separated by a 5-day interval and continued until complete fatigue was achieved. The left arm served as a control and was not subjected to any load. Infrared thermal imaging was employed to record continuous skin temperature, capturing data from 120 s pre-exercise to 480 s post-exercise commencement at ten frames per second. Concurrently, sEMG parameters (RMS—Root Mean Square, MPF—Mean Power Frequency, and MF—Median Frequency) were synchronously collected at a sampling frequency of 1 kHz. Results: During 70% MVC exercise, skin temperature on the exercised arm consistently decreased, reaching its nadir by the end of the exercise, with a statistically significant divergence from the baseline (p < 0.05). At 30% MVC, skin temperature initially slightly declined before gradually increasing. The control arm’s temperature significantly declined across exercise intensities and during recovery. A significant temporal correlation was observed between skin temperature and sEMG parameters. Conclusions: 1. Variability in skin temperature patterns during muscular fatigue is contingent on the level of exercise intensity. 2. The strong correlation between skin temperature and sEMG parameters suggests that infrared thermal imaging is a promising, rapid technique for monitoring exercise-induced muscle fatigue. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

The eddy covariance technique has become one of the most popular methods for measuring CO₂ exchange between ecosystems and the atmosphere. Flux averaging intervals typically range from 15 to 60 min, with 30 min being the most commonly adopted setting. However, due to variations in site conditions and turbulent regimes, the choice of averaging interval can substantially influence flux calculations. In this study, we applied the eddy covariance method to examine how different averaging intervals affect CO₂ flux measurements in a subtropical Moso bamboo forest during winter in Jinggangshan, Jiangxi Province, China. The results showed that the bamboo forest maintained a relatively high CO₂ uptake rate even in winter. When relative humidity exceeded 80%, the averaging interval had a pronounced effect on CO₂ flux estimates, and in some cases even altered the direction of the flux. Based on a comparative analysis, an average interval of 60 min is recommended. These findings offer practical guidance for eddy covariance observations in subtropical Moso bamboo forests and provide useful insights for flux measurements in humid environments more broadly. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

Background: Eyelid reconstruction is particularly challenging because of the delicate anatomy and its critical functional and aesthetic roles. Although various methods have been described for anterior and posterior lamellar repairs, no standardized approach has been established. We developed a single-stage technique integrating reconstruction of both lamellae. Methods: This retrospective case series included seven consecutive patients who underwent full-thickness eyelid reconstruction between 2012 and 2024. Patients were included if they had full-thickness defects requiring reconstruction of both lamellae, underwent reconstruction using a nasal septal chondromucosal graft combined with a large local flap, and had at least 12 months of follow-up. The posterior lamella was reconstructed using nasal septal chondromucosal grafts, and the anterior lamella using large local flaps. Donor sites were managed using various methods. Results: All patients (7/7) achieved complete graft survival without partial or total graft loss. All patients achieved complete eyelid closure without lagophthalmos, and no cases of ectropion, corneal complications, or graft failure were observed. Buccal mucosal grafting demonstrated the most favorable donor-site outcomes, with uneventful healing and no septal perforation or airway-related complications. Conclusions: This single-stage approach combining chondromucosal grafts and local flaps is a feasible and reproducible option for selected patients, providing reliable structural support and satisfactory functional outcomes. Full article

Identifying primary ceramic forming techniques is often problematic when surface traces are altered or erased by secondary shaping on the potter’s wheel, particularly in vessels combining hand-building and wheel use. This study aims to develop a quantitative, non-destructive method to distinguish wheel-throwing and wheel-coiling techniques by analyzing internal fabric features. Experimental replicas of Middle Minoan handleless conical cups (18th cent. BC), produced using wheel-throwing-off-the-hump and wheel-coiling techniques, were investigated using X-ray micro-computed tomography (µCT). Macropores were segmented from complete 3D µCT datasets and their shape preferred orientation was quantitatively assessed through ellipsoid fitting, orientation distribution functions, and pole figure analysis. The results reveal systematic and reproducible differences between the two forming techniques: wheel-coiled vessels show predominantly horizontal pore elongation, expressed as equatorial girdle textures and vertically clustered short axes, whereas wheel-thrown vessels display inclined pore orientations, forming displaced girdles and ring-like short-axis distributions. These contrasting orientation patterns reflect distinct deformation fields imposed during vessel shaping. The study demonstrates that quantitative 3D analysis of pore orientation in whole vessels provides reliable criteria for identifying ceramic forming techniques and confirms previous qualitative observations. This approach offers a robust framework for technological analysis of ceramics and can be applied to both complete vessels and suitably oriented fragments. Full article

In practical engineering applications, rotor–bearing systems inevitably exhibit inherent or residual faults such as imbalance and shaft-bow, originating from manufacturing tolerances, thermal deformation, or operational loading. Accurate monitoring of these faults and their evolution is fundamental to the effectiveness of modern prognostics and health management (PHM) frameworks. However, if such inherent faults are not identified at an early stage, substantial deviations in fault diagnosis may occur, thereby compromising the accuracy of subsequent prognostic assessments and maintenance strategies. This study presents a hybrid diagnostic methodology that integrates a physics-based model with neural network techniques to enhance rotor fault diagnosis. A Jeffcott rotor subjected to simultaneous disk imbalance and shaft-bow is used to demonstrate the methodology, and the results proves its superior capability for simultaneous fault identification. Nonetheless, discrepancies between model predictions and experimental results are observed, attributed to the presence of inherent faults within the rotor system. To address this issue, algorithms for inherent fault identification and compensation, supported by experimental verification, are developed. Following compensation, the accuracy in simultaneously diagnosing and estimating the parameters of imbalance and shaft-bow is significantly improved. The proposed methodology is designed for seamless integration into real-time monitoring systems of industrial rotating machinery. Full article