Search Results (1,432)

Background/Objectives: This study evaluates intratumoral susceptibility signals (ITSS) as imaging markers for glioma grade prediction and their association with molecular and histopathologic features, in the context of the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5). Methods: We retrospectively analyzed patients with adult diffuse gliomas who underwent pretreatment magnetic resonance imaging. ITSS were semiquantitatively graded by two radiologists: grade 0 (no signal), grade 1 (1–5), grade 2 (6–10), and grade 3 (≥11). Relative cerebral blood volume (rCBV) and tumor volume were also obtained. Histopathologic features included tumor grade, Ki-67, mitotic count, necrosis, microvascular proliferation, and molecular alterations (isocitrate dehydrogenase [IDH], 1p/19q, cyclin-dependent kinase inhibitors 2A and 2B [CDKN2A/B], and p53). Regression models predicted tumor grade (low: 1–2, high: 3–4) using ITSS, tumor volume, and rCBV. ROC curves and diagnostic performance metrics were analyzed. Results: 99 patients were included. ITSS grading correlated with rCBV, tumor volume, mitotic count, Ki-67, and tumor grade (p < 0.001). ITSS grades 0–1 were associated with oligodendrogliomas and astrocytomas (p < 0.001), IDH mutations (p < 0.001), and 1p/19q co-deletions (p = 0.01). ITSS grades 2–3 were linked to glioblastomas (p < 0.001), necrosis (p < 0.001), microvascular proliferation (p < 0.001), and CDKN2A/B homozygous deletions (p = 0.02). Models combining ITSS with rCBV and volume showed AUC of 0.94 and 0.96 (p < 0.001), outperforming univariate models. Conclusions: Semiquantitative ITSS grading correlates with key histopathologic and molecular glioma features. Combined with perfusion and volumetric parameters, ITSS enhance non-invasive glioma grading, in alignment with WHO CNS5. Full article

This study presents a detailed investigation of the torque-speed characteristics of a WEG single-phase squirrel-cage induction motor (SPSCIM) of (1/2 hp), 110/220 V at 60 Hz. The primary objective was to derive the motor’s equivalent circuit and validate its performance curves through finite element analysis (FEA), simulation using MATLAB^®/Simulink^®, and experimental testing. Finite element simulations were conducted using the software FEMM (Finite Element Method Magnetics) to model the magnetic flux distribution within the motor’s stator and rotor. These simulations, based on the motor’s dimensions and nameplate data, provided essential insights into the electromagnetic behavior, including flux density and saturation effects, which are crucial for accurate torque-speed curve predictions. For experimental validation, tests were performed under open-circuit and locked-rotor conditions through a universal machine as a load emulator. The torque-speed characteristics were determined using the Suhr method and the classical approach, with the resulting curves compared to experimental measurements. Voltage and current were measured using AC PZEM-004T and DC PZEM-017 meters, while rotor speed was monitored with a Hall effect sensor (A3144). The results revealed strong agreement between the FEM simulations, Surh method, and experimental data, demonstrating the reliability and accuracy of the combined simulation and analytical methods for modeling the motor’s performance. The estimations using classical and Suhr methods, Simulink simulations, and FEMM yielded low error percentages, mostly below 2%. However, in the FEMM simulation, rotor resistance showed a higher error of around 20% due to unavailable data on the exact number of windings turns, a modifiable parameter that can be corrected through further adjustments in the simulation. The torque-speed curves obtained at different voltage levels showed an excellent correlation, confirming the effectiveness of the proposed approach in characterizing the motor’s operational behavior. Full article

Background: Esophageal adenocarcinoma (EAC) diagnosis involves invasive and expensive endoscopy with biopsy, but rising EAC incidence has not been reduced by increased surveillance. This study aimed to develop and clinically validate a novel glycoprotein biomarker blood test for EAC, named PromarkerEso. Methods: Serum glycoprotein relative concentrations were measured using a lectin-based magnetic bead array pulldown method, with multiple reaction monitoring mass spectrometry in 259 samples across three independent cohorts. A panel of glycoproteins: alpha-1-antitrypsin, alpha-1-antichymotrypsin, complement C9 and plasma kallikrein, were combined with clinical factors (age, sex and BMI) in an algorithm to categorize the samples by the risk of EAC. Results: PromarkerEso demonstrated a strong discrimination of EAC from the controls (area under the curve (AUC) of 0.91 in the development cohort and 0.82 and 0.98 in the validation cohorts). The test exhibited a high sensitivity for EAC (98% in the development cohort, and 99.9% and 91% in the validation cohorts) and a high specificity (88% in the development cohort, and 86% and 99% in the validation cohorts). PromarkerEso identified individuals with and without EAC (96% and 95% positive and negative predictive values). Conclusions: This less invasive approach for EAC detection with the novel combination of these glycoprotein biomarkers and clinical factors coalesces in a potential step toward improved diagnosis. Full article

Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on p-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection. Full article

Defects are a direct cause of failure in ferromagnetic components, which can be evaluated via electromagnetic non-destructive testing (ENDT) methods. However, the existing studies exhibit several limitations (e.g., inaccurate quantification, over-reliance on algorithms, and non-intuitive result presentation, among others) in quantitative defect evaluation. To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. Then, a higher precision three-dimensional nonlinear finite element simulation model is established, and the effects of the excitation parameters and detection positions on signal characteristics are investigated. The simulation results indicate that the excitation frequency influences both the detection depth and sensitivity of ENDT, while the voltage amplitude affects the peak strength of the magnetic signal. Consequently, the excitation parameters are determined to be a 10 Hz frequency with a 25 V amplitude. Based on the characterization of signal peaks at positions of 0°, 90°, 180°, and 270°, the characteristic parameter K_A of the peak electrical signal curve is proposed as a quantitative index for evaluating defects. The quantitative experimental results show that K_A is related to the defect dimension. Specifically, the K_A value monotonically decreases from a constant greater than 1 to a constant less than 1 as the defect length increases, K_A is positively correlated with the defect width, and K_A follows a parabolic trend (first increase and then decrease) as the defect depth increases. Notably, K_A values associated with defect width and depth remain below 1. All the above findings provide a basis for evaluating defect dimensions. The results of this paper provide a novel ENDT method for evaluating defects, which is of great significance for improving the accuracy of ENDT and promoting its engineering applications. Full article

Today, numerous advanced options exist for analyzing and measuring magnetic hysteresis loops and core loss across a broad spectrum of applications. Most of these systems are compact and ready to use, fulfilling the measurement and data processing requirements for laminated iron cores according to the standards. However, modeling newly developed materials with complex structures or the high-frequency behavior of iron cores, and the computation of dynamic hysteresis properties’ temperature dependence, are still challenging problems in the field. Moreover, these standardized measurement tools are relatively expensive, and most of them represent a black box that impedes research and further development. This paper presents the development of a cheap and accessible measurement system that is explicitly designed for recording the hysteresis properties of 3D-printed iron cores. The paper presents a comprehensive overview of the design process, components, circuitry, and simulations integral to this project. The paper presents a completed circuit simulation conducted using LTspice and validation of the prototype’s measurement performance. The measurements obtained with the proposed system show good agreement with those of the reference setup, demonstrating its accuracy and practical applicability. Full article

Fine-grained sedimentary rocks are ideal carriers for astronomical cycle analysis as they can record and preserve significant astronomical cycle signals. Spectral analysis using the Multi-taper Method (MTM) and Evolutionary Harmonic Analysis (EHA) using the Fast Fourier Transform (FFT) were conducted on natural gamma data from key wells in the Es₃l sub-member in the Bonan Sag, Bohai Bay Basin, China. Gaussian bandpass filtering was applied using a short eccentricity cycle of 100 ka, and a “floating” astronomical time scale for the Es₃l sub-member (Lower 3rd sub-member of Shahejie Formation in Eocene) was established using magnetic stratigraphic ages as boundaries. Stratigraphic divisions were made for single wells in the Es₃l of the Bonan Sag, and a stratigraphic framework was established based on correlations between key wells. The research results indicate the following: Firstly, the Es₃l of the Bonan Sag records significant astronomical cycle signals, with an optimal sedimentation rate of 8.39 cm/ka identified. Secondly, the cyclical thicknesses corresponding to long eccentricity, short eccentricity, obliquity, and precession cycles are 38.9 m, 9.7 m, 4.6–3.4 m, and 1.96–1.66 m, respectively. Thirdly, the Es₃l sub-member stably records 6 long eccentricity cycles and 26 short eccentricity cycles, and the short eccentricity curve is used as a basis for stratigraphic division for high-precision stratigraphic correlations. Fourthly, the quality of sandstone-interbedded mudrock is jointly controlled by the short eccentricity and precession. Eccentricity maximum values result in thicker sandstone interlayers, while minimum precession values promote the thickness of sandstone interlayers. Through astronomical cycle analysis, the depositional evolution mechanism of sandstone-interbedded mudrock is revealed. Combined with the results of high-precision stratigraphic division, this can provide a basis for fine evaluation and “sweet spot” prediction of lacustrine shale oil reservoirs. Full article

This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study proposes a 2D equivalent analysis method that addresses the unique features of outer rotor type BLDC motors, particularly the permanent magnet (PM) overhang structure. This approach transforms the operating point on the B–H curve to facilitate accurate modeling in a 2D framework, overcoming traditional analysis limitations. An analytical method using spatial harmonics is introduced to derive essential electromagnetic quantities, namely flux linkage and back electromotive force (EMF). The method compensates for slot effects using the Carter coefficient, ensuring precise evaluation of circuit parameters and electromagnetic losses. To optimize motor performance, a multi-objective optimization technique is implemented using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), aiming to maximize both efficiency and power density. The research validates the proposed analytical approach against the finite element analysis method (FEM) results to confirm its accuracy. Full article

Objectives: To investigate the diagnostic value of various lymphatic imaging techniques for thoracic duct (TD) outlet obstruction in patients with chylous leakage. Methods: A retrospective analysis was conducted on 23 patients with chylous leakage who were radiologically diagnosed with a TD outlet obstruction and underwent a TD exploration and reconstruction between January 2022 and February 2025. Non-enhanced magnetic resonance lymphangiography (MRL), ⁹⁹Tc^m-DX lymphoscintigraphy, and intranodal lymphangiography were employed to detect abnormalities in the central lymphatic vessels. The Receiver Operating Characteristic (ROC) curve was utilized to analyze the diagnostic performance of these imaging methods for TD outlet obstruction in lymphatic disorders. Results: Twenty-three patients (fifteen males and eight females) with chylous leakage were included in this study, with an average age of 59.78 ± 13.08 years. Non-enhanced MRL, ⁹⁹Tc^m-DX lymphoscintigraphy, and intranodal lymphangiography revealed TD outlet obstructions in 13, 17, and 18 patients, respectively. Twenty patients exhibited findings consistent with preoperative imaging during TD explorations; the intraoperative microscopic visualization demonstrated the difficulty of white chyle entering the bloodstream for these patients. The ROC curve analysis indicated that “at least two imaging modalities were positive” and had the highest Area Under the Curve (AUC) value (0.90); “intranodal lymphangiography” and “non-enhanced magnetic resonance lymphangiography” followed closely with respective AUC values of 0.76 and 0.73, and ⁹⁹Tc^m-DX lymphoscintigraphy exhibited a lower AUC value 0.63. Conclusions: The combined utilization of multimodal lymphatic imaging techniques demonstrated a high diagnostic accuracy in identifying TD outlet obstruction in patients with chylous leakage. Full article

Background/Objectives: Risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) remains challenging, especially in high-risk cohorts. This study evaluated the predictive utility of the ESC HCM Risk Score and the additive value of myocardial fibrosis assessment via cardiac magnetic resonance (CMR) in HCM patients with implantable cardioverter-defibrillators (ICDs) for primary prevention. Methods: A retrospective analysis was conducted on 108 HCM patients (mean age 49.4 ± 14.2 years; 30.6% female; 63.9% with LVOT obstruction) with ICDs for primary SCD prevention. The primary endpoint was a composite of all-cause mortality or appropriate ICD therapy for ventricular arrhythmia over a mean follow-up of 69.5 ± 22.8 months. ESC HCM Risk Scores, the presence of fibrosis on CMR, and clinical outcomes were analyzed using univariate and multivariate models, ROC curves, and Kaplan–Meier survival estimates. Results: The primary endpoint occurred in 25 patients (23.1%; 3.1%/year). An ESC HCM Risk Score ≥ 4% was common (81.5%) but did not significantly predict the primary outcome (the c-statistic 0.54; p = 0.08) and demonstrated low positive (25%) and high negative predictive values (85%). Severe fibrosis on CMR was significantly associated with events in univariate analysis (p = 0.04), and its inclusion improved the model’s predictive accuracy (the c-statistic increased to 0.65; p = 0.03). Kaplan–Meier analysis revealed worse event-free survival in patients with both elevated ESC scores and more than mild fibrosis (p = 0.028). Conclusions: In this high-risk HCM cohort with ICDs, the ESC risk score showed limited predictive performance, while myocardial fibrosis on CMR added significant prognostic value. Incorporating the fibrosis assessment into future risk models may enhance SCD prediction and refine ICD decision-making in HCM. Further multicenter studies are needed to validate these findings. Full article

By combining density functional theory with the non-equilibrium Green’s function method, we conducted a first-principles investigation of spin-dependent transport properties in a molecular device featuring a dynamic covalent chemical bridge connected to zigzag graphene nanoribbon electrodes. The effects of spin-filtering and spin-rectifying on the I–V characteristics are revealed and explained for the proposed molecular device. Interestingly, our results demonstrate that all three devices exhibit significant single-spin-filtering behavior in parallel (P) magnetization and dual-spin-filtering effects in antiparallel (AP) configurations, achieving nearly 100% spin-filtering efficiency. At the same time, from the I–V curves, we find that there is a weak negative differential resistance effect. Moreover, a high rectifying ratio is found for spin-up electron transport in AP magnetization, which is explained by the transmission spectrum and local density of state. The fundamental mechanisms governing these phenomena have been elucidated through a systematic analysis of spin-resolved transmission spectra and spin-polarized electron transport pathways. These results extend the design principles of spin-controlled molecular electronics beyond graphene-based systems, offering a universal strategy for manipulating spin-polarized currents through dynamic covalent interfaces. The nearly ideal spin-filtering efficiency and tunable rectification suggest potential applications in energy-efficient spintronic logic gates and non-volatile memory devices, while the methodology provides a framework for optimizing spin-dependent transport in hybrid organic–inorganic nanoarchitectures. Our findings suggest that such systems are promising candidates for future spintronic applications. Full article

Background: Multiple sclerosis (MS) is a chronic and heterogeneous disease characterized by demyelination and axonal loss and damage. Magnetic resonance imaging (MRI) has been employed to distinguish these changes in various types of MS lesions. Objectives: We aimed to evaluate intravoxel incoherent motion (IVIM) diffusion and perfusion MRI metrics across different brain regions in healthy individuals and various types of MS lesions, including enhanced, non-enhanced, and black hole lesions. Methods: A prospective study included 237 patients with MS (65 males and 172 females) and 29 healthy control participants (25 males and 4 females). The field strength was 1.5 Tesla. The imaging sequences included three-dimensional (3D) T₁, 3D fluid-attenuated inversion recovery, two-dimensional (2D) T₁, T₂-weighted imaging, and 2D diffusion-weighted imaging (DWI) sequences. IVIM-derived parameters—apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f)—were quantified for commonly observed lesion types (2506 lesions from 224 patients with MS, excluding 13 patients due to MRI artifacts or not meeting the diagnostic criteria for RR-MS) and for corresponding brain regions in 29 healthy control participants. A one-way analysis of variance, followed by post-hoc analysis (Tukey’s test), was performed to compare mean values between the healthy and MS groups. Receiver operating characteristic curve analyses, including area under the curve, sensitivity, and specificity, were conducted to determine the cutoff values of IVIM parameters for distinguishing between the groups. A p-value of ≤0.05 and 95% confidence intervals were used to report statistical significance and precision, respectively. Results: All IVIM parametric maps in this study discriminated among most MS lesion types. ADC, D, and D* values for MS black hole lesions were significantly higher (p < 0.0001) than those for other MS lesions and healthy controls. ADC, D, and D* maps demonstrated high sensitivity and specificity, whereas f maps exhibited low sensitivity but high specificity. Conclusions: IVIM parameters provide valuable diagnostic and clinical insights by demonstrating high sensitivity and specificity in evaluating different categories of MS lesions. Full article

Magnetic resonance imaging (MRI) is widely used as a medical imaging technique due to its non-invasive nature, high spatial contrast, and virtually unlimited depth of penetration. Different modalities can be used for contrast in MRI, including T₁ (spin–lattice) and T₂ or T₂ * (spin–spin) proton relaxation times, and specific contrast agents (CAs) have been developed that locally enhance the contrasts in MRI images. We present a method combining T₁- and T₂-specific CAs in a single imaging technique, referred to as correlation MRI. This technique allows different CAs to be used simultaneously to visualize contrast between multiple types of tissue in the same image when applied as targeted CAs. An obstacle for the quantitative use of correlation MRI is that T₁ and T₂ relaxivity changes generated by CAs are not independent of each other. Here, we measured relaxivities in mixtures with various concentrations of Cas, including Magnevist (Gd³⁺-based, primarily a T₁ CA) and Feridex (Fe²⁺- and Fe³⁺-based, primarily a T₂ CA), and compared them to theoretically predicted values. It was found that, at clinically relevant concentrations, relaxivities of the mixtures deviate from linearly added values. We finally propose a three-dimensional calibration curve to quantitatively determine the concentration in mixtures of CAs, based on the measured relaxivities. Full article

Background: Prostate cancer is a major cause of cancer-related mortality among men, with approximately 15% of newly diagnosed patients having pelvic lymph node metastasis (PLNM). For this reason, PLNM identification before localized PCa treatment would significantly impact treatment planning, clinical judgment, and patient outcome prediction. Radiomics has gained popularity for its ability to predict tumor behavior and prognosis without invasive procedures. Magnetic resonance imaging (MRI) is widely used in radiomic workups, particularly for prostate cancer. This study aims to predict lymph node invasion in prostate cancer patients using clinical information and mp-MRI radiomics features extracted from the suspicious nodule, prostate gland, and periprostatic adipose tissue (PPAT). Methods: A retrospective review of 85 patients who underwent mp-MRI at our radiology department between 2016 and 2022 was conducted. This study included patients who underwent prostatectomy and lymphadenectomy with complete histological examination and previous staging mp-MRI and were divided into two groups based on lymph node status (positive/negative). Data were collected from each patient, including clinical information, radiomics, and semantic data (such as tumor MRI characteristics, histological tumor details, and lymph node status (LNS)). MRI exams were conducted using a 1.5-T system and were used to study the prostate gland. A three-year resident manually segmented the prostate nodule, prostatic gland, and periprostatic tissue using an open-source segmentation program. A random forest (RF) machine learning model was developed and tested using Chat-GPT version 4.0 software. The model’s performance in predicting LNS was assessed using accuracy, precision, recall, F1 score, and area under the curve (AUC) receiver operating characteristic (ROC), with sensitivity and specificity evaluated using DeLong’s test. Results: Random forest demonstrated the best performance in prediction considering features extracted from DWI nodules (67% of accuracy, 0.83 AUC), from T2 fat (78% of accuracy, 0.86 AUC), and from T2 glands (78% of accuracy, 0.97 AUC). The combination of the three sequences in the nodule evaluation was more accurate compared with the single sequences (88%). Combining all the nodule features with gland and PPAT features, an accuracy of 89% with AUC near 1 was obtained. Compared with the analysis of the nodule and the PPAT, the whole-gland evaluation had the best performance (p ≤ 0.05) in predicting LNS when compared with the nodule. Conclusions: Precise nodal staging is essential for PCa patients’ prognosis and therapeutic strategy. When compared with a radiologist’s assessment, radiomics models enhance the diagnostic accuracy of lymph node staging for prostate cancer. Although data are still lacking, deep learning models may be able to further improve on this. Full article

Background/Objectives: Accurate assessment of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer is critical for optimizing treatment strategies. While magnetic resonance imaging (MRI) and mammography are commonly used for response evaluation, they have inherent limitations. Ultrasound (US) has emerged as a promising, cost-effective, and real-time alternative. This study aimed to evaluate the effectiveness of US in tracking tumor regression during NAC and its correlation with pathologic tumor regression grade (TRG). Methods: This study included 282 breast cancer patients undergoing NAC. Tumor size was measured using ultrasound at three key time points: pre-chemotherapy, after four cycles, and post-chemotherapy. Spearman’s correlation was used to assess the relationship between US-measured tumor changes and TRG. Multinomial logistic regression and receiver operating characteristic (ROC) curve analyses were performed to determine the predictive accuracy of the measurements from our US in identifying pathologic complete response (pCR). Conclusions: Ultrasound is a reliable, real-time imaging tool for monitoring NAC response in breast cancer patients. Its ability to predict pCR and track tumor shrinkage highlights its potential for treatment adaptation. Standardization of US protocols and integration with AI-based analysis may further improve its clinical utility, making it a valuable adjunct in breast cancer treatment monitoring. Full article