Spin-lattice Relaxation Research Articles

Spinal extramedullary hematopoiesis mimicking an epidural tumor in a patient with high-risk polycythemia vera: illustrative case.

Here the authors present the case of a 43-year-old male with a history of T-cell lymphoma, which was treated with azacitidine plus cyclophosphamide, doxorubicin, vincristine, and prednisone and autologous hematopoietic cell transplant, and high-risk polycythemia vera (PCV) presenting with severe lower-back pain radiating to the bilateral legs with associated lower-extremity weakness and splenomegaly. T2-weighted magnetic resonance imaging revealed multilevel epidural lesions involving T1-10 and S1-2. Because of severe spinal canal stenosis, the patient underwent surgical decompression of T5-7, with immediate postoperative alleviation of the lower-extremity pain and complete resolution of the lower-leg weakness. Biopsy results revealed extramedullary hematopoiesis (EMH) mimicking a spinal epidural tumor. EMH is radiosensitive and displays a rapid response to low dosages, so the patient was further treated with palliative radiation therapy for residual tumors and symptom alleviation, as well as hydroxyurea and corticosteroids as indicated for cytoreduction. EMH associated with PCV or myeloproliferative conditions occurring within the spine is a rare phenomenon without a standard treatment approach. https://thejns.org/doi/10.3171/CASE23659.

Enzyme replacement therapy for CLN2 disease: MRI volumetry shows significantly slower volume loss compared to a natural history cohort.

Neuronal ceroid lipofuscinoses (NCL) are a group of neurodegenerative disorders. Recently, enzyme replacement therapy (ERT) was approved for CLN2, a subtype of NCL. The aim of this study was to quantify brain volume loss in CLN2 disease of patients on ERT in comparison to a natural history cohort using magnetic resonance imaging (MRI). Nineteen (13 female, 6 male) patients with CLN2 disease at one UK center were studied using serial 3D T1-weighted MRI (follow-up time, 1 to 9 years). Brain segmentation was done using FreeSurfer. Volume measurements for supratentorial grey and white matter, deep grey matter (basal ganglia/thalami), lateral ventricles, and cerebellar grey and white matter were recorded. The volume change over time was analyzed using a linear mixed-effects model excluding scans before treatment start. Comparison was made to a published natural history cohort of 12 patients (8 female, 4 male) which was reanalyzed using the same method. Brain volume loss of all segmented brain regions was much slower in treated patients compared to the natural history cohort. For example, supratentorial grey matter volume in treated patients decreased by 3±0.74% (p<0.001) annually compared to an annual volume loss of 16.8±1.5% (p<0.001) in the natural history cohort. Our treatment cohort showed a significantly slower rate of brain parenchymal volume loss compared to a natural history cohort in several anatomical regions. Our results complement prior clinical data which found a positive response to ERT. We demonstrate that automated MRI volumetry is a sensitive tool to monitor treatment response in children with CLN2 disease. NCL = Neuronal Ceroid Lipofuscinosis, CLN2 = Neuronal Ceroid Lipofuscinosis type 2, TPP1 = tripeptidyl peptidase 1, ERT = enzyme replacement therapy, EMA = European Medicines Agency, ICV = intra-cerebro-ventricular reservoir.

SrCu(OH)3Cl , an isolated equilateral triangle spin S=1/2 model system

We have investigated the magnetic ground state properties of the quantum spin trimer compound strontium hydroxy copper chloride SrCu(OH)3Cl using bulk magnetization, specific heat measurements, nuclear magnetic resonance (NMR), and electron spin resonance (ESR) spectroscopy. SrCu(OH)3Cl consists of layers with isolated Cu2+ triangles and hence provides an opportunity to understand the magnetic ground state of an isolated system of S = 1/2 arranged on an equilateral triangle. Although magnetization measurements do not exhibit a phase transition to a long-range ordered state down to T = 2 K, they reveal the characteristic behavior of isolated trimers with an exchange of J=154 K. The Curie-Weiss behavior changes around 50–80 K, as is also seen in the NMR spin-lattice relaxation rate. In zero magnetic field, our specific heat data establish a second-order phase transition to an antiferromagnetic ground state below T= 1.2 K. We have drawn a magnetic field-temperature (H−T) phase diagram based on the specific heat measurements. The ESR data show divergence of the linewidth at lower temperatures, which precedes the phase transition to an antiferromagnetic long-range ordered state with unconventional critical exponents. The temperature variation of the g-factor further confirms the antiferromagnetic phase transition and reflects the underlying magnetocrystalline anisotropy of the compound. Published by the American Physical Society 2024

Spin-Lattice Relaxation and Spin-Phonon Coupling of ns1 Metal Ions at the Surface.

To use transition metal ions for spin-based applications, it is essential to understand fundamental contributions to electron spin relaxation in different ligand environments. For example, to serve as building blocks for a device, transition metal ion-based molecular qubits must be organized on surfaces and preserve long electron spin relaxation times, up to room temperature. Here we propose monovalent group 12 ions (Zn+ and Cd+) as potential electronic metal qubits with an ns1 ground state. The relaxation properties of Zn+ and Cd+, stabilized at the interface of porous aluminosilicates, are investigated and benchmarked against vanadium (3d1) and copper (3d9) ions. The spin-phonon coupling has been evaluated through DFT modeling and found to be negligible for the ns1 states, explaining the long coherence time, up to 2 μs, at room temperature. These so far unexplored metal qubits may represent viable candidates for room temperature quantum operations and sensing.

Poor Self-Reported Sleep is Associated with Prolonged White Matter T2 Relaxation in Psychotic Disorders.

Schizophrenia (SZ) and bipolar disorder (BD) are characterized by white matter (WM) abnormalities, however, their relationship with illness presentation is not clear. Sleep disturbances are common in both disorders, and recent evidence suggests that sleep plays a critical role in WM physiology. Therefore, it is plausible that sleep disturbances are associated with impaired WM integrity in these disorders. To test this hypothesis, we examined the association of self-reported sleep disturbances with WM transverse (T2) relaxation times in patients with SZ spectrum disorders and BD with psychotic features. 28 patients with psychosis (17 BD-I, with psychotic features and 11 SZ spectrum disorders) were included. Metabolite and water T2 relaxation times were measured in the anterior corona radiata at 4T. Sleep was evaluated using the Pittsburgh Sleep Quality Index. PSQI total score showed a moderate to strong positive correlation with water T2 (r = 0.64, p<0.001). Linear regressions showed that this association was specific to sleep disturbance but was not a byproduct of exacerbation in depressive, manic, or psychotic symptoms. In our exploratory analysis, sleep disturbance was correlated with free water percentage, suggesting that increased extracellular water may be a mechanism underlying the association of disturbed sleep and prolonged water T2 relaxation. Our results highlight the connection between poor sleep and WM abnormalities in psychotic disorders. Future research using objective sleep measures and neuroimaging techniques suitable to probe free water is needed to further our insight into this relationship.

An improved 3D-UNet-based brain hippocampus segmentation model based on MR images

ObjectiveAccurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results. Methods: Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network. Results: The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively. Conclusion: The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.

Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction. This prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed. AI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5. AI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.

Surface Engineering Enhances Vanadium Carbide MXene-Based Nanoplatform Triggered by NIR-II for Cancer Theranostics.

Despite the advantages of high tissue penetration depth, selectivity, and non-invasiveness of photothermal therapy for cancer treatment, developing NIR-II photothermal agents with desirable photothermal performance and advanced theranostics ability remains a key challenge. Herein, a universal surface modification strategy is proposed to effectively improve the photothermal performance of vanadium carbide MXene nanosheets (L-V2C) with the removal of surface impurity ions and generation of mesopores. Subsequently, MnOx coating capable of T1-weighted magnetic resonance imaging can be in situ formed through surface redox reaction on L-V2C, and then, stable nanoplatforms (LVM-PEG) under physiological conditions can be obtained after further PEGylation. In the tumor microenvironment irradiated by NIR-II laser, multivalent Mn ions released from LVM-PEG, as a reversible electronic station, can consume the overexpression of glutathione and catalyze a Fenton-like reaction to produce ·OH, resulting in synchronous cellular oxidative damage. Efficient synergistic therapy promotes immunogenic cell death, improving tumor-related immune microenvironment and immunomodulation, and thus, LVM-PEG can demonstrate high accuracy and excellent anticancer efficiency guided by multimodal imaging. As a result, this study provides a new approach for the customization of 2D surface strategies and the study of synergistic therapy mechanisms, highlighting the application of MXene-based materials in the biomedical field.

Evidence for NMR Relaxation Enhancement in a Protic Ionic Liquid by the Movement of Protons Independent of the Translational Diffusion of Cations.

The molecular dynamics, thermal stability, and ionic conductivity were studied in the protic ionic liquid 1-methylimidazolium bis(trifluoromethylsulfonyl)imide ([MIm][TFSI]). The relaxation of the 1H spin-lattice of cations in the measured frequency range (10 kHz to 20 MHz) and temperature (298 to 343 K) is sensitive mainly to slow processes occurring in the molecular dynamics of protic ionic liquid and dominated by the contribution of intermolecular translational diffusion. Molecular rotations give only a constant contribution and become significant in the higher frequency range. An interesting feature is the observed enhancement of the 1H spin-lattice relaxation below 0.03 MHz attributed to the exchange of protons (order of 10-5 s) between imidazolium cations. The measurements of the self-diffusion coefficient of hydrogen atoms of cation from 298 to 343 K additionally confirm the observed phenomenon. The coefficient for exchangeable protons -NH is higher than for the cation. The nuclear magnetic resonance (NMR) experiments provide unambiguous evidence for proton transport decoupled from molecular diffusion of ions and support the conclusion that the charge transport mechanism in the studied PIL includes contributions from both the vehicular and Grotthus mechanisms. The protic ionic liquid is thermally stable to about 573 K as shown by thermogravimetric analysis and its electrical conductivity is 5 × 10-2 S/cm at 423 K.

CHNet: A multi-task global–local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook the synergistic promotion among multiple tasks and the consideration of both global and local information, which can significantly reduce prediction accuracy. To address these issues, this paper proposes an innovative method called the Multi-task Global–Local Collaborative Hybrid Network (CHNet) aimed at more accurately predicting patients’ KRAS mutation status. CHNet consists of two branches that can extract global and local features from segmentation and classification tasks, respectively, and exchange complementary information to collaborate in executing these tasks. Within the two branches, we have designed a Channel-wise Hybrid Transformer (CHT) and a Spatial-wise Hybrid Transformer (SHT). These transformers integrate the advantages of both Transformer and CNN, employing cascaded hybrid attention and convolution to capture global and local information from the two tasks. Additionally, we have created an Adaptive Collaborative Attention (ACA) module to facilitate the collaborative fusion of segmentation and classification features through guidance. Furthermore, we introduce a novel Class Activation Map (CAM) loss to encourage CHNet to learn complementary information between the two tasks. We evaluate CHNet on the T2-weighted MRI dataset, and achieve an accuracy of 88.93% in KRAS mutation status prediction, which outperforms the performance of representative KRAS mutation status prediction methods. The results suggest that our CHNet can more accurately predict KRAS mutation status in patients via a multi-task collaborative facilitation and considering global–local information way, which can assist doctors in formulating more personalized treatment strategies for patients.

Quality changes in high hydrostatic pressure treated enriched tomato sauce.

Use of high hydrostatic pressure (HHP) with reduced processing times is gaining traction in the food industry as an alternative to conventional thermal treatment. In order to enhance functional benefits while minimizing processing losses, functionalized products are being developed with such novel techniques. In this study, changes in quality parameters for HHP treated enriched tomato sauce were evaluated, with the aim to assess its viability as an alternative to conventional thermal treatment methods. HHP treatments at 500 MPa, 30 °C/50 °C significantly increased the total phenolic and lycopene content of the sauce samples, achieving 6.7% and 7.5% improvements over conventionally treated samples. The antioxidant capacity of the HHP-treated samples was also found to match or be better than conventionally treated samples. Furthermore, a T2 relaxation time study revealed that pressure-temperature processing treatments were effective in maintaining the structural integrity of water molecules. Microbiological analyses revealed that 500 MPa/50 °C 5 min treatment can offer 8 logs reduction colony formation, matching the results of conventional thermal treatment. Combined pressure-temperature treatments improve results, reduce time consumption. 500 MPa/50 °C treatments provided retention of quality parameters and significant reduction in microbial activity. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Advancements in ultrasonography and MRI for enhanced diagnosis of Parkinson's disease: An updated review

Parkinson's disease (PD) is a progressive neurological illness characterized by symptoms such as bradykinesia (slowness of movement), tremor, rigidity, and postural instability. Diagnosing parkinsonian syndromes (PS) can be challenging, even for specialized neurology centers, due to the high rate of diagnostic errors. This review article highlights the challenges in diagnosing PS and the importance of accurately differentiating it from PD. It discusses the utility of various magnetic resonance imaging (MRI) techniques and transcranial ultrasound (TUS) in improving diagnostic accuracy. The focus is on both conventional and cutting-edge MRI procedures, including T2*/T2-weighted MRI, susceptibility-weighted imaging (SWI), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), magnetic resonance spectroscopy (MRS), and ultra-high-resolution MRI. These advanced techniques have shown promising potential in aiding diagnosis and identifying early signs of PD. The review emphasizes the clinical relevance of these imaging modalities in discriminating PD from other parkinsonian disorders and detecting early-stage disease before the onset of clinical symptoms.

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts.

Contrastive Self-supervised Learning for Neurodegenerative Disorder Classification.

Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans. Supervised machine learning approaches classifying neurodegenerative diseases require diagnostic-labels for each sample. However, it can be difficult to obtain expert labels for a large amount of data. Self-supervised learning (SSL) offers an alternative for training machine learning models without data-labels. We investigated if the SSL models can applied to distinguish between different neurodegenerative disorders in an interpretable manner. Our method comprises a feature extractor and a downstream classification head. A deep convolutional neural network trained in a contrastive self-supervised way serves as the feature extractor, learning latent representation, while the classifier head is a single-layer perceptron. We used N=2694 T1-weighted MRI scans from four data cohorts: two ADNI datasets, AIBL and FTLDNI, including cognitively normal controls (CN), cases with prodromal and clinical AD, as well as FTLD cases differentiated into its sub-types. Our results showed that the feature extractor trained in a self-supervised way provides generalizable and robust representations for the downstream classification. For AD vs. CN, our model achieves 82% balanced accuracy on the test subset and 80% on an independent holdout dataset. Similarly, the Behavioral variant of frontotemporal dementia (BV) vs. CN model attains an 88% balanced accuracy on the test subset. The average feature attribution heatmaps obtained by the Integrated Gradient method highlighted hallmark regions, i.e., temporal gray matter atrophy for AD, and insular atrophy for BV. In conclusion, our models perform comparably to state-of-the-art supervised deep learning approaches. This suggests that the SSL methodology can successfully make use of unannotated neuroimaging datasets as training data while remaining robust and interpretable.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model's performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer.

The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.

Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma

BackgroundOral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors.MethodsThis study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.ResultsAfter rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model’s performance, increasing the validation cohort AUC to 0.87.ConclusionIn this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.Clinical relevance statementThis integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.

The associations of peripheral interleukin alterations and hippocampal subfield volume deficits in schizophrenia.

The hippocampus is one of the brain regions most vulnerable to inflammatory insults, and the relationships between peripheral inflammation and hippocampal subfields in patients with schizophrenia remain unclear. In this study, forty-six stably medicated patients with schizophrenia and 48 demographically matched healthy controls (HCs) were recruited. The serum levels of IL - 1β, IL-6, IL-10, and IL-12p70 were measured, and 3D high-resolution T1-weighted magnetic resonance imaging was performed. The IL levels and hippocampal subfield volumes were both compared between patients and HCs. The associations of altered IL levels with hippocampal subfield volumes were assessed in patients. Patients with schizophrenia demonstrated higher serum levels of IL-6 and IL-10 but lower levels of IL-12p70 than HCs. In patients, the levels of IL-6 were positively correlated with the volumes of the left granule cell layer of the dentate gyrus (GCL) and cornu Ammonis (CA) 4, while the levels of IL-10 were negatively correlated with the volumes of those subfields. IL-6 and IL-10 might have antagonistic roles in atrophy of the left GCL and CA4. This suggests a complexity of peripheral cytokine dysregulation and the potential for its selective effects on hippocampal substructures, which might be related to the pathophysiology of schizophrenia.

Advantages of cone beam computed tomography for evaluation of subchondral insufficiency fractures of the knee compared to MRI

To determine the diagnostic yield of cone beam computed tomography (CBCT) compared with 3 T magnetic resonance imaging (MRI) for the evaluation of subchondral insufficiency fractures of the knee. Consecutive patients with subchondral insufficiency fractures of the knee examined by 3 T MRI and CBCT of the femoral condyles were reviewed. Two experienced raters graded the lesion severity on 3 T MRI and CBCT images: grade 1: no signs of a subchondral bone lesion; grade 2: subchondral trabecular fracture or cystic changes, but without infraction of the subchondral bone plate; grade 3: collapse of the subchondral bone plate. Ratings were repeated after six weeks to determine reliability. In addition, the bone lesion size was measured as elliptical area (mm2) and compared between CBCT and T1-weighted MRI sequences. Among 30 patients included (43.3% women; mean age: 60.9 ± 12.8 years; body mass index (BMI) 29.0 ± 12.8 kg/m2), the medial femoral condyle was affected in 21/30 patients (70%). The grading of subchondral lesions between MRI and CBCT did not match in 12 cases (40%). Based on MRI images, an underestimation (i.e., undergrading) compared with CBCT was observed in nine cases (30%), whereas overgrading occurred in three cases (10%). Compared to CBCT, routine T1-weighted 3 T sequences significantly overestimated osseus defect zones in sagittal (84.7 ± 68.9 mm2 vs. 35.9 ± 38.2 mm2, p < 0.01, Cohen’s d = 1.14) and coronal orientation (53.1 ± 24.0 mm2 vs. 22.0 ± 15.2 mm2, p < 0.01, Cohen’s d = 1.23). The reproducibility of the grading determined by intra- and inter-rater agreement was very high in MRI (intra-class correlation coefficient (ICC) 0.78 and 0.90, respectively) and CBCT (ICC 0.96 and 0.96, respectively). In patients with subchondral insufficiency fractures of the knee, the use of CBCT revealed discrepancies in lesion grading compared with MRI. These findings are clinically relevant, as precise determination of subchondral bone plate integrity may influence the decision about conservative or surgical treatment. CBCT represents our imaging modality of choice for grading the lesion and assessing subchondral bone plate integrity. MRI remains the gold standard modality to detect especially early stages.

Effects of contact ultrasound coupled with infrared radiation on drying kinetics, water migration and physical properties of beef during hot air drying

Drying, as a critical step in the production of air-dried beef, has a direct impact on the quality of the final product. Innovatively, a composite system incorporating contact ultrasound (CU) and infrared radiation (IR) as auxiliary measures within a hot air drying (HAD) framework was built in this research, and the effects of these techniques on the drying kinetics, protein denaturation, and moisture transformation of air-dried beef were investigated. In comparison to HAD treatment, the integrated CU and IR (CU-IRD) system displayed marked enhancements in heat and moisture transport efficiency, thereby saving 36.84% of time expenditure and contributing favorably to the improved moisture distribution of the end-product. This was mainly ascribed to the denaturation of myosin induced by IR thermal effect and the micro-channel produced by CU sponge effect, thus increasing T2 relaxation time and the proportion of free water. In conclusion, the composite system solved the problem of surface hardening and reduces hardness and chewiness of air-dried beef by 40.42% and 45.25% respectively, but inevitably increased the energy burden by 41.60%.