Sequential Ring Opening/In Situ SO2-Capture/Alkynylation of Cyclopropanols with Alkynyl Triflones Initiated by Energy Transfer.

A visible-light-triggered ring opening/in situ SO2-capture/alkynylation sequence of cyclopropyl alcohols with alkynyl triflones using 4CzIPN as a triplet energy transfer photocatalyst is herein described. This metal-free protocol provides a straightforward and atom-economical approach to alkynyl-substituted γ-keto sulfones with a broad scope of substituents. In this transformation, alkynyl triflones could be used as both radical acceptors and SO2 donors. Preliminary experimental mechanistic studies and synthetic utility are also demonstrated.

Research progress of residual stress measurement methods.

Residual stress refers to self-equilibrating stress present within materials, with the potential to significantly affect manufacturing processes and performance. Therefore, accurately and quantitatively measuring residual stress is always of great importance. This study provides a comprehensive review of various characterization techniques for residual stress, including their principles, development history, applications, and limitations. Initially, several destructive techniques such as the hole-drilling method, ring-core method, deep hole drilling method, slitting method, and contour method are summarized. Subsequently, three nondestructive techniques based on X-ray/electron diffraction, magnetic signals, and ultrasonic signals are evaluated. In the final part of this overview, special attention is given to a newly-developed technique for measuring residual stress, which combines incremental focused ion beam (FIB) milling and digital image correlation (DIC). Our review aims to guide further investigations on residual stress and identify the future development of techniques for measuring residual stress.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

A Case report: Synovial sarcoma of the mediastinum in an 18-year-old teenager.

Synovial sarcomas (SSs) are a rare group of malignant tumors originating from pluripotential mesenchymal cells, which commonly occur as the primary tumor in the soft tissues near the articular surface, tendons, and articular synovium. Herein, we report a rare case of mediastinal SS in an 18-year-old teenager who initially presented with cough as the primary symptom. In this case, plain chest CT and contrast-enhanced CT clearly revealed the lesion presenting as a round-like and uneven density mass in the mediastinum with heterogeneous enhancement, which compressed the trachea and invaded the adjacent vessels. Based on the results of immunohistochemistry and fluorescence in situ hybridization (FISH), combined with the differential diagnosis with other types of tumors in the mediastinum on imaging, we were able to diagnose the tumor as an SS located in the mediastinum. Subsequent resection of the lesion coupled with chemotherapy and immunotherapy led to an improvement in the patient's symptoms.

The value of preoperative diagnosis of colorectal adenocarcinoma pathological T staging based on dual-layer spectral-detector computed tomography extracellular volume fraction: a preliminary study.

PURPOSE: To investigate the value of preoperative diagnosis of colorectal adenocarcinoma (CRAC) pathological T staging based on dual-layer spectral-detector computed tomography (DLCT) extracellular volume fraction (ECV) of CRAC lesions. METHODS: We prospectively collected clinical and DLCT imaging data from 165 patients with CRAC who attended two hospitals from June 2022 to April 2023. The enrolled patients were divided into a training group (n = 110, from Hospital 1) and an external validation group (n = 55, from Hospital 2). Measuring and calculating DLCT parameters of lesions, including CT values of 40 and 100 keV virtual mono-energetic images (VMI), iodine concentration (IC) and effective atomic number (Eff-Z) in the arterial phases (AP) and venous phases (VP), and ECV in the delayed phase (DP). The differences in clinical characteristics and DLCT parameters were compared between different pT subgroups. The correlation between DLCT parameters and pT stages were evaluated by Spearman correlation analysis. A multifactorial binary logistic stepwise forward regression analysis was performed to obtain independent influences associated with pT stage. Receiver operating characteristic curves (ROCs) were used to assess diagnostic efficacy and were expressed as area under the curve (AUC). RESULTS: Each DLCT parameter was higher in pT3 stage tumors than in pT1-2 stage tumors (all P < 0.05). The highest correlation was found between ECV and pT stage (r = 0.637). ECV were independent influences associated with pT stage. ECV had excellent diagnostic efficacy for CRAC pT staging in both the training and external validation groups (AUC = 0.919 and 0.892). CONCLUSION: ECV based on DLCT measurement can be used for preoperative noninvasive diagnosis of CRAC pT staging with excellent diagnostic efficacy. It can provide a new imaging marker for the preoperative evaluation of CRAC and help clinicians formulate individualized treatment earlier. However, it needs to be confirmed with a larger sample size.

Stimuli-triggered Self-Assembly of Gold Nanoparticles: Recent Advances in Fabrication and Biomedical Applications.

Gold nanoparticles have been widely used in engineering, material chemistry, and biomedical applications owing to their ease of synthesis and functionalization, localized surface plasmon resonance (LSPR), great chemical stability, excellent biocompatibility, tunable optical and electronic property. In recent years, the decoration and modification of gold nanoparticles with small molecules, ligands, surfactants, peptides, DNA/RNA, and proteins have been systematically studied. In this review, we summarize the recent approaches on stimuli-triggered self-assembly of gold nanoparticles and introduce the breakthrough of gold nanoparticles in disease diagnosis and treatment. Finally, we discuss the current challenge and future prospective of stimuli-responsive gold nanoparticles for biomedical applications.

Low Dose Iodinated Contrast Material and Radiation for Virtual Monochromatic Imaging in Craniocervical Dual-Layer Spectral Detector Computed Tomography Angiography: A Prospective and Randomized Study.

RATIONALE AND OBJECTIVES: To investigate the feasibility of virtual monochromatic imaging (VMI) of dual-layer spectral detector computed tomography (SDCT) to reduce iodinated contrast material (CM) and radiation dose in craniocervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 280 consecutively selected patients performed craniocervical CTA with SDCT were prospectively selected and randomly divided into four groups (A, DoseRight index (DRI) 31, iopromide 370mgI/mL, volume 0.8 mL/kg; B, DRI 26, iopromide 370mgI/mL, volume 0.4 mL/kg; C, DRI 26, ioversol 320mgI/mL, volume 0.4 mL/kg; D, DRI 26, iohexol 300mgI/mL, volume 0.4 mL/kg). 50-70 kiloelectron volts (keV) VMIs in group B were reconstructed and compared to group A to select the optimal keV. Then, the optimal keV in groups B, C and D was reconstructed and compared. Objective image quality, including vascular attenuation, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), was evaluated. Subjective image quality was assessed using a 5-point Likert scale. In addition, the effective dose (ED), iodine load and iodine delivery rate (IDR) were compared between groups A and D. RESULTS: 55 keV VMI was the optimal VMI in group B. The objective and subjective image quality of 55 keV VMI in group B were equal to or better than those of the CI in group A. The SNR, CNR and subjective image quality in group D were similar to those in group B (P > 0.05). The ED, iodine load and IDR of group D were reduced by 44%, 59% and 19%, respectively, when compared to those of group A. CONCLUSION: Low dose iodinated CM and radiation for 55 keV VMI in craniocervical CTA using SDCT could still provide equivalent or better image quality than the conventional scanning protocol.

The image quality and diagnostic performance of CT perfusion-derived CT angiography versus that of conventional CT angiography.

Background: The combination of computed tomography angiography (CTA) and computed tomography perfusion (CTP) evaluation of cerebral perfusion status and vascular conditions can improve the diagnostic accuracy of infarction, ischemia, and vascular occlusion in stroke patients, as well as a comprehensive assessment of cerebral edema, collateral circulation, and blood perfusion in the lesion area. However, the consequent radiation safety and contrast agent nephropathy have aroused increasing concern. The purpose of this study was to assess the image quality and diagnostic accuracy of CTA images derived from CTP data, and to explore the feasibility of replacing conventional CTA. Methods: A total of 31 consecutive patients with suspected acute ischemic stroke were retrospectively analyzed. All patients underwent head and neck CTA and brain CTP examinations. All the CTP images were transmitted to the ShuKun artificial intelligence system, which reconstructs CTA derived from CTP (CTA-DF-CTP). The images were divided into 2 groups, including CTA-DF-CTP (Group A) and conventional CTA (Group B). The CT attenuation values, subjective image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image quality, CT volume dose index (CTDIvol), dose length product (DLP), and effective radiation dose (ED) were compared between the 2 groups. Moreover, the consistency of vascular stenosis and stenosis degree between the 2 groups were measured and evaluated. Results: There were no significant differences in image noise, SNR, or CNR between Groups A and B (P>0.05). The CT attenuation values of the arteries were higher in Group A than in B [internal carotid artery (ICA) =548±112 vs. 454±85 Hounsfield units (HU), middle cerebral artery (MCA) =453±118 vs. 388±70 HU, and basilar artery (BA) =431±99 vs. 360±83 HU] (P<0.01). The image quality of the 2 groups met the requirement of clinical diagnosis (4.97±0.18 vs. 4.94±0.25). No significant difference was found in subjective evaluation (P>0.05). In Group A compared with Group B, the following reductions were observed: CTDIvol (10.7%; 100.8 vs. 112.9 mGy), DLP (23.0%; 1,613±0 vs. 2,093±88 mGy·cm), and ED (23.0%; 5.00±0.00 vs. 6.49±0.27 mSv). Conclusions: CTA-DF-CTP data provide diagnostic accuracy and image quality similar to those of conventional CTA of head and neck CTA.

MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments.

The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet. It is based on the UNet network architecture and integrates a Multi-scale Feature Extraction (MFE) module and a Tokenized Multilayer Perceptron (BATok-MLP) module with dynamic sparse attention. The MFE enhances the extraction of detail and semantic features, while the BATok-MLP successfully fuses regional and global attention, striking an effective balance in the extraction capabilities of both global and local information. Additionally, we pioneered the use of a diffusion model for data augmentation. By integrating and analyzing different augmentation methods, we further improved the model's training accuracy and robustness. Experimental results reveal that, compared to other segmentation networks, MFBP-UNet shows a significant improvement across all performance metrics. Specifically, MFBP-UNet achieves scores of 86.15%, 93.53%, 90.89%, and 0.922 on MIoU, MP, MPA, and Dice metrics, marking respective improvements of 5.75%, 5.79%, 1.08%, and 0.074 over the UNet model. These results demonstrate the MFBP-UNet model's superior performance and generalization capabilities in pear leaf disease segmentation and its inherent potential to address analogous challenges in natural environment segmentation tasks.

Regional homogeneity and functional connectivity of freezing of gait conversion in Parkinson's disease.

Background: Freezing of gait (FOG) is common in the late stage of Parkinson's disease (PD), which can lead to disability and impacts the quality of life. Therefore, early recognition is crucial for therapeutic intervention. We aimed to explore the abnormal regional homogeneity (ReHo) and functional connectivity (FC) in FOG converters and evaluate their diagnostic values. Methods: The data downloaded from the Parkinson's Disease Progression Markers Project (PPMI) cohort was subdivided into PD-FOG converters (n = 16) and non-converters (n = 17) based on whether FOG appeared during the 3-year follow-up; 16 healthy controls were well-matched. ReHo and FC analyses were used to explore the variations in spontaneous activity and interactions between significant regions among three groups of baseline data. Correlations between clinical variables and the altered ReHo values were assessed in FOG converter group. Last, logistic regression and receiver operating characteristic curve (ROC) were used to predict diagnostic value. Results: Compared with the non-converters, FOG converters had reduced ReHo in the bilateral medial superior frontal gyrus (SFGmed), which was negatively correlated with the postural instability and gait difficulty (PIGD) score. ReHo within left amygdala/olfactory cortex/putamen (AMYG/OLF/PUT) was decreased, which was correlated with anxiety and autonomic dysfunction. Also, increased ReHo in the left supplementary motor area/paracentral lobule was positively correlated with the rapid eye movement sleep behavior disorder screening questionnaire. FOG converters exhibited diminished FC in the basal ganglia, limbic area, and cognitive control cortex, as compared with non-converters. The prediction model combined ReHo of basal ganglia and limbic area, with PIGD score was the best predictor of FOG conversion. Conclusion: The current results suggested that abnormal ReHo and FC in the basal ganglia, limbic area, and cognitive control cortex may occur in the early stage of FOG. Basal ganglia and limbic area dysfunction combined with higher PIGD score are useful for the early recognition of FOG conversion.

Association of Cortical Gyrification With Imaging and Serum Biomarkers in Patients With Parkinson Disease.

BACKGROUND AND OBJECTIVES: Pathologic progression across the cortex is a key feature of Parkinson disease (PD). Cortical gyrification is a morphologic feature of human cerebral cortex that is tightly linked to the integrity of underlying axonal connectivity. Monitoring cortical gyrification reductions may provide a sensitive marker of progression through structural connectivity, preceding the progressive stages of PD pathology. We aimed to examine the progressive cortical gyrification reductions and their associations with overlying cortical thickness, white matter (WM) integrity, striatum dopamine availability, serum neurofilament light (NfL) chain, and CSF α-synuclein levels in PD. METHODS: This study included a longitudinal dataset with baseline (T0), 1-year (T1), and 4-year (T4) follow-ups and 2 cross-sectional datasets. Local gyrification index (LGI) was computed from T1-weighted MRI data to measure cortical gyrification. Fractional anisotropy (FA) was computed from diffusion-weighted MRI data to measure WM integrity. Striatal binding ratio (SBR) was measured from 123Ioflupane SPECT scans. Serum NfL and CSF α-synuclein levels were also measured. RESULTS: The longitudinal dataset included 113 patients with de novo PD and 55 healthy controls (HCs). The cross-sectional datasets included 116 patients with relatively more advanced PD and 85 HCs. Compared with HCs, patients with de novo PD showed accelerated LGI and FA reductions over 1-year period and a further decline at 4-year follow-up. Across the 3 time points, the LGI paralleled and correlated with FA (p = 0.002 at T0, p = 0.0214 at T1, and p = 0.0037 at T4) and SBR (p = 0.0095 at T0, p = 0.0035 at T1, and p = 0.0096 at T4) but not with overlying cortical thickness in patients with PD. Both LGI and FA correlated with serum NfL level (LGI: p < 0.0001 at T0, p = 0.0043 at T1; FA: p < 0.0001 at T0, p = 0.0001 at T1) but not with CSF α-synuclein level in patients with PD. In the 2 cross-sectional datasets, we revealed similar patterns of LGI and FA reductions and associations between LGI and FA in patients with more advanced PD. DISCUSSION: We demonstrated progressive reductions in cortical gyrification that were robustly associated with WM microstructure, striatum dopamine availability, and serum NfL level in PD. Our findings may contribute biomarkers for PD progression and potential pathways for early interventions of PD.

Energy spectrum CT index-based machine learning model predicts the effect of intravenous thrombolysis in lower limbs.

To develop a noninvasive machine learning (ML) model based on energy spectrum computed tomography venography (CTV) indices for preoperatively predicting the effect of intravenous thrombolytic treatment in lower limbs. A total of 3492 slices containing thrombus regions from 58 veins in lower limbs in a cohort of 18 patients, divided in good and poor thrombolysis prognosis groups, were analyzed. Key indices were selected by univariate analysis and Pearson correlation coefficient test. A support vector machine classifier-based model was developed through ten-fold cross validation. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness at both per-slice and per-vessel levels. Continuous variables and categorical variables were compared between good and poor thrombolysis prognosis group by Mann-Whitney U-test and chi-square test, respectively. A nomogram was built by integrating clinical factors and the energy spectrum CTV index-based score calculated by the model. Six indices selected from 192 indices were used to build the predictive model. The ML model achieved area under the curves (AUCs) of 0.838 and 0.767 [95% CI (confidence interval), 0.825-0.850, 0.752-0.781] in the training and validation datasets at the per-slice level, and the per-vessel level AUCs were 0.945 and 0.876 (95% CI, 0.852-0.988, 0.763-0.948) in the training and validation datasets, respectively. The nomogram showed better performance with the per-vessel level AUC, accuracy, sensitivity and specificity, yielding 0.901(95% CI, 0.793-0.964), 86.2%, 87.9% and 84.0% in the validation dataset, respectively. There was no significant difference in the vessel distribution between good and poor thrombolysis prognosis groups (chi-square test, p = 0.671). The energy spectrum CTV index-based ML model achieved favorable effectiveness in predicting the outcome of vessel-level intravenous thrombolysis. A nomogram integrating clinical factors, and risk score calculated by the developed model showed improved performance and had potential to be used as a noninvasive preoperative tool for clinicians.

Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism.

Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899-1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible.

Organophotoredox and Hydrogen Atom Transfer Cocatalyzed C-H Alkylation of Quinoxalin-2(1H)-ones with Aldehydes, Amides, Alcohols, Ethers, or Cycloalkanes.

Described is a mild method that merges organophotoredox catalysis with hydrogen atom transfer to enable C-H alkylation of quinoxalin-2(1H)-ones with feedstock aldehydes, amides, alcohols, ethers, or cycloalkanes. This reaction occurred under environmentally benign and external oxidant-free reaction conditions, providing a general and sustainable access to various C3-alkylated quinoxalinone derivatives with broad substituent diversity and good functional group compatibility.

Soil bacterial communities of three types of plants from ecological restoration areas and plant-growth promotional benefits of Microbacterium invictum (strain X-18).

Microbial-assisted phytoremediation promotes the ecological restoration of high and steep rocky slopes. To determine the structure and function of microbial communities in the soil in response to changes in soil nutrient content, the bacterial communities of rhizospheric soil from three types of plants, i.e., Robinia pseudoacacia, Pinus massoniana, and Cynodon dactylon, were analyzed using Illumina sequencing technology. High-quality sequences were clustered at the 97% similarity level. The dominant genera were found to be RB41, Gemmatimonas, Sphingomonas, Bradyrhizobium, and Ellin6067. The Tukey HSD (honestly significant difference) test results showed that the abundance of RB41 and Gemmatimonas were significantly different among three types of plants (p < 0.01). The relative abundances of RB41 (13.32%) and Gemmatimonas (3.36%) in rhizospheric soil samples from R. pseudoacacia were significantly higher than that from P. massoniana (0.16 and 0.35%) and C. dactylon (0.40 and 0.82%), respectively. The soil chemical properties analyses suggested that significant differences in rhizospheric soil nutrient content among the three plant types. Especially the available phosphorus, the content of it in the rhizospheric soil of R. pseudoacacia was about 280% (P. massoniana) and 58% (C. dactylon) higher than that of the other two plants, respectively. The soil bacterial communities were further studied using the correlation analysis and the Tax4Fun analysis. A significant and positive correlation was observed between Gemmatimonas and soil nutrient components. Except total nitrogen, the positive correlation between Gemmatimonas and other soil nutrient components was above 0.9. The outcomes of these analyses suggested that Gemmatimonas could be the indicator genus in response to changes in the soil nutrient content. Besides, the genes involved in metabolism were the major contributor to soil nutrients. This study showed that soil nutrients affect the soil bacterial community structure and function. In addition, pot experiments showed that Microbacterium invictum X-18 isolated from the rhizospheric soil of R. pseudoacacia significantly improved soil nutrient content and increased R. pseudoacacia growth. A significant increase in the numbers of nodules of R. pseudoacacia and an increase of 28% in plant height, accompanied by an increase of 94% in available phosphorus was measured in the M. invictum X-18 treatment than the control treatment.

The value of diffusion kurtosis imaging and intravoxel incoherent motion quantitative parameters in predicting synchronous distant metastasis of rectal cancer.

BACKGROUND: The incidence and mortality rate of rectal cancer are still high, the metastasis of rectal cancer are main causes of death. The control of the distant metastasis is one of the main concerns in the treatment of locally advanced rectal cancer, but there are few studies on predicting synchronous distant metastasis (SDM) of rectal cancer. METHOD: The data of patients with rectal adenocarcinoma confirmed by endoscopic biopsy or postoperative pathology from September 2015 to May 2020 in hospital A (center 1) and hospital B (center 2) were analyzed retrospectively, including age, sex, carcinoembryonic antigen, carbohydrate antigen 19-9, tumor location, tumor length, image staging and characteristics. The average age of the 169 patients consisting of 105 males and 64 females in study is 61.2 years. All patients underwent rectal routine rectal MRI, DKI and IVIM examinations on a 3.0-T scanner. Two radiologists sketched regions of interest (ROIs) on b = 1000 s/mm2 DKI and IVIM images to obtain quantitative parameters with FireVoxel manually. We evaluated the difference of histogram analysis, clinical and image data between SDM group and non-SDM group, and evaluated the efficacy of each index in predicting SDM of rectal cancer. RESULTS: The 90th percentile of f values in the SDM group is lower than that in the non-SDM group (29.4 ± 8.4% vs. 35 ± 17.8%, P = 0.005). CA19-9 in the SDM group is higher than that in the non-SDM group (P = 0.003). Low and high rectal cancer are more likely to develop SDM than middle rectal cancer (P = 0.05 and P = 0.047). The combination of these three indexes has a greater area under the curve (AUC) than any one index (0.801 vs. 0.685 (f (90th percentile)) and 0.627 (CA19-9), P = 0.0075 and 0.0058, respectively), and its specificity and sensitivity are 80.0% and 71.6%, respectively. When this combination is incorporated into the predictive nomogram model, the c-index is 0.801 (95% confidence interval (CI): 0.730-0.871). CONCLUSIONS: IVIM quantitative parameters combine with CA19-9 and tumor location can better predict the risk of SDM of rectal cancer.

Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer.

Purpose: To develop deep learning (DL) models based on multiphase dual-energy spectral CT for predicting lymph nodes metastasis preoperatively and noninvasively in papillary thyroid cancer patients. Methods: A total of 293 lymph nodes from 78 papillary thyroid cancer patients who underwent dual-energy spectral CT before lymphadenectomy were enrolled in this retrospective study. The lymph nodes were randomly divided into a development set and an independent testing set following a 4:1 ratio. Four single-modality DL models based on CT-A model, CT-V model, Iodine-A model and Iodine-V model and a multichannel DL model incorporating all modalities (Combined model) were proposed for the prediction of lymph nodes metastasis. A CT-feature model was also built on the selected CT image features. The model performance was evaluated with respect to discrimination, calibration and clinical usefulness. In addition, the diagnostic performance of the Combined model was also compared with four radiologists in the independent test set. Results: The AUCs of the CT-A, CT-V, Iodine-A, Iodine-V and CT-feature models were 0.865, 0.849, 0.791, 0.785 and 0.746 in the development set and 0.830, 0.822, 0.744, 0.739 and 0.732 in the testing set. The Combined model had outperformed the other models and achieved the best performance with AUCs yielding 0.890 in the development set and 0.865 in the independent testing set. The Combined model showed good calibration, and the decision curve analysis demonstrated that the net benefit of the Combined model was higher than that of the other models across the majority of threshold probabilities. The Combined model also showed noninferior diagnostic capability compared with the senior radiologists and significantly outperformed the junior radiologists, and the interobserver agreement of junior radiologists was also improved after artificial intelligence assistance. Conclusion: The Combined model integrating both CT images and iodine maps of the arterial and venous phases showed good performance in predicting lymph nodes metastasis in papillary thyroid cancer patients, which could facilitate clinical decision-making.

Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features.

Purpose: This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods: Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results: After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion: The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Tensile Test Coupled with an EBSD Study of a GH4169 Ring Rolled Product.

An in situ tensile test of the ring-rolled GH4169 alloy is performed to investigate the plastic deformation behavior at the micro level. Slip system activations are identified by slip traces captured by a scanning electron microscope and lattice orientation data acquired by electron backscattered diffraction. Our results demonstrated that the fraction of low-angle grain boundaries gradually increased upon tensile deformation, and the misorientation evolution in the grain interior was severely inhomogeneous. The Schmid factors at the grains of interest are calculated for comparison with the actual activated slip systems. Most of the slip system activation coincides with the Schmid law, as opposed to the initiation of other potential slip systems at some grains.

A Patient With Erdheim-Chester Disease Presenting With Intestinal Obstruction as the Initial Symptom: A Case Report.

Erdheim-Chester disease (ECD) is a rare and systemic non-Langerhans cell histiocytosis. Recently, ECD was classified as an inflammatory medullary tumor that affects a diverse group of organ systems. The purpose of this report is to present the radiological features of this disease in a 51-year-old man with intestinal obstruction as the initial presentation. In this case, X-ray computed tomography (CT) and emission computed tomography (ECT) clearly showed lesions in various systems, especially in the skeletal images. The survival benefit of treatment with interferon α (IFN-α) and BRAF inhibitors is well established, while other treatments focus on symptom relief.