Codelivery of anti-CD47 antibody and chlorin e6 using a dual pH-sensitive nanodrug for photodynamic immunotherapy of osteosarcoma.

Osteosarcoma is a malignant tumor originating from bone tissue that progresses rapidly and has a poor patient prognosis. Immunotherapy has shown great potential in the treatment of osteosarcoma. However, the immunosuppressive microenvironment severely limits the efficacy of osteosarcoma treatment. The dual pH-sensitive nanocarrier has emerged as an effective antitumor drug delivery system that can selectively release drugs into the acidic tumor microenvironment. Here, we prepared a dual pH-sensitive nanocarrier, loaded with the photosensitizer Chlorin e6 (Ce6) and CD47 monoclonal antibodies (aCD47), to deliver synergistic photodynamic and immunotherapy of osteosarcoma. On laser irradiation, Ce6 can generate reactive oxygen species (ROS) to kill cancer cells directly and induces immunogenic tumor cell death (ICD), which further facilitates the dendritic cell maturation induced by blockade of CD47 by aCD47. Moreover, both calreticulin released during ICD and CD47 blockade can accelerate phagocytosis of tumor cells by macrophages, promote antigen presentation, and eventually induce T lymphocyte-mediated antitumor immunity. Overall, the dual pH-sensitive nanodrug loaded with Ce6 and aCD47 showed excellent immune-activating and anti-tumor effects in osteosarcoma, which may lay the theoretical foundation for a novel combination model of osteosarcoma treatment.

Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis.

OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA). MATERIAL & METHODS: A total of 485 patients diagnosed with sacroiliitis related to axSpA (n = 288) or non-sacroiliitis (n = 197) by sacroiliac joint (SIJ) MRI between May 2018 and October 2022 were retrospectively included in this study. The patients were randomly divided into training (n = 388) and testing (n = 97) cohorts. Data were collected using three MRI scanners. We applied a convolutional neural network (CNN) called 3D U-Net for automated SIJ segmentation. Additionally, three CNNs (ResNet50, ResNet101, and DenseNet121) were used to diagnose axSpA-related sacroiliitis using a single modality. The prediction results of all the CNN models across different modalities were integrated using a stacking method based on different algorithms to construct ensemble models, and the optimal ensemble model was used as DLR signature. A combined model incorporating DLR signature with clinical factors was developed using multivariable logistic regression. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Automated deep learning-based segmentation and manual delineation showed good correlation. ResNet50, as the optimal basic model, achieved an area under the curve (AUC) and accuracy of 0.839 and 0.804, respectively. The combined model yielded the highest performance in diagnosing axSpA-related sacroiliitis (AUC: 0.910; accuracy: 0.856) and outperformed the best ensemble model (AUC: 0.868; accuracy: 0.825) (all P < 0.05). Moreover, the DCA showed good clinical utility in the combined model. CONCLUSION: We developed a diagnostic model for axSpA-related sacroiliitis by combining the DLR signature with clinical factors, which resulted in excellent diagnostic performance.

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.

The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one's life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, partial dependence plot, individual conditional expectation, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

Effects of Cold Rolling Reduction Rate on the Microstructure and Properties of Cu-1.16Ni-0.36Cr Alloy after Thermo-Mechanical Treatment.

In this paper, a Cu-Ni-Cr alloy was prepared by adding a Ni-Cr intermediate alloy to copper. The effects of the cold rolling reduction rate on the microstructure and properties of the Cu-1.16Ni-0.36Cr alloy after thermo-mechanical treatment were studied. The results show that the tensile strength of the alloy increased while the electrical conductivity slightly decreased with an increase of the cold rolling reduction rate. At a rolling strain of 3.2, the tensile strength was 512.0 MPa and the conductivity was 45.5% IACS. At a rolling strain of 4.3, the strength further increased to 536.1 MPa and the conductivity decreased to 41.9% IACS. The grain size and dislocation density decreased with an increase of the reduction rate in the thermo-mechanical treatment. However, when the rolling strain reached 4.3, the recrystallization degree of the alloy increased due to an accumulation of the dislocation density and deformation energy, resulting in a slight increase in the grain size and a decrease in the dislocation density. The texture strength of the brass increased due to the induced shear band, with an increase of the cold rolling reduction rate. The reduction rate promoted a uniform distribution of nano-scale Cr precipitates and further enhanced the strength via precipitation strengthening.

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level.

(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model's performance. Pearson's correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level.

Effects of biochar and zero valent iron on the bioavailability and potential toxicity of heavy metals in contaminated soil at the field scale.

Heavy metals (HMs) such as copper, nickel and chromium are toxic, so soil contaminated with these metals is of great concern. In situ HM immobilization by adding amendments can decrease the risk of contaminants being released. A five-month field-scale study was performed to assess how different doses of biochar and zero valent iron (ZVI) affect HM bioavailability, mobility, and toxicity in contaminated soil. The bioavailabilities of HMs were determined and ecotoxicological assays were performed. Adding 5 % biochar, 10 % ZVI, 2 % biochar + 1 % ZVI, and 5 % biochar + 10 % ZVI to soil decreased Cu, Ni and Cr bioavailability. Metals were most effectively immobilized by adding 5 % biochar + 10 % ZVI, and the extractable Cu, Ni, and Cr contents were 60.9 %, 66.1 % and 38.9 % lower, respectively, for soil with 5 % biochar + 10 % ZVI added than unamended soil. The extractable Cu, Ni, and Cr contents were 64.2 %, 59.7 % and 16.7 % lower, respectively, for soil with 2 % biochar + 1 % ZVI added than unamended soil. Experiments using wheat, pak choi and beet seedlings were performed to assess the remediated soil toxicity. Growth was markedly inhibited in seedlings grown in extracts of soil with 5 % biochar, 10 % ZVI, or 5 % biochar + 10 % ZVI added. More growth occurred in wheat and beet seedlings after 2 % biochar + 1 % ZVI treatment than the control, possibly because 2 % biochar + 1 % ZVI simultaneously decreased the extractable HM content and increased the soluble nutrient (carbon and Fe) content of the soil. A comprehensive risk assessment indicated that adding 2 % biochar + 1 % ZVI gave optimal remediation at the field scale. Using ecotoxicological methods and determining the bioavailabilities of HMs can allow remediation methods to be identified to efficiently and cost-effectively decrease the risks posed by multiple metals in soil at contaminated sites.

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge.

Background: Knee osteoarthritis (OA) is harmful to people's health. Effective treatment depends on accurate diagnosis and grading. This study aimed to assess the performance of a deep learning (DL) algorithm based on plain radiographs in detecting knee OA and to investigate the effect of multiview images and prior knowledge on diagnostic performance. Methods: In total, 4,200 paired knee joint X-ray images from 1,846 patients (July 2017 to July 2020) were retrospectively analyzed. Kellgren-Lawrence (K-L) grading was used as the gold standard for knee OA evaluation by expert radiologists. The DL method was used to analyze the performance of anteroposterior and lateral plain radiographs combined with prior zonal segmentation to diagnose knee OA. Four groups of DL models were established according to whether they adopted multiview images and automatic zonal segmentation as the DL prior knowledge. Receiver operating curve analysis was used to assess the diagnostic performance of 4 different DL models. Results: The DL model with multiview images and prior knowledge obtained the best classification performance among the 4 DL models in the testing cohort, with a microaverage area under the receiver operating curve (AUC) and macroaverage AUC of 0.96 and 0.95, respectively. The overall accuracy of the DL model with multiview images and prior knowledge was 0.96 compared to 0.86 for an experienced radiologist. The combined use of anteroposterior and lateral images and prior zonal segmentation affected diagnostic performance. Conclusions: The DL model accurately detected and classified the K-L grading of knee OA. Additionally, multiview X-ray images and prior knowledge improved classification efficacy.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

Homocysteine induces ferroptosis in endothelial cells through the systemXc-/GPX4 signaling pathway.

OBJECTIVES: To investigate whether ferroptosis is involved in HCY-induced endothelial injury and the possible mechanism of HCY-induced ferroptosis. METHODS: EA. hy926 cells were cultured in vitro. Cells were intervened using HCY and Fer-1. The cells were divided into Control groups, HCY (4 mM), HCY (8 mM), HCY + Fer-1 (4 mM HCY + 0.5/2.5/5 µM Fer-1). CCK-8 assay was used to detect cell viability; Flow Cytometry was used to detect cellular Lip-ROS, TBA and Microplate assay was used to detect MDA&GSH, Western blot was used to detect the expression of ferroptosis-related proteins GPX4 and SLC7A11. RESULTS: HCY can inhibited the proliferation of EA. hy926 cells in a time- and concentration-dependent manner; Fer-1 inhibits HCY-induced ferroptosis in EA.hy926 cells in a concentration-dependent manner; Compared with the control group, the cell viability and GSH content in the HCY group was significantly decreased (p < 0.05), and the Lip-ROS and MDA were significantly increased (p < 0.05); After co-culture of HCY and Fer-1, compared with the HCY (4 mM) group, the cell viability and GSH content in the co-culture group were significantly increased (p < 0.05), and the Lip-ROS and MDA were significantly decreased (p < 0.05) in a concentration-dependent manner; Western blotting results showed that the protein expression levels of ferroptosis-related proteins GPX4 and SLC7A11 in each experimental were significantly decreased after HCY treatment (p < 0.05), and Fer-1 could significantly reverse this effect. CONCLUSIONS: (1) HCY can induce ferroptosis in vascular endothelial cells. (2) HCY may induce vascular endothelial cell ferroptosis through the system Xc-GSH-GPX4 signaling pathway.

[Evaluation of Energy Saving and Carbon Reduction Effect of Air Pollution Prevention and Control Action Plan and Innovation Intermediary Effect].

In order to evaluate the effect and mechanism of energy saving and carbon reduction of the Air Pollution Prevention and Control Action Plan (the Policy), on the basis of measuring the energy consumption and CO2 emissions of GDP per unit area in 281 prefecture-level cities and above from 2003 to 2017, the influence, intermediary effect of innovation, and urban heterogeneity of the Policy on energy saving and carbon reduction were explored by using a difference-in-difference model. The results showed that:① the Policy promoted a significant reduction of 17.60% in the energy consumption intensity and 19.99% in the carbon emission intensity in the whole sample city. Based on a series of robustness tests, such as the parallel trend test, overcomed endogenous and placebo, dynamic time window and counterfactual, difference-in-difference-in-differences, and PSM-DID estimation, the above conclusions were still valid. ② Mechanism analysis showed that the Policy achieved energy saving and carbon reduction through the direct innovation intermediary effect of green invention patents as the carrier, and the indirect innovation mediation effect of the industrial structure upgrading effect caused by innovation achieved an energy-saving effect. ③ Heterogeneity analysis showed that the energy saving and carbon reduction rate of the Policy for coal-consuming provinces was 0.86% and 3.25% higher than that of non-coal-consuming provinces. The carbon reduction in the old industrial base city was 36.43% higher than that in the non-old industrial base, but the energy saving effect was 8.93% lower than that of the non-old industrial base. The range of energy saving and carbon reduction in non-resource-based cities was 31.30% and 74.95% higher than that in resource-based cities, respectively. ④ The results showed that it was necessary to strengthen the innovation investment and industrial structure upgrading in key areas such as big coal-consumption provinces, old industrial base cities, and resource-based cities, so as to give full play to the energy saving and carbon reduction effect of the Policy.

Sex-specific bone and muscular morphological features in ischiofemoral impingement: A three-dimensional study.

The study aimed to investigate how hip bone and muscular morphology features differ between ischiofemoral impingement (IFI) patients and healthy subjects among males and females. Three-dimensional models were reconstructed based on magnetic resonance imaging images from IFI patients and healthy subjects of different sexes. Bone morphological parameters and the cross-sectional area of the hip abductors were measured. The diameter and angle of the pelvis were compared between patients and healthy subjects. Bone parameters of the hip and cross-sectional area of the hip abductors were compared between affected and healthy hips. The comparison results of some parameters were significant for females but not males. For females, the comparison results of pelvis parameters showed that the anteroposterior diameter of the pelvic inlet (p = 0.001) and intertuberous distance (p < 0.001) were both larger in IFI patients than in healthy subjects. Additionally, the comparison results of hip parameters showed that the neck shaft angle (p < 0.001) and the cross-sectional area of the gluteus medius (p < 0.001) and gluteus minimus (p = 0.005) were smaller, while the cross-sectional area of the tensor fasciae latae (p < 0.001) was significantly larger in affected hips. Morphological changes in IFI patients demonstrated sexual dimorphism, including bone and muscular morphology. Differences in the anteroposterior diameter of the pelvic inlet, intertuberous distance, neck shaft angle, gluteus medius, and gluteus minimus may explain why females are more susceptible to IFI.

Application of metagenomic next-generation sequencing in patients with infective endocarditis.

Objectives: Metagenomic next-generation sequencing (mNGS) technology is helpful for the early diagnosis of infective endocarditis, especially culture-negative infective endocarditis, which may guide clinical treatment. The purpose of this study was to compare the presence of culture-negative infective endocarditis pathogens versus culture-positive ones, and whether mNGS test results could influence treatment regimens for patients with routine culture-negative infective endocarditis. Methods: The present study enrolled patients diagnosed with infective endocarditis and tested for mNGS in the First Affiliated Hospital of Zhengzhou University from February 2019 to February 2022 continuously. According to the culture results, patients were divided into culture-negative group (Group CN, n=18) and culture-positive group (Group CP, n=32). The baseline characteristics, clinical data, pathogens, 30 day mortality and treatment regimen of 50 patients with infective endocarditis were recorded and analyzed. Results: Except for higher levels of PCT in the Group CN [0.33 (0.16-2.74) ng/ml vs. 0.23 (0.12-0.49) ng/ml, P=0.042], there were no significant differences in the basic clinical data and laboratory examinations between the two groups (all P>0.05). The aortic valve and mitral valve were the most involved valves in patients with infective endocarditis (aortic valve involved: Group CN 10, Group CP 16; mitral valve involved: Group CN 8, Group CP 21; P>0.05) while 9 patients had multiple valves involved (Group CN 2, Group CP 7; P>0.05). The detection rate of non-streptococci infections in the Group CN was significantly higher than that in the Group CP (9/18 vs. 3/32, P=0.004). There was no significant difference in patients with heart failure hospitalization and all-cause death at 30 days after discharge (3 in Group CN vs. 4 in Group CP, P>0.05). It is worth noting that 10 patients with culture-negative infective endocarditis had their antibiotic regimen optimized after the blood mNGS. Conclusions: Culture-negative infective endocarditis should be tested for mNGS for early diagnosis and to guide clinical antibiotic regimen.

Ultrashort echo time magnetization transfer imaging of knee cartilage and meniscus after long-distance running.

OBJECTIVE: To assess the detection of changes in knee cartilage and meniscus of amateur marathon runners before and after long-distance running using a 3D ultrashort echo time MRI sequence with magnetization transfer preparation (UTE-MT). METHODS: We recruited 23 amateur marathon runners (46 knees) in this prospective cohort study. MRI scans using UTE-MT and UTE-T2* sequences were performed pre-race, 2 days post-race, and 4 weeks post-race. UTE-MT ratio (UTE-MTR) and UTE-T2* were measured for knee cartilage (eight subregions) and meniscus (four subregions). The sequence reproducibility and inter-rater reliability were also investigated. RESULTS: Both the UTE-MTR and UTE-T2* measurements showed good reproducibility and inter-rater reliability. For most subregions of cartilage and meniscus, the UTE-MTR values decreased 2 days post-race and increased after 4 weeks of rest. Conversely, the UTE-T2* values increased 2 days post-race and decreased after 4 weeks. The UTE-MTR values in lateral tibial plateau, central medial femoral condyle, and medial tibial plateau showed a significant decrease at 2 days post-race compared to the other two time points (p < 0.05). By comparison, no significant UTE-T2* changes were found for any cartilage subregions. For meniscus, the UTE-MTR values in medial posterior horn and lateral posterior horn regions at 2 days post-race were significantly lower than those at pre-race and 4 weeks post-race (p < 0.05). By comparison, only the UTE-T2* values in medial posterior horn showed a significant difference. CONCLUSIONS: UTE-MTR is a promising method for the detection of dynamic changes in knee cartilage and meniscus after long-distance running. KEY POINTS: • Long-distance running causes changes in the knee cartilage and meniscus. • UTE-MT monitors dynamic changes of knee cartilage and meniscal non-invasively. • UTE-MT is superior to UTE-T2* in monitoring dynamic changes in knee cartilage and meniscus.

Nomogram model based on radiomics signatures and age to assist in the diagnosis of knee osteoarthritis.

BACKGROUND: Knee osteoarthritis (KOA) is a common disease in the elderly. An effective method for accurate diagnosis could affect the management and prognosis of patients. OBJECTIVES: To develop a nomogram model based on X-ray imaging data and age, and to evaluate its effectiveness in the diagnosis of KOA. METHODS: A total of 4403 knee X-rays from 1174 patients (July 2017 to November 2018) were retrospectively analyzed. Radiomics features were extracted and selected from the X-ray image data to quantify the phenotypic characteristics of the lesion region. Feature selection was performed in three steps to enable the derivation of robust and effective radiomics signatures. Then, logistic regression (LR), support vector machine (SVM) AdaBoost, gradient boosting decision tree (GBDT), and multi-layer perceptron (MLP) was adopted to verify the performance of radiomics signatures. In addition, a nomogram model combining age with radiomics signatures was constructed. At last, receiver operating characteristic (ROC) curve, calibration and decision curves were used to evaluate the discriminative performance. RESULTS: The LR model has the best classification performance among the four radiomics models in testing cohort (LR AUC vs. SVM AUC: 0.843 vs. 0.818, DeLong test P = 0.0024; LR AUC vs. GBDT AUC: 0.843 vs. 0.821, P = 0.0028; LR AUC vs. MLP AUC: 0.843 vs. 0.822, P = 0.0019). The nomogram model achieved better predictive efficacy than the radiomics model in testing cohort compared to radiomics models although the statistical difference was not significant (Nomogram AUC vs. Radiomics AUC: 0.847 vs. 0.843, P = 0.06). The decision curve analysis revealed that the constructed nomogram had clinical usefulness. CONCLUSION: The nomogram model combining radiomics signatures with age has good performance for the accurate diagnosis of KOA and may help to improve clinical decision-making.

Comprehensive assessment of osteoporosis in lumbar spine using compositional MR imaging of trabecular bone.

OBJECTIVES: To comprehensively assess osteoporosis in the lumbar spine, a compositional MR imaging technique is proposed to quantify proton fractions for all the water components as well as fat in lumbar vertebrae measured by a combination of a 3D short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) MRI and IDEAL-IQ. METHODS: A total of 182 participants underwent MRI, quantitative CT, and DXA. Lumbar collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), total water proton fraction (TWPF), bone mineral density (BMD), and T-score were calculated in three vertebrae (L2-L4) for each subject. The correlations of the CBWPF, FWPF, and TWPF with BMD and T-score were investigated respectively. A comprehensive diagnostic model combining all the water components and clinical characteristics was established. The performances of all the water components and the comprehensive diagnostic model to discriminate between normal, osteopenia, and osteoporosis cohorts were also evaluated using receiver operator characteristic (ROC). RESULTS: The CBWPF showed strong correlations with BMD (r = 0.85, p < 0.001) and T-score (r = 0.72, p < 0.001), while the FWPF and TWPF showed moderate correlations with BMD (r = 0.65 and 0.68, p < 0.001) and T-score (r = 0.47 and 0.49, p < 0.001). The high area under the curve values obtained from ROC analysis demonstrated that CBWPF, FWPF, and TWPF have the potential to differentiate the normal, osteopenia, and osteoporosis cohorts. At the same time, the comprehensive diagnostic model shows the best performance. CONCLUSIONS: The compositional MRI technique, which quantifies CBWPF, FWPF, and TWPF in trabecular bone, is promising in the assessment of bone quality. KEY POINTS: • Compositional MR imaging technique is able to quantify proton fractions for all the water components (i.e., collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), and total water proton fraction (TWPF)) in the human lumbar spine. • The biomarkers derived from the compositional MR imaging technique showed moderate to high correlations with bone mineral density (BMD) and T-score and showed good performance in distinguishing people with different bone mass. • The comprehensive diagnostic model incorporating CBWPF, FWPF, TWPF, and clinical characteristics showed the highest clinical diagnostic capability for the assessment of osteoporosis.

Analysis of influencing factors for prognosis of patients with ventricular septal perforation: A single-center retrospective study.

Background: Ventricular septal rupture (VSR) is a type of cardiac rupture, usually complicated by acute myocardial infarction (AMI), with a high mortality rate and often poor prognosis. The aim of our study was to investigate the factors influencing the long-term prognosis of patients with VSR from different aspects, comparing the evaluation performance of the Gensini score, Sequential Organ Failure Assessment (SOFA) score and European Heart Surgery Risk Assessment System II (EuroSCORE II) score systems. Methods: This study retrospectively enrolled 188 patients with VSR between Dec 9, 2011 and Nov 21, 2021at the First Affiliated Hospital of Zhengzhou University. All patients were followed up until Jan 27, 2022 for clinical data, angiographic characteristics, echocardiogram outcomes, intraoperative, postoperative characteristics and major adverse cardiac events (MACEs) (30-day mortality, cardiac readmission). Cox proportional hazard regression analysis was used to explore the predictors of long-term mortality. Results: The median age of 188 VSR patients was 66.2 ± 9.1 years and 97 (51.6%) were males, and there were 103 (54.8%) patients in the medication group, 34 (18.1%) patients in the percutaneous transcatheter closure (TCC) group, and 51 (27.1%) patients in the surgical repair group. The average follow-up time was 857.4 days. The long-term mortality of the medically managed group, the percutaneous TCC group, and the surgical repair group was 94.2, 32.4, and 35.3%, respectively. Whether combined with cardiogenic shock (OR 0.023, 95% CI 0.001-0.054, P = 0.019), NT-pro BNP level (OR 0.027, 95% CI 0.002-0.34, P = 0.005), EuroSCORE II (OR 0.530, 95% CI 0.305-0.918, P = 0.024) and therapy group (OR 3.518, 95% CI 1.079-11.463, P = 0.037) were independently associated with long-term mortality in patients with VSR, and this seems to be independent of the therapy group. The mortality rate of surgical repair after 2 weeks of VSR was much lower than within 2 weeks (P = 0.025). The cut-off point of EuroSCORE II was determined to be 14, and there were statistically significant differences between the EuroSCORE II < 14 group and EuroSCORE II≥14 group (HR = 0.2596, 95%CI: 0.1800-0.3744, Logrank P < 0.001). Conclusion: Patients with AMI combined with VSR have a poor prognosis if not treated surgically, surgical repair after 2 weeks of VSR is a better time. In addition, EuroSCORE II can be used as a scoring system to assess the prognosis of patients with VSR.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level. METHODS: A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m2 ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models. RESULTS: The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05). CONCLUSION: A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.

Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma.

PURPOSE: This study aimed to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) for prognosis evaluation in nasopharyngeal carcinoma in order to provide further information for clinical decision making and intervention. METHODS: A total of 154 patients with untreated NPC confirmed by pathological examination were enrolled, and the pretreatment magnetic resonance image (MRI)-including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI)-was collected. The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. Five models, namely model 1 (DWI + ADC), model 2 (T2WI + CE-T1WI), model 3 (DWI + ADC + T2WI), model 4 (DWI + ADC + CE-T1WI), and model 5 (DWI + ADC + T2WI + CE-T1WI), were constructed. The average area under the curve (AUC) of the validation set was determined in order to compare the predictive efficacy for prognosis evaluation. RESULTS: After adjusting the parameters, the RF machine learning models based on extracted imaging features from different sequence combinations were obtained. The invalidation sets of model 1 (DWI + ADC) yielded the highest average AUC of 0.80 (95% CI: 0.79-0.81). The average AUCs of the model 2, 3, 4, and 5 invalidation sets were 0.72 (95% CI: 0.71-0.74), 0.66 (95% CI: 0.64-0.68), 0.74 (95% CI: 0.73-0.75), and 0.75 (95% CI: 0.74-0.76), respectively. CONCLUSION: A radiomics model derived from the MRI DWI of patients with nasopharyngeal carcinoma was generated in order to evaluate the risk of recurrence and metastasis. The model based on MRI DWI can provide an alternative approach for survival estimation, and can reveal more information for clinical decision-making and intervention.