IntroductionThis study aimed to evaluate the impact of varying slice thickness on quantitative values using the Magnetic Resonance Image Compilation (MAGiC) sequence. MethodsIn this retrospective study, 23 healthy subjects underwent the MAGiC sequence (at 3.0 T) with three slice thicknesses: 3 mm (TH3), 4 mm (TH4), and 5 mm (TH5). The T1, T2, and PD values were measured in various knee joint cartilage regions by two experienced radiologists, including the lateral femoral condyle (LFC), lateral tibial plateau (LTP), medial femoral condyle (MFC), medial tibial plateau (MTP), patella (PAT), and trochlea (TRO). The effects of varying slice thicknesses (TH4 vs. TH3 and TH5 vs. TH3) were analyzed using paired t-tests or Wilcoxon signed rank tests, with statistical significance set at P < 0.025. Intra-rater and inter-rater reliability were also assessed. ResultsMeasurements of T1, T2, and PD values demonstrated high intra- and inter-rater reliability. Minimal differences were observed across slice thicknesses for T1WI, T2WI, and PDWI images. T2 and PD values showed little variation, while T1 mapping revealed significant differences. T2 values were consistent across regions, except for the LFC. DiscussionTH4 and TH5 can replace TH3 for knee joint scanning while reducing scan time, with minimal differences in anatomical depiction across sequences. MAGiC technology significantly improves efficiency by acquiring quantitative data in a single scan, demonstrating stable T2 values unaffected by slice thickness, though T1 and PD values are thickness-dependent. This technique holds clinical value for cartilage injury assessment but requires further research on the applicability of multiplanar imaging. ConclusionT2 values obtained with the MAGiC sequence are stable across TH3, TH4, and TH5, allowing for reliable cartilage T2 quantification using TH5 to reduce patient scan time.

BACKGROUND:Lumbar disc herniation (LDH) causes compositional alterations within compressed nerve roots, resulting in low back pain (LBP). The ultrashort echo time magnetization transfer technique (UTE-MT) facilitates assessment of macromolecular changes in collagen- or myelin-rich tissues in nerve roots. PURPOSE:To assess lumbar nerve root composition in LDH using UTE-MT. STUDY TYPE:Prospective. POPULATION:One hundred and seventy-six participants (age range, 20-89; 72 females) with LDH. FIELD STRENGTH/SEQUENCE:3T/UTE-MT, Carr-Purcell-Meiboom-Gill (CPMG). ASSESSMENT:UTE-MT ratio (UTE-MTR) and T2 value in compressed nerve roots (determined on axial T2) were evaluated by UTE-MT and CPMG in LDH patients (L4/5-L5/S1). Additionally, pain and functionality were evaluated using the visual analog scale (VAS) and Oswestry Disability Index (ODI). STATISTICAL TESTS:Linear regression and Bland-Altman assessed UTE-MT reproducibility. One-way ANOVA assessed the statistical significance of UTE-MTR and T2 measures between compressed and intact nerve roots. ROC and DCA evaluated diagnostic performance and clinical value of UTE-MTR and T2 in discriminating between compressed and intact nerve roots. Linear regression correlated UTE-MTR and T2 with pain and functionality scores. The p value < 0.05 was considered significant. RESULTS:Significant increases in UTE-MTR and decreases in T2 values in compressed nerve roots compared to intact ones. High AUC values for UTE-MTR (0.912 at L4/5 and 0.900 at L5/S1) highlighted its superior ability to distinguish between compressed and intact nerve roots, outperforming T2 (AUCs of 0.840 and 0.790, respectively) in cohort discrimination. Strong significant positive correlations were found between UTE-MTR and VAS (R 2 = 0.63) and ODI (R 2 = 0.62), while T2 values showed moderate significant negative correlations with VAS (R 2 = 0.32) and ODI (R 2 = 0.32) for the measurement of the most severely compressed nerve roots (determined on axial T2). DATA CONCLUSION:UTE-MT technique can detect macromolecular alterations in the compressed nerve roots of patients diagnosed with LDH. LEVEL OF EVIDENCE: 1: TECHNICAL EFFICACY:Stage 2.

BACKGROUND:Ankle cartilage is prone to degeneration due to overuse. Developing a non-invasive MRI technique to detect early running-induced lesions enables timely intervention. PURPOSE:To evaluate the value of the ultrashort echo time magnetization transfer (UTE-MT) sequence in monitoring tibiotalar cartilage changes in amateur marathon runners before and after a marathon. STUDY TYPE:Prospective. SUBJECTS:Thirty amateur marathon runners (25 males, 5 females; range: 24-50 years). SEQUENCE:3D UTE-MT (gradient-echo), 3D UTE-T2* (gradient-echo). ASSESSMENT:MRI scans at three time points: 1 week pre-marathon, 2 days post-marathon, and 4 weeks post-marathon. Medial and lateral tibiotalar cartilage was subdivided into 12 subregions, consisting of anterior, middle, and posterior segments for the tibial and talus parts on each side. The UTE-MTR and UTE-T2* values were measured per subregion at each time point. STATISTICAL TESTS:Repeated measures one-way ANOVA and the Tukey test. p < 0.05 was considered statistically significant. RESULTS:Most cartilage subregions showed decreased UTE-MTR values 2 days post-marathon and increased after 4 weeks. Significant differences in UTE-MTR over time were observed in 9 subregions, including the medial and lateral anterior, middle, and posterior tibial cartilage (MTiA, MTiM, MTiP, LTiA, LTiM, LTiP), the medial and lateral posterior talus regions (MTaP, LTaP), and the medial middle talus cartilage (MTaM). Post hoc tests revealed significant UTE-MTR decreases 2 days post-marathon in all 9 regions (Rate: MTiA: -3.9%; MTiM: -2.8%; MTiP: -3.0%; MTaP: -4.5%; MTaM: -4.2%; LTiA: -3.5%; LTiM: -4.7%; LTiP: -5.8%; LTaP: -6.8%), with significant increases in MTiA (3.7%) and MTaM (4.4%) at 4 weeks. UTE-T2* values rose in most cartilage regions at 2 days post-marathon and continued increasing at 4 weeks. Only MTiP, LTiM, and LTaM showed significant changes. DATA CONCLUSION:This study demonstrates that the UTE-MT sequence enables the quantitative assessment of dynamic changes in tibiotalar joint cartilage after a marathon. LEVEL OF EVIDENCE: 2: TECHNICAL EFFICACY:Stage 1.

Background:Patellofemoral osteoarthritis (PFOA) is a common cause of anterior knee pain but is frequently overlooked on routine radiographic assessment. This study aimed to develop and internally validate a radiomics-based nomogram for diagnosing PFOA using lateral knee radiographs combined with clinical features. Methods:This retrospective multicenter study included 1,742 patients with 2,197 knees who underwent knee radiography between July 2017 and July 2020. During screening, 13 patients (15 knees) were excluded because of poor positioning or unqualified image quality, leaving 1,729 patients (2,182 knees) for analysis. PFOA was identified on lateral radiographs according to the Framingham criteria. Radiomic features were extracted from manually delineated rectangular regions of interest (ROIs) on lateral knee radiographs. The dataset was randomly divided into training and internal test sets at a ratio of 7:3. After feature selection, logistic regression (LR), k-nearest neighbors (KNN), and random forest (RF) models were developed and compared. A radiomics-clinical nomogram was subsequently constructed and internally validated. Results:Twenty-five radiomic features were ultimately selected for model construction. In the internal test set, the LR model achieved the best performance, with an area under the curve (AUC) of 0.773. Age was identified as an independent risk factor for PFOA. The nomogram integrating radiomic features, age, and sex showed improved diagnostic performance, with an AUC of 0.842, and demonstrated good calibration and clinical utility. External test set further demonstrated acceptable diagnostic performance of the LR model, with an AUC of 0.751. Conclusions:A radiomics-based nomogram integrating lateral knee radiographs with clinical factors showed good diagnostic performance for PFOA and may serve as a practical tool for radiographic assessment.

Articular cartilage repair remains challenging because of limited intrinsic regeneration and the need for sustained mechanical and biological support. We developed a silk fibroin (SF) scaffold combining directional freezing with HRP/H2O2-mediated enzymatic crosslinking. The resulting EF-SF hydrogel exhibited high compressive strength, limited swelling, aligned porosity, and delayed degradation. Kartogenin (KGN)-loaded PLGA nanoparticles were incorporated to form EF-SF/PK. Compared with direct KGN loading, PLGA encapsulation markedly reduced early release and prolonged KGN delivery. EF-SF/PK supported BMSC viability, proliferation, and chondrogenic gene expression, while LDH and residual peroxide measurements showed no progressive in vitro cytotoxicity. In a rabbit femoral cartilage-defect model, EF-SF/PK improved macroscopic, imaging, and histological repair over 12 weeks. These findings support a hierarchical SF scaffold that integrates mechanical stability with controlled local delivery for cartilage regeneration.

Bone marrow mesenchymal stem cells (BMSCs) possess multidirectional differentiation potential and are regarded as a promising approach for the treatment of cartilage defects. However, how to aggregate and adsorb BMSCs to the injured sites of cartilage and promote their directional differentiation into chondrocytes has always been a key issue to be solved. Here, we fabricated Kartogenin (KGN)-loaded PLGA nanobubbles (KGN-NBs) and swallowed them into BMSCs, obtaining KGN-NBs-containing BMSCs (KGN-NBs@BMSCs). Also, we designed a polylactic acid (PLA) acoustically response scaffold which can produce local sound field under low intensity ultrasound irradiation, and capture KGN-NBs@BMSCs to aggregate on the surface of scaffold. More importantly, KGN could be intracellularly released from KGN-NBs under a short burst of high-energy burst ultrasound irradiation, greatly promoting chondrogenic differentiation of BMSCs and favoring to repair articular cartilage defect. Our study provides a new strategy for the treatment of cartilage defects.

OBJECTIVES:To develop a radiomics model for identifying fresh or old vertebral compression fractures (VCFs) from CT images, thereby assisting physicians in making more effective decisions. METHODS:Patients with VCFs who underwent both CT and MRI within one week were retrospectively enrolled from June 2018 to February 2023. VCFs were categorized as subgroups according to compression grades (mild, moderate or severe) or morphology types (wedge-shaped, biconcave or crush). For each subgroup, a radiomics classification model was built based on 1834 radiomics features extracted from the training dataset. And the diagnostic performance was evaluated in the testing dataset using receiver operating characteristic (ROC). RESULTS:The radiomics model trained on the entire cohort achieved an area under ROC curve (AUC) of 0.824. A nomogram integrating radiomics feature and clinical characteristics reached an AUC of 0.897. We graded the degree of compression as mild, moderate, and severe VCFs. The best performance was observed in the severe subgroup, with an AUC of 0.927, while the AUCs for mild and moderate were 0.633 and 0.774, respectively. In the morphology subgroups, the crush-type VCFs demonstrated the best performance, achieving an AUC of 0.909, while the AUCs for wedge-shaped and biconcave were 0.841 and 0.897, respectively. CONCLUSION:The radiomics models effectively distinguished fresh and old VCFs, performing better when combined with clinical features. However, different grades and morphologies of VCFs showed distinct CT imaging patterns that could impact model performance, warranting consideration in future research and clinical applications.

To evaluate tibiotalar cartilage changes in amateur marathon runners pre- and post-marathon using 3D ultrashort echo time (UTE) bi-component analysis. Amateur runners were prospectively enrolled and underwent ankle MRI at three time points: pre-marathon, 2 days post-marathon, and 4 weeks post-marathon. UTE component analysis was used to obtain single-component values (T2*M), and bi-component values (short (T2*S) and long T2* component values (T2*L), and short T2* fractions) of cartilage. Sagittal images were analyzed by segmenting tibial and talus cartilage into 12 subregions (medial/lateral, anterior/middle/posterior). Thirty-two runners (26 men, 6 women; mean age, 39.80 ± 6.00 years) were evaluated. UTE component analysis parameters increased in most subregions after running, with T2*M increasing further at 4 weeks, while T2*S, T2*L, and short T2* fractions decreased. Repeated-measures analysis of variance (RM-ANOVA) revealed significant T2*S differences in the middle and posterior medial tibia (MTiM, MTiP), the middle medial talus (MTaM), the anterior, middle, and posterior lateral tibia (LTiA, LTiM, and LTiP), and the middle and posterior lateral talus (LTaM and LTaP) (p < 0.05). Short T2* fractions exhibited significant changes in MTiM, MTiP, MTaM, LTiM, and LTaM (RM-ANOVA, p < 0.05). MTiP, MTaP, LTiM, and LTaM showed significant T2*M changes (RM-ANOVA, p < 0.05). Only LTaM showed significant T2*L changes (Friedman’s rank test, p < 0.05). The T2*S and short T2* fractions from UTE bi-component analysis may be more sensitive than T2*M, offering a promising method for detecting dynamic changes in ankle cartilage following long-distance running. Question Long-distance running causes changes in the tibiotalar articular cartilage. Can UTE component analysis of T2* monitor dynamic changes of tibiotalar articular cartilage non-invasively? Findings T2*S and short T2* fractions of UTE bi-component analysis were superior to single-component analysis in monitoring dynamic changes in ankle cartilage. Clinical relevance This study suggests that the UTE bi-component T2* analysis detects exercise-induced cartilage changes, allowing early matrix assessment in at-risk populations and supporting prevention strategies for athletes.

BACKGROUND:Accurate evaluation of the cartilage anatomy of the knee is helpful for clinical evaluation of the source of knee pain and the classification and treatment of knee osteoarthritis (OA). This study proposes a deep learning model for segmentation of knee articular cartilage in conventional proton density fat-saturated MRI sequences to assess cartilage morphology for subsequent injury grading. METHODS:This retrospective study was conducted at two radiology centers, involving 254 knees from 254 patients who had previously undergone MRI scans. The training-internal validation cohort included 219 knees from Center 1. The external validation cohort comprised 35 knees from Center 2. Two musculoskeletal radiology experts manually annotated the cartilage regions. A 3D Res U-net model was employed for segmentation, and its performance was compared with 3D U-net and 3D V-net models. Segmentation results were evaluated using the Dice coefficient and Jaccard index. RESULTS:The 3D Res U-net model demonstrated superior segmentation performance compared to the other deep learning methods. For cartilage in the lateral femorotibial joint, medial femorotibial joint, and patellofemoral joint, the average Dice coefficients with 3D Res U-net were 0.871, 0.860, and 0.858 in internal validation and 0.846, 0.837, and 0.819 in external validation, respectively. The Jaccard index followed a similar trend. CONCLUSION:The 3D Res U-net model improves knee cartilage segmentation in conventional MR imaging, contributing to the understanding of cartilage morphology and the improvement of clinically relevant decisions.

BACKGROUND:Computed tomography is an inadequate method for detecting myocardial focal scar (MFS) due to its moderate density resolution, which is insufficient for distinguishing MFS from artificial beam-hardening (BH). Virtual monochromatic images (VMIs) of dual-energy coronary computed tomography angiography (DECCTA) provide a variety of diagnostic information with significant potential for detecting myocardial lesions. The aim of this study was to assess whether radiomics analysis in VMIs of DECCTA can help distinguish MFS from BH. METHODS:A prospective cohort of patients who were suspected with an old myocardial infarction was assembled at two different centers between Janurary 2021 and June 2024. MFS and BH segmentation and radiomics feature extraction and selection were performed on VMIs images, and four machine learning classifiers were constructed using selected strongest features. Subsequently, an independent validation was conducted, and a subjective diagnosis of the validation set was provided by an radiologist. The AUC was used to assess the performance of the radiomics models. RESULT:The training set included 57 patients from center 1 (mean age, 54 years +/- 9, 55 men), and the external validation set included 10 patients from center 2 (mean age, 59 years +/- 10, 9 men). The radiomics models exhibited the highest AUC value of 0.937 (expressed at 130 keV VMIs), while the radiologist demonstrated the highest AUC value of 0.734 (expressed at 40 keV VMIs). CONCLUSION:The integration of radiomic features derived from VMIs of DECCTA with machine learning algorithms has the potential to improve the efficiency of distinguishing MFS from BH.

PURPOSE:To study the potential advantages of phosphorus magnetic resonance spectroscopy (31P-MRS) in differentiating advanced from mild fibrosis in non-alcoholic fatty liver disease (NAFLD) and early diagnosis at high field strength MR (9.4 Tesla). METHODS:Fibrosis of normal and carbon tetrachloride (CCl4)-treated male rats was staged into: none (F0), perisinusoidal or periportal (F1), perisinusoidal and portal/periportal (F2), bridging fibrosis (F3) and cirrhosis (F4) by Sirius Red staining. The degree of steatosis and inflammatory activity were also graded based on Hematoxylin and Eosin staining. Rats were divided into different groups by different stages of fibrosis (F0, F1-2, F3-4) and laboratory blood tests were performed to verify the degree of liver injury. 31P-MRS was performed at 9.4T MR to obtain signal peaks of different phosphorus metabolites and the changes of the ratios between the peaks were observed. RESULTS:At 9.4 T, phosphoethanolamine (PE), phosphocholine (PC) and glycerophosphorylethanolamine (GPE), glycerophosphorylcholine (GPC) could be separated respectively from the peaks of phosphomonoesters (PME) and phosphodiesters (PDE), meanwhile nicotinamide adenine dinucleotide phosphate (NADPH) and uridine diphosphate glucose (UDPG) showed up as well. The marker of cell membrane metabolism, in F1-2, PME/PDE (P < 0.001), PC/GPE (P < 0.01), PC/GPC (P < 0.05) and PC/(PME + PDE) (P < 0.05) decreased while GPE/(PME + PDE) (P < 0.05) and GPC/(PME + PDE) (P < 0.05) increased significantly. In F3-4, there was a recovery trend of most ratios, especially for PC/(PME + PDE) (P < 0.05). As for the main ratio related to energy metabolism, β-ATP/Ptotal (P < 0.05) decreased in the early stage of the disease (F1-2) and this decline was maintained in advanced stage (F3-4). NADPH/Ptotal (P < 0.01) and β-ATP/Pi (inorganic phosphate) (P < 0.05) ratio was lower in F3-4 comparing with F0. CONCLUSION:31P-MRS can generally stage the liver fibrosis by comparing the ratios of the phosphorus metabolites resonance peaks at 9.4 T and more importantly it can be used for early diagnosis.

To establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model’s effectiveness. Plain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren–Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC). The AUC (macro/micro) of the model using a combination of AP and LAT (AP LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p < 0.05). The overall accuracy of the radiomic model with AP LAT images was 0.727 compared to 0.712 and 0.417 for radiologists with 4 years and 2 years of musculoskeletal diagnostic experience. A radiomic model constructed by combining the AP LAT images of the knee joint can better grade knee OA and assist clinicians in accurate diagnosis and treatment. A radiomic model based on plain radiographs accurately grades knee OA severity. By utilizing the LR classifier and combining AP LAT images, it improves accuracy and consistency in grading, aiding clinical decision-making, and treatment planning.

To investigate the value of synthetic MRI sequences for quantitative detection of the muscles around the knee joints before and after a marathon. Marathon runners were examined with Synthetic MRI sequences of both knees. Quantitative profiles of T1, T2, and PD were obtained after scanning. The differences in T1, T2, and PD values of each muscle were analyzed. Most muscle subregions had elevated T1, T2, and PD values 48 hours after the marathon compared to pre-race, and decreased after 1 month of post-race rest. The synthetic MRI sequences can be useful for detecting dynamic changes in the knee muscles.

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.

Knee osteoarthritis (OA) is the most prevalent degenerative joint disease. When morphological changes become apparent on radiographs, no approved treatment can reverse the disease process. Early diagnosis is an unmet need demanding new molecular and imaging biomarkers to define OA from the earliest stages. In this context, we focus on collagen, the most basic building block of all joint tissues, and interrogate how OA development affects collagen's molecular folding, a previously underexplored area. Here, through whole-joint mapping with a peptide that recognizes unfolded collagen molecules, we report the discovery of collagen denaturation in cartilage before proteolysis and major histopathological degeneration in animal models and patients. Mechanistically, we reveal that such molecular collagen defects can be driven by mechanical overloading without collagenase degradation and are intimately associated with glycosaminoglycan loss. We showcase the advantages of using collagen denaturation as an early-stage OA hallmark for in vivo therapeutic evaluation and molecular magnetic resonance imaging (MRI) of subtle joint defects that are challenging to detect with conventional morphology-based MRI. These results highlight biomolecular integrity as a crucial dimension for characterizing joint degeneration and a molecular foundation for diagnosing early-stage OA and beyond.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis work was supported by the National Natural Science Foundation of China (92059104, 82071977, 82325035, 82172481, 32271409), the 2018 High-level Health Team of Zhuhai, the Six Talent Peaks Project of Jiangsu Province (WSW-079), the Innovation Project of National Orthopedics and Sports Medicine Rehabilitation Clinical Medical Research Center (2021-NCRC-CXJJ-ZH-16), and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (2023LSYS001).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Ethics Committee of Nanjing Drum Tower Hospital of Nanjing University approved these studies (approval no. K228-1).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.

[Objective]To assess the microstructural involvement of gray matter in recovered COVID-19 patients us-ing Synthetic MRI.[Methods]This study was conducted in 29 recovered COVID-19 patients,including severe group(SG,n=11)and ordinary group(OG,n=18).Healthy volunteers matched by age,sex,BMI and years of education were select-ed as a healthy control group(HC=23 cases).Each subject underwent synthetic MRI to generate quantitative T1 and T2 maps,and the T1 and T2 maps were segmented into 90 regions of interest(ROIs)using automatic anatomical labeling(AAL)mapping.T1 and T2 values for each ROI were obtained by averaging all voxels within the ROIs.The T1 and T2 values of the 90 brain regions between the three groups were compared.[Results]Relative to HC,the SG had significantly higher T2 values in bilateral orbital superior frontal gyrus,bilateral parahippocampal gyrus,bilateral putamen,bilateral middle temporal gyrus,bilateral Inferior temporal gyrus,left orbital superior frontal gyrus,left orbital inferior frontal gyrus,left gyrus rectus,left anterior cingulate and paracingulate gyri,right median cingulate and paracingulate gyri,left posterior cingulate gyrus,and left supramarginal gyrus(P＜0.05);Relative to OG,SG showed significantly increased T2 values in the left rectus gyrus,left parahippocampal gyrus,bilateral middle temporal gyrus,and bilateral inferior temporal gyrus(P＜0.05).Relative to HC,the T1 values of SG were significantly increased in bilateral orbital superior frontal gyrus,left rec-tus gyrus,left anterior cingulate and paracingulate gyri,right posterior cingulate gyrus,left parahippocampal gyrus,left lingual gyrus,left putamen,left thalamus(P＜0.05);Relative to OG,the T1 values of SG were significantly higher in the right posterior cingulate gyrus,right calcarine fissure and surrounding cortex,and left putamen(P＜0.05).[Conclusions]Even after recovering from COVID-19,patients may still have persistent or delayed damage to their brain gray matter structure,which is correlated with the severity of the condition.SyMRI can serve as a sensitive tool to assess the extent of microstructural damage to the central nervous system,aiding in early diagnosis of the disease.

Achilles tendinopathy is often attributed to overuse, but its pathophysiology remains poorly understood. Disruption to the molecular structure of collagen is fundamental for the onset and progression of tendinopathy but has mostly been investigated in vitro. Here, we interrogated the in vivo molecular structure changes of collagen in rat Achilles tendons following treadmill running. Unexpectedly, the tendons’ collagen molecules were not mechanically unfolded by running but denatured through proteolysis during physiological post-run remodeling. We further revealed that running induces inflammatory gene expressions in Achilles tendons and that long-term running causes prolonged, elevated collagen degradation, leading to the accumulation of denatured collagen and tendinopathy development. For applications, we demonstrated magnetic resonance imaging of collagenase-induced Achilles tendon injury in vivo using a denatured collagen targeting contrast agent. Our findings may help close the knowledge gaps in the mechanobiology and pathogenesis of Achilles tendinopathy and initiate new strategies for its imaging-based diagnosis.

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.

(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.

针对放射专业住院医师规范化培训消化道造影检查教学中病例资源不足、临床实践难度大等问题,利用教师标准化病人联合Mini-CEX的方法,初步研究证实该方法能够解决上述问题,并有效提升住院医师的沟通能力、减少患者的辐射损害,但在教师标准化病人模拟病种的多样性及Mini-CEX评分量表的专科化改良等方面仍需要进一步的改进.