T1 Pre- and Post-contrast Delta Histogram Parameters in Predicting the Grade of Meningioma and Their Relationship to Ki-67 Proliferation Index.

RATIONALE AND OBJECTIVES: To explore the feasibility of delta histogram parameters (including absolute delta histogram parameters (AdHP) and relative delta histogram parameters (RdHP)) in predicting the grade of meningioma and to further investigate whether delta histogram parameters correlate with the Ki-67 proliferation index. METHODS: 92 patients with meningioma who underwent MRI examination (including T1-weighted (T1) and contrast-enhanced T1-weighted images (T1C)) were enrolled in this retrospective study. A total of 46 low-grade cases formed the low-grade group (grade 1, LGM), and a total of 46 high-grade cases formed the high-grade group (38 grade 2, 8 grade 3, HGM). Histogram parameters (HP) of T1 and T1C were extracted. Subsequently, morphological MRI features, AdHP (AdHP=T1CHP-T1HP), and RdHP (RdHP=(T1CHP-T1HP)/T1HP) were recorded and compared, respectively. Binary logistic regression analysis was used to obtain combined performance of the significant parameters. Diagnostic performance was identified by ROC. Spearman's correlation coefficients were taken to assess the relationship between delta histogram parameters and the Ki-67 proliferation index. RESULTS: In morphological MRI features, HGM is more prone to lobulation and necrosis/cystic changes (all p < 0.05). In delta histogram parameters, HGM exhibits higher mean, Perc.01, Perc.25, Perc.50, Perc.75, Perc.99, SD, and variance of AdHP, maximum, mean, Perc.25, Perc.50, Perc.75, and Perc.99 of RdHP, compared to LGM (all p < 0.00357). The optimal predictive performance was obtained by combining morphological MRI features and delta histogram parameters with an AUC of 0.945. Significant correlations were observed between significant delta histogram parameters and the Ki-67 proliferation index (all p < 0.05). CONCLUSION: Delta histogram parameter is a promising potential biomarker, which may be helpful in noninvasive predicting the grade and proliferative activity of meningioma.

Extracellular vesicles containing circMYBL1 induce CD44 in adenoid cystic carcinoma cells and pulmonary endothelial cells to promote lung metastasis.

Adenoid cystic carcinoma (ACC) is a rare malignant epithelial neoplasm that arises in secretory glands and commonly metastasizes to the lungs. MYBL1 is frequently overexpressed in ACC and has been suggested to be a driver of the disease. Here, we identified a circRNA derived from MYBL1 pre-mRNA that accompanied overexpression of MYBL1 in ACC. Overexpression of circMYBL1 was correlated with increased lung metastasis and poor overall survival in ACC patients. Ectopic circMYBL1 overexpression promoted malignant phenotypes and lung metastasis of ACC cells. Mechanistically, circMYBL1 formed a circRNA-protein complex with CCAAT enhancer binding protein beta (CEBPB), which inhibited ubiquitin-mediated degradation and promoted nuclear translocation of CEBPB. In the nucleus, circMYBL1 increased the binding of CEBPB to the CD44 promoter region and enhanced its transcription. In addition, circMYBL1 was enriched in small extracellular vesicles (sEVs) isolated from the plasma of ACC patients. Treatment with sEVs containing circMYBL1 in sEVs enhanced pro-metastatic phenotypes of ACC cells, elevated the expression of CD44 in human pulmonary microvascular endothelial cells (HPMECs), and enhanced the adhesion between HPMECs and ACC cells. Moreover, circMYBL1 encapsulated in sEVs increased the arrest of circulating ACC cells in the lung and enhanced the lung metastatic burden. This data suggests that circMYBL1 is a tumor-promoting circRNA that could serve as a potential biomarker and therapeutic target in ACC.

Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases.

OBJECTIVE: To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM). METHODS: 186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis. RESULTS: 186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038). CONCLUSION: ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.

Surgical tumor-derived nanoplatform targets tumor-associated macrophage for personalized postsurgical cancer immunotherapy.

Directly activating CD8+ T cells within the tumor through antigen-presenting cells (APCs) hold promise for tumor elimination. However, M2-like tumor-associated macrophages (TAMs), the most abundant APCs in tumors, hinder CD8+ T cell activation due to inefficient antigen cross-presentation. Here, we demonstrated a personalized nanotherapeutic platform using surgical tumor-derived galactose ligand-modified cancer cell membrane (CM)-coated cysteine protease inhibitor (E64)-loaded mesoporous silica nanoparticles for postsurgical cancer immunotherapy. The platform targeted M2-like TAMs and released E64 within lysosomes, which reshaped antigen cross-presentation and directly activated CD8+ T cells, thus suppressing B16-OVA melanoma growth. Furthermore, this platform, in combination with anti-PD-L1 antibodies, enhanced the therapeutic efficacy and substantially inhibited 4T1 tumor growth. CMs obtained from surgically resected tumors were used to construct a personalized nanotherapeutic platform, which, in synergy with immune checkpoint blockade (ICB), effectively inhibited postsurgical tumor recurrence in 4T1 tumor. Our work offered a robust, safe strategy for cancer immunotherapy and prevention of postsurgical tumor recurrence.

ALMS1-IT1: A Key Player in the Novel Disulfidptosis-Related LncRNA Prognostic Signature for Head and Neck Squamous Cell Carcinoma.

Disulfidptosis is a newly discovered form of programmed cell death that is induced by disulfide stress. It is closely associated with various cancers, including head and neck squamous cell carcinoma (HNSCC). However, the factors involved in the modulation of disulfidptosis-related genes (DRGs) still remain unknown. In this study, we established and validated a novel risk score model composed of 11 disulfidptosis-related lncRNAs (DRLs) based on 24 DRGs in HNSCC. The results revealed strong correlations between the 11-DRL prognostic signature and clinicopathological features, immune cell infiltration, immune-related functions, and disulfidptosis-associated pathways, including NADPH and disulfide oxidoreductase activities. Furthermore, we studied and verified the involvement of ALMS1-IT1, one of the 11 model DRLs, in the disulfidptosis of HNSCC cell lines. A series of assays demonstrated that ALMS1-IT1 modulated cell death under starvation conditions in a pentose phosphate pathway (PPP)-dependent manner. Knockdown of ALMS1-IT1 inhibited the PPP, contributing to a decline in NADPH levels, which resulted in the formation of multiple intermolecular disulfide bonds between actin cytoskeleton proteins and the collapse of F-actin in the cytoplasm. Therefore, ALMS1-IT1, which is highly expressed in SLC7A11high cells, can be considered a promising therapeutic target for disulfidptosis-focused treatment strategies for cancer and other diseases.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Toward the Improvement of the Mechanical and Tribological Properties of Braided Ligament for ACL Reconstruction: A "Carrot and Stick" Strategy.

Bone tunnel enlargement has been troubling the clinical adoption of braided artificial ligaments for decades, to which mechanical and tribological performance promotion shall be an effective and promising approach. Herein, a "carrot and stick" strategy has been introduced with two types of polyethylene terephthalate (PET) fibers to fabricate hybrid textures, which is expected to advance fatigue and tribological performance without yielding essential mechanical strength and biocompatibility. Owing to advancements in such a "carrot and stick" strategy, the obtained grafts present three promising properties: i) enhancement of mechanical strength; ii) coefficient of friction (COF) reduction of 25% at the greatest extent, thus lowering the risk of bone tunnel enlargement; iii) final displacement shrinkage of graft length after cyclic loadings, favored in the clinic for isometric reconstruction. The results obtained in this study show that the "carrot and stick" strategy can be a creative and convenient method to optimize the service life, saving the complication rate of artificial ligaments for clinical applications.

Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas.

PURPOSE: To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas. METHODS: Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman's correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index. RESULTS: Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296-0.417, p = 0.001-0.044) and ADC (r = -0.428-0.395, p = 0.003-0.038). CONCLUSION: Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas.

Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients.

RATIONALE AND OBJECTIVES: This study aimed to non-invasively predict epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase computed tomography (CT) radiomics features. MATERIALS AND METHODS: A total of 424 patients with lung adenocarcinoma were recruited from two hospitals who underwent preoperative non-enhanced CT (NE-CT) and enhanced CT (including arterial phase CT [AP-CT], and venous phase CT [VP-CT]). Patients were divided into training (n = 297) and external validation (n = 127) cohorts according to hospital. Radiomics features were extracted from the NE-CT, AP-CT, and VP-CT images, respectively. The Wilcoxon test, correlation analysis, and simulated annealing were used for feature screening. A clinical model and eight radiomics models were established. Furthermore, a clinical-radiomics model was constructed by incorporating multi-phase CT features and clinical risk factors. Receiver operating characteristic curves were used to evaluate the predictive performance of the models. RESULTS: The predictive performance of multi-phase CT radiomics model (AUC of 0.925 [95% CI, 0.879-0.971] in the validation cohort) was higher than that of NE-CT, AP-CT, VP-CT, and clinical models (AUCs of 0.860 [95% CI,0.794-0.927], 0.792 [95% CI, 0.713-0.871], 0.753 [95% CI, 0.669-0.838], and 0.706 [95% CI, 0.620-0.791] in the validation cohort, respectively) (all P < 0.05). The predictive performance of the clinical-radiomics model (AUC of 0.927 [95% CI, 0.882-0.971] in the validation cohort) was comparable to that of multi-phase CT radiomics model (P > 0.05). CONCLUSION: Our multi-phase CT radiomics model showed good performance in identifying the EGFR mutation status in patients with lung adenocarcinoma, which may assist personalized treatment decisions.

NAT10/CEBPB/vimentin signalling axis promotes adenoid cystic carcinoma malignant phenotypes in vitro.

OBJECTIVE: To explore the biological function and mechanisms of CEBPB and NAT10-mediated N4-acetylcytidine (ac4c) modification in salivary adenoid cystic carcinoma (SACC). MATERIALS AND METHODS: CEBPB and NAT10 were knocked down in SACC-LM cells by siRNA transfection and overexpressed in SACC-83 cells by plasmid transfection. Malignant phenotypes were evaluated using CCK-8, Transwell migration and colony formation assays. Real-time PCR, western blotting, ChIP and acRIP were used to investigate the molecular mechanisms involved. RESULTS: We found that CEBPB was highly expressed in SACC tissues and correlated with lung metastasis and unfavourable prognosis. Gain- and loss-of-function experiments revealed that CEBPB promoted SACC malignant phenotypes. Mechanistically, CEBPB exerted its oncogenic effect by binding to the vimentin gene promoter region to enhance its expression. Moreover, NAT10-mediated ac4c modification led to stabilization and overexpression of CEBPB in SACC cells. We also found that NAT10, the only known human enzyme responsible for ac4C modification, promoted SACC cell migration, proliferation and colony formation. Moreover, CEBPB overexpression restored the inhibitory effect of NAT10 knockdown on malignant phenotypes. CONCLUSIONS: Our study reveals the critical role of the newly identified NAT10/CEBPB/vimentin axis in SACC malignant progression, and the findings may be applied to improve treatment for SACC.

Developing a Nomogram to Stratify Intracranial Solitary Fibrous Tumor Recurrence.

RATIONALE AND OBJECTIVES: To develop a nomogram to stratify tumor recurrence (TR) in intracranial solitary fibrous tumors (ISFTs) based on the clinical, radiological, and pathological features. MATERIALS AND METHODS: A total of 215 patients from Beijing Tiantan Hospital, Capital Medical University and 48 patients from Lanzhou University Second Hospital, diagnosed with ISFT based on histopathological findings, were included. The patients were randomly divided into training and test cohorts at a ratio of 8:2. Information regarding clinical, radiological, and histopathological features, and the clinical outcomes was retrospectively analyzed. Univariate and multivariate analyses were performed using the Cox proportional hazard model for TR in the training cohort. A nomogram incorporating the independent risk factors was developed in the training cohort and validated in the test cohort. Its predictive performance was analyzed using the Harrell C-index. Decision curve analysis (DCA) was used to evaluate the net clinical benefit. RESULTS: The Harrell C-indices for TR at 3 and 5 years were 0.845 (0.578-0.944) and 0.807 (0.612-0.901) for the test cohort, respectively. In the test cohort, the nomogram provided a net clinical benefit in the DCA over the TR scheme or non-TR scheme. Although postoperative radiotherapy (PORT) was useful for TR prevention, high doses (≥46 Gy) were not superior to lower doses in prolonging the progression-free survival. CONCLUSION: The nomogram obtained in our study had a good predictive performance and could be used for ISFT patients.

Vasari-Based Features Nomogram to Predict the Tumor-Infiltrating CD8+ T Cell Levels in Glioblastoma.

RATIONALE AND OBJECTIVES: Tumor-infiltrating CD8 + T cells play a key role in glioblastoma (GB) development, malignant progression, and recurrence. The aim of the study was to establish nomograms based on the Visually AcceSAble Rembrandt Images (VASARI) features of multiparametric magnetic resonance imaging (MRI) to determine the expression levels of tumor-infiltrating CD8 + T cells in patients with GB. MATERIALS AND METHODS: Pathological and imaging data of 140 patients with GB confirmed by surgery and pathology were retrospectively analyzed. The levels of tumor-infiltrating CD8 + T cells in tumor tissue samples obtained from patients were quantified using immunohistochemical staining. Patients were divided into high and low CD8 expression groups. The MRI images of patients with GB were analyzed by two radiologists using the VASARI scoring system. RESULTS: A total of 25 MRI-based VASARI imaging features were evaluated by two neuroradiologists. The features with the greatest predictive power for CD8 expression levels were, cystic (OR, 3.063; 95% CI: 1.387, 6.766; P = 0.006), hemorrhage (OR, 2.980; 95% CI: 1.172, 7.575; P = 0.022), and ependymal extension (OR, 0.257; 95% CI: 0.114 0.581; P = 0.001). A logistic regression model based on these three features showed better sample predictive performance (AUC=0.745; 95% CI: 0.665, 0.825; Sensitivity=0.527; Specificity=0.857). CONCLUSION: The VASARI feature-based nomogram model can show promise to predict the level of infiltrative CD8 expression in GB tumors non-invasively for earlier tissue diagnosis and more aggressive treatment.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases.

OBJECTIVE: To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM). METHODS: Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images. RESULTS: All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01). CONCLUSION: Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor.

BACKGROUND: Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE: To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE: Retrospective. POPULATION: Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE: 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT: Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS: Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS: The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION: The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Macrophage inflammarafts in atherosclerosis.

PURPOSE OF REVIEW: Advances in single cell techniques revealed a remarkable diversity in macrophage gene expression profiles in atherosclerosis. However, the diversity of functional processes at the macrophage plasma membrane remains less studied. This review summarizes recent advances in characterization of lipid rafts, where inflammatory receptors assemble, in macrophages that undergo reprogramming in atherosclerotic lesions and in vitro under conditions relevant to the development of atherosclerosis. RECENT FINDINGS: The term inflammarafts refers to enlarged lipid rafts with increased cholesterol content, hosting components of inflammatory receptor complexes assembled in close proximity, including TLR4-TLR4, TLR2-TLR1 and TLR2-CD36 dimers. Macrophages decorated with inflammarafts maintain chronic inflammatory gene expression and are primed to an augmented response to additional inflammatory stimuli. In mouse atherosclerotic lesions, inflammarafts are expressed primarily in nonfoamy macrophages and less in lipid-laden foam cells. This agrees with the reported suppression of inflammatory programs in foam cells. In contrast, nonfoamy macrophages expressing inflammarafts are the major inflammatory population in atherosclerotic lesions. Discussed are emerging reports that help understand formation and persistence of inflammarafts and the potential of inflammarafts as a novel therapeutic target. SUMMARY: Chronic maintenance of inflammarafts in nonfoamy macrophages serves as an effector mechanism of inflammatory macrophage reprogramming in atherosclerosis.

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection.

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

Utility of Apparent Diffusion Coefficient Histogram Analysis in Differentiating Microcystic Meningioma from Intracranial Solitary Fibrous Tumor.

BACKGROUND: To investigate the possibility of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating microcystic meningioma (MM) from intracranial solitary fibrous tumor (SFT). METHODS: Eighteen patients with MM and 23 patients with SFT were enrolled in this retrospective study. Conventional magnetic resonance imaging (MRI) features and 9 ADC histogram parameters (including mean, first (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentiles ADC, as well as variance, skewness, and kurtosis) between MM and SFT were compared. The diagnostic performance of the optimal parameter was determined by the receiver operating characteristic analysis. RESULTS: SFT showed a significantly lower mean, ADC1, ADC10, ADC50, ADC90, and ADC99 than MM (all P < 0.05), while no significant difference was found in conventional MRI features or other ADC histogram parameters (all P > 0.05). ADC1 was identified as the optimal parameter in differentiating between MM and SFT, which achieved an area under the curve of 0.861, with sensitivity, specificity, and accuracy of 78.26%, 88.89%, and 82.93%, respectively. CONCLUSIONS: MM and SFT show overlapping conventional MRI features. ADC histogram analysis helps to differentiate between MM and SFT, with ADC1 being the optimal parameter with the best discrimination performance.

Perihematomal edema-based CT-radiomics model to predict functional outcome in patients with intracerebral hemorrhage.

PURPOSE: The purpose of this study was to identify possible association between noncontrast computed tomography (NCCT)-based radiomics features of perihematomal edema (PHE) and poor functional outcome at 90 days after intracerebral hemorrhage (ICH) and to develop a NCCT-based radiomics-clinical nomogram to predict 90-day functional outcomes in patients with ICH. MATERIALS AND METHODS: In this multicenter retrospective study, 107 radiomics features were extracted from 1098 NCCT examinations obtained in 1098 patients with ICH. There were 652 men and 446 women with a mean age of 60 ± 12 (SD) years (range: 23-95 years). After harmonized and univariable and multivariable screening, seven of these radiomics features were closely associated with the 90-day functional outcome of patients with ICH. The radiomics score (Rad-score) was calculated based on the seven radiomics features. A clinical-radiomics nomogram was developed and validated in three cohorts. The model performance was evaluated using area under the curve analysis and decision and calibration curves. RESULTS: Of the 1098 patients with ICH, 395 had a good outcome at 90 days. Hematoma hypodensity sign and intraventricular and subarachnoid hemorrhages were identified as risk factors for poor outcomes (P < 0.001). Age, Glasgow coma scale score, and Rad-score were independently associated with outcome. The clinical-radiomics nomogram showed good predictive performance with AUCs of 0.882 (95% CI: 0.859-0.905), 0.834 (95% CI: 0.776-0.891) and 0.905 (95% CI: 0.839-0.970) in the three cohorts and clinical applicability. CONCLUSION: NCCT-based radiomics features from PHE are highly correlated with outcome. When combined with Rad-score, radiomics features from PHE can improve the predictive performance for 90-day poor outcome in patients with ICH.

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection.

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle's bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

Dual-energy CT quantification of extracellular liver volume predicts short-term disease progression in patients with hepatitis B liver cirrhosis-acute decompensation.

BACKGROUND: Liver cirrhosis-acute decompensation (LC-AD) has rapid short-term disease progression and difficult early risk stratification. The purpose is to develop and validate a model based on dual-energy CT quantification of extracellular liver volume (ECVIC-liver) for predicting the occurrence of acute-on-chronic liver failure (ACLF) within 90 days in patients with hepatitis B (HBV) LC-AD. METHODS: The retrospective study included patients with HBV LC-AD who underwent dual-energy CT scans of the liver from January 2018 to March 2022 and were randomized to training group (215 patients) and validation group (92 patients). The primary outcome was the need for readmission within 90 days due to ACLF. Based on the training group data, independent risk factors for disease progression in clinical and dual-energy CT parameters were identified and modeled by logistic regression analysis. Based on the training and validation groups data, receiver operating characteristic (ROC) curves, calibration curves, and decision analysis curves (DCA) were used to verify the discrimination, calibration, and clinical validity of the nomogram. RESULTS: Chronic liver failure consortium-acute decompensation score (CLIF-C ADs) (p = 0.008) and ECVIC-liver (p < 0.001) were independent risk factors for ACLF within 90 days. The AUC of the model combined ECVIC-liver and CLIF-C ADs were 0.893 and 0.838 in the training and validation groups, respectively. The calibration curves show good agreement between predicted and actual risks. The DCA indicates that the model has good clinical application. CONCLUSION: The model combined ECVIC-liver and CLIF-C ADs can early predict the occurrence of ACLF within 90 days in HBV LC-AD patients.