Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). RESULTS: 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. CONCLUSIONS: To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Solution-Processable Large-Area Black Phosphorus/Reduced Graphene Oxide Schottky Junction for High-Temperature Broadband Photodetectors.

2D materials-based broadband photodetectors have extensive applications in security monitoring and remote sensing fields, especially in supersonic aircraft that require reliable performance under extreme high-temperature conditions. However, the integration of large-area heterostructures with 2D materials often involves high-temperature deposition methods, and also limited options and size of substrates. Herein, a liquid-phase spin-coating method is presented based on the interface engineering to prepare larger-area Van der Waals heterojunctions of black phosphorus (BP)/reduced graphene oxide (RGO) films at room temperature on arbitrary substrates of any required size. Importantly, this method avoids the common requirement of high-temperature, and prevents the curling or stacking in 2D materials during the liquid-phase film formation. The BP/RGO films-based devices exhibit a wide spectral photo-response, ranging from the visible of 532 nm to infrared range of 2200 nm. Additionally, due to Van der Waals interface of Schottky junction, the array devices provide infrared detection at temperatures up to 400 K, with an outstanding photoresponsivity (R) of 12 A W-1 and a specific detectivity (D*) of ≈2.4 × 109 Jones. This work offers an efficient approach to fabricate large-area 2D Schottky junction films by solution-coating for high-temperature infrared photodetectors.

Exercise ameliorates lipid droplet metabolism disorder by the PLIN2-LIPA axis-mediated lipophagy in mouse model of non-alcoholic fatty liver disease.

Excessive hepatic lipid droplets (LDs) accumulation-induced lipid metabolism disorder contributes to the development of non-alcoholic fatty liver disease (NAFLD). Exercise is a promising therapeutic strategy for NAFLD. However, the mechanism by which exercise ameliorates NAFLD through regulating the catabolism of hepatic LDs remains unclear. In the present study, we investigated the effect of perilipin2 (PLIN2)-lysosomal acid lipase (LIPA) axis mediating exercise-triggered lipophagy in a high-fat diet (HFD)-induced NAFLD mouse model. Our results showed that exercise could reduce HFD-induced hepatic LDs accumulation and change the expression of lipolysis-related enzymes. Moreover, exercise upregulated the expression of microtubule associated protein 1 light chain 3 (LC3) and autophagy-related proteins, and downregulated sequestosome 1 (P62) expression and promoted autophagosomes formation. Interestingly, exercise downregulated PLIN2 expression, upregulated LIPA expression, and increased the activity of hepatic LIPA and serum levels of LIPA in the NAFLD mouse model. Further mechanistic studies demonstrated that adenosine monophosphate-activated protein kinase (AMPK) activator-5-Aminoimidazole-4-carboxamide ribonucleoside (AICAr) treatment significantly increased mRNA levels and protein expression of LIPA and LC3II and decreased levels of PLIN2 and P62 in palmitic acid (PA)-treated HepG2 cells. PLIN2 silencing and LIPA overexpression notably increased the mRNA level and protein expression of LC3II and decreased the mRNA level and protein expression of p62, respectively. In summary, our findings reveal novel insights into the effect of exercise on improving lipid droplet metabolism disorder in NAFLD. Enhancing the PLIN2-LIPA axis-mediated lipophagy may be one of the key mechanisms involved in NAFLD alleviation by exercise.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.

PURPOSE: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions. METHODS: In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. RESULTS: A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08). CONCLUSION: The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.

PURPOSE: We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. METHODS: In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. RESULTS: Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. CONCLUSION: Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

Hypothalamic FTO promotes high-fat diet-induced leptin resistance in mice through increasing CX3CL1 expression.

Long-term consumption of a high-fat diet (HFD) disrupts energy homeostasis and leads to weight gain. The fat mass and obesity-associated (FTO) gene has been consistently identified to be associated with HFD-induced obesity. The hypothalamus is crucial for regulating energy balance, and HFD-induced hypothalamic leptin resistance contributes to obesity. FTO, an N6-methyladenosine (m6A) RNA methylation regulator, may be a key mediator of leptin resistance. However, the exact mechanisms remain unclear. Therefore, the present study aims to investigate the association between FTO and leptin resistance. After HFD or standard diet (SD) feeding in male mice for 22 weeks, m6A-sequencing and western blotting assays were used to identify target genes and assess protein level, and molecular interaction changes. CRISPR/Cas9 gene knockout system was employed to investigate the potential function of FTO in leptin resistance and obesity. Our data showed that chemokine (C-X3-C motif) ligand 1 (CX3CL1) was a direct downstream target of FTO-mediated m6A modification. Furthermore, upregulation of FTO/CX3CL1 and suppressor of cytokine signaling 3 (SOCS3) in the hypothalamus impaired leptin-signal transducer and activator of transcription 3 signaling, resulting in leptin resistance and obesity. Compared to wild-type (WT) mice, FTO deficiency in leptin receptor-expressing neurons of the hypothalamus significantly inhibited the upregulation of CX3CL1 and SOCS3, and partially ameliorating leptin resistance under HFD conditions. Our findings reveal that FTO involved in the hypothalamic leptin resistance and provides novel insight into the function of FTO in the contribution to hypothalamic leptin resistance and obesity.

Targeting CD24 as a novel immunotherapy for solid cancers.

Cluster of differentiation 24 (CD24), a mucin-like highly glycosylated molecule has been extensively studied as a cancer stem cell marker in a variety of solid cancers. The functional role of CD24 is either fulfilled by combining with ligands or participating in signal transduction, which mediate the initiation and progression of neoplasms. Recently, CD24 was also described as an innate immune checkpoint with apparent significance in several types of solid cancers. Herein, we review the current understanding of the molecular fundamentals of CD24, the role of CD24 in tumorigenesis and cancer progression, the possibility as a promising therapeutic target and summarized different therapeutic agents or strategies targeting CD24 in solid cancers. Video Abstract.

ISX9 loaded thermoresponsive nanoparticles for hair follicle regrowth.

There is a high demand for an optimal drug delivery system to treat androgenetic alopecia. Topical application of ISX9, which is a neurogenesis inducer, has been found to stimulate hair follicle (HF) regrowth by upregulating the Wnt/ß-catenin signaling pathway, an essential pathway involved in initiating HF growth and development. In the present study, a temperature-sensitive, biopolymer-based, biocompatible, and eco-friendly drug-delivery system was synthesized. This system comprised chitosan-grafted poly(glycidyl methacrylate-co-N-isopropyl acrylamide) (Poly(GMA-co-NIPAAm)@CS-PGNCS) as the shell component and PF127 as the core polymer. The hydrophobic nature of the PF127 block copolymer efficiently dissolved the partially water-soluble drug, ISX9, and the thermos-responsive shell polymer effectively released the drug at a definite skin temperature. The optimized spherical nanoparticles demonstrated the lowest critical solution temperature (LCST) at 32 ± 2 °C with a diameter of 100-250 nm, which delivered encapsulated ISX9 with greater precision than topical ISX9. In a series of in vivo experiments, we demonstrated that ISX9-coated TBNPs upregulated the expression of ß-catenin, active ß-catenin, Wnt target genes, stemness marker genes, proliferating cell nuclear antigen, HF stem cell markers, and HF markers including VEGF, TGF, and IGF-1 more effectively than topical ISX9. These results suggest that TBNPs could be employed as a platform for effective transdermal delivery of various hydrophobic drugs.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. MATERIAL AND METHODS: We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. RESULTS: We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients. CONCLUSION: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy. CLINICAL RELEVANCE: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients. KEY POINTS: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Advancing Inorganic Microcapsule Fabrication through Frozen-Assisted Interfacial Reactions Utilizing Liquid Marbles.

Inorganic microcapsules (IMs) have gained significant attention as versatile platforms for delivering functional agents in various fields. Traditional template-dependent methods employing hard templates often involve complex and harsh template removal processes. Achieving IMs with diverse composition and structure remains challenging with current preparation strategies. Therefore, in this work, we have for the first time demonstrated an extremely facile and efficient liquid-marbles-based template approach for fabricating pure inorganic microcapsules via interfacial reaction in a mild aqueous solution. The water-water reaction interface is created by changing the wettability of the liquid marble (LM) surface through the icing-melting process. The composition and function of the inorganic shell could be easily adjusted by varying the inorganic reagent species of the interfacial reaction, the hydrophobic particle of the shell, and the reaction environment according to the specific requirements of the application field. Such an approach provides a flexible platform for material preparation.

Preparation of Al3Ti-Al2O3/Al Inoculant and Its Inoculation Effect on Al-Cu-Mn Alloy.

The grain size plays a pivotal role in determining the properties of the alloy. The grain size can be significantly decreased by adding inoculants. Aiming to address the shortcomings of existing inoculants, the Al3Ti-Al2O3/Al inoculant was successfully prepared using Al-Ti master alloy and Al2O3 whiskers as raw materials. With the aid of ultrasonic energy, the Al2O3 whiskers were uniformly dispersed within the inoculants. Under the combined action of ultrasonic and titanium, the Al2O3 whiskers were broken into small particles at high temperature. To enhance the morphology of Al3Ti and achieve even particle dispersion throughout the matrix, vacuum rapid quenching treatment was applied to the inoculant. The SEM test results indicated a significant reduction in particle size after vacuum rapid quenching. The Al3Ti-Al2O3/Al inoculants exhibited excellent grain refinement effects on the weldable Al-Cu-Mn alloy. Crystallographic calculations and HRTEM analysis revealed that Al2O3 and Al have orientation relationships, indicating their potential as effective heterogeneous nucleation sites. The mechanical properties of the Al-Cu-Mn alloy were obviously improved after the Al3Ti-Al2O3/Al inoculant was added.

Rheology of graphene oxide stabilized Pickering emulsions.

Pickering emulgels stabilized by graphene oxide (GO) with didodecyldimethylammonium bromide (DDAB) as an auxiliary surfactant and liquid paraffin as the oil phase have proved to be an excellent 3D printable ink. This paper elucidates the structure of such emulgels by a combination of microscopy before and after intensive shear as well as broadband dielectric spectroscopy and rheology in the linear and nonlinear regime. An increase of the DDAB surfactant and GO-contents leads to a systematic increase of modulus and viscosity, a reduction of the limits of the nonlinear regime and a more complicated variation of the normal forces, with negative normal forces at high shear rate  for low GO-contents and positive normal forces at high GO-contents. The interfacial jamming behavior studied by morphology, rheology and dielectric spectroscopy is explained based on droplet deformation, jamming and recovery phenomena.

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS: This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS: One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION: While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach.

BACKGROUND: Aneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest. OBJECTIVE: We created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method. METHODS: 500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia-). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction. RESULTS: The Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62-88.68), area under the curve 0.780 (95% CI: 0.696-0.864), sensitivity 12.5% (95% CI: -3.7 to 28.7), specificity 94.81% (95% CI: 89.85-99.77), PPV 33.3% (95% CI: -4.39 to 71.05), and NPV 84.1% (95% CI: 76.38-91.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia. CONCLUSION: Machine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.

Large-Area Black Phosphorus/PtSe2 Schottky Junction for High Operating Temperature Broadband Photodetectors.

High operating temperature (HOT) broadband photodetectors are urgently necessary for extreme condition applications in infrared-guided missiles, infrared night vision, fire safety imaging, and space exploration sensing. However, conventional photodetectors show dramatic carrier mobility decreases and carrier losses with low photoresponsivity at HOT due to the increased carrier scattering in channels at high temperatures. Herein, the HOT broadband photodetectors from room temperature to 470 K are developed for the first time by large-area black phosphorus (BP)/PtSe2 films device arrays via a depletion-enhanced photocarrier dynamics strategy. Attributed to the 2D Schottky junction at BP/PtSe2 interface and resulting in full depleted working channels, the BP/PtSe2 photodetector arrays exhibit high tolerance to carrier mobility decrease during the increasing operating temperature in a wide wavelength range from 532 to 2200 nm. Thus, the photodetector shows a state-of-the-art operating temperature at 470 K with the photo-responsivity (R) and specific detectivity (D*) of 25 A W-1 and 6.4 × 1011 Jones under 1850 nm illumination, respectively. Moreover, BP/PtSe2 photodetector arrays show high-uniformity photo-response in a large area. This work provides new strategies for high-performance broadband photodetector arrays with HOT by Schottky junction of large-area BP/PtSe2 films.

The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network.

Background: Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas. Methods: A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks. Results: With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation. Conclusions: The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.

Comparing blood biomarkers to clinical decision rules to select patients suspected of traumatic brain injury for head computed tomography.

INTRODUCTION: Traumatic brain injury (TBI) is a major public health concern in the U.S. Recommendations for patients admitted in the emergency department (ED) to receive head computed tomography (CT) scan are currently guided by various clinical decision rules. OBJECTIVE: To compare how a blood biomarker approach compares with clinical decision rules in terms of predicting a positive head CT in adult patients suspected of TBI. METHODS: We retrospectively identified patients transported to our emergency department and underwent a noncontrast head CT due to suspicion of TBI and who had blood samples available. Published thresholds for serum and plasma glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1), and serum S100ß were used to make CT recommendations. These blood biomarker-based recommendations were compared to those achieved under widely used clinical head CT decision rules (Canadian, New Orleans, NEXUS II, and ACEP Clinical Policy). RESULTS: Our study included 463 patients, of which 122 (26.3%) had one or more abnormalities presenting on head CT. Individual blood biomarkers achieved high negative predictive value (NPV) for abnormal head CT findings (88%-98%), although positive predictive value (PPV) was consistently low (25%-42%). A composite biomarker-based decision rule (GFAP+UCH-L1)'s NPV of 100% and PPV of 29% were comparable or better than those achieved under the clinical decision rules. CONCLUSION: Blood biomarkers perform at least as well as clinical rules in terms of selecting TBI patients for head CT and may be easier to implement in the clinical setting. A prospective study is necessary to validate this approach.

Preparation of CF/PEEK Composites with High Mechanical Performance Using PEEK Derivatives as the Sizing Agent.

Owing to their excellent physical and chemical properties, the carbon fibre reinforced poly(ether-ether-ketone) composites (CF/PEEK) are widely used in aerospace applications such as rockets, missiles, and high-speed vehicles. However, both carbon fibre (CF) and poly(ether-ether-ketone) (PEEK) have inert molecular chain structures, which seriously affect the interfacial properties of CF/PEEK composites. In this study, to improve the properties of CF/PEEK composites, carboxylated PEEK (PEEK-COOH) with different carboxylation degrees is synthesized as the sizing agent by a "two-step" method. Then, the activated CF surface is coated by PEEK-COOH sizing layers with different functionalization degrees to prepare the CF/PEEK composites. The results show that the interfacial properties of CF/PEEK composites are improved after applying the sizing agent. When the carboxylation degree of PEEK-COOH is 19.61%, the flexural strength, flexural modulus, and interlaminar shear strength (ILSS) of CF/PEEK composites reach 489.34 MPa, 25.387 GPa, and 81.3 MPa, respectively. In addition, the use of PEEK-COOH sizing agents can form an excellent transition layer between CF and PEEK, creating an efficient stress transfer system and facilitating an even stress distribution between CF and PEEK. Furthermore, the main mechanism of material fracture changes from CF debonding to CF and resin fracture.

Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence.

OBJECTIVES: To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. METHODS: A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). RESULTS: Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. CONCLUSION: The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. KEY POINTS: • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.

Dynamically bonded, tough, and conductive MXene@oxidized sodium alginate: chitosan based multi-networked elastomeric hydrogels for physical motion detection.

Biopolymer-based conductive hydrogels (HGs) are promising candidates for preparing environmentally friendly flexible electronics. However, it is still a great challenge to synthesize biopolymer-based tough, self-healable, and fast strain recoverable HGs. Herein, a facile strategy is demonstrated to synthesize stretchable, self-recoverable, conductive, and tough HGs strain sensors through the formation of multi-dynamic interactions (i.e., imine bond formation, hydrogen bonds, ionic bonds, and electrostatic bonds) and strong covalent interactions between MXene (Ti3C2Tx), oxidized sodium alginate (OSA), chitosan (CS), polyacrylamide (PAAm), Fe(III) and PEDOT:PSS. Thus, obtaining dynamically and covalently bonded nanocomposite hydrogels (NCHGs) with controllable interfacial interactions exhibited a high mechanical strength (0.91 MPa), toughness (2.99 MJ/m3), stretchability (820 %), elasticity (>600 %) and conductivity (1.31 S/m). In addition, the presence of Fe(III) ions and conducting fillers endows excellent repeatability with high stability in resistance change upon bending or stretching with ultra-broad sensitivity up to 11-gauge factor and consisting lowest resistance change up to 0.5 %.