Association between Dietary Total Vitamin A, ß-carotene, and Retinol Intake and Risk of cardiometabolic multimorbidity: Results from the China Health and Nutrition Survey, 1997-2015.

Background: The association between vitamin A and single cardiometabolic diseases has been extensively studied, but the relationship between dietary vitamin A intake and the risk of cardiometabolic multimorbidity (CMM) has not been studied. Therefore, the present study was conducted to explore the association with CMM risk by analyzing different sources of vitamin A. Methods: This study utilized 13,603 subjects aged ≥ 18 years from 1997-2015 from the China Health and Nutrition Survey (CHNS). Dietary intake was calculated from 3 consecutive 24-h dietary recalls combined with a house hold food inventory. CMM is defined as the development of at least two cardiometabolic diseases. Results: After a median follow-up of 9.1 years, there were 1050 new cases of CMM. The risk of CMM was significantly lower in those with higher vitamin A intake (Q1 vs Q5 HR 0.66, 95% CI 0.54-0.81). ß-carotene (Q1 vs Q5 HR 0.82, 95% CI 0.66-1.02) and retinol (Q1 vs Q5 HR 0.59, 95%CI 0.48-0.73) intake had a similarly negative correlation. Using restricted cubic spline found an L-shaped relationship between retinol intake and CMM (p non-linear < 0.001). In subgroup analyses, protective effects were stronger for participants aged ≥ 44 years (HR 0.72, 95%CI 0.57-0.92) and for the female group (HR 0.62, 95%CI 0.45-0.84). Conclusion: Dietary vitamin A was a protective factor for CMM, and this effect was stronger in age ≥ 44 years and in the female group. There was a ceiling effect on the protective effect of retinol intake on the risk of CMM.

Quantitative Analysis of Hemodynamic Changes in Branch Arteries Covered by Flow Diverters.

BACKGROUND AND OBJECTIVES: Understanding post-treatment hemodynamic alterations and their association with the patency of covered branch arteries is limited. This study aims to identify hemodynamic changes after flow diverter stenting and investigate their correlation with the patency status of covered branch arteries. METHODS: All patients treated with pipeline embolization device for anterior cerebral artery aneurysms at our center between 2016 and 2020 were screened for inclusion. Quantitative digital subtraction angiography was used to analyze changes in hemodynamic parameters pre- and post-stenting. The patency status of covered branch arteries after stenting was categorized as either patent or flow impairment (defined as artery stenosis or occlusion). RESULTS: A total of 71 patients, encompassing 89 covered branch arteries, were enrolled. Flow impairment was observed in 11.2% (10/89) of the branches. The mean transit time and full width at half maximum (FWHM) in covered branches were significantly prolonged post-stenting (P = .004 and .023, respectively). Flow-impaired branch arteries exhibited hemodynamic shifts contrary to those in patent branch arteries. Specifically, flow-impaired branches showed marked reductions in time to peak, FWHM, and mean transit time (decreases of 32.8%, 32.6%, and 29%, respectively; P = .006, .002, and .002, respectively). Further multivariate analysis revealed that reductions in FWHM in the branches (odds ratio = 0.97, 95% CI: 0.95-0.99, P = .007) and smoking (odds ratio = 14.5, 95% CI: 1.39-151.76, P = .026) were independent predictors of flow impairment of covered branches. CONCLUSION: Pipeline embolization device stenting can cause a reduction in blood flow in branch arteries. Compared with patent branches, flow-impaired branches exhibit an increase in blood flow velocity after stenting. Smoking and ΔFWHM in the covered branches indicate flow impairment.

Transition of intracranial aneurysmal wall enhancement from high to low wall shear stress mediation with size increase: A hemodynamic study based on 7T magnetic resonance imaging.

Background: Wall shear stress (WSS) has been proved to be related to the formation, development and rupture of intracranial aneurysms. Aneurysm wall enhancement (AWE) on magnetic resonance imaging (MRI) can be caused by inflammation and have confirmed its relationship with low WSS. High WSS can also result in inflammation but the research of its correlation with AWE is lack because of the focus on large aneurysms limited by 3T MRI in most previous studies.This study aimed to assess the potential association between high or low WSS and AWE in different aneuryms. Especially the relationship between high WSS and AWE in small aneurysm. Methods: Forty-three unruptured intracranial aneurysms in 42 patients were prospectively included for analysis. 7.0 T MRI was used for imaging. Aneurysm size was measured on three-dimensional time-of-flight (TOF) images. Aneurysm-to-pituitary stalk contrast ratio (CRstalk) was calculated on post-contrast black-blood T1-weighted fast spin echo sequence images. Hemodynamics were assessed by four-dimensional flow MRI. Results: The small aneurysms group had more positive WSS-CRstalk correlation coefficient distribution (dome: 78.6 %, p = 0.009; body: 50.0 %, p = 0.025), and large group had more negative coefficient distribution (dome: 44.8 %, p = 0.001; body: 69.0 %, p = 0.002). Aneurysm size was positively correlated with the significant OSI-CRstalk correlation coefficient at the dome (p = 0.012) and body (p = 0.010) but negatively correlated with the significant WSS-CRstalk correlation coefficient at the dome (p < 0.001) and body (p = 0.017). Conclusion: AWE can be mediated by both high and low WSS, and translate from high WSS- to low WSS-mediated pathways as size increase. Additionally, AWE may serve as an indicator of the stage of aneurysm development via different correlations with hemodynamic factors.

Mo3S13 Cluster-Based Cathodes for Rechargeable Magnesium Batteries: Reversible Magnesium Association/Dissociation at the Bridging Disulfur along with Sulfur-Sulfur Bond Break/Formation.

Multivalent cation batteries are attracting increasing attention in energy-storage applications, but reversible storage of highly polarizing multivalent cations is a major difficulty for the electrode materials. In the present study, charge-delocalizing Mo3S13 cluster-based materials (crystalline (NH4)2Mo3S13 and amorphous MoSx) are designed and investigated as cathodes for rechargeable magnesium batteries. Both of the cathodes show high magnesium storage capacities (296 and 302 mAh g-1 at 100 mA g-1) and superior rate performances (76 and 80 mAh g-1 at 15 A g-1). A high area loading of 3.0 mg cm-2 could be achieved. These performances are of the highest level compared with those of reported magnesium storage materials. Further mechanism study and theoretical computation demonstrate the magnesium storage active sites are the bridging disulfur groups of the Mo3S13 cluster. The valence state of bridging disulfur decreases/increases largely during magnesiation/demagnesiation along with breaking/formation of the sulfur-sulfur bond, which makes the Mg-association/dissociation highly reversible. The sulfur-sulfur bond breaking and formation provides high reversible capacities. Prominently, the valence state increase and sulfur-sulfur bond formation of the bridging disulfur during charge weakens the bonding with Mg2+, significantly assisting the magnesium dissociation. The present study not only develops high-performance magnesium storage cathode materials but also demonstrates the importance of constructing favorable magnesium storage active sites in the high-performance cathode materials design. The findings presented herein are of great significance for the development of electrode materials for the storage of multivalent cations.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Chitinases as a potential diagnostic and prognostic biomarker for amyotrophic lateral sclerosis: a systematic review and meta-analysis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the degeneration of motor neurons, and there is currently a lack of reliable diagnostic biomarkers. This meta-analysis aimed to evaluate CHIT1, CHI3L1, and CHI3L2 levels in the cerebrospinal fluid (CSF) or blood and their diagnostic potential in ALS patients. A systematic, comprehensive search was performed of peer-reviewed English-language articles published before April 1, 2023, in PubMed, Scopus, Embase, Cochrane Library, and Web of Science. After a thorough screening, 13 primary articles were included, and their chitinases-related data were extracted for systematic review and meta-analysis. In ALS patients, the CSF CHIT1 levels were significantly elevated compared to controls with healthy control (HC) (SMD, 1.92; 95% CI, 0.78 - 3.06; P < 0.001). CHIT1 levels were elevated in the CSF of ALS patients compared to other neurodegenerative diseases (ONDS) control (SMD, 0.74; 95% CI, 0.22 - 1.27; P < 0.001) and exhibited an even more substantial increase when compared to ALS-mimicking diseases (AMDS) (SMD, 1.15; 95% CI, 0.35 - 1.94, P < 0.001). Similarly, the CSF CHI3L1 levels were significantly higher in ALS patients compared to HC (SMD, 3.16; 95% CI, 1.26 - 5.06, P < 0.001). CHI3L1 levels were elevated in the CSF of ALS patients compared to ONDS (SMD, 0.75; 95% CI, 0.32 - 1.19; P = 0.017) and exhibited a more pronounced increase when compared to AMDS (SMD, 1.92; 95% CI, 0.41 - 3.42; P < 0.001). The levels of CSF chitinases in the ALS patients showed a significant increase, supporting the role of CSF chitinases as diagnostic biomarkers for ALS.

Hybrid surgery can improve neurocognitive function in patients with internal carotid artery occlusion.

Internal carotid artery occlusion (ICAO) is a relatively uncommon but important cause of transient ischaemic attack and cerebral infarction. Hybrid surgery (HS) improves cerebral perfusion, but its impact on neurocognitive function has been controversial. Patients with symptomatic chronic ICAO treated by hybrid surgery or medical treatment from 2016 to 2019 were included. We recorded and analysed the clinical characteristics, angiographic data, outcomes and cognitive status. Functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Barthel Index, and a battery of neuropsychological tests, including the Mini-Mental State Examination (MMSE), Alzheimer Disease Assessment Scale-Cognitive Subtest (ADAS-cog), verbal fluency, and Colour Trail test Parts 1 and 2, were administered. Significant improvements in the ADAS (before, 7.5 ± 6.2 versus after, 5.2 ± 5.7; P = 0.022), MMSE (before, 25.5 ± 2.8 versus after, 28.1 ± 2.3; P = 0.013), and Colour Trail test Part 1 (before, 118.3 ± 26.5 versus after, 96.2 ± 23.1; P = 0.016) were observed six months after HS. Moreover, the abovementioned postprocedure scales were ameliorated in the HS group. This study found that in patients with multiple symptomatic ICAO and objective ipsilateral ischaemia, successful HS leads to improvement in the scores of three cognitive tests.

Thermodynamics and kinetics of Mg2+/Li+ and Mg2+/Na+ co-intercalation into layered titanium disulfide.

Hybrid Mg2+/A+ (A = Li, K, or Na) batteries are promising energy-storage devices combining the merits of a metallic Mg anode and A+ intercalation cathodes. Mg2+/Li+ co-intercalations into the cathodes have been reported, and it is believed that Li-intercalation would reduce the activation energy of the Mg-intercalation and improve the kinetics. Herein, a new understanding of the Mg2+/Li+ and Mg2+/Na+ co-intercalations is revealed for layered TiS2 from the point of view of the thermodynamics and kinetics. In addition to Li-, Na- and Mg-intercalations, Mg-Li (Mg-Na) mixed and Mg-Li (Mg-Na) interlaced intercalations can also be observed. The Mg-Li (Mg-Na) mixed intercalation is co-intercalation of Mg2+ and Li+ (Na+) into the same TiS2 layer, whereas the Mg-Li (Mg-Na) interlaced intercalation means Mg2+ and Li+ (Na+), respectively, intercalate into the adjacent TiS2 layers. The Li- (Na-) intercalation has faster kinetics, whereas the Mg-Li (Mg-Na) interlaced intercalation structure is more stable thermodynamically. The Mg-Li and Mg-Na interlaced intercalations would gradually become the predominant principles of the TiS2-based hybrid Mg2+/Li+ and Mg2+/Na+ batteries, respectively. With this stabilization, both of the hybrid Mg2+/Li+ and Mg2+/Na+ batteries show high capacities, good rate capabilities and stable cycling.

The incidence and predictors of in-stent stenosis after pipeline flow-diverter stenting for intracranial aneurysm treatment.

Background and purpose: Data on in-stent stenosis (ISS) following the flow diverter (FD) implantation method are scarce and inconsistent. In the present study, we sought to determine the incidence of ISS and identify the factors that predict its severity via the use of ordinal logistic regression. Methods: A retrospective review of our center's electronic database was conducted to identify all patients with intracranial aneurysms (IAs) who received pipeline embolization device (PED) implantation between 2016 and 2020. Patient demographics, aneurysm characteristics, procedural information, and clinical and angiographic outcomes were reviewed. ISS was quantitatively assessed on angiographic follow-ups and graded as mild (<25%), moderate (25-50%), or severe (>50%). Ordinal logistic regression was conducted to determine the predictors of stenosis severity. Results: A total of 240 patients with 252 aneurysms treated in 252 procedures were enrolled in this study. ISS has been detected in 135 (53.6%) lesions, with a mean follow-up time of 6.53 ± 3.26 months. The ISS was mild in 66 (48.9%) cases, moderate in 52 (38.5%) cases, and severe in 17 (12.6%) cases. All patients were asymptomatic, except for two of them with severe stenosis who presented with symptoms of acute cerebral thrombosis. Ordinal logistic regression identified that younger age and a longer procedure duration were independent predictors of a higher likelihood of ISS. Conclusion: ISS is a common angiographic finding after PED implantation for IAs and is presented as a largely benign course through long-term follow-up. Patients who were younger in age and had a longer procedure duration were found to be at a greater risk of developing ISS.

Long-term outcomes and dynamic changes of in-stent stenosis after Pipeline embolization device treatment of intracranial aneurysms.

BACKGROUND: Flow diverters have revolutionized the treatment of intracranial aneurysms. However, the delayed complications associated with flow diverter use are unknown. OBJECTIVE: To evaluate the incidence, severity, clinical outcomes, risk factors, and dynamic changes associated with in-stent stenosis (ISS) after treatment with a Pipeline embolization device (PED). METHODS: Patients who underwent PED treatment between 2015 and 2020 were enrolled. The angiographic, clinical, and follow-up data of 459 patients were independently reviewed by four neuroradiologists to identify ISS. Binary logistic regression was conducted to determine ISS risk factors, and an ISS-time curve was established to demonstrate dynamic changes in ISS after PED implantation. RESULTS: Of the 459 treated patients, 69 (15.0%) developed ISS. At follow-up, nine patients (2.0%) with ISS demonstrated reversal, while 18 (3.9%) developed parental artery occlusion. A total of 380 patients (82.8%) achieved complete aneurysm occlusion (O'Kelly-Marotta grade D). Patients with posterior-circulation aneurysm (OR=2.895, 95% CI (1.732 to 4.838; P<0.001) or balloon angioplasty (OR=1.992, 95% CI 1.162 to 3.414; P=0.037) were more likely to develop ISS. Patients aged >54 years (OR=0.464, 95% CI 0.274 to 0.785; P=0.006) or with a body mass index of >28 kg/m2 (OR=0.427, 95% CI 0.184 to 0.991; P=0.026) had a lower ISS risk. Intimal hyperplasia initiated by PED placement peaked within 1 year after the procedure, rarely progressed after 12 months, and tended to reverse within 24 months. CONCLUSIONS: ISS is a common, benign, and self-limiting complication of PED implantation in the Chinese population.

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

Causal relationships between human blood metabolites and intracranial aneurysm and aneurysmal subarachnoid hemorrhage: a Mendelian randomization study.

Objective: The aim of this study was to assess the causal relationships between blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. Methods: Our exposure sample consisted of 7,824 individuals from a genome-wide association study of human blood metabolites. Our outcome sample consisted of 79,429 individuals (7,495 cases and 71,934 controls) from the International Stroke Genetics Consortium, which conducted a genome-wide association study of intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. We identified blood metabolites with a potential causal effect on intracranial aneurysms and conducted sensitivity analyses to validate our findings. Results: After rigorous screening and Mendelian randomization tests, we found four, two, and three serum metabolites causally associated with intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm, respectively (all P < 0.05). Sensitivity analyses confirmed the robustness of these associations. Conclusions: Our Mendelian randomization analysis demonstrated causal relationships between human blood metabolites and intracranial aneurysm, aneurysmal subarachnoid hemorrhage, and unruptured intracranial aneurysm. Further research is required to explore the potential of targeting these metabolites in the management of intracranial aneurysm.

PointTransformer: Encoding Human Local Features for Small Target Detection.

The improvement of small target detection and obscuration handling is the key problem to be solved in the object detection task. In the field operation of chemical plant, due to the occlusion of construction workers and the long distance of surveillance shooting, it often leads to the phenomenon of missed detection. Most of the existing work uses multiple feature fusion strategies to extract different levels of features and then aggregate them into global features, which does not utilize local features and makes it difficult to improve the performance of small target detection. To address this issue, this paper introduces Point Transformer, a transformer encoder, as the core backbone of the object detection framework that first uses a priori information of human skeletal points to obtain local features and then uses both self-attention and cross-attention mechanisms to reconstruct the local features corresponding to each key point. In addition, since the target to be detected is highly correlated with the position of human skeletal points, to further boost Point Transformer's performance, a learnable positional encoding method is proposed by us to highlight the position characteristics of each skeletal point. The proposed model is evaluated on the dataset of field operation in a chemical plant. The results are significantly better than the classical algorithms. It also outperforms state-of-the-art by 12 percent of map points in the small target detection task.

Machine learning to predict in-stent stenosis after Pipeline embolization device placement.

Background: The Pipeline embolization device (PED) is a flow diverter used to treat intracranial aneurysms. In-stent stenosis (ISS) is a common complication of PED placement that can affect long-term outcome. This study aimed to establish a feasible, effective, and reliable model to predict ISS using machine learning methodology. Methods: We retrospectively examined clinical, laboratory, and imaging data obtained from 435 patients with intracranial aneurysms who underwent PED placement in our center. Aneurysm morphological measurements were manually measured on pre- and posttreatment imaging studies by three experienced neurointerventionalists. ISS was defined as stenosis rate >50% within the PED. We compared the performance of five machine learning algorithms (elastic net (ENT), support vector machine, Xgboost, Gaussian Naïve Bayes, and random forest) in predicting ISS. Shapley additive explanation was applied to provide an explanation for the predictions. Results: A total of 69 ISS cases (15.2%) were identified. Six predictors of ISS (age, obesity, balloon angioplasty, internal carotid artery location, neck ratio, and coefficient of variation of red cell volume distribution width) were identified. The ENT model had the best predictive performance with a mean area under the receiver operating characteristic curve of 0.709 (95% confidence interval [CI], 0.697-0.721), mean sensitivity of 77.9% (95% CI, 75.1-80.6%), and mean specificity of 63.4% (95% CI, 60.8-65.9%) in Monte Carlo cross-validation. Shapley additive explanation analysis showed that internal carotid artery location was the most important predictor of ISS. Conclusion: Our machine learning model can predict ISS after PED placement for treatment of intracranial aneurysms and has the potential to improve patient outcomes.

Protocol and Preliminary Results of the Establishment of Intracranial Aneurysm Database for Artificial Intelligence Application Based on CTA Images.

Background and Purpose: Unruptured intracranial aneurysms (UIAs) are increasingly being detected in clinical practice. Artificial intelligence (AI) has been increasingly used to assist diagnostic techniques and shows encouraging prospects. In this study, we reported the protocol and preliminary results of the establishment of an intracranial aneurysm database for AI application based on computed tomography angiography (CTA) images. Methods: Through a review of picture archiving and communication systems, we collected CTA images of patients with aneurysms between January 2010 and March 2021. The radiologists performed manual segmentation of all diagnosed aneurysms on subtraction CTA as the basis for automatic aneurysm segmentation. Then, AI will be applied to two stages of aneurysm treatment, namely, automatic aneurysm detection and segmentation model based on the CTA image and the aneurysm risk prediction model. Results: Three medical centers have been included in this study so far. A total of 3,190 cases of CTA examinations with 4,124 aneurysms were included in the database. All identified aneurysms from CTA images that enrolled in this study were manually segmented on subtraction CTA by six readers. We developed a structure of 3D-Unet for aneurysm detection and segmentation in CTA images. The algorithm was developed and tested using a total of 2,272 head CTAs with 2,938 intracranial aneurysms. The recall and false positives per case (FP/case) of this model for detecting aneurysms were 0.964 and 2.01, and the Dice values for aneurysm segmentation were 0.783. Conclusion: This study introduces the protocol and preliminary results of the establishment of the intracranial aneurysm database for AI applications based on CTA images. The establishment of a multicenter database based on CTA images of intracranial aneurysms is the basis for the application of AI in the diagnosis and treatment of aneurysms. In addition to segmentation, AI should have great potential for aneurysm treatment and management in the future.