Establishment and validation of a 3-month prediction model for poor functional outcomes in patients with acute cardiogenic cerebral embolism related to non-valvular atrial fibrillation.

Objectives: Cardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations. Methods: We included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables. Results: The 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model's Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854-0.902), 0.918 (95% CI, 0.857-0.979), and 0.839 (95% CI, 0.744-0.934), respectively. Conclusion: The N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings.

Predictive Value of the Neutrophil-to-Lymphocyte Ratio and C-Reactive Protein in Patients with Idiopathic Facial Nerve Palsy.

Objective: This study aims to investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) in patients with idiopathic facial nerve palsy. Methods: The clinical data of patients with idiopathic facial nerve palsy were retrospectively analyzed. After three months of follow-up, patients were divided into good prognosis and poor prognosis, and the correlation between NLR, CRP and idiopathic facial nerve palsy was analyzed. Results: Negative correlation of NLR with Portmann score in idiopathic facial nerve palsy (r=-0.788, P<0.05); In contrast to the group with poor prognosis, patients in good prognosis group had low levels of body mass index (BMI), NLR, and C-reactive protein (CRP), and high Portmann score (P<0.05); Multivariate logistic regression analysis showed Portmann score (OR=1.268, 95% CI (1.005-1.616)), NLR (OR=0.262, 95% CI (0.128-0.533)) and CRP levels (OR=0.949, 95% CI (0.895-0.989)) were risk factors of poor prognosis for patients with idiopathic facial nerve palsy. The area under the receiver operator characteristic (ROC) curve of NLR and CRP levels in predicting poor facial nerve function was 0.764 and 0.697, the specificity was 85.5% and 75.0%, and the sensitivity was 74. 0% and 76.0%, respectively. The ROC curve of the combined diagnosis was 0.829, the specificity was 80.7%, and the sensitivity was 82.0%. Conclusion: Elevated NLR and CRP are associated with a poor prognosis of idiopathic facial nerve palsy and can serve as an indicator for clinical prognosis, and can be widely used in clinical.

LSSVC: A Learned Spatially Scalable Video Coding Scheme.

Traditional block-based spatially scalable video coding has been studied for over twenty years. While significant advancements have been made, the scope for further improvement in compression performance is limited. Inspired by the success of learned video coding, we propose an end-to-end learned spatially scalable video coding scheme, LSSVC, which provides a new solution for scalable video coding. In LSSVC, we propose to use the motion, texture, and latent information of the base layer (BL) as interlayer information for compressing the enhancement layer (EL). To reduce interlayer redundancy, we design three modules to leverage the upsampled interlayer information. Firstly, we design a contextual motion vector (MV) encoder-decoder, which utilizes the upsampled BL motion information to help compress high-resolution MV. Secondly, we design a hybrid temporal-layer context mining module to learn more accurate contexts from the EL temporal features and the upsampled BL texture information. Thirdly, we use the upsampled BL latent information as an interlayer prior for the entropy model to estimate more accurate probability distribution parameters for the high-resolution latents. Experimental results show that our scheme surpasses H.265/SHVC reference software by a large margin. Our code is available at https://github.com/EsakaK/LSSVC.

VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision.

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and - as usual - before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities.

Prognostic Value of the Neutrophil-to-Lymphocyte Ratio in Patients with Chronic Internal Carotid Artery Occlusion Complicated by Cerebral Infarction.

Purpose: This study aims to investigate the prognostic value of the peripheral neutrophil-to-lymphocyte ratio (NLR) in patients with chronic internal carotid artery occlusion (CICAO) complicated by cerebral infarction. Patients and Methods: The clinical data of 99 CICAO patients complicated by cerebral infarction were retrospectively analyzed. The modified Rankin Scale (mRS) was used to assess their 3-month prognosis, and a multivariate logistic regression model was established to explore risk factors for poor prognosis. Results: Multivariate logistic regression analysis demonstrated that NLR (OR=2.114; 95% CI: 1.129-3.959) and baseline National Institute of Health Stroke Scale (NIHSS; OR=1.288, 95% CI: 1.053-1.574) score were risk factors of poor prognosis. The area under the receiver operator characteristic (ROC) curve of NLR in predicting the 3-month outcome after onset was 0.717 (95% CI: 0.606-0.828, P<0.000). The optimal cut-off value was 3.22, with a sensitivity of 0.743 and a specificity of 0.791. Conclusion: NLR is an independent risk factor for the poor prognosis of CICAO patients complicated by cerebral infarction and can serve as an indicator for clinical prognosis.

Attribute Artifacts Removal for Geometry-Based Point Cloud Compression.

Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, especially under low bitrate scenarios. In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC. We first construct a graph based on point cloud geometry coordinates and then use the Chebyshev graph convolutions to extract features of point cloud attributes. Considering that one point may be correlated with points both near and far away from it, we propose a multi-scale scheme to capture the short- and long-range correlations between the current point and its neighboring and distant points. To address the problem that various points may have different degrees of artifacts caused by adaptive quantization, we introduce the quantization step per point as an extra input to the proposed network. We also incorporate a weighted graph attentional layer into the network to pay special attention to the points with more attribute artifacts. To the best of our knowledge, this is the first attribute artifacts removal method for G-PCC. We validate the effectiveness of our method over various point clouds. Objective comparison results show that our proposed method achieves an average of 9.74% BD-rate reduction compared with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective comparison results present that visual artifacts such as color shifting, blurring, and quantization noise are reduced.

Decreased Serum Total Bilirubin Level Predicts Early Neurological Deterioration in Patients with Acute Ischemic Stroke.

OBJECTIVE: This study aimed to investigate the relationship between serum total bilirubin (TBil) level and early neurological deterioration (END) in patients with acute ischemic stroke of large artery atherosclerosis. PATIENTS AND METHODS: In this retrospective study, a total of 291 patients with acute ischemic stroke were enrolled. The demographic and laboratory dates were collected. Stroke severity had been assessed using the National Institutes of Health Stroke Scale (NIHSS). Multivariable logistic regression was used to examine the independent association between TBil and END. RESULTS: Approximately 63 (21.6%) of the patients were diagnosed with END within the first seven days. The proportion of hypertension, diabetes mellitus (DM) and previous stroke/transient ischemic attack (TIA) was significant greater in the lowest quartile (<9.8 µmol/l) of TBil. The proportion of patients with an elevated TBil levels was significantly lower in the END group than in the non-END group. After controlling for covariates, the first quartiles (<9.8 µmol/l) of TBil were still associated with END. In addition, an increased level of CRP and age were also associated with an increased risk of END. CONCLUSION: The TBil levels in patients with acute cerebral infarction may be a useful biomarker for the prediction of END.

A Chinese CADASIL Family with a Novel Mutation on Exon 10 of Notch3 Gene.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), which is caused by the Notch3 gene mutation, has its unique clinical and imaging characteristics. Here we present a Chinese family with a novel mutation on exon 10 of Notch3 gene. METHODS: Clinical and MRI data of the three patients in the family during the 7-year follow-up were collected. The CADASIL Scale Score was calculated to evaluate the disease risk of the three patients at their first admission or clinic visit. Five family members underwent genetic test. RESULTS: Genetic test confirmed the diagnosis of CADASIL in this family. A novel mutation of p.C533S on exon 10 of Notch3 gene was detected. The CADASIL score of the proband and her sister was both 17 and that of her brother was 14. CONCLUSIONS: Our report not only expands the mutation spectrum of Notch3 gene in CADASIL, but also shows the distinct heterogeneity of CADASIL patients in the same family with the same mutation.