FLAIR vessel hyperintensities predict functional outcomes in patients with acute ischemic stroke treated with medical therapy.

OBJECTIVES: The prognostic value of fluid-attenuated inversion recovery vessel hyperintensity (FVH) remains controversial in acute ischemic stroke (AIS). The objective was to investigate whether the presence of FVH could predict long-term functional outcomes in patients with AIS receiving medical therapy. METHODS: Consecutive AIS patients with anterior circulation large vessel stenosis (LVS) in multiple centers between January 2019 and December 2020 were studied. Presence of FVH was identified and evaluated as FVH (+). Quantification of FVH was performed using an FVH-Alberta Stroke Program Early CT Score (ASPECTS) system and divided into grades: FVH-ASPECTS of 0 = grade 0; 1-2 = grade 1; 3-7 = grade 2. Poor functional outcome was defined as modified Rankin scale > 2 at 3 months. RESULTS: Overall, 175 patients were analyzed (age, 64.31 ± 13.47 years; men, 65.1%), and 78.9% patients presented with FVH. Larger infarct volume (19.90 mL vs. 5.50 mL, p < 0.001), higher rates of FVH (+) (92.0% vs. 65.9%, p < 0.001), and higher FVH grades (grade 2, 34.5% vs. 10.2%, p < 0.001) were more prone to be observed in patients with poor functional outcomes. FVH (+) with infarct volume larger than 6.265 mL (adjusted odds ratio [aOR] 6.03, 95% confidence interval [CI] 1.82-19.98) and FVH grade (grade 1, aOR 3.07, 95% CI 1.12-8.43; grade 2, aOR 5.80, 95% CI 1.59-21.11) were independently associated with poor functional outcomes. CONCLUSION: FVH (+) combined with large infarct volume and high FVH grade can predict poor long-term functional outcomes in patients with LVS who receive medical therapy. KEY POINTS: • FVH is expected to be a contrast agent-independent alternative for assessing hemodynamic status in the acute stage of stroke. • FVH (+) and high FVH grade, quantified by FVH-ASPECTS rating system and grades, are associated with large infarct volume. • The combination of FVH and DWI-based infarct volume has independent predictive value for long-term functional outcomes in AIS patients with large artery stenosis treated with medical therapy.

Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.

BACKGROUND: Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. METHODS: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model. RESULTS: Fifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I2) of (0.87, 0.84-0.90, [I2 99.0%]) and a weak sensitivity of (0.68, 0.58-0.77, [I2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm's AUC for predicting CKD prognosis was 0.82 (0.79-0.85), with the pool sensitivity of (0.64, 0.49-0.77, [I2 99.20%]) and pool specificity of (0.84, 0.74-0.91, [I2 99.84%]). The ML algorithm's AUC for predicting IgA nephropathy prognosis was 0.78 (0.74-0.81), with the pool sensitivity of (0.74, 0.71-0.77, [I2 7.10%]) and pool specificity of (0.93, 0.91-0.95, [I2 83.92%]). CONCLUSION: Taking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.

Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks.

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.

Distribution and phytotoxicity of soil labile aluminum fractions and aluminum species in soil water extracts and their effects on tall fescue.

Soil acidification can alter the biogeochemistry of ecosystems and adversely affect biota; however, there are still many debates about the toxicity of aluminum (Al) fractions and Al species in soil:water extracts to plants. In this study, five crude soils with different pH values (4.92-8.51) were collected, seeded with tall fescue and grown in rhizosphere boxes for 120 days. Then, soil properties, labile Al fractions and Al species in soil:water extracts were determined, and their toxicities to plants were analyzed. Our study showed that a stable exchangeable Al fraction (ExAl) pool exists and is supplied by other labile Al fractions. Dissolution of Al from adsorbed hydroxyl-Al fraction (HyAl) and organic-Al fraction (OrAl) may play important roles in soil Al toxicity, as HyAl and OrAl account for major parts of soil labile Al. Additionally, Al3+ and mononuclear hydroxyl-Al species in soil:water extracts have few effects to plants. Nevertheless, high negative correlations were found between Al-F- complexes and tall fescue biomass, indicating their toxicity in the natural soil environment. Thus, in many cases, Al3+ toxicity should not be emphasized because of its lower activity in soil water extracts. Moreover, toxicities of AlF3(aq) and AlF4- to plants should be emphasized, because they have been confirmed in soil water extracts in this study.

Impact of viral filters on accuracy of cardiopulmonary testing and spirometry.

The present study found that using viral filters at the proximal end of a spirometry and CPET test circuit did not significantly alter the test results, with the exception of a marginal decrease noted in peak work rate https://bit.ly/3Vkew95.

Evaluation of postcontrast images of intracranial tumors at 7T and 3T MRI: An intra-individual comparison study.

AIM: This study aimed to evaluate the diagnostic value of ultrahigh-field magnetic resonance imaging (MRI) for brain tumors in clinical practice. METHODS: Thirty patients with brain tumors underwent 7- and 3-T MRI. The performance and diagnostic confidence of 7- and 3-T MRI in the visualization of tumor details such as internal structure and feeding artery were evaluated by radiologists. Contrast-enhanced region performance and tumor detail diagnostic confidence score (DCS) were calculated and compared between 7 and 3T using Wilcoxon rank sum test. RESULTS: In 19 with obvious enhancement and 11 cases without obvious enhancement, 7- and 3-T MRI showed similar performance. The tumors' internal structure and feeding artery were more clearly depicted by 7-T MRI (62.2% and 54.4%, respectively) than by 3-T MRI (2.2% and 6.7%, respectively). Furthermore, the mean DCSs of both internal structure and feeding artery were higher at 7T than at 3T (internal structure: 16.29 ± 9.67 vs. -5.79 ± 4.12, p = 0.028; feeding artery: 21.96 ± 6.93 vs. 4.46 ± 7.07, p = 0.028). The DCS was more significantly improved in the senior radiologist group. CONCLUSION: Better visualization of brain tumor details and higher tumor detail diagnostic confidence can be obtained with 7-T MRI.

Association of fluid-attenuated inversion recovery vascular hyperintensity with ischaemic events in internal carotid artery or middle cerebral artery occlusion.

BACKGROUND AND PURPOSE: Individuals with intracranial artery occlusion have high rates of ischaemic events and recurrence. It has been challenging to identify patients who had high-risk stroke using a simple, valid and non-invasive screening approach. This study aimed to investigate whether fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), a specific imaging sign on the FLAIR sequence, could be a predictor of ischaemic events in a population with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusion. METHODS: We retrospectively analysed 147 patients (mean 60.43±12.83 years) with 149 lesions, including 37 asymptomatic and 112 symptomatic cases of ICA or MCA occlusion. Symptomatic occlusion was considered if ischaemic events were present in the relevant territory within 90 days. FVH Alberta Stroke Program Early Computed Tomography Score (FVH-ASPECTS: 0-7, with 0 indicating absence of FVH and 7 suggesting prominent FVH) and collateral circulation grade were assessed for each participant. Multivariable logistic regression analysis was performed to detect independent markers associated with symptomatic status. RESULTS: A lower FVH-ASPECTS was associated with a more favourable collateral circulation grade (rho=-0.464, p<0.0001). The FVH-ASPECTS was significantly lower in the asymptomatic occlusion group than in the symptomatic occlusion group (p<0.0001). FVH-ASPECTS (Odd ratio, 2.973; 95% confidence interval, 1.849 to 4.781; p<0.0001) was independently associated with symptomatic status after adjustment for age, sex, lesion location and collateral circulation grade in the multivariate logistic regression. The area under the curve was 0.861 for the use of FVH-ASPECTS to identify symptomatic occlusion. CONCLUSIONS: The ability to discriminate symptomatic from asymptomatic occlusion suggests that FVH may be a predictor of stroke. As a simple imaging sign, FVH may serve as a surrogate for haemodynamic impairments and can be used to identify high-risk stroke cases early in ICA or MCA occlusion.

Time-restricted feeding's effect on overweight and obese patients with chronic kidney disease stages 3-4: A prospective non-randomized control pilot study.

Background: Time-restricted feeding (TRF) has become a popular weight loss method in recent years. It is widely used in the nutritional treatment of normal obese people and obese people with chronic diseases such as diabetes mellitus and hypertension, and has shown many benefits. However, most TRF studies have excluded chronic kidney disease (CKD) patients, resulting in a lack of sufficient evidence-based practice for the efficacy and safety of TRF therapy for CKD. Therefore, we explore the efficacy and safety of TRF in overweight and obese patients with moderate-to-severe stage CKD through this pilot study, and observe patient compliance to assess the feasibility of the therapy. Methods: This is a prospective, non-randomized controlled short-term clinical trial. We recruited overweight and obese patients with CKD stages 3-4 from an outpatient clinic and assigned them to either a TRF group or a control diet (CD) group according to their preferences. Changes in renal function, other biochemical data, anthropometric parameters, gut microbiota, and adverse events were measured before the intervention and after 12 weeks. Results: The change in estimated glomerular filtration rate (eGFR) before and after intervention in the TRF group (Δ = 3.1 ± 5.3 ml/min/1.73m2) showed significant improvement compared with the CD group (Δ = -0.8 ± 4.4 ml/min/1.73m2). Furthermore, the TRF group had a significant decrease in uric acid (Δ = -70.8 ± 124.2 µmol/L), but an increase in total protein (Δ = 1.7 ± 2.5 g/L), while the changes were inconsistent for inflammatory factors. In addition, the TRF group showed a significant decrease in body weight (Δ = -2.8 ± 2.9 kg) compared to the CD group, and body composition indicated the same decrease in body fat mass, fat free mass and body water. Additionally, TRF shifted the gut microbiota in a positive direction. Conclusion: Preliminary studies suggest that overweight and obese patients with moderate-to-severe CKD with weight loss needs, and who were under strict medical supervision by healthcare professionals, performed TRF with good compliance. They did so without apparent adverse events, and showed efficacy in protecting renal function. These results may be due to changes in body composition and alterations in gut microbiota.

Preventive effect and mechanism of Tibetan tea extract on thrombosis in arachidonic acid-induced zebrafish determined via RNA-seq transcriptome profiles.

Thrombosis is a key pathological event in cardiovascular diseases and is also the most important targeting process for their clinical management. In this study, arachidonic acid (AA) was used to induce thrombus formation in zebrafish larvae. Blood flow, red blood cell (RBCs) aggregation and cellular oxidative stress were measured to evaluate the antithrombotic effect of Tibetan tea (TT). Meanwhile, the potential molecular mechanism was further explored by transcriptome sequencing (RNA-seq). The results indicated that TT could significantly restore heart RBCs intensity of thrombotic zebrafish, whilst decreasing RBCs accumulation in the caudal vein. The transcriptome analysis revealed that the preventive effect of TT on thrombosis could be mostly attributed to changes in lipid metabolism related signaling pathways, such as fatty acid metabolism, glycerollipid metabolism, ECM-receptor interaction and steroid biosynthesis signaling pathway. This study demonstrated that Tibetan tea could alleviate thrombosis by reducing oxidative stress levels and regulating lipid metabolism.

Euler common spatial patterns for EEG classification.

The technique of common spatial patterns (CSP) is a widely used method in the field of feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine distance can enlarge the distance between samples of different classes, we propose the Euler CSP (e-CSP) for the feature extraction of EEG signals, and it is then used for EEG classification. The e-CSP is essentially the conventional CSP with the Euler representation. It includes the following two stages: each sample value is first mapped into a complex space by using the Euler representation, and then the conventional CSP is performed in the Euler space. Thus, the e-CSP is equivalent to applying the Euler representation as a kernel function to the input of the CSP. It is computationally as straightforward as the CSP. However, it extracts more discriminative features from the EEG signals. Extensive experimental results illustrate the discrimination ability of the e-CSP.

A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test.

Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.

Directed Connectivity Analysis of the Brain Network in Mathematically Gifted Adolescents.

The neurocognitive characteristics of mathematically gifted adolescents are characterized by highly developed functional interactions between the right hemisphere and excellent cognitive control of the prefrontal cortex, enhanced frontoparietal cortex, and posterior parietal cortex. However, it is still unclear when and how these cortical interactions occur. In this paper, we used directional coherence analysis based on Granger causality to study the interactions between the frontal brain area and the posterior brain area in the mathematical frontoparietal network system during deductive reasoning tasks. Specifically, the scalp electroencephalography (EEG) signal was first converted into a cortical dipole source signal to construct a Granger causality network over the θ-band and γ-band ranges. We constructed the binary Granger causality network at the 40 pairs of cortical nodes in the frontal lobe and parietal lobe across the θ-band and the γ-band, which were selected as regions of interest (ROI). We then used graph theory to analyze the network differences. It was found that, in the process of reasoning tasks, the frontoparietal regions of the mathematically gifted show stronger working memory information processing at the θ-band. Additionally, in the middle and late stages of the conclusion period, the mathematically talented individuals have less information flow in the anterior and posterior parietal regions of the brain than the normal subjects. We draw the conclusion that the mathematically gifted brain frontoparietal network appears to have more "automated" information processing during reasoning tasks.

Optimization of electronic prescription for parallel external fixator based on genetic algorithm.

PURPOSE: For the parallel external fixator, there are some defects, such as uneven distraction rate, unbearable pain and uncontrollable movement trajectory in practical clinical applications. In order to solve the problems, a new deformity correction algorithm, which is used to calculate the elongation of the six struts, is developed. Meanwhile, the corresponding computer software for getting the electronic prescription is developed. METHODS: First, the trajectory of the moving bone is planned by Cartesian coordinate path control. Next, the prescription is obtained from the trajectory by the inverse pose solution algorithm. Finally, the genetic algorithm is utilized to optimize the achieved prescription. From the year of 2015 to 2018, twenty-three patients with complicated tibia deformity are treated by using parallel external fixator and the developed computer software. All patients have standing, patella-forward, full-length post-operative AP and lateral radiographs of the lower limbs with the complete proximal ring for getting the deformity parameters and frame parameters. These parameters are input into the computer software to calculate a daily prescription schedule for strut adjustment. Radiographs are taken regularly to determine the effects of recovery during the correction process. RESULTS: The mean time of follow-up is 18 months (range 11-40 months). All patients reach the requirements for deformity correction, and their symptoms and appearance are improved significantly. No cases of wound infections or complications occur, and no severe pain came as well during the correction process. CONCLUSIONS: By using the computer-aided parallel external fixator for the correction of lower limb deformities, satisfactory outcomes can be achieved. Hence, this method greatly improved the treatment of these patients in a clinical application.

The computer-aided parallel external fixator for complex lower limb deformity correction.

PURPOSE: Since parameters of the parallel external fixator are difficult to measure and calculate in real applications, this study developed computer software that can help the doctor measure parameters using digital technology and generate an electronic prescription for deformity correction. METHODS: According to Paley's deformity measurement method, we provided digital measurement techniques. In addition, we proposed an deformity correction algorithm to calculate the elongations of the six struts and developed a electronic prescription software. At the same time, a three-dimensional simulation of the parallel external fixator and deformed fragment was made using virtual reality modeling language technology. From 2013 to 2015, fifteen patients with complex lower limb deformity were treated with parallel external fixators and the self-developed computer software. All of the cases had unilateral limb deformity. The deformities were caused by old osteomyelitis in nine cases and traumatic sequelae in six cases. A doctor measured the related angulation, displacement and rotation on postoperative radiographs using the digital measurement techniques. Measurement data were input into the electronic prescription software to calculate the daily adjustment elongations of the struts. Daily strut adjustments were conducted according to the data calculated. The frame was removed when expected results were achieved. Patients lived independently during the adjustment. RESULTS: The mean follow-up was 15 months (range 10-22 months). The duration of frame fixation from the time of application to the time of removal averaged 8.4 months (range 2.5-13.1 months). All patients were satisfied with the corrected limb alignment. No cases of wound infections or complications occurred. CONCLUSIONS: Using the computer-aided parallel external fixator for the correction of lower limb deformities can achieve satisfactory outcomes. The correction process can be simplified and is precise and digitized, which will greatly improve the treatment in a clinical application.