Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. METHODS: Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. RESULTS: Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). CONCLUSIONS: The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

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.

Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing.

Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Assessment of ultrashort echo time (UTE) T2* mapping at 3T for the whole knee: repeatability, the effects of fat suppression, and knee position.

Background: Knee tissues such as tendon, ligament and meniscus have short T2* relaxation times and tend to show little to no signal in conventional magnetic resonance acquisitions. An ultrashort echo time (UTE) technique offers a unique tool to probe fast-decaying signals in these tissues. Clinically relevant factors should be evaluated to quantify the sensitivity needed to distinguish diseased from control tissues. Therefore, the objectives of this study were to (I) quantify the repeatability of UTE-T2* relaxation time values, and (II) evaluate the effects of fat suppression and (III) knee positioning on UTE-T2* relaxation time quantification. Methods: A dual-echo, three-dimensional center-out radially sampling UTE and conventional gradient echo sequences were utilized to image gadolinium phantoms, one ex-vivo specimen, and five in-vivo subjects on a clinical 3T scanner. Scan-rescan images from the phantom and in-vivo experiments were used to evaluate the repeatability of T2* relaxation time values. Fat suppressed and non-suppressed images were acquired for phantoms and the ex-vivo specimen to evaluate the effect of fat suppression on T2* relaxation time quantifications. The effect of knee positioning was evaluated by imaging in-vivo subjects in extended and flexed positions within the knee coil and comparing T2* relaxation times quantified from tissues in each position. Results: Phantom and in-vivo measurements demonstrated repeatable T2* mapping, where the percent difference between T2* relaxation time quantified from scan-rescan images was less than 8% for the phantom and knee tissues. The coefficient of variation across fat suppressed and non-suppressed images was less than 5% for the phantoms and ex-vivo knee tissues, showing that fat suppression had a minimal effect on T2* relaxation time quantification. Knee position introduced variability to T2* quantification of the anterior cruciate ligament, posterior cruciate ligament, and patellar tendon, with percent differences exceeding 20%, but the meniscus showed a percent difference less than 10%. Conclusions: The 3D radial UTE sequence presented in this study could potentially be used to detect clinically relevant changes in mean T2* relaxation time, however, reproducibility of these values is impacted by knee position consistency between scans.

IL-27p28 specifically regulates MHC II expression in macrophages through CIITA.

Antigen-presenting cells (APCs) constantly express major histocompatibility complex II (MHC II), including macrophages and dendritic cells (DCs) which deliver antigens to CD4+ T cells and play an important role in adaptive immunity. The expression of MHC II is controlled by the transcriptional coactivator CIITA. Interleukin-27 (IL-27), a newly discovered IL-12 family cytokine, is composed of p28 and EBI3 subunits. In this study, we used IL-27p28 conditional knock-out mice to investigate the regulatory effects of IL-27p28 on macrophage polarization and the expression of MHC II in macrophages. We found that MHC II expression was upregulated in the bone marrow-derived and peritoneal exudate macrophages (BMDMs; PEMs) from IL-27p28-deficient mice, with their inflammation regulating function unaffected. We also demonstrated that in the APCs, IL-27p28 selectively regulated MHC II expression in macrophages but not in dendritic cells. During Pseudomonas aeruginosa (P. aeruginosa) reinfection, higher survival rate, bacterial clearance, and ratio of CD4+/CD8+ T cells in the spleen during the specific immune phase were observed in IL-27p28 defect mice, as well as an increased MHC II expression in alveolar macrophages (AMs). But these did not occur in the first infection. For the first time we discovered that IL-27p28 specifically regulates the expression of MHC II in macrophages by regulating CIITA, while its absence enhances antigen presentation and adaptive immunity against P. aeruginosa.

FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation.

Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.

Engineered Cytokine-Primed Extracellular Vesicles with High PD-L1 Expression Ameliorate Type 1 Diabetes.

Type 1 diabetes (T1D), which is a chronic autoimmune disease, results from the destruction of insulin-producing ß cells targeted by autoreactive T cells. The recent discovery that mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) function as therapeutic tools for autoimmune conditions has attracted substantial attention. However, the in vivo distribution and therapeutic effects of MSC-EVs potentiated by pro-inflammatory cytokines in the context of T1D have yet to be established. Here, it is reported that hexyl 5-aminolevulinate hydrochloride (HAL)-loaded engineered cytokine-primed MSC-EVs (H@TI-EVs) with high expression of immune checkpoint molecule programmed death-legend 1 (PD-L1) exert excellent inflammatory targeting and immunosuppressive effects for T1D imaging and therapy. The accumulated H@TI-EVs in injured pancreas not only enabled the fluorescence imaging and tracking of TI-EVs through the intermediate product protoporphyrin (PpIX) generated by HAL, but also promoted the proliferative and anti-apoptotic effects of islet ß cells. Further analysis revealed that H@TI-EVs exhibited an impressive ability to reduce CD4+ T cell density and activation through the PD-L1/PD-1 axis, and induced M1-to-M2 macrophage transition to reshape the immune microenvironment, exhibiting high therapeutic efficiency in mice with T1D. This work identifies a novel strategy for the imaging and treatment of T1D with great potential for clinical application.

Identification and Characterization of a Predominant Hydrophobin in the Edible Mushroom Grifola frondosa.

Hydrophobins (HFBs) are a group of small, secreted amphipathic proteins of fungi with multiple physiological functions and potential commercial applications. In this study, HFB genes of the edible mushroom, Grifola frondosa, were systematically identified and characterized, and their transcriptional profiles during fungal development were determined. In total, 19 typical class I HFB genes were discovered and bioinformatically analyzed. Gene expression profile examination showed that Gf.hyd9954 was particularly highly upregulated during primordia formation, suggesting its major role as the predominant HFB in the lifecycle of G. frondosa. The wettability alteration profile and the surface modification ability of recombinant rGf.hyd9954 were greater than for the Grifola HFB HGFII-his. rGf.hyd9954 was also demonstrated to form the typical class I HFB characteristic-rodlet bundles. In addition, rGf.hyd9954 was shown to possess nanoparticle characteristics and emulsification activities. This research sheds light on the regulation of fungal development and its association with the expression of HFB genes.

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.

Background: Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers. Objective: We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers. Methods: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis. Results: A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model. Conclusion: As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: A retrospective, multicentre cohort study.

Background: Acute ischaemic stroke (AIS) is a highly heterogeneous disorder and warrants further investigation to stratify patients with different outcomes and treatment responses. Using a large-scale stroke registry cohort, we applied data-driven approach to identify novel phenotypes based on multiple biomarkers. Methods: In a nationwide, prospective, 201-hospital registry study taking place in China between August 01, 2015 and March 31, 2018, the patients with AIS who were over 18 years of age and admitted to the hospital within 7 days from symptom onset were included. 92 biomarkers were included in the analysis. In the derivation cohort (n=9539), an unsupervised Gaussian mixture model was applied to categorize patients into distinct phenotypes. A classifier was developed using the most important biomarkers and was applied to categorize patients into their corresponding phenotypes in an validation cohort (n=2496). The differences in biological features, clinical outcomes, and treatment response were compared across the phenotypes. Findings: We identified four phenotypes with distinct characteristics in 9288 patients with non-cardioembolic ischaemic stroke. Phenotype 1 was associated with abnormal glucose and lipid metabolism. Phenotype 2 was characterized by inflammation and abnormal renal function. Phenotype 3 had the least laboratory abnormalities and small infarct lesions. Phenotype 4 was characterized by disturbance in homocysteine metabolism. Findings were replicated in the validation cohort. In comparison with phenotype 3, the risk of stroke recurrence (adjusted hazard ratio [aHR] 2.02, 95% confidence intervals [CI] 1.04-3.94), and mortality (aHR 18.14, 95%CI 6.62-49.71) at 3-month post-stroke were highest in phenotype 2, followed by phenotype 4 and phenotype 1, after adjustment for age, gender, smoking, drinking, history of stroke, hypertension, diabetes mellitus, dyslipidemia, and coronary heart disease. The Monte Carlo simulation showed that the patients with phenotype 2 could benefit from high-intensity statin therapy. Interpretation: A data-driven approach could aid in the identification of patients at a higher risk of adverse clinical outcomes following non-cardioembolic ischaemic stroke. These phenotypes, based on different pathophysiology, can suggest individualized treatment plans. Funding: Beijing Natural Science Foundation (grant number Z200016), Beijing Municipal Committee of Science and Technology (grant number Z201100005620010), National Natural Science Foundation of China (grant number 82101360, 92046016, 82171270), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant number 2019-I2M-5-029).

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction.

Objective: To compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment. Background: Bedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis. To overcome these limitations, we developed an automatic workflow to process echocardiograms, including view selection, segmentation, detection of RWMAs and quantification of cardiac function that was trained and validated on image obtained from bedside and standard equipment. Methods: We collected 4,142 examinations from one hospital as training and internal testing dataset and 2,811 examinations from other hospital as the external test dataset. For data pre-processing, we adopted DL model to automatically recognize three apical views and segment the left ventricle. Detection of RWMAs was achieved with 3D convolutional neural networks (CNN). Finally, DL model automatically measured the size of cardiac chambers and left ventricular ejection fraction. Results: The view selection model identified the three apical views with an average accuracy of 96%. The segmentation model provided good agreement with manual segmentation, achieving an average Dice of 0.89. In the internal test dataset, the model detected RWMAs with AUC of 0.91 and 0.88 respectively for standard and bedside ultrasound. In the external test dataset, the AUC were 0.90 and 0.85. The automatic cardiac function measurements agreed with echocardiographic report values (e. g., mean bias is 4% for left ventricular ejection fraction). Conclusion: We present a fully automated echocardiography pipeline applicable to both standard and bedside ultrasound with various functions, including view selection, quality control, segmentation, detection of the region of wall motion abnormalities and quantification of cardiac function.

Neutrophil-mediated delivery of the combination of colistin and azithromycin for the treatment of bacterial infection.

Novel treatment strategies are in urgent need to deal with the rapid development of antibiotic-resistant superbugs. Combination therapies and targeted drug delivery have been exploited to promote treatment efficacies. In this study, we loaded neutrophils with azithromycin and colistin to combine the advantages of antibiotic combinations, targeted delivery, and immunomodulatory effect of azithromycin to treat infections caused by Gram-negative pathogens. Delivery of colistin into neutrophils was mediated by fusogenic liposome, while azithromycin was directly taken up by neutrophils. Neutrophils loaded with the drugs maintained the abilitity to generate reactive oxygen species and migrate. In vitro assays demonstrated enhanced bactericidal activity against multidrug-resistant pathogens and reduced inflammatory cytokine production by the drug-loaded neutrophils. A single intravenous administration of the drug-loaded neutrophils effectively protected mice from Pseudomonas aeruginosa infection in an acute pneumonia model. This study provides a potential effective therapeutic approach for the treatment of bacterial infections.

Elevated Fasting Blood Glucose Levels Are Associated with Worse Clinical Outcomes in COVID-19 Patients Than in Pneumonia Patients with Bacterial Infections.

Aims: We investigate how fasting blood glucose (FBG) levels affect the clinical severity in coronavirus disease 2019 (COVID-19) patients, pneumonia patients with sole bacterial infection, and pneumonia patients with concurrent bacterial and fungal infections. Methods: We enrolled 2761 COVID-19 patients, 1686 pneumonia patients with bacterial infections, and 2035 pneumonia patients with concurrent infections. We used multivariate logistic regression analysis to assess the associations between FBG levels and clinical severity. Results: FBG levels in COVID-19 patients were significantly higher than in other pneumonia patients during hospitalisation and at discharge (all p < 0.05). Among COVID-19 patients, the odds ratios of acute respiratory distress syndrome (ARDS), respiratory failure (RF), acute hepatitis/liver failure (AH/LF), length of stay, and intensive care unit (ICU) admission were 12.80 (95% CI, 4.80−37.96), 5.72 (2.95−11.06), 2.60 (1.20−5.32), 1.42 (1.26−1.59), and 5.16 (3.26−8.17) times higher in the FBG ≥7.0 mmol/L group than in FBG < 6.1 mmol/L group, respectively. The odds ratios of RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with sole bacterial infection (3.70 [2.21−6.29]; 1.56 [1.17−2.07]; 0.98 [0.88−1.11]; 2.06 [1.26−3.36], respectively). The odds ratios of ARDS, RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with concurrent infections (3.04 [0.36−6.41]; 2.31 [1.76−3.05]; 1.21 [0.97−1.52]; 1.02 [0.93−1.13]; 1.72 [1.19−2.50], respectively). Among COVID-19 patients, the incidence rate of ICU admission on day 21 in the FBG ≥ 7.0 mmol/L group was six times higher than in the FBG < 6.1 mmol/L group (12.30% vs. 2.21%, p < 0.001). Among other pneumonia patients, the incidence rate of ICU admission on day 21 was only two times higher. Conclusions: Elevated FBG levels at admission predict subsequent clinical severity in all pneumonia patients regardless of the underlying pathogens, but COVID-19 patients are more sensitive to FBG levels, and suffer more severe clinical complications than other pneumonia patients.

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.

Importance: Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. Objective: To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. Design, Setting, and Participants: This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. Main Outcomes and Measures: Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. Results: A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. Conclusions and Relevance: These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.

Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study.

BACKGROUND: White matter (WM) microstructural abnormalities have been observed in diabetes. However, evidence of prediabetes is currently lacking. This study aims to investigate the WM integrity in prediabetes and diabetes. We also assess the association of WM abnormalities with glucose metabolism status and continuous glucose measures. METHODS: The WM integrity was analyzed using cross-sectional baseline data from a population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. The cohort, including a total of 2218 cases with the mean age of 61.3 ± 6.6 years and 54.1% female, consisted of 1205 prediabetes which are categorized into two subgroups (a group of 254 prediabetes with combined impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) and the other group of 951 prediabetes without combined IFG/IGT), 504 diabetes, and 509 normal control subjects. Alterations of WM integrity were determined by diffusion tensor imaging along with tract-based spatial statistics analysis to compare diffusion metrics on WM skeletons between groups. The mixed-effects multivariate linear regression models were used to assess the association between WM microstructural alterations and glucose status. FINDINGS: Microstructural abnormalities distributed in local WM tracts in prediabetes with combined IFG/IGT and spread widely in diabetes. These WM abnormalities are associated with higher glucose measures. INTERPRETATION: Our findings suggest that WM microstructural abnormalities are already present at the prediabetes with combined IFG/IGT stage. Preventative strategies should begin early to maintain normal glucose metabolism and avert further destruction of WM integrity. FUNDING: Partially supported by National Key R&D Program of China (2016YFC0901002).

Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review.

Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the brain and the external world. Neuroscientific research in the past two decades has indicated a tremendous potential of BCI systems for the rehabilitation of patients suffering from poststroke impairments. By promoting the neuronal recovery of the damaged brain networks, BCI systems have achieved promising results for the recovery of poststroke motor, cognitive, and language impairments. Also, several assistive BCI systems that provide alternative means of communication and control to severely paralysed patients have been proposed to enhance patients' quality of life. In this article, we present a perspective review of the recent advances and challenges in the BCI systems used in the poststroke rehabilitation of motor, cognitive, and communication impairments.

The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts.

Stroke induced by basal ganglia infarction often impair cognitive function. The exploration of topological patterns in structural and functional networks associated cognitive impairment after stroke may contribute to understand the pathological mechanism of cognitive impairment caused by stroke. In this paper, graph theory analysis was applied to diffusion-weighted imaging (DWI) data and resting-state functional MRI (fMRI) data from 23 post-stroke patients with cognitive impairment (PSCI), 17 post-stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC). Structural and functional connectivity between 90 cortical and subcortical brain regions was estimated and set threshold to construct a set of undirected graphs. Network-based statistics (NBS) was used to characterize altered connectivity patterns among the three groups. Compared to HC, the PSCI group demonstrated substantial reductions in all three types of connections-rich club, feeder, and local-in structural and functional networks. Specifically, in structural network analysis, reduced connections were observed within basal ganglia and basal ganglia-frontal networks, whereas in the functional network analysis, reduced connections were observed in fronto-parietal network (FPN) and cingulo-opercular networks (CON). Meanwhile, compared to HC, the NPSCI group demonstrated reductions in both feeder and local connections only within occipital area and occipital-temporal structural networks. The findings of reduced structural connectivity in regions stemming from a basal ganglia core and reduced functional connectivity in FPN and CON may indicate a bottom-up cognitive impairment induced by stroke. Graph analysis and connectomics may aid clinical diagnosis and serve as potential imaging biomarkers for post-stroke patients with cognitive impairment.

De novo biosynthesis of p-coumaric acid and caffeic acid from carboxymethyl-cellulose by microbial co-culture strategy.

BACKGROUND: Aromatic compounds, such as p-coumaric acid (p-CA) and caffeic acid, are secondary metabolites of various plants, and are widely used in diet and industry for their biological activities. In addition to expensive and unsustainable methods of plant extraction and chemical synthesis, the strategy for heterologous synthesis of aromatic compounds in microorganisms has received much attention. As the most abundant renewable resource in the world, lignocellulose is an economical and environmentally friendly alternative to edible, high-cost carbon sources such as glucose. RESULTS: In the present study, carboxymethyl-cellulose (CMC) was utilized as the sole carbon source, and a metabolically engineered Saccharomyces cerevisiae strain SK10-3 was co-cultured with other recombinant S. cerevisiae strains to achieve the bioconversion of value-added products from CMC. By optimizing the inoculation ratio, interval time, and carbon source content, the final titer of p-CA in 30 g/L CMC medium was increased to 71.71 mg/L, which was 155.9-fold higher than that achieved in mono-culture. The de novo biosynthesis of caffeic acid in the CMC medium was also achieved through a three-strain co-cultivation. Caffeic acid production was up to 16.91 mg/L after optimizing the inoculation ratio of these strains. CONCLUSION: De novo biosynthesis of p-CA and caffeic acid from lignocellulose through a co-cultivation strategy was achieved for the first time. This study provides favorable support for the biosynthesis of more high value-added products from economical substrates. In addition, the multi-strain co-culture strategy can effectively improve the final titer of the target products, which has high application potential in the field of industrial production.

Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".

Objective: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). Method: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis. Results: In the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average. Conclusion: Based on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions.

Bacteroides acidifaciens in the gut plays a protective role against CD95-mediated liver injury.

The intestinal flora plays an important role in the development of many human and animal diseases. Microbiome association studies revealed the potential regulatory function of intestinal bacteria in many liver diseases, such as autoimmune hepatitis, viral hepatitis and alcoholic hepatitis. However, the key intestinal bacterial strains that affect pathological liver injury and the underlying functional mechanisms remain unclear. We found that the gut microbiota from gentamycin (Gen)-treated mice significantly alleviated concanavalin A (ConA)-induced liver injury compared to vancomycin (Van)-treated mice by inhibiting CD95 expression on the surface of hepatocytes and reducing CD95/CD95L-mediated hepatocyte apoptosis. Through the combination of microbiota sequencing and correlation analysis, we isolated 5 strains with the highest relative abundance, Bacteroides acidifaciens (BA), Parabacteroides distasonis (PD), Bacteroides thetaiotaomicron (BT), Bacteroides dorei (BD) and Bacteroides uniformis (BU), from the feces of Gen-treated mice. Only BA played a protective role against ConA-induced liver injury. Further studies demonstrated that BA-reconstituted mice had reduced CD95/CD95L signaling, which was required for the decrease in the L-glutathione/glutathione (GSSG/GSH) ratio observed in the liver. BA-reconstituted mice were also more resistant to alcoholic liver injury. Our work showed that a specific murine intestinal bacterial strain, BA, ameliorated liver injury by reducing hepatocyte apoptosis in a CD95-dependent manner. Determination of the function of BA may provide an opportunity for its future use as a treatment for liver disease.