Dual regulatory actions of LncBMP4 on BMP4 promote chicken primordial germ cell formation.

The unique characteristics of chicken primordial germ cells (PGCs) provide potential strategies for transgenic animal generation; however, insufficient PGC availability has limited their application. Regulation of bone morphogenic protein 4 (BMP4), a crucial factor for PGCs formation, may provide new strategies for PGC generation. We here identify a long noncoding RNA (lncRNA) that targets BMP4 (LncBMP4). LncBMP4 has similar functions as BMP4, in that it facilitates the formation and migration of PGCs. LncBMP4 promotes BMP4 expression by adsorbing the miRNA gga-mir-12211, thus reducing its inhibitory effect on BMP4 expression. In addition, the small peptide EPC5 encoded by LncBMP4 promotes the transcription of BMP4. The competing endogenous RNA (ceRNA) effect of LncBMP4 requires N6-methyladenosine (m6A) modification, in a dose-dependent manner, and high levels of m6A modification hinder EPC5 translation. Understanding the molecular mechanisms through which LncBMP4 promotes BMP4 expression during PGC formation may provide new avenues for efficient PGC generation.

Dietary intake and gastrointestinal symptoms are altered in children with Autism Spectrum Disorder: the relative contribution of autism-linked traits.

BACKGROUND: Dietary and gastrointestinal (GI) problems have been frequently reported in autism spectrum disorder (ASD). However, the relative contributions of autism-linked traits to dietary and GI problems in children with ASD are poorly understood. This study firstly compared the dietary intake and GI symptoms between children with ASD and typically developing children (TDC), and then quantified the relative contributions of autism-linked traits to dietary intake, and relative contributions of autism-linked traits and dietary intake to GI symptoms within the ASD group. METHODS: A sample of 121 children with ASD and 121 age-matched TDC were eligible for this study. The dietary intake indicators included food groups intakes, food variety, and diet quality. The autism-linked traits included ASD symptom severity, restricted repetitive behaviors (RRBs), sensory profiles, mealtime behaviors, and their subtypes. Linear mixed-effects models and mixed-effects logistic regression models were used to estimate the relative contributions. RESULTS: Children with ASD had poorer diets with fewer vegetables/fruits, less variety of food, a higher degree of inadequate/unbalanced dietary intake, and more severe constipation/total GI symptoms than age-matched TDC. Within the ASD group, compulsive behavior (a subtype of RRBs) and taste/smell sensitivity were the only traits associated with lower vegetables and fruit consumption, respectively. Self-injurious behavior (a subtype of RRBs) was the only contributing trait to less variety of food. Limited variety (a subtype of mealtime behavior problems) and ASD symptom severity were the primary and secondary contributors to inadequate dietary intake, respectively. ASD symptom severity and limited variety were the primary and secondary contributors to unbalanced dietary intake, respectively. Notably, unbalanced dietary intake was a significant independent factor associated with constipation/total GI symptoms, and autism-linked traits manifested no contributions. CONCLUSIONS: ASD symptom severity and unbalanced diets were the most important contributors to unbalanced dietary intake and GI symptoms, respectively. Our findings highlight that ASD symptom severity and unbalanced diets could provide the largest benefits for the dietary and GI problems of ASD if they were targeted for early detection and optimal treatment.

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy.

BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

The Differences in Structure and Function of the Cerebellum Between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals: a Multi-model MRI Study.

The effects of the long-term bilingual experience on structure and function of the cerebellum remain unclear. To explore whether there are differences in cerebellar gray matter structure between Cantonese-Mandarin bilinguals and Mandarin monolinguals and whether these different cerebellar structures have different resting-state functional connectivity (RSFC) with the cerebrum between the two groups, 30 Cantonese-Mandarin bilingual and 30 Mandarin monolingual college students were scanned by the T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Voxel-based morphology (VBM) analysis and RSFC analysis were used to analyze the cerebellar gray matter volume (GMV) and cerebellar-cerebro functional connectivity, respectively. Correlation analysis was performed between GMV/RSFC and the rapid automatized naming (RAN) and cognitive control. Compared to Mandarin monolinguals, Cantonese-Mandarin bilinguals showed larger GMV in bilateral cerebellar inferior posterior lobe (including bilateral VIIIa, VIIIb,IX, and right X, Vermis VIIIb, and Vermis IX) and a significant increase in RSFC coupling of the right inferior cerebellar posterior lobe with orbital part of left inferior frontal gyrus (IFG). In addition, there was a positive correlation between average response time (RT) of Mandarin alphanumeric RAN and RSFC between the right inferior posterior lobe of cerebellum and left IFG of all participants. The long-term Cantonese-Mandarin bilingual experience can increase the GMV of the bilateral cerebellar inferior posterior lobe and the RSFC between the right inferior cerebellar posterior lobe with orbital part of left inferior frontal gyrus (IFG).

Identification of patients with epilepsy using automated electronic health records phenotyping.

OBJECTIVE: Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS: The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS: Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE: A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.

Association of diet quality during pregnancy with maternal glucose metabolism in Chinese women.

Overall diet quality during pregnancy has played an important role on maternal glucose metabolism. However, evidence based on the adherence to the dietary guideline is limited, especially for Asian populations. We aimed to examine the association between adherence to the Chinese dietary guideline measured by the Diet Balance Index for Pregnancy (DBI-P) and maternal glucose metabolism, including gestational diabetes mellitus (GDM) status, fasting and 2-h plasma glucose. Data were obtained from the baseline survey of the Yuexiu birth cohort. We recruited 942 pregnant women at 20-28 weeks of gestation in 2017-2018. Dietary intakes during the past month were collected using a validated semi-quantitative FFQ. The scores of DBI-P were calculated to assess dietary quality. Lower absolute values of the scores indicate higher adherence to the Chinese dietary guidelines. All participants underwent a 75 g of oral glucose tolerance test (OGTT). Multiple linear regression and logistic regression were conducted. The Benjamini-Hochberg method was used to adjust multiple comparisons across DBI-P food components. The value of high bound score indicator, reflecting excessive total food intake, was positively associated with OGTT-2h glucose levels (ß = 0·037, P = 0·029). After adjustment for multiple comparisons, the score of animal food intake was positively associated with OGTT-2 h glucose levels (ß = 0·045, P = 0·045) and risk of GDM (OR = 1·105, P = 0·030). In conclusion, excessive total food intake was associated with higher postprandial glucose in pregnant women. Lower compliance with the dietary guideline for animal food was associated with both higher postprandial glucose and increased risk of GDM during pregnancy.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography.

OBJECTIVE: To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG). MATERIALS AND METHODS: We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results. RESULTS: One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%. SIGNIFICANCE: A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

LncRNA KCNQ1OT1 promotes osteogenic differentiation via miR-205-5p/RICTOR axis.

Osteoporosis is a prevalent degenerative disease that is characterized by decreased bone density and strength, resulting in gradually increasing bone fragility. Osteoporosis is caused by an imbalance between osteoblastic bone formation and osteoclastic bone resorption. Recently, increasing evidence has suggested that long non-coding RNAs (lncRNAs) participate in the occurrence and development of osteoporosis. Herein, we explored the role of lncRNA KCNQ1OT1 in osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). QPCR results indicated that KCNQ1OT1 and RICTOR were down-regulated, while miR-205-5p was up-regulated in the osteoporotic patients, as compared with non-osteoporotic controls. During the osteogenic differentiation of BMSCs, the expression of KCNQ1OT1 and RICTOR was upregulated, whereas miR-205-5p was downregulated. The interaction among KCNQ1OT1, miR-205-5p and RICTOR was validated by dual luciferase reporter system. KCNQ1OT1 promoted RICTOR expression via inhibiting miR-205-5p, therefore promoting osteogenesis as demonstrated by ALP assay, alizarin red staining and the increased expression of osteogenic markers (OPN, RUNX2 and OCN). Furthermore, KCNQ1OT1 overexpression or miR-205-5p inhibition could promote ALP activity and mineralization of BMSCs, while overexpressed miR-205-5p could reverse the effects of overexpressed KCNQ1OT1, and knockdown of RICTOR could reverse the effects of miR-205-5p inhibition. In conclusion, our study illustrated that KCNQ1OT1 might inhibit miR-205-5p in BMSCs, thus upregulating the expression of RICTOR and promoting osteogenic differentiation.

Genetic predisposition to white blood cells in relation to the risk of frailty.

BACKGROUND: Observational studies have suggested an association between white blood cells (WBCs) and frailty, but considering the susceptibility to reverse causality and confounding, the causal direction and magnitude of this association remain ambiguous. Our aim was to investigate the causal effect of WBCs on frailty by means of a Mendelian randomization (MR) analysis. METHODS: Based on the genome-wide association study (GWAS) summary statistics data provided by the European Bioinformatics Institute (EBI), we carried out a two-sample MR study. We applied the genetically predicted independent WBCs from GWAS as a measure of exposure data. The Rockwood Frailty Index (FI) was used as outcome measure, which was derived from a meta-analysis from GWAS in UK Biobank European ancestry participants and Swedish TwinGene participants. Our study applied inverse variance weighted (IVW), weighted median, Mendelian randomization-Egger (MR-Egger) and outlier test (MR-PRESSO) methods to explore relationships between various WBCs and frailty. RESULTS: In our study, a possible causal relationship between eosinophil levels and frailty was demonstrated by two-sample MR analysis. Eosinophils were associated with FI (beta:0.0609; 95% CI 0.0382, 0.0836; P = 1.38E-07). Our results suggest that as the level of eosinophils increases, so does the risk of frailty. No meaningful causal relationship between neutrophils, lymphocytes, monocytes or basophils and FI was found in the MR results (P > 0.05). CONCLUSIONS: According to this MR study, higher eosinophil counts are related to an increased risk of frailty. To validate these findings and investigate the mechanisms underlying these connections, future studies are warranted.

Lipid sponge droplets as programmable synthetic organelles.

Living cells segregate molecules and reactions in various subcellular compartments known as organelles. Spatial organization is likely essential for expanding the biochemical functions of synthetic reaction systems, including artificial cells. Many studies have attempted to mimic organelle functions using lamellar membrane-bound vesicles. However, vesicles typically suffer from highly limited transport across the membranes and an inability to mimic the dense membrane networks typically found in organelles such as the endoplasmic reticulum. Here, we describe programmable synthetic organelles based on highly stable nonlamellar sponge phase droplets that spontaneously assemble from a single-chain galactolipid and nonionic detergents. Due to their nanoporous structure, lipid sponge droplets readily exchange materials with the surrounding environment. In addition, the sponge phase contains a dense network of lipid bilayers and nanometric aqueous channels, which allows different classes of molecules to partition based on their size, polarity, and specific binding motifs. The sequestration of biologically relevant macromolecules can be programmed by the addition of suitably functionalized amphiphiles to the droplets. We demonstrate that droplets can harbor functional soluble and transmembrane proteins, allowing for the colocalization and concentration of enzymes and substrates to enhance reaction rates. Droplets protect bound proteins from proteases, and these interactions can be engineered to be reversible and optically controlled. Our results show that lipid sponge droplets permit the facile integration of membrane-rich environments and self-assembling spatial organization with biochemical reaction systems.

Design and Experimental Study of a Large Beam Waist Streak Tube in an ICF Experiment.

In order to realize in situ multi-frame framing, this paper designed and developed a large-waist framing converter tube. The size ratio between the waist and the object was about 1.16:1. The subsequent test results showed that the static spatial resolution of the tube could reach 10 lp/mm (@ 72.5%) under the premise of this adjustment, and the transverse magnification could reach 2.9. Once the MCP (Micro Channel Plate) traveling wave gating unit is equipped at the output end, it is expected to promote the further development of in situ multi-frame framing technology.

Secondary Amplifier Sampling Component Design of an X-ray Framing Detector Based on a Streak Tube.

The development of inertial confinement fusion (ICF) experiments necessitates the diagnostic instrument to have multiple frames with a high spatial and temporal resolution for the two-dimensional detection of the hot spot at the implosion end of the ICF. The existing sampling two-dimensional imaging technology in the world has superior performance; however, its subsequent development requires a streak tube with large lateral magnification. In this work, an electron beam separation device was designed and developed for the first time. The device can be used without changing the structure of the streak tube. It can be combined directly with the corresponding device and matched with a special control circuit. Based on the original transverse magnification, 1.77 times the secondary amplification can be achieved, which is conducive to expanding the recording range of the technology. The experimental results showed that the static spatial resolution of the streak tube after the inclusion of the device can still reach 10 lp/mm.

Chinese medicinal herbs for reducing endocrine therapy-induced side effects in patients with hormone receptor-positive breast cancer: a systematic review and meta-analysis.

CONTEXT: Chinese medicinal herbs (CMH) have been considered a potentially efficacious approach for patients with breast cancer that experience adverse effects from endocrine treatment. OBJECTIVE: To investigate the impact of CMH on endocrine therapy-induced side effects in patients with hormone receptor-positive (HR+) breast cancer. METHODS: Ten databases (e.g., PubMed, Web of Science, Cochrane Library, China National Knowledge Information Database and other databases) were searched up to 20 May 2022. The search terms included Chinese herb, breast cancer, endocrine therapy, clinical trial and their mesh terms. The study selection and data extraction were performed by two independent reviewers. The risk of bias was evaluated using the Cochrane risk of bias method. RESULTS: A total of 31 studies with 2288 patients were included. There were significant improvements in bone mineral density (BMD) [lumbar BMD (MD 0.08, 95% CI 0.07 to 0.09, p < 0.00001) and femoral neck BMD (MD 0.08, 95% CI 0.07 to 0.10, p < 0.00001)] and bone gal protein (BGP) (MD 0.24, 95% CI 0.17 to 0.31, p < 0.00001), with a significant reduction in triglycerides (MD -0.53, 95% CI -1.00 to -0.07, p < 0.05) and no effect on estradiol levels (MD 0.90, 95% CI -0.31 to 2.12, p = 0.15). CONCLUSIONS: CMH combined with complementary therapy can moderately reduce endocrine therapy-induced side effects, including bone loss and dyslipidemia in patients with HR + breast cancer, revealing the potential role of CMH in treating (HR+) breast cancer. More high-quality RCTs are warranted to further validate the effectiveness and safety of CMH.

Focal Sleep Spindle Deficits Reveal Focal Thalamocortical Dysfunction and Predict Cognitive Deficits in Sleep Activated Developmental Epilepsy.

Childhood epilepsy with centrotemporal spikes (CECTS) is the most common focal epilepsy syndrome, yet the cause of this disease remains unknown. Now recognized as a mild epileptic encephalopathy, children exhibit sleep-activated focal epileptiform discharges and cognitive difficulties during the active phase of the disease. The association between the abnormal electrophysiology and sleep suggests disruption to thalamocortical circuits. Thalamocortical circuit dysfunction resulting in pathologic epileptiform activity could hinder the production of sleep spindles, a brain rhythm essential for memory processes. Despite this pathophysiologic connection, the relationship between spindles and cognitive symptoms in epileptic encephalopathies has not been previously evaluated. A significant challenge limiting such work has been the poor performance of available automated spindle detection methods in the setting of sharp activities, such as epileptic spikes. Here, we validate a robust new method to accurately measure sleep spindles in patients with epilepsy. We then apply this detector to a prospective cohort of male and female children with CECTS with combined high-density EEGs during sleep and cognitive testing at varying time points of disease. We show that: (1) children have a transient, focal deficit in spindles during the symptomatic phase of disease; (2) spindle rate anticorrelates with spike rate; and (3) spindle rate, but not spike rate, predicts performance on cognitive tasks. These findings demonstrate focal thalamocortical circuit dysfunction and provide a pathophysiological explanation for the shared seizures and cognitive symptoms in CECTS. Further, this work identifies sleep spindles as a potential treatment target of cognitive dysfunction in this common epileptic encephalopathy.SIGNIFICANCE STATEMENT Childhood epilepsy with centrotemporal spikes is the most common idiopathic focal epilepsy syndrome, characterized by self-limited focal seizures and cognitive symptoms. Here, we provide the first evidence that focal thalamocortical circuit dysfunction underlies the shared seizures and cognitive dysfunction observed. In doing so, we identify sleep spindles as a mechanistic biomarker, and potential treatment target, of cognitive dysfunction in this common developmental epilepsy and provide a novel method to reliably quantify spindles in brain recordings from patients with epilepsy.

Self-Calibration 3D Hybrid SERS Substrate and Its Application in Quantitative Analysis.

Surface-enhanced Raman spectroscopy (SERS) has been widely applied in many fields as a sensitive vibrational fingerprint technique. However, SERS faces challenges in quantitative analysis due to the heterogeneity of hot spots. An internal standard (IS) strategy has been employed for correcting the variation of hot spots. However, the method suffers from limitations due to the competitive adsorption between the IS and the target analyte. In this work, we combined the IS strategy with the 3D hybrid nanostructures to develop a bifunctional SERS substrate. The substrate had two functional units. The bottom self-assembly layer consisted of Au@IS@SiO2 nanoparticles, which provided a stable reference signal and functioned as the calibration unit. The top one consisted of appropriate-sized Au octahedrons for the detection of target analytes, which was the detection unit. Within the 3D hybrid nanostructure, the calibration unit improved the SERS performance of the detection unit, which was demonstrated by the 6-fold increase of SERS intensity when compared with the 2D substrate. Meanwhile, the reproducibility of the detection was greatly improved by correcting the hot spot changes through the calibration unit. Two biomedical molecules of cotinine and creatinine in ultrapure water and artificial urine, respectively, were sensitively determined by the 3D hybrid substrate. We expect that the developed bifunctional 3D substrate will open up new ways to advance the applications of SERS.

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients. METHODS: A single center retrospective analysis was made of 1,967 neurologic, medical, and surgical patients who underwent >16 hours of continuous electroencephalography (EEG) between 2011 and 2017. We developed an artificial intelligence algorithm to annotate 11.02 terabytes of EEG and quantify epileptiform activity burden within 72 hours of recording. We evaluated burden (1) in the first 24 hours of recording, (2) in the 12-hours epoch with highest burden (peak burden), and (3) cumulatively through the first 72 hours of monitoring. Machine learning was applied to estimate the effect of epileptiform burden on outcome. Outcome measure was discharge modified Rankin Scale, dichotomized as good (0-4) versus poor (5-6). RESULTS: Peak epileptiform burden was independently associated with poor outcomes (p < 0.0001). Other independent associations included age, Acute Physiology and Chronic Health Evaluation II score, seizure on presentation, and diagnosis of hypoxic-ischemic encephalopathy. Model calibration error was calculated across 3 strata based on the time interval between last EEG measurement (up to 72 hours of monitoring) and discharge: (1) <5 days between last measurement and discharge, 0.0941 (95% confidence interval [CI] = 0.0706-0.1191); 5 to 10 days between last measurement and discharge, 0.0946 (95% CI = 0.0631-0.1290); >10 days between last measurement and discharge, 0.0998 (95% CI = 0.0698-0.1335). After adjusting for covariates, increase in peak epileptiform activity burden from 0 to 100% increased the probability of poor outcome by 35%. INTERPRETATION: Automated measurement of peak epileptiform activity burden affords a convenient, consistent, and quantifiable target for future multicenter randomized trials investigating whether suppressing epileptiform activity improves outcomes. ANN NEUROL 2021;90:300-311.

Electroencephalographic Abnormalities are Common in COVID-19 and are Associated with Outcomes.

OBJECTIVE: The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. METHODS: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. RESULTS: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio [HR] 4.07 [1.44-11.51], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). INTERPRETATION: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes. ANN NEUROL 2021;89:872-883.

Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) electroencephalography (EEG) recordings. METHODS: We evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED definition to assess the automated detections grouped into clusters by the algorithms. We used three previously developed AI algorithms: Encevis, SpikeNet, and Persyst. The diagnostic gold standard (epilepsy or not) was derived from video-EEG recordings of patients' habitual clinical episodes. We compared the algorithms with the gold standard at the recording level (epileptic or not). The independent validation data set (not used for training) consisted of 20-min EEG recordings containing sharp transients (epileptiform or not) from 60 patients: 30 with epilepsy (with a total of 340 IEDs) and 30 with nonepileptic paroxysmal events. We compared sensitivity, specificity, overall accuracy, and the review time-burden of the fully automated and hybrid approaches, with the conventional visual assessment of the whole recordings, based solely on unrestricted expert opinion. RESULTS: For all three AI algorithms, the specificity of the fully automated approach was too low for clinical implementation (16.67%; 63.33%; 3.33%), despite the high sensitivity (96.67%; 66.67%; 100.00%). Using the hybrid approach significantly increased the specificity (93.33%; 96.67%; 96.67%) with good sensitivity (93.33%; 56.67%; 76.67%). The overall accuracy of the hybrid methods (93.33%; 76.67%; 86.67%) was similar to the conventional visual assessment of the whole recordings (83.33%; 95% confidence interval [CI]: 71.48-91.70%; p > .5), yet the time-burden of review was significantly lower (p < .001). SIGNIFICANCE: The hybrid approach, where human raters apply the operational IED criteria to automated detections of AI-based algorithms, has high specificity, good sensitivity, and overall accuracy similar to conventional EEG reading, with a significantly lower time-burden. The hybrid approach is accurate and suitable for clinical implementation.

4-Trifluoromethyl-(E)-cinnamoyl]-L-4-F-phenylalanine acid exerts its effects on the prevention, post-therapeutic and prolongation of the thrombolytic window in ischemia-reperfusion rats through multiple mechanisms of action.

Ischemic stroke is one of the leading causes of death and disability worldwide. The severe sequelae caused by ischemic thrombolysis and the narrow time window are now the main clinical challenges. Our previous study has reported 4-Trifluoromethyl-(E)-cinnamoyl]-L-4-F-phenylalanine Acid (AE-18) was a promising candidate for Parkinson's Disease. In this study, the preventive and therapeutic effects of AE-18 on focal cerebral ischemia-reperfusion injury and the mechanisms are explored. In oxygen glucose deprivation/reoxygenation (OGD/R)-induced well-differentiated PC12 cells model, AE-18 (10 or 20 µM) can significantly reduce nerve damage when administered before or after molding. In middle cerebral artery occlusion-reperfusion (MCAO/R) rat model, pre-modelling, or post-modelling administration of AE-18 (5 or 10 mg/kg) was effective in reducing neurological damage, decreasing infarct volume and improving motor disturbances. In addition, AE-18 (5 mg/kg) given by intravenous injection immediately after occlusion significantly reduce the infarct volume caused by reperfusion for different durations, indicating that AE-18 could extend the time window of thrombolytic therapy. Further studies demonstrate that AE-18 exerts the effects in the prevention, treatment, and prolongation of the time window of cerebral ischemic injury mainly through inhibiting excitotoxicity and improving BBB permeability, VEGF and BDNF. These results suggest that AE-18 is a good candidate for the treatment of ischemic stroke.

The epidemiological evidence linking exposure to ambient particulate matter with neurodevelopmental disorders: A systematic review and meta-analysis.

BACKGROUND: There has been increasing attention on the associations between ambient particulate matter (PM) in early-life and neurodevelopmental disorders (NDDs). However, the associations remained unclear when considering different types of NDDs and different sizes of PM, and vulnerable exposure windows during early-life were not identified yet. OBJECTIVE: To synthesize the published literature on the associations between ambient particulate matter (PM) and risk of different types of neurodevelopmental disorders (NDDs) in a systematic review and meta-analysis. METHODS: A systematic search of Medline, Embase, PubMed, Cochrane Library, and Web of Science was performed from inception through 24 January 2022. Two reviewers conducted the study selection, data extraction, and quality appraisal. A random-effects model was used for meta-analyses with two quality-of-evidence assessments (the Grading of Recommendations Assessment, Development, and Evaluation system and the best evidence synthesis system). RESULTS: A total of 6554 articles were screened, of which 31 were included in the review, and 20 provided adequate data for meta-analyses. Exposures to particulate matter of 2.5 µm or less (PM2.5) during prenatal periods (OR, 1.32 [95%CI, 1.03-1.69]), the first year after birth (OR, 1.62 [95%CI, 1.22-2.15]) and the second year after birth (OR, 3.13 [95%CI, 1.47-6.67]) were associated with increased risk of autism spectrum disorders (ASD) in children. The quality of evidence for these associations during early postnatal periods was somewhat moderate with limited studies. We found inconsistent evidence when considering other types of NDDs and different sizes of PM. CONCLUSIONS AND RELEVANCE: Current evidence indicated that there might be an association between PM2.5 exposure and higher risk of ASD, and early postnatal periods appeared to be the critical exposure window. High-quality studies are needed to assess the evidence for other types of NDDs.