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Abstract Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases.

Abstract Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer’s disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at

Abstract Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual analysis by clinicians is time-consuming, complicated and prone to human error during interpretation; therefore, automated analysis using deep learning has been attempted. We aimed to develop a model for the automatic measurement of various temporal parameters of swallowing using deep learning. Overall, 547 VFSS video clips were included. Seven temporal parameters were manually measured by two physiatrists as ground-truth data: oral phase duration, pharyngeal delay time, pharyngeal response time, pharyngeal transit time, laryngeal vestibule closure reaction time, laryngeal vestibule closure duration, and upper esophageal sphincter opening duration. ResNet3D was selected as the base model for the deep learning of temporal parameters. The performances of ResNet3D variants were compared with those of the VGG and I3D models used previously. The average accuracy of the proposed ResNet3D variants was from 0.901 to 0.981. The F1 scores and average precision were 0.794 to 0.941 and 0.714 to 0.899, respectively. Compared to the VGG and I3D models, our model achieved the best results in terms of accuracy, F1 score, and average precision values. Through the clinical application of this automatic model, temporal analysis of VFSS will be easier and more accurate.

Abstract The purpose of this study was to clarify the association between improvement of spasticity in hemiplegic patient’s upper extremity with Botulinum toxin injection and improvement in postural balance and gait function. For this prospective cohort study, sixteen hemiplegic stroke patients with upper extremity spasticity were recruited. The plantar pressure with gait parameters, postural balance parameters, Modified Ashworth Scale, and Modified Tardieu Scale were evaluated before, 3 weeks and 3 months after Botulinum toxin A (BTxA) injection. Spasticity of hemiplegic upper extremity before, and after BTxA injection were significantly changed. Plantar pressure overload in affected side was reduced after BTxA injection. The mean X-speed and the horizontal distance decreased in postural balance analysis with eyes-opened test. Improvement in hemiplegic upper extremity spasticity showed positive correlation with gait parameters. In addition, improvement in hemiplegic upper extremity spasticity was positively correlated with change in balance parameters in postural balance analysis with eyes-closed and dynamic tests. This study focused on the effect of stroke patient’s hemiplegic upper extremity spasticity on their gait and balance parameters and identified that the BTxA injection on hemiplegic patient’s spastic upper extremity improve postural balance and gait function.

Abstract In brain magnetic resonance imaging (MRI), white matter hyperintensity (WMH) is a commonly encountered finding and is known to reflect cerebral small vessel disease. The aim of our study was to investigate the association of coronary artery calcium (CAC) with WMH and elucidate the relationship between WMH and atherosclerotic risk factors in a large-scale healthy population. This retrospective study included 1337 individuals who underwent brain MRI and CAC scoring computed tomography at healthcare centers affiliated with a tertiary hospital. Cerebral WMH was defined as Fazekas score greater than 2 on brain MRI. Intracranial artery stenosis (ICAS) was also assessed and determined to be present when stenosis was more than 50% on angiography. The associations of risk factors, CAC score, and ICAS with cerebral WMH were assessed by multivariable regression analysis. In multivariable analysis, categories of higher CAC scores showed increased associations with both periventricular and deep WMHs in a dose-dependent relationship. The presence of ICAS was also significantly related to cerebral WMH, and among the clinical variables, age and hypertension were independent risk factors. In conclusion, CAC showed a significant association with cerebral WMH in a healthy population, which might provide evidence for referring to the CAC score to identify individuals with risk of cerebral WMH.

Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Abstract Raman spectroscopic-based biosensing strategies are often complicated by low signal and the presence of multiple chemical species. While surface-enhanced Raman spectroscopy (SERS) nanostructured platforms are able to deliver high quality signals by focusing the electromagnetic field into a tight plasmonic hot-spot, it is not a generally applicable strategy as it often depends on the specific adsorption of the analyte of interest onto the SERS platform. This paper describes a strategy to address this challenge by using surface potential as a physical binding agent in the context of microneedle sensors. We show that the potential-dependent adsorption of different chemical species allows scrutinization of the contributions of different chemical species to the final spectrum, and that the ability to cyclically adsorb and desorb molecules from the surface enables efficient application of multivariate analysis methods. We demonstrate how the strategy can be used to mitigate potentially confounding phenomena, such as surface reactions, competitive adsorption and the presence of molecules with similar structures. In addition, this decomposition helps evaluate criteria to maximize the signal of one molecule with respect to others, offering new opportunities to enhance the measurement of analytes in the presence of interferants.

Abstract Even a tiny functioning pituitary adenoma could cause symptoms; hence, accurate diagnosis and treatment are crucial for management. However, it is difficult to diagnose a small pituitary adenoma using conventional MR sequence. Deep learning-based reconstruction (DLR) using magnetic resonance imaging (MRI) enables high-resolution thin-section imaging with noise reduction. In the present single-institution retrospective study of 201 patients, conducted between August 2019 and October 2020, we compared the performance of 1 mm DLR MRI with that of 3 mm routine MRI, using a combined imaging protocol to detect and delineate pituitary adenoma. Four readers assessed the adenomas in a pairwise fashion, and diagnostic performance and image preferences were compared between inexperienced and experienced readers. The signal-to-noise ratio (SNR) was quantitatively assessed. New detection of adenoma, achieved using 1 mm DLR MRI, was not visualised using 3 mm routine MRI (overall: 6.5% [13/201]). There was no significant difference depending on the experience of the readers in new detections. Readers preferred 1 mm DLR MRI over 3 mm routine MRI (overall superiority 56%) to delineate normal pituitary stalk and gland, with inexperienced readers more preferred 1 mm DLR MRI than experienced readers. The SNR of 1 mm DLR MRI was 1.25-fold higher than that of the 3 mm routine MRI. In conclusion, the 1 mm DLR MRI achieved higher sensitivity in the detection of pituitary adenoma and provided better delineation of normal pituitary gland than 3 mm routine MRI.

Abstract To evaluate the association between thyroid echogenicity and heterogeneity seen on ultrasonography (US) and thyroid function in pediatric and adolescent populations with autoimmune diffuse thyroid diseases (AITD). From 2000 to 2020, we reviewed thyroid ultrasound (US) images and thyroid function statuses in 133 children and adolescent AITD patients. Our review of the images focused on decreased echogenicity and heterogeneity, which were classified into four grades. Among patients with overt hypothyroidism or overt hyperthyroidism, 94.2% (65/69) showed a US grade of 3 or 4. In patients with subclinical hyper/hypothyroidism or euthyroidism, 45.3% (29/64) showed grades 1 or 2. There were no overt hyper/hypothyroidism patients with US grade 1. When we compared US grades according to thyroid status, more severe thyroid dysfunction was significantly associated with higher US grade (p = 0.047). Thyroid stimulating hormone (TSH) level differed significantly according to US grades when we evaluated hyperthyroid (p = 0.035) and hypothyroid (p = 0.027) states independently. 11 patients showed both US grade and thyroid function status changes on follow-up US. In children and adolescent AITD patients, there was an association between decreased echogenicity and heterogeneity on US and thyroid dysfunction.

Abstract From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p

Abstract Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P

Abstract Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).

Abstract Hydroxychloroquine has recently received attention as a treatment for COVID-19. However, it may prolong the QTc interval. Furthermore, when hydroxychloroquine is administered concomitantly with other drugs, it can exacerbate the risk of QT prolongation. Nevertheless, the risk of QT prolongation due to drug-drug interactions (DDIs) between hydroxychloroquine and concomitant medications has not yet been identified. To evaluate the risk of QT prolongation due to DDIs between hydroxychloroquine and 118 concurrent drugs frequently used in real-world practice, we analyzed the electrocardiogram results obtained for 447,632 patients and their relevant electronic health records in a tertiary teaching hospital in Korea from 1996 to 2018. We repeated the case–control analysis for each drug. In each analysis, we performed multiple logistic regression and calculated the odds ratio (OR) for each target drug, hydroxychloroquine, and the interaction terms between those two drugs. The DDIs were observed in 12 drugs (trimebutine, tacrolimus, tramadol, rosuvastatin, cyclosporin, sulfasalazine, rofecoxib, diltiazem, piperacillin/tazobactam, isoniazid, clarithromycin, and furosemide), all with a p value of

Abstract The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN. .

Abstract Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

The proteins of the chromosomal passenger complex help chromosomes condense before cell division, but how this complex arrives at chromosomes was not known. Here the authors show that PRMT6 methylates histone H3 to recruit the chromosomal passenger complex.