Normal Control Subjects Research Articles

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI.

Objective: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. Methods: In this study, we selected tau-PET scans from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. Results: Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. Conclusions: Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.

A diagnostic index based on pseudo-continuous arterial spin labeling and T1-mapping improves efficacy in discriminating Alzheimer's disease from normal cognition.

BackgroundPseudo-continuous arterial spin labeling (pCASL) is widely used to quantify cerebral blood flow (CBF) abnormalities in patients with Alzheimer’s disease (AD). T1-mapping techniques assess microstructural characteristics in various pathologic changes, but their application in AD remains in the exploratory stage. We hypothesized that combining quantitative CBF and T1 values would generate diagnostic results with higher accuracy than using either method alone in discriminating AD patients from cognitively normal control (NC) subjects.Materials and methodsA total of 45 patients diagnosed with AD and 33 NC subjects were enrolled, and cognitive assessment was performed for each participant according to the Chinese version of the Mini-Mental State Examination (MMSE). T1-weighted magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) and pCASL sequence were scanned on a 3T MR scanner. A brain morphometric analysis was integrated into prototype sequence, providing tissue classification and morphometric segmentation results. Quantitative CBF and T1 values of each brain region were automatically generated inline after data acquisition. Independent samples t-test was used to compare regional CBF and T1 values controlled by false discovery rate correction (corrected p < 0.01). The model with combined CBF and T1 values was compared with the individual index by performing receiver operating characteristic curves analysis. The associations between the MMSE score and CBF and T1 values of the brain were investigated using partial correlations.ResultsCerebral blood flow of the right caudate nucleus (RCc) and left hippocampus (LHc) was significantly lower in the AD group compared with the NC group, while the T1 values of the right caudate nucleus (RCt) and left hippocampus (LHt) increased in the AD group. Prediction accuracies of 73.1, 77.2, 75.9, and 81.3% were achieved for each of the above parameters, respectively. In distinguishing patients from controls using the corresponding optimized cut-off values, most combinations of parameters were elevated (area under curve = 0.775–0.894). The highest area under curve value was 0.944, by combining RCc, LHc, RCt, and LHt.ConclusionIn this preliminary study, the combined model based on pCASL and T1-mapping improved the diagnostic performance of discriminating AD and NC groups. T1-mapping may become a competitive technique for quantitatively measuring pathologic changes in the brain.

Impact of age and gender disparity on CD4+ cell counts to control disease progression using specific HAART in HIV-1 positive patients: A case-control study.

Highly Active Antiretroviral Therapy (HAART) is composed of several drugs in the antiretroviral class to better treat human immunodeficiency virus type 1 (HIV-1) patients. The estimation of CD4+ T cell counts and HIV-1 viral load in plasma is required to evaluate the treatment success of a specific HAART. The study included the effects of NRTIs (nucleoside reverse transcriptase inhibitors) and novel protease inhibitors (HAART) on normal control subjects and HIV-1 positive subjects from SGPGIMS, Lucknow, with different age groups and genders. Furthermore, the study was conducted by the estimation of HIV through ELISA, measurement of absolute CD4+ cell count, and the measurement of viral load through qRT-PCR. Furthermore, NRTIs (Retrovir and Epivir) were administered orally one tablet daily in the morning followed by newly FDA-approved protease inhibitors (fosamprenavir and darunavir) orally in the evening at the same dose. Furthermore, CD4+T cell counts and HIV-1 viral load were investigated and correlated in patients with different genders and age groups. Administration of NRTIs and novel protease inhibitors (HAART) in HIV patients had a significant effect on the CD4+ cell count in various age intervals among males and females. The mean comparison of viral load distribution based on gender in CD4 +ve patients in the case group exhibited a viral load higher in females compared to males, indicating a statistically significant difference between males and females (p<0.05). A notable association between virological and immunological parameters was observed with a reciprocal relationship between viral load and CD4 cell count in CD4 +ve patients, demonstrating multiple correlation coefficients with an R-value of 0.853. The administration of specific HAART (NRTIs and novel protease inhibitors) in HIV patients had a notable improvement in the CD4+ cell count and viral load with significant age and gender disparity.

Semiautomated Algorithm for the Diagnosis of Multiple System Atrophy With Predominant Parkinsonism.

ObjectivePutaminal iron deposition is an important feature that helps differentiate multiple system atrophy with predominant parkinsonism (MSA-p) from Parkinson’s disease (PD). Most previous studies used visual inspection or quantitative methods with manual manipulation to perform this differentiation. We investigated the value of a new semiautomated diagnostic algorithm using 3T-MR susceptibility-weighted imaging for MSA-p.MethodsThis study included 26 MSA-p, 68 PD, and 41 normal control (NC) subjects. The algorithm was developed in 2 steps: 1) determine the image containing the remarkable putaminal margin and 2) calculate the phase-shift values, which reflect the iron concentration. The next step was to identify the best differentiating conditions among several combinations. The highest phase-shift value of each subject was used to assess the most effective diagnostic set.ResultsThe raw phase-shift values were present along the lateral margin of the putamen in each group. It demonstrates an anterior-to-posterior gradient that was identified most frequently in MSA-p. The average of anterior 5 phase shift values were used for normalization. The highest area under the receiver operating characteristic curve (0.874, 80.8% sensitivity, and 86.7% specificity) of MSA-p versus PD was obtained under the combination of 3 or 4 vertical pixels and one dominant side when the normalization methods were applied. In the subanalysis for the MSA-p patients with a longer disease duration, the performance of the algorithm improved.ConclusionThis algorithm detected the putaminal lateral margin well, provided insight into the iron distribution of the putaminal rim of MSA-p, and demonstrated good performance in differentiating MSA-p from PD.

835 Evaluation of differences in c acnes, s epidermidis and demodex between rosacea subjects and normal controls

The role of the microbiome in the etiology and exacerbation of rosacea has been highlighted in recent studies. Increased Demodex mite density is frequently reported in association with rosacea, but multiple studies have failed to correlate Demodex and rosacea severity. S. epidermidis has been isolated from rosacea pustules, and decreased abundance of C. acnes, dominant on healthy facial skin, has been demonstrated in rosacea subjects. We sought to better understand these players in the rosacea microbiome and to elucidate the differences between rosacea and normal control subjects.

MicroRNA 21as a novel biomarker in hepatitis C virus-related hepatocellular carcinoma

BackgroundHepatocellular carcinoma is considered one of the most common cancers occurring in human population all over the world. It became an increasingly threatening malignancy due to both morbidity and mortality. Chronic viral hepatitis B and hepatitis C are two risk factors, which account for 80–90% of all HCC cases worldwide. Alfa Feto protien is used as a tumor marker for HCC diagnosis and prognosis prediction; however, its false negative rate when used alone is as high as 40% for patients with early-stage HCC. AFP levels remain normal in 15–30% of all the patients, even patients with advanced HCC. It has been demonstrated that miRNAs (MicroRNAs) are an important class of non-coding RNAs. They act as tumor oncogenes or suppressors and are involved in the HCC development. MiRNAs are endogenous nucleotides that can be found in intra- and extracellular spaces, such as the blood, urine, and saliva.The study evaluated the miRNA 21 as a novel biomarker in patients with HCV related hepatocellular carcinoma.ResultsThe study was conducted on three groups. Group (1) included 25 patients with liver cirrhosis due to hepatitis C virus infection. Group (2) included 25 patients with hepatocellular carcinoma (HCC) on top of liver cirrhosis due to hepatitis C virus infection. Group (3) included 10 normal control subjects. There was a significant difference in the mean level of miRNA between the three groups with p value < 0.001 with the highest value in group 2 ( 8.28 ± 2.55), then in group1 (5.04 ± 2.11) and the lowest in group 3 (control) (1.02 ± 0.07). MiRNA 21 has a sensitivity of 68% and a specificity of 96%, to differentiate between the liver cirrhosis group and HCC group.ConclusionmiRNA 21 can be a promising marker for detection of patients with HCV-related hepatocellular carcinoma, with higher specificity compared to α feto protein; however, its cost is higher.

Mechanical versus bioprosthetic valves in chronic dialysis: a systematic review and meta-analysis.

Background:Many patients with end-stage kidney disease (ESKD) have valvular heart disease requiring surgery. The optimal prosthetic valve is not established in this population. We performed a systematic review and meta-analysis to assess outcomes of patients with dialysis-dependent ESKD who received mechanical or bioprosthetic valves.Methods:We searched Cochrane Central, Medline and Embase from inception to January 2020. We performed screening, full-text assessment, risk of bias and data collection, independently and in duplicate. Data were pooled using a random-effects model.Results:We identified 28 observational studies (n = 9857 patients, including 6680 with mechanical valves and 3717 with bioprosthetic valves) with a median follow-up of 3.45 years. Twenty-two studies were at high risk of bias and 1 was at critical risk of bias from confounding. Certainty in evidence was very low for all outcomes except bleeding. Mechanical valves were associated with reduced mortality at 30 days (relative risk [RR] 0.79, 95% confidence interval [CI] 0.65–0.97, I2 = 0, absolute effect 27 fewer deaths per 1000) and at 6 or more years (mean 9.7 yr, RR 0.83, 95% CI 0.72–0.96, I2 = 79%, absolute effect 145 fewer deaths per 1000), but increased bleeding (incidence rate ratio [IRR] 2.46, 95% CI 1.41–4.27, I2 = 59%, absolute effect 91 more events per 1000) and stroke (IRR 1.63, 95% CI 1.21–2.20, I2 = 0%, absolute effect 25 more events per 1000).Conclusion:Mechanical valves were associated with reduced mortality, but increased rate of bleeding and stroke. Given very low certainty for evidence of mortality and stroke outcomes, patients and clinicians may choose prosthetic valves based on factors such as bleeding risk and valve longevity.Study registration:PROSPERO no. CRD42017081863

A Supervised Contrastive Learning-based Analysis of rs-tMRI Data Captures Gender Differences in Nonlinear Functional Network Coupling.

Many studies in neuroscience have focused on interpreting brain activity using functional connectivity (FC). The most widely used approach for measuring FC is based on linear correlation (e.g., the Pearson correlation), where the temporal cofluctuations between functional brain regions are computed. However, such approaches ignore nonlinear dependencies among regions that might carry distinctive information across groups of subjects. In this study, we offer a deep learning-based approach that also captures nonlinear temporal relationships between brain networks. Our approach consists of two main parts: an encoder that learns domain-specific embeddings of time courses estimated from independent component analysis (ICA) and a similarity metric that measures the similarities between the embeddings. We call such similarities as nonlinear functional relationships between networks. Our findings on a large dataset (including above 11k normal control subjects) suggest that male subjects exhibit stronger nonlinear network-network relationships than female subjects in most cases. Furthermore, we observe that, unlike FC, our approach could capture some intra-network relationships, especially between cognitive control and visual networks, which are significantly different between males and females, suggesting that our approach can provide a complementary interpretation of the functional brain activity to FC.

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

SummaryBackgroundWhite 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.MethodsThe 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.FindingsMicrostructural 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.InterpretationOur 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.FundingPartially supported by National Key R&D Program of China (2016YFC0901002).

Automated brain volumetric measures with AccuBrain: version comparison in accuracy, reproducibility and application for diagnosis

BackgroundAutomated brain volumetry has been widely used to assess brain volumetric changes that may indicate clinical states and progression. Among the tools that implement automated brain volumetry, AccuBrain has been validated for its accuracy, reliability and clinical applications for the older version (IV1.2). Here, we aim to investigate the performance of an updated version (IV2.0) of AccuBrain for future use from several aspects.MethodsPublic datasets with 3D T1-weighted scans were included for version comparisons, each with Alzheimer’s disease (AD) patients and normal control (NC) subjects that were matched in age and gender. For the comparisons of the brain volumetric measures quantified from the same scans, we investigated the difference of hippocampal segmentation accuracy (using Dice similarity coefficient [DSC] as the major measurement). As AccuBrain generates a composite index (AD resemblance atrophy index, AD-RAI) that indicates similarity with AD-like brain atrophy pattern, we also compared the two versions for the diagnostic accuracy of AD versus NC with AD-RAI. Also, we examined the intra-scanner reproducibility of the two versions for the scans acquired with short-intervals using intraclass correlation coefficient.ResultsAccuBrain IV2.0 presented significantly higher accuracy of hippocampal segmentation (DSC: 0.91 vs. 0.89, p < 0.001) and diagnostic accuracy of AD (AUC: 0.977 vs. 0.921, p < 0.001) than IV1.2. The results of intra-scanner reproducibility did not favor one version over the other.ConclusionsAccuBrain IV2.0 presented better segmentation accuracy and diagnostic accuracy of AD, and similar intra-scanner reproducibility compared with IV1.2. Both versions should be feasible for use due to the small magnitude of differences.

Evaluation of artificial intelligence models for the detection of asymmetric keratoconus eyes using Scheimpflug tomography.

To evaluate artificial intelligence (AI) models based on objective indices and raw corneal data from the Scheimpflug Pentacam HR system (OCULUS Optikgeräte GmbH, Wetzlar, Germany) for the detection of clinically unaffected eyes in patients with asymmetric keratoconus (AKC) eyes. A total of 1108 eyes of 1108 patients were enrolled, including 430 eyes from normal control subjects, 231 clinically unaffected eyes from patients with AKC, and 447 eyes from keratoconus (KC) patients. Eyes were divided into a training set (664 eyes), a test set (222 eyes) and a validation set (222 eyes). AI models were built based on objective indices (XGBoost, LGBM, LR and RF) and entire corneal raw data (KerNet). The discriminating performances of the AI models were evaluated by accuracy and the area under the ROC curve (AUC). The KerNet model showed great overall discriminating power in the test (accuracy=94.67%, AUC=0.985) and validation (accuracy=94.12%, AUC=0.990) sets, which were higher than the index-derived AI models (accuracy=84.02%-86.98%, AUC=0.944-0.968). In the test set, the KerNet model demonstrated good diagnostic power for the AKC group (accuracy=95.24%, AUC=0.984). The validation set also proved that the KerNet model was useful for AKC group diagnosis (accuracy=94.12%, AUC=0.983). KerNet outperformed all the index-derived AI models. Based on the raw data of the entire cornea, KerNet was helpful for distinguishing clinically unaffected eyes in patients with AKC from normal eyes.

Written Spelling in Patients with Subcortical Vascular Dementia and Subcortical Vascular Mild Cognitive Impairment

Objectives: The purpose of this study is to examine the spelling processing patterns in patients with subcortical vascular dementia (SVaD) and subcortical vascular mild cognitive impairment (svMCI).Methods: The subjects consisted of 10 patients with SVaD, 15with svMCI, and 30 normal controls (NC) subjects. Prior to this study, Korean- Mini Mental Status Examination (K-MMSE), Montreal Cognitive Assessment (MoCA-K), and health screening questionnaires were conducted as pre-tests. After that, subjects were presented with 20 words grouped according to word type (regular word, irregular word, non-word), and then they were asked to dictate the words they heard. After the tests were completed, the number of correct responses and the number of error patterns were compared between groups.Results: First, by word type between groups, the svMCI and SVaD groups showed a significantly lower number of correct responses for regular and irregular words compared to the NC group. In addition, the SVaD group showed a significantly lower number of forward responses than the svMCI group in irregular words. Non-words showed significantly lower number of forward responses in the SVaD group compared to the NC and svMCI groups. Second, the comparison of the number of error types between groups showed a significant difference between the three groups in terms of grapheme substitution; and the SVaD group showed significantly more errors compared with the NC and svMCI groups in addition and omission of graphemes. Third, the SVaD group showed significantly more errors in comparison to the NC and svMCI groups for phonological implausible errors, and the svMCI and SVaD groups showed significantly more errors than the NC group for phonological plausible errors. Fourth, lexicalization errors within non-words were observed only in the SVaD group.Conclusion: For early detection of svMCI, it is necessary to pay attention to irregular word performance.

GSTP1 rs1695 polymorphism, oxidative stress markers, and antioxidants in coronary artery disease

Oxidative damage is among the essential factors in the progression of cardiovascular disease. Objectives: This study aims to evaluate the molecular role of GST genotypic polymorphism involved in the development of CAD. This study also aimed to compare the levels of oxidative stress and antioxidant markers in subjects with CAD with age and sex-matched controls. Result: There was no significant difference in allele frequency (p= 0.85) or genotype frequency (p= 0.85) between the examined case and control groups. Compared to healthy controls, F2-Isoprostanes and MDA levels were considerably elevated in individuals with coronary artery disease. CAD patients' GST, SOD, Vitamine E, and Vitamin C levels were considerably lower than in normal control subjects. Conclusion: This study observed that oxidative stress markers were significantly higher, whereas, in CAD patients, enzymatic and nonenzymatic-enzymatic antioxidants were significantly lower. This study could not find a good connection between GSTP1 gene polymorphism rs1695 and coronary artery disease.

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.

BackgroundDetection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.MethodsOur study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.Results(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.ConclusionFeature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Increased Expression of LASI lncRNA Regulates the Cigarette Smoke and COPD Associated Airway Inflammation and Mucous Cell Hyperplasia.

Research ImpactCigarette smoke (CS) exposure is strongly associated with chronic obstructive pulmonary disease (COPD). In respiratory airways, CS exposure disrupts airway barrier functions, mucous/phlegm production, and basic immune responses of airway epithelial cells. Based on our recent identification of a specific immunomodulatory long noncoding RNA (lncRNA), we investigated its role in CS-induced responses in bronchial airways of cynomolgus macaque model of CS-induced COPD and in former smokers with and without COPD. The lncRNA was significantly upregulated in CS-induced macaque airways and in COPD airways that exhibited higher mucus expression and goblet cell hyperplasia. Experimental models of cells derived from COPD subjects recapitulated the augmented inflammation and mucus expression following the smoke challenge. Blocking of lncRNA expression in cell culture setting suppressed the smoke-induced and COPD-associated dysregulated mucoinflammatory response suggesting that this airway specific immunomodulatory lncRNA may represent a novel target to mitigate the smoke-mediated inflammation and mucus hyperexpression.RationaleIn conducting airways, CS disrupts airway epithelial functions, mucociliary clearances, and innate immune responses that are primarily orchestrated by human bronchial epithelial cells (HBECs). Mucus hypersecretion and dysregulated immune response are the hallmarks of chronic bronchitis (CB) that is often exacerbated by CS. Notably, we recently identified a long noncoding RNA (lncRNA) antisense to ICAM-1 (LASI) that mediates airway epithelial responses.ObjectiveTo investigate the role of LASI lncRNA in CS-induced airway inflammation and mucin hyperexpression in an animal model of COPD, and in HBECs and lung tissues from former smokers with and without COPD. To interrogate LASI lncRNA role in CS-mediated airway mucoinflammatory responses by targeted gene editing.MethodsSmall airway tissue sections from cynomolgus macaques exposed to long-term mainstream CS, and those from former smokers with and without COPD were analyzed. The structured-illumination imaging, RNA fluorescence in-situ hybridization (FISH), and qRT-PCR were used to characterize lncRNA expression and the expression of inflammatory factors and airway mucins in a cell culture model of CS extract (CSE) exposure using HBECs from COPD (CHBEs) in comparison with cells from normal control (NHBEs) subjects. The protein levels of mucin MUC5AC, and inflammatory factors ICAM-1, and IL-6 were determined using specific ELISAs. RNA silencing was used to block LASI lncRNA expression and lentivirus encoding LASI lncRNA was used to achieve LASI overexpression (LASI-OE).ResultsCompared to controls, LASI lncRNA was upregulated in CS-exposed macaques and in COPD smoker airways, correlating with mucus hyperexpression and mucus cell hyperplasia in severe COPD airways. At baseline, the unstimulated CHBEs showed increased LASI lncRNA expression with higher expression of secretory mucin MUC5AC, and inflammatory factors, ICAM-1, and IL-6 compared to NHBEs. CSE exposure of CHBEs resulted in augmented inflammation and mucus expression compared to controls. While RNA silencing-mediated LASI knockdown suppressed the mucoinflammatory response, cells overexpressing LASI lncRNA showed elevated mRNA levels of inflammatory factors.ConclusionsAltogether, LASI lncRNA may represent a novel target to control the smoke-mediated dysregulation in airway responses and COPD exacerbations.

Potential Therapeutic Role of Bone Morphogenic Protein 7 (BMP7) in the Pathogenesis of Graves' Orbitopathy.

PurposeWe investigated a role of bone morphogenic protein 7 (BMP7), a member of the TGF-β superfamily on pathogenic mechanism of Graves’ orbitopathy (GO). The therapeutic effects of BMP7 on inflammation and fibrosis were evaluated in cultured Graves’ orbital fibroblasts.MethodsExpression of BMP7 was compared in cultured orbital tissue explants from GO (n = 12) and normal control (n = 12) subjects using real-time PCR. Orbital fibroblasts were cultured from orbital connective tissues obtained from GO (n = 3) and normal control patients (n = 3). Cells were pretreated with recombinant human BMP7 (rhBMP7) before stimulation with TGF-β, IL-1β, and TNF-α. Fibrosis-related proteins and inflammatory cytokines were analyzed by Western blotting. The activation of signaling molecules in inflammation and fibrosis was also analyzed.ResultsThe expressions of BMP7 mRNA were lower in GO orbital tissues than control. Fibrosis-related proteins, fibronectin, collagen 1α, and α-SMA induced by TGF-β were suppressed by treating rhBMP7, and rhBMP7 upregulated TGF-β induced SMAD1/5/8 protein expression, whereas downregulated SMAD2/3. Increased pro-inflammatory molecules, IL-6, IL-8, and intercellular adhesion molecule-1 (ICAM-1) by IL-1β or TNF-α were blocked by rhBMP7 treatment, and the expression of phosphorylated NFκB and Akt was suppressed by rhBMP7 treatment.Conclusions BMP7 transcript levels were downregulated in Graves’ orbital tissues. Exogenous BMP7 treatment showed inhibitory effects on the production of profibrotic proteins and proinflammatory cytokines in orbital fibroblasts. Our results provide a molecular basis of BMP7 as a new potential therapeutic agent through the opposing mechanism of profibrotic TGF-β/SMAD signaling and proinflammatory cytokine production.

8. Optical genome mapping analysis of FMR1 expansions in fragile X syndrome

Fragile X syndrome (FXS) is associated with cognitive impairment and intellectual disability. Nearly all cases of FXS are associated with an expansion of a CGG triplet repeat in the FMR1 gene. Phenotype severity is often correlated with the expansion size; thus, accurate sizing of the CCG repeat array is crucial. The repetitive nature of these regions presents difficulties: 1. PCR is unable to traverse through long repeats; 2. sequencing has limited read lengths; 3. Southern blot may be inaccurate, time consuming, and expensive. Optical genome mapping (OGM) has the potential to address some of these shortcomings. OGM utilizes images of ultra-long DNA molecules, labeled at specific sequence motifs linearized in nanochannel arrays and can be used for structural and copy number variation calling and absence of heterozygosity analysis. Bionano Genomics has developed a targeted analysis workflow for analysis of FMR1. To evaluate the capability of measuring repeat arrays consistent with normal (<45 repeats), premutation (55-200 repeats), and full mutation (>200 repeats), we analyzed the FMR1 repeats in 75 samples. We also analyzed the repeat region on 20 phenotypically normal control subjects. We observed alleles consistent with annotation across the entire range of repeat counts. Analytical sensitivity was measured at 97% with 100% PPV for expansions >200 repeats. The largest expansion we detected was ∼1000 repeats. In the control samples, we measured repeat counts below the full mutation cutoff in all cases. Repeatability studies were carried out to show analytical consistency and to establish minimum molecule quality requirements. EnFocus™ analysis report provides pass/fail for QC metrics for input data quality as well as analytic measurement quality using internal control regions of the genome on each autosome. OGM performance for FMR1 repeat lengths analyzed here show a much higher dynamic range compared to PCR and higher precision compared to Southern blot.

Difference in the Standard and Novel Lipid Profile Parameters Between Patients With Alzheimer's Disease and Vascular Dementia Stratified by the Degree of Cognitive Impairment.

Background:Lipids and lipoproteins are significantly involved in maintaining structural and functional components of the human brain and neurons, but their role in the development of Alzheimer’s disease (AD) and vascular dementia (VD) remains unclear.Objective:The aim of the present study was to explore the differences in the standard and novel lipid profile parameters in patients with AD and VD, stratified by the degree of cognitive impairment (CI).Methods:Present study included 66 patients with AD, 50 patients with VD, and 60 control subjects. For an evaluation of the global cognitive function the Montreal Cognitive Assessment (MoCA) test was used. In order to distinguish patients with VD from those with AD the Hachinski ischemic score was used. Plasma total cholesterol (TC), high-density lipoprotein -cholesterol (HDL-C), and triglycerides (TG) levels were determined using standard enzymatic colorimetric techniques, whereas the Friedewald formula was used to calculate low-density lipoprotein-cholesterol (LDL-C) levels. The non-traditional lipid indices such as TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C ratio were separately calculated. The differences between the groups were analyzed with the Kruskal Wallis test followed by the Mann-Whitney test or with ANOVA followed by the Tuckey posthoc test.Results:Results of the conducted study have found that the patients in AD group with moderate CI and patients in AD group with severe CI exhibited significantly lower levels of serum TC, TG, LDL-C, VLDL-C, Non- HDL-C, atherogenic index, TG/HDL-C, TC/HDL-C and LDL-C/HDL-C compared to cognitively normal control subjects. Moreover, patients in VD group with severe and moderate CI had significantly lower level of TG compared to control group of subjects. Our results have also shown that patients in AD group with moderate CI had significantly lower level of TC, TG, LDL-C, Non-HDL-C, atherogenic index, TG/HDL-C, TC/HDL-C compared to VD patients with moderate CI. In addition, patients in AD group with severe CI had significantly lower level of TC, LDL-C, Non-HDL-C and TC/HDL-C compared to VD patients with severe CI.Conclusion:The results of this study have shown dysregulation of lipid metabolism in AD and VD patients with different degree of CI. In both moderate and in severe CI, patients with AD had lower levels of majority of standard and novel lipid parameters compared to patients with VD. Further larger prospective studies are required to elucidate the accuracy of standard and novel lipid parameters in the assessment of different degree of CI in AD and VD.

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.

The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.

Photopic negative response recorded with RETeval system in eyes with optic nerve disorders

Electroretinography (ERG) is used to evaluate the physiological status of the retina and optic nerve. The purpose of this study was to determine the usefulness of ERGs recorded with the RETeval system in diagnosing optic nerve diseases. Forty-eight patients with optic nerve disorders, including optic neuritis, ischemic optic neuropathy, traumatic optic neuropathy, and dominant optic atrophy, and 36 normal control subjects were studied. The amplitudes of the photopic negative response (PhNR) were recorded with the RETeval system without mydriasis. The circumpapillary retinal nerve fiber layer thickness (cpRNFLT) was determined by optical coherence tomography (OCT). The significance of the correlations between the PhNR and cpRNFLT parameters were determined, and the receiver operating curve (ROC) analyses were performed for the PhNR and cpRNFLT. Patients with optic nerve disorders had significantly smaller PhNRs compared to the control subjects (P = 0.001). The ROC analyses indicated that both PhNR and cpRNFLT had comparable diagnostic abilities of detecting optic nerve disorders with PhNR at 0.857 and cpRNFLT at 0.764. The PhNR components recorded with the RETeval system have comparable diagnostic abilities as the cpRNFLT in diagnosing optic nerve disorders.