Health Control Research Articles

Differential diagnostic value of simultaneous detection of CD69 and HLA-DR on host T and NK cells in QFT-TB assay for identifying active tuberculosis

BackgroundInterferon-gamma release assay (IGRA) for tuberculosis (TB) remains limited in its ability to discriminate between active TB (ATB) and latent TB infection (LTBI). Activation markers on host T and NK cells are currently considered to be promising markers in the diagnosis of ATB. MethodsThis prospective observational study enrolled 213 participants and the participants were divided into ATB, LTBI, other lung-related diseases (ORD), and health control (HC) groups. CD69 and HLA-DR on T and NK cells were detected in QFT-TB assay, and a composite scoring system (TB-Flow) was created for the diagnosis of ATB. ResultsThe expression of activation markers (CD69 and HLA-DR) were significantly increased in ATB. HLA-DR on NK cells, CD69 on T cells, and QFT-TB in the differential diagnosis of ATB and HC were all of good diagnostic value (AUC>0.90). In addition, the TB-Flow greatly improved the efficiency of differential diagnosis between ATB and LTBI (AUC=0.90, 95%CI: 0.84–0.96), with sensitivity and specificity of 79.17 % (95%CI: 64.60%–89.04 %) and 88.68 % (95%CI: 76.28%–95.31 %). ConclusionsCD69 and HLA-DR on host T and NK cells are promising markers in distinguishing different TB infection status. Our blood-based TB-Flow scoring system can distinguish ATB from LTBI with good diagnostic efficacy.

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia.

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

Potential different immune phenotypes of macrophages in oral lichen planus by integrating immunofluorescence double staining and single-cell RNA sequencing

Background/purposeOral lichen planus (OLP) is a chronic inflammatory disease with obscure etiopathogenesis. Macrophages play an important role in interaction between innate and adaptive immunity. This study aimed to investigate the macrophage phenotypes and obtain more comprehensive gene characteristics of macrophages in OLP. Materials and methodsDouble cluster of differentiation (CD) 68/CD86 and CD68/CD206 immunofluorescence staining was conducted in 11 biopsy-proven OLP tissue samples and 5 health control (HC) to represent M1 and M2 macrophages, respectively. The number of positively stained cells was manually counted, and the density was calculated. Furtherly, OLP single-cell dataset GSE211630 was downloaded from Gene Expression Omnibus. Gene characteristics and functional analysis of the macrophages were elucidated. ResultsIn the OLP group, the densities of M1 (P < 0.001), M2 macrophages (P < 0.001) and M1/M2 ratio (P = 0.001) were significantly higher than those in HC group. Single-cell RNA sequencing revealed that proportions of CXCL10 macrophages (P = 0.003), IL1B/MMP19 macrophages (P < 0.001) were increased in OLP tissues compared with those in HC. Macrophages in OLP tissues had a stronger ability to cell chemotaxis, positive regulation of cell adhesion and antigen processing and presentation. Functional analysis revealed macrophages in OLP tissues could interact with multiple immune cells, and multiple signaling pathways were associated with macrophages in OLP. ConclusionA pro-inflammatory status of macrophages with different gene characteristics was found in the microenvironment of OLP by integrating immunofluorescence double staining and single-cell RNA sequencing, which provided a potential target for clinical treatment of OLP.

In Vitro Lactic Acid Bacteria Anti-Hepatitis B Virus (HBV) Effect and Modulation of the Intestinal Microbiota in Fecal Cultures from HBV-Associated Hepatocellular Carcinoma Patients.

Hepatocellular carcinoma (HCC), being ranked as the top fifth most prevalent cancer globally, poses a significant health challenge, with a considerable mortality rate. Hepatitis B virus (HBV) infection stands as the primary factor contributing to HCC, presenting substantial challenges in its treatment. This study aimed to identify lactic acid bacteria (LAB) with anti-HBV properties and evaluate their impact on the intestinal flora in HBV-associated HCC. Initially, two LAB strains, Levilactobacillus brevis SR52-2 (L. brevis SR52-2) and LeviLactobacillus delbrueckii subsp. bulgaicus Q80 (L. delbrueckii Q80), exhibiting anti-HBV effects, were screened in vitro from a pool of 498 LAB strains through cell experiments, with extracellular expression levels of 0.58 ± 0.05 and 0.65 ± 0.03, respectively. These strains exhibited the capability of inhibiting the expression of HBeAg and HBsAg. Subsequent in vitro fermentation, conducted under simulated anaerobic conditions mimicking the colon environment, revealed a decrease in pH levels in both the health control (HC) and HCC groups influenced by LAB, with a more pronounced effect observed in the HC group. Additionally, the density of total short-chain fatty acids (SCFAs) significantly increased (p < 0.05) in the HCC group. Analysis of 16S rRNA highlighted differences in the gut microbiota (GM) community structure in cultures treated with L. brevis SR52-2 and L. delbrueckii Q80. Fecal microflora in normal samples exhibited greater diversity compared to HBV-HCC samples. The HCC group treated with LAB showed a significant increase in the abundance of the phyla Firmicutes, Bacteroidetes and Actinobacteria, while Proteobacteria significantly decreased compared to the untreated HCC group after 48 h. In conclusion, the findings indicate that LAB, specifically L. brevis SR52-2 and L. delbrueckii Q80, possessing antiviral properties, contribute to an improvement in gastrointestinal health.

Screening of diagnostic biomarkers for ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Background: Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective: The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and methods: Download ferroptosis-related genes and OA synovial chip datasets separately from the FerrDB and Gene Expression Omnibus databases. Identify ferroptosis differentially expressed genes using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and health control synovium. Results: Obtain 38 ferroptosis differentially expressed genes through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and health control. Conclusion: This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.

The Association Between a Mediterranean Diet and Symptoms of Irritable Bowel Syndrome

Low adherence to Mediterranean diet (MD) has been shown to be associated with a higher prevalence of irritable bowel syndrome (IBS), but its association with IBS symptoms is not established. We aim to assess the association between MD and IBS symptoms, identify components of MD associated with IBS symptoms, and determine if a symptom-modified MD is associated with changes in the gut microbiome. One hundred and six Rome+IBS and 108 health control participants completed diet history and gastrointestinal symptom questionnaires. Adherence to MD was measured using Alternate Mediterranean Diet and Mediterranean Diet Adherence Screener. Sparse partial least squares analysis identified MD food items associated with IBS symptoms. Stool samples were collected for 16S ribosomal RNA gene sequencing and microbial composition analysis in IBS subjects. Alternate Mediterranean Diet and Mediterranean Diet Adherence Screener scores were similar between IBS and health control subjects and did not correlate with Irritable Bowel Syndrome Severity Scoring System, abdominal pain, or bloating. Among IBS participants, a higher consumption of fruits, vegetables, sugar, and butter was associated with a greater severity of IBS symptoms. Multivariate analysis identified several MD foods to be associated with increased IBS symptoms. A higher adherence to symptom-modified MD was associated with a lower abundance of potentially harmful Faecalitalea, Streptococcus, and Intestinibacter, and higher abundance of potentially beneficial Holdemanella from the Firmicutes phylum. A standard MD was not associated with IBS symptom severity, although certain MD foods were associated with increased IBS symptoms. Our study suggests that standard MD may not be suitable for all patients with IBS and likely needs to be personalized in those with increased symptoms.

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

Galectin-9 in synergy with NF-κB inhibition restores immune regulatory capability in dendritic cells of subjects with food allergy.

The pathogenesis of immune tolerance disruption is not fully understood. Galectin-9 (Gal9) has immune regulatory functions. The objective of the present study is to assess the role of Gal9 in maintaining immune tolerance. Blood and intestinal biopsies were taken from patients with food allergy (FA). The status of tolerogenic dendritic cells (tDC) and type 1 regulatory T cells (Tr1 cells) in the samples was evaluated and used as representative parameters of immune tolerance. An FA mouse model was established to assess the role of Gal9 in maintaining immune tolerance. We found that peripheral CD11c+ CD5+ CD1d+ tDC frequency was significantly lower in FA patients as compared to health control (HC) subjects. There was no significant change in CD11c+ DC frequency between the FA group and the HC group. The expression of IL-10 in peripheral tDCs was lower in the FA group than that in the HC group. A positive correlation was detected between the serum levels of IL-10 and Gal9. The expression of Gal9 was observed in intestinal biopsies, which was positively correlated with the serum levels of Gal9 as well as serum IL-10 levels. Peripheral Tr1 cells had lower frequencies in the FA group than in the non-FA (Con) group. tDCs demonstrated the ability to generate Tr1 cells, which was weaker in the FA group as compared with the Con group. Exposure of FA tDCs to Gal9 in culture restored the ability to generate Tr1 cells. In summary, the lower frequency of tDC and Tr1 cell of FA patients was associated with the levels of Gal9. The presence of Gal9 restored the capacity of tDC to generate Tr1 cells.

SUVR cut off using Optimal time of the Early‐phase 18F‐florbetaben brain image compared to the 18F‐fluorodeoxyglucose brain image

AbstractBackgroundEarly phase amyloid positron emission tomography (PET) imaging is known to correlated with glucose metabolism. However, studies on the optimal time frame and SUVR cut off for early‐phase amyloid PET have not been sufficiently progressed. In this study, the optimal time of early‐phase 18F‐FBB was determined and SUVR cut off was obtained.MethodTo determine the optimal time frame, the correlation between FBB and FDG SUVR of 67 subjects(34, Alzheimer’s disease(AD), 4 mild cognitive impairment(MCI), and 29 health control(HC)) was compared. In this method, 83 brain regions were treated as gray matter masks using an integrated brain map of PMOD 3.6. After obtaining the optimal time of FBB, which has a high correlation compared to FDG, a total of 225 subjects(188 AD, 37 HC) were targeted. SUVR cut off of dual‐phase 18F‐FBB PET of 11 brain regions was obtained. Calculate the composite mean SUVR and regional SUVR using MedCalc.ResultThe highest correlation between early‐phase 18F‐FBB and 18F‐FDG peaked at ‐.8747 at 270‐330s. Considering the minimum time and correlation between image noise and visual analysis, 90 to 330 seconds were set as the optimal time frame(r‐ratio ≥0.875).In the early‐phase FBB, cut off values in the order of HC&gt;AD were valid. The classification accuracy of the composite mean SUVR was 72%(AD 69.15%, HC 86.49%), and the regional area accuracy was 85.78%(AD 93.62%, HC 45.95%). In the delay‐phase FBB, cut off vlaues in the order of AD&gt;HC were valid. The classification accuracy of the composite mean SUVR was 77,33%(AD 72,87%, HC 100%), and the regional area accuracy was 84.89%(AD 86.17%, HC 78.38%). In the early and delay‐phase FBB, the classification accuracy of the composite mean SUVR was 90.67%(AD 92.02%, HC 86.49%), and the regional area accuracy was 88%(AD 98.4%, HC 37.84%).ConclusionIn this study, it is possible to know the optimal time frame of the early‐phase 18F‐FBB showing a high correlation with 18F‐FDG. Composite mean SUVR and regional SUVR cut off of the dual‐phase 18F‐FBB are helpful for visually ambiguous analysis.

Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network

Schizophrenia (ScZ) is a devastating mental disorder of the human brain that causes a serious impact of emotional inclinations, quality of personal and social life and healthcare systems. In recent years, deep learning methods with connectivity analysis only very recently focused into fMRI data. To explore this kind of research into electroencephalogram (EEG) signal, this paper investigates the identification of ScZ EEG signals using dynamic functional connectivity analysis and deep learning methods. A time-frequency domain functional connectivity analysis through cross mutual information algorithm is proposed to extract the features in alpha band (8–12 Hz) of each subject. A 3D convolutional neural network technique was applied to classify the ScZ subjects and health control (HC) subjects. The LMSU public ScZ EEG dataset is employed to evaluate the proposed method, and a 97.74 ± 1.15% accuracy, 96.91 ± 2.76% sensitivity and 98.53 ± 1.97% specificity results were achieved in this study. In addition, we also found not only the default mode network region but also the connectivity between temporal lobe and posterior temporal lobe in both right and left side have significant difference between the ScZ and HC subjects.

Mass Cytometry Reveals the Imbalanced Immune State in the Peripheral Blood of Patients with Essential Hypertension.

Mounting evidence has confirmed that essential hypertension (EH) is closely related to low-grade inflammation, but there is still a lack of in-depth understanding of the state of immune cells in the circulating blood of patients with EH. We analyzed whether hypertensive peripheral blood immune cell balance was destroyed. The peripheral blood mononuclear cells (PBMCs) of all subjects were analyzed using time-of-flight cytometry (CyTOF) based on 42 kinds of metal-binding antibodies. CD45+ cells were categorized into 32 kinds of subsets. Compared with the health control (HC) group, the percentage of total dendritic cells, two kinds of myeloid dendritic cell subsets, one intermediate/nonclassical monocyte subset and one CD4+ central memory T cell subset in the EH group, was significantly higher; the percentage of low-density neutrophils, four kinds of classical monocyte subsets, one CD14lowCD16- monocyte subset, one naive CD4+ and one naive CD8+ T cell subsets, one CD4+ effector and one CD4+ central memory T cell subsets, one CD8+ effector memory T cell subset, and one terminally differentiated γδ T cell subset, decreased significantly in EH. What is more, the expression of many important antigens was enhanced in CD45+ immune cells, granulocytes, and B cells in patients with EH. In conclusion, the altered number and antigen expression of immune cells reflect the imbalanced immune state of the peripheral blood in patients with EH.

Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques

Early detection of Parkinson’s disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.

EMCI: A Novel EEG-Based Mental Workload Assessment Index of Mild Cognitive Impairment.

As aging deepens, early detection of mild cognitive impairment (MCI) is increasingly important to prevent Alzheimer Dementia (AD) and improve the quality of life of older adults. In recent years, a large number of studies focus on the abnormal brain cognitive function of MCI, while ignoring the quantitative evaluation of MCI's mental workload. In this study, we propose a workload index for MCI screening, named EMCI, which is a linear discriminant cumulative estimate of subjects' electroencephalography (EEG) power spectra in α and β rhythms. Then, we design a matched prototype system to verify the effectiveness of EMCI. The results show that the EMCI is sensitive to changes of subjects' mental workload, and is significantly lower in MCI than in HC (Health control), which may be precisely caused by cognitive dysfunction. The proposed EMCI index can be used for online assessment of mental workload in older adults, which can help achieve quick screening of MCI and provide a critical window for clinical treatment interventions.

Partly recovery and compensation in anterior cingulate cortex after SSRI treatment—evidence from multi-voxel pattern analysis over resting state fMRI in depression

BackgroundAnterior cingulate cortex (ACC) plays an essential role in the pathophysiology of major depressive disorder (MDD) and its treatment. However, it's still unclear whether the effects of disease and antidepressant treatment on ACC perform diversely in neural mechanisms. MethodsFifty-nine MDD patients completed resting-state fMRI scanning twice at baseline and after 12-week selective serotonin reuptake inhibitor (SSRI) treatment, respectively in acute state and remission state. Fifty-nine demographically matched healthy controls were enrolled. Using fractional amplitude of low-frequency fluctuation (fALFF) in ACC as features, we performed multi-voxel pattern analysis over pretreatment MDD patients vs health control (HC), and over pretreatment MDD patients vs posttreatment MDD patients. ResultsDiscriminative regions in ACC for MDD impairment and changes after antidepressants were obtained. The intersection set and difference set were calculated to form ACC subregions of recovered, unrecovered and compensative, respectively. The recovered ACC subregion mainly distributed in rostral ACC (80 %) and the other two subregions had nearly equal distribution over dorsal ACC and rostral ACC. Furthermore, only the compensative subregion had significant changed functional connectivity with cingulo-opercular control network (CON) after antidepressant treatment. LimitationsThe number of subjects was relatively small. The results need to be validated with larger sample sizes and multisite data. ConclusionsThis finding suggested that the local function of ACC was partly recovered on regulating emotion after antidepressant by detecting the common subregional targets of depression impairment and antidepressive effect. Besides, changed fALFF in the compensative ACC subregion and its connectivity with CON may partly compensate for the cognition deficits.

Anxiety and depression prevalence and their risk factors in lupus nephritis patients: A case-control study.

IntroductionAnxiety and depression exhibit a high prevalence in systemic lupus erythematosus (SLE) patients, while this issue is seldom explored in lupus nephritis (LN). Hence, the current study aimed to investigate the prevalence of anxiety and depression, and the risk factors for these mental disorders in LN patients.MethodsFifty LN patients, 50 non‐LN SLE patients, and 50 health control (HCs) were enrolled. The Hospital Anxiety and Depression Scale (HADS) for anxiety (HADS‐A) score and HADS for depression (HADS‐D) score were evaluated.ResultsHADS‐A score was highest in LN patients (median 7.0, interquartile range [IQR]: 6.0–10.0), followed by non‐LN SLE patients (median 6.0, IQR: 5.0–8.0), and lowest in HCs (median 5.0, IQR: 3.0–7.0) (p < .001). Besides, the anxiety rate was most frequent in LN patients (38.0%), followed by non‐LN SLE patients (28.0%), least common in HCs (12.0%) (p = .011). HADS‐D score was highest in LN patients (median 7.5, IQR: 6.0–11.0), followed by non‐LN SLE patients (median 6.0, IQR: 5.0–8.3), and lowest in HCs (median 4.0, IQR: 2.0–6.3) (p < .001). Similarly, the depression rate was most prevalent in LN patients (50.0%), subsequently the non‐LN SLE patients (30.0%), and rarest in HCs (10.0%) (p < .001). Furthermore, in LN patients, age (p = .009), LN activity index (p = .020), alopecia (p = .023), 24 h proteinuria (p = .044), and C‐reactive protein (p = .049) were independently correlated with higher anxiety risk; meanwhile, age (p = .001) and LN activity index (p = .009) were independently correlated with higher depression risk.ConclusionAnxiety and depression are highly prevalent, which link to aging, alopecia, inflammation, and severe renal involvement in LN patients.

The Variation in the Diastolic Period with Interventricular Septal Displacement and Its Relation to the Right Ventricular Function in Pulmonary Hypertension: A Preliminary Cardiac Magnetic Resonance Study.

Background: Pulmonary hypertension (PH) is known to alter the biventricular shape and temporal phases of the cardiac cycle. The presence of interventricular septal (IVS) displacement has been associated with the severity of PH. There has been limited cardiac magnetic resonance (CMR) data regarding the temporal parameters of the cardiac cycle in PH. This study aimed to quantify the temporal changes in the cardiac cycle derived from CMR in PH patients with and without IVS displacement and sought to understand the mechanism of cardiac dysfunction in the cardiac cycle. Methods: Patients with PH who had CMR and right heart catheterization (RHC) examinations were included retrospectively. Patients were divided into an IVS non-displacement (IVSND) group and an IVS displacement (IVSD) group according to IVS morphology, as observed on short-axis cine CMR images. Additionally, age-matched healthy volunteers were included as the health control (HC). Temporal parameters, IVS displacement, ventricular volume and functional parameters were obtained by CMR, and pulmonary hemodynamics were obtained by RHC. The risk stratification of the PH patients was also graded according to the guidelines. Results: A total of 70 subjects were included, consisting of 33 IVSD patients, 15 IVSND patients, and 22 HC patients. In the IVSND group, only the right ventricle ejection fraction (RVEF) was decreased in the ventricular function, and no temporal change in the cardiac cycle was found. A prolonged isovolumetric relaxation time (IRT) and shortened filling time (FT) in both ventricles, along with biventricular dysfunction, were detected in the IVSD group (p < 0.001). The IRT of the right ventricle (IRTRV) and FT of the right ventricle (FTRV) in the PH patients were associated with pulmonary vascular resistance, right cardiac index, and IVS curvature, and the IRTRV was also associated with the RVEF in a multivariate regression analysis. A total of 90% of the PH patients in the IVSD group were stratified into intermediate- and high-risk categories, and they showed a prolonged IRTRV and a shortened FTRV. The IRTRV was also the predictor of the major cardiovascular events. Conclusions: The temporal changes in the cardiac cycle were related to IVS displacement and mainly impacted the diastolic period of the two ventricles in the PH patients. The IRT and FT changes may provide useful pathophysiological information on the progression of PH.

Plasma neurofilament light chain: A biomarker predicting severity in patients with acute ischemic stroke.

Neurofilament light chain (NfL) levels have proved to be a good biomarker in cerebrospinal fluid (CSF) correlating with the degree of neuronal injury and neurodegeneration. However, little is known about the value of plasma neurofilament light chain (pNfL) levels in predicting the clinical prognosis of patients with acute cerebral infarction. This study aimed to explore whether pNfL could be used as a biomarker to predict the severity of the outcomes of acute ischemic stroke (AIS).Patients with AIS were included from the Department of Neurology of the First People’s Hospital of Bengbu City from January 2018 to May 2019, as well as health control (HC). The plasma levels of NfL in patients with AIS (n = 60) at 2 days, 7 days, and 6 months after stroke, as well as in HCs (n = 60) were measured by electrochemiluminescence immunoassay(ECL) on the Meso Scale Discovery platform. Stroke severity was analyzed at admission using the National Institutes of Health Stroke Scale score. Functional outcomes were assessed at different times using the modified Rankin Scale (mRS) and Barthel Index.The mean level of pNfL in patients with ischemic stroke (IS) at 2 days (225.86 pg/L) after stroke was significantly higher than that in HC (107.02 pg/L) and gradually increased 7 days after stroke (316.23 pg/L) (P < .0001). The mean level of pNfL in patients with IS at 6 months after stroke was 173.38 pg/L, which was still significantly higher than that of HC. The levels of pNfL at 7 days after stroke independently predicted modified Rankin Scale scores (mRS) (R = 0.621, P < .001), Barthel Index (R = –0.716, P < .001), and National Institutes of Health Stroke Scale (R = –0.736, P < .001). The diagnostic severity and prognosis were evaluated by ROC curve, an area under the receiver operator curve of 0.812 (P = .001, 95% CI: 0.69–0.93) at 7 days.Plasma NfL levels reflect neuronal injury after AIS. It changes with time and has a certain relationship with prognosis and may be a promising biomarker for predicting the severity of neuroaxonal injury in patients with acute IS.

EEG Oscillatory Networks in Peri-Ictal Period of Absence Epilepsy.

ObjectiveTo investigate the dynamical brain network changes before and after an absence seizure episode in absence epilepsy (AE).Methods21 AE patients with a current high frequency of seizures and 21 sex- and age-matched health control (HC) who reported no history of neurological or psychiatric disorders and visited the hospital for routine physical examinations were included. Each included subject underwent a 2-h and 19-channel video EEG examination. For AE patients, five epochs of 10-s EEG data in inter-ictal, pre-ictal, and post-ictal states were collected. For the HC group, five 10-s resting-state EEG epochs were extracted. Functional independent components analysis (ICA) was carried out using the LORETA KEY tool.ResultsCompared with the resting-state EEG data of the HC group, the EEG data from AE patients during inter-ictal periods showed decreased alpha oscillations in regions involving the superior frontal gyrus (SFG) (BA11). From inter-ictal to pre-ictal, SFG (BA10) showed maximum decreased delta oscillations. Additionally, from pre-ictal to post-ictal, superior temporal gyrus (STG) (BA 22) presented maximum increased neural activity in the alpha band. Moreover, compared with inter-ictal EEG, post-ictal EEG showed significantly decreased theta activity in SFG (BA8).ConclusionThe changes in SFG alpha oscillations are the key brain network differences between inter-ictal EEG of AE patients and resting-state EEG of HCs. The brain networks of EEG oscillatory during peri-ictal episodes are mainly involving SFG and STG. Our study suggests that altered EEG brain networks dynamics exist between inter-ictal EEG of AE patients and resting-state EEG of HCs and between pre- and post-ictal EEG in AE patients.

Role of orbital magnetic resonance imaging in early detection of graves’ orbitopathy and disease activity

We aim to evaluate feasibility of using orbital diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) of extraocular muscles (EOMs) in patients with Graves’ orbitopathy (GO) versus clinical activity score (CAS) in early detection of GO activity. This case control study was conducted on 20 consecutive patients (10 males, 10 females with a mean age of 35.50±13.65 years) with Graves’ orbitopathy and 10 age- and sex-matched volunteers without any thyroid abnormality as a health control (HC). The patients and HCs underwent DTI MRI of the orbit in the coronal plane. The mean diffusivity (MD) was calculated. Thyroid stimulating hormone receptor antibody (TRAb) was measured using third generation thyroid binding inhibiting immunoglobulin (TBII) with ELISA method. CAS score, according to the standardized criteria of the European Group on Graves Orbitopathy (EUGOGO), was calculated for the patients. GO patients’ EOMs showed significantly higher MD values and higher compared to HCs’ (P &lt; 0.001). Median CAS score was 4. A statistically significant positive correlation was only detected between CAS score and inferior rectus (IR) MD (r=.360, p=.023).

Succinate/IL-1β Signaling Axis Promotes the Inflammatory Progression of Endothelial and Exacerbates Atherosclerosis.

Inflammation is an important driver of atherosclerosis. Succinate is a new extracellular inflammatory alarm released by activated macrophages. Succinate is sensed by succinate receptor 1 (Sucnr1) and then transferred to effector cells. It is worth exploring whether succinate is capable of facilitating the inflammatory response in atherosclerosis. In this study, we firstly found that arterial serum of Coronary Heart Disease (CHD) patients contained significantly higher succinate and interleukin (IL)-1β than Health control (HC) subjects, and succinate was positively correlated with IL-1β. As demonstrated by the in vitro study, succinate/hypoxia-inducible factor 1α (Hif)-1α/IL-1β signal axis existed and significantly facilitated the inflammatory program in human umbilical vein endothelial cells (HUVECs). Under the coculture, activated macrophages released succinate, which would be transferred to HUVECs via Sucnr1 and then activate Hif-1α to produce a greater amount of IL-1β. Likewise, the aortic sinus’s inflammatory phenotype was found to be more significant within Apoe-/- mice that were injected with succinate. Furthermore, Sucnr1 inhibitor (NF-56-EJ40) could significantly interrupt succinate/IL-1β signal in HUVECs and macrophages. As revealed by this study, glycolytic metabolism following the release of succinate could be found in atherosclerotic pathology, and succinate would drive succinate/IL-1β signal dependent on Sucnr1 and then exacerbate inflammatory responses. Sucnr1 might be a novel target for cutting off the transduction of succinate signal to prevent the inflammation of atherosclerosis.