Deletion of the Mitochondrial Membrane Protein Fam210b Is Associated with the Development of Systemic Lupus Erythematosus.

Mitochondrial dysfunction has been increasingly recognized as a trigger for systemic lupus erythematosus (SLE). Recent bioinformatics studies have suggested Fam210b as a significant candidate for the classification and therapeutic targeting of SLE. To experimentally prove the role of Fam210b in SLE, we constructed Fam210b knockout (Fam210b-/-) mice using the CRISPR-Cas9 method. We found that approximately 15.68% of Fam210b-/- mice spontaneously developed lupus-like autoimmunity, which was characterized by skin ulcerations, splenomegaly, and an increase in anti-double-stranded DNA (anti-dsDNA) IgG antibodies and anti-nuclear antibodies(ANA). Single-cell sequencing showed that Fam210b was mainly expressed in erythroid cells. Critically, the knockout of Fam210b resulted in abnormal erythrocyte differentiation and development in the spleens of mice. Concurrently, the spleens exhibited an increased number of CD71+ erythroid cells, along with elevated levels of reactive oxygen species (ROS) in the erythrocytes. The co-culture of CD71+ erythroid cells and lymphocytes resulted in lymphocyte activation and promoted dsDNA and IgG production. In summary, Fam210b knockout leads to a low probability of lupus-like symptoms in mice through the overproduction of ROS in CD71+ erythroid cells. Thus, Fam210b reduction may serve as a novel key marker that triggers the development of SLE.

Mendelian randomization study on the causal effect of herpes simplex virus infection on idiopathic pulmonary fibrosis.

BACKGROUND: Previous observational studies have shown that past infection of herpes simplex virus (HSV) is associated with idiopathic pulmonary fibrosis (IPF). The present study aims to identify the causal link between HSV infection (exposure factor) and IPF (outcome factor). RESEARCH DESIGN AND METHODS: To date, the largest publicly available genome-wide association study (GWAS) for HSV infection (1,595 cases and 211,856 controls from Finnish ancestry) and for IPF (1,028 cases and 196,986 controls from Finnish ancestry) were used to perform this two-sample Mendelian randomization (MR) study. RESULTS: We found no significant pleiotropy or heterogeneity of all selected nine HSV infection-associated genetic instrumental variants (IVs) in IPF GWAS dataset. Interestingly, we found that as HSV infection genetically increased, IPF risk increased based on an inverse-variance weighted (IVW) analysis (odds ratio [OR] = 1.280, 95% confidence interval [CI]: 1.048-1.563; p = 0.015) and weighted median (OR = 1.321, 95% CI: 1.032-1.692; p = 0.027). CONCLUSIONS: Our analysis suggests a causal effect of genetically increased HSV infection on IPF risk. Thus, HSV infection may be a potential risk factor for IPF.

Human C15orf39 Inhibits Inflammatory Response via PRMT2 in Human Microglial HMC3 Cell Line.

Microglia-mediated inflammatory response is one key cause of many central nervous system diseases, like Alzheimer's disease. We hypothesized that a novel C15orf39 (MAPK1 substrate) plays a critical role in the microglial inflammatory response. To confirm this hypothesis, we used lipopolysaccharide (LPS)-and interferon-gamma (IFN-γ)-induced human microglia HMC3 cells as a representative indicator of the microglial in vitro inflammatory response. We found that C15orf39 was down-regulated when interleukin-6 (IL-6) and tumor necrosis factor-α (TNFα) expression increased in LPS/IFN-γ-stimulated HMC3 cells. Once C15orf39 was overexpressed, IL-6 and TNFα expression were reduced in LPS/IFN-γ-stimulated HMC3 cells. In contrast, C15orf39 knockdown promoted IL-6 and TNFα expression in LPS/IFN-γ-stimulated HMC3 cells. These results suggest that C15orf39 is a suppressive factor in the microglial inflammatory response. Mechanistically, C15orf39 interacts with the cytoplasmic protein arginine methyltransferase 2 (PRMT2). Thus, we termed C15orf39 a PRMT2 interaction protein (PRMT2 IP). Furthermore, the interaction of C15orf39 and PRMT2 suppressed the activation of NF-κB signaling via the PRMT2-IκBα signaling axis, which then led to a reduction in transcription of the inflammatory factors IL6 and TNF-α. Under inflammatory conditions, NF-κBp65 was found to be activated and to suppress C15orf39 promoter activation, after which it canceled the suppressive effect of the C15orf39-PRMT2-IκBα signaling axis on IL-6 and TNFα transcriptional expression. In conclusion, our findings demonstrate that in a steady condition, the interaction of C15orf39 and PRMT2 stabilizes IκBα to inhibit IL-6 and TNFα expression by suppressing NF-κB signaling, which reversely suppresses C15orf39 transcription to enhance IL-6 and TNFα expression in the microglial inflammatory condition. Our study provides a clue as to the role of C15orf39 in microglia-mediated inflammation, suggesting the potential therapeutic efficacy of C15orf39 in some central nervous system diseases.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography. METHODS: In a retrospective study, magnetic resonance images with radiological reports of intracranial arterial stenosis and occlusion were extracted. The images were randomly divided into a training set and a test set. The manual annotation of lesions with a bounding box labeled "moderate stenosis," "severe stenosis," "occlusion," and "absence of signal" was considered as ground truth. A deep learning algorithm based on you only look once version 5 (YOLOv5) detection model was developed with the training set, and its sensitivity and positive predictive values to detect lesions were evaluated in the test set. RESULTS: A dataset of 200 examinations consisted of a total of 411 lesions-242 moderate stenoses, 84 severe stenoses, 70 occlusions, and 15 absence of signal. The magnetic resonance images contained 291 lesions in the training set and 120 lesions in the test set. The sensitivity and positive predictive values were 64.2 and 83.7%, respectively. The detection sensitivity in relation to the location was greatest in the internal carotid artery (86.2%). CONCLUSIONS: Applying deep learning algorithms in the automated detection of intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography is feasible and has great potential.

Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features.

Background: Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis. Methods: Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated. Results: Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999). Conclusion: COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission.

BACKGROUND: To investigate the CT imaging and clinical features of three atypical presentations of coronavirus disease 2019 (COVID-19), namely (1) asymptomatic, (2) CT imaging-negative, and (3) re-detectable positive (RP), during all disease stages. METHODS: A consecutive cohort of 79 COVID-19 patients was retrospectively recruited from five independent institutions. For each presentation type, all patients were classified into atypical vs. typical groups (i.e., asymptomatic vs.symptomatic, CT imaging-negative vs. CT imaging-positive, and RP and non-RP,respectively). The chi-square test, Student's t test, and Kruskal-Wallis H test were performed to compare CT imaging and clinical features of atypical vs. typical patients for all three presentation categories. RESULTS: In our COVID-19 cohort, we found 12.7% asymptomatic patients, 13.9% CT imaging-negative patients, and 8.9% RP patients. The asymptomatic patients had fewer hospitalization days (P=0.043), lower total scores for bilateral lung involvement (P< 0.001), and fewer ground-glass opacities (GGOs) in the peripheral area (P< 0.001) than symptomatic patients. The CT imaging-negative patients were younger (P=0.002), had a higher lymphocyte count (P=0.038), had a higher lymphocyte rate (P=0.008), and had more asymptomatic infections (P=0.002) than the CT imaging-positive patients. The RP patients with moderate COVID-19 had lower total scores of for bilateral lung involvement (P=0.030) and a smaller portion of the left lung affected (P=0.024) than non-RP patients. Compared to their first hospitalization, RP patients had a shorter hospitalization period (P< 0.001) and fewer days from the onset of illness to last RNA negative conversion (P< 0.001) at readmission. CONCLUSIONS: Significant CT imaging and clinical feature differences were found between atypical and typical COVID-19 patients for all three atypical presentation categories investigated in this study, which may help provide complementary information for the effective management of COVID-19.

Glutamate Chemical Exchange Saturation Transfer (GluCEST) Magnetic Resonance Imaging in Pre-clinical and Clinical Applications for Encephalitis.

BACKGROUND: Encephalitis is a common central nervous system inflammatory disease that seriously endangers human health owing to the lack of effective diagnostic methods, which leads to a high rate of misdiagnosis and mortality. Glutamate is implicated closely in microglial activation, and activated microglia are key players in encephalitis. Hence, using glutamate chemical exchange saturation transfer (GluCEST) imaging for the early diagnosis of encephalitis holds promise. METHODS: The sensitivity of GluCEST imaging with different concentrations of glutamate and other major metabolites in the brain was validated in phantoms. Twenty-seven Sprague-Dawley (SD) rats with encephalitis induced by Staphylococcus aureus infection were used for preclinical research of GluCEST imaging in a 7.0-Tesla scanner. For the clinical study, six patients with encephalitis, six patients with lacunar infarction, and six healthy volunteers underwent GluCEST imaging in a 3.0-Tesla scanner. RESULTS: The number of amine protons on glutamate that had a chemical shift of 3.0 ppm away from bulk water and the signal intensity of GluCEST were concentration-dependent. Under physiological conditions, glutamate is the main contributor to the GluCEST signal. Compared with normal tissue, in both rats and patients with encephalitis, the encephalitis areas demonstrated a hyper-intense GluCEST signal, while the lacunar infarction had a decreased GluCEST signal intensity. After intravenous immunoglobulin therapy, patients with encephalitis lesions showed a decrease in GluCEST signal, and the results were significantly different from the pre-treatment signal (1.34 ± 0.31 vs 5.0 ± 0.27%, respectively; p = 0.000). CONCLUSION: Glutamate plays a role in encephalitis, and the GluCEST imaging signal has potential as an in vivo imaging biomarker for the early diagnosis of encephalitis. GluCEST will provide new insight into encephalitis and help improve the differential diagnosis of brain disorders.

Glymphatic System Visualized by Chemical-Exchange-Saturation-Transfer Magnetic Resonance Imaging.

Dysfunction of the glymphatic system may play a significant role in the development of neurodegenerative diseases. However, in vivo imaging of the glymphatic system is challenging. In this study, we describe an unconventional MRI method for imaging the glymphatic system based on chemical exchange saturation transfer, which we tested in an in vivo porcine model of impaired glymphatic function. The blood, lymph, and cerebrospinal fluid (CSF) from one pig were used for testing the MRI effect in vitro at 7 Tesla (T). Unilateral deep cervical lymph node ligation models were then performed in 20 adult male Sprague-Dawley rats. The brains were scanned in vivo dynamically after surgery using the new MRI method. Behavioral tests were performed after each scanning session and the results were tested for correlations with the MRI signal intensity. Finally, the pathological assessment was conducted in the same brain slices. The special MRI effect in the lymph was evident at about 1.0 ppm in water and was distinguishable from those of blood and CSF. In the model group, the intensity of this MRI signal was significantly higher in the ipsilateral than in the contralateral hippocampus. The correlation between the signal abnormality and the behavioral score was significant (Pearson's, R2 = 0.9154, p < 0.005). We conclude that the novel MRI method can visualize the glymphatic system in vivo.

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.

OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.