Alcoholic liver disease(ALD)is the most frequent liver disease worldwide,resulting in severe harm to personal health and posing a serious burden to public health.Based on the reported antioxidant and anti-inflammatory capacities of scutellarin(SCU),this study investigated its protective role in male BALB/c mice with acute alcoholic liver injury after oral administration(10,25,and 50 mg/kg).The results indicated that SCU could lessen serum alanine aminotransferase(ALT)and aspartate aminotransferase(AST)levels and improve the histopathological changes in acute alcoholic liver;it reduced alcohol-induced malondialdehyde(MDA)content and increased glutathione peroxidase(GSH-Px),catalase(CAT),and superoxide dismutase(SOD)activity.Furthermore,SCU decreased tumor necrosis factor-α(TNF-α),interleukin-6(IL-6),and IL-1β messenger RNA(mRNA)expression levels,weakened inducible nitric oxide synthase(iNOS)activity,and inhibited nucleotide-binding oligomerization domain(NOD)-like receptor protein 3(NLRP3)inflammasome activation.Mechanistically,SCU suppressed cytochrome P450 family 2 subfamily E member 1(CYP2E1)upregulation triggered by alcohol,increased the expression of oxidative stress-related nuclear factor erythroid 2-related factor 2(Nrf2)and heme oxygenase-1(HO-1)pathways,and suppressed the inflammation-related degradation of inhibitor of nuclear factor-κB(NF-κB)-α(IκBα)as well as activation of NF-κB by mediating the protein kinase B(AKT)and p38 mitogen-activated protein kinase(MAPK)pathways.These findings demonstrate that SCU protects against acute alcoholic liver injury via inhibiting oxidative stress by regulating the Nrf2/HO-1 pathway and suppressing inflammation by regulating the AKT,p38 MAPK/NF-κB pathways.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Adolescent ; Adult ; Age Distribution ; Aged ; Basic Reproduction Number ; COVID-19/virology* ; China/epidemiology* ; Disease Outbreaks ; Female ; Humans ; Male ; Middle Aged ; SARS-CoV-2 ; Sex Ratio ; Young Adult

Objectives:The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19.Methods:The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method.Results:A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 ( P25- P75: 3.472-7.318) days, 5.083 ( P25- P75: 3.511-7.314) days, and 5.695 ( P25- P75: 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions:The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Objective:To investigate the pathological characteristics and the clinical significance of novel coronavirus (2019-nCoV)-infected pneumonia (termed by WHO as coronavirus disease 2019, COVID-19).Methods:Minimally invasive autopsies from lung, heart, kidney, spleen, bone marrow, liver, pancreas, stomach, intestine, thyroid and skin were performed on three patients died of novel coronavirus pneumonia in Chongqing, China. Hematoxylin and eosin staining (HE), transmission electron microcopy, and histochemical staining were performed to investigate the pathological changes of indicated organs or tissues. Immunohistochemical staining was conducted to evaluate the infiltration of immune cells as well as the expression of 2019-nCoV proteins. Real time PCR was carried out to detect the RNA of 2019-nCoV.Results:Various damages were observed in the alveolar structure, with minor serous exudation and fibrin exudation. Hyaline membrane formation was observed in some alveoli. The infiltrated immune cells in alveoli were majorly macrophages and monocytes. Moderate multinucleated giant cells, minimal lymphocytes, eosinophils and neutrophils were also observed. Most of infiltrated lymphocytes were CD4-positive T cells. Significant proliferation of type Ⅱ alveolar epithelia and focal desquamation of alveolar epithelia were also indicated. The blood vessels of alveolar septum were congested, edematous and widened, with modest infiltration of monocytes and lymphocytes. Hyaline thrombi were found in a minority of microvessels. Focal hemorrhage in lung tissue, organization of exudates in some alveolar cavities, and pulmonary interstitial fibrosis were observed. Part of the bronchial epithelia were exfoliated. Coronavirus particles in bronchial mucosal epithelia and type Ⅱ alveolar epithelia were observed under electron microscope. Immunohistochemical staining showed that part of the alveolar epithelia and macrophages were positive for 2019-nCoV antigen. Real time PCR analyses identified positive signals for 2019-nCoV nucleic acid. Decreased numbers of lymphocyte, cell degeneration and necrosis were observed in spleen. Furthermore, degeneration and necrosis of parenchymal cells, formation of hyaline thrombus in small vessels, and pathological changes of chronic diseases were observed in other organs and tissues, while no evidence of coronavirus infection was observed in these organs.Conclusions:The lungs from novel coronavirus pneumonia patients manifest significant pathological lesions, including the alveolar exudative inflammation and interstitial inflammation, alveolar epithelium proliferation and hyaline membrane formation. While the 2019-nCoV is mainly distributed in lung, the infection also involves in the damages of heart, vessels, liver, kidney and other organs. Further studies are warranted to investigate the mechanism underlying pathological changes of this disease.

Major depressive disorder (MDD) is the most common nonfatal disease burden worldwide. Systemic chronic low-grade inflammation has been reported to be associated with MDD progression by affecting monoaminergic and glutamatergic neurotransmission. However, whether various proinflammatory cytokines are abnormally elevated before the first episode of depression is still largely unclear. Here, we evaluated 184 adolescent patients who were experiencing their first episode of depressive disorder, and the same number of healthy individuals was included as controls. We tested the serum levels of high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α), IgE, 14 different types of food antigen-specific IgG, histamine, homocysteine, S100 calcium-binding protein B, and diamine oxidase. We were not able to find any significant differences in the serum levels of hs-CRP or TNF-α between the two groups. However, the histamine level of the patients (12.35 μM) was significantly higher than that of the controls (9.73 μM, P < 0.001, Mann-Whitney U test). Moreover, significantly higher serum food antigen-specific IgG positive rates were also found in the patient group. Furthermore, over 80% of patients exhibited prolonged food intolerance with elevated levels of serum histamine, leading to hyperpermeability of the blood-brain barrier, which has previously been implicated in the pathogenesis of MDD. Hence, prolonged high levels of serum histamine could be a risk factor for depressive disorders, and antihistamine release might represent a novel therapeutic strategy for depression treatment.
Adolescent ; Biomarkers ; blood ; C-Reactive Protein ; Chronic Disease ; Cytokines ; Depressive Disorder, Major ; blood ; epidemiology ; etiology ; Female ; Food Hypersensitivity ; blood ; complications ; Histamine ; blood ; Homocysteine ; blood ; Humans ; Immunoglobulin E ; blood ; Immunoglobulin G ; blood ; immunology ; Inflammation Mediators ; blood ; Male ; Risk Factors ; S100 Calcium Binding Protein beta Subunit ; blood ; Young Adult

BACKGROUND:At present, mouse embryonic neural stem cells (NSCs) culture has been skillfully operated by many labs, but there are differences existing about which part are dissociated to get NSCs. Embryonic 14 days (E14) mouse brain tissues are widely used for culturing NSCs, but there are less studies about the detailed percentage and difference of NSCs separated from different brain tissues. OBJECTIVE: To test the proportion and difference of NSCs and neurons percentage from E14 mouse whole brain, cortex and forebrain, providing quantized data for optimizing the isolation of high-purity NSCs. METHODS:E14 C57BL/6 mouse whole brain, cortex and forebrain tissues were separated and dissociated into single cells that were adherently cultured for 3.0-4.0 hours and labeled by DAPI. Then the cells were immunostained with NSCs specific marker, Nestin, and neuron specific marker, Tuj1, to identify NSCs and neurons percentage by calculating Nestin+/DAPI and Tuj1+/DAPI. In addition, real-time PCR assay was used to test Nestin and Tuj1 mRNA expression in the E14 mouse whole brain, cortex and forebrain. RESULTS AND CONCLUSION: (1) Immunocytochemical results showed that there were a large amount of Nestin+ and Tuj1+ cells in the whole brain, cortex and forebrain of E14 mice. NSCs percentage in the forebrain was obviously higher than that in the whole brain (P < 0.01) and cortex (P < 0.05), while the percentage of neurons in the forebrain was significantly lower than that in the whole brain (P < 0.05) and in the cortex (P < 0.001). (2) Real-time PCR results showed that the Nestin mRNA expression in the forebrain was significantly higher than that in the whole brain (P < 0.05) and slightly higher than that in the cortex (P > 0.05); the Tuj1 mRNA expression in the forebrain was significantly lower than that in the whole brain (P < 0.05) and in the cortex (P < 0.05). These findings indicated that the forebrain had the most NSCs and the least neurons compared with the whole brain and the cortex. In summary, E14 mouse forebrain has the highest percentage of NSCs compared with the whole brain and cortex, which is a better source to obtain NSCs for the following cell culture experiments.

Objective To understand and compare the proportion of neural stem cells (NSCs) in the whole brain and cerebral cortex of mice at different embryonic days, and provide quantitative data for the later optimization of NSCs isolation and culture.Methods The whole brains (at embryonic 12.5, 14, 16 and 18 days) and cerebral cortex (at embryonic14, 16 and 18 days) were isolated and digested into single cell suspension, and were adherently cultured for 3-4 h.Immunofluorescence staining of Nestin, a NSCs specific marker, was used to statistically analyze the proportion of NSCs in each group.Expression of Nestin mRNA in the cerebral cortex of mice at E12.5, E14, E16, and E18 was detected by real-time fluorescence quantitative PCR.Results The result of immunofluorescence assay showed that there were Nestin-positive cells in the whole brain and cerebral cortex of mice at different embryonic days.In the whole brain,the proportion of NSCs was highest at E12.5 (53.42±1.57%) and lowest at E18(25.96±1.31%), and the proportions at E14 and E16 were placed in the middle among the groups.In the cerebral cortex, the highest proportion of NSCs was at E14 (33.65±0.29%), and the lowest at E18(25.29±0.28%), and the middle at E16 (26.82±0.30%).The result of real-time PCR showed that when the mRNA expression of Nestin in the cerebral cortex was set to 1, the relative mRNA expression of Nestin was 0.83±0.04 at E14, 0.77±0.05 at E16, and 0.44 ±0.05 at E18.Thus, the mRNA expression level of Nestin in the mouse cerebral cortex was gradually decreasing with the increase of embryonic days.Conclusions During the brain development, the proportion of NSCs is gradually decreasing in the whole brain and cerebral cortex of mice with the increase of embryonic days.