Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae

Analysis of the Potential Relationship between Aging and Pulmonary Fibrosis Based on Transcriptome

Idiopathic pulmonary fibrosis (IPF) is an age-related interstitial lung disease with a high incidence in the elderly. Although many reports have shown that senescence can initiate pulmonary fibrosis, the relationship between aging and pulmonary fibrosis has not been explained systematically. In our study, young and old rats were intratracheally instilled with bleomycin (1 mg/kg), and the basic pathological indexes were determined using a commercial kit, hematoxylin, and eosin (H&E) and Masson’s Trichrome staining, immunohistochemistry, immunohistofluorescence, and q-PCR. Then, the lung tissues of rats were sequenced by next-generation sequencing for transcriptome analysis. Bioinformatics was performed to analyze the possible differences in the mechanism of pulmonary fibrosis between aged and young rats. Finally, the related cytokines were determined by q-PCR and ELISA. The results indicate that pulmonary fibrosis in old rats is more serious than that in young rats under the same conditions. Additionally, transcriptomic and bioinformatics analysis with experimental validation indicate that the differences in pulmonary fibrosis between old and young rats are mainly related to the differential expression of cytokines, extracellular matrix (ECM), and other important signaling pathways. In conclusion, aging mainly affects pulmonary fibrosis through the ECM–receptor interaction, immune response, and chemokines

Towards Personalized Intervention for Alzheimer’s Disease

AbstractAlzheimer’s disease (AD) remains to be a grand challenge for the international community despite over a century of exploration. A key factor likely accounting for such a situation is the vast heterogeneity in the disease etiology, which involves very complex and divergent pathways. Therefore, intervention strategies shall be tailored for subgroups of AD patients. Both demographic and in-depth information is needed for patient stratification. The demographic information includes primarily APOE genotype, age, gender, education, environmental exposure, life style, and medical history, whereas in-depth information stems from genome sequencing, brain imaging, peripheral biomarkers, and even functional assays on neurons derived from patient-specific induced pluripotent cells (iPSCs). Comprehensive information collection, better understanding of the disease mechanisms, and diversified strategies of drug development would help with more effective intervention in the foreseeable future

The frontline of immune response in peripheral blood

<div><p>Peripheral blood is an attractive source for the discovery of disease biomarkers. Gene expression profiling of whole blood or its components has been widely conducted for various diseases. However, due to population heterogeneity and the dynamic nature of gene expression, certain biomarkers discovered from blood transcriptome studies could not be replicated in independent studies. In the meantime, it’s also important to know whether a reliable biomarker is shared by several diseases or specific to certain health conditions. We hypothesized that common mechanism of immune response in blood may be shared by different diseases. Under this hypothesis, we surveyed publicly available transcriptome data on infectious and autoimmune diseases derived from peripheral blood. We examined to which extent common gene dys-regulation existed in different diseases. We also investigated whether the commonly dys-regulated genes could serve as reliable biomarkers. First, we found that a limited number of genes are frequently dys-regulated in infectious and autoimmune diseases, from which we selected 10 genes co-dysregulated in viral infections and another set of 10 genes co-dysregulated in bacterial infections. In addition to its ability to distinguish viral infections from bacterial infections, these 20 genes could assist in disease classification and monitoring of treatment effect for several infectious and autoimmune diseases. In some cases, a single gene is sufficient to serve this purpose. It was interesting that dys-regulation of these 20 genes were also observed in other types of diseases including cancer and stroke where certain genes could also serve as biomarkers for diagnosis or prognosis. Furthermore, we demonstrated that this set of 20 genes could also be used in continuous monitoring of personal health. The rich information from these commonly dys-regulated genes may find its wide application in clinical practice and personal healthcare. More validation studies and in-depth investigations are warranted in the future.</p></div

Discrimination of viral and bacterial infections.

<p>Discrimination of viral and bacterial infections.</p

Module assignment and relevant functions of the 20 genes.

<p>Module assignment and relevant functions of the 20 genes.</p

A co-expression network for the top genes in infectious and autoimmune diseases.

<p>The 55 genes with fold change (FC>2.0) in at least 8 of the 20 discovery datasets were selected for co-expression analysis. The dataset GSE48348 with 734 blood samples was used to construct the co-expression network. A cutoff 0.50 for Pearson correlation coefficient was used to retain only strong connections among these 55 genes. Blue color indicates the 10 selected virus response genes (VRGs), while pink color indicates the 10 selected bacteria response genes (BRGs).</p

Discrimination of SLE and other diseases.

<p>Discrimination of SLE and other diseases.</p

Single-gene biomarker for stroke and RIA.

<p>Most of the patients with stroke or RIA displayed 2-fold induction of <i>ARG1</i> expression. RIA, ruptured intracranial aneurysms.</p

Single-gene biomarker for JIA.

<p>Systemic and non-systemic JIA can be distinguished using the expression of <i>ANXA3</i>. Treatment effect can also be monitored using the expression of <i>ANXA3</i>. In the left panel, fold induction was calculated against the median level of healthy controls. In the right panel, fold reduction was calculated against the pre-treatment level of the same patient.</p