Identification of Vernonia patula Merr. and Its Similar Varieties Based on a Combination of HPLC Fingerprinting and Chemical Pattern Recognition.

Vernonia patula Merr. (VP) is a traditional medicine used by the Zhuang and Yao people, known for its therapeutic properties in treating anemopyretic cold and other diseases. Distinguishing VP from similar varieties such as Praxelis clematidea (PC), Ageratum conyzoides L. (AC) and Ageratum houstonianum Mill (AH) was challenging due to their similar traits and plant morphology. The HPLC fingerprints of 40 batches of VP and three similar varieties were established. SPSS 20.0 and SIMCA-P 13.0 were used to statistically analyze the chromatographic peak areas of 37 components. The results showed that the similarity of the HPLC fingerprints for each of the four varieties was >0.9, while the similarity between the control chromatogram of VP and its similar varieties was <0.678. Cluster analysis and partial least squares discriminant analysis provided consistent results, indicating that all four varieties could be individually clustered together. Through further analysis, we found isochlorogenic acid A and isochlorogenic acid C were present only in the original VP, while preconene II was present in the three similar varieties of VP. These three components are expected to be identification points for accurately distinguishing VP from PC, AC and AH.

SERS microfluidic chip integrated with double amplified signal off-on strategy for detection of microRNA in NSCLC.

In this work, based on Fe3O4@AuNPs and double amplified signal Off-On strategy, a simple and sensitive SERS microfluidic chip was constructed to detect microRNA associated with non-small cell lung cancer (NSCLC). Fe3O4@AuNPs have two advantages of SERS enhanced and magnetic adsorption, the introduction of microfluidic chip can realize double amplification of SERS signal. First, the binding of complementary ssDNA and hpDNA moved the Raman signaling molecule away from Fe3O4@AuNPs, at which point the signal was turned off. Second, in the presence of the target microRNA, they were captured by complementary ssDNA and bound to them. HpDNA restored the hairpin conformation, the Raman signaling molecule moved closer to Fe3O4@AuNPs. At this time, the signal was turned on and strong Raman signal was generated. And last, through the magnetic component of SERS microfluidic chip, Fe3O4@AuNPs could be enriched to realize the secondary enhancement of SERS signal. In this way, the proposed SERS microfluidic chip can detect microRNA with high sensitivity and specificity. The corresponding detection of limit (LOD) for miR-21 versus miR-125b was 6.38 aM and 7.94 aM, respectively. This SERS microfluidic chip was promising in the field of early detection of NSCLC.

Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer.

Purpose: Esophageal cancer (EC) is a lethal digestive tumor worldwide with a dismal clinical outcome. Endoplasmic reticulum (ER) stress poses essential implications for a variety of tumor malignant behaviors. Here, we set up an ER stress-based risk classifier for assessing patient outcome and exploiting robust targets for medical decision-making of EC cases. Methods: 340 EC cases with transcriptome and survival data from two independent public datasets (TCGA and GEO) were recruited for this project. Cox regression analyses were employed to create a risk classifier based on ER stress-related genes (ERGs) which were strongly linked to EC cases' outcomes. Then, we detected and confirmed the predictive ability of our proposed classifier via a host of statistical methods, including survival analysis and ROC method. In addition, immune-associated algorithm was implemented to analyze the immune activity of EC samples. Results: Four EGRs (BCAP31, HSPD1, PDHA1, and UBE2D1) were selected to build an EGR-related classifier (ERC). This classifier could distinguish the patients into different risky subgroups. The remarkable differences in patient outcome between the two groups were observed, and similar results were also confirmed in GEO cohort. In terms of the immune analysis, the ERC could forecast the infiltration level of immunocytes, such as Tregs and NK cells. Conclusion: We created a four-ERG risk classifier which displays the powerful capability of survival evaluation for EC cases.