Efficient Separation of Methanol Single-Micron Droplets by Tailing Phenomenon Using a PDMS Microfluidic Device.

Microdroplet-based fluidic systems have the advantages of small size, short diffusion time, and no cross-contamination; consequently, droplets often provide a fast and precise reaction environment as well as an analytical environment for individual molecules. In order to handle diverse reactions, we developed a method to create organic single-micron droplets (S-MDs) smaller than 5 µm in diameter dispersed in silicone oil without surfactant. The S-MD generation microflow device consists of a mother droplet (MoD) generator and a tapered separation channel featuring multiple side channels. The tapered channel enhanced the shear forces to form tails from the MoDs, causing them to break up. Surface treatment with the fluoropolymer CYTOP protected PDMS fluid devices from organic fluids. The tailing separation of methanol droplets was accomplished without the use of surfactants. The generation of tiny organic droplets may offer new insights into chemical separation and help study the scaling effects of various chemical reactions.

To explore the mechanism of Maiwei Dihuang decoction in the treatment of non-small cell lung cancer based on network pharmacology combined with LC-MS.

OBJECTIVE: To explore the mechanism of Maiwei Dihuang decoction in the treatment of non-small cell lung cancer (NSCLC) by using network pharmacology and LC-MS technology. METHODS: The effective components in Maiwei Dihuang decoction were detected by liquid chromatography-mass spectrometry (LC-MS). Use the SuperPred database to collect the relevant targets of the active ingredients of Mai Wei Di Tang, and then collect the relevant targets of non-small cell lung cancer from GeneCards, DisgenNET and OMIM databases. On this basis, PPI network construction, GO enrichment analysis and KEGG pathway annotation analysis were carried out for target sites. Finally, AutoDock Vina is used for molecular docking. RESULTS: We further screened 16 effective Chinese herbal compounds through LC-MS combined with ADME level. On this basis, we obtained 77 core targets through protein interaction network analysis. Through GO, KEGG analysis and molecular docking results, we finally screened out the potential targets of Maiwei Dihuang Decoction for NSCLC: TP53, STAT3, MAPK3. CONCLUSION: Maiwei Dihuang decoction may play a role in the treatment of NSCLC by co-regulating TP53/STAT3/MAPK3 signal pathway.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes. / åŸºäºŽç¦»å­é€šé“ç›¸å ³åŸºå› çš„è†€èƒ±ç™Œæ‚£è€ é¢„åŽé£Žé™©è¯„ä¼°æ¨¡åž‹æž„å»º.

OBJECTIVES: To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRGs). METHODS: ICRGs were obtained from the existing researches. The clinical information and the expression of ICRGs mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and qRT-PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median risk score. Immune cell infiltration was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed. RESULTS: By comparing the expression levels of ICRGs in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were dentified, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRGs-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1, 3 and 5 year overall survival rate of bladder cancer patients. CONCLUSIONS: The study shows the potential value of ICRGs in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRGs risk score has high accuracy in predicting the prognosis of bladder cancer patients.

Integrated analysis to identify the prognostic and immunotherapeutic roles of coagulation-associated gene signature in clear cell renal cell carcinoma.

The coagulation system is closely related to the physiological status and immune response of the body. Recent years, studies focusing on the association between coagulation system abnormalities and tumor progression have been widely reported. In clear cell renal cell carcinoma (ccRCC), poor prognosis often occurs in patients with venous tumor thrombosis and coagulation system abnormalities, and there is a lack of research in related fields. Significant differences in coagulation function were also demonstrated in our clinical sample of patients with high ccRCC stage or grade. Therefore, in this study, we analyzed the biological functions of coagulation-related genes (CRGs) in ccRCC patients using single-cell sequencing and TCGA data to establish the 5-CRGs based diagnostic signature and predictive signature for ccRCC. Univariate and multivariate Cox analyses suggested that prognostic signature could be an independent risk factor. Meanwhile, we applied CRGs for consistent clustering of ccRCC patients, and the two classes showed significant survival and genotype differences. The differences in individualized treatment between the two different subtypes were revealed by pathway enrichment analysis and immune cell infiltration analysis. In summary, we present the first systematic analysis of the significance of CRGs in the diagnosis, prognosis, and individualized treatment of ccRCC patients.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis. / åŸºäºŽå ç–«ç»†èƒžæµ¸æ¶¦è¯„åˆ†å®žçŽ°è†€èƒ±ç™Œåˆ†åž‹åŠé¢„åŽé£Žé™©è¯„ä¼°.

OBJECTIVES: To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer. METHODS: The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified. RESULTS: B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. CONCLUSIONS: According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.