Exosomes are small homogenous membrane vesicles that derive from the exocytosis process of cells and can contain DNA, microRNAs (miRNAs), and/or proteins. Characterization of the content profile of exosomes may reflect the state of the cells that release them, and this could be predictive of disease. In this study, to explore the potential biomarkers for melanoma, we isolated serous exosomes from 30 patients with melanoma and 30 healthy individuals using the ultracentrifugation method. Five miRNAs were subsequently detected in each sample by quantitative reverse transcription-PCR: miRNA-532-5p, miRNA-106b, miRNA-200c, miRNA-199a-5p, and miRNA-210. Only the levels of exo-miRNA-532-5p and exo-miRNA-106b differed between the two groups (Z=-4.17 and -4.57, respectively, P<0.0001). When these two miRNAs were evaluated individually and in combination in 95 melanoma patients and 95 healthy individuals serum samples, the area under the receiver operating characteristic curve values were 0.867, 0.820, and 0.936, respectively. Furthermore, in blinded tests of samples from 25 melanoma patients and 25 healthy individuals, this panel of miRNAs identified 23/25 patients with melanoma (92.0% sensitivity) and 22/25 healthy individuals (88.0% sensitivity). Our exo-miRNA panel also distinguished patients with metastasis from those without metastasis, patients with stage I-II disease from those with stage III-IV disease, and patients who had received pembrolizumab treatment from those who were untreated. Overall, these results indicate that serum exosomal miRNAs, especially exo-miRNA-532-5p and exo-miRNA-106b, have the potential to be used for monitoring and/or a diagnosis of melanoma in a clinical setting.

Higher dosage of methadone is recommended as a way to improve treatment adherence.

No abstract

Due to the Sysmex XN-20 BF model's imperfect agreement with manual microscopy and its weak diagnostic accuracy for malignant diseases, the current evidence does not support replacing manual microscopy with this model in clinical practice.

Background/Aims: Long noncoding RNAs (lncRNAs) are important regulators of biological processes and they contribute to the pathological developments of various diseases, including autoimmune diseases. To gain the further understanding, we estimate the expression of lncRNAs in primary immune thrombocytopenia (ITP). Methods: In this study, microarray studies were performed to characterize expression profiles of various lncRNAs and mRNAs in blood samples collected from ITP patients. Quantitative real-time PCR (qRT-PCR) was performed to confirm the results, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene ontology analysis were used to provide functional annotations, co-expression network construction (CNC) analysis was made to reveal the relations between lncRNAs and their targeted genes. Results: A total of 1177 and 632 lncRNAs were significantly up-regulated or down-regulated, respectively, in “newly diagnosed ITP” patients versus healthy individuals. In addition, 1182 genes and 737 genes were up-regulated or down-regulated, respectively, in “chronic recurrent ITP” patients versus healthy individuals. In a KEGG analysis, “TNF signaling pathway-Homo sapiens (human)” was a key result. In a gene ontology analysis, “Granulocyte macrophage colony-stimulating factor production (GO: 0032604, ontology: Biological process, P = 1.69577E-05)” and “coreceptor activity (GO: 0015026, ontology: molecular function, P = 4.67594E-06)” were the two most critical results. Data from qRT-PCR and receiver operating characteristic curves further demonstrated that ENST00000440492, ENST00000528366, NR_038920, and ENST00000552576 can efficiently distinguish different stages of ITP, especially NR_038920 and ENST00000528366. In a CNC analysis, four lncRNAs were emphasized, and NR_038920 and ENST00000528366 were both associated with proteins with important roles in autoimmune diseases. Conclusions: These results suggest that lncRNAs act through targeted genes to mediate their functions and to mediate their functions and affect the pathogenesis of ITP.

Background/Aims: MicroRNAs (miRNAs) have been described to have important roles in primary immune thrombocytopenia (ITP). To gain additional understanding, we have now further evaluated the involvement of miRNAs in ITP. Methods: Microarray experiments were performed to examine the expression profiles of miRNAs and mRNAs in samples from subjects with newly diagnosed ITP (G1), chronic ITP (G2), and normal controls. The systematic Pipeline of Outlier MicroRNA Analysis framework was applied to identify key miRNAs expressed in the G1 and G2 samples. Quantitative PCR and receiver operator characteristic curves were used to confirm the performance of key miRNAs. Results: Compared with normal controls, 14 miRNAs (12 over-expressed and 2 under-expressed) and 7 over-expressed miRNAs were identified as key in G1 and G2 samples, respectively. miR-106b-5p, miR-200c-3p, and miR-92a-3p exhibited significantly different expression profiles among the groups. In particular, miR-106b-5p and miR-200c-3p were expressed at higher levels in patients with ITP compared to the normal controls. Furthermore, these two miRNAs expressions were even higher in patients with chronic ITP. Conclusion: MiR-106b-5p and miR-200c-3p may represent valuable biomarkers of ITP, although further studies are needed to confirm and assess the value of these potential biomarkers at various stages of ITP.

No abstract

Early diagnosis and clear differentiation of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) is clinically challenging. A machine learning model is developed for the diagnosis of PDAC. The model is induced using a dataset of 13  987 participants, of which 12  402 are used for training the model and the remaining 1585 for testing purposes. One thousand sixty‐six laboratory variables are reduced to 18 measures using standard filtering and feature importance methods. Then, five machine learning classifiers are evaluated for the study. Hyperparameter optimization for each classifier is carried out, and the optimal algorithm is established using a tenfold cross validation on the training data. Finally, gradient boosting decision tree‐based ternary classifier composed of 18 routine laboratory variables (GBDT‐TC18) is established. In the test cohort, GBDT‐TC18 differentiates PDAC from CP and healthy control (HC) with an accuracy better than carbohydrate antigen 19‐9 (CA19‐9)‐based diagnosis. It also maintains a high diagnostic accuracy for stages I, IIA, and IIB PDAC, small‐sized PDAC, body and tail adenocarcinoma, CA19‐9‐negative PDAC, and nonjaundice PDAC. What's more, GBDT‐TC18 shows a higher accuracy than CA19‐9 in distinguishing PDAC from CP. GBDT‐TC18 can be used to augment the capability of doctors for early and differential diagnosis of PDAC.