Medulloblastoma (MB), a prevalent pediatric central nervous system tumor, is influenced by microRNAs (miRNAs) that impact tumor initiation and progression. However, the specific involvement of miRNAs in MB tumorigenesis remains unclear. Using single-cell RNA sequencing, we identified ROR2 expression in normal human fetal cerebellum. Subsequent analyses, including immunofluorescence, quantitative real-time PCR (qRT-PCR), and Western blot, assessed ROR2 expression in MB tissues and cell lines. We investigated miR-124-3p and miR-194-5p and their regulatory role in ROR2 expression through the dual-luciferase reporter, qRT-PCR, and western blot assays. Mechanistic insights were gained through functional assays exploring the impact of miR-124-3p, miR-194-5p, and ROR2 on MB growth in vitro and in vivo. We observed significantly reduced miR-124-3p and miR-194-5p expression and elevated ROR2 expression in MB tissues and cell lines. High ROR2 expression inversely correlated with overall survival in WNT and SHH subgroups of MB patients. Functionally, overexpressing miR-124-3p and miR-194-5p and inhibiting ROR2 suppressed in vitro malignant transformation and in vivo tumorigenicity. Mechanistically, miR-124-3p and miR-194-5p synergistically regulated the ROR2/PI3K/Akt pathway, influencing MB progression. Our findings indicate that miR-124-3p and miR-194-5p function as tumor suppressors, inhibiting MB progression via the ROR2/PI3K/Akt axis, suggesting a key mechanism and therapeutic targets for MB patients.

To develop an XGBoost model to predict the occurrence of acute lung injury (ALI) in patients with acute pancreatitis (AP). Using the case database of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, 1231 cases suffering from AP were screened, and after 137 variables were identified, the clinical characteristics of the samples were statistically analyzed, and the data were randomly divided into a training set (75%) to build the XGBoost model and a test set (25%) for validation. Finally, the performance of the model was evaluated based on accuracy, specificity, sensitivity, and subject characteristics working characteristic curves. The model performance is also compared with that of three other commonly used machine learning algorithms (support vector machine (SVM), logistic regression, and random forest). The age and laboratory tests of patients with AP combined with ALI differed from those of patients without combined acute lung injury. The area under the receiver operating characteristic (ROC) curve of the test set after model evaluation was 0.9534, the specificity was 0.7333, and the sensitivity was 0.7857, with arterial partial pressure of oxygen, bile acid, aspartate transaminase, urea nitrogen, and arterial blood pH as its most important influencing factors. In this study, the XGBoost model has advantages compared with other three machine learning algorithms. The XGBoost model has potential in the application of predicting acute lung injury after acute pancreatitis.

Monitoring of cerebrospinal fluid (CSF) microRNAs (miRs) offers a promising option for the diagnosis and management of patients with central nervous system tumors. However, the sensitive detection of miRs in clinical CSF samples has been hindered by the ultra-low abundance of target miRs. Here, we report an electrochemical biosensor for the highly sensitive label-free detection of CSF miR-21 relying on target-induced redox signal amplification (eTIRSA). The biosensor was developed by covalently assembling the capture stands partially complementary to miR-21 on the gold nanoparticle-coated glassy carbon electrode. In the presence of miR-21, the short capture stand hybridized with the partial bases of miR-21, allowing the rest sequence of the target molecule to further bind with a long guanine-rich sequence which could specifically adsorb a number of methylene blue indicators, thus generating an amplified electrochemical redox signal, typically at a working potential of − 0.19 V (vs. SCE). The response of the surface-bound methylene blue indicators was positively correlated to the concentration of miR-21, providing a dynamic range of 0.5–80 pM and a limit of detection down to 56 fM. Moreover, the eTIRSA biosensor had high specificity with single-base resolution and exhibited good performance for label-free quantification of miR-21 in medulloblastoma cell extracts and clinical CSF samples and for accurate discrimination of medulloblastoma against non-cancer controls, indicating its potential application in CSF miR-based liquid biopsy of brain cancers.

Background: To explore the specific prognosis related microRNAs (miRNAs) of glioma. Methods: The miRNA-Seq data and clinical information of glioma patients were downloaded from the TCGA (510 cases) and GEO (GSE112009, 25 cases) database. LASSO & COX regression was used to develop a miRNA-based model for predicting patient survival in the training set (n=255), to carry out glioma prognostic related miRNAs screening, and to construct a linear risk model based on the expression profiles of seven miRNAs. COX regression analysis was used to determine whether the miRNAs risk model was an independent prognostic factor. Results: Seven survival-related miRNAs (miR-140-5p, miR-145-5p, miR-148a-3p, miR-183-5p, miR222-3p, miR-223-3p, and miR-374a-5p) were identified in the training set. This showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group in the training set, prediction set, and validation set (P<0.05). Further analysis revealed that age and Karnofsky score both affected the risk of glioma. By crossing seven potential target genes of microRNAs, 620 effective target genes were obtained and GO analysis showed that these were related to the positive regulation of cell migration, neuron migration, and the response of transforming growth factor, and KEGG analysis showed they were related to the TGF-beta signaling pathway, MAPK signaling, and AGE-RAGE signaling pathway in diabetic complications. Conclusions: Seven miRNAs which regulate target genes to participate in related signaling pathways and lead to a poor prognosis were identified as biomarkers of glioma.

Techniques of rare event dynamics were reviewed, including string methods, which will be implemented with the biochemical simulation packages. The existing methods were applied to study biological systems with relevance to drug design and drug metabolism. The rare event dynamics simulations were performed to understand the kinetic and thermodynamic free energy information on the drug binding sites in the M2 proton channel, the free energy of insertion and association of membrane proteins and membrane active peptides. Results give a theoretical framework to interpret and reconcile existing and often conflicting opinions.