LIN28 Is Involved in Glioma Carcinogenesis and Predicts Outcomes of Glioblastoma Multiforme Patients

LIN28, an evolutionarily conversed RNA binding protein which can bind to the terminal loops of let-7 family microRNA precursors and block their processing to maturation, is highly expressed in several subsets of tumors that carry poor prognoses, such as ovarian carcinoma, hepatocellular carcinoma, colon carcinoma and germ cell carcinoma. However, there has been no study on the expression of LIN28 in glioma tissues or their importance as a prognostic predictor of glioma patients. This study aimed to examine the expression of LIN28 in glioma and correlate the results to patient outcome. We found that LIN28 expression was significantly higher in the group of patients with a poor prognosis compared to patients with a good prognosis by gene microarray. Log-rank analysis showed patients with higher LIN28 expression level in tumor had a shorter progression-free survival and overall survival times compared to those with lower LIN28 expression level. Similar results were also obtained from the tissue microarray analysis. Univariate and multivariate analyses showed high LIN28 expression was an independent prognostic factor for a shorter progression-free survival and overall survival in GBM patients. Furthermore in vitro experiments showed that down-regulation of LIN28 in U251 and U373 cells caused cell cycle arrest in the G1 phase, delayed cell proliferation, increased apoptosis, and resulted in fewer colonies compared to controls. Summarily, our data provides a potential target for cancer therapy as an approach to overcome the poor options currently available for GBM patients

Identification and Validation of Two Novel Prognostic lncRNAs in Kidney Renal Clear Cell Carcinoma

Background/Aims: Kidney renal clear cell carcinoma (KIRC) is one of the most fatal malignancies due to late diagnosis and poor treatment. To improve its prognosis, a screening for molecular biomarkers of KIRC is urgently needed. Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and prognosis of cancers. However, it is not clear whether lncRNAs can be used as molecular biomarkers in predicting the survival of KIRC patients. Methods: In this study, our aim was to identify lncRNAs/mRNAs signatures and their prognostic values in KIRC. The aberrant expression profile of mRNAs and lncRNAs in 529 KIRC tissues and 72 adjacent non-tumor pancreatic tissues were obtained from the Cancer Genome Atlas (TCGA). A weighted gene co-expression network analysis (WGCNA) of two key lncRNAs was constructed. We constructed an aberrant lncRNA-mRNA-miRNA ceRNA network in CESC. In addition, Gene Ontology (GO) and KEGG pathway analysis were performed. Results: Using lncRNA/mRNA expression profiling data, the overall analysis revealed that two novel lncRNA signatures (DNM1P35 and MIR155HG) and several mRNAs were found to be significantly correlated with KIRC patient’s overall analysis. Based on the target gene of the two lncRNA in co-expression network, the GO and KEGG analysis were also performed. A dysregulated lncRNA-related ceRNA network was also observed. Conclusion: These results suggested that the two novel lncRNAs signatures may act as independent prognostic biomarkers for predicting the survival of KIRC patient

HIV and Stigma in Liuzhou, China

To describe emergent stigma-related themes from individual descriptions of living with HIV in Liuzhou, China

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains

Rapid detection of influenza A viruses using a real-time reverse transcription recombinase-aided amplification assay

IntroductionInfluenza A viruses (IAVs) are important pathogens of respiratory infections, causing not only seasonal influenza but also influenza pandemics and posing a global threat to public health. IAVs infection spreads rapidly, widely, and across species, causing huge losses, especially zoonotic IAVs infections that are more harmful. Fast and sensitive detection of IAVs is critical for controlling the spread of this disease.MethodsHere, a real-time reverse transcription recombinase-aided amplification (real-time RT-RAA) assay targeting conserved positions in the matrix protein gene (M gene) of IAVs, is successfully established to detect IAVs. The assay can be completed within 20 min at 42°C.ResultsThe sensitivity of the real-time RT-RAA assay was 142 copies per reaction at 95% probability, which was comparable to the sensitivity of the RT-qPCR assay. The specificity assay showed that the real-time RT-RAA assay was specific to IAVs, and there was no cross-reactivity with other important viruses. In addition, 100%concordance between the real-time RT-RAA and RT-qPCR assays was achieved after testing 120 clinical specimens.DiscussionThe results suggested that the real-time RT-RAA assay we developed was a specific, sensitive and reliable diagnostic tool for the rapid detection of IAVs

An efficient method for computing slope reliability calculation based on rigorous limit equilibrium

Traditional rigorous limit equilibrium methods satisfy all equilibrium conditions and usually have high accuracy, however, which are less efficient for slope reliability analysis. The main reason is that the limit state functions are highly nonlinear implicit functions of safety factor. Complex numerical iterations are required, which may sometimes lead to computational convergence problems. A new method for computing slope reliability calculation with high efficiency and accuracy was proposed. This method was based on the rigorous limit equilibrium method by modifying normal stresses over the slip surface. The critical horizontal acceleration factor Kc{K}_{c}, which can be expressed explicitly, was used to replace the implicit safety factor as a representation of slope stability. The difference between Kc{K}_{c} and the known value Kc0{K}_{c0} was used as the limit state function. Two slope examples were analyzed. The results showed that the calculation results of this method were in good agreement with those of the traditional Morgenstern–Price limit equilibrium method, but the computational efficiency was significantly improved. When this method was combined with the subset simulation method, the calculation time was only a few seconds. Therefore, this method can be used for rapid calculation of slope reliability

Failure Mechanism of the Qianjiangping Slope in Three Gorges Reservoir Area, China

The Qianjiangping landslide is the first large-scale rock slide in Three Gorges Reservoir (TGR) Area, China, after the impoundment of the TGR. Previous studies on the slope showed that most researchers agreed that reservoir impoundment and rainfall were the two main triggering factors of the slope failure. However, there were different views about the influence degrees of the two factors on the slope failure. In order to clarify the influence degrees of each of three conditions (reservoir impoundment, rainfall, and combined effect of reservoir impoundment and rainfall) on the failure of the Qianjiangping slope and reveal the failure mechanism of the slope, underground water tables and stresses in the slope were calculated under the three conditions, respectively, based on fluid-solid coupling theory using the Abaqus software in this paper; then, the failure approach index (FAI) was adopted to analyze the failure characteristics of the slope under each of the three conditions. Research results show that the influence degree of rainfall is greater than that of reservoir impoundment on the slope failure, and the influence degree of the combined effect of reservoir impoundment and rainfall is greater than that of rainfall; the sliding surface runs through only in the condition of the combined effect of reservoir impoundment and rainfall. Study results suggest that with the reservoir water level rising, the toe of the slope was gradually submerged in reservoir water and the strength of rock mass submerged by reservoir water decreased due to water-rock interaction; furthermore, the heavy rainfall was rapidly injected into the slope through the interlayer staggered zone and slope surface, the groundwater table in the middle part of the slope rose rapidly, the sliding force of the slope increased, and the stress concentration appeared at the lower part of the slope; finally, the rock bridges submerged by reservoir water in the front of the slope fractured, and the failure of the slope occurred