Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.

Background: Differential diagnosis of patients with suspected infections is particularly difficult, but necessary for prompt diagnosis and rational use of antibiotics. A substantial proportion of these patients have non-infectious diseases that include malignant tumors. Metagenomic next-generation sequencing (mNGS) technologies are used with increasing frequency to aid clinical diagnosis of patients with suspected infections. Methods: Based upon mNGS technologies and chromosomal copy number variation (CNV) analysis on abundant human genome, a new workflow named Onco-mNGS was established to simultaneously detect pathogens and malignant tumors in patients with suspected infections. Results: Of 140 patients screened by Onco-mNGS testing at four hospitals in Shanghai, 115 patients were diagnosed with infections; 17 had obvious abnormal CNV signals indicating malignant tumors that were confirmed clinically. The sensitivity and specificity of mNGS testing for diagnosis of a clinically relevant infection was 53.0% (61/115) and 60% (15/25), respectively, vs 20.9% (24/115) and 96.0% (24/25), respectively, for conventional microbiological testing (both P<0.01). Klebsiella pneumoniae was the most common pathogen detected by mNGS, followed by E. coli and viruses. The chromosomal abnormalities of the 17 cases included genome-wide variations and local variations of a certain chromosome. Five of 17 patients had a final confirmed with malignant tumors, including three lung adenocarcinomas and two hematological tumors; one patient was highly suspected to have lymphoma; and 11 patients had a prior history of malignant tumor.Conclusions: This preliminary study demonstrates the feasibility and clinical value of using Onco-mNGS to simultaneously search for potential pathogens and malignant tumors in patients with suspected infections.

Japan Glioblastoma (GBM), one of the most frequently occurring malignancies in the central nervous system, still has a very poor prognosis. To improve the prognosis of GBM patients, various attempts have been made. Immunotherapy targeting Wilms' tumor 1 (WT1) has proved to be effective in GBM (Izumoto et al. 2008). However, the functional roles of WT1 in GBM have not been intensively studied. In this study, we aim to examine the functional roles of WT1 in GBM. We established WT1 shRNA knocked down GBM cell lines (U87 and U251) and examined the functional roles in vitro and in vivo. In cell-proliferation assays, we plated 3 × 10 4 cells in 12-well plates. On day 4 the numbers of the proliferated cells were (39 + 7.8) ×10 4 cells in the U87 control shRNA, U87 WT1 shRNA, U251 control shRNA, and U251 WT1 shRNA, respectively (P , 0.05). Furthermore, an Annexin V apoptosis assay showed the numbers of the apoptotic cells per 30 times magnified microscopic field were 2.5 + 0.6, 15.3 + 5.7, 3.0 + 1.7, and 9.0 + 1.0 cells in the U87 control shRNA, U87 WT1 shRNA, U251 control shRNA, and U251 WT1 shRNA, respectively (P , 0.05). In the in vivo experiment, U87 control shRNA and U87 WT1 shRNA cells were intracranially injected into newborn pups of immunodeficient mice. At day 30, all of the mice transplanted with U87MG control shRNA developed GBM, whereas none of the mice transplanted with U87MG WT1 shRNA developed GBM. These results suggest that WT1 is involved in GBM cell proliferation, apoptosis, and tumor formation. Malignant gliomas are highly invasive and chemoresistant brain tumors with extremely poor prognosis. Glioma invasion is strongly associated with the resistance of these tumors to therapy, but the mechanisms that underlie this association are poorly understood. Targeting soluble factors triggering invasion and resistance could substantially affect the difficult-to-reach, infiltrative glioma cells that are a major source of recurrence. Fibulin-3, a matrix protein absent in normal brain tissue but upregulated in gliomas, promotes tumor invasion by unknown mechanisms. We show here that fibulin-3 is a novel soluble activator of Notch signaling that antagonizes DLL3, an autocrine inhibitor of Notch, and promotes tumor-cell survival and invasion in a Notch-dependent manner. Using a strategy for inducible knockdown, we demonstrate that controlled downregulation of fibulin-3 reduces Notch signaling and leads to increased apoptosis, reduced self-renewal of glioblastoma-initiating cells, and impaired growth and dispersion of intracranial tumors. Finally, we show that fibulin-3 expression correlates with expression levels of Notch-dependent genes (Hes1, Hes5) and is a marker of Notch activation in clinical gliomas. These results underscore a major role of the tumor extracellular matrix in regulating glioma invasion and resistance to apoptosis via activation of the key Notch pathway. More importantly, this is the first description of a noncanonical, soluble activator of Notch in a cancer model and a demonstration of how Not...

Fisheye camera calibration is an essential task in photogrammetry. However, previous calibration patterns and the robustness of the adjoint processing methods are limited due to the fisheye distortion and various lighting. This problem leads to additional manual intervention in the data collection. Moreover, it is arduous to accurately detect the board target under fisheye's distortion. To increase the robustness in this task, we present a novel encoded board “Meta‐Board” and a learning‐based target detection method. Additionally, an automatic image orthorectification is integrated to alleviate the distortion effect on the target iteratively until convergence. A low‐cost control field with the proposed boards is built for the experiment. Results on both virtual and real camera lenses and multi‐camera rigs show that our method can be robustly used in calibrating the fisheye camera and reaches state‐of‐the‐art accuracy.