Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space.

BACKGROUND AND OBJECTIVES: Compared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. METHODS: To fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. RESULTS: The proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. CONCLUSIONS: The proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

Orthogonal light-triggered multiple effects based on photochromic nanoparticles for DNA cleavage and beyond.

Efficient and spatiotemporally controllable cleavage of deoxyribonucleic acid (DNA) is of great significance for both disease treatment (e.g. tumour, bacterial infection, etc) and molecular biology applications (e.g. gene editing). The recently developed light-induced cleavage strategy based on catalytic nanoparticles has been regarded as a promising strategy for DNA controllable cleavage. Although the regulation based on orthogonal light in biomedical applications holds more significant advantages than that based on single light, nanoparticle-mediated DNA cleavage based on orthogonal light has yet to be reported. In this article, for the first time, we demonstrated an orthogonal light-regulated nanosystem for efficient and spatiotemporal DNA cleavage. In this strategy, tungsten oxide (WO3) nanoparticles with photochromic properties were used as nano-antennae to convert the photoenergy from the orthogonal visible light (405 nm) and near-infrared light (808 nm) into chemical energy for DNA cleavage. We verified that only the orthogonal light can trigger high cleavage efficiency on different types of DNA. Moreover, such an orthogonal light-response nano-system can not only induce significant apoptosis of tumour cells, but also effectively eliminate bacterial biofilms.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer.

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm-1, 1004 cm-1, 1449 cm-1, 1089-1128 cm-1, 1311-1367 cm-1 and 1585-1665 cm-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Superoxide dismutase 3 as an inflammatory suppressor in A549 cells infected with Mycoplasma pneumoniae.

Herein, we found that serum concentration of superoxide dismutase 3 (SOD3) was significantly reduced in children with mycoplasma pneumonia (MP) infection. To study the roles of SOD3 in inflammatory regulation of MP infection, human A549 type II alveolar epithelial cells were stimulated with 107 CCU/ml of MP to build MP infection in vitro. Secretion of pro-inflammatory cytokine interleukin (IL)-8 and tumor necrosis factor (TNF)-α were measured via enzyme-linked immunosorbent assay (ELISA) to assess the inflammatory response of A549 cells. Levofloxacin (LVFX) was used as an anti-inflammatory drug while recombinant TNF-α was used as an inflammatory promotor in MP-infected cells. Transcriptional activity of nuclear factor (NF)-κB was assessed by detecting protein levels of nuclear NF-κB and cytoplasm NF-κB using Western blot analysis. Our data suggested that the expression of SOD3 mRNA and protein, as well as content of SOD3 in cultured supernatant, were time-dependently inhibited in MP-infected A549 cells. However, lentiviruses-mediated SOD3 overexpression alleviated inflammatory response of MP-infected A549 cells, and prevented the unclear translocation of NF-κB, as evidenced by obviously reducing the production of IL-8 and TNF-α in cell cultured supernatant, as well as decreasing nuclear NF-κB while increasing cytoplasm NF-κB. Inspiringly, SOD3 overexpression induced anti-inflammatory effect and the inactivation of NF-κB was similar to that of 2 lg/ml of LVFX, but reversed by additional TNF-α treatment. Therefore, we can conclude that transcriptional activity of NF-jB was the underlying mechanism, by which SOD3 regulated inflammatory response in MP infection in vitro.