Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.Comment: ICCV 201

MRI-based radiomics features uncover the micro-change of dorsal root ganglia lesion for patients with post-herpetic neuralgia

ObjectiveTo create and authenticate MRI-based radiomic signatures to identify dorsal root ganglia (DRG) lesions in post-herpetic neuralgia (PHN) patients generalizable and interpretable.MethodThis prospective diagnostic study was conducted between January 2021 and February 2022. Lesioned DRG in patients with PHN and normal DRG in age-, sex-, height-, and weight-matched healthy controls were selected for assessment and divided into two groups (8:2) randomly: training and testing sets. The least absolute shrinkage and selection operator algorithm was employed to generate feature signatures and construct a model, followed by the assessment of model efficacy using the area under the curve (AUC) of the receiver operating characteristic (ROC), as well as sensitivity and specificity metrics.ResultsThe present investigation involved 30 patients diagnosed with postherpetic neuralgia (PHN), consisting of 18 males and 12 females (mean age 60.70 ± 10.18 years), as well as 30 healthy controls, comprising 18 males and 12 females (mean age 58.13 ± 10.54 years). A total of 98 DRG were randomly divided into two groups (8:2), namely a training set (n = 78) and a testing set (n = 20). Five radiomic features were chosen to construct the models. In the training dataset, the area under the curve (AUC) was 0.847, while the sensitivity and specificity were 71.79 and 97.44%, respectively. In the test dataset, the AUC was 0.87, and the sensitivity and specificity were 80.00 and 100.00%, respectively.ConclusionAn MRI-based radiomic signatures model has the capacity to uncover the micro-change of damaged DRG in individuals afflicted with postherpetic neuralgia

Enhanced γ-Glutamyltranspeptidase Imaging That Unravels the Glioma Recurrence in Post-radio/Chemotherapy Mixtures for Precise Pathology via Enzyme-Triggered Fluorescent Probe

Accurate pathological diagnosis of gliomas recurrence is crucial for the optimal management and prognosis prediction. The study here unravels that our newly developed γ-glutamyltranspeptidase (GGT) fluorescence probe (Figure 1A) imaging in twenty recurrent glioma tissues selectively recognizes the most malignant portion from treatment responsive tissues induced by radio/chemo-therapy (Figure 1B). The overexpression of GGT in recurrent gliomas and low level in radiation necrosis were validated by western blot analysis and immunohistochemistry. Furthermore, the ki-67 index evaluation demonstrated the significant increase of malignancy, aided by the GGT-responsive fluorescent probe to screen out the right specimen through fast enhanced imaging of enzyme activity. Importantly, our GGT-targeting probe can be used for accurate determination of pathologic evaluation of tumor malignancy, and eventually for guiding the following management in patients with recurrent gliomas

Alterations of the gut microbiota in patients with postherpetic neuralgia

Abstract Postherpetic neuralgia (PHN) is a prevalent, intricate, and intractable form of neuropathic pain. The available evidence indicates that alterations in the gut microbiota are significant environmental determinants in the development of chronic neuropathic pain. Nevertheless, the correlation between the gut microbiota and PHN remains elusive. A cross-sectional study was performed on a cohort of 27 patients diagnosed with PHN and 27 matched healthy controls. Fecal samples were collected and subjected to microbiota analysis using 16S ribosomal RNA gene sequencing. Comparable levels of bacterial richness and diversity were observed in the gut microbiota of PHN patients and healthy controls. A significant difference was observed in 37 genera between the two groups. Furthermore, the LEfSe method revealed that the abundance levels of Escherichia-Shigella, Streptococcus, Ligilactobacillus, and Clostridia_UCG-014_unclassified were elevated in PHN patients, while Eubacterium_hallii_group, Butyricicoccus, Tyzzerella, Dorea, Parasutterella, Romboutsia, Megamonas, and Agathobacter genera were reduced in comparison to healthy controls. Significantly, the discriminant model utilizing the predominant microbiota exhibited efficacy in distinguishing PHN patients from healthy controls, with an area under the curve value of 0.824. Moreover, Spearman correlation analysis demonstrated noteworthy correlations between various gut microbiota and clinical symptoms, including disease course, anxiety state, sleep quality, heat pain, pain intensity, and itching intensity. Gut microbiota dysbiosis exists in PHN patients, microbiome differences could be used to distinguish PHN patients from normal healthy individuals with high sensitivity and specificity, and altered gut microbiota are related to clinical manifestations, suggesting potentially novel prevention and therapeutic directions of PHN