Efficacy of Zhenjingdingzhi decoction in treating insomnia with Qi-deficiency of heart and gallbladder: a randomized, double-blind, controlled trial

AbstractObjectiveTo evaluate the clinical efficacy of Zhenjingdingzhi decoction in treating insomnia with Qi-deficiency of heart and gallbladder.MethodsWe conducted a double-blind, randomized, controlled trial involving 100 patients with insomnia of Qi-deficiency of heart and gallbladder. Patients were randomly divided into the treatment group (n = 50) and the control group (n = 50) according to a random number table. The treatment group was given Zhenjingdingzhi decoction, while the control group was treated with Suanzaoren decoction. the pharmacological treatment lasted for 8 weeks. The clinical efficacy was assessed by using Spiegel scale, Pittsburgh sleep quality index (PSQI) and Traditional Chinese Medicine (TCM) syndrome scores.ResultsComparing Spiegel scores between the two groups at 4 and 8 weeks, the differences in curative effect between the two groups were both significant (both P < 0.05). The total effective rate was 46% in the treatment group and 27.7% in the control group at 4 weeks, and 80% and 53.2% at 8 weeks, respectively; After 8 weeks, PSQI scores showed that the total effective rates differed significantly between the two groups (P < 0.01): 84% in the treatment group and 59.6% in the control group; In improving sleep quality and sleep duration, the curative effect of the treatment group was better than that of the control group (P < 0.05). TCM syndrome, especially insomnia and palpitation, was improved better in the treatment group after 8 weeks as compared to that in the control group (P < 0.05). The total effective rate of the two groups was 84% and 66%, respectively.ConclusionZhenjingdingzhi decoction is effective and safe for the treatment of insomnia with Qi-deficiency of heart and gallbladder, especially for improving sleep quality and sleep duration

Silencing of c-Ski augments TGF-b1-induced epithelial-mesenchymal transition in cardiomyocyte H9C2 cells

Background: The shRNA lentiviral vector was constructed to silence c-Ski expression in cardiac mus-  cle cells, with the aim of exploring the role of c-Ski in transforming growth factor b1 (TGF-b1)-induced epithelial-mesenchymal transitions (EMT) in H9C2 cells. Methods: Real-time polymerase chain reaction (RT-PCR) and western blot were used to detect c-Ski ex- pression at protein and messenger ribonucleic acid (mRNA) levels in 5 different cell lines. Then, lentiviral vector was constructed to silence or overexpress c-Ski in H9C2 cells. MTT and/or soft agar assay and tran- swell assay were used to detect cell proliferation and migration, respectively. The expression levels of c-Ski under different concentrations of TGF-b1 stimulation were detected by RT-qPCR and immunocytochemi- cal analysis. In the presence or absence of TGF-b1 stimulation, the proteins’ expression levels of a-SMA, FN and E-cadherin, which are closely correlated with the process of EMT, were measured by western blot after c-Ski silencing or overexpression. Meanwhile, the effect of c-Ski on Samd3 phosphorylation with TGF-b1 stimulation was investigated.  Results: There is a high expression of c-Ski at protein and mRNA levels in H9C2 cell line, which first demonstrated the presence of c-Ski expression in H9C2 cells. Overexpression of c-Ski significantly increased H9C2 cell proliferation. The ability of c-Ski gene silencing to suppress cell proliferation was gradually enhanced, and inhibition efficiency was the highest after 6 to 7 d of transfection. Moreover, H9C2 cells with c-Ski knockdown gained significantly aggressive invasive potential when compared with the control group. TGF-b1 stimulation could dose-independently reduce c-Ski expression in H9C2 cells and lead to obvious down-regulated expression of E-cadherin. Interestingly, c-Ski could restore E-cadherin expression while suppressing a-SMA and/or FN expression stimulated by TGF-b1. How- ever, shRNA-induced c-Ski knockdown aggravated only the TGF-b1-induced EMT. Moreover, c-Ski- -shRNA also promoted the phosphorylation of Samd3 induced by TGF-b1.  Conclusions: c-Ski expression in cardiac muscle cells could be down-regulated by TGF-b1. Silencing of c-Ski gene was accompanied by down-regulation of E-cadherin, up-regulation of a-SMA and/or FN and Smad3 phosphorylation induced by TGF-b1, promoting EMT process. Therefore, c-Ski may be closely associated with TGF-b1-induced EMT and play an important role in cardiac fibrosis develop- ment and progression.

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.Comment: MICCAI202

Exosomes from Adipose-Derived Stem Cells Promotes VEGF-C-Dependent Lymphangiogenesis by Regulating miRNA-132/TGF-β Pathway

Background/Aims: Lymphangiogenesis plays an important role in the pathogenesis of inflammatory bowel diseases (IBD), and vascular endothelial growth factor-C (VEGF-C) is a powerful lymphangiogenic factor. Adipose-derived stem cells (ADSCs) are a promising therapeutic modality for several diseases because ADSCs secret growth factors and exosomes, which modulate hostile microenvironments affected by diseases. However, the effect of exosomes on VEGF-C-dependent lymphangiogenesis and its mechanism remain unclear. Methods: ADSCs were cultured in media with or without recombinant VEGF-C and exosomes were extracted from conditioned medium (CM). Lymphatic endothelial cells (LECs) were treated with ADSCs-derived exosomes, then proliferation, migration and tube formation of LECs were assayed using cell counting Kit-8 (CCK-8), transwell chamber inserts and matrigel-based tube formation assay respectively. Results: We identified significantly higher levels of miR-132 in exosomes isolated from VEGF-C-treated ADSCs (ADSCs/VEGF-C) than in those from ADSCs control. miR-132 was directly transferred from ADSCs to the LECs by the mediation of exosomes. The exosomes from ADSCs/VEGF-C promoted LECs proliferation, migration, and tube formation more potently than the exosomes from ADSCs, whereas pretreatment of ADSCs with miR-132 inhibitor attenuates VEGF-C-dependent lymphangiogenic response. Finally we reveal that miR-132 promotes lymphangiogenic response by directly targeting Smad-7 and regulating TGF-β/Smad signaling. Conclusion: These data provide new insights into the role of ADSCs-derived exosomes as an important player in VEGF-C-dependent lymphangiogenesis

Pressure-induced structural modulations in coesite

Silica phases, SiO2, have attracted significant attention as important phases in the fields of condensed-matter physics, materials science, and (in view of their abundance in the Earth's crust) geoscience. Here, we experimentally and theoretically demonstrate that coesite undergoes structural modulations under high pressure. Coesite transforms to a distorted modulated structure, coesite-II, at 22–25 GPa with modulation wave vector q=0.5b∗. Coesite-II displays further commensurate modulation along the y axis at 36–40 GPa and the long-range ordered crystalline structure collapses beyond ∼40GPa and starts amorphizing. First-principles calculations illuminate the nature of the modulated phase transitions of coesite and elucidate the modulated structures of coesite caused by modulations along the y-axis direction. The structural modulations are demonstrated to result from phonon instability, preceding pressured-induced amorphization. The recovered sample after decompression develops a rim of crystalline coesite structure, but its interior remains low crystalline or partially amorphous. Our results not only clarify that the pressure-induced reversible phase transitions and amorphization in coesite originate from structural modulations along the y-axis direction, but also shed light on the densification mechanism of silica under high pressure

LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images

Diffusion-weighted (DW) magnetic resonance imaging is essential for the diagnosis and treatment of ischemic stroke. DW images (DWIs) are usually acquired in multi-slice settings where lesion areas in two consecutive 2D slices are highly discontinuous due to large slice thickness and sometimes even slice gaps. Therefore, although DWIs contain rich 3D information, they cannot be treated as regular 3D or 2D images. Instead, DWIs are somewhere in-between (or 2.5D) due to the volumetric nature but inter-slice discontinuities. Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images. To tackle this problem, we propose a new neural network architecture tailored for segmenting highly-discontinuous 2.5D data such as DWIs. Our network, termed LambdaUNet, extends UNet by replacing convolutional layers with our proposed Lambda+ layers. In particular, Lambda+ layers transform both intra-slice and inter-slice context around a pixel into linear functions, called lambdas, which are then applied to the pixel to produce informative 2.5D features. LambdaUNet is simple yet effective in combining sparse inter-slice information from adjacent slices while also capturing dense contextual features within a single slice. Experiments on a unique clinical dataset demonstrate that LambdaUNet outperforms existing 3D/2D image segmentation methods including recent variants of UNet. Code for LambdaUNet is released with the publication to facilitate future research

Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Vision Transformers, it employs Patcher blocks that segment an image into large patches, each of which is further divided into small patches. Transformers are applied to the small patches within a large patch, which constrains the receptive field of each pixel. We intentionally make the large patches overlap to enhance intra-patch communication. The encoder employs a cascade of Patcher blocks with increasing receptive fields to extract features from local to global levels. This design allows Patcher to benefit from both the coarse-to-fine feature extraction common in CNNs and the superior spatial relationship modeling of Transformers. We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel. The use of MoE enables better specializations of the expert features and reduces interference between them during inference. Extensive experiments demonstrate that Patcher outperforms state-of-the-art Transformer- and CNN-based approaches significantly on stroke lesion segmentation and polyp segmentation. Code for Patcher is released with publication to facilitate future research.Comment: MICCAI 202