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Trans-endothelial electrical resistance (TEER) is one of the most widely used indicators to quantify the barrier integrity of endothelial layers. Over the last decade, the integration of TEER sensors into organ-on-a-chip (OOC) platforms has gained increasing interest for its efficient and effective measurement of TEER in OOCs. To date, microfabricated electrodes or direct insertion of wires has been used to integrate TEER sensors into OOCs, with each method having advantages and disadvantages. In this study, we developed a TEER-SPE chip consisting of carbon-based screen-printed electrodes (SPEs) embedded in a poly(methyl methacrylate) (PMMA)-based multi-layered microfluidic device with a porous poly(ethylene terephthalate) membrane in-between. As proof of concept, we demonstrated the successful cultures of hCMEC/D3 cells and the formation of confluent monolayers in the TEER-SPE chip and obtained TEER measurements for 4 days. Additionally, the TEER-SPE chip could detect changes in the barrier integrity due to shear stress or an inflammatory cytokine (i.e., tumor necrosis factor-& alpha;). The novel approach enables a low-cost and facile fabrication of carbon-based SPEs on PMMA substrates and the subsequent assembly of PMMA layers for rapid prototyping. Being cost-effective and cleanroom-free, our method lowers the existing logistical and technical barriers presenting itself as another step forward to the broader adoption of OOCs with TEER measurement capability., QC 20231009

Chen Z, Mou L, Pan Y, Feng C, Zhang J, Li J. Onco Targets Ther. 2019;12:8125–8138. The Editor and Publisher of OncoTargets and Therapy wish to retract the published article. Concerns were raised following the authors request to make corrections to images in Supplemental Figures 3, 4 and 5. Our investigation found that images within these figures appeared to have duplicated and further image duplication was found within Figures 3 and 4 from the main body of the article. Specifically, Figure 3B, U-251MG, None, appears to have been duplicated with the same image in Figure 3C, U-87MG, sh-CXCL8#1, which has been rotated. Figure 4A, U-251MG, LV-Vector, N-cadherin, appears to have been duplicated with regions from the image used in Figure 4A, U-251MG, LV-Vector, Vimentin. Figure 4A, U-87MG, CXCL8-shRNA, DAPI/CXCL8, appears to have been duplicated with regions from the image used in Figure 4A, U-251MG, LV-Vector, Vimentin. Figure S3C, U-87MG, None, appears to have been duplicated with the same image in Figure S3D, U-251MG, Vector. Figure S3C, U-87MG, CXCL8(KD), appears to have been duplicated with the same image in Figure S4D, U-251MG, Vector. Figure S4E, U-87MG, None, appears to have been duplicated with the same image in Figure S4E, U-87MG, Vector. The authors cooperated with the investigation and provided data associated with the study. However, despite the authors’ assistance, it was still not clear how the images came to be duplicated, and, as we cannot verify the validity of the published work, we are therefore retracting the article and the authors were notified of this. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS 2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics.

This paper has been retracted. Chen Z, Mou L, Pan Y, Feng C, Zhang J, Li J. Onco Targets Ther. 2019;12:8125–8138. The Editor and Publisher of OncoTargets and Therapy wish to retract the published article. Concerns were raised following the authors request to make corrections to images in Supplemental Figures 3, 4 and 5. Our investigation found that images within these figures appeared to have duplicated and further image duplication was found within Figures 3 and 4 from the main body of the article. Specifically, Figure 3B, U-251MG, None, appears to have been duplicated with the same image in Figure 3C, U-87MG, sh-CXCL8#1, which has been rotated. Figure 4A, U-251MG, LV-Vector, N-cadherin, appears to have been duplicated with regions from the image used in Figure 4A, U-251MG, LV-Vector, Vimentin. Figure 4A, U-87MG, CXCL8-shRNA, DAPI/CXCL8, appears to have been duplicated with regions from the image used in Figure 4A, U-251MG, LV-Vector, Vimentin. Figure S3C, U-87MG, None, appears to have been duplicated with the same image in Figure S3D, U-251MG, Vector. Figure S3C, U-87MG, CXCL8(KD), appears to have been duplicated with the same image in Figure S4D, U-251MG, Vector. Figure S4E, U-87MG, None, appears to have been duplicated with the same image in Figure S4E, U-87MG, Vector. The authors cooperated with the investigation and provided data associated with the study. However, despite the authors’ assistance, it was still not clear how the images came to be duplicated, and, as we cannot verify the validity of the published work, we are therefore retracting the article and the authors were notified of this. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.