Use of pyridazinediones for tuneable and reversible covalent cysteine modification applied to peptides, proteins and hydrogels.

Reversible cysteine modification has been found to be a useful tool for a plethora of applications such as selective enzymatic inhibition, activity-based protein profiling and/or cargo release from a protein or a material. However, only a limited number of reagents display reliable dynamic/reversible thiol modification and, in most cases, many of these reagents suffer from issues of stability, a lack of modularity and/or poor rate tunability. In this work, we demonstrate the potential of pyridazinediones as novel reversible and tuneable covalent cysteine modifiers. We show that the electrophilicity of pyridazinediones correlates to the rates of the Michael addition and retro-Michael deconjugation reactions, demonstrating that pyridazinediones provide an enticing platform for readily tuneable and reversible thiol addition/release. We explore the regioselectivity of the novel reaction and unveil the reason for the fundamental increased reactivity of aryl bearing pyridazinediones by using DFT calculations and corroborating findings with SCXRD. We also applied this fundamental discovery to making more rapid disulfide rebridging agents in related work. We finally provide the groundwork for potential applications in various areas with exemplification using readily functionalised "clickable" pyridazinediones on clinically relevant cysteine and disulfide conjugated proteins, as well as on a hydrogel material.

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image. METHODS: 1022 enhanced abdomen CT images of 121 patients with hepatocellular carcinoma and impaired liver reserve function were retrospectively collected. Firstly, CT images were pre-processed by de-noising, data amplification and normalization. Then, new branches were added between the dense blocks of the DenseNet structure, and the center clipping operation was introduced to obtain a lightweight deep learning model liver reserve function network (LRFNet) with rich liver scale features. LRFNet extracted depth features related to liver reserve function from CT images. Finally, the extracted features are input into a deep learning classifier composed of fully connected layers to classify CT images into Child-Pugh A, B and C. Precision, Specificity, Sensitivity, and Area Under Curve are used to evaluate the performance of the model. RESULTS: The AUC by our LRFNet model based on CT image for Child-Pugh A, B and C classification of liver reserve function was 0.834, 0.649 and 0.876, respectively, and with an average AUC of 0.774, which was better than the traditional clinical subjective Child-Pugh classification method. CONCLUSION: Deep learning model based on CT images can accurately classify Child-Pugh grade of liver reserve function in hepatocellular carcinoma patients, provide a comprehensive method for clinicians to assess liver reserve function before surgery.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks.

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation. To address these problems, we propose a novel metal artifact reduction method based on generative adversarial networks to simultaneously reduce metal artifacts and enhance texture structure of corrected CT images. Specifically, we firstly incorporate interactive information (text) and imaging CT (image) into a comprehensive feature to yield multi-modal feature-fusion representation, which overcomes the representative ability limitation of single-modal data. The incorporation of interaction information constrains the feature generation to ensure symptom consistency between corrected and target CT. Then, we design an edge-enhance sub-network to avoid second artifact and suppress noise. Besides, we invite three professional physicians to evaluate corrected CT image subjectively. In this paper, We achieved average increment of 11.3% PSNR and 12.1% SSIM on DeepLesion dataset. The subjective evaluations by physicians show that ours outperforms over 6.3%, 7.1%, 5.50% and 6.9% in term of sharpness, resolution, invariance and acceptability, respectively. Our proposed method can achieve high-quality metal artifact reduction results.

Phytoremediation potential of Pterocypsela laciniata as a cadmium hyperaccumulator.

To identify new cadmium (Cd) hyperaccumulators, the artificially high soil Cd concentration method was used to screen six common farmland weeds. Among them, only Pterocypsela laciniata (Houtt.) C. Shih showed characteristics of a Cd hyperaccumulator and was selected for further studies. In pot experiments, soil Cd concentrations of 5, 10, and 25 mg kg-1 increased the biomass and photosynthetic pigment concentrations in P. laciniata when compared with the control, whereas 75 and 100 mg kg-1 decreased them (the maxima were at 10 mg kg-1 soil Cd). The antioxidant enzyme activities and the soluble protein concentrations of P. laciniata showed similar trends as biomass. The Cd concentrations in roots and shoots of P. laciniata increased as soil Cd concentration increased. When the soil Cd concentration was 50 mg kg-1, the Cd concentration in the shoots of P. laciniata was 116 mg kg-1 (the critical value for Cd hyperaccumulators is 100 mg kg-1). Both the root and shoot bioconcentration factors of P. laciniata were larger than 1.0, and the translocation factor exceeded 1.0 in almost all treatments. The Cd extractions by the shoots and whole plants of P. laciniata reached maxima at 208 and 375 µg plant-1, respectively. The Cd extractions by P. laciniata were different between two ecotypes. Therefore, P. laciniata is a Cd hyperaccumulator that could remediate Cd-contaminated soils, but the ecotypes should be considered when using P. laciniata for phytoremediation.