Bioactive triterpenoid compounds of Poria cocos (Schw.) Wolf in the treatment of diabetic ulcers via regulating the PI3K-AKT signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic ulcers represent a chronic condition characterized by prolonged hyperglycemia and delayed wound healing, accompanied by endocrine disorders, inflammatory responses, and microvascular damage in the epidermal tissue, demanding effective clinical treatment approaches. For thousands of years, ancient Chinese ethnopharmacological studies have documented the use of Poria cocos (Schw.) Wolf in treating diabetic ulcers. Recent research has substantiated the diverse pharmacological effects of Poria cocos (Schw.) Wolf, including its potential to alleviate hyperglycemia and exhibit anti-inflammatory, antioxidant, and immune regulatory properties, which could effectively mitigate diabetic ulcer symptoms. Furthermore, being a natural medicine, Poria cocos (Schw.) Wolf has demonstrated promising therapeutic effects and safety in the management of diabetic ulcers, holding significant clinical value. Despite its potential clinical efficacy and applications in diabetic ulcer treatment, the primary active components and underlying pharmacological mechanisms of Poria cocos (Schw.) Wolf remains unclear. Further investigations are imperative to establish a solid foundation for drug development in this domain. AIM OF THE STUDY AND MATERIALS AND METHODS: In this study, we aimed to identify the active compounds and potential targets of Poria cocos (Schw.) Wolf using UHPLC-Q-TOF-MS and TCMSP databases. Additionally, we attempt to identify targets related to diabetic ulcers. Following enrichment analysis, a network of protein-protein interactions was constructed to identify hub genes based on the common elements between the two datasets. To gain insights into the binding activities of the hub genes and active ingredients, molecular docking analysis was employed. Furthermore, to further validate the therapeutic effect of Poria cocos (Schw.) Wolf, we exerted in vitro experiments using human umbilical vein vascular endothelial cells and human myeloid leukemia monocytes (THP-1). The active ingredient of Poria cocos (Schw.) Wolf was applied in these experiments. Our investigations included various assays, such as CCK-8, scratch test, immunofluorescence, western blotting, RT-PCR, and flow cytometry, to explore the potential of Poria cocos (Schw.) Wolf triterpenoid extract (PTE) in treating diabetic ulcers. RESULTS: The findings here highlighted PTE as the primary active ingredient in Poria cocos (Schw.) Wolf. Utilizing network pharmacology, we identified 74 potential targets associated with diabetic ulcer treatment for Poria cocos (Schw.) Wolf, with five hub genes (JUN, MAPK1, STAT3, AKT1, and CTNNB1). Enrichment analysis revealed the involvement of multiple pathways in the therapeutic process, with the PI3K-AKT signaling pathway showing significant enrichment. Through molecular docking, we discovered that relevant targets within this pathway exhibited strong binding with the active components of Poria cocos (Schw.) Wolf. In vitro experiments unveiled that PTE (10 mg/L) facilitated the migration of human umbilical vein vascular endothelial cells (P < 0.05). PTE also increased the expression of CD31 and VEGF mRNA (P < 0.05) while activating the expressions of p-PI3K and p-AKT (P < 0.05). Moreover, PTE demonstrated its potential by reducing the expression of IL-1ß, IL-6, TNF-α, and NF-κB mRNA in THP-1 (P < 0.05) and fostering M2 macrophage polarization. These results signify the potential therapeutic effects of PTE in treating diabetic ulcers, with its beneficial actions mediated through the PI3K-AKT signaling pathway. CONCLUSIONS: PTE is the main active ingredient in Poria cocos (Schw.) Wolf that exerts therapeutic effects. Through PI3K-AKT signaling pathway activation and inflammatory response reduction, PTE promotes angiogenesis, thereby healing diabetic ulcers.

Double filter iterative ghost imaging for high quality edge and image acquisition.

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three dimensional filtering and guided filtering to achieve high-quality image reconstruction under low measurement and low iteration times. This method combines the advantages of ill-posed problem solution of projected Landweber iterative regularization with double filtering joint iterative de-noising and edge preservation. The numerical simulation results show that our method outperforms the comparison method by 4 to 6 dB in terms of peak signal-to-noise ratio for complex binary target 'rice' and grayscale target 'aircraft' after 1500 measurements. The comparison results of experiments and numerical simulations using similar aircraft targets show that this method is superior to the comparison method, especially in terms of richer and more accurate edge detection results. This method can simultaneously obtain high quality reconstructed image and edge feature information under low measurement and iteration times, which is of great value for the practical application fields of imaging and edge detection at the same time, such as intelligent driving, remote sensing and other fields.

MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering.

Deep neural network-based models have achieved great success in extractive question answering. Recently, many works have been proposed to model multistage matching for this task, which usually first retrieve relevant paragraphs or sentences and then extract an answer span from the retrieved results. However, such a pipeline-based approach suffers from the error propagation problem, especially for sentence-level retrieval that is usually difficult to achieve high accuracy due to the severe data imbalance problem. Furthermore, since the paragraph/sentence selector and the answer extractor are closely related, modeling them independently does not fully exploit the power of multistage matching. To solve these problems, we propose a novel end-to-end multigranularity reading comprehension model, which is a unified framework to explicitly model three matching granularities, including paragraph identification, sentence selection, and answer extraction. Our approach has two main advantages. First, the end-to-end approach alleviates the error propagation problem in both the training and inference phases. Second, the shared features in a unified model improve the learning of representations of different matching granularities. We conduct a comprehensive comparison on four large-scale datasets (SQuAD-open, NewsQA, SQuAD 2.0, and SQuAD Adversarial) and verify that the proposed approach outperforms both the vanilla BERT model and existing multistage matching approaches. We also conduct an ablation study and verify the effectiveness of the proposed components in our model structure.

TopicBERT: A Topic-Enhanced Neural Language Model Fine-Tuned for Sentiment Classification.

Sentiment classification is a form of data analytics where people's feelings and attitudes toward a topic are mined from data. This tantalizing power to "predict the zeitgeist" means that sentiment classification has long attracted interest, but with mixed results. However, the recent development of the BERT framework and its pretrained neural language models is seeing new-found success for sentiment classification. BERT models are trained to capture word-level information via mask language modeling and sentence-level contexts via next sentence prediction tasks. Out of the box, they are adequate models for some natural language processing tasks. However, most models are further fine-tuned with domain-specific information to increase accuracy and usefulness. Motivated by the idea that a further fine-tuning step would improve the performance for downstream sentiment classification tasks, we developed TopicBERT-a BERT model fine-tuned to recognize topics at the corpus level in addition to the word and sentence levels. TopicBERT comprises two variants: TopicBERT-ATP (aspect topic prediction), which captures topic information via an auxiliary training task, and TopicBERT-TA, where topic representation is directly injected into a topic augmentation layer for sentiment classification. With TopicBERT-ATP, the topics are predetermined by an LDA mechanism and collapsed Gibbs sampling. With TopicBERT-TA, the topics can change dynamically during the training. Experimental results show that both approaches deliver the state-of-the-art performance in two different domains with SemEval 2014 Task 4. However, in a test of methods, direct augmentation outperforms further training. Comprehensive analyses in the form of ablation, parameter, and complexity studies accompany the results.

Bone Tissue Engineering in the Treatment of Bone Defects.

Bones play an important role in maintaining exercise and protecting organs. Bone defect, as a common orthopedic disease in clinics, can cause tremendous damage with long treatment cycles. Therefore, the treatment of bone defect remains as one of the main challenges in clinical practice. Today, with increased incidence of bone disease in the aging population, demand for bone repair material is high. At present, the method of clinical treatment for bone defects including non-invasive therapy and invasive therapy. Surgical treatment is the most effective way to treat bone defects, such as using bone grafts, Masquelet technique, Ilizarov technique etc. In recent years, the rapid development of tissue engineering technology provides a new treatment strategy for bone repair. This review paper introduces the current situation and challenges of clinical treatment of bone defect repair in detail. The advantages and disadvantages of bone tissue engineering scaffolds are comprehensively discussed from the aspect of material, preparation technology, and function of bone tissue engineering scaffolds. This paper also summarizes the 3D printing technology based on computer technology, aiming at designing personalized artificial scaffolds that can accurately fit bone defects.

Correction: The hangman effect boosts hydrogen production by a manganese terpyridine complex.

Correction for 'The hangman effect boosts hydrogen production by a manganese terpyridine complex' by Qianqian Wu et al., Chem. Commun., 2022, 58, 5128-5131, https://doi.org/10.1039/D2CC00757F.

The hangman effect boosts hydrogen production by a manganese terpyridine complex.

The manganese terpyridine complex 1 with a coordinated carboxylate in the axial position was obtained in situ. By virtue of a hangman effect, complex 1 catalyzes electrochemical hydrogen evolution from phenol in acetonitrile solution with a turnover frequency of 525 s-1 at a low overpotential of ca. 230 mV.

Rapid evolution of latitudinal clines in growth and defence of an invasive weed.

Re-establishment of heritable latitudinal clines in growth-related traits has been recognised as evidence for adaptive evolution in invasive plants. However, less information is known about latitudinal clines in defence and joint clinal evolution of growth and defence in invasive plants. We planted 14 native Argentinean populations and 14 introduced Chinese populations of Alternanthera philoxeroides in replicate common gardens in China. We investigated the latitudinal clines of traits related to growth and defence, and plasticity of these traits in relation to experiment site and soil nitrogen. We found that chemical defence decreased with latitude in introduced populations but increased with latitude in native populations. For growth rate, latitudinal clines were positive in introduced populations but nonexistent in native populations. There were also parallel positive latitudinal clines in total/shoot biomass and specific leaf area. Experiment site affected the occurrence or magnitude of latitudinal clines in growth rate, branch intensity and triterpenoid saponins concentration. Introduced populations were more plastic to experiment site and soil nitrogen than native populations. We provide evidence for rapid evolution of clines in growth and defence in an invasive plant. Altered herbivory gradients and trade-off between growth and defence may explain nonparallel clines between the native and introduced ranges.

Ni Hierarchical Structures Supported on Titania Nanowire Arrays as Efficient Nonenzymatic Glucose Sensor.

Developing new advanced nonenzymatic electrochemical nano-sensors for glucose detection has attracted intensive attraction. In this work, we designed a novel nanocomposite nonenzymatic glucose sensor by fabricating hierarchically nanostructured metal nickel on titania nanowire arrays, which was loaded on a transparent conductive substrate (i.e., fluorine-doped tin oxide, FTO) surface by mild hydrothermal method. Due to the large surface area of the hierarchically nanostructured Ni and fast electron transfer of the TiO2 nanowire arrays electrode, the nanocomposite electrode shows excellent electrochemical activity toward the oxidation of glucose. The electrode exhibits high sensitivity in detecting glucose concentration (1472 µA mM-1 cm-2) with a wide linear range from 2×10-4 M to 2×10-3 M, fast response time (within 5 s), and small detection limit (10 µM) (S/N = 3). The good analytical performance, low cost and simple preparation method make this novel electrode material promising for the development of effective glucose nonenzymatic glucose sensor.

Block matching low-rank for ghost imaging.

High-quality ghost imaging (GI) under low sampling is very important for scientific research and practical application. How to reconstruct high-quality image from low sampling has always been the focus of ghost imaging research. In this work, based on the hypothesis that the matrix stacked by the vectors of image's nonlocal similar patches is of low rank and has sparse singular values, we both theoretically and experimentally demonstrate a method that applies the projected Landweber regularization and blocking matching low-rank denoising to obtain the excellent image under low sampling, which we call blocking matching low-rank ghost imaging (BLRGI). Comparing with these methods of "GI via sparsity constraint," "joint iteration GI" and "total variation based GI," both simulation and experiment show that the BLRGI can obtain better ghost imaging quality with low sampling in terms of peak signal-to-noise ratio, structural similarity index and visual observation.

Edge detection based on joint iteration ghost imaging.

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system requires large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method has been proposed, which can improve the feature detection quality in ghost imaging. This method can also achieve high-quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging and can satisfy the practical application fields of imaging and edge detection at the same time.

MR image reconstruction via guided filter.

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.

Dictionary learning approach for image deconvolution with variance estimation.

In this paper, we propose a new dictionary learning approach for image deconvolution, which effectively integrates the Fourier regularization and dictionary learning technique into the deconvolution framework. Specifically, we propose an iterative algorithm with the decoupling of the deblurring and denoising steps in the restoration process. In the deblurring step, we involve a regularized inversion of the blur in the Fourier domain. Then we remove the colored noise using a dictionary learning method in the denoising step. In the denoising step, we propose an approach to update the estimation of noise variance for dictionary learning. We will show that this approach outperforms several state-of-the-art image deconvolution methods in terms of improvement in signal-to-noise ratio and visual quality.

Noise-aware image deconvolution with multidirectional filters.

In this paper we propose an approach for handling noise in deconvolution algorithm based on multidirectional filters. Most image deconvolution techniques are sensitive to the noise. Even a small amount of noise will degrade the quality of image estimation dramatically. We found that by applying a directional low-pass filter to the blurred image, we can reduce the noise level while preserving the blur information in the orthogonal direction to the filter. So we apply a series of directional filters at different orientations to the blurred image, and a guided filter based edge-preserving image deconvolution is used to estimate an accurate Radon transform of the clear image from each filtered image. Finally, we reconstruct the original image using the inverse Radon transform. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of the improvement in signal-to-noise ratio and visual quality.