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The purpose of this study is to develop and validate the intelligent diagnosis of severe DR with lesion recognition based on color fundus photography. The Kaggle public dataset for DR grading is used in the project, including 53,576 fundus photos in the test set, 28,101 in the training set, and 7,025 in the validation set. We randomly select 4,192 images for lesion annotation. Inception V3 structure is adopted as the classification algorithm. Both 299 × 299 pixel images and 896 × 896 pixel images are used as the input size. ROC curve, AUC, sensitivity, specificity, and their harmonic mean are used to evaluate the performance of the models. The harmonic mean and AUC of the model of 896 × 896 input are higher than those of the 299 × 299 input model. The sensitivity, specificity, harmonic mean, and AUC of the method with 896 × 896 resolution images as input for severe DR are 0.925, 0.907, 0.916, and 0.968, respectively. The prediction error mainly occurs in moderate NPDR, and cases with more hard exudates and cotton wool spots are easily predicted as severe cases. Cases with preretinal hemorrhage and vitreous hemorrhage are easily identified as severe cases, and IRMA is the most difficult lesion to recognize. We have studied the intelligent diagnosis of severe DR based on color fundus photography. This artificial intelligence–based technology offers a possibility to increase the accessibility and efficiency of severe DR screening.

We aimed to explore biomarkers associated with diagnosis and prognosis of colorectal cancer. Differentially expressed protein (DEP) genes were obtained and validated. Moreover, co-expressed genes were screened and their prognostic value was evaluated. In addition, miRNAs that were negatively correlated with DEP genes were identified and used to construct a competitive endogenous RNA network. Furthermore, a support vector machine model was built using DEP genes, and a receiver operating characteristic curve was implemented to confirm its prediction performance. The results showed that only one DEP gene, CCL26, was obtained. Moreover, 43 genes co-expressed with CCL26 were identified, among which six (AP3M2, DAPK1, ISYNA1, PPM1K, PRR4 and RNF122) were linked with the prognosis of colorectal cancer. Besides, the axis RP11-47122.2/RP11-527N22.1-hsa-miR-3192-5p-CCL26 was identified as an lncRNA-miRNA-target gene network. Support vector machine model analysis showed that the area under the curve of CCL26 reached 0.878 based on GEO data and 0.743 based on our protein data. In conclusion, AP3M2, DAPK1, ISYNA1, PPM1K, PRR4, RNF122, CCL26 and hsa-miR-3192-5p appear to be related to the progression of colorectal cancer.

Steam explosion (SE) pretreatment is a widely adopted method for enhancing enzymatic hydrolysis of lignocellulosic biomass in biorefinery. In this work, moderate SE pretreatment was employed to overcome the recalcitrance of cell wall through systematically assessing physicochemical and structural modifications. The hydrolysis yield of pretreated poplar by SE pretreatment (up to 69.7%) was higher than untreated poplar (26.8%). With increasing SE severity from 3.5 to 4.5, the contents of hemicellulose and lignin were reduced from 15.7 to 5.3% and 25.4 to 20.7%, respectively. Furthermore, it was observed that the cell wall structure became rough and collapsed. Meanwhile, channels emerged in the internal cell wall primarily due to the removal of hemicellulose and lignin along with the migration of lignin from inside to the out surface of cell wall. These results induced more exposure of cellulose to enzyme attacking, thus improved accessibility to cellulolytic enzymes. Addition of bovine serum albumin (BSA) obtained equal hydrolysis efficiency of 70.1% with a 33% reduction of enzyme loading, which was advantageous for enzyme costs effective.

Aims To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images. Methods A retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset. Results On a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well. Conclusion A bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.

Background/aimsThe aim of this study was to generate and evaluate individualised post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of antivascular endothelial growth factor therapy for typical neovascular age-related macular degeneration (nAMD) based on pretherapeutic images using generative adversarial network (GAN).MethodsA total of 476 pairs of pretherapeutic and post-therapeutic OCT images of patients with nAMD were included in training set, while 50 pretherapeutic OCT images were included in the tests set retrospectively, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. The pix2pixHD method was adopted for image synthesis. Three experiments were performed to evaluate the quality, authenticity and predictive power of the synthetic images by retinal specialists.ResultsWe found that 92% of the synthetic OCT images had sufficient quality for further clinical interpretation. Only about 26%–30% synthetic post-therapeutic images could be accurately identified as synthetic images. The accuracy to predict macular status of wet or dry was 0.85 (95% CI 0.74 to 0.95).ConclusionOur results revealed a great potential of GAN to generate post-therapeutic OCT images with both good quality and high accuracy.

Previous report suggested that the expression of ten-eleven translocation (TET) proteins was abnormal in different cancers. Quercetin, has been demonstrated as anti-caner role in cancer development. To analyze the expression of TETs, quercetin treated with uveal melanoma cells in the present study. Our results suggested that the expression of TET1 was increased following treated with quercetin in OCM-1, SK-MEL-1 and B16 cells. In addition, quercetin treatment induced apoptosis and inhibited invasion. To further investigate if the expression of TET1 was associate with cell growth, apoptosis, migration and invasion, knocked down and overexpressing of TET1 was constructed. The results showed that the increased expression of TET1 induced cell apoptosis, increased 5-hydroxymethylcytosine (5hmC) and inhibited cell invasion. TET1 was a target gene of miR-17. Our results showed that inhibited expression of miR-17 increased TET1 expression in OCM-1 cells. To further confirm the effect of TET1 on melanoma tumors, nude mice was used. The results indicated that quercetin treatment increased TET1 expression and inhibited tumor growth. Taken together, these results suggested that quercetin can regulate cell proliferation and apoptosis through TET1 via miR-17 in melanoma cells.

A cost efficient synergistic strategy combining mild alkaline pretreatment (0.5–5% NaOH at 70 °C for 60 min) and bovine serum albumin (BSA) blocking of lignin was evaluated for effective conversion of poplar. The highest glucose yield of 69.2% was obtained for 5% alkaline pretreated sample, which was 4.4 times that of untreated sample. The enhanced enzymatic hydrolysis was attributed to significant hemicelluloses removal with limited delignification. Delignification mainly occurred in secondary wall, leading to more open cell wall structure, thus facilitating better transport of enzyme. Hemicelluloses removal helped split adjacent microfibrils, thus increased the specific sites for cellulase binding. After BSA addition in enzymatic hydrolysis, cellulose conversion further improved to 78.4% with 33% reduction of cellulase dosage due to decreased non-specific adsorption of cellulase on residual lignin. The utilization of synergistic alkaline pretreatment – BSA strategy may improve the overall economics of biomass conversion and successful commercial implementation of biorefineries.

Ionic conductive hydrogels have been widely applied in sensors, energy storage and soft electronics recently. However, most of the polyvinyl alcohol (PVA) based ionic hydrogels are mainly fabricated by soaking the hydrogels in high concentration electrolyte solution which can induce the waste of electrolyte and solvent. Herein, we have designed cellulose nanofibrils (CNF) and ZnSO4 reinforced PVA based hydrogels through a one-pot simple freezing-thawing method at low ZnSO4 concentration without any soaking process. Furthermore, the hydrogel with 0.4% CNF exhibited stress up to 0.79 MPa (242% strain) and high ionic conductivity of 0.32 S m−1 (0.07 M ZnSO4). Moreover, hydrogel sensor displayed high linear gauge factor 1.70 (0–200% strain), excellent stability, durability and reliability. The integrated hydrogel sensor also showed excellent sensor performance for human motion monitoring. This work provides a new prospect for the design of cellulose reinforced conductive hydrogels via a facile method.

Colorectal cancer (CRC) is one of the most common cancer, and the early detection of CRC is essential to improve the survival rate of patients. To identify diagnostic markers for colorectal cancer (CRC) by screening differentially expressed proteins (DEPs) in CRC. The DEPs were initially obtained from 12 CRC samples and 12 healthy control samples, and verification analysis was performed in another 34 CRC samples and 34 normal controls. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment with DEPs was analyzed by the R package clusterProfiler (Version 3.2.11), and the DEP-associated protein-protein interaction (PPI) network was created from the STRING database. Additionally, Support Vector Machine (SVM) model prediction and survival analyses were conducted on the key DEPs. Preliminary screening and functional analysis showed that the DEPs mainly overrepresented in pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, Rap1, Ras, and MAPK signaling pathways. The key DEPs, including AgRP, ANG-2, Dtk, EOT3, FGF-4, FGF-9, HCC-4, IL-16, IL-8, MIF, MSPa, TECK, TPO, TRAIL R3, and VEGF-D, were used to construct a custom chip. The drug-gene interaction network suggested that TPO was a key drug target. ROC curve showed the SVM diagnostic model with the DEPs IL-8, MSPa, MIF, FGF-9, ANG-2, and AgRP had better diagnostic performance with an AUC of 0.933. Survival analysis showed the expression of FGF9, TPO, TRAIL R3, Dtk, TECK and FGF4 were associated with prognosis. This study revealed the important serum proteins in the pathogenesis of CRC, which might serve as useful and noninvasive predictors for the diagnosis of CRC.

γ-Valerolactone (GVL), a biomass-derived green chemical, offers an environmentally responsible solvent for conversion of lignocellulose to high value-added chemicals. Herein, we report a two-step process for directly producing cellulosic residual, furfural and lignin from Miscanthus × giganteus (M. × giganteus) bypassing the isolation of xylose, which exhibits promising advantage in energy reduction. The optimized pretreatment (100 mM FeCl

Aims To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning-based lesion detection and stage grading. Materials and methods A set of 12,252 eligible fundus images of diabetic patients were manually annotated by 45 licenced ophthalmologists and were randomly split into training, validation, and internal test sets (ratio of 7:1:2). Another set of 565 eligible consecutive clinical fundus images was established as an external test set. For automated referable DR identification, four deep learning models were programmed based on whether two factors were included: DR-related lesions and DR stages. Sensitivity, specificity and the area under the receiver operating characteristic curve (AUC) were reported for referable DR identification, while precision and recall were reported for lesion detection. Results Adding lesion information to the five-stage grading model improved the AUC (0.943 vs. 0.938), sensitivity (90.6% vs. 90.5%) and specificity (80.7% vs. 78.5%) of the model for identifying referable DR in the internal test set. Adding stage information to the lesion-based model increased the AUC (0.943 vs. 0.936) and sensitivity (90.6% vs. 76.7%) of the model for identifying referable DR in the internal test set. Similar trends were also seen in the external test set. DR lesion types with high precision results were preretinal haemorrhage, hard exudate, vitreous haemorrhage, neovascularisation, cotton wool spots and fibrous proliferation. Conclusions The herein described automated model employed DR lesions and stage information to identify referable DR and displayed better diagnostic value than models built without this information.

To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions.Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation-based co-decision model.This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all0.95; the pixel-level F1 values for segmenting optic disc and peripapillary atrophy were both0.9; the pixel-level F1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230.The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions.The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

This paper tackles automated screening of Retinopathy of Prematurity (ROP), one of the most common causes of visual loss in childhood. Clinically, ROP screening per case requires multiple color fundus image instances that capture different zones of the (premature) retina. A desirable model shall not only make a decision at the case level, but also pinpoint which instances and what part of the instances are responsible for the decision. This paper makes the first attempt to accomplish three tasks, i.e. ROP case classification, instance selection and abnormality localization in a unified framework. To that end, we propose a new model that effectively combines instance-attention based deep multiple instance learning (MIL) and spatial attention (SA). The propose model, which we term MIL-SA, identifies positive instances in light of their contributions to case-level decision. Meanwhile, abnormal regions in the identified instances are automatically localized by the SA mechanism. Moreover, MIL-SA is learned from case-level binary labels exclusively, and in an end-to-end manner. Experiments on a large clinical dataset of 2,186 cases with 11,053 fundus images show the viability of the proposed model for all the three tasks.

Domain adaptation is an important research topic in the field of computer vision, where the goal is to solve the difference of data distribution between different scenarios of the same task. In recent times, adversarial learning method becomes a mainstream approach to generate complicated images across diverse domains through optimizing deep networks, and it can also improve the recognition accuracy rate of deep networks despite existing domain shift or dataset bias. However, there are few effective efforts of domain adaptation for the disease diagnosis on fundus images. Fundus images are normally captured on different medical devices with different rules. When diagnosing glaucoma, there is a serious homogeneous domain shift, which means feature spaces between target domain and source domain images have a distribution shift although they are very similar. We propose a unified framework to solve this problem. Previous studies have shown that glaucoma can be monitored by analyzing the optic disc/cup and its surroundings. So we exploit a novel reconstruction loss which not only leverages unsupervised data to bring the source and target distributions closer but also keeps original target domain images label unchanged. The experimental results on several public and private datasets demonstrate that our method could increase the classification accuracy of glaucoma diagnosis.

The electrochemical performance of supercapacitors using porous carbon as electrodes is strongly affected by the fabrication process of carbon material. KOH is commonly used as an activator combined with urea as a nitrogen dopant. However, the roles of KOH and urea in pore structure configuration and the electrochemical behavior of porous carbon electrodes are still ambiguous. Herein, the optimum porous carbon is obtained when KOH and urea are used simultaneously. KOH is used as a pore-forming substance, whereas urea is employed as a nitrogen source for the nitrogen doping of porous carbon, which increases its defect sites while reducing the graphitization degree. More importantly, urea also expands pores as a pore-enlarging agent, inducing interconnected porous structures. As a result, a hierarchical porous structure is formed and ascribed to the synergistic effect of KOH and urea, and the specific surface area reached 3282 m2 g−1 for sample PC800-4. The specific capacitance is 319 F g−1 at 0.5 A g−1 with excellent cycling stability over 2500 cycles. Furthermore, the symmetric supercapacitor reaches an excellent energy density of 11.6 W h kg−1 under 70.0 W kg−1 in a 6 M KOH electrolyte. Our work contributes to the rational designation of the porous carbon structure for supercapacitor applications.

Colorectal cancer (CRC) is a common malignancy, most of which remain unresponsive to chemotherapy. Methotrexate (MTX) is one of the earliest cytotoxic drugs and serves as an anti-metabolite and anti-folate chemotherapy for various types of cancer. However, MTX resistance prevents its clinical application in cancer therapy. Thereby, overcoming the drug resistance is an alternative strategy to maximize the efficacy of MTX therapies in clinics. Long non-coding RNAs (lncRNAs) have gained widespread attention in recent years. More and more evidences have shown that lncRNAs play regulatory roles in various biological activities and disease progression including drug resistance in cancer cells. Here, we observed lncRNA TUG1 was associated to the MTX resistant in colorectal cancer cells. Firstly, quantitative analysis indicated that TUG1 was significantly increased in tumors which were resistant to MTX treatment. TUG1 knockdown re-sensitized the MTX resistance in colorectal cancer cells, which were MTX-resistant colorectal cell line. Furthermore, bioinformatics analysis showed that miR-186 could directly bind to TUG1, suggesting TUG1 might worked as a ceRNA to sponge miR-186. Extensively, our study also showed that CPEB2 was the direct target of miR-186 in colorectal cancer cells. Taken together, our study suggests that lncRNA TUG1 mediates MTX resistance in colorectal cancer via miR-186/CPEB2 axis.

Colorectal cancer is a commonly diagnosed gastrointestinal malignancy worldwide with a high mortality rate. Accumulating evidence has indicated that the expression of a number of microRNAs (miRNAs) is associated with the development of colorectal cancer. However, the precise molecular mechanism of these miRNAs in regulating cancer progression is yet to be determined. In the present study, miR‑3651 was demonstrated to be overexpressed in colorectal cancer tissues compared with normal tissues, and to be associated with the tumor‑node‑metastasis stage. The downregulation of miR‑3651 was found to induce growth arrest and apoptosis in colorectal cancer cells. In addition, western blot analysis demonstrated that the downregulation of miR‑3651 inactivated PI3K/AKT and MAPK/ERK signaling in colorectal cancer cells. Bioinformatics analysis predicted T‑box transcription factor 1 (TBX1) as a potential target gene of miR‑3651, and a dual‑luciferase reporter assay confirmed that TBX1 was directly repressed by miR‑3651. The results of the current study also indicated that TBX1 was associated with the miR‑3651 mediated activation of oncogenic signaling and colorectal cancer cell proliferation. In conclusion, the results of the current study revealed the oncogenic potential of miR‑3651 in colorectal cancer.

A nuclear cataract is an age-related chronic and priority ophthalmic disease in which a clouding of the lens in the human eye affects vision. Automatic segmentation of nuclear region based on slit-lamp photographs is a basic step for computer-aided diagnosis such as nuclear cataract grading. However, slit-lamp photographs collected from a clinic scenario often have complex background containing the eyelids, sclera and cornea with spectral highlights. The existing efforts using traditional image processing that have unsatisfactory results, and the deep learning method using standard Faster R-CNN tends to obtain a bigger nuclear contour. In this paper, we propose a coarse-to-fine deep learning solution to localize nuclear regions by cascading the Faster R-CNN in a two-stage framework. First, a nuclear ROI (region of interest) predictor is pre-trained to localize a rough position and remove complex backgrounds. Then, a fine nuclear locator is applied to predict a more compact nuclear bounding box. Finally, an ellipse-like nuclear contour is fitted based on its bounding box. Evaluated on a clinical dataset of 884 slit-lamp photographs, the proposed method outperforms the state-of-the-art, improving the overlapping rate (IoU) by 0.33% from 67.98% to 68.31%, and increasing the success rate by 2.55% from 85.71% to 88.26%.

AimTo explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.MethodsDiagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted.ResultsThe area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity.ConclusionThe proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world.

Thorough understanding of how hemicelluloses removal influences cell wall nanoscale architecture and cellulose digestion is of crucial importance for enabling low-cost industrial conversion of lignocellulosic biomass to renewable biofuels. In this work, delignified poplar cell walls, after various degrees of hemicelluloses removal, were characterized by Fourier transform infrared imaging spectroscopy and atomic force microscopy to evaluate enhancement in cell wall digestibility. There was a gradual decrease in hemicelluloses content with dilute alkali treatment, which resulted in alterations in the nanoscale architecture and crystallinity of cell walls. Removal of hemicelluloses did not disrupt the integrity of microfibrils but resulted in exposure of microfibrils and a decrease in the diameter of microfibrils. X-ray analysis indicated that the increase in crystallinity beyond natural variations in the crystallinity of cellulose was mainly attributable to removal of hemicelluloses. In conclusion, alterations in the architecture and crystallinity of cell walls facilitated enzymatic digestion of delignified poplar, enhancing cellulose conversion from 68.24 to 75.16 %.

As the main hemicellulose of poplar, the interaction of xylan with lignin was expected to have profound effect on biomass recalcitrance. In this paper, the dynamic changes of xylan and lignin in poplar cell wall during a mild pretreatment using γ-valerolactone (GVL) was investigated using chemical and microscopic techniques. Synergetic dissolution of branched xylan and lignin from the secondary wall of the fiber cell was found to play a major role in opening the cell wall structure for enzymatic attack. In the case of the removal of xylan and lignin reaching a certain level, β-O-4′ cleavage of lignin which destroyed its interaction with hydrophobic cellulose face was found to make great contribution to the enhanced enzymatic hydrolysis. The deep understanding of this process could lead to a new insight into the understanding of the plant cell wall architecture and provide basic information for biomass processing.

Although many studies have been conducted on the wood property and chemical changes caused by thermal modification, little has been reported on the microstructural and topochemical changes occurring in the cell wall during heat treatment. In this study, poplar (Populus cathayaha) was treated within a temperature range from 180 to 220 °C for 4 h. Chemical analyses by Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) indicated that heat treatment resulted in deacetylation of hemicelluloses and cleavage of lignin chains, thus generating new carbonyl and phenolic linkages. Transformation of matrix substances contributed to microstructural changes that appeared in clearly distorted and collapsed fiber and vessel walls along with the delamination of compound middle lamella (CML) and secondary walls (S), which showed a reduced capability to resist deformation. It was also observed by fluorescence microscopy (FM) and scanning electron microscope coupled with energy dispersive X-ray analysis (SEM-EDXA) that the concentration of lignin increased, probably because of the degradation of hemicelluloses and the generation of new carbonyl groups. These results on cell wall microstructure and topochemistry can help explain the altered wood properties revealed by dynamic mechanical analysis (DMA) and equilibrium moisture content (EMC) testing after heat treatment.

Background/Aims: Effects of preoperative one week enteral nutrition (EN) support on the postoperative nutritional status, immune function and inflammatory response of gastric cancer patients were investigated. Materials and Methods: 106 cases of gastric cancer patients were randomly divided into preoperative one week EN group (trial group) and early postoperative EN group (control group), which were continuously treated with EN support until the postoperative 9th day according to different treatment protocols. All the patients were checked for their body weight, skinfold thickness, upper arm circumference, white blood cell count (WBC), albumin (ALB), prealbumin (PA), C-reactive protein (CRP), humoral immunity (IgA, IgG), T cell subsets (CD4, CD8 and CD4/CD8), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), etc. on the preoperative and the postoperative 1 st and 10 th day, respectively. Results: PA and IgG levels of the experimental group were higher than those of the control group on the postoperative 10 th day, whereas IL-6 level of the experimental group was lower than that of the control group. Conclusion: EN support for preoperative gastric cancer patients will improve the postoperative nutritional status and immune function, alleviate inflammatory response, and facilitate the recovery of patients.

This investigation was aimed at working out the combined role of lncRNA H19, miR-29b and Wnt signaling in the development of colorectal cancer (CRC). In the aggregate, 185 CRC tissues and corresponding para-carcinoma tissues were gathered. The human CRC cell lines (i.e. HT29, HCT116, SW480 and SW620) and normal colorectal mucosa cell line (NCM460) were also purchased. Si-H19, si-NC, miR-29b-3p mimics, miR-29b-3p inhibitor, si-PGRN and negative control (NC) were, respectively, transfected into the CRC cells. Lucif-erase reporter plasmids were prepared to evaluate the transduction activity of Wnt/β-catenin signaling pathway, and dual-luciferase reporter gene assay was arranged to confirm the targeted relationship between H19 and miR-29b-3p, as well as between miR-29b-3p and PGRN. Finally, the proliferative and invasive capacities of CRC cells were appraised through transwell, MTT and scratch assays. As a result, over-expressed H19 and down-expressed miR-29b-3p displayed close associations with the CRC patients’ poor prognosis (P < 0.05). Besides, transfection with si-H19, miR-29b-3p mimic or si-PGRN were correlated with elevated E-cadherin expression, decreased snail and vimentin expressions, as well as less-motivated cell proliferation and cell metastasis (P < 0.05). Moreover, H19 was verified to directly target miR-29b-3p based on the luciferase reporter gene assay (P < 0.05), and miR-29b-3p also bound to PGRN in a direct manner (P < 0.05). Finally, addition of LiCl (Wnt/β-catenin pathway activator) or XAV93920 (Wnt/β-catenin pathway inhibitor) would cause remarkably altered E-cadherin, c-Myc, vimentin and snail expressions, as well as significantly changed transcriptional activity of β-catenin/Tcf reporter plasmid (P < 0.05). In conclusion, the lncRNA H19/miR-29b-3p/PGRN/Wnt axis counted a great deal for seeking appropriate diagnostic biomarkers and treatment targets for CRC.

The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named “automatic pre-processing method for Raman imaging data set (APRI)”, which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.

The favorable metabolic effects of telmisartan are supposedly related to the changes in carbohydrate and lipid metabolism driven by peroxisome proliferators-activated receptor-gamma (PPARgamma). The fatty acid translocase CD36 is one of the PPARgamma targets that mediate these actions. We studied the metabolic effects of telmisartan in the NIH-derived strain of spontaneously hypertensive rats (SHR/NIH), which harbors a deletion mutation in CD36, in comparison to the original SHRs (SHR/Izm), which express wild-type CD36. In SHR/Izm, administration of telmisartan was associated with significantly lower serum levels of free fatty acids (42%), triglycerides (29%), glucose (11%), insulin (31%), and lower hepatic triglyceride (17%) levels, as well as larger epididymal fat pads (1.19-fold) than in SHR/NIH. Additionally, insulin-stimulated glucose incorporation into epididymal fat tissues was significantly augmented in SHR/Izm (1.33-fold) compared with SHR/NIH. In the epididymal fat pads of SHR/Izm treated with telmisartan, CD36 mRNA transcript (1.55-fold) and protein expression (1.37-fold) were also significantly enhanced. However, after 4 weeks of treatment with telmisartan, in SHR/NIH only serum free fatty acid levels were slightly reduced (20%). Overall, these results showed marked discrepancies in the metabolic actions of telmisartan in SHR/Izm and SHR/NIH and further supported the involvement of CD36 in the actions of this drug, suggesting that this pharmacogenetic interaction may be of particular importance in CD36-deficient patients.

In this study we report that cobratoxin (CbTX), a long-chain postsynaptic α-neurotoxin isolated from the Thailand cobra, Naja naja kaouthia, has antinociceptive effect in rats with neuropathic pain. The neuropathic pain model was established in rats with partial sciatic nerve ligature (PSNL) method. The pain response was examined behaviorally with mechanical paw withdrawal and thermal paw withdrawal method. Different doses (0.56, 1.12 and 4.50 μg/kg) of CbTX were injected intrathecally. Injection of CbTX resulted in a significant dose-dependent antinociception as evidenced by increased mechanical withdrawal threshold and thermal withdrawal latency. CbTX also induces a significant dose-dependent inhibition of pain-evoked unit discharges of thalamic parafascicular neurons. Both the behavioral mechanical and thermal antinociception and the inhibition of pain-evoked discharges of neurons in thalamic parafascicular nucleus in PSNL model could be mimicked by PUN282987, selective α7 nicotinic AChR (α7 nAChR) agonist and reversed by methyllycaconitine (MLA) selective α7 nAChR antagonist. In summary, these results suggested that AChR α7 subunit was involved in the antinociceptive action of CbTX for neuropathic pain and might be the candidate target for analgesic drug design.

The analytic hierarchy process (AHP) is a method to help people make a complex decision by analyzing and synthesizing multiple criteria for the decision objective in a hierarchy. We adopt the AHP as a new strategy for the semantic concept detection (SCD) in video so that multiple factors involved in SCD, including multiple modalities and relating concepts, can be hierarchically analyzed and synthesized. In this paper, we first explain, by an example, why and how the SCD problem can be analyzed by the AHP. Then, following the idea of the AHP, we develop a new rank aggregation (RA) method, called AHP-RA. Experimental results of RA for SCD in video show the effectiveness of this method.