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Despite the remarkable progression in colon cancer treatment in recent years, the pathological mechanism underlying this disease remains unclear. This study aimed to discuss the potential of luteolin in the treatment of colon cancer, from the perspective of traditional Chinese medicine, with a particular focus on the tumor microenvironment.Reverse transcription quantitative polymerase chain reaction and western blot were used to analyze the effects of luteolin on interleukin (IL)-6/signal transducer and activator of transcription 3 (STAT3)/Phospho-STAT3. Enzyme-linked immunosorbent assay was used to analyze the protein secretion of IL-6. Proliferation and transwell assays were used to analyze the growth and migration of luteolin and IL-6 in colon cancer cells, respectively.Stimulation with lipopolysaccharides (LPS) promoted the M1 polarization of macrophages and increased the expression and secretion of IL-6. However, the presence of luteolin inhibited the effects of LPS. M1 polarization increased the proliferation rate, migration and invasion ability, and phosphorylation of STAT3 in colon cancer cells (SW620 and SW480). Luteolin inhibited these effects by reducing M1 polarization. To confirm that the action of luteolin is mediated by IL-6/STAT3 signaling, we treated SW620 and SW480 cells with recombinant IL-6 protein and anti-IL-6 antibody. IL-6 was observed to promote cell proliferation, enhance migration and invasion, and increase STAT3 phosphorylation. The opposite effect was observed with the anti-IL-6 antibody. In addition, IL-6 promoted LPS-induced M1 polarization, while the anti-IL-6 antibody enhanced the decrease in luteolin-induced M1 polarization.Luteolin suppressed the growth and migration/invasion potential of colon cancer cells by inhibiting the IL-6/STAT3 signaling pathway.

Neuropathic pain affects 7-10% of the global population, and one of its characteristics is sensitization of somatosensory nervous system. Altered expression of ion channels and receptors has been found to be involved in neuronal hyperexcitability after injury to somatosensory nervous system, it is, however, unknown that if ion channels and receptors could gain qualitative changes on the level of structure organization when they are excessively expressed in same one neuron during the development of neuropathic pain. Here we show first that not only the expression of voltage-gated sodium channels Nav1.7 (SCN9A), Nav1.8 (SCN10A) and TRKB (also named NTRK2) increased in DRG neurons of patients with over 3-month severe neuropathic pain induced by brachial plexus avulsion (BPA), but also Nav1.7 and Nav1.8 formed supramolecular active clusters with or without TRKB in DRG neurons of mice with chronic neuropathic pain induced by spared nerve injury or diabetic neuropathy and of BPA pain patients with neuropathic pain. Nav1.7, Nav1.8 and TRKB might function in a coordinated manner in orderly organized supramolecular active clusters to geometrically increase the hyperexcitability of pathological DRG neurons. Our findings suggest that supramolecular active clusters of Nav1.7, Nav1.8 and TRKB might need be targeted for curing neuropathic pain, and that inhibition of both Nav1.7 and Nav1.8 might be required to achieve efficient relief of neuropathic pain.

Objectives: To determine the values of quantitative metrics derived from synthetic MRI (SyMRI) and apparent diffusion coefficient (ADC) in evaluating the prognostic factors of cervical carcinoma (CC). Methods: In this prospective study, 74 patients with pathologically confirmed CC were enrolled. Pretreatment quantitative metrics including T1, T2 and ADC values were obtained from SyMRI and diffusion-weighted imaging (DWI) sequences. The values of all metrics were compared for different prognostic features using Student’s t-test or Mann-Whitney U-test. The receiver operating characteristic (ROC) curve and multivariate logistic regression analysis were utilized to evaluate the diagnostic performance of quantitative variables. Results: T1 and T2 values of parametrial involvement (PMI)-negative were significantly higher than those of PMI-positive (p = 0.002 and < 0.001), while ADC values did not show a significant difference. The area under curve (AUC) of T1 and T2 values for identifying PMI were 0.743 and 0.831. Only the T2 values showed a significant difference between the lymphovascular space involvement (LVSI)-negative and LVSI-positive (p < 0.001), and the AUC of T2 values for discriminating LVSI was 0.814. The differences of T1, T2, and ADC values between the well/moderately and the poorly differentiated CC were significant (all p < 0.001). The AUCs of T1, T2 and ADC values for predicting differentiation grades were 0.762, 0.830, and 0.808. The combined model of all metrics proved to achieve good diagnostic performance with the AUC of 0.866. Conclusion: SyMRI may be a potential noninvasive tool for assessing the prognostic factors such as PMI, LVSI, and differentiation grades in CC. Moreover, the overall diagnostic performances of synthetic quantitative metrics were superior to the ADC values, especially in identifying PMI and LVSI. Advances in knowledge: This is the first study to assess the utility of SyMRI-derived parameters and ADC value in evaluating the prognostic factors in CC.

Review question / Objective: Inclusion criteria: population: 1) A randomized controlled study on the impact of music intervention on the QOL of patients with AD; 2) The participants in this study is patients with AD; 3) There is no significant difference among age, gender and education background in sorted groups before analysis which make these groups comparable; intervention: 1)Intervention Modality Music-based intervention; comparison: 1) All data were sorted into two groups: the music intervention group and the control group without any music intervention; outcome: 1) The indicators evaluated in the literature included the score of QOL-AD or WHOQOL-BERF scale, at least one of the two scales summarized in selected publications; language: 1) Only articles published in English and Chinese were considered. Exclusion criteria: 1) The participants were not diagnosed with AD; 2) Non-musical intervention;3) Non-RCTs; 4) No specific values for outcome variables; 5) Articles lacking original data; 6) Repeat published reports; 7) Full text could not be obtained.

Crack detection is important for evaluating pavement conditions. In this paper, we propose an automatic pavement crack detection method called as U2CrackNet. The architecture of U2CrackNet is a two-level nested U-structure which is an encoding and decoding architecture. Firstly, the crack features are extracted by the encoding layer so that the preliminary effective feature layer is obtained. Subsequently, the encoder and decoder are connected by an atrous spatial pyramid pooling (ASPP) model. The atrous convolution with different expansion rates can be used to capture the multi-scale crack information. Besides, in order to make the network pay more attention to the features of cracks, the crack feature map channels are given different weights through the effective channel attention mechanism after upsampling in the decoding layer. Finally, the crack saliency probability maps generated by each layer of the network are fused into the final crack saliency map through cascade operation. The proposed method has been evaluated on an expanded pavement crack dataset containing 8700 images. The experimental results demonstrate that the proposed U2CrackNet can obtain more clear and continuous cracks. Specifically, the precision, accuracy, F1-score, and Mean Intersection over Union (MIoU) are 89.51%, 98.95%, 81.45%, and 69.19%, respectively.

The challenge in fine-grained visual classification (FGVC) is that the similarity within intra-class may be larger than inter-class, where the discriminative details require more attention than traditional classification tasks. To generate channel-wise complementary and discriminative features in beneficial details of FGVC, we propose a multi-branch channel-wise enhancement network (MCEN), which includes multi-pattern spatial disruption mechanism, inter-channel complementarity module(ICM), and novel soft target loss. The raw images are scrambled in multi-pattern and then the sub-images with different degrees of confusion are combined into three pairs as inputs, where the scrambled operation can force the channel to look for the discriminative details. And ICM can measure the complementarity between key features and overall features to restrain the redundancy of features. The soft target loss is designed for classification and the semantic relationship between the blocks is learned to judge the degree of the chaos of the image. Our designed multi-branched structure utilizes the shallow visual and deep semantic features to judge the outcome jointly, where the image pairs obtained by segmentation and rearrangement are input into the different branches to extract more complementary features from different patterns of the raw image. Our method is trained end-to-end with only class labels. Experimental results show that our model outperforms the state-of-the-art performance on three fine-grained benchmarks.