[Pulmonary PET /CT image instance segmentation based on dense interactive feature fusion Mask RCNN].

There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model's perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.

Prediction and analysis of quality markers for Chuantieling gel patches based on HPLC fingerprinting and network pharmacology.

The Chuantieling gel patch (CGP), a traditional Chinese medicine compound, is an external treatment for asthma. It has shown remarkable effectiveness in alleviating asthma-related airway hyperresponsiveness and inflammation. Nevertheless, there is currently no information available regarding the analysis of quality markers for CGP, and there is a need for further improvement in quality control research. In this study, we developed an HPLC fingerprinting method for CGP and conducted a comprehensive methodological investigation. We assessed the similarity among 10 batches of CGP, identified common peaks, and quantified the content of seven major quality markers. Furthermore, we built a network pharmacology-based 'active ingredients-targets-pathways-diseases' network to forecast the potential mechanisms of action for the primary active components in asthma treatment. Our findings demonstrated that the developed CGP fingerprinting and content determination methods were consistent and trustworthy. We verified the existence of 25 shared peaks and successfully identified 7 chromatographic peaks, including sinigrin thiocyanate, ephedrine hydrochloride, methyleugenol, imperatorin, cinnamaldehyde, emodin, and 6-gingerol, using reference standards. The network pharmacology analysis suggested that these seven active components may target proteins such as STAT3 (signal transducer and activator of transcription 3), MAPK3 (mitogen-activated protein kinase 3), and TP53 (tumor protein P53) and influence various diseases through pathways including cancer pathways, hepatitis B, and PI3K-Akt (phosphoinositide 3-kinase-protein kinase B) signaling. This study provides insight into the complex multicomponent composition of CGP, and the predictive analysis through network pharmacology sets the stage for uncovering the mechanisms responsible for the therapeutic effects of CGP.

Therapeutic Potential of Ginsenoside Rb1-PLGA Nanoparticles for Heart Failure Treatment via the ROS/PPARα/PGC1α Pathway.

(1) Background: Ginsenoside Rb1-PLGA nanoparticles (GRb1@PLGA@NPs) represent a novel nanotherapeutic system, yet their therapeutic efficacy and underlying mechanisms for treating heart failure (HF) remain unexplored. This study aims to investigate the potential mechanisms underlying the therapeutic effects of GRb1@PLGA@NPs in HF treatment; (2) Methods: The left anterior descending coronary artery ligation was employed to establish a HF model in Sprague-Dawley rats, along with an in vitro oxidative stress model using H9c2 myocardial cells. Following treatment with GRb1@PLGA@NPs, cardiac tissue pathological changes and cell proliferation were observed. Additionally, the serum levels of biomarkers such as NT-proBNP, TNF-α, and IL-1ß were measured, along with the expression of the ROS/PPARα/PGC1α pathway; (3) Results: GRb1@PLGA@NPs effectively ameliorated the pathological status of cardiac tissues in HF rats, mitigated oxidative stress-induced myocardial cell damage, elevated SOD and MMP levels, and reduced LDH, MDA, ROS, NT-proBNP, TNF-α, and IL-1ß levels. Furthermore, the expression of PPARα and PGC1α proteins was upregulated; (4) Conclusions: GRb1@PLGA@NPs may attenuate myocardial cell injury and treat HF through the ROS/PPARα/PGC1α pathway.

GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images.

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

Diagnostic value of exfoliated tumor cells combined with DNA methylation in bronchoalveolar lavage fluid for lung cancer.

BACKGROUND: To evaluate the diagnostic value of exfoliated tumor cells (ETCs) numbers combined with DNA methylation levels in bronchoalveolar lavage fluid (BALF) in lung cancer. METHODS: BALF samples were collected from 43 patients with lung cancer and 23 with benign lung disease. ETCs were detected by the nano-enrichment method, and the methylation status of the short stature homeobox gene 2 (SHOX2) and the RAS association domain family 1, isoform A (RASSF1A) gene were detected by RT-PCR. The diagnostic value of each metric was evaluated by receiver operating characteristic curve analysis, specificity and sensitivity. RESULTS: The sensitivity/specificity of RASSF1A and SHOX2 methylation detection were 44.12%/76.47% and 93.75%/87.50%, respectively. When "RASSF1A/SHOX2 methylation" was used as a positive result, the sensitivity increased to 88.24%, and the specificity decreased to 81.25%. When "RASSF1A + SHOX methylation" was used as positive, the sensitivity was reduced to 32.35%, but the specificity was increased to 100.00%. The sensitivity and specificity of ETCs detection in BALF were 89.47% and 16.67%, respectively. When "SHOX2/RASSF1A methylation + ETCs was used as a positive result, the sensitivity and specificity of the detection were 79.31% and 81.82%, respectively. When "SHOX2 + RASSF1A + ETCs" was used as positive, the sensitivity was 34.48% and the specificity was 90.91%. Receiver operating characteristic curve analysis showed that when SHOX2, RASSF1A methylation and ETCs were combined, the diagnostic sensitivity increased to 0.778. CONCLUSION: ETCs counting in combination with SHOX2 and RASSF1A methylation assays in BALF samples has demonstrated excellent sensitivity for lung cancer diagnosis and is an effective complementary tool for clinical diagnosis of lung cancer.

CCGL-YOLOV5:A cross-modal cross-scale global-local attention YOLOV5 lung tumor detection model.

BACKGROUND: Multimodal medical image detection is a key technology in medical image analysis, which plays an important role in tumor diagnosis. There are different sizes lesions and different shapes lesions in multimodal lung tumor images, which makes it difficult to effectively extract key features of lung tumor lesions. METHODS: A Cross-modal Cross-scale Clobal-Local Attention YOLOV5 Lung Tumor Detection Model (CCGL-YOLOV5) is proposed in this paper. The main works are as follows: Firstly, the Cross-Modal Fusion Transformer Module (CMFTM) is designed to improve the multimodal key lesion feature extraction ability and fusion ability through the interactive assisted fusion of multimodal features; Secondly, the Global-Local Feature Interaction Module (GLFIM) is proposed to enhance the interaction ability between multimodal global features and multimodal local features through bidirectional interactive branches. Thirdly, the Cross-Scale Attention Fusion Module (CSAFM) is designed to obtain rich multi-scale features through grouping multi-scale attention for feature fusion. RESULTS: The comparison experiments with advanced networks are done. The Acc, Rec, mAP, F1 score and FPS of CCGL-YOLOV5 model on multimodal lung tumor PET/CT dataset are 97.83%, 97.39%, 96.67%, 97.61% and 98.59, respectively; The experimental results show that the performance of CCGL-YOLOV5 model in this paper are better than other typical models. CONCLUSION: The CCGL-YOLOV5 model can effectively use the multimodal feature information. There are important implications for multimodal medical image research and clinical disease diagnosis in CCGL-YOLOV5 model.

Transcriptome Data Reveal Geographic Heterogeneity in Gene Expression in Patients with Prostate Cancer.

Background: The incidence of prostate cancer (PC) exhibits geographical heterogeneity. However, the metabolic mechanisms underlying this geographic heterogeneity remain unclear. This study aimed to reveal the metabolic mechanism of the geographic heterogeneity in the incidence of PC.This study aimed to investigate the anti-cancer effects of different gum extracts on metabolic changes and their impact on gene expression in HT-29 cell. Methods: Transcriptomic data from public databases were obtained and analyzed to screen geographic-differentially expressed genes and metabolic pathways. Associations between these differentially expressed genes and the incidence of PC were determined to identify genes that were highly associated with PC incidence. A co-expression network analysis was performed to identify geographic-specific regulatory pathways. Results: A total of 175 differentially expressed genes were identified in four countries and were associated with the regulation of DNA replication and the metabolism of pyrimidine, nucleotides, purines, and galactose.Additionally, the expression of the genes CLVS2, SCGB1A1, KCNK3, HHIPL2, MMP26, KCNJ15, and PNMT was highly correlated with the incidence of PC. Geographic-specific differentially expressed genes in low-incidence areas were highly correlated with KCNJ15, MMP26, KCNK3, and SCCB1A1, which play a major role in ion channel-related functions. Conclusions: This study suggests that geographic heterogeneity in PC incidence is associated with the expression levels of genes associated with amino acid metabolism, lipid metabolism, and ion channels.

Assessment of Naples prognostic score in predicting survival for small cell lung cancer patients treated with chemoradiotherapy.

BACKGROUNDS: The Naples prognosis score (NPS) is a novel prognostic biomarker-based immune and nutritional status and that can be used to evaluate prognosis. Our study aimed to investigate the prognostic role of NPS in SCLC patients. METHODS: Patients treated with chemoradiotherapy were retrospectively analyzed between June 2012 and August 2017. We divided patients into three groups depending on the NPS: group 0, n = 31; group 1, n = 100; and group 2, n = 48, and associations between clinical characteristics and NPS group were analyzed. The univariable and multivariable Cox analyses were used to evaluate the prognostic value of clinicopathological characteristics and laboratory indicators for overall survival (OS) and progression-free survival (PFS). RESULTS: Data from 179 patients were analyzed. Treatment modality (p < 0.001) and serum CEA (p = 0.03) were significantly different among the NPS groups. The age, sex, smoking status, KPS, Karnofsky performance score (KPS), disease extent, and number of metastatic sites were not correlated with NPS (all p > 0.05). KPS, disease extent, prophylactic cranial irradiation, treatment response and NPS Group were associated with OS. In addition, KPS, disease extent, prophylactic cranial irradiation, treatment response and NPS Group were associated with PFS. Multivariate analysis results showed that NPS was identified as an independent prognostic factor for OS (Group 1: hazard ratio [HR] = 2.704, 95% confidence interval [CI] = 1.403-5.210; p = 0.003; Group 2: HR = 5.154, 95% CI = 2.614-10.166; p < 0.001) and PFS (Group 1: HR = 2.018, 95% CI = 1.014-4.014; p = 0.045; Group 2: HR = 3.339, 95% CI = 1.650-6.756; p = 0.001). CONCLUSIONS: NPS is related to clinical outcomes in patients with SCLC.

Despite the high clinical curative effect to radiation therapy and chemotherapy in SCLC, most patients subsequently experience tumor recurrence or metastasis.Whether NPS has prognostic values in SCLC has not been investigated to date.NPS is related to clinical outcomes in patients with SCLC.NPS as an innovative scoring system, can improves prediction of survival in SCLC patients.

Nested multi-scale transform fusion model: The response evaluation of chemoradiotherapy for patients with lung tumors.

BACKGROUND AND OBJECTIVE: The response evaluation of chemoradiotherapy is an important method of precision treatment for patients with malignant lung tumors. In view of the existing evaluation criteria for chemoradiotherapy, it is difficult to synthesize the geometric and shape characteristics of lung tumors. In the present, the response evaluation of chemoradiotherapy is limited. Therefore, this paper constructs a response evaluation system of chemoradiotherapy based on PET/CT images. METHODS: There are two parts in the system: a nested multi-scale fusion model and an attribute sets for the Response evaluation of chemoradiotherapy (AS-REC). In the first part, a new nested multi-scale transform method, i.e., latent low-rank representation (LATLRR) and non-subsampled contourlet transform (NSCT), is proposed. Then, the average gradient self-adaptive weighting is used for the low-frequency fusion rule, and the regional energy fusion rule is used for the high-frequency fusion rule. Further, the low-rank part fusion image is obtained by the inverse NSCT, and the fusion image is generated by adding the low-rank part fusion image and the significant part fusion image. In the second part, AS-REC is constructed to evaluate the growth direction of the tumor, the degree of tumor metabolic activity, and the tumor growth state. RESULTS: the numerical results clearly show that the performance of our proposed method outperforms in comparison with several existing methods, among them, the value of Qabf increased by up to 69%. CONCLUSIONS: Through the experiment of three reexamination patients, the effectiveness of the evaluation system of radiotherapy and chemotherapy are proved.

Phenols from Potentilla anserina L. Improve Insulin Sensitivity and Inhibit Differentiation in 3T3-L1 Adipocytes in Vitro.

Potentilla anserina L., a well-known perennial herb, is widely used in traditional Tibetan medicine and used as a delicious food in humans. The present investigation reports on the activity of P. anserina phenols (PAP) in regulating glycolipid metabolism in 3T3-L1 adipocytes. Insulin sensitivity tests showed that PAP improved insulin-stimulated glucose uptake by promoting the phosphorylation of serine/threonine kinase Akt. Moreover, an assay involving the differentiation of 3T3-L1 preadipocytes demonstrated that PAP also decreased the accumulation of lipid droplets by suppressing the expression of adipokines during the differentiation process. In addition, the underlying mechanism from the aspects of energy metabolism and oxidative stress is also discussed. The improvement in energy metabolism was supported by an increase in mitochondrial membrane potential (MMP) and intracellular ATP. Amelioration of oxidative stress was supported by decreased levels of intracellular reactive oxygen species (ROS). In summary, our findings suggest that PAP can ameliorate the disorder of glycolipid metabolism in insulin resistant 3T3-L1 adipocytes by improving energy metabolism and oxidative stress and might be an attractive candidate for the treatment of diabetes.

[Medical image instance segmentation: from candidate region to no candidate region].

In recent years, the task of object detection and segmentation in medical image is the research hotspot and difficulty in the field of image processing. Instance segmentation provides instance-level labels for different objects belonging to the same class, so it is widely used in the field of medical image processing. In this paper, medical image instance segmentation was summarized from the following aspects: First, the basic principle of instance segmentation was described, the instance segmentation models were classified into three categories, the development context of the instance segmentation algorithm was displayed in two-dimensional space, and six classic model diagrams of instance segmentation were given. Second, from the perspective of the three models of two-stage instance segmentation, single-stage instance segmentation and three-dimensional (3D) instance segmentation, we summarized the ideas of the three types of models, discussed the advantages and disadvantages, and sorted out the latest developments. Third, the application status of instance segmentation in six medical images such as colon tissue image, cervical image, bone imaging image, pathological section image of gastric cancer, computed tomography (CT) image of lung nodule and X-ray image of breast was summarized. Fourth, the main challenges in the field of medical image instance segmentation were discussed and the future development direction was prospected. In this paper, the principle, models and characteristics of instance segmentation are systematically summarized, as well as the application of instance segmentation in the field of medical image processing, which is of positive guiding significance to the study of instance segmentation.

APU-Net: An Attention Mechanism Parallel U-Net for Lung Tumor Segmentation.

Lung cancer is one of the malignant tumors with high morbidity and mortality, and lung nodules are the early stages of lung cancer. The symptoms of pulmonary nodules are not obvious in the clinic, and the optimal treatment time is missed due to the missed diagnosis in the clinic. A parallel U-Net network called APU-Net is proposed. Firstly, two parallel U-Net networks are used to extract the features of different modalities. Among them, the subnetwork UNet_B extracts the CT image features, and the subnetwork UNet_A consists of two encoders to extract the PET/CT and PET image features. Secondly, multimodal feature extraction blocks are used to extract features for PET/CT and PET images in UNet_B network. Thirdly, a hybrid attention mechanism is added to the encoding paths of the UNet_A and UNet_B. Finally, a multiscale feature aggregation block is used for extracting feature maps of different scales of decoding path. On the lung tumor 18FDGPET/CT multimodal medical images dataset, experiments' results show that the DSC, Recall, VOE, and RVD coefficients of APU-Net are 96.86%, 97.53%, 3.18%, and 3.29%, respectively. APU-Net can improve the segmentation accuracy of the adhesion between the lesion of complex shape and the normal tissue. This has positive significance for computer-aided diagnosis.

Anti-hepatitis B virus activity and hepatoprotective effect of des(rhamnosyl) verbascoside from Lindernia ruellioides in vitro.

Although clinically approved hepatitis B virus (HBV) polymerase inhibitors (lamivudine-3TC, entecavir, etc.) serve as effective therapeutics, the virus can easily generate resistance to them. Therefore, the treatment of HBV infection remains a public health problem. Numerous studies have shown that natural products have prospective anti-HBV activity. The purpose of this study was to isolate and extract des(rhamnosyl) verbascoside from Lindernia ruellioides (Colsm.) Pennell and explore its anti-HBV and hepatoprotective effects. Anti-HBV activity was evaluated in HepG2.2.15 cells, a human hepatocellular carcinoma cell line with HBV-stable infection, and its protective effect was evaluated in HL-7702 cells, a normal human liver cell line. HepG2.2.15 cells maintained normal growth morphology within the selected concentration range of des(rhamnosyl) verbascoside. It also inhibited the expression of HBV antigens and HBV DNA in a dose- and time-dependent manner in vitro. Further, western blot experiments showed that it could downregulate HBV X protein (HBx) expression in a dose-dependent manner. In the H2 O2 -induced hepatocyte injury model, the cell-survival rate of the HL-7702 cells with the highest drug dose reached 85.25%, which was significantly improved compared with that of the model group. Most of the cells returned to normal morphology, showing polygonal or fusiform structures. Thus, it may be stated that des(rhamnosyl) verbascoside exhibits anti-HBV activity and hepatoprotective effects in vitro and may exert an anti-HBV effect via antigen inhibition, HBV DNA secretion, and HBx protein expression.

Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features.

Based on the better generalization ability and the feature learning ability of the deep convolutional neural network, it is very significant to use the DCNN on the computer-aided diagnosis of a lung tumor. Firstly, a deep convolutional neural network was constructed according to the fuzzy characteristics and the complexity of lung CT images. Secondly, the relation between model parameters (iterations, different resolution) and recognition rate is discussed. Thirdly, the effects of different model structures for the identification of a lung tumor were analyzed by changing convolution kernel size, feature dimension, and depth of the network. Fourthly, the different optimization methods on how to influence the DCNN performance were discussed from three aspects containing pooling methods (maximum pooling and mean pooling), activation function (sigmoid and ReLU), and training algorithm (batch gradient descent and gradient descent with momentum). Finally, the experimental results verified the feasibility of DCNN used on computer-aided diagnosis of lung tumors, and it can achieve a good recognition rate when selecting the appropriate model parameters and model structure and using the method of gradient descent with momentum.

The ensemble deep learning model for novel COVID-19 on CT images.

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

Effectiveness of Huai Qi Huang Granules on Juvenile Collagen-induced Arthritis and Its Influence on Pyroptosis Pathway in Synovial Tissue.

Huai Qi Huang (HQH) exerts great effects in clinic, such as anti-inflammation, immune-regulation, anti-cancer, and so on. However, the mechanism by which HQH protects juvenile idiopathic arthritis (JIA) is obscure. Thus, we explored deeply the protective mechanisms in juvenile collagen-induced arthritis (CIA) rat model. Pyroptosis is Gasdermin D (GSDMD)-dependent programmed cell death, involved in many diseases, such as sepsis. We investigated whether GSDMD-induced pyroptosis take part in mechanisms of juvenile CIA arthritis. Juvenile Wistar rats (3-4 weeks) were injected intradermally with fully emulsified bovine type II collagen and complete Freund's adjuvant to establish CIA rat models. Later, the CIA rats received oral administration of HQH (4.16 g/kg) once a day from the day 21 of modeling, with the treatment lasting for 28 days. Varieties of indicators were measured for evaluation of anti-inflammation effect of HQH, including hind paw swelling, arthritis scores, micro CT, and histopathological changes and the level of pro-inflammatory cytokines in the serum, including tumor necrosis factor alpha (TNF-±) and interleukin-18 (IL-18). The expression of GSDMD and caspase-1 in the joint synovial tissues was detected. The results demonstrated that the expression of the pyroptotic protein GSDMD and its upstream caspase-1 was significantly increased in the synovial tissues of CIA rats. The treatment of HQH ameliorated the symptoms in CIA rats, reduced levels of pro-inflammatory cytokines and hind paw swelling, down-regulated the expression of GDSMD and caspase-1. GSDMD-induced pyroptosis participated in the pathogenesis of CIA rats. The study supported that HQH can effectively improve joints inflammation of juvenile collagen-induced arthritis rats by inhibiting pyroptosis pathway in the joint synovial tissues.

Activating transcription factor 1 promoted migration and invasion in lung cancer cells through regulating EGFR and MMP-2.

Lung cancer is among the most frequently occurring cancers and the leading cause of cancer-related deaths worldwide. Nonsmall cell lung cancer is accountable for 85% to 90% of all lung cancer cases and develops distant metastases with high mortality. In this work, we elucidated the role of activating transcription factor 1 (ATF1) in migration and invasion of lung cancer cells. We found that the migration and invasion were inhibited with ATF1 silencing in lung cancer cells. By contrast, ATF1 overexpression led to promotion in migration and invasion. The alteration in ATF1 expression induced a change in the epidermal growth factor receptor (EGFR) and matrix metalloproteinases (MMP)-2 expression level in the same tendency. Thus, we provided a potential new candidate for therapies against lung cancer, showing the possible mechanism underlying the invasion and migration of lung cancer cells.

A novel circular RNA, hsa_circ_0043278, acts as a potential biomarker and promotes non-small cell lung cancer cell proliferation and migration by regulating miR-520f.

More and more circular RNAs (circRNAs) revealed to play a critical role in the initiation and progression of cancer, however, the effects of circRNAs on non-small cell lung cancer (NSCLC) remain largely undetermined. In the present study, we screened the dysregulated circRNAs in paired NSCLC and normal samples from GEO database and identified circ_0043278 was to be significantly up-regulated in NSCLC and demonstrated it promotes NSCLC progression in vitro and in vivo. Then, we revealed the expression of miR-520f, a downstream factor of circ_0043278, was significantly down-regulated in NSCLC and acted as a tumor inhibitor. In addition, we revealed that circ_0043278 sponged miR-520f, which was demonstrated to target ROCK1, CDKN1B, and AKT3 in NSCLC cells. In conclusion, circ_0043278 promoted NSCLC cell proliferation, invasion, and migration by increasing ROCK1, CDKN1B, and AKT3 expressions through direct inhibition of miR-520f.

Developing a Web-Based Comic for Newly Diagnosed Women With Breast Cancer: An Action Research Approach.

BACKGROUND: Personal narratives have been seen as a useful way of communicating about cancer treatment options and providing recovery information. Many printed versions of such material are available, including comics that explore the individual memories of patients who have gone through cancer treatment. These studies have been used to orientate patients, patients' relatives, and physicians. However, only a few Web-based comics have been specifically designed for patients with breast cancer and used as aids to decision making. OBJECTIVE: We aimed to describe the developmental process of creating an animated comic as a Web-based surgery decision-making tool; the comic was aimed at illustrating the feelings, thoughts, and meanings when a patient suffers from breast cancer. This was done by recounting the symptoms, diagnostic process, treatments, and treatment effects of such women from the diagnosis stage onward. METHODS: Using cycles of planning, action, evaluation, and reflection, which involved collaborative work, action research was conducted to develop a Web-based animated comic. The stages of action research consisted of (1) semistructured and in-depth interviews to collect experiences of women with breast cancer; (2) construction of an animated comic by editors, graphics designers, dubbers, and information technology engineers; (3) redrawing of pictures of the comic after gathering feedback from a breast surgeon; and (4) evaluation of the Web-based animated comic using 6 patient focus groups. RESULTS: The comic was produced and showcased on the website "The Network of Making-decision Aids for Breast Cancer Surgery"; the comic was accompanied by soft music and audio explanations. The comic functions as a personal statement that describes experiencing breast cancer. The animated comic consists of 8 chapters, based on the 8 themes deducted from the findings obtained during the analysis of relevant interviews. The 8 chapters include (1) the appearance of a lump; (2) confirmation by medical diagnosis; (3) the uncertainty of waiting (4) fear of life-threatening disease; (5) choosing life over despair; (6) being brave and deciding to undergo treatment; (7) choosing the type of surgery; and (8) being reborn. CONCLUSIONS: Using action research, this study illustrated that the comic that sheds light on issues of feelings, emotions, and thoughts that are present when a woman is diagnosed with breast cancer and provides a communication medium to explain the steps in the process. Meanwhile, it implies that hope will be able to overcome the challenges that will be faced. Within the Web-based decision aid for patients with breast cancer, the animated comic acts as an information resource and is aimed at patients' understanding of impacts of emotions arising when suffering from breast cancer. It is potentially applicable as a therapeutic tool that facilitates self-reflection and self-healing among newly diagnosed patients with breast cancer.

[Serum levels of interleukin-38 and interleukin-1ß in the acute phase of Kawasaki disease in children].

OBJECTIVE: To study the expression of serum cytokines, interleukin-38 (IL-38) and interleukin-1ß (IL-1ß) in the acute phase of Kawasaki disease (KD) in children and the association of IL-38 and IL-1ß with inflammatory response in the acute phase and the development of coronary artery lesion (CAL). METHODS: A total of 40 children with KD who were hospitalized in the hospital between July 2015 and June 2016 were enrolled, with 21 children in the CAL group and 19 in the non-CAL (NCAL) group. Thirty healthy children and 19 children with infection and pyrexia, who were matched for sex and age, were enrolled as healthy control group and pyrexia control group respectively. ELISA was used to measure the serum levels of IL-38 and IL-1ß in the 40 children in the acute phase of KD. Spearman's rank correlation analysis was used to investigate the correlations of IL-1ß and IL-38 with interleukin-6 (IL-6), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide (NT-proBNP), triglyceride (TG), and total cholesterol (TC). RESULTS: The serum level of IL-38 in the children in the acute phase of KD was significantly lower than that in the healthy control group (P<0.05), but significantly higher than that in the pyrexia control group (P<0.05). There was no significant difference in the level of IL-38 between the CAL and NCAL groups (P>0.05). The children in the acute phase of KD had a significantly higher level of IL-1ß than the healthy control group (P<0.05), while there was no significant difference between this group and the pyrexia control group (P>0.05). There was also no significant difference in the level of IL-1ß between the CAL and NCAL groups (P>0.05). Serum IL-1ß and IL-38 levels were not correlated with serum levels of CRP, ESR, PCT, IL-6, and NT-ProBNP or blood lipids (TG and TC) (P>0.05). CONCLUSIONS: IL-38 is involved in an inflammatory response in the acute phase of KD and may exert an anti-inflammatory effect, which is opposite to the effect of IL-1ß to promote inflammatory response. However, there is no significant correlation between these two cytokines and the development of CAL in KD.