LINC01268 promotes epithelial-mesenchymal transition,invasion and metastasis of gastric cancer via the PI3K/Akt signaling pathway and targeting MARCKS

BACKGROUND Long non-coding RNAs(lncRNAs)with differential expression characteristics have been found to be closely related to the tumorigenesis and development of gastric cancer(GC),but their specific mechanisms and roles still need to be further elucidated.AIM To investigate the expression of LINC01268 in GC and its mechanism of affecting GC progression.METHODS Real-time quantitative polymerase chain reaction was used to detect the expression of LINC01268 in GC tissues,cell lines and plasma.The Kaplan-Meier method was used to evaluate the value of LINC01268 in the prognostication of GC patients.An receiver operating characteristic curve was constructed to evaluate the value of LINC01268 in the diagnosis of GC.Transwell migration and invasion assays and wound healing assays were used to confirm the effect of LINC01268 on the invasion and migration of GC cells.The regulatory relationship between LINC01268 and myristoylated alanine rich protein kinase C substrate(MARCKS),the PI3K/Akt signaling pathway,and the epithelial-mesenchymal transition(EMT)process in GC was demonstrated by western blot analysis.RESULTS The expression of LINC01268 was increased in GC tissues and cell lines.The expression level of LINC01268 was significantly correlated with lymph node metastasis,TNM stage,and tumor differentiation in patients with GC.Over-expression of LINC01268 indicated a poor prognosis for patients with GC,and it had a certain auxiliary diagnostic value for GC.In vitro functional experiments proved that the abnormal expression of LINC01268 further activated the PI3K/Akt signaling pathway and promoted EMT by targeting and regulating MARCKS and ultimately promoted the invasion and metastasis of GC.CONCLUSION This study elucidates that LINC01268 in GC may be an oncogene that further activates the PI3K/Akt signaling pathway and EMT by targeting and regulating MARCKS,and ultimately promotes the invasion and metastasis of GC.LINC01268 may be a potential effective target for the treatment of GC.

Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Breast mass identification is of great significance for early screening of breast cancer,while the existing detection methods have high missed and misdiagnosis rate for small masses.We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once(RCM-YOLO),which improves the identifiability of small masses by increasing the resolution of feature maps,adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters,and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations.In the training process,we propose an adaptive positive sample selection algorithm to automatically select positive samples,which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model.To verify the performance of our model,we used public datasets to carry out the experiments.The results showed that the mean Average Precision(mAP)of RCM-YOLO reached 90.34%,compared with YOLOv5,the missed detection rate for small masses of RCM-YOLO was reduced to 11%,and the single detection time was reduced to 28 ms.The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features.Our method can help doctors in batch screening of breast images,and significantly promote the detection rate of small masses and reduce misdiagnosis.

Identification of Tibetan Medical Material Dracocephalum tanguticum Maxim.

[Objectives]To explore morphological identification,macroscopical identification,microscopic identification,thin layer chromatography(TLC)identification of Tibetan medical material Dracocephalum tanguticum Maxim.,and provide experimental data for its identification and application.[Methods]The Tibetan medical material was identified by means of original plant,characters,powder,paraffin section and thin layer chromatography(TLC).[Results]Tibetan medical material D.tanguticum Maxim.was obviously distinguished in character identification and microscopic identification,and the TLC method was simple and feasible.[Conclusions]The results will provide the source work foundation for the formulation of the quality standard of Sichuan Province(draft)for Tibetan medicinal material"D.tanguticum Maxim."and the development of pharmaceutical preparations for medical institutions.

Maclaurin Symmetric Mean Aggregation Operators and Their Application to Hesitant Q-Rung Orthopair Fuzzy Multiple Attribute Decision Making

The Maclaurin symmetric mean(MSM)operator exhibits a desirable characteristic by effectively capturing the correlations among multiple input parameters,and it serves as an extension of certain existing aggregation operators through adjustments to the parameter k.The hesitant q-rung orthopair set(Hq-ROFSs)can serve as an extension of the existing orthopair fuzzy sets,which provides decision makers more freedom in describing their true opinions.The objective of this paper is to present an MSM operator to aggregate hesitant q-rung orthopair numbers and solve the multiple attribute decision making(MADM)problems in which the attribute values take the form of hesitant q-rung orthopair fuzzy sets(H-qROFSs).Firstly,the definition of H-qROFSs and some operational laws of H-qROFSs are proposed.Then we develop a family of hesitant q-rung orthopair fuzzy maclaurin symmetric mean aggregation operators,such as the hesitant q-rung orthopair fuzzy maclaurin symmetric mean(Hq-ROFMSM)operator,the hesitant q-rung orthopair fuzzy weighted maclaurin symmetric mean(Hq-ROFWMSM)operator,the hesitant q-rung orthopair fuzzy dual maclaurin symmetric mean(Hq-ROFDMSM)operator,the hesitant q-rung orthopair fuzzy weighted dual maclaurin symmetric mean(Hq-ROFWDMSM)operator.And the properties and special cases of these proposed operators are studied.Furthermore,an approach based on the Hq-ROFWMSM operator is proposed for multiple attribute decision making problems under hesitant q-rung orthopair fuzzy environment.Finally,a numerical example and comparative analysis is given to illustrate the application of the proposed approach.

Imaging features of intraductal papillary mucinous neoplasms of the pancreas in multi-detector row computed tomography

AIM:To retrospectively evaluate the imaging features of pancreatic intraductal papillary mucinous neoplasms (IPMNs) in multi-detector row computed tomography (MDCT).METHODS: A total of 20 patients with pathologically-confirmed intraductal papillary mucinous neoplasms (IPMNs) were included in this study. Axial MDCT images combined with CT angiography (CTA) and multiplanar volume reformations (MPVR) or curved reformations (CR) were preoperatively acquired. Two radiologists (Tan L and Wang DB) reviewed all the images in consensus using an interactive picture archiving and communication system. The disputes in readings were resolved through consultation with a third experienced radiologist (Chen KM). Finally, the findings and diagnoses were compared with the pathologic results.RESULTS: The pathological study revealed 12 malignant IPMNs and eight benign IPMNs. The diameters of the cystic lesions and main pancreatic ducts (MPDs) were significantly larger in malignant IPMNs compared with those of the benign IPMNs (P<0.05). The combined-type IPMNs had a higher rate of malignancy than the other two types of IPMNs (P<0.05). Tumors with mural nodules and thick septa had a significantly higher incidence of malignancy than tumors without these features (P<0.05). Communication of side-branch IPMNs with the MPD was present in nine cases at pathologic examination. Seven of them were identified from CTA and MPVR or CR images. From comparison with the pathological diagnosis, the sensitivity, specificity, and accuracy of MDCT in characterizing the malignancy of IPMN of the pancreas were determined to be 100%, 87.5% and 95%, respectively.CONCLUSION: MDCT with CTA and MPVR or CR techniques can elucidate the imaging features of IPMNs and help predict the malignancy of these tumors.

Tunable Syngas Synthesis from Photocatalytic CO2 Reduction Under Visible-Light Irradiation by Interfacial Engineering

Visible-light-driven CO2 photoreduction to achieve renewable materials,such as syngas,hydrocarbons,and alcohols,is a key process that could relieve environmental problems and the energy crisis simultaneously.Reduction of syngas products with diff erent H2:CO proportions is highly expected to produce high value-added chemicals in the industry.However,the development of technologies employing long-wavelength irradiation to achieve CO2 photoreduction and simultaneous tuning of the resultant H2:CO proportion remains a challenging endeavor.In this work,we carried out interfacial engineering by designing a series of heterostructured layered double-hydroxide/MoS2 nanocomposites via electrostatic self-assembly.The syngas proportion(H 2:CO)obtained from CO2 photoreduction could be modulated from 1:1 to 9:1 by visible-light irradiation(λ>400 nm)under the control of the interface-rich heterostructures.This work provides a cost-eff ective strategy for solar-tofuel conversion in an artificial photosynthetic system and describes a novel route to produce syngas with targeted proportions.

Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning

Aortic dissection(AD)is a kind of acute and rapidly progressing cardiovascular disease.In this work,we build a CTA image library with 88 CT cases,43 cases of aortic dissection and 45 cases of health.An aortic dissection detection method based on CTA images is proposed.ROI is extracted based on binarization and morphology opening operation.The deep learning networks(InceptionV3,ResNet50,and DenseNet)are applied after the preprocessing of the datasets.Recall,F1-score,Matthews correlation coefficient(MCC)and other performance indexes are investigated.It is shown that the deep learning methods have much better performance than the traditional method.And among those deep learning methods,DenseNet121 can exceed other networks such as ResNet50 and InceptionV3.

Tuning the selectivity of photoreduction of CO2 to syngas over Pd/layered double hydroxide nanosheets under visible light up to 600 nm

Photocatalytic reduction of CO2 with H2 O to syngas is an effective way for producing high value-added chemical feedstocks such as methanol and light olefins in industry.Nevertheless,the precise control of CO/H2 ratio from photocatalytic CO2 reduction reaction still poses a great challenge for the further application.Herein,we prepared a series of highly efficient heterostructure based on highly dispersed palladium supported on ultrathin Co Al-layered double hydroxide(LDH).In conjunction with a Ru-complex sensitizer,the molar ratios of CO/H2 can be tuned from 1:0.74 to 1:3 under visible-light irradiation(λ>400 nm).More interestingly,the syngas can be obtained under light irradiation atλ>600 nm.Structure characterization and density functional theory calculations revealed that the remarkable catalytic activity can be due to the supported palladium,which improved the charge transfer efficiency.Meanwhile,more H atoms were used to generate H2 on the supported palladium for further tunable CO/H2 ratio.This work demonstrates a new strategy for harnessing abundant solar-energy to produce syngas from a CO2 feedstock.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Scaled-up synthesis of defect-rich layered double hydroxide monolayers without organic species for efficient oxygen evolution reaction

The scaled-up synthesis of organic-free monolayer nanomaterials is highly desirable,especially in obtaining green energy by electrocatalysis.In this study,a method for the scaled-up synthesis of the series of monolayer layered double hydroxides(LDHs)without the addition of organic solvents is reported via the separate nucleation and aging steps process.The resulting monolayer LDHs with the thicknesses of less than 1 nm showed a narrow thickness distribution.X-ray absorption fine-structure revealed that monolayer NiFe-LDH nanosheets have a number of oxygen and metal vacancies defects.As a practical application,monolayer NiFe-LDH nanosheets containing defects showed an enhanced electrocatalytic water oxidation activity compared with that of bulk NiFe-LDH.Density functional theory calculations uncovered that excellent catalytic activity is attributed to vacancies defects.The proposed method is an economical and universally applicable strategy for the scaled-up production of monolayer LDHs.

Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography.For a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity comparison.Then the mapping indexes can be constructed based on LBP feature and hash generation.After mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user.The user can extract the privacy information successfully with a similar method of feature analysis and index construction.The simulation results show good performance of robustness.And the hiding success rate also shows good feasibility and practicability for application.Since the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.

Prognostic impact of at least 12 lymph nodes after neoadjuvant therapy in rectal cancer: A meta-analysis

BACKGROUND The number of dissected lymph nodes(LNs)in rectal cancer after neoadjuvant therapy has a controversial effect on the prognosis.AIM To investigate the prognostic impact of the number of LN dissected in rectal cancer patients after neoadjuvant therapy.METHODS We performed a systematic review and searched Pub Med,Embase(Ovid),MEDLINE(Ovid),Web of Science,and Cochrane Library from January 1,2000 until January 1,2020.Two reviewers examined all the publications independently and extracted the relevant data.Articles were eligible for inclusion if they compared the number of LNs in rectal cancer specimens resected after neoadjuvant treatment(LNs≥12 vs LNs<12).The primary endpoints were the overall survival(OS)and disease-free survival(DFS).RESULTS Nine articles were included in the meta-analyses.Statistical analysis revealed a statistically significant difference in OS[hazard ratio(HR)=0.76,95%confidence interval(CI):0.66-0.88,I2=12.2%,P=0.336],DFS(HR=0.76,95%CI:0.63-0.92,I2=68.4%,P=0.013),and distant recurrence(DR)(HR=0.63,95%CI:0.48-0.93,I2=30.5%,P=0.237)between the LNs≥12 and LNs<12 groups,but local recurrence(HR=0.67,95%CI:0.38-1.16,I2=0%,P=0.348)showed no statistical difference.Moreover,subgroup analysis of LN negative patients revealed a statistically significant difference in DFS(HR=0.67,95%CI:0.52-0.88,I2=0%,P=0.565)between the LNs≥12 and LNs<12 groups.CONCLUSION Although neoadjuvant therapy reduces LN production in rectal cancer,our data indicate that dissecting at least 12 LNs after neoadjuvant therapy may improve the patients’OS,DFS,and DR.

Combinative Approaches of Chemistry, Pharmacology and Toxicology for the Optimal Pharmaceutical Preparation of an Anti-arthritic Chinese Medicine Formulation QFJBT

Objective Qing Fu Juan Bi Tang(QFJBT)is an anti-arthritic Chinese medicine formula consisting of five herbs:Aconiti Lateralis Radix Praeparata(Fu Zi,附子),Sinomenii Caulis(Qing Feng Teng,青风藤),Astragali Radix(Huang Qi,黄芪),Paeoniae Radix Alba(Bai Shao,白芍)and Moutan Cortex(Mu Dan Pi,牡丹皮),which have well-established histories of use for treatment of rheumatic and arthritic diseases.We intended to establish the optimized and standardized pharmaceutical procedures and manufacturing processes for the pilot production of QFJBT to develop it as a novel botanical drug product for treatment of rheumatoid arthritis(RA).Methods The combinative approaches of chemical assessment,toxicological and pharmacological evaluation were explored to define the pharmaceutical preparation of QFJBT.Results The optimized and standardized pharmaceutical procedures and manufacturing processes for the pilot production of QFJBT were established in terms of greatest chemical contents of bioactive constituents,potent anti-inflammatory and antinociceptive activities,and favorable safety profile.Quality analysis of the pilot product of QFJBT by high-performance liquid chromatography(HPLC)demonstrated that the chromatographic fingerprint profiles of three batches of QFJBT were basically identical and the contents of four characteristic and bioactive markers were relatively consistent.General toxicological studies showed a favorable safety profile of QFJBT.The maximum tolerated single dose of QFJBT was determined in both sexes of rats to be 33.63 g/kg body weight which is equivalent to 346 times of clinical dose.In the chronic oral toxicity study,the results of laboratory investigation showed that QFJBT at doses of 3.89,6.80 and 9.72 g/kg body weight(equivalent to 40,70 and 100-fold clinical doses,respectively)caused no changes in all hematological parameters and blood biochemical parameters of rats.No mortality or specific toxic responses were observed in animals after three months of repeated dosing with QFJBT.Conclusion The optimized and standardized pharmaceutical and manufacturing processes for the production of QFJBT have been successfully screened and identified through established rigorous in-process controls.

Soil Physical and Chemical Properties of Pure Pinus massoniana and Its Mixed Forests in Different Ages in Southern Guangxi

Six forest stands of 59,34 and 24 year-old Pinus massoniana forests and their mixed forests were selected at the Experimental Center of Tropical Forestry of Chinese Academy of Forestry,and 20 m × 20 m plot was set up and soil samples were taken from 0-60 cm soil layers,to analyze the changes of soil nutrient content under different forest stands and forest ages. The results showed that soil moisture and the bulk density in the mesophytic forest land were higher than those of other forest lands. The highest soil porosity value was observed in the early forest land. Soil p H of different forest was 4. 45-4. 75,indicating the variation was small. Besides,it indicated that the mixed forest was more able to increase the soil fertility than the pure forest because that the variation of soil acidity,organic matter content and total P and K in 34 and24 year-old mixed forests were higher than those in pure forests of the same year old. However,the content of soil available P and K decreased with the increase of soil depth,and varied in terms of forest ages. From the changes of soil indicators in different forest lands,soil nutrients in the 34 year-old P. massoniana forest was superior to that of other forest stands.

Research on Indoor Passive Positioning Technology Based on WiFi

In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.

Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

During the COVID-19 pandemic,the treatment of aortic dissection has faced additional challenges.The necessary medical resources are in serious shortage,and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection.In this work,we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic.A general scheme of medical data processing is proposed,which includes five modules,namely problem definition,data preprocessing,data mining,result analysis,and knowledge application.Based on effective data preprocessing,feature analysis and boosting trees,our proposed fusion decision model can obtain 100%accuracy for early postoperative mortality prediction,which outperforms machine learning methods based on a single model such as LightGBM,XGBoost,and CatBoost.The results reveal the critical factors related to the postoperative mortality of aortic dissection,which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance.

Aortic Dissection Diagnosis Based on Sequence Information and Deep Learning

Aortic dissection(AD)is one of the most serious diseases with high mortality,and its diagnosis mainly depends on computed tomography(CT)results.Most existing automatic diagnosis methods of AD are only suitable for AD recognition,which usually require preselection of CT images and cannot be further classified to different types.In this work,we constructed a dataset of 105 cases with a total of 49021 slices,including 31043 slices expertlevel annotation and proposed a two-stage AD diagnosis structure based on sequence information and deep learning.The proposed region of interest(RoI)extraction algorithm based on sequence information(RESI)can realize high-precision for RoI identification in the first stage.Then DenseNet-121 is applied for further diagnosis.Specially,the proposed method can judge the type of AD without preselection of CT images.The experimental results show that the accuracy of Stanford typing classification of AD is 89.19%,and the accuracy at the slice-level reaches 97.41%,which outperform the state-ofart methods.It can provide important decision-making information for the determination of further surgical treatment plan for patients.

Study on the Forming Process of Yi Medicine Tongfeng Granules

[Objectives]To optimize the forming process of Yi medicine Tongfeng Granules.[Methods]The forming process of Yi medicine Tongfeng Granules was optimized,with paste density,ethanol volume fraction,and type and proportion of excipient as influencing factors,and granule formability,solubility,moisture absorption,and angle of repose as evaluation indicators.Critical relative humidity(CRH)was investigated to select optimal storage conditions.[Results]Maltodextrin was selected as the excipient,and the best process parameters was the ratio of drug to excipient at 1∶2(g/g),under which the forming rate,solubility,moisture absorption rate,and angle of repose were 81.38%,98.90%,8.81%,and 27.5°,respectively.The critical relative humidity was 72%.[Conclusions]The forming process adopted is reasonable and feasible,and can provide a reference for large-scale production of Yi medicine Tongfeng Granules.

Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

At present,segmentation for medical image is mainly based on fully supervised model training,which consumes a lot of time and labor for dataset labeling.To address this issue,we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries.The network is mainly composed of two parts:a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results.In the initial stage of network training,a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities.Then a semi-supervised method is adopted to train the model,in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network.The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.