syedzayyan/paper_mill_benchmark huggingface dataset:
Development of Health Digital GIS Map for Tuberculosis Disease Distribution Analysis in Sudan

Health digital gis map provides a great solution for medical geographical distribution to efficiently explore diseases and health services.In sudan, tuberculosis disease is expanding in different areas, which requires a digital gis map to collect information about the patients and support medical institutions by geographical distribution based on health services, drug supply, and consumption.This paper developed a health digital gis map to provide a fair geographical distribution of tuberculosis health centers and control the drug supply according to medical reports.The proposed approach extracts the unfair distribution of medicine, as some centers receive medicine but do not receive patients, while others receive a large number of patients but limited amounts of medicine.The analysis results show that there is a defect in some states representing the distribution of tuberculosis centers.In the northern state, there are 15 tuberculosis centers distributed over all localities, serving about 84 tuberculosis‐infected patients only.

Journal: Journal of Healthcare Engineering (2023)
Publication types: Research Article;
Development and Validation of a Nomogram for Predicting Mortality in Patients with Atrial Fibrillation and Acute Coronary Syndrome Who Underwent Percutaneous Coronary Intervention in a Chinese Multicenter Cohort

This study is aimed at to establish an effective prognostic nomogram for patients with atrial fibrillation (af) and acute coronary syndrome (acs) underwent percutaneous coronary intervention (pci).Methods.The nomogram was based on a retrospective study of 977 patients with af and acs who underwent pci who were admitted to any of the 11 tertiary hospitals in the beijing area between 2009 and 2015.The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index ( c -index) and calibration curve and were compared using current risk scores such as grace, crusade, cha2ds2-vasc, and has-bled.The results were validated using bootstrap resampling and a retrospective cohort study of 409 patients enrolled in fuwai hospital at the same institution.Results.Independent factors derived from multivariable analysis of the primary cohort to predict all-cause mortality were age, pattern of acs, red blood cell distribution width, n-terminal pro bnp, and serum creatinine, all of which were assembled into the nomogram.The calibration curve for the probability of recurrence showed that the nomogram-based predictions were in good agreement with actual observations.The c -index of the nomogram for predicting mortality was 0.764 (95% ci, 0.718-0.810), which was statistically higher than the c -index values for the current risk scores (from 0.573 to 0.681).In the validation cohort, the c -index of the nomogram for predicting all-cause death was 0.706 (95% ci 0.601-0.811), with no significant differences compared with grace and crusade, but better than that of cha2ds2-vasc and has-bled.Conclusions.The nomogram has good prognostic prediction for patients with af and acs who underwent pci.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme

The need for computerized medical assistance for accurate detection of brain hemorrhage from computer tomography (ct) images is more mandatory than conventional clinical tests.Recent technologies and advanced computerized algorithms follow artificial intelligence (ai), machine learning (ml), and deep learning (dl) techniques to improve medical diagnosis platforms.This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance.Methods.As the existing ct image analysis models use standard procedures to detect hemorrhages, the need for dl-based data analysis is essential to provide more accurate results.Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules.On the scope, this work develops a dl-based automated analysis of ct scan slices to find various levels of brain hemorrhages.Notably, this proposed system integrates convolutional neural network (cnn) and generative adversarial network (gan) architectures as integrated generative adversarial-convolutional imaging model (igacm) for extracting the ct image features for detecting brain hemorrhages.Results.This system produces good results and takes lesser training time than existing techniques.This proposed system effectively works over ct images and classifies the abnormalities with more accuracy than current techniques.The experiments and results deliver the optimal detection of hemorrhages with better accuracy.It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques.Conclusion.The complex nature of ct images leads to noncorrelated feature complexities in diagnosis models.Considering the issue, the proposed system used gan-based effective sampling techniques for enriching complex image samples into cnn training phases.This concludes the effective contribution of the proposed igacm technique for detecting brain hemorrhages than the existing diagnosis models.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3d magnetic resonance and design corresponding models for practical applications.A series of medical image segmentation models based on convolutional neural networks is proposed.In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block.This structure has multiple parallel data paths, which increases the width of the network and therefore improves the feature extraction capability of the model.Then, this paper proposes a bidirectional feature pyramid for medical image segmentation task, which has top-down and bottom-up data paths, and, together with jump connections, can fully interact with feature maps at different scales.After that, a new activation function mish is introduced, and its advantages over other activation functions are experimentally demonstrated.Finally, for the situation that medical image annotations are not easy to obtain, a semisupervised learning method is introduced in the model training process, and the effectiveness of this method is experimentally demonstrated.The joint network first denoises the input image, then super-resolution mapping is performed on the noise-removed feature map, and finally, the super-resolution 3d-mr image is obtained.We update the network by combining the denoising loss and super-resolution loss during the joint network training process.The experimental results show that the joint network with denoising first and then super-resolution outperforms the joint network with other task order and outperforms the method that performs the two tasks separately and the proposed method in this paper has the optimal performance.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Circular RNA circFBXO11 Modulates Hepatocellular Carcinoma Progress and Oxaliplatin Resistance Through miR-605/FOXO3/ABCB1 Axis

Increasing findings suggest the critical role of circular rna (circ rna) in human cancer, and chemotherapy resistance is a poor prognostic factor for hepatocellular carcinoma (hcc).The function of circ rna in the hcc oxaliplatin (oxa) resistance remains largely unknown.In this study, we found that circ rna circ fbxo11 was significantly up‐regulated in hcc tissues, and the circ fbxo11 overexpression was associated with poor prognosis.Circ fbxo11 was found to promote the hcc proliferation, cycle progress and oxa resistance.Mechanistically, circ fbxo11 was predominantly localized in the cytoplasm and harboured the mi r‐605, thereby targeting foxo3 protein.Furthermore, foxo3 targeted the promoter region of abcb1 to accelerate its expression.In conclusion, this research reveals the role of circ fbxo11/mi r‐605/foxo3/abcb1 axis in the hcc oxa resistance, providing new insight for circ rna‐based diagnostic and therapeutic strategies.

Journal: Journal of Cellular and Molecular Medicine (JCMM) (2020)
Publication types: Research Article;
Design and Discussion on Information Management and Control System of Agricultural Machinery and Equipment

In order to solve the problem of low development efficiency of agricultural mechanization information network system, a design and discussion method of agricultural machinery equipment information management and control system is proposed.This paper introduces the technologies required to realize the agricultural machinery operation management control system: android development technology required to develop mobile phone positioning software, internet map api for displaying location data, and construction of agricultural machinery resource allocation model for the realization of the scheduling module.Different objective functions are set, and the model results in different situations are obtained through experimental data.The experimental data are in the case of schedulable agricultural machinery, if the target is short-distance short-distance operation, it can save time by 0.16 h, and the existing m1 model is in changying.Village work, the work end time is about 2.5 hours; m4 and m2 models work in the xuzhuang village, and the work end time is about 0.5 h; m3 model works in the jiangtang village, and the calculation results for different goals are as follows: m1 model first dispatches short-distance operations; the m3 type is dispatched first for short-distance operations; the m4 type operating in xuzhuang village is given priority to dispatch short-distance and short-distance operations.According to the analysis of system requirements, each submodule of the system is designed, including the function description and realization method of the module.The history track query module, mobile phone positioning module, and agricultural machinery scheduling module are introduced in detail.Using the android development technology required by mobile phone positioning software, the information management of user interface, server system, and database is realized, which provides conditions for the collection of agricultural machinery and equipment information and the generation of information management control.

Journal: Journal of Control Science and Engineering (2022)
Publication types: Research Article;
Long Noncoding RNA LEF1-AS1 Silencing Suppresses the Initiation and Development of Prostate Cancer by Acting as a Molecular Sponge of miR-330-5p via LEF1 Repression

Prostate cancer (pca) is one of the major cancers affecting males with high mortality around the world.Recent studies have found that some long noncoding rnas play a critical part in the cellular processes of pca.In our study, aberrant expressed lymphoid enhancer‐binding factor‐1 antisense rna 1 (lef1‐as1), micro rna‐330‐5p (mi r‐330‐5p), and lymphoid enhancer‐binding factor‐1 (lef1) were screened out from a microarray database, the role of the novel noncoding rna regulatory circuitry in the initiation and development of pca was investigated.Lef1‐as1 and lef1 were highly expressed while mi r‐330‐5p was poorly expressed in pca.Following that, the pca pc‐3 cell line was adopted for subsequently experiments, in which the expression of lef1‐as1 and mi r‐330‐5p was subsequently altered by means of exogenous transfection.After that, the effects of up‐ or downregulation of lef1‐as1 and mi r‐330‐5p on epithelial–mesenchymal transition (emt) and the cell ability for proliferation, invasion, migration in vitro, and tumorigenesis and lymph node metastasis (lnm) in vivo were evaluated.Rna crosstalk revealed that lef1‐as1 bound to mi r‐330‐5p and lef1 was the target gene of mi r‐330‐5p.Silenced lef1‐as1 or elevated mi r‐330‐5p exhibited inhibited emt processes, reduced ability of proliferation, invasion and migration, coupling with decreased tumorigenesis and lnm in nude mice.The key findings of this study collectively propose downregulation of lef1‐as1 competing with mi r‐330‐5p to inhibit emt, invasion and migration of pca by lef1 repression.

Journal: Journal of Cellular Physiology (2019)
Publication types: Research Article;
Defending against Internal Attacks in Healthcare-Based WSNs

In view of the spatiotemporal limitations of traditional healthcare services, the use of wireless communication has become one of the main development directions for the medical system.Compared with the traditional methods, applying the potential and benefits of the wireless sensor networks has more advantages such as low cost, simplicity, and flexible data acquisition.However, due to the limited resources of the individual wireless sensor nodes, traditional security solutions for defending against internal attacks cannot be directly used in healthcare based wireless sensor networks.To address this issue, a negative binomial distribution trust with energy consideration is proposed in this study.The proposed method is lightweight and suitable to be operated on the individual healthcare sensors.Simulations show that it can effectively deal with the internal attacks while taking the energy saving into consideration.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Long Noncoding RNA LINC00958 Regulates Cell Sensitivity to Radiotherapy Through RRM2 by Binding to microRNA-5095 in Cervical Cancer

Long noncoding rnas (lnc rnas) have been implicated in the regulation of resistance to radiotherapy in cervical cancer, which is a type of gynecological disease with high mortality in women around the world.Hence, our purpose is to delineate the involvement of linc00958 in regulating cell sensitivity to radiotherapy in cervical cancer.Linc00958 expression in cervical cancer was assayed, followed by verification of the relationship among linc00958, micro rna‐5095 (mi r‐5095) and ribonucleotide reductase subunit m2 (rrm2).Hela cells were transduced with up‐/downregulation of mi r‐5095 or rrm2, or linc00958 silencing, respectively, and then treated with or without a 6 gy dose of x‐ray irradiation.Then the cell proliferation, apoptosis, survival fraction rate, as well as sensitivity to radiotherapy, were assessed.Finally, xenograft tumor in nude mice was established by transplanting hela cells transfected with sh‐linc00958 and irradiated with 6 gy of x‐ray.High expression of linc00958 was revealed in the cancer genome atlas and gene expression profiling interactive analysis, as well as in radiation‐resistant patients, which was associated with lower sensitivity to radiotherapy in cervical cancer.Moreover, cervical cancer patients with higher linc00958 expression exhibited a shorter overall survival according to kaplan–meier analysis.In addition, linc00958 could regulate the expression of rrm2 by competing for mi r‐5095.A combination of radiotherapy with linc00958 silencing, rrm2 downregulation or mi r‐5095 overexpression was found to inhibit cervical cancer cell proliferation and tumor growth, while promoting cell apoptosis both in vitro and in vivo.Collectively, our results suggest that linc00958 could regulate rrm2 by competing to mi r‐5095, which regulates cell sensitivity to radiotherapy in cervical cancer.

Journal: Journal of Cellular Physiology (2019)
Publication types: Research Article;
Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

It can be challenging for doctors to identify eye disorders early enough using fundus pictures.Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated.Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures.Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities.A deep-learning-based approach to targeted ocular detection is presented in this study.For this study, we used state-of-the-art image classification algorithms, such as vgg-19, to classify the odir dataset, which contains 5000 images of eight different classes of the fundus.These classes represent different ocular diseases.However, the dataset within these classes is highly unbalanced.To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications.Then, the binary classifications were trained with vgg-19.The accuracy of the vgg-19 model was 98.13% for the normal (n) versus pathological myopia (m) class; the model reached an accuracy of 94.03% for normal (n) versus cataract (c), and the model provided an accuracy of 90.94% for normal (n) versus glaucoma (g).All of the other models also improve the accuracy when the data is balanced.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Deconstruction of the Prevention of Knee Osteoarthritis by Swimming Based on Data Mining Technology

With the continuous development of big data and the continuous improvement of people’s living standards, increasingly attention is paid to physical health.Swimming in this sport is effective in preventing the occurrence of arthritis.This paper analyzes the prevention and exploration of arthritis and relies on the traditional method of retrieving clinical literature on the treatment of knee osteoarthritis with traditional chinese medicine and internal medicine, which requires a lot of manpower and material resources.At this time, the role of data mining technology is brought into play.This article analyzes the prevention of arthritis by swimming.If you rely on the traditional retrieval of clinical literature on the treatment of knee osteoarthritis with traditional chinese medicine and internal medicine, you will find a lot of disordered data.It takes a lot of manpower and material resources to sort out the summary, and at this time, the role of data mining (dm) technology is brought into play.In this paper, the relevant information of the literature that meets the requirements is established in an excel database, and the data of the relevant information is entered.Through sorting and analysis, the tcm syndrome types of knee osteoarthritis are summarized.Then, dm technology was used to carry out statistical analysis of frequency and prescription, to summarize the distribution characteristics of the corresponding knee osteoarthritis, tcm syndrome types, and the weight of each syndrome type, and to make a preliminary discussion at the same time.Finally, it is concluded that there are better prevention methods for arthritis in the research methods of traditional chinese medicine.Dm technology has been increasingly applied to all aspects of traditional chinese medicine.Dm technology has improved its research efficiency by 38% and achieved great results, which will play a greater role in promoting the research process of tcm syndrome.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

The knee joint is the second largest joint in the human body, with a wide range of functional activities and strong support for the human body.Moreover, the cartilage of the knee joint is hyaline cartilage, which is relatively brittle, so it is most vulnerable to trauma.In clinical work, the damage of articular cartilage is a disease with a high rate of orthopedic visits.In this paper, all the experimental group cases included in the observation were patients with acute articular cartilage injury or oa diagnosed by knee arthroscopy.All experimental groups and control groups did not have any strenuous exercise one day before mri (magnetic resonance imaging), and they sat for 30 minutes before the examination.Conventional scanning sagittal fse‐t1wi, fse‐t2wi, fs‐fse‐t1wi, fs‐fse‐t2wi, fs‐pdwi, and coronal fs‐pdwi sequence.In the normal control group, after the t 2 color map was generated in the workstation, the articular cartilage was divided on the midsagittal plane, and the patellar cartilage and tibial plateau were roughly divided into upper, middle, lower and anterior, middle, and posterior thirds.In order to ensure the maximum comparability of the results, an artificial intelligence segmentation algorithm is used to divide the region of interest equally, and the central part of each partition is selected as much as possible for measurement.The t 2 values of the three partitions of each cartilage were measured one by one and averaged.For the comparison results of t 2 value of cartilage in the same part: according to patellar cartilage, femoral cartilage, and tibial cartilage, the p values are 0.973, 0.150, and 0.525, respectively.Therefore, early detection and early treatment of articular cartilage injury are of great significance to the performance of athletes’ competition level and the extension of sports life.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Clinical Study;
Deconstruction of Clinical Treatment of Pneumonia and Respiratory Tract Infection Based on MRI Molecular Imaging

Pneumonia is a major research core topic in the medical field, and clinical trials of pneumonia and respiratory tract infection have been ongoing.The purpose of this study was to investigate the clinical efficacy and safety of shuanghuanghua granules in the treatment of pneumonia and respiratory tract infection based on nmr molecular imaging, and to lay a foundation for the development of new drugs.In this paper, 126 patients were randomly divided into the control group, the treatment group 1, the and treatment group 2, and were given fengreganmao granules, shuanghuanghua granules (ultrafine preparation), and shuanghuanghua granules (extract preparation), respectively.The main symptom scores, sign scores, antipyretic time, and virus content before and after treatment were recorded.Statistical analysis was carried out on this basis.The experiment showed that the body temperature of the subjects in the three groups before treatment was: control group (37.59 ± 0.78), treatment group 1 (37.8 ± 0.81), and treatment group 2 (37.6 ± 0.76).After treatment, the body temperature of subjects in the three groups was: control group (36.67 ± 0.71), treatment group 1 (36.49 ± 0.43), and treatment group 2 (36.19 ± 0.25).Experiments show that shuanghuanghua granules can significantly reduce the nasal virus, adenovirus, parainfluenza virus, etc.In the patient’s body.And it can shorten the antipyretic time of patients and has good clinical application.In addition, this study gave full play to the role of mri molecular imaging and provided ideas and references for the clinical treatment of pneumonia and respiratory tract infection.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Clinical Study;