syedzayyan/paper_mill_benchmark huggingface dataset:
Identification of Diabetic Retinopathy through Machine Learning

A cross-sectional study of patients with suspected diabetic retinopathy (dr) who had an ophthalmological examination and a retinal scan is the focus of this research.Specialized retinal images were analyzed and classified using opf and rbm models (restricted boltzmann machines).Classification of retinographs was based on the presence or absence of disease-related retinopathy (dr).The rbm and opf models extracted 500 and 1000 characteristics from the images for disease classification after the system training phase for the recognition of retinopathy and normality patterns.There were a total of fifteen different experiment series, each with a repetition rate of 30 cycles.The study included 73 diabetics (a total of 122 eyes), with 50.7% of them being men and 49.3% being women.The population was on the older side, at 59.7 years old on average.The rbm-1000 had the highest overall diagnostic accuracy (89.47) of any of the devices evaluated.The rbm-500 had a better autodetection system for dr signals in fundus images than the competition (100% sensitivity).In terms of specificity, rbm-1000 and opf-1000 correctly identified all of the images that lacked dr signs.In particular, the rbm model of machine learning automatic disease detection performed well in terms of diagnostic accuracy, sensitivity, and application in diabetic retinopathy screening.

Journal: Mobile Information Systems (2021)
Publication types: Research Article;
Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

Intervertebral disc degeneration (idd) is a major cause of lower back pain.However, to date, the molecular mechanism of the idd remains unclear.Gene expression profiles and clinical traits were downloaded from the gene expression omnibus (geo) database.Firstly, weighted gene coexpression network analysis (wgcna) was used to screen idd-related genes.Moreover, least absolute shrinkage and selection operator (lasso) logistic regression and support vector machine (svm) algorithms were used to identify characteristic genes.Furthermore, we further investigated the immune landscape by the cell-type identification by estimating relative subsets of rna transcripts (cibersort) algorithm and the correlations between key characteristic genes and infiltrating immune cells.Finally, a competing endogenous rna (ce rna) network was established to show the regulatory mechanisms of characteristic genes.A total of 2458 genes were identified by wgcna, and 48 of them were disordered.After overlapping the genes obtained by lasso and svm-rfe algorithms, genes including linc01347, asap1-it1, lnc-sept7l-1, b3gnt8, chrnb3, clec4f, loc102724000, serinc2, and loc102723649 were identified as characteristic genes of idd.Moreover, differential analysis further identified asap1-it1 and serinc2 as key characteristic genes.Furthermore, we found that the expression of both asap1-it1 and serinc2 was related to the proportions of t cells gamma delta and neutrophils.Finally, a ce rna network was established to show the regulatory mechanisms of asap1-it1 and serinc2.In conclusion, the present study identified asap1-it1 and serinc2 as the key characteristic genes of idd through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of idd.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
High-Resolution Computer Tomography Image Features of Lungs for Patients with Type 2 Diabetes under the Faster-Region Recurrent Convolutional Neural Network Algorithm

The objective of this study was to adopt the high-resolution computed tomography (hrct) technology based on the faster-region recurrent convolutional neural network (faster-rcnn) algorithm to evaluate the lung infection in patients with type 2 diabetes, so as to analyze the application value of imaging features in the assessment of pulmonary disease in type 2 diabetes.In this study, 176 patients with type 2 diabetes were selected as the research objects, and they were divided into different groups based on gender, course of disease, age, glycosylated hemoglobin level (hb a1c), 2 h c peptide (2 h c-p) after meal, fasting c peptide (fc-p), and complications.The research objects were performed with hrct scan, and the faster-rcnn algorithm model was built to obtain the imaging features.The relationships between hrct imaging features and 2 h c-p, fc-p, hb a1c, gender, course of disease, age, and complications were analyzed comprehensively.The results showed that there were no significant differences in hrct scores between male and female patients, patients of various ages, and patients with different hb a1c contents ( p &gt; 0.05 ).As the course of disease and complications increased, hrct scores of patients increased obviously ( p &lt; 0.05 ).The hrct score decreased dramatically with the increase in the contents of 2 h c-p and fc-p after the meal ( p &lt; 0.05 ).In addition, the results of the spearman rank correlation analysis showed that the course of disease and complications were positively correlated with the hrct scores, while the 2 h c-p and fc-p levels after meal were negatively correlated with the hrct scores.The receiver operating curve (roc) showed that the accuracy, specificity, and sensitivity of hrct imaging based on faster-rcnn algorithm were 90.12%, 90.43%, and 83.64%, respectively, in diagnosing lung infection of patients with type 2 diabetes.In summary, the hrct imaging features based on the faster-rcnn algorithm can provide effective reference information for the diagnosis and condition assessment of lung infection in patients with type 2 diabetes.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
G-Protein-Coupled Receptor Kinase-Interacting Protein 1 (GIT1) Promotes Head and Neck Squamous Cell Carcinoma Metastases via Activating the PI3K/AKT/mTOR Signal Pathway

Git1 is identified as a novel tumor oncogene in breast cancer.In this article, we aimed to explore the role of git1 in the progression of head and neck squamous cell carcinoma (hnscc).Methods.Git1 expression in hnscc was detected by rt-q pcr, immunohistochemistry assay, and western blot.Hnscc cell proliferation, migration, and invasion were examined by cck-8 assay, wound healing assay, and transwell assay, respectively.Cell apoptosis was detected by flow cytometric analysis.Results.In our study, git1 was notably upregulated in hnscc tissues and cells.Moreover, git1 expression level had positive corelation with pathological grade and nodal status of hnscc.Functional experiments showed that knockdown of git1 restrained hnscc proliferation, invasion, migration, and emt and facilitated cell apoptosis.Furthermore, git1 knockdown was found to restrain hnscc tumor growth and lung metastasis.Additionally, pi3k/akt/m tor signal pathway inhibitors suppressed the effect of git1 on hnscc cell progression.Conclusion.Git1 was upregulated in hnscc and facilitated hnscc cell progression by inducing pi3k/akt/m tor signal pathway.Therefore, we suggested that git1 might be a potential target for hnscc treatment.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Healthcare Communication Role in the Detection of Unhealthy Behavior in University Students

The current study aims to identify unhealthy behaviors among university students, establish means of detection of unhealthy behavior, identify obstacles to digital volunteering, and explore the relationship between volunteer preferred style of volunteering and the obstacles to volunteering.Data for the study was gathered by administering an unstructured, anonymous questionnaire to 207 female university students and staff.The survey design included questions about sociodemographic characteristics, views on different facets of volunteering, unhealthy behaviors, and correlation between volunteering and unhealthy behaviors.The results revealed unhealthy behavior detected by the respondents (51.7%).Twenty-eight (13.5%) of the 207 respondents reported using social media in detecting women with offending behavior.The value of pearson’s r is 0.245; thus, it is considered as weak or no correlation.There is hence no correlation between how respondents preferred volunteer work and the obstacle to volunteering.There is not much difference in the obstacles to volunteering faced by respondents despite their preferred style of volunteering.The findings reveal that digital volunteering effectively gains ground in detecting and managing unhealthy behaviors among university students.Much more could be achieved through digital volunteering if more awareness is created and volunteering programs are designed to be more interesting and less time-consuming to allow more students to participate.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Genistein Protects against Spinal Cord Injury in Mice by Inhibiting Neuroinflammation via TLR4-Mediated Microglial Polarization

The present study was designed to study the effect of genistein on spinal cord injury (sci) in mice and to explore its underlying mechanisms.Methods.We established sci mouse model, and genistein was administered for treatment.We used the basso, beattie, and bresnahan (bbb) exercise rating scale to evaluate exercise recovery, and the detection of spinal cord edema was done using the wet/dry weight method.Apoptosis was determined by tunel staining, and inflammation was evaluated by measuring inflammatory factors by an elisa kit.The expression of m1 and m2 macrophage markers was determined using flow cytometry, and the expression of proteins was detected using immunoblotting.Results.Genistein treatment not only improved the bbb score but also reduced spinal cord edema in sci mice.Genistein treatment reduced apoptosis by increasing bcl2 protein expression and decreasing bax and caspase 3 protein expression.It also reduced the expression of inflammatory cytokines (tnf-α, il-1β, il-6, and il-8) in the sci area of sci mice.Flow cytometry analysis indicated that genistein treatment significantly decreased the ratio of m1 macrophages (cd45+/gr-1-/cd11b+/i nos+) and increased the ratio of m2 macrophages (cd45+/gr-1-/cd11b+/arginase 1+) in the sci area of sci mice on the 28th day after being treated with genistein.We also found that genistein treatment significantly decreased the expression of tlr4, my d88, and traf6 protein in the sci area of sci mice on 28th day after being treated with genistein.Conclusion.Our findings suggested that genistein exerted neuroprotective action by inhibiting neuroinflammation by promoting the activation of m2 macrophages, and its underlying mechanisms might be related to the inhibition of the tlr4-mediated my d88-dependent signaling pathway.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Functionalized Ferroferric Oxide Nanomagnetic Beads for Extraction of Nucleic Acid and Its Application in Early Screening of Colorectal Cancer

Early screening is an important way to reduce the incidence and mortality of colorectal cancer (crc).Fecal dna testing stands out among many screening methods due to its high sensitivity.However, at this stage, researchers have not found a high-efficiency method for fecal dna extraction.To this end, this work carried out a new round of exploration.Here, this experiment synthesized a kind of nanomagnetic beads (nh2-si o2@fe3o4) with good stability for nucleic acid extraction.A comparative study with the centrifugal adsorption column method revealed the significant advantages of the magnetic bead method in extracting fecal dna.The dna extracted by the magnetic bead method is of high purity, can also achieve high-throughput tests, and is more suitable for polymerase chain reaction detection, greatly simplifying the stool dna detection process and providing a basis for the widespread promotion of early screening.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Finite Element Prediction on Fracture Load of Femur with Osteogenesis Imperfecta under Various Loading Conditions

Osteogenesis imperfecta (oi) is an inherited disorder characterized by extreme bone fragility due to collagen defects.It is an incurable disease.Bone fractures can occur frequently without prior notice, especially among children.Early quantitative prediction of fracture loads due to oi tends to alert patients to avoid unnecessary situations or dangerous conditions.This study is aimed at investigating the fracture loads of femur with oi under various types of loading.Ten finite element models of an oi-affected bone were reconstructed from the normal femur with different bowing angles ranging from 7.5 to 30.0°.The boundary conditions were assigned on an oi-affected femoral head under three types of load: medial-lateral impacts, compression-tension, and internal-external torsions, and various loading direction cases that reflect the stance condition.The fracture load was examined based on the load that can cause bone fracture for each case.The results show that the loads bearable by the femur before fracture were decreased with respect to the increase of oi bowing angles in most of the loading cases.The risk of fracture for the femur with oi was directly proportional to the increase of bowing angles in the frontal plane.This study provides new insights on fracture load prediction in oi-affected bone with respect to various loading types, which could help medical personnel for surgical intervention judgement.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Feasibility and Validity of Cambridge Neuropsychological Test Automated Battery in Mild Cognitive Impairment Screening for Patients with Atrial Fibrillation

Atrial fibrillation (af) is associated with the worsening of cognitive function.Strategies that are both convenient and reliable for cognitive screening of af patients remain underdeveloped.We aimed to analyze the sensitivity and specificity of computerized cognitive screening strategies using subtests from cambridge neuropsychological test automated battery (cantab) in af patients.Methods.The multitasking test (mtt), rapid visual information processing (rvp), and paired associates learning (pal) subtests from cantab were performed in 105 af patients.Traditional standard neuropsychological tests were used as a reference standard.Cognitive screening models using different cantab subtests were established using multivariable logistic regression.Further stepwise regression using the akaike information criterion (aic) was applied to optimize the models.Receiver operating characteristic curve analyses were used to study the sensitivity and specificity of these models.Results.Fifty-eight (55%) patients were diagnosed with mild cognitive impairment (mci).Mtt alone had reasonable sensitivity (82.8%) and specificity (74.5%) for mci screening, while rvp (sensitivity 72.4%, specificity 70.2%) and pal (sensitivity 70.7%, specificity 57.4%) were less effective.Stepwise regression of all available variables revealed that a combination of mtt and rvp brought about higher specificity (sensitivity 82.8%, specificity 85.8%), while pal was not included in the optimal model.Moreover, adding education to the models did not result in improved validity for mci screening.Conclusion.The cantab subtests are feasible and effective strategies for mci screening among af patients independent of patients’ education levels.Hence, they are practical for cardiologists or general practitioners.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Factors Associated with Cranial Nerve Injury after Radiotherapy for Large Brain Metastases

This paper mainly studied the correlation factors of cranial nerve injury after radiotherapy for large brain metastases by investigating the influencing factors and predictors of cranial nerve injury, which can provide a good reference and idea for radiotherapy.Through a large number of experiments, it is proved that the research idea proposed in this paper is reasonable and correct.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models

Autism spectrum disorder (asd) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face.Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (td) children.This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework.Mobile net, xception, and inception v3 were the pretrained models used for classification.The facial images were taken from a publicly available dataset on kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children.Given the accuracy of the classification results for the validation data, mobile net reached 95% accuracy, xception achieved 94%, and inception v3 attained 0.89%.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Exosomes Derived from Human Bone Marrow Mesenchymal Stem Cells Stimulated by Deferoxamine Accelerate Cutaneous Wound Healing by Promoting Angiogenesis

The exosomes are derived from mesenchymal stem cells (mscs) and may be potentially used as an alternative for cell therapy, for treating diabetic wounds, and aid in angiogenesis.This study, aimed to investigate whether exosomes originated from bone marrow-derived mscs (bmscs) preconditioned by deferoxamine (dfo-exos) exhibited superior proangiogenic property in wound repair and to explore the underlying mechanisms involved.Human umbilical vein endothelial cells (huvecs) were used for assays involving cell proliferation, scratch wound healing, and tube formation.To test the effects in vivo, streptozotocin-induced diabetic rats were established.Two weeks after the procedure, histological analysis was used to measure wound-healing effects, and the neovascularization was evaluated as well.Our findings demonstrated that dfo-exos activate the pi3k/akt signaling pathway via mi r-126 mediated pten downregulation to stimulate angiogenesis in vitro.This contributed to enhanced wound healing and angiogenesis in streptozotocin-induced diabetic rats in vivo.Our results suggest that, in cell-free therapies, exosomes derived from dfo preconditioned stem cells manifest increased proangiogenic ability.

Journal: BioMed Research International (2019)
Publication types: Research Article;
Evaluation of Cardiac Function Characteristics after Patent Ductus Arteriosus Closure in Children and Adults by Echocardiographic Data

This study was to investigate the value of echocardiographic data in assessing changes in cardiac function before and after transcatheter closure in children and adult patients with patent ductus arteriosus (pda).In this study, 150 patients with isolated pda treated by cardiac catheterization and transcatheter closure were selected as the study sample.Real-time color doppler echocardiography was used both after and after operation.The results showed that the left ventricle returned to normal in 75 patients one day after operation, with an average age of 10.95 ± 3.27 years; the left ventricle did not return to normal in 10 patients 360 days after operation, with an average age of 64.31 ± 7.05 years.Left ventricular end diastolic volume index (lvedvi) and left ventricular end systolic volume index (lvesvi) of patients decreased significantly one day after operation and remained at 51.95 ± 9.55 m l/m2 and 20.36 ± 8.11 m l/m-2, respectively.In summary, echocardiographic data have a high reference value in assessing cardiac function characteristics in children and adult patients with pda and are worthy of further promotion.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Evaluation of Angiogenesis and Pathological Classification of Extrahepatic Cholangiocarcinoma by Dynamic MR Imaging for E-Healthcare

For staging cholangiocarcinoma and determining respectability, mr is an accurate noninvasive method which provides size of tumor and vascular patency information.Dynamic contrast-enhanced magnetic resonance imaging (dce-mri) is a noninvasive inspection method for evaluating the vascular structure and functional characteristics of tumor tissue.However, some limitations should be noted about the technology.At present, the technology cannot be used alone, which is just an assisted method during the conventional mri examination.50 ecc patients, admitted to indira gandhi medical college and hospital between 2016 and 2019, were selected as research subjects.They were classified pathologically according to the steiner classification system.After image processing, regions of interest (rois) were selected from the image to measure the rate constant (kep), extravascular space volume fraction (ve), and tissue volume transfer constant (ktrans).There were 15 cases with highly differentiated carcinoma, 23 cases with moderately differentiated carcinoma, and 12 cases with lowly differentiated carcinoma.Non-vegf expression was noted in 21 cases, with low expression noted in 15 cases, moderate expression noted in 14 cases, and no high expression case noted.The relevant parameters in the dynamic mri image can quantitatively reflect the angiogenesis and pathological classification of ecc, which is suggested in the clinical treatment of ecc.The ktrans, kep, and ve values of the ecc patients were all not associated with the pathological classification, with no significant difference ( p &lt; 0.05 ).Besides, due to the fact that the patient cannot completely hold his breath, the air leak reduces the image quality.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Sars-co v-2 is a novel virus, responsible for causing the covid-19 pandemic that has emerged as a pandemic in recent years.Humans are becoming infected with the virus.In 2019, the city of wuhan reported the first-ever incidence of covid-19.Covid-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult.A real-time reverse transcriptase-polymerase chain reaction (rt-pcr) kit is used to diagnose the disease.Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread.To develop an alternative, radiologists looked at the changes in radiological imaging, like ct scans, that produce comprehensive pictures of the body of excellent quality.The suspected patient’s computed tomography (ct) scan is used to distinguish between a healthy individual and a covid-19 patient using deep learning algorithms.A lot of deep learning methods have been proposed for covid-19.The proposed work utilizes cnn architectures like vgg16, dese net121, mobile net, nasnet, xception, and efficient net.The dataset contains 3873 total ct scan images with “covid” and “non-covid.” the dataset is divided into train, test, and validation.Accuracies obtained for vgg16 are 97.68%, dense net121 is 97.53%, mobile net is 96.38%, nasnet is 89.51%, xception is 92.47%, and efficient net is 80.19%, respectively.From the obtained analysis, the results show that the vgg16 architecture gives better accuracy compared to other architectures.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;