syedzayyan/paper_mill_benchmark huggingface dataset:
Effect and Significance of High-Quality Nursing on Blood Glucose, Pregnancy Outcome, and Neonatal Complications of Patients with Gestational Diabetes Mellitus

To explore the role of high-quality nursing on blood glucose, pregnancy outcome, and neonatal complications in patients with gestational diabetes mellitus.Methods.Altogether, 148 patients with gdm admitted to our hospital were selected as the research participants, 83 of whom received high-quality nursing as the observation group (og).Another 65 patients received only routine care as the control group (cg).The blood sugar level, blood pressure value, and adverse pregnancy outcomes of the og and the cg of patients after intervention were compared.The changes of psychological state and nursing satisfaction of the og and the cg of patients were observed.The blood lipid level and neonatal complications of the og and the cg were detected.Results.The blood sugar level of the og was lower than that of the cg ( p &lt; 0.05 ).The systolic pressure and diastolic pressure of patients in the og were lower than those in the cg after nursing intervention ( p &lt; 0.05 ).The abnormal delivery rate in the cg was evidently higher than that in the og ( p &lt; 0.05 ).After intervention, sas and sds scores in the og were lower than those in the cg ( p &lt; 0.05 ).The number of people who needed improvement and were dissatisfied in the og was evidently lower than that in the cg ( p &lt; 0.05 ).Tg and ldl-c in the og were lower than those in the cg ( p &lt; 0.05 ).The total incidence of complications in the cg was evidently higher than that in the og ( p &lt; 0.05 ).Conclusion.High-quality nursing can reduce blood sugar and blood pressure of gdm patients and has a great protective effect on maternal and infant health, which is worthy of clinical practice.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Effect and Clinical Value of Protective Motivation Intervention Rehabilitation Model on Pain Perception and Dysfunction in Patients with Lumbar Disc Herniation: Based on a Retrospective Cohort Study

To examine the potential medical benefits of protective motivation intervention rehabilitation mode on pain perception and dysfunction in patients with lumbar disc herniation (ldh).Methods.140 ldh patients hospitalized from january 2021 to september 2021 were totally selected.The control group received regular rehabilitation, and the research group received protective motivation intervention rehabilitation.The comparisons of scores of disease knowledge, visual analogue scale (vas), pain belief and perception scale (pbpi), japanese orthopedic association score (joa), roland-morris dysfunction (rmdq), and quality of life scale (sf-36) were made across different groups.Results.The scores of disease knowledge in the two cohorts at 1 month, 2 months, and 3 months after intervention were greater than those before intervention, and the difference is statistically significant ( p &lt; 0.05 ).The scores of vas, pbpi, joa, and rmdq at 1 month, 2 months, and 3 months after intervention were downregulated.At 1 month, 2 months, and 3 months after intervention, the experimental scores of vas, pbpi, joa, and rmdq were markedly fewer than the control group, and the difference is statistically significant ( p &lt; 0.05 ).The scores of sf-36 after intervention were statistically upregulated, and the difference is statistically significant ( p &lt; 0.05 ).After intervention, the score of sf-36 in the research group was significantly higher than that in the control group, and the difference is statistically significant ( p &lt; 0.05 ).Conclusion.The application of protective motivation intervention in rehabilitation of ldh patients can more effectively improve their cognitive level, reduce their pain perception, improve their lumbar function, and enhance their well-being.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries.Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population.In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram.It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors.Electrocardiogram indicates the potential activity of patient’s heart and directly reflects the changes in it.In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients.For this purpose, we have used two datasets, physio-bank and mimic-iii, which are publicly available, to extract ecg signals and thoroughly examine heart failure.Initially, a heart failure diagnosis model which is based on attention convolutional neural network (cbam-cnn) is proposed to automatically extract features.Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ecg signal to perform classification diagnosis.To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals.Influence of signal preprocessing on the performance of model is also discussed.These results show that the proposed cbam-cnn model performance is better for both classifications of ecg signals.Likewise, the cbam-cnn model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Dynamic Display Design of Cultural and Creative Products Based on Digital Augmented Reality Technology

The development of science and technology has brought about a change in life, and this has also caused people to use technology more and more frequently.In the dynamic display design of cultural and creative products, people are also beginning to look for a technology that can better deal with it.Its characteristics are displayed, which is the research of this article.This article aims to study how to carry out a dynamic display design for cultural and creative products.To this end, this paper proposes a method of digital augmented reality technology, optimizes the visual search technology in augmented reality technology, studies and analyzes its specific digital implementation process, divides and understands the technical principles, and designs relevant experiments to study its specific display effect and carry out the relevant effect analysis in the analysis part.The experimental results of this article show that the improved augmented reality technology can better enhance the consumer’s interactive experience.At the same time, the consumers’ purchase intentions have also been improved by 43%.It can be said that the dynamic effects of cultural and creative products are well demonstrated.

Journal: Mobile Information Systems (2022)
Publication types: Research Article;
DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm

A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in dsa, and its clinical characteristics were analyzed, and targeted nursing measures were formulated.Methods.Using a retrospective multicenter study method based on radiology reports, dsa images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set.Using a computer-aided detection method based on the three-dimensional u-net (3d u-net), after preprocessing dsa images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians’ annotations are introduced.The 3d u-net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area.Results.Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of 94.4 ± 1.1 % was obtained.Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 fps/case (false positives/case).The resulting sensitivity was 82.9%.The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~&lt;10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of &lt;3.00 mm and 3.00~&lt;5.00 mm (50.0% and 72.7%).The sensitivity of patients aged 50-60 years and &gt;60 years (90.0% and 87.5%) was higher than that of patients aged &lt;50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females).Conclusion.The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.

Journal: Scanning (2022)
Publication types: Research Article;
Downregulation of miR-146a-5p Promotes Acute Pancreatitis through Activating the TLR9/NLRP3 Signaling Pathway by Targeting TRAF6 In Vitro Rat Model

Acute pancreatitis (ap) is mainly caused by acinar cells releasing various inflammatory factors, causing inflammatory storms and leading to severe pancreatitis.Detection methods and treatment targets for pancreatitis are lacking, raising the urgency of identifying diagnostic markers and therapeutic targets for ap.Micro rnas (mi rnas) have recently been identified as molecular markers for various biological processes such as tumors, immunity, and metabolism, and the involvement of mi rnas in inflammatory responses has been increasingly studied.To explore the role of mi rnas in ap is the primary objective of this study.By using q pcr on our cerulein-induced pancreatitis cell model, it is worth noting that the change of mi r-146a-5p expression in inflammation-related mi rnas in ap was predominant.Next, elisa, cck8, and flow cytometry were used to inspect the impact of mi r-146a-5p on pancreatitis.Bi bi serv bioinformatics anticipated binding ability of mi r-146a-5p and 3 ′ -untranslated region (3 ′ utr) of tnf receptor-associated factor 6 (traf6), and the dual-luciferase assay verified the combination of the two.Traf6 knockdown verified the effect of traf6 on the progression of pancreatitis.Finally, rescue experiments verified the capability of mi r-146a-5p and traf6 interaction on the toll-like receptor 9 (tlr9)/nod-like receptor protein 3 (nlrp3) signaling pathway and cell function.The expression of mi r-146a-5p decreased in cerulein-induced ar42j pancreatic acinar cells.Functional experiments verified that mi r-146a-5p facilitated the proliferation of ar42j pancreatic acinar cells and inhibited their apoptosis.Bioinformatic predictions and dual-luciferase experiments verified the actual binding efficiency between mi r-146a-5p and 3 ′ utr of traf6.Our study confirmed that knockdown of traf6 restrained the progression of pancreatitis, and knockdown of traf6 rescued pancreatitis caused by mi r-146a-5p downregulation by the tlr9/nlrp3 signaling pathway.Therefore, downregulation of mi r-146a-5p in the induced pancreatitis cell model promotes the progression of pancreatitis via the tlr9/traf6/nlrp3 signaling pathway.There is potential for mi r-146a-5p to serve as a diagnostic marker and therapeutic nucleic acid drug for ap.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Distribution Characteristics of ST-Segment Elevation Myocardial Infarction and Non-ST-Segment Elevation Myocardial Infarction Culprit Lesion in Acute Myocardial Infarction Patients Based on Coronary Angiography Diagnosis

This research was aimed at exploring the application value of coronary angiography (cag) based on a convolutional neural network algorithm in analyzing the distribution characteristics of st-segment elevation myocardial infarction (stemi) and non-st-segment elevation myocardial infarction (nstemi) culprit lesions in acute myocardial infarction (ami) patients.Methods.Patients with ami treated in hospital from june 2019 to december 2020 were selected as subjects.According to the results of an echocardiogram, the patients were divided into the stemi group (44 cases) and the nstemi group (36 cases).All patients received cag.All images were denoised and edge detected by a convolutional neural network algorithm.Then, the number of diseased vessels, the location of diseased vessels, and the degree of stenosis of diseased vessels in the two groups were compared and analyzed.Results.The number of patients with complete occlusion (3 cases vs.12 cases) and collateral circulation (5 cases vs.20 cases) in the nstemi group was significantly higher than that in the stemi group, and the difference was statistically significant, p &lt; 0.05 .There was a statistically significant difference in the number of lesions between the distal lad (1 case vs.10 cases) and the distal lcx (4 cases vs.11 cases), p &lt; 0.05 .There was a statistically significant difference in the number of patients with one lesion branch (1 vs.18) and three lesion branches (25 vs.12) between the two groups, p &lt; 0.05 .The image quality after the convolution neural network algorithm is significantly improved, and the lesion is more prominent.Conclusion.The convolutional neural network algorithm has good performance in dsa image processing of ami patients.Stemi and nstemi as the starting point of ami disease analysis to determine the treatment plan have high clinical application value.This work provided reference and basis for the application of the convolutional neural network algorithm and cag in the analysis of the distribution characteristics of stemi and nstemi culprit lesions in ami patients.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Prostate cancer (pca) is one of the highest frequent malignant tumors with very complicated pathogenesis.Genes of neurodegenerative diseases can influence tumor progression.But its role in the progression of pca remains unclear.The purpose of the present academic work was to identify significant genes with poor outcome and their underlying mechanism.Methods.The gse70768, gse88808, and gse134051 datasets were downloaded to screen the differentially expressed genes (degs).The deg screening criteria were as follows: p &lt; 0.05 and differential fold change log fc ≥ 1 .The common degs (co-degs) of the three datasets were obtained by the robust rank aggregation (rra) method.Gene ontology (go) function annotation and kyoto encyclopedia of genes and genome (kegg) pathway analysis were performed using r software.Protein-protein interaction (ppi) network analysis was performed for co-degs using string to screen critical genes.Differential expression and prognosis of key genes were analyzed by the online tool gene expression profiling interactive analysis 2 (gepia2).The intersection gene between key genes and neurodegenerative genes was identified by constructing a venn diagram.Results.A total of 263 co-degs were identified from the three datasets.Go analysis showed that co-degs were mainly involved in muscle contraction and blood circulation regulation.The top ten key genes were actg2, apoe, f5, cald1, myh11, myl9, mylk, tpm1, tpm2, and calm1.Gepia2 analysis showed that apoe, myh11, and mylk differ dramatically between tumor and normal tissues.These key genes are related to disease-free survival (dfs) in pca.Apoe was the intersection gene between key genes and alzheimer-related genes.Conclusion.The neurodegenerative gene apoe may be a potential prognostic and diagnostic biomarker for pca.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Digital Subtraction Angiography Image Features under the Deep Learning Algorithm in Cardiovascular Interventional Treatment and Nursing for Vascular Restenosis

The objective of this study was to explore the application value of digital subtraction angiography (dsa) images optimized by deep learning algorithms in vascular restenosis patients undergoing cardiovascular intervention and their nursing efficacy.In this study, a network model for removing artifacts was constructed based on a deep algorithm.60 patients with coronary artery restenosis were selected as the research objects, and they were randomly divided into the cta group guided by ct angiography (cta) and digital subtraction angiography (dsa) group, with 30 cases in each group.The antiartifact network model constructed based on the depth algorithm was applied to the images of cta and dsa for experiments.After cardiovascular intervention and clinical pathway nursing intervention, it was found that the diameter stenosis rate in the dsa group decreased from 65.82 ± 12.9 % to 4.7 ± 1.3 % , and the area stenosis rate decreased from 88.4 ± 14.3 % to 5.4 ± 1.7 % .During the follow-up period of 3-24 months, 3 out of 46 lesions in the dsa group showed restenosis, so the restenosis rate was 6.5%, which was significantly lower than the 18.4% in the cta group ( p &lt; 0.05 ).In the dsa group, there was 1 case of bleeding, 0 case of hematoma, 2 cases of urinary retention, and 0 case of hypotension, so the total incidence of adverse reactions was 10%, which was significantly lower than the 30% of the cta group ( p &lt; 0.05 ).The high-sensitivity c-reactive protein (hs-crp) levels of the two groups of patients were 3.58 ± 2.02 mg / l and 4.36 ± 3.11 mg / l before surgery and 3.49 ± 2.18 mg / l and 4.57 ± 3.4 mg / l after the surgery.The postoperative hs-crp level in the cta group was slightly lower than that before the surgery and the postoperative hs-crp level in the dsa group was slightly higher than that before the surgery, but they were not statistically significant ( p &gt; 0.05 ).The hs-crp level of the dsa group before and after the surgery was slightly higher than that of the cta group, but there was no significant difference ( p &gt; 0.05 ).In summary, the network model based on the deep learning algorithm can remove the artifacts in dsa images and present high-quality clear images, and convolutional neural network (cnn) algorithms had a strong ability to automatically learn features in the field of medical image processing and were worthy of being widely used and popularized.In addition, the dsa-guided intervention can reduce the rate of vascular stenosis in patients, showing low probability of postoperative restenosis and adverse reactions and a good clinical effect.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Dictionary Learning-Based Ultrasound Image Combined with Gastroscope for Diagnosis of Helicobacter pylori-Caused Gastrointestinal Bleeding

The study is aimed at evaluating the application value of ultrasound combined with gastroscopy in diagnosing gastrointestinal bleeding (gib) caused by helicobacter pylori (hp).An ultrasound combined with a gastroscopy diagnostic model based on improved k -means singular value decomposition (n-ksvd) was proposed first.86 patients with peptic ulcer (pu) and gib admitted to our hospital were selected and defined as the test group, and 86 pu patients free of gib during the same period were selected as the control group.The two groups were observed for clinical manifestations and hp detection results.The results showed that when the noise ρ was 10, 30, 50, and 70, the peak signal to noise ratio (psnr) values of n-ksvd dictionary after denoising were 35.55, 30.47, 27.91, and 26.08, respectively, and the structure similarity index measure (ssim) values were 0.91, 0.827, 0.763, and 0.709, respectively.Those were greater than those of dct dictionary and global dictionary and showed statistically significant differences versus the dct dictionary ( p &lt; 0.05 ).In the test group, there were 60 hp-positives and 26 hp-negatives, and there was significant difference in the numbers of hp-positives and hp-negatives ( p &lt; 0.05 ), but no significant difference in gender and age ( p &gt; 0.05 ).Of the subjects with abdominal pain, hp-positives accounted for 59.02% and hp-negatives accounted for 37.67%, showing significant differences ( p &lt; 0.05 ).Finally, the size of the ulcer lesion in hp-positives and hp-negatives was compared.It was found that 71.57% of hp-positives had ulcers with a diameter of 0-1 cm, and 28.43% had ulcers with a diameter of ≥1 cm.Compared with hp-negatives, the difference was statistically significant ( p &lt; 0.05 ).In conclusion, n-ksvd-based ultrasound combined with gastroscopy demonstrated good denoising effects and was effective in the diagnosis of gib caused by hp.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Clinical Study;
Diagnostic Value of Model-Based Iterative Algorithm in Tuberculous Pleural Effusion

Although there are several diagnostic modalities for tuberculous pleurisy, there is still a lack of easy, cost-effective, and rapid methods for confirming the diagnosis.In order to facilitate clinicians to diagnose patients with tuberculous pleurisy at an early stage, help patients to obtain treatment early, and reduce lung damage, it is hoped that new techniques will be available in the future to help diagnose tuberculous pleurisy rapidly in the clinic.To this end, this paper investigates the problem of bidirectional consistency based on event-triggered iterative learning.Firstly, a dynamic linearized data model of tb pleurisy intelligent system is established using compact-form dynamic linearization method, and a parameter estimation algorithm of tb pleurisy data model is proposed; then, based on this data model, an output observer and a dead zone controller are designed, and an event-triggered distributed model-free iterative learning bidirectional consistency control strategy is constructed by combining with signal graph theory.In this paper, 112 patients with pleural effusion were collected, including 76 patients with confirmed or clinically diagnosed tuberculous pleural effusion and 36 patients with nontuberculous pleural effusion.Pleural effusion t-spot.tb, blood t-spot.tb, pleural effusion xpert mtb/rif, and pleural effusion adenosine deaminase (ada) tests were performed before treatment in the included patients.The sensitivity of pleural effusion t-spot.tb was higher than that of peripheral blood t-spot.tb (76.32%, 58/76), pleural effusion xpert mtb/rif (65.79%, 50/76), and pleural effusion ada (28.95%, 22/76); the differences were statistically significant (x2 = 14.74, 25.22, and 76.45, p &lt; 0.01).The specificity of the xpert mtb/rif test for pleural effusion (100%, 36/36) was higher than that for pleural effusion t-spot.tb (77.78%, 28/36), peripheral blood t-spot.tb, and pleural effusion t-spot.tb.The sensitivity of the combined xpert mtb/rif test (64.47%, 49/76) was lower than that of the pleural effusion t-spot.tb alone (97.37%, 74/76).

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;