syedzayyan/paper_mill_benchmark huggingface dataset:
Causes of Emergency Bleeding after Nonsurgical Periodontal Therapy in Adult Periodontitis Patients: A Retrospective Analysis

Emergency gingival uncontrollable bleeding after nonsurgical periodontal therapy (nspt) could be caused by a variety of factors; local oral factors are the main cause of gingival bleeding in most patients.Because the doctor will do a good job of evaluating the patient’s physical condition before nonsurgical periodontal therapy.This study is subjected to evaluate the possible factors associated with emergency uncontrollable bleeding within 24-48 hours after nspt.Material and methods.A total of fifty-eight patients with emergency bleeding after nspt in the past four years were enrolled.The related factors in patients, such as age, gender, clotting function, systemic diseases, and baseline periodontitis severity, were analyzed.The site-related factors, such as tooth type, tooth distribution, and alveolar bone resorption at the bleeding site, were compared.The possible relationship of the parameters to the causes of emergency bleeding with nspt was also evaluated.Results.Gingival bleeding after nspt was registered.In this retrospective study, a total of 58 patients were selected.There were 29 males and 29 females, aged from 20 to 67 years old, with an average age of 35.21 ± 10.09 years.Among them, 8.6% were over 50 years old, and 91.4% were under 50 years old.Completed evaluations were performed in 15.5% gingivitis and 84.5% periodontitis.The causes of emergency bleeding after nonsurgical periodontal therapy in this study were residual subgingival calculus or granulation tissue in 63.79% of cases: severe gingival inflammation, 29.32%; gum trauma, 3.45%; and poor compliance, 3.45%.The therapy method before bleeding includes supragingival scaling accounted for 72.4% and subgingival scaling accounted for 27.6%.23 cases of horizontal absorption at the bleeding site accounted for 39.66%, and 35 cases of angular absorption accounted for 60.34%.Bleeding of maxillary posterior teeth accounted for 34.48%; mandibular anterior teeth accounted for 15.52%; mandibular anterior teeth accounted for 8.62%; and mandibular posterior teeth accounted for 18.97%; multiple sites accounted for 22.41%; eliminating residual subgingival calculus and granulation tissue were the main and most effective hemostatic methods, 86.21%.Conclusion.Residual subgingival calculus or granulation tissue and severe gingival inflammation were the main causes of emergency gingival bleeding after nonsurgical periodontal therapy.Severe gingival inflammation causing emergency bleeding was more common in maxillary posterior teeth areas.Angular alveolar bone resorption was more likely to cause bleeding than horizontal resorption.Careful debridement of residual subgingival calculus and granulation tissue was the main hemostatic method.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

One of the most common visual disorders is cataracts, which people suffer from as they get older.The creation of a cloud on the lens of our eyes is known as a cataract.Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition.These symptoms frequently result in difficulty doing a variety of tasks.As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness.This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset.In this observation, four different convolutional neural network (cnn) meta-architectures, including inception v3, inception resnet v2, xception, and dense net121, were applied by using the tensor flow object detection framework.By using inception resnet v2, we were able to attain the avant-garde in cataract disease detection.This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset.This model also has a sensitivity of 96.55% and a specificity of 100%.In addition, the model greatly minimizes training loss while boosting accuracy.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Breastfeeding and Reduced Risk of Breast Cancer: A Systematic Review and Meta-Analysis

Evidences which prove relation between breastfeeding women and risk of breast cancer have been limited.Objective.A meta-analysis was carried out on the basis of published literature from clinical trials and studies among different parts of the world.Methods.Studies were analyzed and extracted using prisma flowchart.Rev man 5.4.1 was used for analyzing the extracted data.Included studies were fully cited texts with complete information about studies, trails conducted for risk of breast cancer, and breastfeeding correlations.Results.Menarche age, family history, lactation duration, and menopausal status have a strong effect on the risks of breast cancer.Family history studies concluded that for 95% ci, the risk ratio was 2.66 (2.00, 3.52).Conclusion.Findings have suggested that family history and lactation duration affect the risks of breast cancer.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Meta-Analysis;
Bioinformatics Analysis and Experimental Verification Identify Downregulation of COL27A1 in Poor Segmental Congenital Scoliosis

Congenital scoliosis (cs) represents the congenital defect disease, and poor segmental congenital scoliosis (pscs) represents one of its types.Delayed intervention can result in disability and paralysis.In this study, we would identify the core biomarkers for pscs progression through bioinformatics analysis combined with experimental verification.Methods.This work obtained the gse11854 expression dataset associated with somite formation in the geo database, which covers data of 13 samples.Thereafter, we utilized the edge r of the r package to obtain degs in this dataset.Then, go annotation, kegg analyses, and do annotation of degs were performed by “cluster profiler” of the r package.This study performed lasso regression for screening the optimal predicting factors for somite formation.Through rna sequencing based on peripheral blood samples from healthy donors and pscs cases, we obtained the rna expression patterns and screen out degs using the r package deseq2.The present work analyzed col27a1 expression in pscs patients by the rt-pcr assay.Results.A total of 443 genes from the gse11854 dataset were identified as degs, which were involved in bp associated with dna replication, cc associated with chromosomal region, and mf associated with atpase activity.These degs were primarily enriched in the tgf-β signaling pathway and spinal deformity.Further, lasso regression suggested that 9 degs acted as the signature markers for somite formation.We discovered altogether 162 degs in pscs patients, which were involved in bp associated with cardiac myofibril assembly and mf associated with structural constituent of muscle.However, these 162 degs were not significantly correlated with any pathways.Finally, col27a1 was identified as the only intersected gene between the best predictors for somite formation and pscs-related degs, which was significantly downregulated in pscs patients.Conclusion.This work sheds novel lights on degs related to the pscs pathogenic mechanism, and col27a1 is the possible therapeutic target for pscs.Findings in this work may contribute to developing therapeutic strategies for pscs.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Benefits of Contrast-Enhanced Ultrasonography to the Differential Diagnosis of TI-RADS 4-5 Thyroid Nodules

The early detection, diagnosis, and treatment of thyroid cancer are of great significance to the prognosis for patients.This study was aimed at exploring the benefits of contrast-enhanced ultrasonography (ceus) to the differential diagnosis of thyroid nodules classified as ti-rads class 4 or 5.Method.A total of 46 patients with ti-rads 4-5 thyroid nodules admitted in peking university people’s hospital from january 2019 to january 2021 were selected to study.The sensitivity, specificity, accuracy, and positive and negative predictive values of conventional ultrasonography (us) and conventional ultrasonography combined with contrast-enhanced ultrasonography (us + ceus) in the diagnosis of benign and malignant thyroid nodules were compared by referring to the results of the surgical pathology report, which is seen as the “gold standard” for diagnosis, followed by the construction of receiver operating characteristic curves (rocs).Result.Among 57 thyroid nodules, there were statistically significant differences between benign and malignant thyroid nodules in terms of echogenicity, margin characteristics, aspect ratio, and calcification ( p &lt; 0.01 ).In the case of ceus, there was no statistically significant difference among contrast agent perfusion patterns in distinguishing between benign and malignant thyroid nodules ( p &gt; 0.05 ).However, there were statistically significant differences among different enhancement degrees, enhanced borders, and enhancement patterns.By comparing the ceus results of ti-rads 4-5 thyroid nodules with the results of pathology report, the malignancy rate was found to pathology report results, the malignancy rate was 53.85% in ti-rads class 4 thyroid nodules and 100.00% in ti-rads class 5 thyroid nodules.Among thyroid nodules diagnosed using us, 6 benign nodules were misdiagnosed as malignant and 7 malignant nodules were misdiagnosed as benign.Among those diagnosed using us + ceus, 2 benign nodules were misdiagnosed as malignant and 2 malignant nodules were misdiagnosed as benign.The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of un + ceus significantly outperformed those of un alone in diagnosing thyroid nodules ( p &lt; 0.05 ).The roc curve analysis showed that the area under the curve (auc) derived from us + ceus was 0.849, while the auc from us was only 0.726.Conclusion.Using us + ceus to diagnose thyroid nodules classified as ti-rads category 4 or 5 can further improve distinguishing between benign and malignant nodules.The ceus is of important value to clinical applications as it can provide effective supplementary information and quantitative analysis for the differentiation between benign and malignant thyroid nodules.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Association between the Predicted Value of APACHE IV Scores and Intensive Care Unit Mortality: A Secondary Analysis Based on EICU Dataset

The evidence regarding the relationship between acute physiological and chronic health assessment (apache) iv scores and emergency intensive care unit (eicu) mortality in patients following organ transplantation remains controversial.The purpose of this study was to investigate the relationship between apache iv score and eicu mortality.Methods.Data from 391 american men and women admitted to the eicu after undergoing organ transplants including heart, bone marrow, liver, kidney, lung, and pancreas in the united states.We used this data to analyze the relationship between apache iv scores and in-hospital mortality in the postoperative eicu.The primary endpoint was icu hospitalization mortality after organ transplantation.The entire study data was extracted from the eicu database and uploaded to the data dryad website.Results.Interaction tests indicate age, respiratory failure, and hormone use can modify the association between apache iv and eicu mortality.A stronger association of apache and mortality can be observed at &lt;60 years old, no respiratory failure, and no use of hormones.In contrast, there was no association between respiratory failure, hormone use, apache, and icu mortality in patients over 60 years of age.Conclusion.When using the apache score for risk stratification of critically ill patients after transplantation, the patient’s age, respiratory failure, and use of hormones should be taken into account.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Artificial Intelligence-Based MRI in Diagnosis of Injury of Cranial Nerves of Premature Infant and Its Correlation with Inflammation of Placenta

The study focused on the effects of artificial intelligence algorithms in magnetic resonance imaging (mri) for diagnosing cranial nerve inflammation of placenta and the correlation between cranial nerve injury with placental inflammation was explored.The subjects were selected from 132 premature infants in the hospital.According to the pathological examination of placenta, 81 cases with chorioamnionitis were taken as the experimental group and 51 cases without chorioamnionitis were taken as the control group.The incidence of cranial nerve injury in different groups of premature infants was analyzed by mri diagnosis based on the principal component analysis (pca) artificial intelligence algorithm, so as to analyze the correlation between cranial nerve injury and placental inflammation in premature infants.It was found that when the pca artificial intelligence algorithm was incorporated into mri examination of cranial nerve injury of premature infant, the a (accuracy), p (precision), r (recall), and f 1 values under the pca algorithm were 92%, 93.75%, 90%, and 92.87%, respectively.The a , p , r , and f 1 of the control group were 54%, 54.1%, 52%, and 53.03%, respectively; there were statistically significant differences between the two groups, p &lt; 0.05.As for the correlation of placental inflammation and cranial nerve injury, the positive detection rate of the experimental group was 53.09%, and the positive detection rate of the control group was 15.69%, and the difference was statistically significant, p &lt; 0.05.In conclusion, the pca artificial intelligence algorithm has high effectiveness and high accuracy in auxiliary diagnosis of premature brain nerve injury, and placental inflammation greatly increases the chance of premature infant suffering from brain nerve injury.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Research Article;
Artificial Intelligence Algorithm-Based Computerized Tomography Image Features Combined with Serum Tumor Markers for Diagnosis of Pancreatic Cancer

The objective of this study was to analyze the value of artificial intelligence algorithm-based computerized tomography (ct) image combined with serum tumor markers for diagnoses of pancreatic cancer.In the study, 68 hospitalized patients with pancreatic cancer were selected as the experimental group, and 68 hospitalized patients with chronic pancreatitis were selected as the control group, all underwent ct imaging.An image segmentation algorithm on account of two-dimensional (2d)-three-dimensional (3d) convolution neural network (cnn) was proposed.It also introduced full convolutional network (fcn) and unet network algorithm.The diagnostic performance of ct, serum carbohydrate antigen-50 (ca-50), serum carbohydrate antigen-199 (ca-199), serum carbohydrate antigen-242 (ca-242), combined detection of tumor markers, and ct-combined tumor marker testing (ct-stum) for pancreatic cancer were compared and analyzed.The results showed that the average dice coefficient of 2d-3d training was 84.27%, which was higher than that of 2d and 3d cnns.During the test, the maximum and average dice coefficient of the 2d-3d cnn algorithm was 90.75% and 84.32%, respectively, which were higher than the other two algorithms, and the differences were statistically significant ( p &lt; 0.05 ).The penetration ratio of pancreatic duct in the experimental group was lower than that in the control group, the rest were higher than that in the control group, and the differences were statistically significant ( p &lt; 0.05 ).Ca-50, ca-199, and ca-242 in the experimental group were 141.72 u/m l, 1548.24 u/m l, and 83.65 u/m l, respectively, which were higher than those in the control group, and the differences were statistically significant ( p &lt; 0.05 ).The sensitivity, specificity, positive predictive value, and authenticity of combined detection of serum tumor markers were higher than those of ca-50, ca-199, and ca-242, and the differences were statistically significant ( p &lt; 0.05 ).The results showed that the proposed algorithm 2d-3d cnn had good stability and image segmentation performance.Ct-stum had high sensitivity and specificity in diagnoses of pancreatic cancer.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Applications of Bladder Cancer Data Using a Modified Log-Logistic Model

In information science, modern and advanced computational methods and tools are often used to build predictive models for time-to-event data analysis.Such predictive models based on previously collected data from patients can support decision-making and prediction of clinical data.Therefore, a new simple and flexible modified log-logistic model is presented in this paper.Then, some basic statistical and reliability properties are discussed.Also, a graphical method for determining the data from the log-logistic or the proposed modified model is presented.Some methods are applied to estimate the parameters of the presented model.A simulation study is conducted to investigate the consistency and behavior of the discussed estimators.Finally, the model is fitted to two data sets and compared with some other candidates.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Application of Data Mining Technology in Public Welfare Sports Education in the Era of Artificial Intelligence

Data mining refers to extracting the implicit prediction information from a massive dataset.It has very application prospects.Some data mining tools can develop things.The purpose of this article mainly discusses the public welfare sports education in the artificial intelligence era.The article discusses the research background and significance, development of education data mining, and decision tree technology and enumerates the application of education data mining in real life.The concept of educational data mining is given, and several common typical decision tree algorithms and their connections and differences are described; then, the concepts of multivalue decision tables and decision trees are discussed in detail.This article aims to build a nonprofit physical education system to manage and analyze the attendance data of students’ physical health assessment, so as to improve the enthusiasm of students to exercise, such as bp neural network, decision tree classification algorithm, and cluster analysis, discusses the calculation and analysis process of the relevant body side data of the sports teaching platform, and emphatically discusses and analyzes the application effect of data mining technology in public welfare sports teaching.In addition, this article has built a public welfare physical education system, allowing us to clearly understand various factors that affect students’ exercise and the relationship between various project indicators.Based on these data, educators can adjust technical means.Experimental results can efficiently and conveniently understand the pass rate of students in various sports.The pass rates of students in the six tests, grip strength, and sitting forward bending were 58%, 65%, 78%, 78%, 85%, and 65%, respectively.Using mathematical methods and computer technology, we can dig out valuable education management information from massive education data, so as to provide a reference for improving school enrollment.

Journal: Mobile Information Systems (2021)
Publication types: Research Article;
Anterior Cruciate Ligament Reconstruction with Hybrid Graft versus Autograft: A Systematic Review and Meta-Analysis

The standard surgical treatment for acl tear is acl reconstruction.There is a debate of a choice between autograft or hybrid graft for treating acl reconstruction.The purpose of this paper is to compare both case scenarios.Methods.A lot of libraries were searched like pub med, cochrane, and embase library for clinical trials which were then compared and analyzed via meta-analysis.The systematic review and meta-analysis were performed as per prisma guidelines, and rev man software was used to perform the meta-analysis.Results.We analyzed 6 studies where patients of both autograft and hybrid graft were studied.The study outcomes, graft failures, graft diameters, reoperations, and so on were compared via forest plot and funnel plot.No significant difference was noted in both cases.Conclusions.In this meta-analysis, the performance of both autograft and hybrid graft was similar.Though the diameters were larger in hybrid, other factors also had an influence like graft failures, reoperations, and age at reconstruction which must be further investigated in detail.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Meta-Analysis;
Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in shanghai, china, using location-based social network (lbsn) data and a variety of spatiotemporal analytic techniques.The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose.We obtained the geolocation data from sina weibo, one of the biggest renowned chinese microblogs (weibo).The composed data is converted to geographic information system (gis) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (kde) assessment.We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others.The location cataloguing is accomplished via the use of facts about the characteristics of physical places.The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown.However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior.These findings may be used in destination management, metro planning, and the creation of digital cities.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;