syedzayyan/paper_mill_benchmark huggingface dataset:
Analysis of the Effectiveness of Transradial Access Puncture in the Application of Complications and Comfort after Cerebral Angiography

To investigate the analysis of the effectiveness of transradial access puncture in the application of complications and comfort after cerebral angiography.Methods.Retrospectively analyzed 80 patients who met the inclusion and exclusion criteria and were randomly divided into the control group (femoral artery group n = 40) and test group (radial artery group n = 40) using a random number table from january 2021 to january 2022 admitted to the department of neurology and department of vascular interventions in our hospital and compared the incidence of postoperative puncture site bleeding, time to first postoperative urination, and incidence of postoperative urinary retention and postoperative changes in comfort level.Results.There was 1 case of postoperative puncture site bleeding in the test group and 6 cases of postoperative puncture site bleeding in the control group, with statistically significant differences ( p &lt; 0.05); the time to first urination in the test group (62.47) was significantly better than that in the control group (85.97), with statistically significant differences ( p &lt; 0.05); there were 0 cases of urinary retention in the test group and 6 cases in the control group, with statistically significant differences ( p &lt; 0.05).The gcq scores of patients in the test group were significantly higher than those in the control group, and the difference was statistically significant ( p &lt; 0.05).Conclusion.Transradial access puncture has a good clinical effect and can effectively reduce the complication rate of patients, which is worth promoting.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;
Analysis of the Effect of the Communication Ability of Nurses in Outpatient Infusion Room on the Treatment Experience of Patients and Their Families

To observe the effect of nursing communication ability in outpatient infusion room on the treatment experience of outpatients and their families and analyze whether improving nurses’ communication skills can reduce the incidence of doctor-patient disputes.Methods.According to the results of the communication ability survey, the outpatient nurses in our hospital are divided into groups a ( score &lt; 65 ), b ( 65 ≤ score &lt; 80 ), c ( 80 ≤ score &lt; 95 ), and d ( score ≥ 95 ).Taking outpatients and their families in our hospital from september 2019 to december 2020 as the research objects, they were divided into corresponding groups according to the nurses who served them.Finally, there were 73 cases in group a, 85 in group b, 92 in group c, and 65 in group d.The mental toughness scale (connor-davidson resilience scale chinese (cd-risc)) scores and herth hope index (hhi) scores of the four groups of patients were compared, and the correlation between nurse communication scores and cd-risc scores and hhi scores was analyzed.The incidence of negative events (patient-nurse disputes, complaints, etc.) and the results of nursing satisfaction surveys during the visits of the four study groups were also counted.Results.The four groups of cd-risc and hhi total scores were ranked from highest to lowest as groups d, c, b, and a, respectively ( p &lt; 0.05 ).There was a positive correlation between the resilience dimension scores of the family members’ cd-risc and the toughness, strength, optimism dimension scores of the hhi and the nurses’ communication dimension scores ( r = 0.191 -0.472, p &lt; 0.05 ).Besides, the incidence of negative events was higher in group a than in the other three groups, while the satisfaction with care was lower than in the other three groups ( p &lt; 0.05 ).Conclusion.Improving nurses’ communication skills in infusion room nursing quality management can improve the mental toughness and hope level of patients’ accompanying family members, which is conducive to controlling their emotions and avoiding the occurrence of dispute incidents.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Analysis of Surveying and Mapping Method of Landownership and Environmental Resources Confirmation Based on GPS Technology

The establishment of the agricultural and forestry land management information system is predicated on the acquisition of spatial positioning data of agricultural and forestry land, i.e., the surveying and mapping of agricultural and forestry land.It also serves as the central component of the overall management information system.It is necessary to find solutions to the issues of large plot areas and far‐reaching boundary lines in light of the current situation of land right confirmation.With its high efficiency, the use of gps technology for land right confirmation can not only increase work productivity and speed up construction but also lower operating costs.The method of confirming landownership through surveying and mapping is studied in this paper using gps technology.With respect to various land types, various classification methods have varying degrees of classification accuracy.This method is one of the three algorithms that classify green land, which includes grassland, cultivated land, and forest land; however, it does not clearly distinguish between forest land and grassland, with forest land having an average classification accuracy of 58.69 percent.The average accuracy of classifying forest land is 73.14 percent.The classification accuracy of forest land using this method is 88.45 percent, which is a relatively high percentage.It can be seen that this method is more accurate in identifying minute differences between ground objects.This is demonstrated by comparing the accuracy of forest land classification with that of other methods.A lot of surveying and mapping work has been done on the assumption that urban control networks have been established in many cities.The most ideal option is to combine gps technology with high‐precision photogrammetry.

Journal: Journal of Environmental and Public Health (2022)
Publication types: Research Article;
Analysis of Risk Factors for Rheumatoid Arthritis in Yunnan: A Small-Scale Case-Control Study

To study the risk factors of rheumatoid arthritis (ra) in yunnan and provide reference for its clinical prevention and treatment.Methods.From january 2014 to february 2022, a total of 249 ra patients who were admitted to the first people’s hospital of yunnan province were selected and 149 healthy people were selected as the controls.The medical records, clinical data of blood test results of anticyclic citrullinated polypeptide (anti-ccp) antibody, antikeratin antibody (aka), and antiperinuclear factor (apf) were collected.Logistic regression and receiver operating curve (roc) were used to analyze the correlation between blood anti-ccp antibody, aka, and apf and the occurrence of ra.Results.Univariate analysis showed that the age, proportion of hypertension, and diabetes patients in ra patients were significantly different from those in the control group (p &lt; 0.05), but there was no statistical difference in sex (p &gt; 0.05).The area under the curve (auc) of the serum anti-ccp antibody level in the diagnosis of ra was 0.76 (95% ci: 0.72–0.81, p &lt; 0.01), the sensitivity was 85.23%, the specificity was 61.45%, and the cutoff value was 18.07au/m l.The positive rates of aka (33.73%) and apf (43.37%) in the ra patients were significantly higher than those in the controls (4.03% and 6.04%), and the differences were statistically significant (p &lt; 0.001).After adjusting for age, sex, hypertension, and diabetes, logistic regression analysis showed that anti-ccp positivity, aka positive, and apf positive were all independent risk factors for ra (or = 20.24, 95% ci: 9.36–43.77, p &lt; 0.01; or = 4.33, 95% ci: 1.62–11.60, p &lt; 0.01; or = 5.28, 95% ci: 2.33–11.99, p &lt; 0.01).Conclusion.Anti-ccp positive, aka positive, and apf positive are independent risk factors for the occurrence of ra.Serum anti-ccp level has high sensitivity but low specificity in the diagnosis of ra.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Analysis of Perioperative Nursing Intervention Effect of Cerebrovascular Intervention Patients Based on Intelligent Internet of Things

The postoperative results of cerebrovascular surgery patients have been successfully used in medical practice using the internet.The results obtained through data analysis were used in the study.So far, 120 patients who underwent cerebrovascular surgery from february 2018 to december 2018 have been enrolled.The selected class was divided into two groups: 60 psychiatric patients, a control group and an observation group.The former is medical treatment and the latter is postoperative treatment.Results : the results showed that the blood pressure of control group was lower than that of control group, and the incidence of adverse events was lower than that of control group ( p &lt; 0.05).Meanwhile, the average hospitalization rate of cerebrovascular disease patients in control group was lower than that in control group ( p &lt; 0.05).Conclusion : for patients with cerebrovascular disease, postoperative nursing can reduce the incidence of postoperative complications, reduce the risk of surgery, and improve the effect of surgery.Acute ischemic stroke refers to a kind of clinical syndrome caused by abnormal blood supply in the brain, resulting in ischemia, hypoxic brain tissue necrosis, and focal or comprehensive neurological deficiency.Among them, progressive cerebral infarction accounted for about 20~35%, and most occurred in the early stage of the disease (48~72)h.

Journal: BioMed Research International (2022)
Publication types: Clinical Study;
Analysis of Nursing Effect of Comprehensive Nursing Intervention on Hemodialysis Patients with Uremia

Uremic pruritus affects 50–90% of hemodialysis patients, making it one of the most frequent medical issues among this group.Pruritus can lead to skin infections, desquamation, pathological skin changes, sleep problems, anxiety, depression, and social problems.The epidemic of uremia pneumonia has put a lot of stress on hemodialysis patients, resulting in negative feelings.As a result, during the prevention and control of uremia, rigorous management and improved nursing intervention are critical.During the uremia disease outbreak, this study will examine and assess the impact of clinically refined nurse intervention on patients receiving maintenance hemodialysis.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Research Article;
Analysis of miRNA Associated with Coronary Artery Calcification

Cardiovascular diseases seriously endanger human physical and mental health and life safety, to investigate correlation between mi r-let-7b and mi r-29b and coronary artery calcification of various patients.At present, real-time fluorescence quantitative pcr (q rt-pcr) was used to detect the expression levels of plasma mi r-let-7b and mi r-29b in patients with coronary artery calcification and noncoronary artery calcification and to analyze whether the expression levels of mi r-let-7b and mi r-29b were different between the two groups.It was shown that there was no significant difference in the expression of mi r-let-7d-3p between the two groups.But the expression of mi r-29b in the observation group was significantly lower than that in the control group.Taken together, mi r-29b might be a risk factor for coronary artery calcification and may be a marker for early diagnosis of coronary artery calcification.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Analysis of Maternal and Infant Outcomes and Related Factors of Vaginal Delivery of Second Pregnancy after Cesarean Section

To explore the clinical effect of different delivery methods and the safety of vaginal delivery of second pregnancy after cesarean section and analyze the related factors.Methods.A total of 738 eligible pregnant women who underwent cesarean section from september 2018 to august 2020 were randomly selected from our hospital.Among them, 527 pregnant women successfully delivered vaginally were selected as the observation group, and 211 pregnant women who failed vaginal delivery were selected as the control group.To analyze the factors that influence the success of vaginal delivery of second pregnancy after cesarean section and compare the outcomes of mother and infant in two groups.Results.There was no significant difference in age, prenatal body mass index (bmi), and thickness of lower uterine segment between the two groups ( p &gt; 0.05 ).There were significant differences in fetal head orientation, fetal abdominal circumference, fetal biparietal diameter, uterine height, premature rupture of membranes, bishop score, and epidural anesthesia during labor between the two groups ( p &lt; 0.05 ).Multivariate logistic regression analysis showed that fetal abdominal circumference, fetal head orientation, bishop score, and epidural anesthesia during labor were independent factors affecting the success of vbac ( p &lt; 0.05 ).There was no significant difference in the incidence of uterine rupture between the two groups ( p &gt; 0.05 ).The amount of postpartum hemorrhage in the observation group was significantly lower than that in the control group ( p &lt; 0.05 ).There was no significant difference in apgar score, asphyxia rate, and hospitalization rate between the two groups ( p &gt; 0.05 ).There was no significant difference in the incidence of complications between the two groups ( p &gt; 0.05 ).Conclusion.There are many factors that influence the success of vaginal delivery after cesarean section.Through prenatal comprehensive evaluation of vaginal delivery conditions, we can guide the parturient to choose a reasonable mode of delivery, reduce the incidence of complications, and improve the outcome of mother and baby.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis.The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes.It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases.To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and powell.Through the linear search strategy of golden section method and powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions.According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently.The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display.The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization.The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
An Evaluation Model of Agricultural Entrepreneurial Ecological Environment Quality Oriented to Public Health Topic Using the Optimized Neural Network Algorithm

As the primary industry, agriculture is a prerequisite for human survival and a basic industry of the national economy.As a result, the growth of agriculture is vital for the nation’s socioeconomic development.The construction of ecological civilization and the reform and innovation of farmers’ digital entrepreneurial behavior in the region are intimately correlated with the agricultural eco‐environmental quality.Therefore, it is crucial for us to improve control and maintenance of the agricultural eco‐environment.A requirement for directing the sustainable growth of the agricultural economy is evaluating and identifying the quality of the regional eco‐environment and the issues it faces.In view of this, the study employs jiangsu province as a research example, builds a comprehensive index evaluation system, and uses the modified bp neural network to evaluate the agricultural eco‐environmental quality in the target area between 2010 and 2020.First, the evaluation system is constructed from four dimensions: ecological resource status, environmental pollution degree, eco‐environmental protection efforts, and agricultural development level, and 14 influencing factors are then screened out as specific secondary evaluation indicators.Second, a ga‐bp model will be applied to comprehensively assess the quality of the agricultural eco‐environment in the province over the past 10 years.The comprehensive score of the province’s agricultural eco‐environmental quality was calculated, and the quality level was classified by this.Meanwhile, the indicators’ scores were paired with an analysis of each contributing factor’s degree of change.The findings indicate that from 2010 to 2020, jiangsu province’s agricultural eco‐environment demonstrated a favorable trend, a reasonably high‐quality level, and a tendency of continuous improvement from bad to good.The results of each indicator measuring the quality of the agricultural environment fluctuated, but overall they showed an upward trend.Finally, in order to offer a reference for agricultural eco‐environment control and protection and a good ecological environment for farmers’ digital entrepreneurship in the near future, countermeasures and suggestions are proposed for the restoration of the agricultural eco‐environment in jiangsu province referring to the results of the research and the problems already present in the area.

Journal: Journal of Environmental and Public Health (2022)
Publication types: Research Article;
An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Lung cancer has the highest death rate of any other cancer in the world.Detecting lung cancer early can increase a patient’s survival rate.The corresponding work presents the method for improving the computer-aided detection (cad) of nodules present in the lung area in computed tomography (ct) images.The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data.After the analysis, a model is proposed consisting of three main steps.In the first step, threshold values and component labeling of 3d components were used to segment the lung volume.In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming.It also selects 2d and 3d features from the candidate segmented node.In the final step, the selected features are used to train the svm and classify the nodes and classify the non-nodes.To assess the performance of the proposed framework, experiments were performed on the lidc data set.As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 fp per scan with a sensitivity of 95%.

Journal: Journal of Healthcare Engineering (2023)
Publication types: Research Article;
An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications.Classification of human activities is one of the emerging research areas in the field of computer vision.It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring.In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis.Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ml) and soft computational algorithms.However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames.This paper proposes an effective automated approach using feature fusion and ml methods.It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps.Two available public benchmark datasets are utilized to train, validate, and test ml classifiers of the developed approach.The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively.Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
An Auxiliary Scoring Model for Patients with Acute Pulmonary Embolism Complicated with Atrial Fibrillation Was Established Based on Random Forests

The purpose of this study was to explore the establishment of an auxiliary scoring model for patients with acute pulmonary embolism (ape) complicated with atrial fibrillation (af) based on random forest (rf) and its application effect.A retrospective analysis was performed on the general data, underlying diseases, laboratory indicators, and cardiac indicators of 100 patients with ape admitted to our hospital from 2018 to 2021.The occurrence of atrial fibrillation in patients with pulmonary embolism was taken as a categorical variable, and the general data, underlying diseases, laboratory indicators, and cardiac indicators were taken as input variables.Then, the risk auxiliary scoring model for patients with ape complicated with af was established based on rf and logistic regression.Finally, the accuracy, sensitivity, specificity, recall rate, accuracy, f1 value, and the receiver operator characteristic (roc) curve were used to evaluate the predictive value of the models.After statistical analysis, the optimal node value was 3 and the optimal number of decision trees was 500 in the rf model.The importance of predictors in descending order were hcy, diabetes mellitus, ft3 level, ua level, left atrial diameter, hypertension, and smoking history.The prediction accuracy of the rf model was 0.934, sensitivity 0.966, specificity 0.876, recall rate 0.9660, accuracy 0.934, and f1 value 0.950.The logistic regression model prediction accuracy was 0.816, sensitivity 0.915, specificity 0.125, recall rate 0.902, accuracy 0.811, and f1 value 0.896.The rf model and logistic regression prediction model auc values were 0.984 and 0.883, respectively.From this, we conclude that the rf model was better than the logistic regression model in predicting af in ape patients.So, the rf model had the clinical application value.

Journal: Emergency Medicine International (2022)
Publication types: Clinical Study;