syedzayyan/paper_mill_benchmark huggingface dataset:
Data Fusion Algorithm for Myocardial Proteomics and Its Research in Sports

Sport is a type of comprehensive activity that the human body consciously engages in to improve physical fitness.Proteomics is a comprehensive technology dedicated to the study of all protein profiles expressed by a species, individual organ, tissue, or cell under specific conditions and specific times.Proteomics is a science that studies the protein composition of cells, tissues, or organisms and their changing laws with proteomics as the research object.Related technologies are now widely used in sports and other fields.The purpose of this article is to study myocardial proteomic technology and its application in sports.During the research process, the main methods used in this study are literature survey and controlled experiment.The results achieved and the problems in this field, followed by selecting 30 sd rats into 3 groups for control experiments.The results of the study showed that among the three groups of rats, the left ventricular ejection fraction of the sham operation group was the highest, which was 7.7% and 4.6% higher than that of the operation group and the model group, respectively.The operation group had the highest left ventricular short axis shortening rate, and the left ventricle diastolic inner diameter is the longest.It can be seen that myocardial proteomics can accurately reflect the heart condition of rats.In addition, the length, diastolic velocity, and diastolic time of cardiomyocytes of the three groups of rats were different.Among them, the cardiomyocytes of the operation group had the longest time and the longest diastolic time, which were 37.1% and 8.5% higher than those of the sham operation group and the model group.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
CT Image Features of the FBP Reconstruction Algorithm in the Evaluation of Fasting Blood Sugar Level of Diabetic Pulmonary Tuberculosis Patients and Early Diet Nursing

The study was aimed at exploring the application value of the ct image based on a filtered back projection (fbp) algorithm in the diagnosis of patients with diabetes complicated with tuberculosis and at analyzing the influence of dietary nursing on patients with diabetes complicated with tuberculosis.In this study, the fbp algorithm was used to optimize ct images to effectively obtain reconstructed roi images.Then, the deviation from measurement values of reconstructed images at different pixel levels was analyzed.138 patients with diabetes complicated with tuberculosis were selected as research subjects to compare the number of lung segments involved and the ct imaging manifestations at different fasting glucose levels.All patients were divided into the control group (routine drug treatment) and observation group (diet intervention on the basis of drug treatment) by random number table method, and the effect of different nursing methods on the improvement of patients’ clinical symptoms was discussed.The results showed that the distance measurement value decreased with the increase in pixel level, there was no significant difference in the number of lung segments involved in patients with different fasting glucose levels ( p &gt; 0.05 ), and there were statistically significant differences in the incidence of segmental lobar shadow, bronchial air sign, wall-less cavity, thick-walled cavity, pulmonary multiple cavity, and bronchial tuberculosis in patients with different fasting glucose levels ( p &lt; 0.05 ).Compared with the control group, 2 h postprandial blood glucose level in the observation group was significantly improved ( p &lt; 0.05 ), there was a statistical significance in the number with reduced pleural effusion and the number with reduced tuberculosis foci in the two groups ( p &lt; 0.05 ), and the level of hemoglobin in the observation group was 7.1 ± 1.26 , significantly lower than that in the control group ( 8.91 ± 2.03 , p &lt; 0.05 ).It suggested that the changes of ct images based on the fbp reconstruction algorithm were correlated with fasting blood glucose level.Personalized diet nursing intervention can improve the clinical symptoms of patients, which provides a reference for the clinical diagnosis and treatment of patients with diabetes complicated with tuberculosis.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
CT Image Feature under Intelligent Algorithm in the Evaluation of Continuous Blood Purification in the Treatment and Nursing of Pulmonary Infection-Caused Severe Sepsis

This study was to explore the ct image features based on intelligent algorithm to evaluate continuous blood purification in the treatment of severe sepsis caused by pulmonary infection and nursing.50 patients in the hospital were selected as the research objects.Convolutional neural network algorithm was used to segment ct images of severe sepsis caused by pulmonary infection.They were randomly divided into 25 cases of experimental group and 25 cases of control group.The experimental group was given continuous blood purification treatment, combined with comprehensive nursing.The control group was given routine treatment and basic nursing.Fasting plasma glucose (fpg) and fasting insulin (fin), interleukin-6 (il-6), tumor necrosis factor (tnf-α), high-sensitivity c-reactive protein (hs-crp) levels, cd3+, cd4+, cd4+/cd8+ levels, icu monitoring time, malnutrition inflammation score (mis), and incidence of adverse events were compared between the two groups before and after treatment.There was no difference in fpg and fin between the two groups before treatment.After treatment, the fpg and fin of the experimental group were lower than those of the control group, and there was statistical significance ( p &lt; 0.05 ).There was no difference in il-6, tnf-α, and hs-crp between the two groups before treatment.After treatment, il-6, tnf-α, and hs-crp in the experimental group were lower than those in the control group.There was no difference in the percentage of cd3+, cd4+, and cd4+/cd8+ between the two groups before treatment.After treatment, the cd3+, cd4+, and cd4+/cd8+ in the experimental group were higher than those in the control group.The icu monitoring time, mis, and incidence of adverse events in the experimental group were lower than those in the control group ( p &gt; 0.05 ).Convolutional neural network algorithm can accurately identify and segment ct images of patients with severe sepsis, which has high clinical application value.Continuous blood purification therapy can effectively control blood glucose level, improve immune function, and reduce the content of inflammatory factors in patients with severe sepsis caused by pulmonary infection.Effective nursing measures can improve the therapeutic effect.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Cost-Effectiveness of Life Cycle Cost Theory-Based Large Medical Equipment

The purpose of this study is to use the life cycle cost theory to analyze the efficiency of large medical equipment in hospitals, so as to implement life cycle cost (lcc) management and solve the current problems in hospitals.The analysis model of cost benefit of large medical equipment is established, and the cost-effectiveness of 4 large medical equipment between 2019 and 2021 is investigated and analyzed.In terms of the data in each information system of hospitals, the utilization of large medical equipment is quantitatively evaluated and analyzed by life cycle theory.The results show that the revolution 256 row has the highest revenue of 113.29%.The annual depreciation of signa 3.0 t hdxt is the highest, amounting to 4,160,000 yuan.However, there is lack of quality control and preventive maintenance of most equipment during use.The cost and benefit of large medical equipment in hospitals are analyzed, which demonstrates that signa 3.0 t hdxt shows better effectiveness.Too high hospital warranty cost reflects the weak maintenance strength of hospital engineering technicians.The fundamental point of the maintenance and management of large medical equipment is to strengthen the performance evaluation of medical engineering technicians.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Correlation of Impaired NF- k B Activation in Sepsis-Induced Acute Lung Injury (ALI) in Diabetic Rats

The allergic lung inflammation is reduced in the diabetic rats which can be restored by treating it with insulin.As observed in multiple studies, the diabetic patients are more vulnerable to infections and their inflammatory reactions.There are confirmations on insulin and its effects control the inflammatory reactions.Objective.This study was performed to understand the correlation of impaired nf-k b activation in sepsis-induced acute lung injury (ali) in diabetic rats.Material and methods.Streptozotocin was used for induction of diabetes and sepsis was induced by colon ligation puncture surgery.Post 6 hours of clp, the lungs in the groups were analyzed for cell infiltration using broncho-alveolar lavage.The lungs were removed for histopathological analysis at the end of study where the bronchioles, alveoli, and edema were analyzed and compared.Cell expressions quantified by the help of antibodies and inflammatory events were analyzed.Results.Diabetic rats developed mild acute lung injury due to the suppression of activation of nf-k b in alveolar macrophages.Conclusion.Even the diabetic rats were more susceptible to sepsis in comparison to the nondiabetic rats, but the nf-k b suppression has a major role to play in the faint symptoms of ali.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Correlation between Breast Ultrasound Microcalcification and the Prognosis of Breast Cancer

Breast cancer is a common gynecological disease, and its incidence and mortality are higher than those of other common malignant tumors.Breast ultrasound technology is a new surgical method, which has the advantages of reducing postoperative complications, improving the quality of life of patients, and improving the prognosis of patients.Breast microcalcification is a new method for the treatment of tumors.Its mechanism is that the proliferation of breast cancer cell walls increases the inflammatory factors in the cancer tissues and enhances the formation of tumors and peripheral vascular thrombosis.Breast microcalcification in the treatment of breast cancer patients will have a more significant impact compared to ordinary antibiotics alone.For this reason, the microcalcification performance of breast ultrasound is worthy of study, and related research on prognosis is also indispensable.The purpose of this study is to improve the understanding of the ultrasound manifestations of breast cancer microcalcification and the prognosis of breast cancer.This article mainly applied statistical analysis as well as experimental and survey methods to conduct breast ultrasound examination on 100 patients and analyzed the ultrasound manifestations of breast cancer mcs from three aspects: location, shape, and distribution.The experimental results show that there is no correlation between the location and distribution of breast cancer mcs and the diameter of the cancer foci, but there is a certain correlation between the morphology (non-gravel-like calcification) and the diameter of the cancer foci (&gt;5 cm).Among them, her-2 overexpression accounted for 11.9% in the grit-like mcs group and 51% in the non-grit-like mcs group.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Corneal Biomechanics Computational Analysis for Keratoconus Diagnosis

For machine learning techniques to be used in early keratoconus diagnosis, researchers aimed to find and model representations of corneal biomechanical characteristics from exam images generated by the corvis st.Image segments were used to identify and convert anterior data into vectors for representation and representation of apparent posterior surfaces, apparent pachymetry, and the composition of apparent anterior data in images.Chained (batch images) and simplified with wavelet, the vectors were also arranged as 2d histograms for deep learning use in a neural network.An interval of 0.7843 to 1 and a significance level of 0.0157 were used in the scoring, with the classifications getting points for being as sensitive as they could be while also being as precise as they could be.In order to train and validate the used data from examination bases in europe and iraq, in grades i to iv, researchers looked at data from 686 healthy eyes and 406 keratoconus-afflicted eyes.With a score of 0.8247, sensitivity of 89.49%, and specificity of 92.09%, the european database found that apparent pachymetry from batch images applied with level 4 wavelet and processed quickly had the highest accuracy.This is a 2d histogram of apparent pachymetry with a score of 0.8361, which indicates that it is 88.58 percent sensitive and 94.389% specific.According to the findings, keratoconus can be diagnosed using biomechanical models.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Construction of Rheumatoid Arthritis Risk Prediction and Medical Image Applications from Rheumatoid Factor Levels

To study the value of rheumatoid factor (rf) levels in the risk assessment of rheumatoid arthritis (ra) and combined hypertension and diabetes mellitus (dm) and construct ra risk prediction and medical image applications from rheumatoid factor levels.Methods.A total of 249 ra patients who were treated in the first people’s hospital of yunnan province, and another 149 non-ra people were selected as the controls.The clinical data and the detection results of serum circulating rf_ig a, rf_ig g, and rf_ig m were collected.The receiver operating curve (roc) and logistic regression were used to analyze the value of rf levels in the risk assessment of ra and combined hypertension and dm.Results.After adjusting for age, bmi, smoking, drinking, hypertension, and diabetes, logistic regression analysis showed that rf_ig a positive, rf_ig g positive, and rf_ig m positive were all independent risk factors for ra ( p &lt; 0.05 ).The area under the curve (auc) of circulating rf_ig a, rf_ig g, and rf_ig m levels in predicting ra was 0.79 (95% ci: 0.74-0.83, p &lt; 0.001 ), 0.73 (95% ci: 0.68-0.78, p &lt; 0.001 ), and 0.87 (95% ci: 0.84-0.91, p &lt; 0.001 ), respectively.The auc for predicting ra was 0.88 (95% ci: 0.85-0.92, p &lt; 0.001 ) when combined detection of circulating rf_ig a, rf_ig g, and rf_ig m levels in peripheral blood.After adjusting for age and sex, logistic regression analysis showed that rf_ig a positive, rf_ig g positive, and rf_ig m positive were not independent risk factors for dm in ra patients ( p &gt; 0.05 ).Conclusion.The levels of serum circulating rf_ig a, rf_ig g, and rf_ig m are valuable indicators for predicting the risk of ra, but not for the risk of ra complicated with hypertension and dm.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

The area of medical diagnosis has been transformed by computer-aided diagnosis (cad).With the advancement of technology and the widespread availability of medical data, cad has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created.Ultrasound (us) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems.However, owing to patient movement and equipment constraints, certain artefacts make identification of these us pictures challenging.To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required.Hence, we proposed a three-stage image segmentation method using u-net and iterative random forest classifier (irfc) to detect orthopedic diseases in ultrasound images efficiently.Initially, the input dataset is preprocessed using enhanced wiener filter for image denoising and image enhancement.Then, the proposed segmentation method is applied.Feature extraction is performed by transform-based analysis.Finally, obtained features are reduced to optimal subset using principal component analysis (pca).The classification is done using the proposed iterative random forest classifier.The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score.The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Conservative Treatment and Rehabilitation Training for Rectus Femoris Tear in Basketball Training Based on Computer Vision

Computer vision is an emerging artificial intelligence subject, whose purpose is to make computers have the same ability to perceive and understand image semantic information as humans.Computer vision technology is based on high-performance computers, which can obtain massive amounts of information and data in a short period of time and use intelligent algorithms to perform high-speed data processing on the information, which is conducive to the integration of information related to product design, production process management, etc.Due to the rapid development of visual sensing technology, computer technology, and image processing technology, computer vision technology has been widely used in the fields of food, medicine, construction, chemical industry, electronics, packaging, and automobiles.This article uses computer vision technology to compare four conservative treatments and rehabilitation training for rectus femoris in basketball training and analyze the best rehabilitation treatment for rectus femoris tear.The experimental results show that the average electroacupuncture plus muscle stretching exercise group after treatment has an average emg value of 55.49, an average muscle strength rating of five, an average motor function score of 23.45, and an average treatment recovery time of 11.6 days.This group has the best treatment effect.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Clinical Study;
Computer-Assisted Quantitative Analysis of Skeletal Muscles of Snowboarding Parallel Giant Slalom Athletes after Exercise Based on Artificial Intelligence and Complex Networks

The snowboarding project has the characteristics of high risk and high technical level.The current publicity level is not high, and the number of participants is also very limited.Another potential advantage medal breakthrough project that is expected to be achieved in the winter olympics has received a lot of attention, creating favorable opportunities for the promotion and development of this project in china.The event requires good special physical support, skeletal muscle contraction is the body to produce motor function, and special physical training and recovery are key factors for athletes to obtain excellent results in the competition.This article is aimed at performing ultrasonic quantitative analysis on the skeletal muscles of skiers after exercise based on artificial intelligence and complex networks and at studying the skeletal muscle conditions of snowboarders after exercise, so as to provide a certain theoretical basis for coaches in future scientific training.Based on a large amount of literature, this paper uses variational optical flow calculation and split bregman method to solve the typical hs model, l1-l2 model, and l1-high-order model, respectively, and uses the motion estimation method to describe the movement of muscles.An experiment was designed to collect ultrasound images of the gastrocnemius and quadriceps muscles during contraction.In addition, a motion target positioning algorithm was used to obtain some motion parameters, which provided direct help for athletes in rationally arranging training load and scientific training.The experimental results in this paper show that the muscle motion features extracted from the ultrasound sequence images can quantitatively express a lot of important information about the skeletal muscle motion form and function and have potential practical application value.And the different invariants of each type of ski trajectory vary greatly, floating between 1.5429 and 7.6759.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

The research is aimed at investigating computed tomography (ct) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer.The images of ordinary ct detection were improved.In this study, 60 bladder cancer patients were selected and performed with ordinary ct detection, and the detection results were processed by ct based on deep learning algorithms and compared with pathological diagnosis.In addition, western blot technology was used to detect the expression of glucose ceramide synthase (gcs) in the cell membrane of tumor tissues and normal tissues of bladder.The comparison results found that, in simple ct clinical staging, the coincidence rates of t1 stage, t2a stage, t2b stage, t3 stage, and t4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of ct clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis ( p &lt; 0.05 ).In the clinical staging of algorithm-based ct test results, the coincidence rates of t1 stage and t2a stage were 50.01% and 91.65%, respectively; and those of t2b stage, t3 stage, and t4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis ( p &gt; 0.05 ).Therefore, it could be concluded that the algorithm-based ct detection results were more accurate, and the use of ct scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value.In addition, it was found that the expression level of gcs in normal bladder tissues was much lower than that in bladder cancer tissues.This indicated that the changes in gcs were closely related to the development and prognosis of bladder cancer.Therefore, it was believed that gcs may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.

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