syedzayyan/paper_mill_benchmark huggingface dataset:
Diagnostic Value of LH Peak Value of the GnRH Stimulation Test for Girls with Precocious Puberty and Its Correlation with Body Mass Index

To analyze the diagnostic value of luteinizing hormone (lh) peak value of the gonadotropin-releasing hormone (gn rh) stimulation test for girls with precocious puberty and its correlation with body mass index (bmi).Methods.A total of 230 girls with precocious puberty who came to our hospital for testing from june 2019 to june 2021 were selected and divided into a true group ( n = 130 ) and sham group ( n = 100 ) according to the results of the gn rh stimulation test.According to the bmi, the true group was further divided into a normal group (48 cases), overweight group (43 cases), and obese group (39 cases).The gn rh stimulation test was performed on all subjects, and the basal value and peak value of lh and the basal value and peak value of follicle-stimulating hormone (fsh) were recorded.The general data and serological indexes of the true group and the sham group were compared.Indicators of the gn rh stimulation test, breast stage, bone age, bmi, uterine volume, ovarian volume, and serological indicators (leptin, sex hormone-binding protein (shbg), and adiponectin (apn)) were compared among the normal group, the overweight group, and the obese group.Results.There were no significant differences in age and breast stage between the true group and the sham group ( p &gt; 0.05 ).There were statistically significant differences in bone age, bmi, uterine volume, and ovarian volume between the two groups ( p &lt; 0.05 ).The lh base value, lh peak value, fsh base value, and fsh peak value in the true group were higher than those in the sham group, and the differences were statistically significant ( p &lt; 0.05 ).Roc curve analysis showed that the auc of lh peak value in diagnosing girls with precocious puberty was 0.973, which was higher than 0.895, 0.875, and 0.912 of lh base value, fsh base value, and fsh peak value, respectively.There were statistically significant differences in lh base value, lh peak value, fsh base value, breast development stage, bone age, bmi, shbg, leptin, and apn among the normal group, overweight group, and obese group ( p &lt; 0.05 ), but there were no significant differences in fsh peak value, uterine volume, and ovarian volume among the three groups ( p &gt; 0.05 ).There was a negative correlation between bmi, lh peak value, and fsh base value ( p &lt; 0.05 ), but there was no significant correlation between bmi and fsh peak value ( p &gt; 0.05 ).Conclusion.The lh peak value of the gn rh stimulation test has high diagnostic value for girls with precocious puberty, and bmi is negatively correlated with the lh peak value of cpp children.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Diagnostic Value of IGFBP-2 in Predicting Preeclampsia before 20 Weeks of Pregnancy: A Prospective Nested Case-Control Study

Backgroud.Preeclampsia (pe) is a critical type of hypertensive disorder of pregnancy, which seriously affects maternal and infant health.The etiology of pe is unclear, and there is no clear prediction model.In this study, new biomarkers were identified before 20 weeks of gestation to construct an early pe prediction model.Purpose.To identify novel biomarker insulin-like growth factor binding protein-2 (igfbp-2) associated with preeclampsia (pe) before 20 weeks of gestation and to explore the predictive value of plasma igfbp-2 in pe.Methods.A prospective nested case-control investigation involving 122 pe patients and 122 normal controls (nc) that were matched 1 : 1 in terms of age and week of pregnancy was carried out in guangzhou women and children’s medical center (guangzhou, china, 2018030306) from april 2016 to december 2019.At 8 to 20 weeks, blood samples from the mother were taken.To calculate the correlations, univariate conditional logistic regression was employed.Results.Herein, 12 clinical indices were significantly different between the pe and nc groups (uric acid (ua), cystatin c (cys c), aspartate aminotransferase (ast), glutamyl transpeptidase (γ-gt), total bilirubin (tb), prothrombin time (pt), red blood cell (rbc), hematocrit (hct), red cell distribution width (rdw), platelets (plt), mean platelet volume (mpv), and thrombocytocrit (pct)).Compared with the nc group ( 36.79 ± 19.91 pg/m l), the expression level of igfbp2 in the pe group ( 19.76 ± 19.40 pg/m l) before 20 weeks of pregnancy was significantly decreased ( p &lt; 0.01 ).Two high-risk factors were found to be significantly associated with pe independently of confounders: anemia 4.35 (2.20-8.45) ( p &lt; 0.01 ) and cesarean section history 8.25 (2.67-26.67) ( p &lt; 0.01 ).As a result of the univariate logistic regression analysis, the following three variables were included in the final logistic regression model.: y = − 18.841 − 0.085 × igfbp ‐ 2 + 0.630 × rdw + 0.165 × ast + 0.863 × mpv .In comparison to igfbp-2 alone as an independent predictor of pe ( auc = 0.897 , 95% ci 0.830–0.964), the model’s discriminatory power was considerably higher ( auc = 0.953 , 95% ci 0.911–0.995).Conclusion.Plasma igfbp-2 before 20 weeks of pregnancy combined with high-risk factors and routine blood indexes has a high early predictive value for pe.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Diagnostic Value of CT Window Technique for Primary Omentum Infarction

The diagnostic value of ct window width technique in primary omentum infarction was evaluated by this study.Methods.The abdominal ct data of 32 patients with clinically diagnosed abdominal omentum infarction were retrospectively selected and analyzed.The fixed window position was 50 hu, and the window width was 135 hu, 250 hu (abdomen), 350 hu (mediastinum), and 500 hu, respectively.The detection rate of lesions was analyzed and compared.Results.Window widths of 135 hu, 250 hu (abdomen), 350 hu (mediastinum), and 500 hu have a detection rate of 12.5% (4 cases), 62.5% (20 cases), 100% (32 cases), 100% (32 cases) for abdominal omental lesions, respectively.However, 500 hu showed worse abdominal bowel and parenchymal organs than 350 hu.Conclusion.According to the comprehensive image quality, the ideal window width for diagnosis of primary omentum infarction is 350hu (mediastinal) window width.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Diagnostic Performance of Atherosclerotic Carotid Plaque Neovascularization with Contrast-Enhanced Ultrasound: A Meta-Analysis

S.To evaluate the diagnostic performance of contrast-enhanced ultrasound (ceus) for atherosclerotic carotid plaque neovascularization.Methods.The electronic databases like pub med, embase, ovid, and web of science were used to search for the relevant studies, which are involved in the evaluation of the diagnostic parameters of qus for atherosclerotic carotid plaque neovascularization.Review manager 5.4 and stata 14.0 were used to estimate the pooled diagnostic value of ceus.Forest plots, sensitivity analysis, and deeks’ funnel plots were performed on the included studies.Results.Ten studies eventually met the final inclusion criteria.For diagnostic performance, cues showed that the pooled values of sensitivity, specificity, positive likelihood odds ratios, negative likelihood odds ratios, and diagnostic odds ratios were 0.83 (95% ci 0.78-0.86), 0.77 (95% ci 0.68-0.84), 3.61 (95% ci 2.59-5.03), 0.23 (95% ci 0.18-0.28), and 16.02 (95% ci 10.02-25.60), respectively.The estimate of the area under curve (auc) was 0.85 (95% ci 0.82-0.88).Conclusion.Our research supported that ceus had high sensitivity and specificity in the diagnosis of atherosclerotic carotid plaque neovascularization.More high-quality prospective multicenter studies focusing on the accuracy of ceus for carotid atherosclerotic plaque should be performed to verify our conclusions.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Meta-Analysis;
Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm

Dilated cardiomyopathy (dcm) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction.It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation.Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac mri.However, it takes time for cardiologists to measure dcm-related parameters to decide whether patients have this disease.We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing dcm.In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac mr image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart.Machine learning classifiers use extracted parameters as input to predict normal people and patients with dcm, among which random forest classifier gives the highest accuracy.The results show that the proposed system can be effectively utilized to detect and diagnose dcm automatically.The experimental results suggest the capabilities and advantages of the proposed method to diagnose dcm.A small amount of sample input can generate results comparable to more complex methods.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Clinical Study;
Diagnostic Accuracy of Procalcitonin for Bacterial Infection in Liver Failure: A Meta-Analysis

The purpose of our studies was to systematically assess the accuracy and clinical value of plasma calcitonin in patients with liver failure complicated with bacterial infection.In this study, we included prospective observational studies or randomized controlled trials on pct.The quality of the studies was evaluated using the quality assessment of diagnostic accuracy studies-2 (quadas-2) tool.Heterogeneity, pooled diagnostic odds ratio (dor), pooled sensitivity, pooled specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, the area under the summary receiver operating characteristic curve (sroc), and metaregression analysis were performed using stata16.0 software.Consequently, the studies revealed substantial heterogeneity (i2 = 96, 95% confidence interval (95% ci) = 94–99).The results of meta-analysis using random effect models suggested that the combined dor was 10.67 (95% ci = 3.73–30.53).In addition, the threshold effect analysis showed that the threshold effect was 0.23 and the correlation coefficient was −0.48, indicating that there was no threshold effect.In the forest map, the dor of each study and the combined dor are not distributed along the same line, and q = 2.2 × 1014, p ≤ 0.001 .Furthermore, the metaregression analysis of pct study design, bacterial infection site, and mean age displayed that the p values were &gt;0.05.The combined sensitivity was 0.77 (95% ci = 0.54–0.90), the combined specificity was 0.76 (95% ci = 0.70–0.82), the combined positive likelihood ratio was 3.25 (95% ci = 2.33–4.52), the combined negative likelihood ratio was 0.30 (95% ci = 0.14–0.67), and the combined auc was 0.80 (95% ci = 0.76–0.83).In conclusion, pct has moderate diagnostic value for adult liver failure complicated with bacterial infection, and it is a better auxiliary diagnostic index for liver failure with bacterial infection.However, the results of procalcitonin must be carefully interpreted combined with medical history, physical examination, and microbiological assessment.

Journal: Bioinorganic Chemistry and Applications (2021)
Publication types: Meta-Analysis;
Development of NLP-Integrated Intelligent Web System for E-Mental Health

As the covid-19 pandemic continues, the need for a better health care facility is highlighted more than ever.Besides physical health, mental health conditions have become a significant concern.Unfortunately, there are few opportunities for people to receive mental health care.There are inadequate facilities for seeking mental health support even in big cities, let alone remote areas.This paper presents the structure and implementation procedures for a mental health support system combining technology and professionals.The system is a web platform where mental health seekers can register and use functionalities like nlp-based chatbot for personality assessment, chatting with like-minded people, and one-to-one video conferencing with a mental health professional.The video calling feature of the system has emotion detection capabilities using computer vision.The system also includes downloadable prescription facilities and a payment gateway for secure transactions.From a technological aspect, the conversational nlp-based chatbot and computer vision-powered video calling are the system’s most important features.The system has a documentation facility to analyze the mental health condition over time.The web platform is built using react.js for the frontend and express.js for the backend.Mongo db is used as the database of the platform.The nlp chatbot is built on a three-layered deep neural network model that is programmed in the python language and uses the nltk, tensor flow, and keras sequential api.Video conference is one of the most important features of the platform.To create the video calling feature, express.js, socket.io, and socket.io-client have been used.The emotion detection feature is implemented on video conferences using computer vision, haar cascade, and tensor flow.All the implemented features are tested and work fine.The targeted users for the platform are teenagers, youth, and the middle-aged population.Mental health-seeking is still considered taboo in some societies today.Apart from basic established facilities, this social dilemma of undergoing treatment for mental health is causing severe damage to individuals.A solution to this problem can be a remote platform for mental health support.With this goal in mind, this system is designed to provide mental health support to people remotely from anywhere worldwide.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (hcc) is one of the most common diseases that threaten millions of lives annually.Evidence supports that bile acid (ba) affects hcc through inflammation, dna damage, or other mechanisms.Methods.A total of 127 ba-associated genes were analyzed in hcc tumor and nontumor samples using the cancer genome atlas data.Genes correlated to the prognosis of patients with hcc were identified using univariate and multivariate cox regression analyses.Furthermore, a prediction model with identified genes was constructed to evaluate the risk of patients with hcc for prognosis.Results.Out of 26 genes with differential expressions between the hcc and nontumor samples, 19 and 7 genes showed upregulated and downregulated expressions, respectively.Three genes, npc1, abcc1, and slc51b, were extrapolated to construct a prediction model for the prognosis of patients with hcc.Conclusion.The three-gene prediction model was more reliable than the pathological staging characters of the tumor for the prognosis and survival of patients with hcc.In addition, the upregulated genes facilitating the transport of bas are associated with poor prognosis of patients with hcc, and genes of de novo synthesis of bas benefit patients with hcc.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Detection of BRCA1/2 Mutation and Analysis of Clinicopathological Characteristics in 141 Cases of Ovarian Cancer

Breast cancer susceptibility genes 1 and 2 (brca1 and brca2) are known biomarkers for hereditary ovarian cancer (oc).However, a comprehensive association study between brca1/2 mutation spectrum and clinicopathological characteristics in chinese ovarian cancer patients has not been performed yet to our best knowledge.To fill in this gap, we collected brca1/2 sequencing data and clinical information of 141 oc patients from fujian cancer hospital between april 2018 and march 2020.The clinical information includes the age of onset, figo staging, pathological types, serum 125 detection level, lymph node metastasis, distant metastasis, the expression of ki67, and disease history of the patient and his/her family.We then studied their associations by software sci py 1.0.As a result, we detected pathogenic and potentially pathogenic brca1/2 mutations in 27 out of 141 patients (19.15%).Among the 27 patients with mutations, the major type of mutation was frameshift, which was observed in 12 patients (44.4%).Most of the mutation sites were distributed on exons 10 and 11, accounting for 48.1% (13/27) and 22.2% (6/27), respectively.In terms of histological classification, high-grade serous adenocarcinoma accounted for 79.43% of the 141 samples.The brca1/2 mutation group was all high-grade serous adenocarcinoma, accounting for 24.1% (27/112) of this group.The incidence of pathogenic mutation in brca1 and brca2 was 15.7% (19/112) and 7.27% (8/112), respectively.Univariate analysis showed that there was no significant difference between patients with brca1/2 mutation and others in age-of-onset, figo stage, pathological types, serum ca125 level, lymph node metastasis, the expression of ki67, and personal and family disease history.However, there are significant differences between patients with brca1/2 mutation and others in distant metastasis rate ( p &lt; 0.002 ).In addition, the brca1/2 mutation rate in 141 ovarian cancer patients was similar to those reported in other studies in china.Nearly one-quarter of high-grade serous carcinomas had brca1/2 mutations.In conclusion, our study indicated that patients with brca1/2 mutations were more likely to undergo distant metastasis, and brca1/2 mutation detection should be performed for patients with high-grade serous adenocarcinoma to guide the selection of clinical treatment options.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

Nursing medical histories are copied randomly, leading to the occurrence of incorrectly altered histories, and medical errors are on the rise year by year.Moreover, erroneous falsification of nursing medical history is a challenging issue that needs to be urgently addressed in domestic nursing medical history.In this manuscript, a caregiver association method, which is based on a multiattribute decision model, is proposed which is specifically designed for the caregiver association selection problem in intelligent caregiving decision making.In this mode, through the selection and modeling of key performance indicators and the design of combined weights, the purpose of regulating the priority of attributes is achieved.At the same time, a comprehensive evaluation index is designed to realize the joint optimization of multiple attributes.Simulation results show that the proposed nursing workstation can not only tradeoff the quality of nursing staff experience and system performance but also balance the distribution of nursing work and effectively reduce the association delay and system energy consumption.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

This work takes the production and usage scenarios of the data glove as the research object and studies the method of applying the flexible sensor to the data glove.Many studies are also devoted to exploring the transplantation of flexible sensors to data gloves.However, this type of research still lacks the display of specific application scenarios such as gesture recognition or hand rehabilitation training.A small amount of experimental data and theoretical analysis are difficult to promote the development of flexible sensors and flexible data gloves design schemes.Therefore, this study uses the self-made flexible sensor of the research group as the core sensing unit to produce a flexible data glove to monitor the bending changes of the knuckles and then use it for simple gesture recognition and rehabilitation training.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Deep Scale-Variant Network for Femur Trochanteric Fracture Classification with HP Loss

Achieving automatic classification of femur trochanteric fracture from the edge computing device is of great importance and value for remote diagnosis and treatment.Nevertheless, designing a highly accurate classification model on 31a1/31a2/31a3 fractures from the x-ray is still limited due to the failure of capturing the scale-variant and contextual information.As a result, this paper proposes a deep scale-variant (dsv) network with a hybrid and progressive (hp) loss function to aggregate more influential representations of the fracture regions.More specifically, the dsv network is based on the res net and integrated with the designed scale-variant (sv) layer and hp loss, where the sv layer aims to enhance the representation ability to extract the scale-variant features, and hp loss is intended to force the network to condense more contextual clues.Furthermore, to evaluate the effect of the proposed dsv network, we carry out a series of experiments on the real x-ray images for comparison and evaluation, and the experimental results demonstrate that the proposed dsv network could outperform other classification methods on this classification task.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer.In recent years, both morbidity and mortality rates, particularly in our country, are getting higher and higher.At present, diagnosis of colorectal cancer, specifically in the emergency department of a hospital, is based on the doctor's pathological diagnosis, and it is heavily dependent on the doctor's clinical experience.The doctor's workload is heavy, and misdiagnosis events occur from time to time.Therefore, computer-aided diagnosis technology is desperately needed for colorectal pathological images to assist pathologists in reducing their workload, improve the efficiency of diagnosis, and eliminate misdiagnosis.To address these issues, a gland segmentation of emergency colorectal pathology images and diagnosis of benign and malignant pathology is presented in this paper.Initially, a multifeatured auxiliary diagnosis is designed to enable diagnosis of benign and malignant diagnosis of emergency colorectal pathology.The proposed algorithm constructs an svm-enabled pathological diagnosis model which is based on contour, color, and texture features.Additionally, their combination is used for pathological benign and malignant pathological diagnosis of two types of data sets d1 (original pathological image dataset) and d2 (dataset that has undergone glandular segmentation) diagnosis.Experimental results show that the proposed pathological diagnosis model has higher diagnostic accuracy on d2.Among these datasets, svm based on the multifeature fusion of contour and texture achieved the highest diagnostic accuracy rate, i.e., 83.75%, which confirms that traditional image processing methods have limitations.Diagnosing benign and malignant colorectal pathology in an emergency is more difficult and must be treated on a priority basis.Finally, an emergency colorectal pathology diagnosis method, which is based on deep convolutional neural networks such as cifar and vgg, is proposed.After configuring and training process of the two networks, trained cifar and vgg network models are applied to the diagnosis of both datasets, i.e., d1 and d2, respectively.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;