syedzayyan/paper_mill_benchmark huggingface dataset:
Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer

The incidence of cancer is increasing today, particularly lung and chest cancer.Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed.The goal of the proposed study is to investigate the value of automated breast volume scanning (abus) in conjunction with contrast‐enhanced ultrasonography (ceus) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (nac) in treating the disease.For the research study, information on 98 patients who had nac and surgery in the breast surgery department of the shaanxi provincial cancer hospital has been gathered.All patients have received four cycles of nac and underwent conventional ultrasound (huss), ceus, abus, and pathological examination.At the same time, receiver operating characteristic (roc) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and nac efficacy evaluation results.The study of this paper is totally based on the data collected from shaanxi provincial cancer hospital.The statistical and computational analyses are performed on the data collected for drawing inferences.When the findings are compared to the results of the pathological examination, huss has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of p &lt; 0.05.abus combined with ceus has shown no considerable differences in correlation study.Except for negative likelihood ratio, the diagnostic performance indexes of ceus+ abus are substantially higher than hhus with p &lt; 0.05.Roc curve analysis is also performed which shows that ceus and abus combination has higher precision in the analysis of breast cancer.Abus pooled with ceus shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images

Cancer has a disproportionately large influence on the death rate of adults.A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness.Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body.Nevertheless, manual diagnosis may be time‐consuming and subjective owing to the wide range of interobserver variability induced by the enormous number of medical imaging data.This variability is caused by the fact that medical imaging data are collected.Because of this, the process of accurately diagnosing a patient could become more difficult.To execute jobs that included machine learning and the interpretation of complicated imagery, cutting‐edge computer technology was necessary.Since the 1980s, researchers have been working on developing a computer‐aided diagnostic system that would help medical professionals in the early diagnosis of various malignancies.According to the most recent projections, prostate cancer will be discovered in the body of one out of every seven men at some time throughout the course of their life.It is unacceptable how many men are being told that they have prostate cancer, and the condition is responsible for the deaths of a rising number of men every year.Because of the high quality and multidimensionality of the mri pictures, you will also need a powerful diagnosis system in addition to the cad tools.Since it has been shown that cad technology is beneficial, researchers are looking at methods to improve the accuracy, precision, and speed of the systems that use it.The effectiveness of cad technology has been shown.This research proposes a strategy that is both effective and efficient for the processing of images and the extraction of features as well as for machine learning.This work makes use of mri scans and machine learning in an effort to detect prostate cancer at an early stage.Histogram equalization is used while doing the preliminary processing on photographs.The image’s overall quality is elevated as a result.The fuzzy c means approach is used in order to segment the images.Using a gray level cooccurrence matrix (glcm), it is feasible to extract features from a dataset.The knn, random forest, and ada boost classification algorithms are used in the classification process.

Journal: BioMed Research International (2023)
Publication types: Research Article;
Development of Niacinamide/Ferulic Acid-Loaded Multiple Emulsion and Its In Vitro/In Vivo Investigation as a Cosmeceutical Product

Multiple emulsions have the ability to incorporate both lipophilic and hydrophilic actives in the same preparation and facilitate permeation of active ingredients through skin.The current study was aimed at formulating niacinamide/ferulic acid‐loaded stable multiple emulsion (mnf) and its in vitro / in vivo characterization as a cosmeceutical product.Methods .Both the compounds were evaluated for their radical scavenging potential by the dpph method and ftir analysis.Then, placebo and active formulations were prepared using a double emulsification method and were investigated for stability testing (changes in color, odor, and liquefaction on centrifugation, p h, and globule size) for a period of three months.Afterwards, mnf was investigated for in vitro sun protection factor, rheological studies, entrapment efficiency, zeta potential, zeta size, and ex vivo permeation.Moreover, after ensuring the hypoallergenicity and safety, it was also checked for its cosmeceutical effects on human skin using noninvasive biophysical probes in comparison with placebo.Results .Results demonstrated that mnf showed a non‐newtonian behavior rheologically and both mnf and placebo were stable at different storage conditions.Entrapment efficiency, zeta potential, and zeta size were 93.3%, ‐5.88 m v, and 0.173 μ m, respectively.Moreover, melanin, sebum, and skin erythema were significantly reduced while skin elasticity and hydration were improved.Conclusion .It is evident that niacinamide and ferulic acid can be successfully incorporated in a stable multiple emulsion which has potent cosmeceutical effects on human skin.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN

Computer tomography is an extensively used method for the detection of the disease in the subjects.Basically, computer‐aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system.Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process.This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique.By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description.Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi.With the unsupervised machine learning, the extracted features are used for various applications.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (hcc) is one of the most lethal cancers worldwide, which is associated with a variety of risk factors.Cancer stem cells are self‐renewal cells, which can promote the occurrence and metastasis of tumors and enhance the drug resistance of tumor treatment.This study aimed to develop a stemness score model to assess the prognosis of hepatocellular carcinoma (hcc) patients for the optimization of treatment.The single‐cell sequencing data gse149614 was downloaded from the geo database.Then, we compared the gene expression of hepatic stem cells and other hepatocytes in tumor samples to screen differentially expressed genes related to stemness.R package “cluster profiler” was used to explore the potential function of stemness‐related genes.We then constructed a prognostic model using lasso regression analysis based on the tcga and gse14520 cohorts.The associations of stemness score with clinical features, drug sensitivity, gene mutation, and tumor immune microenvironment were further explored.R package “rms” was used to construct the nomogram model.A total of 18 stemness‐related genes were enrolled to construct the prognosis model.Kaplan‐meier analysis proved the good performance of the stemness score model at predicting overall survival (os) of hcc patients.The stemness score was closely associated with clinical features, drug sensitivity, and tumor immune microenvironment of hcc.The infiltration level of cd8 + t cells was lower, and tumor‐associated macrophages were higher in patients with high‐stemness score, indicating an immunosuppressive microenvironment.Our study established an 18 stemness‐related gene model that reliably predicts os in hcc.The findings may help clarify the biological characteristics and progression of hcc and help the future diagnosis and therapy of hcc.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences.The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required.Accurate cancer‐type prediction is essential for cancer diagnosis and treatment.Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes.The convolution neural network (cnn) and neural pattern recognition (npr) approaches are used in this study paper to detect and predict the type of cancer.Different convolution neural networks (cnns) have been proposed by various researchers up to this point.Each model concentrated on a certain set of parameters to simulate the expression of genes.We have developed a novel cnn‐npr architecture that predicts cancer type while accounting for the tissue of origin using high‐dimensional gene expression inputs.The 5000‐person sample of the 1‐d cnn integrated with npr is trained and tested on the gene profile, mapping with various cancer kinds.The proposed model’s accuracy of 94% suggests that the suggested combination may be useful for long‐term cancer diagnosis and detection.Fewer parameters are required for the suggested model to be efficiently trained before prediction.

Journal: BioMed Research International (2023)
Publication types: Research Article;
Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five‐year survival rate is very less.The majority of cases of this condition may be traced back to pancreatic cancer.Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage.The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people.This article presents detection of pancreatic cancer in ct scan images using machine pso svm and image processing.The gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images.The k means algorithm uses a partitioning technique to separate the image into its component parts.The process of identifying objects in an image and determining the regions of interest is aided by image segmentation.The pca method is used to extract important information from digital photographs.Pso svm, naive bayes, and ada boost are the algorithms that are used to perform the classification.Accuracy, sensitivity, and specificity of the pso svm algorithm are better.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Detection and Prediction of Peripheral Arterial Plaque Using Vessel Wall MR in Patients with Diabetes

S .To evaluate the predictive performance of a newly developed delay alternating with nutation for tailored excitation (dante) pulse sequence for detecting lower extremity artery wall morphology and distribution in patients with peripheral artery disease (pad) with diabetes.Methods .Seventy‐four pad patients diagnosed according to 2011 who criteria were enrolled, who has diabetic diagnosis by 1999 who diabetes criteria.All patients received sequential dante, t2wi, dante‐enhance, and ce‐mra scans.The images consisted of three parts: the iliac artery (segment 1), femoral artery (segment 2), and popliteal artery (segment 3).Regions of interest (rois) were drawn on vessels, muscle, and background, and multiple imaging metrics compared between modalities, including image quality score, image noise, signal‐to‐noise ratio (snr), and contrast‐to‐noise ratio (cnr).In the images with a score greater than 2, the lumen area (la), total vessel area (tva), and vessel thickness (vt) can be identified using semiautomatic image analysis vessel morphology parameters.Results .All 222 arterial segments were successfully analyzed from 71 patients, after exclusion of three subjects with poor image quality (iq &lt; 2) in segment 3.There were 54 diabetic and 17 nondiabetic patients.Quantitative analysis shows that the cnr difference between diabetic patients and nondiabetic patients was statistically significant for the same segment, while there was no significant difference among the three segments of snr and cnr.There were a total of 54 diabetics with plaque distribution data, which showed that la of segments 1 and 2 was higher than that of segment 3.The vwi of segments 1 and 2 was lower than segment 3.Diabetic was associated with vascular wt 3 and wa3, which increased by 0.23 and 0.83 units on average compared without diabetic foot, respectively.Diabetic foot was associated with vascular wt 3, which increased by 0.37 units on average compared without diabetic foot.The incidence of segment 3 plaques was higher than that of segment 1.The incidence of the left and right plaques was different.Conclusions .Mr imaging using the dante and multicontrast sequence could evaluate plaque morphology, and distribution of lower extremities and the occurrence of diabetic foot development are closely related; it may predict occurrence of pad with diabetic foot.

Journal: BioMed Research International (2021)
Publication types: Research Article;
Design and Characterization of Agarose/HPMC Buccal Films Bearing Ondansetron HCl In Vitro and In Vivo: Enhancement Using Iontophoretic and Chemical Approaches

The study was aimed at designing and characterizing the ondansetron hydrochloride (ond) bearing agarose (ag), and hydroxypropyl methyl cellulose (hpmc) mucoadhesive buccal films employing glycerol as a plasticizer.The buccal delivery of ondansetron hydrochloride was remarkably boosted by employing physical (iontophoresis) and chemical enhancement approaches (chemical penetration enhancers).To explore the influence of different formulation components, i.e., agarose, hydroxypropyl methyl cellulose (hpmc), and glycerol on various evaluating parameters, i.e., tensile strength, swelling index, ex vivo mucoadhesion time, and subsequently on in vitro drug release, a d‐optimal design was opted.A buccal film bearing ond was mounted on bovine buccal mucosa for ex vivo permeation studies and impact of chemical and physical enhancement techniques on the permeation profile was also analysed.A linear release profile was revealed in in vitro drug release of ond over 60 minutes and outcomes ascertained the direct relationship between hpmc content and in vitro drug release and inverse relationship was depicted by ag content.The ftir and dsc thermal analysis was executed to determine the physicochemical interactions and results exposed no chemical interactions between drug and polymers.The drug (ond) appeared as tiny crystals on smooth film surface during scanning electron microscopy (sem) analysis.A notable enhancement in permeation flux, i.e., 761.02 μ g/min of ond during ex vivo permeation studies was witnessed after the application of current (0.5‐1 m a) without any time lag and with enhancement ratio of 3.107.A time lag of 15 minutes, 19 minutes, and 26 minutes with permeation flux of 475.34 μ g/min, 399.35 μ g/min, and 244.81 μ g/min was observed after chemical enhancer pretreatment with propylene glycol, tween 80, and passive, respectively.Rabbit was employed as the experimental animal for pharmacokinetic studies (in vivo) and cats for pharmacological activity (in vivo), and the results illustrated the enhanced bioavailablity (2.88 times) in the iontophoresis animal group when compared with the rabbits of control group.Likewise, a remarkable reduction in emesis events was recorded in cats of iontophoresis group.Conclusively, the histopathological examinations on excised buccal mucosa unveiled no severe necrotic or cytopathetic outcomes of current.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Enterprise Credit Security Prediction and Evaluation Based on Multimodel Fusion

Based on the industry data and enterprise data from tens of thousands of small and medium-sized enterprises, a deep learning and machine learning model of credit prediction is constructed through the division of data sets, processing, and integration of models.At first, with the help of two characteristic selection methods, several subsets separated from the dataset are analyzed based on convolutional neural network as coarse prediction.Then, combined with the tree model, the precise prediction is further made for the enterprise credit evaluation.Finally, the model fusion is carried out to obtain high-precision results.In the simulation experiment, this paper takes a data set of 14,366 small and medium-sized enterprise credit evaluations as the analysis samples to verify the results.The accuracy of the model is 97%, which is far more than 93% of single model with metadata set.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Ellagic Acid: A Review on Its Natural Sources, Chemical Stability, and Therapeutic Potential

Ellagic acid (ea) is a bioactive polyphenolic compound naturally occurring as secondary metabolite in many plant taxa.Ea content is considerable in pomegranate (punica granatum l.) and in wood and bark of some tree species.Structurally, ea is a dilactone of hexahydroxydiphenic acid (hhdp), a dimeric gallic acid derivative, produced mainly by hydrolysis of ellagitannins, a widely distributed group of secondary metabolites.Ea is attracting attention due to its antioxidant, anti-inflammatory, antimutagenic, and antiproliferative properties.Ea displayed pharmacological effects in various in vitro and in vivo model systems.Furthermore, ea has also been well documented for its antiallergic, antiatherosclerotic, cardioprotective, hepatoprotective, nephroprotective, and neuroprotective properties.This review reports on the health-promoting effects of ea, along with possible mechanisms of its action in maintaining the health status, by summarizing the literature related to the therapeutic potential of this polyphenolic in the treatment of several human diseases.

Journal: Oxidative Medicine and Cellular Longevity (2022)
Publication types: Research Article;
Engineering Management Model Analysis Using Partial Differential Equation Hilbert Space

In the process of the rapid development of science and technology, people study many problems with the help of the independent variables, but the according descriptions have not been accurate enough.Therefore, many problems must be described by a function of more than one variable to more accurately get the results that people need to use the functions containing more than one variable and their partial derivatives to study the real problems of the method.Partial differential equations are closely and directly related to many problems in physics, mechanics, and engineering.It becomes an important bridge between mathematical theory and practical problems in physics, mechanics, and engineering.And the elements in hilbert space, vectors, can represent vectors, functions, and phase quantities, while the special solutions of partial differential equations in solving physical problems can satisfy the common properties of hilbert space.The analogy of functions and phase quantities to vectors can establish the function space and phase quantity space with geometric intuition.Based on hilbert space theory, the key and difficult problems in engineering management can be explained by clear mathematical models by using the analogy method.The purpose of this study is to enrich the means of the building engineering management model, to broaden the horizon of managers, and to solve the problems in engineering management from a new dimension.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Delivery of miR-654-5p via SonoVue Microbubble Ultrasound Inhibits Proliferation, Migration, and Invasion of Vascular Smooth Muscle Cells and Arterial Thrombosis and Stenosis through Targeting TCF21

Abnormal proliferation of vascular smooth muscle cells (vsmcs) is an important cause of vascular stenosis.The study explored the mechanism of inhibition of vascular stenosis through the molecular mechanism of smooth muscle cell phenotype transformation.Methods.Coronary heart disease-related genes were screened by bioinformatics, and the target genes of mi r-654-5p were predicted by dual-luciferase method and immunofluorescence method.Mi r-654-5p mimic stimulation and transfection of tcf21 and mtap into cells.Sono vue microbubble sonication was used to deliver mi r-654-5p into cells.Cell proliferation, migration, and invasion were detected by cck-8, wound scratch, and transwell.He and ihc staining were performed to study the effect of mi r-654-5p delivery via sono vue microbubble ultrasound on vessel stenosis in a model of arterial injury.Gene expression was determined by q rt-pcr and wb.Results.Tcf21 and mtap were predicted as the target genes of mi r-654-5p.Cytokines induced smooth muscle cell proliferation, migration, and invasion and promoted mi r-654-5p downregulation; noticeably, downregulated mi r-654-5p was positively associated with the cell proliferation and migration.Overexpression of tcf21 promoted proliferation, invasion, and migration, and mimic reversed such effects.Mi r-654-5p overexpression delivered by sono vue microbubble ultrasound inhibited proliferation, migration, and invasion of cells.Moreover, in arterial injury model, we found that sono vue microbubble ultrasound transmitted mi r-654-5p into the arterial wall to inhibit arterial thrombosis and stenosis, while tcf21 was inhibited.Conclusion.Ultrasound delivery of mi r-654-5p via sono vue microbubbles was able to inhibit arterial thrombosis and stenosis by targeting tcf21.

Journal: Oxidative Medicine and Cellular Longevity (2022)
Publication types: Research Article;