syedzayyan/paper_mill_benchmark huggingface dataset:
Effect of Cold Fluid Compensatory Swallowing Combined with Balloon Dilation on the Treatment of Poststroke Cricopharyngeal Achalasia: A Pilot Randomized Controlled Trial

This study is aimed at comparing the treatment efficacy between catheter balloon dilation combined with cold fluid compensatory swallowing training and catheter balloon dilation alone on poststroke cricopharyngeal achalasia (cpa).Methods .We conducted a single‐blind, randomized controlled trial (rct).Poststroke patients with cpa were divided into two groups: the control group (treated with catheter balloon dilation) and the trial group (catheter balloon dilation combined with cold fluid compensatory swallowing).Videofluoroscopic swallowing study (vfss) was performed, and functional oral intake scale (fois) was used to evaluate and compare the swallowing function of patients in the 2 groups before and after intervention.Posttreatment vas pain scores and recovery time were also measured.Results .Vfss and fois scores in the two groups were improved after treatment ( p &lt; 0.05).In the trial group, vfss scores in the pharyngeal phase and aspiration degree were significantly higher compared with the control group ( p &lt; 0.05) but not in the oral phase ( p &gt; 0.05).The difference in fois scores and patients’ recovery time from intervention to eating mushy food between the trial and control groups was significant ( p &lt; 0.05), but not the vas scores ( p &gt; 0.05).Conclusion .The catheter balloon dilation combined with cold fluid compensatory swallowing was superior to catheter balloon dilation alone in terms of relieving dysphagia and reducing aspiration in patients with cpa following stroke.Long‐term efficacy should be followed up with more objective and quantitative indicators in future studies.

Journal: BioMed Research International (2022)
Publication types: Clinical Study;
Ecological Concept of Basic Computer Vision and Visual Communication of Advertising under the Concept of Ecological Design

With the importance people attach to the harmony of ecosystem in modern society, the concept of ecological design has been adopted in some industries and is gradually being valued and recognized by people.In real life, advertising design and production is an important field of art design.With the rapid development of society and the continuous updating of network information technology, network advertising and visual communication design inevitably collide.As a new form of advertising, network advertising had become a part of people’s network life.This paper analyzes the technical characteristics of image transformation, image enhancement, image restoration, image coding, and image recognition in computer vision processing.Through the image processing algorithm and program of computer, the advertising image can be processed to realize the functions of recognition, restoration, coding, enhancement, and transformation of advertising image.This thesis had studied the visual communication design in modern online advertising under the concept of ecological design.Using computer vision processing technology, this thesis had studied how to process the ecological concept and advertising visual communication content through computer vision technology, which will help to better improve the configuration of online advertising and create a more pleasant visual environment.It is of great significance to promote the rapid development of online advertising industry.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Developing a Multimodal Model for Detecting Higher-Grade Prostate Cancer Using Biomarkers and Risk Factors

A technique to predict crucial clinical prostate cancer (pc) is desperately required to prevent diagnostic errors and overdiagnosis.To create a multimodal model that incorporates long‐established messenger rna (m rna) indicators and conventional risk variables for identifying individuals with severe pc on prostatic biopsies.Urinary has gathered for m rna analysis following a dre and before a prostatic examination in two prospective multimodal investigations.A first group ( n = 489) generated the multimodal risk score, which was then medically verified in a second group ( n = 283).The reverse transcription qualitative polymerase chain reaction determined the m rna phase.Logistic regression was applied to predict risk in patients and incorporate health risks.The area under the curve (auc) was used to compare models, and clinical efficacy was assessed by using a dca.The amounts of sixth homeobox clustering and first distal‐less homeobox m rna have been strongly predictive of high‐grade pc detection.In the control subjects, the multimodal method achieved a total auc of 0.90, with the most important aspects being the messenger riboneuclic acid features’ psa densities and previous cancer‐negative tests as a nonsignificant design ability to contribute to psa, aging, and background.An auc of 0.86 was observed for one more model that added dre as an extra risk component.Two methods were satisfactorily verified without any significant changes within the area under the curve in the validation group.Dca showed a massive net advantage and the highest decrease in inappropriate costs.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Detection of CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and Their Relationship with CT Signs

Lung cancer is a common clinical thoracic malignant tumor, which had a serious impact on the safety of patients, currently ranking first in all malignant tumors in morbidity and mortality, with generally less than 5% survival rate in 5 years.Objective .To investigate the relationship and significance between carcinoembryonic antigen (cea) and precursor gastrin‐releasing peptide (pro grp) changing levels in bronchoalveolar lavage fluid (balf) and ct imaging features in patients with peripheral lung cancer.Methods .A total of 90 patients diagnosed with peripheral lung cancer as the lung cancer group and 60 patients with benign lung diseases as the control group in our hospital from may 2019 to october 2021 were selected to compare the differences of cea and pro grp in balf by the classification of ct features.Results .The levels of cea and pro grp in the balf of the lung cancer group were significantly higher than those of the control group; the proportion of patients with lobulation sign, burr sign, ground glass sign, pleural effusion, and lesion diameter ≥ 3.0 cm in the lung cancer group was higher than that in the control group; the cea level in balf of lung cancer patients with spicule sign, pleural effusion, and lesion diameter ≥ 3.0 cm was significantly higher than that without these symptoms, while pro grp level in the balf of lung cancer patients with lobulation sign, burr sign, ground glass sign, pleural effusion, and lesion diameter ≥ 3.0 cm was significantly higher than that of lung cancer patients without these symptoms.Conclusion .The check of cea and pro grp in balf in combination with ct features has a certain clinical value for the diagnosis of lung cancer.At the same time, the increased levels of cea and pro grp in balf have a certain correlation with the changes of malignant signs of lung cancer in ct examination.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Demographics and Economic Burden of Nasopharyngeal Carcinoma Inpatients

Nasopharyngeal carcinoma is particularly prevalent in guangdong and guangxi (southern china); the economic burden of nasopharyngeal cancer patients is heavy in china.This study is aimed at retrospectively analyzing the basic features and economic burden of newly diagnosed nasopharyngeal carcinoma patients admitted to the first affiliated hospital of guangxi medical university and at providing a scientific basis for nasopharyngeal carcinoma prevention and control strategies.Methods .The data of 3,727 nasopharyngeal carcinoma inpatients diagnosed from january 2012 to december 2020 were extracted from the guangxi nasopharyngeal carcinoma healthcare big data management information platform.Basic demographic characteristics, duration of hospital stay, and hospitalization cost of nasopharyngeal carcinoma patients were collected and analyzed statistically.Results .The incidence period of nasopharyngeal carcinoma was primarily from 30 to 69 years of age, with the 40–49‐year age group comprising the largest proportion of nasopharyngeal carcinoma patients, accounting for 34.18% of the patients with newly diagnosed nasopharyngeal carcinoma in the hospital.The male‐to‐female ratio was 2.87 : 1.There were 2,223 cases from rural areas, 2,153 from the han ethnic group, and 1,460 from the zhuang ethnic group, accounting for 59.65%, 55.77%, and 39.17% of the total number of cases, respectively.The average duration of hospitalization decreased whereas the average hospitalization cost increased annually.Multivariate analysis of hospitalization cost showed that the duration of hospital stay, rural/urban, and ethnicity was the main influencing factors: the longer the duration of hospital stay, the higher the hospitalization cost; patients from rural incurred lower costs than from urban; ethnic zhuang patients incurred significantly lower costs than patients from other ethnicities.Conclusion .Early diagnosis and treatment should be actively carried out to reduce the incidence of nasopharyngeal carcinoma, especially for rural, ethnic zhuang, and males in the 40–49‐year age group patients.The future research on nasopharyngeal carcinoma will focus on exploring the pathogenesis of nasopharyngeal carcinoma, improving the screening system, and reducing the burden on patients, in order to further improve the survival rate and quality of life of patients with nasopharyngeal carcinoma.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Depletion of Fibroblast Growth Factor 12 Restrains the Viability, Stemness, and Motility of Colorectal Cancer

Colorectal cancer (crc) is a leading cause of cancer‐related death.Crc patients have a poor prognosis due to tumor metastasis and recurrence.Fibroblast growth factor 12 (fgf12), a member of the fgf family, is highly expressed in several cancers.However, little is known about the roles of fgf12 in crc progression.Methods .The overall survival (os) of crc patients was detected via kaplan–meier analysis.The fgf12 expression in both crc tissues and cells was analyzed by q rt‐pcr, immunohistochemistry (ihc), and western blotting (wb).Lo vo and sw480 cells were transfected with sh fgf12 lentivirus to silence fgf12 .In vivo and in vitro experiments were performed to explore the fgf12 functions in crc, including cck‐8, edu, flow cytometry, transwell, emt, cancer stemness, and tumor xenograft experiments.Results .Fgf12 was upregulated in both crc cells and tissues.High expression of fgf12 indicated a shorter os in crc patients.Fgf12 knockdown inhibited the proliferation, invasion, stemness, and emt of crc cells.Fgf12 knockdown promoted crc cell apoptosis in vitro .740 y‐p (a pi3k/akt pathway activator) restored the proliferation, stemness, invasion, and emt in fgf12‐deficient cells and reversed lo vo cell apoptosis induced by fgf12 depletion.Depletion of fgf12 inhibited tumor growth, emt, cancer stemness, and pi3k/akt pathway in a xenograft mouse model.Conclusions .Fgf12 predicts bad clinical outcome and modulates the viability, stemness, and motility of crc cells.Our study may provide a new insight for the diagnosis and treatment of crc.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment.Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications.One of the important tools is photoacoustic multimodal imaging (pami) which combines the concepts of optics and ultrasonic systems.At the same time, earlier detection of breast cancer becomes essential to reduce mortality.The recent advancements of deep learning (dl) models enable detection and classification the breast cancer using biomedical images.This article introduces a novel social engineering optimization with deep transfer learning‐based breast cancer detection and classification (seodtl‐bdc) model using pai.The intention of the seodtl‐bdc technique is to detect and categorize the presence of breast cancer using ultrasound images.Primarily, bilateral filtering (bf) is applied as an image preprocessing technique to remove noise.Besides, a lightweight lednet model is employed for the segmentation of biomedical images.In addition, residual network (res net‐18) model can be utilized as a feature extractor.Finally, seo with recurrent neural network (rnn) model, named seo‐rnn classifier, is applied to allot proper class labels to the biomedical images.The performance validation of the seodtl‐bdc technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the seodtl‐bdc approach over the existing methods.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women.Early diagnosis has improved further treatments and reduced the level of mortality.A unique deep learning algorithm is presented for predicting breast cancer in its early stages.This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs.It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis.The dataset of axillary lymph nodes from the breast cancer patients was collected from erasmus medical center.A total of 1050 images were studied from the 850 patients during the years 2018 to 2021.For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute.The existence of axillary lymph nodes was confirmed by pathologic examination.The feed forward, radial basis function, and kohonen self‐organizing are the artificial neural networks (anns) which are used to train 84% of the erasmus medical center dataset and test the remaining 16% of the independent dataset.The proposed model performance was determined in terms of accuracy (ac), sensitivity (sn), specificity (sf), and the outcome of the receiver operating curve (roc), which was compared to the other four radiologists’ mechanism.The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists’ models (90% sensitivity, 92% specificity, and 94% accuracy).Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients.This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity

As an epidemic, covid‐19’s core test instrument still has serious flaws.To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic.Because of the contagious characteristics of the unique coronavirus (covid‐19) infection, an overwhelming comparison with patients queues up for pulmonary x‐rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention.Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories.This research describes a novel use of the deep convolutional neural network (cnn) technique to covid‐19 illness assessment seriousness.Utilizing chest x‐ray images as contribution, an unsupervised dcnn model is constructed and suggested to split covid‐19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent.The efficiency of the dcnn model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest x‐ray scans.To the evidence relating, it is the first covid‐19 disease incidence evaluation research with four different phases, to use a reasonably high number of x‐ray images dataset and a dcnn with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Dense Convolutional Neural Network for Detection of Cancer from CT Images

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model.The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection.The method involves preprocessing of computerized tomography (ct) images for optimal classification at the testing stages.A 10‐fold cross‐validation is conducted to test the reliability of the model for cancer detection.The experimental validation is conducted in python to validate the effectiveness of the model.The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets.The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods.Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.

Journal: BioMed Research International (2022)
Publication types: Research Article;
COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

The covid‐19 pandemic has been a global crisis affecting billions of people and causing countless economic losses.Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict covid‐19 cases.While there is much attention being paid to the usa and china, little research attention has been drawn to less developed countries, e.g., india.In this study, i conduct an analysis of the covid‐19 epidemic in india, with datasets collected from different sources.Several machine learning models have been built to predict the covid‐19 spread, with different combinations of input features, in which the transformer is proven as the most precise one.I also find that the facebook mobility dataset is the most useful for predicting the number of confirmed cases.However, i find that the datasets from different sources are not very effective when predicting the number of deaths caused by the covid‐19 infection.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Correlations between Intestinal Microbiota and Clinical Characteristics in Colorectal Adenoma/Carcinoma

Most of colorectal cancer (crc) cases are sporadic and develop along the adenoma‐carcinoma sequence.Intestinal microbial dysbiosis is involved in the development of colorectal cancer.However, there are still no absolute markers predicting the progression from adenoma to carcinoma.Aims .To investigate the characteristics of intestinal microbiota in colorectal adenoma and carcinoma patients and the correlations with clinical characteristics.Methods .Fecal samples were collected from 154 colorectal carcinoma patients (crc group), 20 colorectal adenoma patients (ad group), and 199 healthy controls (control group).The intestinal microbiota was investigated by 16s r rna gene sequencing.Results .Compared to the healthy controls, microbial diversity was dramatically decreased in ad/crc.At the genus level, acidaminococcus significantly decreased with the order of control‐ad‐crc ( p &lt; 0.05).Parvimonas , peptostreptococcus , prevotella , butyricimonas , alistipes , and odoribacter were the key genera in the network of colorectal adenoma/carcinoma‐associated bacteria.Combination of the top 10 most important species, including butyricimonas synergistica , agrobacterium larrymoorei , bacteroides plebeius , lachnospiraceae bacterium feline oral taxon 001, clostridium scindens , prevotella heparinolytica , bacterium ld2013, streptococcus mutans , lachnospiraceae bacterium 19gly4, and eubacterium hallii , showed the best performance in distinguishing ad patients from crc (auc = 85.54 % , 95% ci: 78.83%‐92.25%).The clinicopathologic features, including age, sex, tumor location, differentiation degree, and tnm stage, were identified to be closely linked to the intestinal microbiome in crc.Conclusion .Several intestinal bacteria changed along the adenoma‐carcinoma sequence and might be the potential markers for the diagnosis and treatment of colorectal adenoma/carcinoma.Intestinal microbiota characteristics in crc should account for the host factors.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Controlled Decompression Alleviates Brain Injury via Attenuating Oxidative Damage and Neuroinflammation in Acute Intracranial Hypertension

The benefits of controlled decompression (cdc) for patients with acute intracranial hypertension especially in terms of alleviating the complications caused by rapid decompression (rdc) have been confirmed by clinical studies.This study is aimed at evaluating the therapeutic potency of cdc with ubiquitin c‐terminal hydrolase‐l1 (uch‐l1) and glial fibrillary acidic protein (gfap) by investigating the potential molecular mechanism in the acute intracranial hypertension (aich) rabbit model.Methods .Male new zealand white rabbits were randomly subdivided into the sham‐operated (sh) group, cdc group, and rdc group.Blood plasma samples and brain tissue were collected 2 days before operation (baseline) and at 3, 6, 24, and 72 hours after operation to measure the levels of uch‐l1, gfap, oxidative stress indicators, and inflammatory cytokines by performing elisa or western blot.The neurological score of the rabbits and brain water content was graded 24 h after surgery.Q pcr, immunofluorescence, and fj‐c staining were conducted.Results .Cdc improved neurological function, lowered brain water content, ameliorated neuronal degeneration, attenuated oxidative damage, and inflammatory responses to a greater extent than rdc.Plasma uch‐l1 level was significantly lower in the cdc group at 3 h postoperatively than in the rdc group.Cdc reduced plasma gfap levels to various degrees at 3 h, 6 h, and 24 h postoperatively compared with rdc.Immunofluorescence confirmed that the expression of uch‐l1 and gfap in the cortex of the cdc group was lower than that of the rdc group.Conclusions .Our data collectively demonstrate that cdc could attenuate oxidative damage and inflammatory responses, downregulate uch‐l1 and gfap levels, and contribute to an improved neuroprotective effect compared with rdc.

Journal: BioMed Research International (2022)
Publication types: Research Article;