syedzayyan/paper_mill_benchmark huggingface dataset:
A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI

Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in mri as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction.The existing approaches are time-consuming, incursive, and susceptible to human mistake.These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors.The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment.As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed.In the deep learning-based brain tumor segmentation and classification technique, the cnn model has an excellent brain segmentation and classification effect.In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain mri tumor.A cnn is proposed in the first stage to learn the feature map from image space of brain mri into the tumor marker region.In the second step, a faster region-based cnn is developed for the localization of tumor region followed by region proposal network (rpn).In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings.The proposed model’s performance is assessed based on evaluation metrics extensively used in medical image processing.The experimental results validate that the proposed deep cnn and svm-rbf classifier achieved an accuracy of 98.3% and a dice similarity coefficient (dsc) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on figshare dataset, it achieved an accuracy of 98.0% and a dsc of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary.The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
A Critical Scrutiny on Liposomal Nanoparticles Drug Carriers as Modelled by Topotecan Encapsulation and Release in Treating Cancer

The medical field is looking for drugs and/or ways of delivering drugs without harming patients.A number of severe drug side effects are reported, such as acute kidney injury (aki), hepatotoxicity, skin rash, and so on.Nanomedicine has come to the rescue.Liposomal nanoparticles have shown great potential in loading drugs, and delivering drugs to specific targeted sites, hence achieving a needed bioavailability and steady state concentration, which is achieved by a controlled drug release ability by the nanoparticles.The liposomal nanoparticles can be conjugated to cancer receptor tags that give the anticancer-loaded nanoparticles specificity to deliver anticancer agents only at cancerous sites, hence circumventing destruction of normal cells.Also, the particles are biocompatible.The drugs are shielded by attack from the liver and other cytochrome p450 enzymes before reaching the desired sites.The challenge, however, is that the drug release is slow by these nanoparticles on their own.Scientists then came up with several ways to enhance drug release.Magnetic fields, uv light, infrared light, and so on are amongst the enhancers used by scientists to potentiate drug release from nanoparticles.In this paper, synthesis of liposomal nanoparticle formulations (liposomal-quantum dots (l-qds), liposomal-quantum dots loaded with topotecan (l-qd-tpt)) and their analysis (cytotoxicity, drug internalization, loading efficiency, drug release rate, and the uptake of the drug and nanoparticles by the he la cells) are discussed.

Journal: Evidence-Based Complementary and Alternative Medicine (2022)
Publication types: Research Article;
A Clinical Study on the Treatment of Children’s Short Stature with Auxiliary Comprehensive Management Combined with Growth Patch

To explore the clinical effect of auxiliary comprehensive management combined with growth patch in the treatment of childhood idiopathic short stature (iss).Methods.From september 2017 to december 2019, 120 children with iss who met the selection criteria were collected.Random number table method divided them into 2 groups: one group was given auxiliary comprehensive management and recorded as the routine group (n = 60), and the other group was given auxiliary comprehensive management and combined growth patch treatment and recorded as the combination group (n = 60).The course of treatment was 12 months.The effects of the two methods on children’s height, bone age, body weight, and insulin-like growth factor (igf)-1 and igf-binding protein (igfbp)-3 levels were compared.Results.There was no statistical difference between the two groups in baseline height, genetic height, baseline bone age, baseline body weight, and body weight before and after treatment ( p &gt; 0.05 ).After treatment, the heights of the two groups were higher than before for the same group, the height growth values and predicted adult height of the combination group were higher than those of the routine group, and the predicted adult height of the combination group was higher than the genetic height of the same group p &lt; 0.001 .There was no statistical difference in igf-1 and igfbp-3 levels before treatment between the two groups ( p &gt; 0.05 ).The levels of igf-1 and igfbp-3 after treatment in the two groups were higher than those in the same group before treatment, and the combination group was higher than that in the routine group ( p &lt; 0.05 ).Conclusion.On the basis of auxiliary comprehensive management, combined with growth patch for the treatment of children with iss, it can effectively increase the height of the children, improve the levels of serum igf-1 and igfbp-3, and have significant clinical effects, which is beneficial to the healthy growth of the children.

Journal: Evidence-Based Complementary and Alternative Medicine (2021)
Publication types: Clinical Study;
A Clinical Study of the Effect of Estradiol Valerate on Sleep Disorders, Negative Emotions, and Quality of Life in Perimenopausal Women

In this prospective study, we randomly divided 100 patients with perimenopausal syndrome equally into the control group (n = 50) receiving conventional treatment and the study group (n = 50) receiving estradiol valerate.The indicators observed were endometrial thickness, uterine volume, and the levels of lh (luteinizing hormone), fsh (follicle-stimulating hormone), and e2 (estradiol) of the patients before and after treatment.The pittsburgh sleep quality index (psqi), hamilton anxiety/depression scale (hama/hamd), kupperman symptom score, and menopause-specific quality of life (menqol) were also applied to assess the sleep quality, negative emotions, severity of the condition, and quality of life of all patients, respectively.Our findings were that estradiol valerate is beneficial in improving serum sex hormone levels, sleep disturbances, negative mood, and quality of life in patients with perimenopausal syndrome and that its safety profile is high enough to warrant clinical promotion.

Journal: Evidence-Based Complementary and Alternative Medicine (2021)
Publication types: Clinical Study;
A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Recently, severe acute respiratory syndrome coronavirus 2 (sars-co v-2), the causative agent of coronavirus disease 2019 (covid-19), has infected millions of individuals worldwide.While covid-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system.Hypertrophic cardiomyopathy (hcm) is a common genetic cardiovascular disorder.Studies have shown that hcm patients with covid-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated.Herein, we conducted transcriptomic analyses to identify shared biomarkers between hcm and covid-19 to bridge this knowledge gap.Differentially expressed genes (degs) were obtained using the gene expression omnibus ribonucleic acid (rna) sequencing datasets, gse147507 and gse89714, to identify shared pathways and potential drug candidates.We discovered 30 degs that were common between these two datasets.Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules.We discovered that hcm is linked to covid-19 progression based on a functional analysis under ontology terms.Based on the degs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and micro rnas was also discovered.Lastly, our research suggests that the potential drugs we identified might be helpful for covid-19 therapy.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless eeg-based brain-computer interface system.Overall performance of a brain-computer interface system depends on information transfer rate.Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate.In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network.According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively.This both results significantly higher than the other methods that we used in experiments.Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network.Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine

Though artificial intelligence (ai) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (dl) and machine learning (ml), which have driven the development of new ai abilities in the field.Anns are used in both deep learning and machine learning in nuclear medicine.Alternatively, if 3d convolutional neural network (cnn) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs.In nuclear medicine, artificial intelligence reimagines and reengineers the field’s therapeutic and scientific capabilities.Understanding the concepts of 3d cnn and u-net in the context of nuclear medicine provides for a deeper engagement with clinical and research applications, as well as the ability to troubleshoot problems when they emerge.Business analytics, risk assessment, quality assurance, and basic classifications are all examples of simple ml applications.General nuclear medicine, spect, pet, mri, and ct may benefit from more advanced dl applications for classification, detection, localization, segmentation, quantification, and radiomic feature extraction utilizing 3d cnns.An ann may be used to analyze a small dataset at the same time as traditional statistical methods, as well as bigger datasets.Nuclear medicine’s clinical and research practices have been largely unaffected by the introduction of artificial intelligence (ai).Clinical and research landscapes have been fundamentally altered by the advent of 3d cnn and u-net applications.Nuclear medicine professionals must now have at least an elementary understanding of ai principles such as neural networks (anns) and convolutional neural networks (cnns).

Journal: Scanning (2022)
Publication types: Research Article;
Transcranial Electrical Motor Evoked Potential in Predicting Positive Functional Outcome of Patients after Decompressive Spine Surgery: Review on Challenges and Recommendations towards Objective Interpretation

Spine surgeries impose risk to the spine’s surrounding anatomical and physiological structures especially the spinal cord and the nerve roots.Intraoperative neuromonitoring (ionm) is a technology developed to monitor the integrity of the spinal cord and the nerve roots via the surgery.Transcranial motor evoked potential (tc mep) (one of the ionm modalities) is adopted to monitor the integrity of the motor pathway of the spinal cord and the motor nerve roots.Recent research suggested that the ionm is conducive as a prognostic tool towards the patient’s functional outcome.This paper summarizes the researches of ionm being adopted as a prognostic tool.In addition, this paper highlights the problems associated with the signal parameters as the improvement criteria in the previous researches.Lastly, we review the challenges of tc mep to achieve a prognostic tool focusing on the factors that could interfere with the generation of a stable tc mep response.The final section will discuss recommendations for ionm technology to achieve an objective prognostic tool.

Journal: Behavioural Neurology (2021)
Publication types: Review Article;
Three-Dimensional Finite Element Numerical Simulation and Analysis of Solid-State Processing of Metal Material

Solid-state processing of metal material is a very complex physical and chemical process, which is coupled by a series of variations including heat transfer, momentum transfer, mass transfer, and phase change.Applying three-dimensional (3d) finite element numerical method to the simulation of solid-state processing can perform analysis of metal material’s forging processes before production trial production, can obtain their relevant information such as material flow law, temperature field, and strain field under the minimum physical test conditions, thereby predicting metal material’s forming defects and improving their forging quality.On the basis of summarizing and analyzing previous research works, this paper expounded the current status and significance of solid-state processing of metal materials, elaborated the development background, current status, and future challenges of 3d finite element numerical simulation, introduced the discrimination method and free surface solution method of numerical simulation calculation, conducted finite element model’s geometric assumptions, material selection, element division, model establishment, parameter selection, and initial and boundary condition determination, and simulated and analyzed rheological casting, remelting heating, thixoforming, and rotary piercing processes of metal materials.The results show that the 3d finite element numerical method can not only simulate various processes of flow field, temperature field, stress field, and microstructure in solid-state processing but also can provide a reliable basis for effectively obtaining a reasonable description and finding a more optimized design plan for metal material processing in a short time, which plays an important role in understanding and analyzing solid metal forming process, controlling and optimizing process parameters, guiding and mastering rheological casting, and secondary heating and rotary piercing of metal materials.

Journal: Complexity (2020)
Publication types: Research Article;
The Impact of Innovative Industrial Cluster Policy on Regional Innovation: Evidence from a Quasi-Natural Experiment in China

As important carriers of local innovation activities, innovative industrial clusters are attracting increasing attention.Therefore, several countries have started promotion policies for innovative industrial clusters.However, there are few empirical studies on relevant policies.This paper investigates the impact of china’s “innovative industrial cluster pilot” (iicp) policy on regional innovation.Taking the implementation of iicp policy as a quasi-natural experiment and using the panel data of 266 prefecture-level cities in china in 2008-2019, this paper provides strong evidence that iicp policy promotes regional innovation.The conclusion still holds after a battery of robustness checks.The heterogeneity test shows that the promoting effect of iicp policy on innovation is more significant in central and western region than in eastern region.Moreover, the lower the city administrative level and the lower the dependence on natural resource, the more prominent the innovation effect of iicp policy.Further, the mechanism test shows that the iicp policy can promote regional innovation indirectly by strengthening government support for innovation and attracting the agglomeration of science and technological talents, but the mediation effect of industrial structure has not been verified.

Journal: Discrete Dynamics in Nature and Society (2021)
Publication types: Research Article;
Silver Nanoparticles Stabilized by Poly (Vinyl Pyrrolidone) with Potential Anticancer Activity towards Prostate Cancer

Tumor necrosis factor (tnf‐ α ) and inflammatory cytokine (il‐6) play a vital role in various cellular incidents such as the proliferation and death of cells during carcinogenesis.Hence, regulation of these biomarkers could be a promising tool for controlling tumor progression using nanoformulations.Silver nanoparticles‐poly (vinyl pyrrolidone) (ag nps‐pvp) were prepared using the reduction of silver nitrate and stabilized with pvp.They are characterized through yield percentage, uv‐vis, ft‐ir, size, charge, and morphology.The obtained ag nps were tested for anticancer activity against prostate cancer (pc 3) and human skin fibroblast (hfs) cell lines.Moreover, biomarker‐based confirmations like tnf‐ α and il‐6 were estimated.The synthesized ag nps‐pvp were stable, spherical in shape, with particle sizes of 122.33 ± 17.61 nm, a polydispersity index of 0.49 ± 0.07, and a negative surface charge of −19.23 ± 0.61 m v.In vitro cytotoxicity testing showed the ag nps‐pvp exhibited antiproliferation properties in pc3 in a dose‐dependent manner.In addition, when compared to control cells, ag nps‐pvp has lower tnf‐ α with a significant value ( ∗ p &lt; 0.05); the value reached 16.84 ± 0.71 pg/ml versus 20.81 ± 0.44 pg/ml, respectively.In addition, hsf cells showed a high level of reduction ( ∗∗∗ p &lt; 0.001) in il‐6 production.This study suggested that ag nps‐pvp could be a possible therapeutic agent for human prostate cancer and anti‐il‐6 in cancerous and noncancerous cells.Further studies will be performed to investigate the effect of ag nps‐pvp in different types of cancer.

Journal: Bioinorganic Chemistry and Applications (2022)
Publication types: Research Article;
The Expression of miR-34a in Gingival Crevicular Fluid of Chronic Periodontitis and Its Connection with the TLR/NF-κB Signaling Pathway

This study analyzed the expression of mi r-34a in gingival crevicular fluid (gcf) of patients with chronic periodontitis and its connection with the toll-like receptor (tlr)/nuclear factor kappa-b (nf-κb) signaling pathway.Methods.We collected the gcf of the two groups of subjects, using rt-pcr to detect the expression of mi r-34a and nf-κb p65 m rna and tlr4 m rna and elisa to detect the inflammatory factor degree in gcf, and performed periodontal examinations on both groups.Results.The gingival index, bleeding index, probe depth, and attachment loss indexes of periodontal examination in the observation group were remarkably superior to those in the control group ( p &lt; 0.05 ).The levels of il-1β, il-6, and tnf-α in the gcf of the observation group were higher than those of the control group ( p &lt; 0.05 ).The m rna relative expression levels of mi r-34a, nf-κb p65, and tlr4 in the gcf of the observation group were dramatically higher than those of the control group ( p &lt; 0.05 ).Correlation analysis showed that mi r-34a was highly expressed in patients with chronic periodontitis.Conclusion.There is an abnormally high expression of mi r-34a in gcf of chronic periodontitis.Its expression is associated with the degree of periodontal inflammation, periodontal tissue damage, and the activation of the tlr/nf-κb signaling pathway and could be used as a potential index for auxiliary diagnosis and severity of the disease.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
The Evaluation Method of Community Emergency Resource Allocation Based on Coordination Sensor Information Collection

This paper analyzes the information collection data of coordination sensors and designs a corresponding evaluation method to conduct an in-depth assessment of the configuration of community emergency resources.Based on the principles of assessment index system construction and the actual research, an assessment index system of community emergency resource allocation level is established.In this paper, the g1 method was selected to determine the weights of the evaluation indexes, and the gray clustering method was applied to construct the whitening weight function, determine the gray level to which each index belongs and the affiliation degree of each gray class, and establish a comprehensive evaluation model of the community emergency resource allocation level.In response to the problem that the increase of state variables leads to a decrease in real-time map building, a dynamic local window size mechanism is proposed, which can reduce the time consumption and save computational resources under the condition of ensuring the accuracy of positioning and map building.Therefore, it is urgent to design an efficient self-interference cancellation mechanism to resist the influence of self-interference on the signal-to-noise ratio of the first-hop link.For example, the imu pre integration theory is combined with the wheel range method to solve the problem of frequency synchronization; an initialization algorithm is proposed to recover the scale information of the camera and optimize the external parameters; design a graphic optimization framework integrating imu, wheel range finder and camera.The monitoring terminal sends the data information of coordination transportation to the monitoring platform in real-time, and the monitoring platform is responsible for storing and displaying the data information, thus realizing the real-time monitoring of the coordination transportation process.Finally, the functions of the monitoring terminal and the display system of the monitoring platform experiment, respectively, and the test results verify the effectiveness and integrity of the system data communication, which proves the correctness of the terminal design of the integrated coordination monitoring system based on multidimensional information and has practical engineering value.

Journal: Journal of Sensors (2022)
Publication types: Research Article;