syedzayyan/paper_mill_benchmark huggingface dataset:
Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses.50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study.They all underwent ultrasound scans.Different deep learning convolutional neural network models dense convolutional network (dense net121), google inception net (goog le net), and microsoft’s residual network (res net) were used for training and diagnoses.Then, the diagnostic results were compared with professional image physicians’ artificial diagnoses.The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians’ artificial diagnoses.Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians’ artificial diagnoses.The areas under curves (aucs) of the three algorithms were significantly higher than aucs of doctors’ diagnosis results.The loss function parameters of dense net121 and goog le net were significantly lower than that of res net, with the statistically significant difference ( p &lt; 0.05 ).There was no significant difference in training time of res net, goog le net, and dense net121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively ( p &gt; 0.05 ).In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians’ artificial judgments and had great clinical value for the diagnoses and treatments of the disease.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Accident Liability Determination of Autonomous Driving Systems Based on Artificial Intelligence Technology and Its Impact on Public Mental Health

With the rise of self‐driving technology research, the establishment of a scientific and perfect legal restraint and supervision system for self‐driving vehicles has been gradually paid attention to.The determination of tort liability subject of traffic accidents of self‐driving cars is different from that of ordinary motor vehicle traffic accident tort, which challenges the application of traditional fault liability and product liability.The tort issue of self‐driving cars should be discussed by distinguishing two kinds of situations: assisted driving cars and highly automated driving, and typological analysis of each situation is needed.When the car is in the assisted driving mode, the accident occurs due to the quality defect or product damage of the self‐driving car, and there is no other fault cause; then, the producer and seller of the car should bear the product liability according to the no‐fault principle; if the driver has a subjective fault and fails to exercise a high degree of care; the owner and user of the car should bear the fault liability.This paper analyzes the study of the impact of autonomous driving public on public psychological health, summarizes the key factors affecting the public acceptance of autonomous driving, and dissects its impact on public psychological acceptance.In order to fully study the responsibility determination of autonomous driving system accidents and their impact on public psychological health, this paper proposes an autonomous driving risk prediction model based on artificial intelligence technology, combined with a complex intelligent traffic environment vehicle autonomous driving risk prediction method, to complete the risk target detection.The experimental results in the relevant dataset demonstrate the effectiveness of the proposed method.

Journal: Journal of Environmental and Public Health (2022)
Publication types: Research Article;
A Survey Research on the Willingness of Gynecologists to Employ Mobile Health Applications

In gynecological care, m health (mobile health) technology may play an important role.Medical professionals’ willingness to use this technology is the key to its acceptance.Most doctors utilize mobile health technology; however, there is still room for improvement in the use of m health.Gynecologists were asked to participate in this research to see how open they were to use mobile health technologies.In this descriptive‐analytical investigation, the researchers determined the average scores for each variable.The overall mean for preparedness to embrace mobile medical technology is 1.8 out of 2, as shown in table 1.When it came to their desire to embrace mobile health technology, doctors’ years of experience correlated negatively with their age.According to our findings, the amount of interest in mobile health technology is high.Patients’ private information must be protected throughout the usage of this technology though.Mobile health technology may effectively reach patients in remote areas, but it is not a substitute for face‐to‐face encounters with medical professionals.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
A Study on Fucosyltransferase 2 Gene Polymorphism and Secretion Status Related to Neonatal Necrotizing Enterocolitis

To detect the single nucleotide polymorphism (snp) of alpha-(1,2) fucosyltransferase 2 gene (fut2) and the secretion status in the newborns of chongqing china and explore the relationship between genotype or phenotype of fut2 and neonatal necrotizing enterocolitis (nec).Methods.Newborns who were hospitalized in children’s hospital of chongqing medical university from august 2014 to december 2015 and in line with the inclusion criteria were chosen as the research subjects; 34 cases of them in accordance with the diagnostic criteria of nec stage ii or iii were nec group, and 36 other cases of them were the control group.Total dna was extracted from oral epithelial cells of patients which were collected with cotton buds.Fut2 snp genotype was detected by gene sequencing.H antigen was detected with saliva samples by saliva agglutination inhibition test.Related clinical data were collected for analysis.Results.There are three genotypes on the rs1047781 (a385t) allele of the fut2 encoding sequence: aa, at, and tt.The number of genotypes aa, at, and tt in the nec group was 9 (26.47%), 12 (35.29%), and 13 (38.24%), respectively.In the control group, the number of genotypes aa, at, and tt was 12 (33.33%), 17 (47.23%), and 7 (19.44%).There were no differences in genotypes between the two groups according to the chi-square test ( p &gt; 0.05 ).There were 22 cases of secretors (64.7%) and 12 cases of nonsecretors (35.3%) in the nec group.The number of secretors and nonsecretors in the control group was 31 (88.89%) and 5 (11.11%).Statistical difference was found in the phenotype between two groups through the chi-square test ( p &lt; 0.05 ).In addition, no g428a homozygous mutation, which causes nonsecretor phenotype in caucasians, was seen in all the subjects of this study.Conclusions.These findings indicate that secretion status (nonsecretor) was significantly associated with nec in chongqing, china.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
A Stochastic SEIRS Epidemic Model with Infection Forces and Intervention Strategies

The spread of epidemics has been extensively investigated using susceptible-exposed infectious-recovered-susceptible (seirs) models.In this work, we propose a seirs pandemic model with infection forces and intervention strategies.The proposed model is characterized by a stochastic differential equation (sde) framework with arbitrary parameter settings.Based on a markov semigroup hypothesis, we demonstrate the effect of the proliferation number r 0 s on the sde solution.On the one hand, when r 0 s &lt; 1 , the sde has an illness-free solution set under gentle additional conditions.This implies that the epidemic can be eliminated with a likelihood of 1.On the other hand, when r 0 s &gt; 1 , the sde has an endemic stationary circulation under mild additional conditions.This prompts the stochastic regeneration of the epidemic.Also, we show that arbitrary fluctuations can reduce the infection outbreak.Hence, valuable procedures can be created to manage and control epidemics.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
A Review on the Application of PD-1 Blockade in EBV-Associated Nasopharyngeal Carcinoma Immunotherapy

Epstein-barr virus (ebv) linked with nasopharyngeal carcinoma (npc) is considered to be one of the most prevalent head and neck malignancies in east and southeast asia.Although radiotherapy and chemotherapy are effective treatments for npc, they have immunosuppressive effects.Immunotherapy has got considerable attention of clinicians for cancer treatment in recent years due to proven success of pd-1/pd-l 1 inhibition in solid tumors trials.The distinct immunological environment of ebv-associated npc presents a reasonable therapeutic target for pd-1/pd-l 1 inhibition.Immune checkpoint blockade therapy targeting the programmed cell death-1 (pd-1) and programmed cell death ligand-1 (pd-l 1) receptors have shown efficacy in early phase i clinical trials, with ongoing phase iii clinical trials.Herein, we have extensively addressed the role of the pd-1/pd-l1 axis in the immunotherapy of ebv-associated npc.Immunotherapeutic strategies are anticipated to enter mainstream clinical practise and provide long-term remissions in patients with severe npc.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Review Article;
A Retrospective Study of Recurrent Bacterial Meningitis in Children: Etiology, Clinical Course, and Treatment

S.Recurrent bacterial meningitis (rbm) is a rare but life-threatening disease.This study aims to analyze the clinical features, potential causes, and therapeutic outcomes of rbm in children.Methods.This article retrospectively reviews the clinical characteristics, etiologies, and treatments in children with rbm hospitalized in hebei children’s hospital from 2012 to 2020.Results.A total of 10 children with rbm, five males and five females, were included in this study.The age of rbm in children spans from the neonatal stage to the childhood stage.The underlying illnesses were identified and classified as cerebrospinal fluid rhinorrhea (1 case), humoral immunodeficiency with mondini dysplasia (1 case), common cavity deformity with cerebrospinal fluid ear leakage (1 case), mondini malformations (2 cases), incomplete cochlear separation type i with a vestibular enlargement (2 cases), local inflammation of the sphenoid bone caused by cellulitis (1 case), congenital skull base defects (1 case), and congenital dermal sinus with intraspinal abscess (1 case).6 patients chose targeted therapy for potential reasons.Conclusions.Congenital abnormalities or acquired injuries lead to intracranial communication with the outside world, which can quickly become a portal for bacterial invasion of the central nervous system, resulting in repeated infections.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
A Novel Variable Pseudonym Scheme for Preserving Privacy User Location in 5G Networks

Due to the development in 5g mobile communications, user privacy becomes the main challenge, especially with the multiplicity of services and applications that can be accessed.Location privacy is related to the user privacy in terms of the possibility of tracking and unwanted advertisements, as well as the possibility of exposure to suspicious activities and terrorist attacks based on the user location.Accordingly, previous mobile systems use pseudonyms instead of a permanent identity to preserving the user’s location privacy in mobile networks, by what is known as the cellular radio network temporary identifiers (c-rntis).The c-rnti protects the user privacy relatively, but it faces some problems due to the clear text of the user in crni exchange, which will make the user easily trackable by man-in-the-middle attack.This article aims at proposing a new algorithm that improves the user’s location privacy and enhances the capabilities of the 5g infrastructure in terms of confidentiality and privacy.The idea is based on the use of a novel variable pseudonym (v-rnti) algorithm that acts as a radio channel identifier for the user, which improves the allocation of pseudonyms to identify users.The proposed algorithm uses different v-rnti values by the ue and can be changed frequently to improve the pseudonym allocation procedure.This approach can be implemented in 3gpp standard architecture by upgrading ues and e nb by minor modifications.During this study, the proposed 5g v-rnti authentication protocol model was built.And then, the automated analysis of the protocol model is performed by using pro verif model checker.The results showed that the model works well without any noticeable problems.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
A Novel Prognostic Stratified System Based on Tumor Budding and the Cell Nest Size in Ureter Urothelial Carcinoma

At present, malignant tumor stratification based on the tnm stage is very important for predicting patient prognosis and selecting appropriate treatment.The prognostic factor of ureter urothelial carcinoma is mainly based on the stage according to ajcc (8th) tnm classification.None of the histomorphologic features is recommended to assess patient’s prognosis.Recently, a novel three-tiered grading system based on tumor budding and the cell nest size (referred as tbns system) has been applied to be highly prognostic for some squamous cell carcinomas, including esophageal, pulmonary, uterine cervix cancer, and endocervical endocarcinoma.In this study, we explored the application of this tbns grading system in ureter urothelial carcinoma consisting 87 surgically resected cases and no neoadjuvant therapy.Tumor budding and the cell nest size were assessed and correlated with clinicopathological data and survival.The results showed that higher tumor budding, cell nest size, and tbns grading system were strongly related to shorter overall survival (os), disease-specific survival (dss), and disease-free survival (dfs).Multivariate survival analysis showed the tbns grading system to be closely related to the independent prognosis of dfs and dss.In conclusion, the tbns grading system based on tumor budding and cell nest size, if further validated, could satisfactorily predict the prognosis of uterine urothelial carcinoma and be applicable in routine pathologic description of this cancer type.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
A Novel Game-Based Intelligent Test for Detecting Elderly Cognitive Function Impairment

S.This research explores the game-based intelligent test (gbit), predicts the possibilities of mini-mental state examination (mmse) scores and the risk of cognitive impairment, and then verifies gbit as one of the reliable and valid cognitive assessment tools.Methods.This study recruited 117 elderly subjects in taiwan (average age is 79.92 ± 8.68 , average height is 156.91 ± 8.01 , average weight is 59.14 ± 9.67 , and average mmse score is 23.33 ± 6.16 ).A multiple regression model was used to analyze the gbit parameters of the elderly’s reaction, attention, coordination, and memory to predict their mmse performance.The binary logistic regression was then utilized to predict their risk of cognitive impairment.The statistical significance level was set as α = 0.05 .Results.Multiple regression analysis showed that gender, the correct number of reactions, and the correct number of memory have a significantly positive predictive power on mmse of the elderly ( f = 37.60 , r 2 = 0.69 , and p &lt; 0.05 ).Binary logistic regression analysis noted that the correct average number of reactions falls by one question, and the ratio of cognitive dysfunction risk increases 1.09 times ( p &lt; 0.05 ); the correct average number of memory drops by one question, the ratio of cognitive dysfunction risk increases 3.76 times ( p &lt; 0.05 ), and the overall model predictive power is 88.20% (sensitivity: 84.00%; specificity: 92.30%).Conclusions.This study verifies that gbit is reliable and can effectively predict the cognitive function and risk of cognitive impairment in the elderly.Therefore, gbit can be used as one of the feasible tools for evaluating older people’s cognitive function.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
A Meta-Analysis on Evaluation of Nosocomial Infections Amongst Patients in a Tertiary Care Hospital

Hospital-acquired infections, also known as nosocomial infections, are one of the many severe outcomes amongst patients in tertiary care hospitals.Hospital-acquired influenza is amongst the most common infection which has affected huge population.Objective.We have performed a meta-analysis in order to summarize the effects of epidemiology and clinical characteristics in hai.Methods.We performed literature review with help of pub med, cochrane library, embase, scopus, web of science, china national knowledge infrastructure (cnki), the global index medicus (gim), and other clinical databases till 2021.Many random models were used in order to obtain pooled proportions, mean difference, odds ratio, and ci.Results.A total of six studies were analyzed, where a total of 491 nosocomial and 4030 nonnosocomial infection cases were reported.The odds ratio of mortality was 0.02 with 95% ci and the risk ration for males was 1.08 with 95% ci.Conclusion.The proportion of nosocomial infections in cases of influenza was higher in patients admitted in tertiary care hospitals.Thus, a surveillance system for vaccination for all the high-risk patients must be made mandatory.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Meta-Analysis;
A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

In the field of intelligent transportation systems (its), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations.Object detection and shadow elimination are the main tasks in this area.Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence.Additionally, other challenges arise in regard to video surveillance, including the recognition of text.Based on shadow elevation, we present in this work an inner-outer outline profile (ioopl) algorithm for detecting the three levels of object boundaries.A system of video surveillance monitoring of traffic can be incorporated into this method.It is essential to identify the type of detected objects in intelligent transportation systems (its) to track safely and estimate traffic parameters correctly.This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation.This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (widrow-hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types.An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly.Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels.The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques.In regard to video text recognition, number plate recognition is a challenging task.

Journal: Wireless Communications and Mobile Computing (2022)
Publication types: Research Article;
A Dynamic Equilibrium Mechanism of Core Layer Interests in the Mobile Medical Platform Ecosystem

In recent years, with the development of the mobile internet, big data, and cloud computing, the mobile medical platforms such as ding xiang yuan aggregating platform ecological resources have played an irreplaceable role in improving efficiency, optimizing resource allocation, and even promoting the transformation and upgrading of the medical industry.Despite all this, most mobile medical platforms in china still face many problems, including the immature business model, the stagnation of the interaction of knowledge and information among platform members, and the weak platform competitiveness.Based on a review of the platform and commercial ecosystems, this paper adopts the evolutionary game method and simulation to analyze the evolutionary stability strategy of operators, partners, and users in the core layer of the platform during preflow and postflow periods of a mobile medical platform, hence, to construct a beneficial dynamic equilibrium model of a platform business ecosystem under the optimal decisions made by all parties involved in the platform: the goal in the early stage (preflow period) is to increase platform user flow.Hence, the knowledge/information sharing of platform users is needed to enhance platform’s visibility.While in the late period (postflow period), when the platform user flow reaches a certain scale, platform’s goal is to promote revenue, which relies mainly on the pricing strategy.It is critical to promote the stability of the platform and the dynamic balance of interests at the core layer in the pricing process.This paper applies the platform business ecosystem theory and the evolutionary game theory to mobile medical platform development, contributing theoretically and practically in the following: (1) providing a more solid theoretical support for the mobile medical platform research and enriching the theoretical framework of the platform business ecosystem; (2) proposing the dynamic equilibrium model based on the optimal decisions of the platform core layers, which help to reveal the inherent law of the evolution of the mobile medical platform; (3) providing policy suggestions and management implications in constructing an appropriate business ecosystem and achieving sustainable development in mobile medical platforms.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;