syedzayyan/paper_mill_benchmark huggingface dataset:
Predictive Value of CT Perfusion Imaging on the Basis of Automatic Segmentation Algorithm to Evaluate the Collateral Blood Flow Status on the Outcome of Reperfusion Therapy for Ischemic Stroke

Our objective was to study the predictive value of ct perfusion imaging based on automatic segmentation algorithm for evaluating collateral blood flow status in the outcome of reperfusion therapy for ischemic stroke.All data of 30 patients with ischemic stroke reperfusion in our hospital were collected and examined by ct perfusion imaging.Convolutional neural network (cnn) algorithm was used to segment perfusion imaging map and evaluate the results.The patients were grouped by regional leptomeningeal collateral score (r lmcs).Binary logistic regression was used to analyze the independent influencing factors of collateral blood flow on brain ct perfusion.The modified scandinavian stroke scale was used to evaluate the prognosis of patients, and the effects of different collateral flow conditions on prognosis were obtained.The accuracy of cnn segmentation image is 62.61%, the sensitivity is 87.42%, the similarity coefficient is 93.76%, and the segmentation result quality is higher.Blood glucose (95% ci = 0.943, p = 0.028 ) and ischemic stroke history (95% ci = 0.855, p = 0.003 ) were independent factors affecting the collateral blood flow status of stroke patients.Cbf (95% ci = 0.818, p = 0.008 ) and cbv (95% ci = 0.796, p = 0.016 ) were independent influencing factors of ct perfusion parameters.After 3 weeks of onset, the prognostic function defect score of the good collateral flow group (11.11%) was lower than that of the poor group (41.67%) ( p &lt; 0.05 ).The automatic segmentation algorithm has more accurate segmentation ability for stroke ct perfusion imaging and plays a good auxiliary role in the diagnosis of clinical stroke reperfusion therapy.The collateral blood flow state based on ct perfusion imaging is helpful to predict the treatment outcome of patients with ischemic stroke and further predict the prognosis of patients.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm

Timely prediction of the mechanism and characteristics of chronic liver disease using next-generation information technology is an effective way to improve the diagnosis rate of chronic liver disease.In this paper, we have proposed a modified backpropagation (bp) neural network with improved ant colony optimization algorithm to process multiple index attribute items describing chronic liver disease and construct a chronic liver disease assessment model.The proposed model is very effective in detecting chronic liver disease on time with acceptable level of accuracy and precision ratio.To verify these claims, the proposed scheme is checked experimentally where 125 groups of 20-dimensional medical test index data items of patients with chronic liver disease were analyzed.Moreover, 13-dimensional index items were preferentially selected as test index attribute items with high sensitivity to chronic liver disease using well-known roc curves.The 13-dimensional index items were reduced to 5-dimensional comprehensive data items by principal component analysis.The proposed neural network-based model was trained with 115 sets of test indicator sample sets, and the remaining 10 sets of sample sets were used as test samples.Compared with the original 20-dimensional data as the neural network input, the proposed model not only reduces the complexity but also improves the prediction accuracy by 15.07%.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors.However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (hcc) remains unknown.The cancer genome atlas (tcga) database was analyzed using weighted gene coexpression network analysis (wgcna) and differential analysis to discover essential inflammatory response-related genes (ifrgs).Cox regression studies, both univariate and multivariate, were employed to develop a prognostic ifrgs signature.Additionally, gene set enrichment analysis (gsea) was used to deduce the biological function of the ifrgs signature.Finally, we estimated immune cell infiltration using a single sample gsea (ss gsea) and x-cell.Our results revealed that, among the major hcc ifrgs, two (dnase1l3 and klkb1) were employed to create a predictive ifrg signature.The ifrg signature could correctly predict overall survival (o.s) as per kaplan-meier time-dependent roc curves analysis.It was also linked to pathological tumor stage and t stage and might be used as a prognostic predictor in hcc.Gsea analysis concluded that the ifrg signature might influence the immune response in hcc.Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups.As a result of our findings, dnasile may play a role in the tumor microenvironment.However, more research is necessary to confirm the role of dnase1l3 and klkb1.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Prediction of Cancer-Specific Survival of Brainstem Glioma in Children Based on Risk Stratification Model

To develop and authenticate a risk stratification framework and nomogram for ascertaining cancer-specific survival (css) among the pediatric brainstem gliomas.Methods.For patients less than 12 years, according to surveillance, epidemiology, and end results (seer), information from 1998 to 2016 is found in their databases.The survival outcomes, treatments, and demographic clinicopathologic conditions are scrutinized per the database validation, and training cohorts are divided and validated using multivariate cox regression analysis.A nomogram was designed, and predominantly, the risk stratification conceptualization engaged selected tenets according to the multivariate analysis.The model’s authenticity was substantiated through c-index measure and calibration curves.Results.There are 806 pediatric concerns of histologically concluded brainstem glioma in the research.According to multivariate analysis, age, grade, radiotherapy, and race (with p value &lt; 0.05) depicted independent prognostic variations of the pediatric gliomas.The nomogram’s c-index was approximately 0.75 and an accompanied predictive capability for css.Conclusion.The nomogram constructed in this glioma’s context is the primary predictor of using risk stratification.A combination of nomograms with the risk stratification mechanism assists clinicians in monitoring high-risk individuals and engage targeted accessory treatment.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Porous Lactose as a Novel Ingredient Carrier for the Improvement of Quercetin Solubility In Vitro

In this work, quercetin was loaded in the highly-porous lactose via the adsorption of quercetin molecules in ethanol.The method aims to improve the quercetin solubility and the loading capacity of lactose.The method relates to the synthesis of the highly-porous lactose with a particle size of ∼35 μm, a mean pore width of ∼30 nm, a bet surface area of 35.0561 ± 0.4613 m2/g, and a bjh pore volume of ∼0.075346 cc/g.After the quercetin loading in ethanol, bet surface area and bjh pore volume of porous lactose were reduced to 28.8735 ± 0.3526 m2/g and 0.073315 cc/g, respectively.The reduction rate was based on the quercetin loading efficiency of highly-porous lactose.Dsc analysis and xrd analysis suggest that the sediments of quercetin in the nanopores of porous lactose are crystalline.Ftir spectroscopy results suggest that there is no significant interaction between quercetin and lactose.The highly-porous lactose had a higher loading efficiency of 20.3% (w/w) compared to the α-lactose (with 5.2% w/w).The release rates of quercetin from the highly-porous lactose tablets were faster compared to the conventional α-lactose carrier.

Journal: Bioinorganic Chemistry and Applications (2021)
Publication types: Research Article;
Polymorphisms in MicroRNA Genes Associated with Schizophrenia Susceptibility but Not with Effectiveness of MECT

Schizophrenia (scz) is a common and complex psychiatric disease associated with hereditary and environmental risk factors.Micro rnas (mi rnas or mi rs) are small, noncoding rna molecules that endogenously regulate gene expression.Single nucleotide polymorphisms (snps) in related mi rna genes are associated with susceptibility of the disorder.We wonder if the snps have influence on the effectiveness of modified electroconvulsive therapy (mect) for scz.Rs1625579 within mi r-137, rs6577555 within mi r-34, and rs2296616 within mi r-107 were sequenced in 150 cases and 150 controls to check the potential association between the snps and scz.Our results showed that allele g in rs1625579 ( p = 0.005 , adjusted or = 1.379 , 95 % ci = 1.108 − 1.634 ), allele a in rs6577555 ( p = 0.014 , adjusted or = 1.246 , 95 % ci = 1.045 − 1.463 ), allele g in rs2296616 ( p &lt; 0.001 , adjusted or = 1.646 , 95 % ci = 1.374 − 1.879 ) are positively associated with the disorder risk.Mect courses did significantly decrease the level of the mi rnas, except for the variant of rs2296616 with the aa genotype.Schizophrenic phenotypes assessed by the positive and negative syndrome scale (panss) were improved after mect, and there was no significant relevance observed between the effectiveness of mect and the variants of these loci.Thus, our findings indicate that polymorphisms within the loci may be involved in the pathogenesis of scz, and mect is effective and unbiased for patients harboring different genotypes of the loci.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Period Analysis of Intraracial Differences in Incidence and Survival Rates in Epithelial Ovarian Cancer

To explain the difference in the incidence and relative survival in a population-based cohort of women with epithelial ovarian cancer (eoc) postdiagnosis in the last forty years.Eoc is the most common type of all ovarian cancers, but there is inadequate information about the variations related to long-term eoc survival.Methods.We acquired the incidence and relative survival rate data from the surveillance, epidemiology, and end results (seer) registries to analyze the epidemiological variations from 1974 to 2013 in eoc-affected individuals.The survival disparities in eoc-specific individuals due to age, race, and socioeconomic status (ses) were performed by kaplan-meier analysis.The results.The overall incidence of eoc progressively declined to 9.0 per 100,000 from 11.4 in the last forty years.The median survival rate improved to 48 months in the first decade from a previous of 27 months in the fourth decade.The 5-year relative survival rate (rsr) increased to 44.3% that was previously 32.3% at the same time.However, between whites and blacks, an increase from 11 to 18 months was observed in the median survival differences.Between the low and high poverty groups, it was increased from 7 months to 12 months, respectively.Conclusions.The incidence rate of rsr and eoc-specific individuals in the last forty years was improved.However, the survival rates among different races and ses differed over time.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Performance Evaluation System Based on Online Monitoring of Internet of Things

Research on performance evaluation system based on online monitoring of internet of things technology, taking medical imaging equipment as an example.Build the performance management platform of the internet of things through four aspects: data acquisition, network architecture, cpu, and display output.Select 100 medical imaging devices in clinical use in the hospital and divide them into the control group (50 sets) and the observation group (50 sets) according to different management modes.The control group adopts conventional performance management mode, and the observation group implements internet of things performance management mode.Compare the differences of equipment operation performance, cost-effectiveness, and service effect between the two groups.The experimental results showed that the equipment utilization rate, operation rate, inspection schedule balance, and technician schedule balance of the observation group were better than those of the control group, and the difference was statistically significant ( p &lt; 0.05).The quarterly cost-benefit increase of x-ray examination equipment, ct equipment, mr equipment, interventional radiotherapy equipment, color ultrasound instrument, and nuclear medicine equipment in the observation group was higher than that in the control group ( p &lt; 0.05).The satisfaction of imaging doctors, technicians, engineers, and patients with the equipment service quality of the observation group was higher than that of the control group; the difference was statistically significant ( p &lt; 0.05).It is proved that the internet of things performance management platform of medical imaging equipment can solve the problems of untimely information collection and unscientific scheduling during equipment operation, realize the real-time display and statistics of equipment operation performance, and improve the operation efficiency and service level of medical imaging equipment.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Performance Evaluation Model of Short-Term Mutual Funds Based on Return-Variance-Liquidity

In the past, research on evaluating the portfolio efficiency of mutual funds was mostly based on the mean-variance framework proposed by markowitz.However, for chinese short-term mutual funds (cstmf), short-term mutual funds with different lock-up periods will automatically roll over after the lock up period to restart the funds unless instructed to terminate.By reducing the liquidity of their investments, the lock-up period generates an opportunity cost for investors.The liquidity impacts of different cstmfs will depend on their lock-up periods.In this paper, we evaluate the portfolio efficiency of the funds by justifying using the lock-up period as a measurement to measure the liquidity risk faced by the investors from the perspective of investors.Therefore, here, we propose to use the return-variance-liquidity framework to evaluate the performance of the funds.Furthermore, we select 28 cstmf funds to conduct empirical analysis with appropriate dea models.Firstly, we carry out some comparisons with the traditional mean-variance framework.The results show that the length of the lock-up periods has a significant impact on the fund’s performance, which has important guidance for the investment strategy.Finally, we conduct the benchmarking analysis for the 28 funds and introduce a new dea model, present how to reduce liquidity risk under the premise of given return and variance, and provide valuable investment advices by combining these with sensitivity analysis.Overall, there are three conclusions: (1) most inefficient funds can be improved using portfolios consisting of funds with shorter lock-up period.(2) for some inefficient funds with shorter lock-up period, the current return and variance levels can be achieved by a portfolio.(3) among the funds with higher efficiency scores, funds with shorter lock-up period have higher stability.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

Due to the high amount of electronic health records, hospitals have prioritized data protection.Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed.Because of the large number of e-health records, hospitals have made data security a major concern.The cloud’s security cannot be guaranteed because it uses parallel processing and is distributed.The blockchain (bc) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks.An overview of blockchain (bc) technology in cloud storage to improve healthcare system security can be obtained by reading this paper.First, we will look at the benefits and drawbacks of using a basic cloud storage system.After that, a brief overview of blockchain cloud storage technology will be offered.Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided.This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data.Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection.Because of blockchain and it, business warfare has emerged, and governments in the middle east have embraced it.Thus, this research focused on the qualities that influence customers’ interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people’s knowledge of blockchain.One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the united theory of acceptance and use of technology (utaut).A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in middle eastern poor countries.A total of 443 randomly selected responses were tested using spss.Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (si), facilitation factors, personal innovativeness (pinn), and a perception of security risk (psr).Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (si), facilitating conditions, personal innovativeness (pinn), and perceived security risk (psr) during the covid-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Pattern and In-Hospital Mortality of Thoracoabdominal Injuries Associated with Motor Vehicle Accident-Related Spinal Injury: A Retrospective Single-Center Study

Motor vehicle accident (mva) is a global health hazard that results in spinal, thoracic, and abdominal injuries.Detailed studies on the association between mva-related traumatic spinal injury (tsi) and thoracoabdominal injuries are lacking.This study aims to elucidate the prevalence, pattern of association between these injuries, and related outcomes in terms of in-hospital mortality.Methods.This is a retrospective single-center study of mva-related tsi with thoracoabdominal associated injuries.Descriptive analysis was performed for gender, age, spinal injury level, thoracoabdominal injury region, admission day, hospital stay duration, and discharge category.The association between tsi and thoracoabdominal injury was analyzed, and the chi-square test was used to test the significance of differences.A statistically significant difference was considered at p values less than 0.05.Results.The cohort had a mean age of 33.6 ± 17.7 years with predominantly more males (85.1%).Thoracoabdominal injuries were present in 10.5% of mva-related tsis, and 9.2% of victims died during their hospital stay.There is a significant ( p = 0.045 ) association between the level of the spinal and the region of thoracoabdominal injuries.The presence of tsi-associated thoracic injury significantly ( p = 0.041 ) correlated with increased in-hospital mortality more than abdominal injury.Conclusion.Thoracoabdominal injuries concomitant with mva-related tsi cause considerable mortality.A pattern of association exists between the level of spinal and region of thoracoabdominal injury.Knowledge of this pattern is helpful in the routine practice of trauma health partitioners.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Passive Prescription of Secondary Prevention Medical Therapy during Index Hospitalization for Acute Myocardial Infarction Is Prevalent and Associated with Adverse Clinical Outcomes

Secondary prevention therapy reduces death and reinfarction after acute myocardial infarction (ami), but it is underutilized in clinical practice.Mechanisms for this therapeutic gap are not well established.In this study, we have explored and evaluated the impact of passive continuation compared to active initiation of secondary prevention therapy for ami during the index hospitalization.For this purpose, we have analyzed 1083 consecutive patients with ami to a tertiary referral hospital in hong kong and assessed discharge prescription rates of secondary prevention therapies (aspirin, beta-blockers, statins, and acei/arbs).Multivariate analysis was used to identify independent predictors of discharge medication, and kaplan–meier survival curve was used to evaluate 12-month survival.Overall, prescription rates of aspirin, beta-blocker, statin, and acei/arbs on discharge were 94.8%, 64.5%, 83.5%, and 61.4%, respectively.Multivariate analysis showed that prior use of each therapy was an independent predictor of prescription of the same therapy on discharge: aspirin (odds ratio (or) = 4.8, 95% ci = 1.9–12.3, p &lt; 0.01 ), beta-blocker (or = 2.5, 95% ci = 1.8–3.4, p &lt; 0.01 ); statin (or = 8.3, 95% ci = 0.4–15.7, p &lt; 0.01 ), and acei/arbs (or = 2.9, 95% ci = 2.0–4.3, p &lt; 0.01 ).Passive continuation of prior medication was associated with higher 1-year mortality rates than active initiation in treatment-naïve patients (aspirin (13.7% vs.5.7%), beta-blockers (12.9% vs.5.6%), and statins (11.0% vs.4.6%); all p &lt; 0.01 ).Overall, the use of secondary prevention medication for ami was suboptimal.Our findings suggested that the practice of passive continuation of prior medication was prevalent and associated with adverse clinical outcomes compared to active initiation of secondary preventive therapies for acute myocardial infarction during the index hospitalization.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Clinical Study;