syedzayyan/paper_mill_benchmark huggingface dataset:
Identification of Key Genes and Immune Infiltrate in Nonalcoholic Steatohepatitis: A Bioinformatic Analysis

Nonalcoholic steatohepatitis (nash) can progress to cirrhosis and hepatic carcinoma and is closely associated with changes in the neurological environment.The discovery of new biomarkers would aid in the treatment of nash.Methods .Data gse89632 were downloaded from the gene expression omnibus (geo) database, and r package “limma” was used to identify differentially expressed genes (degs) for nash vs.Normal tissues.The string database was used to construct a protein‐protein interaction (ppi) network, and the cytoscape software program (version 3.80) was used to visualize the ppi network and identify key genes.The immune infiltration of nash was determined using the r package “cibersort”.Results .We screened 41 degs.Go and kegg enrichment analyses of the degs revealed the enrichment of pathways related to nafld steatosis and inflammation.A ppi network analysis was also performed on the degs, and seven genes (myc, cxcl8, fos, socs1, socs3, il6, and ptgs2) were identified as hub genes.An immune infiltration assessment revealed that macrophages m2, memory resting cd4+ t cells, and γδ t cells play important roles in the immune microenvironment of nash, which may be mediated by the seven identified hub genes.

Journal: BioMed Research International (2021)
Publication types: Research Article;
Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved.Now society is gradually moving towards the era of experience economy.From the perspective of experience economy, patients’ demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements.Decision tree algorithm is a kind of data mining algorithm.Data mining technology is a young technology for data analysis.It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy.This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as id3, c4.5, and c5.0.Based on the analysis of patients’ satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%.The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%.It can be seen that the effect of the model is good and can be effectively predicted.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Identification and Validation of Immune Markers in Coronary Heart Disease

Coronary heart disease (chd) is an ischemic heart disease involving a variety of immune factors.This study was aimed at investigating unique immune and m6a patterns in patients with chd by gene expression in peripheral blood mononuclear cells (pbmcs) and at identifying novel immune biomarkers.Methods.The cibersort algorithm and single-sample gene set enrichment analysis (ss gsea) were applied to assess the population of specific infiltrating immunocytes.Weighted gene coexpression network analysis (wgcna) was utilized on immune genes matching chd.A prediction model based on core immune genes was constructed and verified by a machine learning model.Unsupervised cluster analysis identified various immune patterns in the chd group according to the abundance of immune cells.Methylation of n6 adenosine- (m6a-) related gene was identified from the literature, and t-distributed stochastic neighbor embedding (t-sne) analysis was used to determine the rationality of the m6a classification.The association between m6a-related genes and various immune cells was estimated using heat maps.Results.22/28 immune-associated cells differed between the chd and normal groups, and a significant difference was detected in the expression of 21 m6a-related genes.The proportion of immune-related cells (activated cd4+ t cells and cd8+ t cells) in the peripheral blood of the chd group was lower than that of the normal group.The immune genes were divided into four modules, of which the turquoise modules showed a significant association with coronary heart disease.Eight hub immune genes (pdgfra, gnly, osmr, nudt6, fgfr2, il2rb, tpm2, and s100a1) can well distinguish the chd group from the normal group.Two different immune patterns were identified in the chd group.Interestingly, a significant association was detected between the m6a-related genes and immune cell abundance.Conclusion.In conclusion, we identified different immune and m6a patterns in chd.Thus, it could be speculated that the immune system plays a crucial role in chd, and m6a is correlated with immune genes.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

S.Colon cancer (crc), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis.So it is urgent to employ a reasonable manner to assess the prognosis of patients.We developed and validated a gene model for predicting crc risk.Methods.The gene expression omnibus (geo) database was used to extract the gene expression profiles of crc patients ( n = 181 ) from geo to identify genes that were differentially expressed between crc patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms.Cluster analysis using the longest distance method was drawn out, and kaplan–meier (km) survival analysis was used to compare the clusters.Meanwhile, the risk score was evaluated in three independent datasets including the geo and illumina hi seq sequencing platforms.The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median.And survival roc analysis was used to evaluate the prognostic model.Finally, the gene set enrichment analysis (gsea) was performed for further functional enrichment analyses.Results.A 10-gene model was obtained, including 7 negative impact factors (slc39a14, aacs, erp29, lamp3, tmem106c, tmed2, and slc25a3) and 3 positive ones (cnpy2, grb10, and pbk), which related with several important oncogenic pathways (kras signaling, tnf-α signaling pathway, and wnt signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis).By using colon cancer datasets from the cancer genome atlas (tcga), the model was validated in km survival analysis ( p ≤ 0.001 ) and significant analysis with recurrence time ( p = 0.0018 ).Conclusions.This study firstly developed a stable and effective 10-gene model by using novel combined methods, and crc patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
C-Reactive Protein, Procalcitonin, and a Novel Pathogenesis and Therapeutic Target of Thrombocytopenia in Sepsis

The aim of the study is to analyze the clinical characteristics, pathogen distribution, and drug sensitivity information of adult sepsis, and to provide reference for empirical clinical use; to explore the relationship between c-reactive protein (crp) and calcitonin (pct) the clinical value in the diagnosis of adult sepsis.Methods.We collected 455 cases of hospitalized patients with positive blood culture, including 352 cases with sepsis and 103 cases without sepsis; 1609 cases of hospitalized patients with suspected infection and negative blood culture, including 287 cases of sepsis, and 518 cases of non-infectious systemic inflammatory response syndrome (sirs) and 804 cases of local infection.Age, gender, route of admission, admission status, crp, pct, and white blood cell (wbc) levels were collected from the patients.The differences between the factors were statistically analyzed, and the receiver operating characteristic curve (roc curve) was plotted to obtain the optimal cut-off values of crp and pct and their area under the curve (auc), and to compare the crp, pct and pct, and the crp + pct tandem to diagnose sepsis sensitivity and specificity.Results.(1) 387 pathogenic strains were isolated from blood cultures of patients with sepsis, 71.06% gram-negative, 26.87% gram-positive and 2.07% fungi.(2) among gram-positive bacteria, staphylococcus aureus was 87.5% resistant to penicillin and sensitive to vancomycin, milantropine, and teicoplanin; among gram-negative bacteria, escherichia coli was resistant to piracillin the resistance rate was 73.1%, fully susceptible to viraemia (100%) and resistant to imipenem, amikacin, and lacillin/tazole.(3) among patients with positive blood cultures, crp and pct levels were higher in patients with sepsis than in those without sepsis.Pairwise comparison of roc curves showed that the diagnostic value of pct was greater than that of crp ( p = 0.016 ).Conclusion.Crp and pct have a good reference value for diagnosis of sepsis patients and determination of the degree of infection in septic patients, especially pct is more valuable for diagnosis of sepsis.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;
Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

With the advent of the internet of things (io t), human-assistive technologies in healthcare services have reached the peak of their application in terms of diagnosis and treatment process.These devices must be aware of human movements to provide better aid in clinical applications as well as the user’s daily activities.In this context, real-time gait analysis remains to be key catalyst for developing intelligent assistive devices.In addition to machine and deep learning algorithms, gait recognition systems have significantly improved in terms of high accuracy recognition.However, most of the existing models are focused on improving gait recognition while ignoring the computational overhead that affects the accuracy of detection and even remains unsuitable for real-time implementation.In this research paper, we proposed a hybrid gated recurrent unit (gru) based on bat-inspired extreme convolutional networks (bat-ecn) for the effective recognition of human activities using gait data.The gait data are collected by implanting the wearable internet of things (wio t) devices invasively.Then, a novel gru and ecn networks are employed to extract the spatio-temporal features which are then used for classification to realize gait recognition.Extensive and comprehensive experimentations have been carried out to evaluate the proposed model using real-time datasets and also other benchmarks such as whu gait and ou-isir datasets.To prove the excellence of the proposed learning model, we have compared the model’s performance with the other existing hybrid models.Results demonstrate that the proposed model has outperformed the other learning models in terms of high gait classification and less computational overhead.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Higher Chronic Endometritis Incidences within Infertile Polycystic Ovary Syndrome Clinical Cases

Clinical cases of a polycystic ovarian syndrome (pcos) have prolonged subclinical inflammation.Hysteroscopy has revealed worsened chronic endometritis (ce), particularly endometrial diffuse hyperemia, in pcos patients.However, the possible relationships between pcos and ce remain largely unexplored.Methods.This retrospective-based investigation was conducted on 3336 infertile patients.The pcos group consisted of 508 patients, while the non-pocs group consisted of 2828 individuals with normal ovarian function.Their clinical features and ce prevalence diagnosed with hysteroscopy were compared.The risk factors affecting the incidence of diffuse endometrial hyperemia were analyzed by binary logistic regression.Results.The pcos cohort and the non-pcos cohort showed marked variations in age, body mass index (bmi), infertility (primary, secondary), basal hormone level (b fsh, b lh, b t, and prl), anti-müllerian hormone (amh), and ca125 p &lt; 0.05 .The prevalence of ce in pcos women was 41.73% (212/508), markedly higher than the 28.50% in the non-pcos cohort (806/2828).Variations within diffuse endometrial hyperemia prevalence were especially marked p &lt; 0.05 .Furthermore, we found that the variables of bmi, b lh, b t, and amh correlated with diffuse endometrial hyperemia.Conclusions.Ce prevalence was elevated in clinical cases of infertility associated with pcos, and diffuse endometrial hyperemia was prevalent, as indicated by hysteroscopy.Furthermore, increased bmi, b lh, b t, and amh levels all contribute to the risk of diffuse endometrial hyperemia.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Heart Rate Variability and Chronic Kidney Disease in Patients with Type 2 Diabetes

To evaluate whether heart rate variability (hrv) as a measure of cardiac autonomic neuropathy (can) is associated with chronic kidney disease (ckd) in chinese adults with type 2 diabetes mellitus (t2dm) in china.392 individuals of t2dm were entered in this study, all these subjects undertook the holter electrocardiogram for 24 hours to get the hrv parameters.Of these t2dm patients, 126 (37.3%) had ckd, and most of the hrv parameters were lower in this group than in those without ckd.Decreased hrv parameters were strongly related with ckd in spearman’s correlation analysis.After adjustments for variables, the logistic regression showed that standard deviation of the averaged normal rr intervals for all 5-minute segments (sdann) was independently associated with decreased estimated glomerular filtration rate ( e gfr &lt; 60 m l/min/1.73 m2) ( or = 0.988 ; 95% ci, 0.978-0.998; p = 0.015 ) and increased urine albumin : creatinine ratio uacr ≥ 30 mg/g cr ( or = 0.992 ; 95% ci, 0.985-0.998; p = 0.015 ).A decreased 24-hour time domain hrv parameter, sdann, was strongly associated with both e gfr and uacr among chinese t2dm.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Health Promotion Effects of Sports Training Based on HMM Theory and Big Data

In order to better analyze human health status and guide people to carry out reasonable physical training, this paper puts forward the construction method of human health status evaluation model after sports training based on big data.Firstly, the characteristic information of human health status after sports training is collected based on big data technology, and the evaluation index and evaluation algorithm of human health status after sports training are constructed.The evaluation system of human health status after sports training is constructed.Finally, the experiment proves that the proposed evaluation model of human health status after sports training based on big data has high practicability in the process of practical application and fully meets the research requirements.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Geological Mineral Energy and Classification Based on Machine Learning

In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed.Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (svm) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated.The results show that the ratio of ore‐bearing unit and non‐ore‐bearing unit is 1 : 1, and the best random forest training model is obtained.70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model.The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%.Both model training accuracy and model testing accuracy are very high, which proves the accuracy of rf model construction and achieves satisfactory results.In this study, a wireless sensor is successfully applied to 3d mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future.Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.

Journal: Wireless Communications and Mobile Computing (2021)
Publication types: Research Article;
Genome-Wide Screening Identifies Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma

Hepatocellular carcinoma (hcc) is a common malignancy with a poor prognosis.Therefore, there is an urgent call for the investigation of novel biomarkers in hcc.In the present study, we identified 6 upregulated lnc rnas in hcc, including linc01134, rhpn1‐as1, nrav, cmb9‐22p13.1, mkln1‐as, and mapkapk5‐as1.Higher expression of these lnc rnas was correlated to a more advanced cancer stage and a poorer prognosis in hcc patients.Enrichment analysis revealed that these lnc rnas played a crucial role in hcc progression, possibly through a series of cancer‐related biological processes, such as cell cycle, dna replication, histone acetyltransferase complex, fatty acid oxidation, and lipid modification.Moreover, competing endogenous rna (ce rna) network analysis revealed that these lnc rnas could bind to certain mi rnas to promote hcc progression.Loss‐of‐function assays indicated that silencing of rhpn1‐as1 significantly suppressed hcc proliferation and migration.Though further validations are still needed, these identified lnc rnas could serve as valuable potential biomarkers for hcc prognosis.

Journal: BioMed Research International (2021)
Publication types: Research Article;
FLG Is a Potential Biomarker of Prognosis and Immunotherapy in Skin Cutaneous Melanoma

Skin cutaneous melanoma is one of most aggressive type of cancers worldwide.Therefore, the identification of skcm biomarkers is of great importance.Flg gene is one of the genes that encode proteins involved in epidermal formation.This was the first time to study the role of flg in the prognosis and immune infiltrates of skin cutaneous melanoma.Methods.We downloaded the somatic mutation data of 471 skcm patients from the cancer genome atlas (tcga) database and analyzed the mutation profiles with “maf tools” package.The expression of flg and the overall survival in skcm were analyzed by gepia.Additionally, univariate and multivariate cox analyses were used to compare several clinical features with survival rates.We used timer to investigate flg expression and collection of immune infiltration levels in skcm, as well as cumulative survival in skcm.Meanwhile, we also used cibersort to investigate the association between flg and cancer immune infiltration.In addition, gene set enrichment analysis (gsea) was performed using the tcga dataset.Furthermore, data from geo and hpa was used to validate the results.Results.Single nucleotide polymorphism (snp) happened more frequently than insertion or deletion, and c &gt; t was the most common of snv in skcm.We selected the first 15 mutated genes by analyzing 471 melanoma samples, and the prognosis analysis showed that only the high expression of mutated flg gene was significantly correlated with the poor prognosis of skcm.Multivariate cox analysis showed that age, the worse tumor status, less lymph node metastasis, and flg expression were independent factors for prognosis.Specifically, lower infiltration levels of b cell, cd8+ t cells, neutrophils, and dendritic cells correlated with poor survival outcomes in skcm.Gsea revealed that flg is closely related to cancer pathways and epidermal cell proliferation.In addition, the previous conclusions can be verified from external data from geo and hpa.Conclusion.The discovery of mutant gene flg as a biomarker of skcm helps elucidate how changes in the immune environment promote the occurrence of cutaneous melanoma.Further analysis suggested that flg might be a new predictor of skcm prognosis.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Ferroptosis-Related Long Noncoding RNAs as Prognostic Marker for Colon Adenocarcinoma

The incidence of colon adenocarcinoma (coad) has been increasing over time.Although ferroptosis and long noncoding rnas (lnc rnas) have been extensively reported to participate in the tumorigenesis and development of coad, few studies have investigated the role of ferroptosis-related lnc rnas in the prognosis of coad.Methods.Gene-sequencing and clinical data for coad were obtained from the cancer genome atlas database.The coexpression network was constructed using known ferroptosis-related genes.Cox and least absolute shrinkage and selection operator regression were used to screen ferroptosis-related lnc rnas with prognostic value and to identify a predictive model of coad.Patients with coad were divided into low- and high-risk groups according to their risk score.Cases of coad in the international cancer genome consortium database were included as the testing cohort.Results.In total, nine lnc rnas (linc02381, ac105219.1, ac009283.1, linc01011, elfn1-as1, eif3j-dt, nkila, linc01063, and snhg16) were considered prognostic factors for coad.Then, a risk score model was established.The overall survival rate of coad patients was negatively associated with the risk score.Kaplan–meier analyses in the original and testing cohorts showed similar results.The expression of the lnc rnas in tissue was consistent with the risk score, and the relationship with tumor mutation burden, immunity, and drug sensitivity presented a marked link between the signature and coad.A nomogram was established for clinical applications.Conclusions.Nine ferroptosis-related lnc rnas and the established signature have a certain predictive value for prognosis of coad patients and can be used as potential research targets for exploring treatment of coad.

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