syedzayyan/paper_mill_benchmark huggingface dataset:
Infiltration and Significance of CD103+CD8+ T Cells in Gastrointestinal Adenocarcinoma

Immunohistochemical staining is a common technique to study tissue morphology and in situ protein expression.With the rise of immunotherapy, more and more markers in the tumor microenvironment have been discovered.In this paper, the multiplex immunofluorescence staining method was used to stain paraffin sections of 35 cases of gastric adenocarcinoma tissue and 35 cases of colorectal adenocarcinoma tissue and its adjacent tissue.The infiltration of cd8+ t cells and cd103+cd8+ t cells in the slices was detected by using the slice scanner to form the map and case viewer2.0 software analysis.The experimental results in this paper showed that compared with adjacent tissues, the infiltration degree of cd8+ t cells in gastric adenocarcinoma tissue was significantly lower ( z = 2.244, p = 0.025, p &lt; 0.05), and the infiltration degree of cells was significantly lower ( z = 2.785, p = 0.005, p &lt; 0.05).In gastric adenocarcinoma tissue, the infiltration of cd8+ t cells was correlated with the tumor diameter of gastric adenocarcinoma ( p = 0.002).There was a statistically significant difference.The degree of cd103+cd8+ t cell infiltration was related to the survival of patients with gastric adenocarcinoma ( p = 0.002) and the degree of tumor differentiation ( p = 0.004).The infiltration of cd103+cd8+ t cells in gastric cancer tissue and colorectal cancer tissue is related to the survival of patients.Cd103+cd8+ t cells are thought to play a role in the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma.Experiments have shown that the more the cd103+cd8+ t cells, the better the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma, so the prognosis of gastric adenocarcinoma and colorectal adenocarcinoma can be improved by increasing cd103+cd8+ t cells.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification

The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage.This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities.The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities).The proposed approach overlapping activity recognition using cluster-based classification (oar-cb c) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level.Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level.We examine four partitioning and classification techniques with the same hierarchy for a fair comparison.The oar-cb c evaluates on smart home datasets aruba and milan using threefold and leave-one-day-out cross-validation.We used evaluation metrics: precision, recall, f score, accuracy, and confusion matrices to ensure the model’s reliability.The oar-cb c shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Implicit Environmental Injustice in Global Trade: Based on the MRIO Model

To answer these questions in light of the mrio model, this paper presents a study of environmental injustice affecting the global economy.Practical ideas and lifespan measurements are often used in studies of the embodied carbon industry.The input‐output table method is an important method for industrial embodied carbon research, which can be divided into the regional input‐output table method, bilateral input‐output table method, and multiregional input‐output table method.Bilateral and multistakeholder consultations are more accurate than regional proposals.Therefore, when studying the carbon industry implied by the two countries, the input‐output table of the two countries is usually used, and the multilateral input‐output table is more reliable for determining the input‐output calculation.Therefore, when studying local problems, it is advisable to adopt a variety of display strategies.The results show that in 2010, the carbon content of the carbon industry was 26,593 thousand tons, down 34.6% from 17,383 thousand tons in 2011, calculated at 2%.From 2012 to 2018, the carbon content grew from 31,051 tons in 2014 to 84,248 tons in 2018, with an average annual increase rate of 18%.The experimental results show that there is a large incidence of carbon emissions in the bilateral trade between china, the united states, and japan.The expansion of export industries is the main reason for the increase in carbon emissions between the two industries.The role of technology has narrowed this difference to some extent.

Journal: Journal of Environmental and Public Health (2022)
Publication types: Research Article;
Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning

To explore and evaluate the imaging manifestations of postoperative complications of bone and joint infections based on deep learning, a retrospective study was performed on 40 patients with bone and joint infections in the department of orthopedics of orthopedics hospital of henan province of luoyang city.Sensitivity and dice similarity coefficient (dsc) were used to evaluate the image results by convolutional neural network (cnn) algorithm.Imaging features of postoperative complications in 40 patients were analyzed.Then, three imaging methods were used to diagnose the features.Sensitivity and specificity were used to evaluate the diagnostic performance of three imaging methods for imaging features.Compared with professional doctors and biomarker algorithms, the sensitivity of cnn algorithm proposed was 90.6%, and dsc was 84.1%.Compared with traditional methods, the cnn algorithm has higher image resolution and wider and more accurate lesion area recognition and division.The three manifestations of soft tissue abscess, periosteum swelling, and bone damage were postoperative imaging features of bone and joint infections.In addition, compared with x-ray, ct examination and mri examination were better for the examination of imaging characteristics.Ct and mri had higher sensitivity and specificity than x-ray.The experimental results show that cnn algorithm can effectively identify and divide pathological images and assist doctors to diagnose the images more efficiently in clinic.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity.The forced vital capacity (fvc) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease.This paper proposes a deep learning-based fvc-net architecture to predict the progression of the disease from the patient’s computed tomography (ct) scan and the patient’s metadata.The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata.This input is then fed to a 3-layer net to obtain the final output.The performance of the proposed fvc-net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset.The model showcased significant improvement in the performance over other models for modified laplace log-likelihood (−6.64).Finally, the paper concludes with some prospects to be explored in the proposed study.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
IFN-γ induces PD-L1 through p38/JNK/ERK signaling pathways and counteracts the tumor promoting effect mediated by PD-L1 in Glioblastoma

Glioblastoma is the most malignant primary glioma.Conventional treatment methods that include surgery, radiotherapy, and chemotherapy have a limited curative effect on the tumor.With the deepening of molecular biology research, molecular targeted therapy has opened a new era of tumor therapy.Programmed death ligand 1 (pd-l1) has been proved to play a pivotal role in the tumor immune evasion process.Previous studies have confirmed the specific expression of pd-l1 in glioblastoma tissues and cells, but there are few studies on inflammation regulating pd-l1 in glioblastoma.In this study, real-time pcr, flow cytometry, and western blot were applied to detect pd-l1 in glioblastoma cells.Short hairpin rna was used to knock down pd-l1 in glioblastoma cells.Cell counting kit-8 experiment and wound-healing assay were used to detect the proliferation and migration of glioblastoma cells.Here we demonstrated that pd-l1 was overexpressed in glioblastoma cells, and interferon-gamma (ifn-γ) induces pd-l1 in glioblastoma cells via activating p38/jnk/erk signaling pathways.To summarize, pd-l1 promotes the occurrence and development of glioblastoma.Ifn-γ counteracts the tumor-promoting effects mediated by pd-l1 in glioblastoma.Ifn-γ regulates pd-l1 through multiple signaling pathways, but the total effect of ifn-γ-mediated inflammatory signals still need to be further explored in glioblastoma.Pd-l1 enhances the proliferation and migration of glioblastoma cells by regulating cdk4, cdk6, mmp-2, and vimentin molecules.Most importantly, targeting pd-l1 can be applied in the treatment of glioblastoma.We speculate that ifn-γ may affect glioblastoma through other pathways, and we will continue to further explore the mechanisms in the future.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Covid‐19, a severe respiratory disease caused by a new type of coronavirus sars‐co v‐2, has been spreading all over the world.Patients infected with sars‐co v‐2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients.Both patients could further spread the virus to other susceptible people, thereby making the control of covid‐19 difficult.The two major challenges for covid‐19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus.Therefore, new biomarkers at different omics levels are required for the large‐scale screening and diagnosis of covid‐19.Although some initial analyses could identify a group of candidate gene biomarkers for covid‐19, the previous work still could not identify biomarkers capable for clinical use in covid‐19, which requires disease‐specific diagnosis compared with other multiple infectious diseases.As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with covid‐19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, covid‐19, and other kinds of coronavirus.This dataset was first analysed by boruta, max‐relevance and min‐redundancy feature selection methods one by one, resulting in a feature list.This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules.The capacity of these findings to distinguish covid‐19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of covid‐19 diagnosis.

Journal: BioMed Research International (2021)
Publication types: Research Article;
Identification of Tumor Tissue of Origin with RNA-Seq Data and Using Gradient Boosting Strategy

Cancer of unknown primary (cup) is a type of malignant tumor, which is histologically diagnosed as a metastatic carcinoma while the tissue‐of‐origin cannot be identified.Cup accounts for roughly 5% of all cancers.Traditional treatment for cup is primarily broad‐spectrum chemotherapy; however, the prognosis is relatively poor.Thus, it is of clinical importance to accurately infer the tissue‐of‐origin of cup.Methods .We developed a gradient boosting framework to trace tissue‐of‐origin of 20 types of solid tumors.Specifically, we downloaded the expression profiles of 20,501 genes for 7713 samples from the cancer genome atlas (tcga), which were used as the training data set.The rna‐seq data of 79 tumor samples from 6 cancer types with known origins were also downloaded from the gene expression omnibus (geo) for an independent data set.Results .400 genes were selected to train a gradient boosting model for identification of the primary site of the tumor.The overall 10‐fold cross‐validation accuracy of our method was 96.1% across 20 types of cancer, while the accuracy for the independent data set reached 83.5%.Conclusion .Our gradient boosting framework was proven to be accurate in identifying tumor tissue‐of‐origin on both training data and independent testing data, which might be of practical usage.

Journal: BioMed Research International (2021)
Publication types: Research Article;
Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

The aim of this study was to identify prognostic fatty acid metabolism lnc rnas and potential molecular targeting drugs in uveal melanoma through integrated bioinformatics analysis.Methods.In the present study, we obtained the expression matrix of 309 fam-m rnas and identified 225 fam-lnc rnas by coexpression network analysis.We then performed univariate cox analysis, lasso regression analysis, and cross-validation and finally obtained an optimized uvm prognosis prediction model composed of four pfam-lnc rnas (ac104129.1, sos1-it1, idi2-as1, and dlgap1-as2).Results.The survival curves showed that the survival time of uvm patients in the high-risk group was significantly lower than that in the low-risk group in the train cohort, test cohort, and all patients in the prognostic prediction model ( p &lt; 0.05 ).We further performed risk prognostic assessment, and the results showed that the risk scores of the high-risk group in the train cohort, test cohort, and all patients were significantly higher than those of the low-risk group ( p &lt; 0.05 ), patient survival decreased and the number of deaths increased with increasing risk scores, and ac104129.1, sos1-it1, and dlgap1-as2 were high-risk pfam-lnc rnas, while idi2-as1 were low-risk pfam-lnc rnas.Afterwards, we further verified the accuracy and the prognostic value of our model in predicting prognosis by pca analysis and roc curves.Conclusion.We identified 24 potential molecularly targeted drugs with significant sensitivity differences between high- and low-risk uvm patients, of which 13 may be potential targeted drugs for high-risk patients.Our findings have important implications for early prediction and early clinical intervention in high-risk uvm patients.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Identification of Paxillin as a Prognostic Factor for Glioblastoma via Integrated Bioinformatics Analysis

Glioblastoma (gbm) is the most prevalent and aggressive type of brain tumor in the central nervous system.Clinical outcomes for patients with gbm are unsatisfactory.Here, we aimed to identify novel, reliable prognostic factors for gbm.Cox and interactive analyses were used to identify hub genes from the cancer genome atlas and the chinese glioma genome atlas datasets.After validation using various cohorts, survival analysis, meta‐analysis, and prognostic analysis were performed.Coexpression and enrichment analyses were performed to elucidate the biological pathways of hub genes involved in gbm.Estimate and cibersort methods were applied to analyze the association of hub genes with the tumor microenvironment (tme).Paxillin ( pxn ) was identified as a hub gene with a high expression in gbm.Pxn expression was negatively correlated with overall survival, progression‐free survival, and disease‐free survival in patients with gbm.Meta‐analysis and cox analysis revealed that pxn could act as an independent prognostic factor in gbm.In addition, pxn was significantly coexpressed with signal transducer and activator of transcription 3 and transforming growth factor β 1 and participated in focal adhesion, extracellular matrix/receptor interactions, and the phosphatidylinositol 3‐kinase/akt signaling pathway.The results of estimate and cibersort analyses revealed that pxn was implicated in tme alterations, particularly the infiltration of regulatory t cells, activated memory t cells, and activated natural killer cells.Pxn may be a reliable prognostic factor for gbm.Further studies are needed to validate these findings.

Journal: BioMed Research International (2022)
Publication types: Meta-Analysis;Research Article;
Identification of lncRNAs Associated with the Pathogenesis of Diabetic Retinopathy: From Sequencing Analysis to Validation via In Vivo and In Vitro Experiments

This study is aimed at screening for differentially expressed long noncoding rnas (lnc rnas) associated with the pathogenesis of diabetic retinopathy and verifying the role of lnc znrd1 in high glucose-induced injury of retinal microvascular endothelial cells.The retinal tissues of normal and diabetic rats were collected for high-throughput sequencing of differentially expressed lnc rnas.Retinal microvascular endothelial cells were treated with 50 m m glucose for 4 h, 8 h, 24 h, 48 h, and 72 h.Our results showed that compared with the control group, there were 736 differentially expressed lnc rnas in the retina tissue of the model group, including 226 upregulated genes and 736 downregulated genes.Based on the differentially expressed lnc rnas, kyoto encyclopedia of genes and genomes (kegg) analysis indicated that the erb b signaling pathway, transforming growth factor- (tgf-) β signaling pathway, pi3k − akt signaling pathway, cyclic adenosine 3,5-monophosphate (c amp) signaling pathway, mitogen-activated protein kinase (mapk) signaling pathway, and hypoxia-inducible factor-1 (hif-1) signaling pathway were likely involved in the regulation of diabetic retinopathy.Compared with the control group, the expression of lnc znrd1-as1 was significantly increased in retinal microvascular endothelial cells after treatment with high glucose for 24 h.Silencing lnc znrd1 promoted high glucose-induced apoptosis of microvascular endothelial cells.Additionally, silencing lnc znrd1 increased the expression levels of aldh7a1 and aldh3a2.In conclusion, lnc znrd1-as1 demonstrated potentially beneficial function against high glucose-induced retina cell injury by regulating aldh7a1 and aldh3a2 expressions.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Identification of Key Genes Related to the Obesity Patients with Osteoarthritis Based on Weighted Gene Coexpression Network Analysis (WGCNA)

Increasing evidence has suggested that obesity affects the occurrence and progression of osteoarthritis (oa).However, the underlying molecular mechanism that obesity affects the course of oa is not fully understood and remains to be studied.Methods.The gene expression profiles of the gse117999 and gse98460 datasets were derived from the gene expression omnibus (geo) database.Firstly, we explored the correlation between obesity and oa using chi-square test.Next, weighted gene coexpression network analysis (wgcna) was executed to identify obesity patients with oa- (obesity oa-) related genes in the gse117999 dataset by “wgcna” package.Moreover, differential expression analysis was performed to select the hub genes by “limma” package.Furthermore, ingenuity pathway analysis (ipa) and functional enrichment analysis (“cluster profiler” package) were conducted to investigate the functions of genes.Finally, the regulatory networks of hub genes and protein-protein interaction (ppi) network were created by the cytoscape 3.5.1 software and string.Results.A total of 15 differentially expressed obesity oa-related genes, including 9 lnc rnas and 6 protein coding genes, were detected by overlapping 66 differentially expressed genes (degs) between normal bmi samples and obesity oa samples and 451 obesity oa-related genes.Moreover, ccr10, leng8, qrfpr, uhrf1bp1, and hla-drb4 were identified as hub genes.Ipa results indicated that the hub genes were noticeably enriched in antimicrobial response, inflammatory response, and humoral immune response.Ppi network showed that ccr10 interacted more with other proteins.Gene set enrichment analysis (gsea) indicated that the hub genes were related to protein translation, cancer, chromatin modification, antigen processing, and presentation.Conclusion.Our results further demonstrated the role of obesity in oa and might provide new targets for the treatment of obesity oa.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Identification of Key Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

This study aimed to identify key genes associated with the pathogenesis of nasopharyngeal carcinoma (npc) by bioinformatics analysis.Methods.Datasets (gse13597 and gse34573) were screened and downloaded from the comprehensive gene expression database (geo).Geo2r online tool was adopted to analyze microarray data gse13597 and gse34573 related to npc.Volcano plot was generated using bioconductor in r software.“pheatmap” was used to draw heatmaps based on the top 10 regulated genes of gse13597 and gse34573.Go and kegg analyses were conducted via online tool david.We uploaded the degs of npc to string software and then used cytoscape software to draw ppi network of degs.Results.216 degs were obtained in gse13597 between patient and control group (111 up-regulated degs and 105 down-regulated degs).1101 degs were obtained in gse34573 (470 up-regulated degs and 641 down-regulated degs).63 common differential genes were screened named co-degs in the two datasets.These degs were mainly associated with defense response to bacterium, cell-matrix adhesion, chemokine-mediated signaling pathway, tissue homeostasis, humoral immune response, cilium movement, cilium organization, cilium assembly, and epithelial cilium movement.Kegg pathway enrichment analysis showed that degs were mainly involved in viral protein interaction with cytokine and cytokine receptor, salivary secretion, p53 signaling pathway, il-17 signaling pathway, cell cycle, pi3k-akt signaling pathway, and ecm-receptor interaction.We identified seven hub genes, including fn1, mmp-10, muc1, kif23, cdk1, muc5b, and muc5ac.Conclusions.Seven hub genes, including fn1, mmp-10, muc1, kif23, cdk1, muc5b, and muc5ac, might be therapeutic potential biomarkers of npc.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;