Nonzero solutions for a class of set-valued variational inequalities in reflexive Banach spaces

AbstractIn this paper, we study the existence of nonzero solutions for a class of set-valued variational inequalities involving set-contractive mappings by using the fixed point index approach in reflexive Banach spaces. Some new existence theorems of nonzero solutions for this class of set-valued variational inequalities are established

Effect of a structurally modified human granulocyte colony stimulating factor, G-CSFa, on leukopenia in mice and monkeys

<p>Abstract</p> <p>Background</p> <p>Granulocyte colony stimulating factor (G-CSF) regulates survival, proliferation, and differentiation of neutrophilic granulocyte precursors, Recombinant G-CSF has been used for the treatment of congenital and therapy-induced neutropenia and stem cell mobilization. Due to its intrinsic instability, recombinant G-CSF needs to be excessively and/or frequently administered to patients in order to maintain a plasma concentration high enough to achieve therapeutic effects. Therefore, there is a need for the development of G-CSF derivatives that are more stable and active in vivo.</p> <p>Methods</p> <p>Using site-direct mutagenesis and recombinant DNA technology, a structurally modified derivative of human G-CSF termed G-CSFa was obtained. G-CSFa contains alanine 17 (instead of cysteine 17 as in wild-type G-CSF) as well as four additional amino acids including methionine, arginine, glycine, and serine at the amino-terminus. Purified recombinant G-CSFa was tested for its in vitro activity using cell-based assays and in vivo activity using both murine and primate animal models.</p> <p>Results</p> <p>In vitro studies demonstrated that G-CSFa, expressed in and purified from <it>E. coli</it>, induced a much higher proliferation rate than that of wild-type G-CSF at the same concentrations. In vivo studies showed that G-CSFa significantly increased the number of peripheral blood leukocytes in cesium-137 irradiated mice or monkeys with neutropenia after administration of clyclophosphamide. In addition, G-CSFa increased neutrophil counts to a higher level in monkeys with a concomitant slower declining rate than that of G-CSF, indicating a longer half-life of G-CSFa. Bone marrow smear analysis also confirmed that G-CSFa was more potent than G-CSF in the induction of granulopoiesis in bone marrows of myelo-suppressed monkeys.</p> <p>Conclusion</p> <p>G-CSFa, a structurally modified form of G-CSF, is more potent in stimulating proliferation and differentiation of myeloid cells of the granulocytic lineage than the wild-type counterpart both in vitro and in vivo. G-CSFa can be explored for the development of a new generation of recombinant therapeutic drug for leukopenia.</p

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field

nCD64 index as a novel inflammatory indicator for the early prediction of prognosis in infectious and non-infectious inflammatory diseases: An observational study of febrile patients

BackgroundGenerally, febrile patients admitted to the Department of Infectious Diseases, Fudan University Affiliated Huashan Hospital, China may eventually be diagnosed as infectious (ID) or non-infectious inflammatory diseases (NIID). Furthermore, mortality from sepsis remains incredibly high. Thus, early diagnosis and prognosis evaluation of sepsis is necessary. Here, we investigated neutrophil (n)CD64 index profile in a cohort of febrile patients and explored its diagnostic and prognostic value in ID and NIID.MethodsThis observational cohort study enrolled 348 febrile patients from the Emergency Department and Department of Infectious Diseases. nCD64 index were detected using flow cytometry, and dynamically measured at different timepoints during follow-up. Procalcitonin (PCT), C-reactive protein (CRP), and ferritin levels were measured routinely. Finally, the diagnostic and prognostic value of nCD64 index were evaluated by receiver operating characteristic (ROC) analysis and Kaplan-Meier curve analysis.ResultsOf included 348 febrile patients, 238, 81, and 29 were categorized into ID, NIID, and lymphoma groups, respectively. In ID patients, both SOFA score and infection site had impact on nCD64 index expression. In NIID patients, adult-onset Still’s disease patients had the highest nCD64 index value, however, nCD64 index couldn’t distinguish between ID and NIID. Regardless of the site of infection, nCD64 index was significantly higher in bacterial and viral infections than in fungal infections, but it could not discriminate between bacterial and viral infections. In bloodstream infections, gram-negative (G-) bacterial infections showed an obvious increase in nCD64 index compared to that of gram-positive (G+) bacterial infections. nCD64 index has the potential to be a biomarker for distinguishing between DNA and RNA virus infections. The routine measurement of nCD64 index can facilitate septic shock diagnosis and predict 28-day hospital mortality in patients with sepsis. Serial monitoring of nCD64 index in patients with sepsis is helpful for evaluating prognosis and treatment efficacy. Notably, nCD64 index is more sensitive to predict disease progression and monitor glucocorticoid treatment in patients with NIID.ConclusionsnCD64 index can be used to predict 28-day hospital mortality in patients with sepsis and to evaluate the prognosis. Serial determinations of nCD64 index can be used to predict and monitor disease progression in patients with NIID

CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models

We are currently in an era of fierce competition among various large language models (LLMs) continuously pushing the boundaries of benchmark performance. However, genuinely assessing the capabilities of these LLMs has become a challenging and critical issue due to potential data contamination, and it wastes dozens of time and effort for researchers and engineers to download and try those contaminated models. To save our precious time, we propose a novel and useful method, Clean-Eval, which mitigates the issue of data contamination and evaluates the LLMs in a cleaner manner. Clean-Eval employs an LLM to paraphrase and back-translate the contaminated data into a candidate set, generating expressions with the same meaning but in different surface forms. A semantic detector is then used to filter the generated low-quality samples to narrow down this candidate set. The best candidate is finally selected from this set based on the BLEURT score. According to human assessment, this best candidate is semantically similar to the original contamination data but expressed differently. All candidates can form a new benchmark to evaluate the model. Our experiments illustrate that Clean-Eval substantially restores the actual evaluation results on contaminated LLMs under both few-shot learning and fine-tuning scenarios

Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and atherosclerosis

BackgroundAtherosclerosis (AS) is one of the leading causes of the cardio-cerebral vascular incident. The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.MethodsThe common differentially expressed genes (co-DEGs) with a consistent trend were identified via bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established via the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.ResultsA total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971–1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938–0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.ConclusionWe identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS