Cation exchange chromatography removes FXIa from a 10% intravenous immunoglobulin preparation

The presence of residual activated coagulation factor XI (FXIa) in some commercial intravenous immunoglobulin (IVIG) products has been identified as the root cause of a small number of thromboembolic events in patients who had received such therapy. Our objectives here were to design and evaluate the manufacturing process of GC5107, a 10% glycine-stabilized IVIG product, for its capacity to remove FXIa. The manufacturing process included a cation exchange chromatography (CEX) step, which employs a resin that binds immunoglobulin G (IgG) with high capacity. Procoagulant activity was assessed using Western blot analysis, enzyme-linked immunosorbent assay, thrombin generation assay, chromogenic FXIa assay, and non-activated partial thromboplastin time (NaPTT) assay. A spiking study in which large quantities of FXIa were added to samples before CEX chromatography was used to examine the robustness of the process to remove FXIa. Western blot and ELISA analyses demonstrated that residual FXIa remained in the intermediate manufacturing products until after CEX chromatography, when it was reduced to undetectable levels. The spiking study demonstrated that CEX chromatography removed >99% of FXI protein and reduced FXI activity to below detection limits, even in samples containing 158-fold greater FXIa levels than that of normal samples. Procoagulant activity in 9 consecutive lots of GC5107 was reduced to below the detection limits of the thrombin generation and chromogenic FXIa assays (<1.56 IU/ml and <0.16 IU/ml, respectively). The NaPTT of >250 s in all 9 lots indicated very low levels of procoagulant activity. We demonstrate that a novel 10% IVIG manufacturing process including CEX chromatography is a robust means of removing FXIa from the final preparation

Mumefural Ameliorates Cognitive Impairment in Chronic Cerebral Hypoperfusion via Regulating the Septohippocampal Cholinergic System and Neuroinflammation

Chronic cerebral hypoperfusion (CCH) causes cognitive impairment and neurogenic inflammation by reducing blood flow. We previously showed that Fructus mume (F. mume) improves cognitive impairment and inhibits neuroinflammation in a CCH rat model. One of the components of F. mume, Mumefural (MF), is known to improve blood flow and inhibit platelet aggregation. Whether MF affects cerebral and cognitive function remains unclear. We investigated the effects of MF on cognitive impairment and neurological function-related protein expression in the rat CCH model, established by bilateral common carotid arterial occlusion (BCCAo). Three weeks after BCCAo, MF (20, 40, or 80 mg/kg) was orally administrated once a day for 42 days. Using Morris water maze assessment, MF treatment significantly improved cognitive impairment. MF treatment also inhibited cholinergic system dysfunction, attenuated choline acetyltransferase-positive cholinergic neuron loss, and regulated cholinergic system-related protein expressions in the basal forebrain and hippocampus. MF also inhibited myelin basic protein degradation and increased the hippocampal expression of synaptic markers and cognition-related proteins. Moreover, MF reduced neuroinflammation, inhibited gliosis, and attenuated the activation of P2X7 receptor, TLR4/MyD88, NLRP3, and NF-κB. This study indicates that MF ameliorates cognitive impairment in BCCAo rats by enhancing neurological function and inhibiting neuroinflammation

Mumefural Improves Blood Flow in a Rat Model of FeCl3-Induced Arterial Thrombosis

Mumefural (MF), a bioactive component of the processed fruit of Prunus mume Sieb. et Zucc, is known to inhibit platelet aggregation induced by agonists in vitro. In this study, we investigated the anti-thrombotic effects of MF using a rat model of FeCl3-induced arterial thrombosis. Sprague–Dawley rats were intraperitoneally injected with MF (0.1, 1, or 10 mg/kg) 30 min before 35% FeCl3 treatment to measure the time to occlusion using a laser Doppler flowmeter and to assess the weight of the blood vessels containing thrombus. MF treatment significantly improved blood flow by inhibiting occlusion and thrombus formation. MF also prevented collagen fiber damage in injured vessels and inhibited the expression of the platelet activation-related proteins P-selectin and E-selectin. Moreover, MF significantly reduced the increased inflammatory signal of nuclear factor (NF)-κB, toll-like receptor 4 (TLR4), tumor necrosis factor (TNF)-α, and interleukin (IL)-6 in blood vessels. After administration, MF was detected in the plasma samples of rats with a bioavailability of 36.95%. Therefore, we suggest that MF may improve blood flow as a candidate component in dietary supplements for improving blood flow and preventing blood circulation disorders

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.Y

Quad-phased data mining modeling for dementia diagnosis

Abstract Background The number of people with dementia is increasing along with people’s ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians. Methods Therefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups. Results The proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013. Conclusions This research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients

Haplotype of Wild Korean Boars Infected by Classical Swine Fever Virus Subgenotype 2.1d

Classical swine fever virus (CSFV) is one of the major pathogens that causes severe economic damage to the swine industry. Circulation of CSFV in wild boars carries the potential risk of reintroducing the virus into CSFV-free pig farms. This study carried out a genetic analysis of CSFV isolates from wild boars and analyzed the mtDNA haplotypes of the wild boars. Blood samples (n = 2140) from wild Korean boars captured in 2020 were subjected to qRT-PCR to detect CSFV, which was classified as subgenotype 2.1d based on phylogenetic analysis. CSFV had been detected in wild boars only in northern regions (Gangwon and Gyeonggi) of South Korea between 2011 and 2019. However, CSFV was identified in wild boars in the more southern regions (Chungbuk and Gyeongbuk) in 2020. Based on mitochondrial DNA analysis, all wild boars with CSFV were haplotype 01 (H01). Thus, we presume that the H01 haplotype is more susceptible to CSFV. In the future, infection of wild boars by CSFV is expected to occur intermittently every year, and we predict that most wild boars infected with CSFV will be haplotype H01

Biphasic Functional Regulation in Hippocampus of Rat with Chronic Cerebral Hypoperfusion Induced by Permanent Occlusion of Bilateral Common Carotid Artery

<div><p>Background</p><p>Chronic cerebral hypoperfusion induced by permanent occlusion of the bilateral common carotid artery (BCCAO) in rats has been commonly used for the study of Alzheimer’s disease and vascular dementia. Despite the apparent cognitive dysfunction in rats with BCCAO, the molecular markers or pathways involved in the pathological alternation have not been clearly identified.</p><p>Methods</p><p>Temporal changes (sham, 21, 35, 45, 55 and 70 days) in gene expression in the hippocampus of rats after BCCAO were measured using time-course microarray analysis. Gene Ontology (GO) and pathway analyses were performed to identify the functional involvement of temporally regulated genes in BCCAO.</p><p>Results</p><p>Two major gene expression patterns were observed in the hippocampus of rats after BCCAO. One pattern, which was composed of 341 early up-regulated genes after the surgical procedure, was dominantly involved in immune-related biological functions (false discovery rate [FDR]<0.01). Another pattern composed of 182 temporally delayed down-regulated genes was involved in sensory perception such as olfactory and cognition functions (FDR<0.01). In addition to the two gene expression patterns, the temporal change of GO and the pathway activities using all differentially expressed genes also confirmed that an immune response was the main early change, whereas sensory functions were delayed responses. Moreover, we identified <i>FADD</i> and <i>SOCS3</i> as possible core genes in the sensory function loss process using text-based mining and interaction network analysis.</p><p>Conclusions</p><p>The biphasic regulatory mechanism first reported here could provide molecular evidence of BCCAO-induced impaired memory in rats as well as mechanism of the development of vascular dementia.</p></div

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations