Search

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models.

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the electroencephalogram (EEG) signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposed a novel EEG stroke signal feature extraction method by combining fuzzy entropy and hierarchical theory. Fuzzy entropy not only took the advantages of sample entropy, but also had less dependence on the length of time series and possessed better robustness to noise signals. It measured the similarity of two vectors based on Gaussian function instead of Heaviside function, avoiding discontinuity problems of sample entropy and approximate entropy. Hierarchical theory efficiently took advantages of the approximation information in low-frequency and the detail information in high-frequency. This was benefit for capturing a wealth of dynamic information and retaining redundant components. Support vector machine (SVM) was further used as the stroke signal classification model for classifying ischemic stroke and hemorrhagic stroke. The experimental results showed that, compared with other benchmarks, the classification accuracy based on the features of hierarchical fuzzy entropy is much higher than those benchmarks methods. Compared with the features of fuzzy entropy without using hierarchical theory, the classifier based on the features of hierarchical fuzzy entropy gave a much more improvement in classification performance by increasing accuracy from 68.03% to 96.72%. It meant that the proposed EEG stroke signal hierarchical fuzzy entropy feature extraction method was an efficient measure in classifying ischemic and hemorrhagic stroke.

In this study, omics-based analysis tools were used to explore the effect of glucose starvation and culture duration on monoclonal antibody (mAb) production in fed-batch CHO cell culture to gain better insight into how these parameters can be controlled to ensure optimal mAb productivity and quality. Titer and N-glycosylation of mAbs, as well as proteomic signature and metabolic status of the production cells in the culture were assessed. We found that the impact of glucose starvation on the titer and N-glycosylation of mAbs was dependent on the degree of starvation during early stationary phase of the fed-batch culture. Higher degree of glucose starvation reduced intracellular concentrations of UDP-GlcNAc and UDP-GalNAc, but increased the levels of UDP-Glc and UDP-Gal. Increased GlcNAc and Gal occupancy correlated well with increased degree of glucose starvation, which can be attributed to the interplay between the dilution effect associated with change in specific productivity of mAbs and the changed nucleotide sugar metabolism. Herein, we also show and discuss that increased cell culture duration negatively affect the maturation of glycans. In addition, comparative proteomics analysis of cells was conducted to observe differences in protein abundance between early growth and early stationary phases. Generally higher expression of proteins involved in regulating cellular metabolism, extracellular matrix, apoptosis, protein secretion and glycosylation was found in early stationary phase. These analyses offered a systematic view of the intrinsic properties of these cells and allowed us to explore the root causes correlating culture duration with variations in the productivity and glycosylation quality of monoclonal antibodies produced with CHO cells.

Takket være nylige fremskridt inden for den "omic" revolution af Chinese hamster ovary (CHO) celler, er udvikling og produktion af rekombinante terapeutiske proteiner i CHO cellefabrikker begyndt at blive præget med afsæt i systembiologi. En holistisk forståelse af titer og N-glykosylering af et produceret rekombinant protein i forhold til den tilknyttede celle kultiverings proces, samt en genomisk, proteomisk, metabolisk og fysiologisk forståelse, vise sig at være yderst effektiv når der samtidig er fokus på sikkerhed, effektivitet og pris, af det enkelte protein. Ved at kombinere og sammensætte viden indenfor CHO celle kultiverings teknologi, upstream proces udvikling, metabolisk optimering, og glyco-biologi, til et systematisk framework med fokus på kvalitet og kvantitet, kan produktionen af rekombinante terapeutiske proteiner blive optimeret.I det præsenterede arbejde er det seneste know-how indenfor analyse, kontrol og optimering af N-glykosylering, samt indflydelsen af N-glykosylering på terapeutiske proteiner, blevet grundigt revideret og analyseret. Mere specifikt er det blevet undersøgt hvordan man kan kontrollere og optimere N-glykosylering i CHO celler. Hoved fokus i dette PhD projekt har været at udvikle effektive metoder til modelering af N-glycosylering af CHO-producerede recombinante monoklonale antistoffer (mAb), således at ønskede glycosylerings mønstre kan opnåes, samtidig med at udvide forståelsen for de dybere mekanismer der styrer N-glycosylering set fra et systembiologisk perspektiv. To forskellige strategier blev brugt til at glyko-optimere med stor succes: 1) optimering af medie og kultiverings processer; 2) genetisk forbedring af CHO som cellefabrik.I den første del af tesen, er den første strategi demonstreret af flere successfulde case-studies, hvor process- og medieoptimering blev brugt til at styre N-glykosyleringen. Balancen mellem glukose- og aminosyremetabolismen blev kontrolleret ved at bruge galaktose som feed additiv og ved at ændre process parametre såsom udsåningstætheden og længden af kultiveringen. Ved at kontrollere balancen mellem glukose- og aminosyremetabolismen, kunne N-glycosyleringen påvirkes. Der er flere forklaringer på, hvorfor denne balance styrer glykosyleringen, herunder mekanismer associeret med produktion, metabolisme, proteomet, og fysiologi.I den anden del af tesen bliver både literaturen og eksperimentelle applikationer undersøgt, for at demonstrere hvorledes omics data og implementering af systembiologi kan udnyttes til at forstår biologiske mekanismer, herunder N-glycosylering i CHO celler.I den tredje og sidste del af tesen, bliver den anden strategi demonstreret. Lovende resultater viser at det er muligt at optimere N-glykosylering ved at modificere genetikken og metabolismen i CHO celler. Ved at overudtrykke enten N-acetylglucosaminyltransferase I (GnTI) proteiner kunne GlcNAc tilgængeligheden øges, med en medfølgende forbedring af matureringen af glykaner i mAbs.Resultaterne demonstrerer integrationen af systembiologi koncepter og process- og metabolisme modifikationer, som en effektiv måde hvorved N-glykosylering af CHO-producerede rekombinante terapeutiske proteiner kan kontrolleres og optimeres. Thanks to the recent advances in Chinese hamster ovary (CHO) “omic” revolution, the development of recombinant therapeutic protein bioprocessing using CHO cell factory started to merge with the new biological mindset called systems biology. In order to produce a CHO-derived recombinant therapeutic protein with ensured safety, efficacy and cost-effectiveness, holistic understanding of titer and N-glycosylation of the protein in relation to cell culture process as well as genomic, proteomic, metabolic and physiological status of the cells becomes a superior approach. Combining the knowledge of CHO cell culture technology, upstream process engineering, metabolic engineering, and glycobiology into a systematic framework allow us to improve the production of recombinant therapeutic protein towards an optimal balance between quantity and quality.In the presented work, recent know-how on impact, analysis, control and optimization of N-glycosylation were thoroughly reviewed. In particular, how to control and optimize N-glycosylation in CHO cells was exclusively studied. The main focus of this PhD project is to find effective approaches of modulating N-glycosylation of CHO-derived recombinant monoclonal antibody (mAb) towards desired patterns, and at the same time try to understand the underlying mechanisms of that from a systems biology perspective. Two different strategies were used and achieved great success in glyco-optimization: 1) optimize media and culture process; 2) Genetically optimize CHO cell factory.In the early part of the thesis, the first strategy was displayed by a number of successful case studies, in which process and media engineering approach was successfully used to direct N-glycosylation. Controlling the balance between glucose and amino acid metabolism, using galactose as feed additives, changing process parameters such as seeding density and cultivation duration are all demonstrated to be effective. The causal explanation of their impact on glycosylation can be various, including product, metabolism, proteome and physiology-associated mechanism.In the middle part of the thesis, both literature reviews and experimental applications were provided to demonstrate how to use omics data and implement systems biology to understand biological activities, especially N-glycosylation in CHO cells.In the last part of the thesis, the second strategy that apply genetic and metabolic engineering approach to improve N-glycosylation capability of CHO cells was also presented promising results. Overexpression of either N-acetylglucosaminyltransferase I (GnTI) in CHO cells was confirmed to improve the maturation of glycans in mAb.In conclusion, integrating the concept of systems biology with process and metabolic engineering has been demonstrated through a number of studies to be a superior way of controlling and optimizing N-glycosylation of CHO-derived recombinant therapeutic protein.