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Home > Capability of Bioactive Compounds from Medicinal Plants to Ameliorate the Neuronal Destruction in Animal Model of Parkinson Disease

Capability of Bioactive Compounds from Medicinal Plants to Ameliorate the Neuronal Destruction in Animal Model of Parkinson Disease

Thesis Info

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Author

Muhammad Rashid Ahmed

Program

PhD

Country

Pakistan

Thesis Completing Year

2018

Thesis Completion Status

Completed

Subject

Anatomy

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/11196/1/Muhammad%20Rashid%20Ahmed_Anatomy_2018_Baqai_PRR.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676725661351

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OBJECTIVE: To study the capability of bioactive compounds to ameliorate the neuronal destruction in animal model of Parkinson disease. MATERIALS AND METHODS:Rotenone was used to produce the Parkinsonism in mammalian model of rats and the bioactive compounds, chrysin, polydatin and CGA were used to ameliorate the neuronal destruction and the resulting effets of Parkinson in this study. A total of hundred male Sprague Dawley albino rats of weight 200 to 250 gm were divided randomly into five groups with twenty animals in each: control, rotenone+chrysin, rotenone+polydatin and rotenone+chlorogenic acid (CGA) group. Rotenone was dissolved in DMSO and migloyl 812 N at ratio of 2:98 respectively and was administered intraperitoneally at a dose of 3 mg/kg, daily for four week to produce the rat model of Parkinson disease. Mortality and weight changes were assessed, various behavioral tests were performed throughout the experiment to assess the neuro behavioral changes. At the end of experiment, animals were sacrificed, brains were fixed by perfusion method and light microscopic sections were prepared using H & E and cresyl violet method. Immunohistochemical sections were also prepared using anti tyrosine hydroxylase antibody, anti α synuclein antibody, anti ubiquitin antibody, to localize the damaged neurons and amelioration done by bioactive compounds. Anti GFAP antibody was used to localize the Astrocytes immunohistochemically. Dopaminergic neurons, α synuclein and ubiquitin aggregations and astrocytes were analysed for anychanges in their morphology and count in substantia nigra and striatum in Parkinsonism and the ameliorating effects of the bioactive compounds used in this study on them. RESULTS:Rotenone-induced Parkinsonism in animals showed a significant loss of body weight and increase in mortality.Parkinson animals showed deteriorating motor behaviour over the period of experiment. Parkinson animals showed a decrease in dopaminergic neurons in nigrostriatal region. TH immunohistochemistry (IHC) showed decreased immunoreactivity and number of TH positive dopaminergic neurons.Ubiquitin and α-synuclein IHC showed accumulation of α-synuclein & ubiquitin within the neuronal cytoplasm. Astrogliosis with increase in number and amoeboid morphology was also seen. Bioactive compounds not only significantly restored the rotenone-induced weight loss but also the behavioural changes. These compounds also showed protective effects in terms of less degeneration of nigrostriatal dopaminergic neurons and diminished immunoreactivity to anti ubiquitin and anti α-synuclein antibodies. These compounds also decreased the activation and number of astrocytes. CONCLUSION:This study concludes that bioactive compounds confer protection in rat model of Parkinson disease. They ameliorated the degeneration of the dopaminergic neurons, accumulation of α-synuclein and ubiquitin in the cytoplasm of neurons and activation & morphological alteration of astrocytes.
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Fast Speech Processing Algorithms for Real-Time Identification of Wanted Persons Using Digital Communication Networks

Telephony networks are frequently connected to computers for speech processing to extract useful information such as automatic speaker identification (ASI). Matching of feature vectors extracted from speech sample of an unknown speaker, with models of registered speakers is the most time consuming component in real-time speaker identification systems. Time controlling parameters are size d and count T of extracted test feature vectors as well as size M , complexity and count N of models of registered speakers. Reported speedup techniques for Vector quantization (VQ) and Gaussian mixture model (GMM) based ASI systems reduce test feature vector count T by pre-quantization and reduce candidate registered speakers N by pruning unlikely models which introduces accuracy degradation. Vantage point tree (VPT) indexing of code vectors has also been used to decrease the effect of parameter M on ASI speed for VQ based systems. Somehow parameter d has remained unexplored in ASI speedup studies. Speedup techniques for VQ based and GMM based real-time ASI without loss of accuracy are presented in this thesis. Speeding up closest code vector search (CCS) is focused for VQ based systems. Capability of partial distortion elimination (PDE), through reducing d parameter of codebook, was found more promising than VPT to speedup CCS. Advancing in this direction, speech signal stationarity has been capitalized to a greater extent than previously proposed technique of cluster size based sorting of code vectors to speedup PDE. Proximity relationship among code vectors established through Linde Buzo Gray (LBG) process of codebook generation has been substantiated. Based upon the high correlation of proximate code vectors, circular partial distortion elimination (CPDE) and toggling-CPDE algorithms have been proposed to speedup CCS. Further speedup for ASI is proposed through test feature vector visequence pruning (VSP) when a codebook proves unlikely during search of best match speaker. Empirical results presented in this thesis show that an average speedup factor up to 5.8 for 630 registered speakers of TIMIT 8kHz corpus and 6.6 for 230 speakers of NIST-1999 database have been achieved through integrating VSP and TCPDE. Speeding up potential of hierarchical speaker pruning (HSP) for faster ASI has also been demonstrated in this thesis. HSP prunes unlikely candidate speakers based on ranking results of coarse speaker models. Best match is then found from the detailed models of remaining speakers. VQ based and GMM based ASI systems are explored in depth for parameters governing the speedup performance of HSP. Using the smallest possible coarse model and pruning the largest number of detailed candidate models is the key objective for speedup through HSP. City block distance (CBD) is proposed instead of Euclidean distance (EUD) for ranking speakers in VQ based systems. This allows use of smaller codebook for ranking and pruning greater number of speakers. HSP has been ignored by previous authors for GMM based ASI systems due to discouraging speedup results in their studies of VQ-based systems. However, we achieved speedup factors up to 6.61 and 10.40 for GMM based ASI systems using HSP for 230 speaker from NIST-1999 and 630 speakers from TIMIT data, respectively. While speedup factors of up to 22.46 and 34.78 are achieved on TIMIT and NIST-1999 data for VQ based systems, respectively. All the speedup factors reported are with out any accuracy loss.