Rat Model of Pre-Motor Parkinson\u27s Disease: Behavioral and MRI Characterization.

Background: Parkinson\u27s disease (PD) is a chronic, progressive, neurodegenerative disorder with currently no known cure. PD has a significant impact on quality of life of the patients, as well as, the caregivers and family members. It is the second most common cause of chronic neurological disability in US and Europe. According to National Parkinson\u27s Foundation, there are almost 1 million patients in the Unites States and 50,000 to 60,000 new cases of PD are diagnosed each year. The total number of cases of PD is predicted to double by 2030. The annual cost associated with this disease is estimated to be $10.8 billion in the United States, including the cost of treatment and the cost of the disability. Although it is primarily thought of as a movement-disorder and is clinically diagnosed based on motor symptoms, non-motor symptoms such as cognitive and emotional deficits are thought to precede the clinical diagnosis by almost 20 years. By the time of clinical diagnosis, there is 80% loss in the dopamine content in the striatum and 50% degeneration of the substantia nigra dopamine cells. The research presented in this thesis was an attempt to develop an animal model of PD in its pre-motor stages. Such a model would allow us to develop pre-clinical markers for PD, and facilitate the development and testing of potential treatment strategies for the non-motor symptoms of the disorder. Specific Aims: There were five specific aims for this research: * The first specific aim dealt with development of a rat model of PD with slow, progressive onset of motor deficits, determination of timeline for future studies, and quantification the dopamine depletion in this model at a pre-motor stage. * The second and the third specific aims focused on testing for emotional (aversion) deficits and cognitive (executive functioning) deficits in this rat model at the 3 week timepoint determined during specific aim 1. * The fourth specific aim was to determine the brain network changes associated with the behavioral changes observed our rat model using resting state connectivity as a measure. * The fifth and the final specific aim was to test sodium butyrate, a drug from the histone deacetylase inhibitor family, as a potential treatment option for cognitive deficits in PD. Results: The 6-hydroxy dopamine based stepwise striatal lesion model of pre-motor PD, developed during this research, exhibits delayed onset of Parkinsonian gait like symptoms by week 4 after the lesions. At 3 weeks post lesion (3WKPD), the rats exhibit 27% reduction in striatal dopamine and 23%reduction in substantia nigra dopamine cells, with lack of any apparent motor deficits. The 3WKPD rats also exhibited changes in aversion. The fMRI study with the aversive scent pointed towards possible amygdala dysfunction sub-serving the aversion deficits. The executive function deficits tested using a rat analog of the Wisconsin card sorting test, divulged an extra-dimensional set shifting deficit in the 3WKPD rats similar to those reported in PD patients. The resting state connectivity study indicated significant changes in the 3WKPD rats compared to age matched controls. We observed increased overall connectivity of the motor cortex and increased CPu connectivity with prefrontal cortex, cingulate cortex, and hypothalamus in the 3WKPD rats compared to the controls. These observations parallel the observations in unmedicated early-stage PD patients. We also observed negative correlation between amygdala and prefrontal cortex as reported in humans. This negative correlation was lost in 3WKPD rats. Sodium butyrate treatment, tested in the cognitive deficit study, was able to ameliorate the extra-dimensional set shifting deficit observed in this model. This treatment also improved the attentional set formation. Conclusion: Taken together, our observations indicate that, the model of pre-motor stage PD developed during this research is a very high face validity rat model of late Braak stage 2 or early Braak stage 3 PD. Sodium butyrate was able to alleviate the cognitive deficits observed in our rat model. Hence, along with the prior reports of anti-depressant and neuroprotective effects of this drug, our results point towards a possible treatment strategy for the non-motor deficits of PD

Establishing a model workplace tobacco cessation program in India

Background: Tobacco use is highly prevalent and culturally accepted in rural Maharashtra, India. Aims: To study the knowledge, attitude, and practices (KAP) regarding tobacco consumption, identify reasons for initiation and continuation of tobacco use, identify prevalence of tobacco consumption and its relation with different precancerous lesions, provide professional help for quitting tobacco, and develop local manpower for tobacco cessation activities. Settings, Design, Methods and Material: The present study was conducted for one year in a chemical industrial unit in Ratnagiri district. All employees (104) were interviewed and screened for oral neoplasia. Their socio-demographic features, habits, awareness levels etc. were recorded. Active intervention in the form of awareness lectures, focus group discussions, one-to-one counseling and, if needed, pharmacotherapy was offered to the tobacco users. Results: All employees actively participated in the program. Overall, 48.08% of the employees were found to use tobacco, among which the smokeless forms were predominant. Peer pressure and pleasure were the main reasons for initiation of tobacco consumption, and the belief that, though injurious, it would not harm them, avoiding physical discomfort on quitting and relieving stress were important factors for continuation of the habit. Employees had poor knowledge regarding the ill-effects of tobacco. 40% of tobacco users had oral precancerous lesions, which were predominant in employees consuming smokeless forms of tobacco. Conclusions: Identifying reasons for initiation and continuation of tobacco consumption along with baseline assessment of knowledge, attitudes, and practices regarding tobacco use, are important in formulating strategies for a comprehensive workplace tobacco cessation program

Can plant bio-regulators minimize crop productivity losses caused by drought, salinity and heat stress? An integrated review

Ratnakumar P, Khan MIR, Minhas PS, et al. Can plant bio-regulators minimize crop productivity losses caused by drought, salinity and heat stress? An integrated review. JOURNAL OF APPLIED BOTANY AND FOOD QUALITY. 2016;89:113-125.Plant bio-regulators (PBRs), are biochemical compounds stimulates plant growth and productivity when applied, even in small quantities at appropriate plant growth stages. These are being extensively used in agriculture to enhance the productivity particularly in horticultural crops but are not as prevalent in field crops. Their central role in plant growth and development is through nutrient allocation and source-sink transitions while most of the PBRs stimulate redox signaling under abiotic stress conditions. Since climate change and degrading natural resources are projected to amplify the stresses, particularly soil moisture deficit, high temperature and soil salinity, PBRs are likely to play a crucial role in plant growth regulation. However, the utility of PBRs to enhance crop productivity under stresses induced by abiotic factors needs critical evaluation. Research efforts so far have centered on the crop and agro-ecosystem specificity, optimal doses and schedule of their application for optimizing crop yields under stress conditions. These efforts are being complemented by investigations on genes and gene regulatory network at molecular level to tailor crop plants for climate resilience. In addition to complying with regulation governing use of bio-chemicals, issues related to crop yield losses in case of excessive doses as well as their impacts on soil health are being addressed. In this review, prospects and pathways of PBRs are thrashed out as an emerging stress alleviating technology for crop production in harsh agro-ecosystems, specifically those featured by drought, heat and salinity stress

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset