Hibernation impact on the catalytic activities of the mitochondrial D-3-hydroxybutyrate dehydrogenase in liver and brain tissues of jerboa (Jaculus orientalis)

BACKGROUND: Jerboa (Jaculus orientalis) is a deep hibernating rodent native to subdesert highlands. During hibernation, a high level of ketone bodies i.e. acetoacetate (AcAc) and D-3-hydroxybutyrate (BOH) are produced in liver, which are used in brain as energetic fuel. These compounds are bioconverted by mitochondrial D-3-hydroxybutyrate dehydrogenase (BDH) E.C. 1.1.1.30. Here we report, the function and the expression of BDH in terms of catalytic activities, kinetic parameters, levels of protein and mRNA in both tissues i.e brain and liver, in relation to the hibernating process. RESULTS: We found that: 1/ In euthemic jerboa the specific activity in liver is 2.4- and 6.4- fold higher than in brain, respectively for AcAc reduction and for BOH oxidation. The same differences were found in the hibernation state. 2/ In euthermic jerboa, the Michaelis constants, K(M )BOH and K(M )NAD(+ )are different in liver and in brain while K(M )AcAc, K(M )NADH and the dissociation constants, K(D )NAD(+)and K(D )NADH are similar. 3/ During prehibernating state, as compared to euthermic state, the liver BDH activity is reduced by half, while kinetic constants are strongly increased except K(D )NAD(+). 4/ During hibernating state, BDH activity is significantly enhanced, moreover, kinetic constants (K(M )and K(D)) are strongly modified as compared to the euthermic state; i.e. K(D )NAD(+ )in liver and K(M )AcAc in brain decrease 5 and 3 times respectively, while K(D )NADH in brain strongly increases up to 5.6 fold. 5/ Both protein content and mRNA level of BDH remain unchanged during the cold adaptation process. CONCLUSIONS: These results cumulatively explained and are consistent with the existence of two BDH enzymatic forms in the liver and the brain. The apoenzyme would be subjected to differential conformational folding depending on the hibernation state. This regulation could be a result of either post-translational modifications and/or a modification of the mitochondrial membrane state, taking into account that BDH activity is phospholipid-dependent

A new framework for sustainable development in US healthcare sector: the case of the Lower Rio Grande Valley Medicaid project

This paper discusses on the outcome of a large urban renewal planning project in the Lower Rio Grande Valley, a US developing region. The proposed outcome to increase demand for medical services by extending Medicaid enrolment was arrived at through a sustainable development framework, which combines theory and methodology originating in Michael Porter's work on competition with that stemming from United Nations work on human and environmental development. This new methodology can be transplanted to additional locations and may provide a more viable solution to sustainable development policy formation than those provided by those by the World Bank and other institutions.sustainable development; healthcare economics; US Medicare; US Medicaid; human development; environmental development; Michael Porter; competition; cluster theory; healthcare sustainability; urban renewal; urban planning; USA; United States; medical services; policy formation.

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches

Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms

The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter α adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature

An expeditious synthesis of spinasterol and schottenol, two phytosterols present in argan oil and in cactus pear seed oil, and evaluation of their biological activities on cells of the central nervous system

International audienceSpinasterol and schottenol, two phytosterols present in argan oil and in cactus pear seed oil, were synthesized from commercially available stigmasterol by a four steps reactions. In addition, the effects of these phytosterols on cell growth and mitochondrial activity were evaluated on 158N murine oligodendrocytes, C6 rat glioma cells, and SK-N-BE human neuronal cells with the crystal violet test and the MTT test, respectively. The effects of spinasterol and schottenol were compared with 7-ketocholesterol (71CC) and ferulic acid, which is also present in argan and cactus pear seed oil. Whatever the cells considered, dose dependent cytotoxic effects of 71CC were observed whereas no or slight effects of ferulic acid were found. With spinasterol and schottenol, no or slight effects on cell growth were detected. With spinasterol, reduced mitochondrial activities (30-50%) were found on 158N and C6 cells; no effect was found on SK-N-BE. With schottenol, reduced mitochondrial activity were revealed on 158N (50%) and C6 (10-20%) cells; no effect was found on SK-N-BE. Altogether, these data suggest that spinasterol and schottenol can modulate mitochondrial activity and might therefore influence cell metabolism

Differential regulation of peroxisome proliferator-activated receptor (PPAR)-alpha1 and truncated PPARalpha2 as an adaptive response to fasting in the control of hepatic peroxisomal fatty acid beta-oxidation in the hibernating mammal.

International audienceSeasonal obesity and fasting-associated hibernation are the two major metabolic events governing hepatic lipid metabolism in hibernating mammals. In this process, however, the role of the nuclear receptor known as peroxisome proliferator-activated receptor (PPAR)-alpha has not been elucidated yet. Here we show, as in human, that jerboa (Jaculus orientalis) liver expresses both active wild-type PPARalpha (PPARalpha1wt) and truncated PPARalpha forms and that the PPARalpha1wt to truncated PPARalpha2 ratio, which indicates the availability of active PPARalpha1wt, is differentially regulated during fasting-associated hibernation. Functional activation of hepatic jerboa PPARalpha, during prehibernating and hibernating states, was demonstrated by the induction of its target genes, which encode peroxisomal proteins such as acyl-CoA oxidase 1, peroxisomal membrane protein 70, and catalase, accompanied by a concomitant induction of PPARalpha thermogenic coactivator PPARgamma coactivator-1alpha. Interestingly, sustained activation of PPARalpha by its hypolipidemic ligand, ciprofibrate, abrogates the adaptive fasting response of PPARalpha during prehibernation and overinduces its target genes, disrupting the prehibernation fattening process. In striking contrast, during fasting-associated hibernation, jerboas exhibit preferential up-regulation of hepatic peroxisomal fatty acid oxidation instead of the mitochondrial pathway, which is down-regulated. Taken together, our results strongly suggest that PPARalpha is subject to a hibernation-dependent splicing regulation in response to feeding-fasting conditions, which defines the activity of PPARalpha and the activation of its target genes during hibernation bouts of jerboas