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Quantal potential fields around individual amphibian motor-nerve terminal active zones for the in silico rationally designed drug Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure molecules for the possible potentiating of the efficient delivery of gene constructs through the internalization successes in experimental therapy of muscular dystrophies

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Abstract

The release of a quantum from a nerve terminal is accompanied by the flow of extracellular current, which creates a field around the site of transmitter action. We provide a solution for the extent of this field for the case of a quantum released from a site on an amphibian motor-nerve terminal branch onto the receptor patch of a muscle fiber and compare this with measurements of the field using three extracellular electrodes. Numerical solution of the equations for the quantal potential field in cylindrical coordinates show that the density of the field at the peak of the quantal current gives rise to a peak extracellular potential, which declines approximately as the inverse of the distance from the source at distances greater than about 4 microm from the source along the length of the fiber. The peak extracellular potential declines to 20% of its initial value in a distance of about 6 microm, both along the length of the fiber and in the circumferential direction around the fiber. Simultaneous recordings of quantal potential fields, made with three electrodes placed in a line at right angles to an FM1-43 visualized branch, gave determinations of the field strengths in accord with the numerical solutions. In addition, the three electrodes were placed so as to straddle the visualized release sites of a branch. The positions of these sites were correctly predicted on the basis of the theory and independently ascertained by FM1-43 staining of the sites. It is concluded that quantal potential fields at the neuromuscular junction that can be used on Quantal potential fields around individual amphibian motor-nerve terminal active zones for the in silico rationally designed drug Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure molecules for the possible potentiating of the efficient delivery of gene constructs through the internalization successes in experimental therapy of muscular dystrophies.

Keywords

Quantal potential fields; individual active zones; amphibian motor-nerve terminals; in silico; rationally designed; drug molecules; Peptide-mimic; pharmacologic low mass predicted; chemorecored; poly-druggable-structure; possible potentiating; efficient delivery; gene constructs; internalization successes; experimental therapy; muscular dystrophies.

Quantum Discord of a Two-Qubit Anisotropy XXZ Heisenberg Chain with Dzyaloshinskii-Moriya Interaction of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor activities on viral naive tropism comprising replication-competent therapeutic properties for as a pancreatic cancer

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Abstract

We investigate the quantum discord of a two-qubit anisotropy XXZ Heisenberg chain with Dzyaloshinskii-Moriya (DM) interaction under magnetic field. It is shown that the quantum discord highly depends on the system’s temperature T, DM interaction D, homogenous magnetic field B and the anisotropy Δ. For lower temperature T, by modulating D and B, the quantum discord can be controlled and the quantum discord switch can be utilised for the Quantum Discord of a Two-Qubit Anisotropy XXZ Heisenberg Chain with Dzyaloshinskii-Moriya Interaction of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor activities on viral naive tropism comprising replication-competent therapeutic properties for as a pancreatic cancer.

Keywords

Quantum Discords; Two-Qubit Anisotropy; XXZ Heisenberg Chain; Dzyaloshinskii-Moriya Interaction; in silico rational; designed adenovirus library; displaying random peptide-mimic; pharmacophoric ligand; supressor activities; viral naive tropism; replication-competent; therapeutic properties; pancreatic cancer;Quantum Discord, Heisenberg Chain, Dzyaloshinskii-Moriya Interaction, Anisotropy, Magnetic Field

A surface representation Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model of a designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications

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Abstract

Cavitation bubble collapse near rough solid wall is modeled by the multi- relaxation-time (MRT) pseudopotential lattice Boltzmann (LB) model. The modified forcing scheme, which can achieve LB model’s thermodynamic consistency by tuning a parameter related with the particle interaction range, is adopted to achieve desired stability and density ratio. The bubble collapse near rough solid wall was simulated by the improved MRT pseudopotential LB model. The mechanism of bubble collapse is studied by investigating the bubble profiles, pressure field and velocity field evolution. The eroding effects of collapsing bubble are analyzed in details. It is found that the process and the effect of the interaction between bubble collapse and rough solid wall are affected seriously by the geometry of solid boundary. At the same time, in this scientific study we demonstrate that the MRT pseudopotential LB model is a potential tool for the investigation of the interaction mechanism between the surface representation Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model of a designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Modeling for Collapsing; Cavitation Bubble; Rough Solid Wall; Mulit-Relaxation-Time; Pseudopotential Lattice; Boltzmann Model; surface representation; IHMVYSK peptide-mimo based; chemo-ligand; therapeutic vaccine-like; agonistic properties; novel druggable; synthetic regulator; future allergic; autoimmune treatment applications; Cavitation Bubble, Bubble Collapse, Lattice Boltzmann Method, Pseudopotential Model, Rough Solid Wall.

Quantum representation algorithms for topological and geometric analysis of a surface designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications

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Abstract

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis.Abstract: Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Allergic and autoimmune diseases are forms of immune hypersensitivity that increasingly cause chronic ill health. Most current therapies treat symptoms rather than addressing underlying immunological mechanisms. The ability to modify antigen-specific pathogenic responses by therapeutic vaccination offers the prospect of targeted therapy resulting in long-term clinical improvement without nonspecific immune suppression. Examples of specific immune modulation can be found in nature and in established forms of immune desensitization. Targeting pathogenic T cells using vaccines consisting of synthetic peptides representing T cell epitopes is one such strategy that is currently being evaluated with encouraging results. Future challenges in the development of therapeutic vaccines include selection of appropriate antigens and peptides, optimization of peptide dose and route of administration and identifying strategies to induce bystander suppression. Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given peptide based on their structural complementarity. Compared to other peptide and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Peptidomimetics, deriving from structure-based, combinatorial or protein dissection approaches, can play a key role as hit compounds. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. Here, in Biogenea we have for the first time generated Quantum representation algorithms for topological and geometric analysis of a surface designed IHMVYSK peptide-mimo based chemo-ligand comprising therapeutic vaccine-like agonistic properties as a potential novel druggable synthetic regulator for future allergic and autoimmune treatment applications.

Keywords

Quantum algorithms, topological, geometric, analysis, surface, representation, peptide-mimo, chemo-ligand, therapeutic, vaccine-like, agonistic, potential, druggable, synthetic, regulator, allergic, autoimmune, treatment, applications; Quantum representation algorithms; topological; geometric analysis; surface designed; IHMVYSK peptide-mimo based; chemo-ligand; therapeutic vaccine-like; agonistic properties; novel druggable; synthetic regulator ; allergic and autoimmune treatment applications.

Oncolytic virus potential Quantum algorithms for topological and geometric analysis of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor comprising viral naive tropism replication-competent pancreatic cancer therapeutic properties

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Abstract

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. A conditionally replicative adenovirus is a novel anticancer agent designed to replicate selectively in tumor cells. However, a leak of the virus into systemic circulation from the tumors often causes ectopic infection of various organs. Therefore, suppression of naive viral tropism and addition of tumor-targeting potential are necessary to secure patient safety and increase the therapeutic effect of an oncolytic adenovirus in the clinical setting. It has also recently been developed a direct selection method of targeted vector from a random peptide library displayed on an adenoviral fiber knob to overcome the limitation that many cell type-specific ligands for targeted adenovirus vectors are not known. In previous studies it has also been further examined whether the addition of a tumor-targeting ligand to a replication-competent adenovirus ablated for naive tropism enhances its therapeutic index. Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple short linear motif like peptide structures of a protein-drug active binding conserved site. Statistical approaches that rely upon residue equivalence, but not superposition, are developed as chemogenomic informatic tasks can be performed includinig the identification of hinge regions, allosteric conformational changes and transient binding sites identified by Oncolytic virus potential Quantum algorithms for topological and geometric analysis of an in silico rational designed adenovirus library displaying random peptide-mimic pharmacophoric ligand supressor comprising viral naive tropism replication-competent pancreatic cancer therapeutic properties.

Keywords

Quantum algorithms, topological, geometric, analysis, in silico, rational, adenovirus library, displaying, random, peptide-mimic, pharmacophoric, ligand, supressor, vira,l naive, tropism, comprising, replication-competent, Oncolytic, virus, potential, therapeutic, properties pancreatic, cancer.

Biomarker Tests and Ageing Science

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DOI: 10.31038/ASMHS.2017112

Short commentary

Major interests in geriatric medicine have accelerated with diet and lifestyle changes that may stabilize mitochondrial apoptosis [1,2] and organ diseases in these communities. Geriatrics are susceptible to the global increase in chronic diseases with diabetes and neurodegenerative disease predicted to effect and determine the increased death rate of the geriatric population in the next 40 years. A defect in a single gene [3] versus multi gene effects may be responsible for accelerated ageing connected to mitochondrial apoptosis and programmed cell death with relevance to insulin resistance and the increased death rate in geriatrics. In the United States and Europe the geriatric population (> 65 years) is expected to double by the year 2060 with the death rate in the European Union in the geriatric population to be greater than 80% when compared with individuals < 65 years [4, 5].

The science of ageing has become of critical interest [6] and the anti-ageing market now relevant to geriatric medicine with nutritional diets and lifestyles changes that may stabilize mitochondrial apoptosis [7] and organ diseases in these communities. The assessment of healthy ageing has created several difficulties with interpretation from various biomarker studies that biomarker analysis may not necessarily translate to diagnosis. Comprehensive review of the literature have been conducted with biomarkers that may be relevant to five age related domains and include physical/cognitive capability, physiological/musculoskeletal, endocrine and immune functions [8]. The comprehensive assessment of these biomarkers may allow interpretations of the science of ageing but may not allow the diagnosis of programmed cell death with mitophagy as the inevitable defect in the geriatric population. Biomarkers that may allow diagnosis of various mental health conditions have become of major importance in psychiatry [8] but still present a major challenge to psychiatry research. Extensive analysis of biomarkers [8] now hypothesize that both genetic and non-genetic factors determine the different patterns of ageing and extensive new biomarker tests are required to interpret healthy ageing from accelerated ageing science [8].

Environmental factors such as stress, anxiety and depression are important to consider in many communities with the global increase in chronic diseases [12-14] and brain metabolic diseases associated with malfunction of the gene Sirtuin 1 (Sirt 1) that regulates immunometabolism [15]. Analysis of biomarkers may indicate immunometabolism disorders [16] with epigenetic alterations and autoimmunity disorders (Figure 1) that may supersede the connections between plasma biomarkers related to accelerated ageing in geriatric disease [17] and ageing science. The stress sensitive gene Sirt 1 [14] may be completely repressed in these individuals with the defective immune system related to heat shock protein (HSP) metabolism, autoimmunity and mitophagy (Figure 1). Stress and HSP are closely connected to cell survival [18,19] and Sirt 1 repression leads to an elevation of plasma HSP in man [7,20]. Sirt 1 may be the major gene that has malfunctioned under various genetic and stress conditions associated with autoimmune disease [21-24] and mitophagy [7, 25] connected to geriatric disease and ageing science.

In the developing world the projected cost for chronic disease [26] is predicted to increase to 100 billion dollars by the year 2020 with the increased involvement of diseases such as diabetes, cardiovascular disease and neurodegenerative diseases. The developing world is a model for the assessment of the stress sensitive gene Sirt 1 with the importance of plasma Sirt 1 biomarker analysis connected to various chronic diseases. In these individuals the increased plasma bacterial lipopolysaccharides (LPS) levels may repress Sirt 1 [27] with reduced plasma Sirt 1 levels [28-29] and increased HSP levels [7, 18-20] in these individuals connected to programmed cell death.

Conclusion

Ageing science has now become important to geriatric medicine with nutritional diets and lifestyles changes critical to maintain mitochondrial survival with the prevention of various chronic diseases. Healthy ageing assessment require billion dollars in cost for the treatment of future chronic disease and interpretation from several plasma biomarker analysis that do not diagnose immunometabolism defects have created problems with uncontrolled accelerated ageing connected to the global chronic disease epidemic.

Acknowledgements

This work was supported by grants from Edith Cowan University, the McCusker Alzheimer’s Research Foundation and the National Health and Medical Research Council.

ASMHS2017-102Australia_f1

Figure 1. The science of ageing requires improved diagnostic technologies to determine biomarkers for autoimmune disease and mitophagy. The stress sensitive gene Sirt 1 is one of the major gene defects that is related to programmed cell death and accelerated ageing with the analysis of other biomarkers insensitive to the progression of early chronic disease.

References

  1. Martins IJ (2016) Early diagnosis of neuron mitochondrial dysfunction may reverse global metabolic and neurodegenerative disease. Glob J Med Res 2: 1–8.
  2. Payne BA, Chinnery PF (2015) Mitochondrial dysfunction in aging: Much progress but many unresolved questions. Biochim Biophys Acta 1847: 1347–1353. [crossref] 
  3. Martins IJ (2017) Single Gene Inactivation with Implications to Diabetes and Multiple Organ Dysfunction Syndrome. J Clin Epigenet. (3): 24.
  4. Causes_of_death_statistics_-_people_ over_65
  5. Administration on Aging (AoA)s
  6. Halldór Stefánsson (2005) The science of ageing and anti-ageing. EMBO Rep 6 (Suppl 1): S1–S3.
  7. Martins IJ (2017) Regulation of Core Body Temperature and the Immune System Determines Species Longevity. Curr Updates Gerontol 1: 6.1.
  8. Kenessary A, Zhumadilov Z, Nurgozhin T, Kipling D, Yeoman M, et al. (2013) Biomarkers, interventions and healthy ageing. N Biotechnol 30: 373–377. [crossref] 
  9. Boksa P (2013) A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci38: 75–77. [crossref]
  10. Lara J, Cooper R, Nissan J, Ginty AT, Khaw KT, et al. (2015) A proposed panel of biomarkers of healthy ageing. BMC Med 13: 222. [crossref]
  11. Sebastiani P, Thyagarajan B, Sun F, Schupf N, Newman AB, et al. (2017) Biomarker signatures of aging. Aging Cell 16: 329–338. [crossref]
  12. Ising M, Holsboer F (2006) Genetics of stress response and stress-related disorders. Dialogues Clin Neurosci 8: 433–44.
  13. Klengel T, Binder EB (2015) Epigenetics of Stress-Related Psychiatric Disorders and Gene × Environment Interactions. Neuron 86: 1343–1357. [crossref]
  14. Martins IJ. Nutritional diets accelerate amyloid beta metabolism and prevent the induction of chronic diseases and Alzheimer’s disease. J Clin Endocrino. Metab. Photon ebooks.
  15. Martins IJ (2017) Defective Interplay between Adipose Tissue and Immune System Induces Non Alcoholic Fatty Liver Disease. Updates Nutr Disorders Ther 1: 3.
  16. Mathis D, Shoelson SE (2011) Immunometabolism: an emerging frontier. Nat Rev Immunol 11: 81. [crossref]
  17. Martins IJ (2016) Geriatric Medicine and Heat Shock Gene Therapy in Global Populations. Curr Updates Gerontol 1: 1–5.
  18. Beere HM (2004) “The stress of dying”: the role of heat shock proteins in the regulation of apoptosis. J Cell Sci117: 2641–2651. [crossref]
  19. Feder ME, Hofmann GE (1999) Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annu Rev Physiol 61: 243-282. [crossref]
  20. Martins IJ (2016) Type 3 diabetes with links to NAFLD and Other Chronic Diseases in the Western World. International Journal of Diabetes 1: 1–5.
  21. Genetics and Stress: Is There a Link? Beyond Genes: Epigenetics Can Stress Modify Our Genes? Does Stress make our cell age faster.
  22. Stojanovich L, Marisavljevich D (2008) Stress as a trigger of autoimmune disease. Autoimmun Rev7: 209–213. [crossref]
  23. Stojanovich L (2010) Stress and autoimmunity. Autoimmun Rev9: A271-276. [crossref]
  24. Grolleau-Julius A, Ray D, Yung RL (2010) The role of epigenetics in aging and autoimmunity. Clin Rev Allergy Immunol 39: 42–50. [crossref]
  25. Martins IJ (2017) The Future of Biomarkers Tests and Genomic Medicine in Global Organ Disease. Arch Infect Dis Ther 1: 1–6.
  26. Nugent R1 (2008) Chronic diseases in developing countries: health and economic burdens. Ann N Y Acad Sci 1136: 70–79. [crossref]
  27. Mariani S, Fiore D, Basciani S, Persichetti A, Contini S, et al. (2015) Plasma levels of SIRT1 associate with non-alcoholic fatty liver disease in obese patients. Endocrine 49: 711–716. [crossref]
  28. Kumar R, Chaterjee P, Sharma PK, Singh AK, Gupta A, et al. (2013) Sirtuin1: a promising serum protein marker for early detection of Alzheimer’s disease. PLoS One 8: e61560. [crossref]
  29. Mariani S, Fiore D, Persichetti A, Basciani S, Lubrano C, et al. (2016) Circulating SIRT1 Increases After Intragastric Balloon Fat Loss in Obese Patients. Obes Surg 26: 1215–1220. [crossref]

Aging: Time, Timing, Genetics, and Environment

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DOI: 10.31038/ASMHS.2017111

Editorial

Time and timing represent some of the most influential aspects of human and animal life. Defining time has historically been, and continues to be, one of the toughest challenges for the minds of philosophers, theologians, physicists, biologists, and lay people [1-3]. Although the definition of time is a “hard problem” in philosophy, and philosophy of science in particular, and an exhaustive description of it is far from being proposed, the experience of time and timing is a very common one. We need to be on time for an appointment, music is essentially timing, circadian rhythm is about different temporal phases of our day, seasons have timing, human history is a continuous series of relevant and less relevant timed events.

In a neurobiological perspective, timing is about spatio-temporal coordination of events beginning from the moment of conception until to the end of life. While the concept of timing in neuroembriology and brain development in general, seems to be easier to grasp, and in fact it finally started to be considered by researchers as an essential aspect to consider to better understand highly complex genetic and neuroanatomical events of the central nervous system (CNS) during its formation [4-6], an operational concept and definition of timing during aging has been instead, much less considered. Investigating specific genetic, neuroanatomical, neuroplasticity, and long-term environmental aspects that could model human brain function and neuroanatomical structures (?) during the last temporal phases of human life has received much less attention.

Importantly, focusing on the molecular mechanisms (and at a larger scale at clinical level as well) of timing aspects in an older brain imply also to take in account that the CNS is part of an organism with other apparatuses and systems that are integrated (with their own timings) to each other and that can have functional consequences on each other.

A series of fundamental questions still remain unanswered, for example: what is the timing of those molecular phenomena determined by gene activations and “residual?” neuroplasticity capacities in a centenarian brain? Are there “pathologic experiences” during in-utero life that could be temporarily buffered (for example, thanks to neuroplasticity and adaptive/compensatory synaptic capacities of the human CNS – with possible additional positive effects from the interaction of the CNS with beneficial environmental stimulation such as education, physical and intellectual exercising, and other) and so determine a clinically normal brain functioning during infancy, youth and early adulthood, but that can later manifest (or re-manifest) at different levels of clinical severity (e.g. from isolated cognitive disorders such as mild cognitive impairment (MCI) to dementia – e.g. Alzheimer’s disease or Parkinson’s disease)? Which are the relative biological influence, or intrinsic biological power, of programmed (genetic) and non-programmed (environment, traumatic brain injury, unbalanced nutrition, etc.) factors? How can those factors be reliably quantified at clinical level?

Albert Einstein defined time as a quantity [7], but which are the effects of those temporal quantitative aspects on the human brain? Is aging a neurodevelopmental timing going backwards as to infancy? Which are the temporal (and neuroanatomical?) synchronized events in relationship to those genes determining those peculiar biological (neuronal) events characterizing the period of life that we conventionally termed aging? Is longevity a question of biological timing? If so, which are the internal “clocks” that establish that timing?

At the beginning of this new editorial adventure, we ought to receive investigations describing these fascinating aspects of human life: time, timing, genetics and environment as interplay of events across the lifespan, and especially across the brain lifespan.

Giving the opportunity to either senior and junior investigators in the field of aging to submit their scientific works to our new journal will potentially offer the tremendous opportunity to contribute to a more detailed understanding of human biology during aging [8-10], and especially the biology of the brain during human aging, and so shed light on the mysteries of this still so unknown and surprising organ.

References

  1. St. Augustine, Confessions (2012) Simon & Brown Publishers.
  2. Martin Heidegger (1962) Being and Time. Translated by John Macquarrie & Edward Robinson. London: SCM Press, United Kingdom.
  3. Clarke EF (1999) Rhythm and Timing in Music, in: Diana Deutsch (ed.), Psychology of Music, 2nd (edn) University of California, San Diego, USA: pp 473–500.
  4. Sarnat HB, Philippart M, Flores-Sarnat L, Wei XC (2015). Timing in neural maturation: arrest, delay, precociousness, and temporal determination of malformations. Pediatr Neurol 52: 473–486. [crossref]
  5. Acosta M, Gallo V, Batshaw ML (2002) Brain development and the ontogeny of developmental disabilities. Adv Pediatr49: 1–57. [crossref]
  6. Hua JY, Smith SJ (2004) Neural activity and the dynamics of central nervous system development. Nat Neurosci 7: 327-332. [crossref]
  7. Einstein A (1916) Relativity: The Special and General Theory (Translation 1920) New York: H. Holt and Company, USA.
  8. Jin K (2010) Modern Biological Theories of Aging. Aging Dis 1: 72–74. [crossref]
  9. Jones OR, Vaupel JW (2017) Senescence is not inevitable. Biogerontology Aug 28. [crossref]
  10. Greenberg E, Vatolin S (2017) Symbiotic origin of aging. Rejuvenation Res. [crossref]

Relapse of Graves’ Disease Thirty-Two Years After Treatment with Radioactive Iodine

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DOI: 10.31038/EDMJ.2017132

Abstract

Background: Relapse of Graves’ disease (GD) after a prolonged period of radioactive iodine (RAI)-induced hypothyroidism is very unusual. We report a case of GD with the longest time-to-relapse so far published, i.e. 32 years after therapy with RAI.

Case Presentation: A 69-year-old woman was referred in 2016 for evaluation of hyperthyroidism. Her history was significant for GD diagnosed at the age of 37. A RAI uptake and scan in 1984 showed an increase uptake of 72% at 24 hours in a diffusely enlarged gland with no focal lesions. She was treated with 9 mCi of I-131 and was rendered hypothyroid requiring levothyroxine therapy. Her hyperthyroidism work-up in 2016 confirmed the first known relapse of her GD. Her thyroid-stimulating hormone level was suppressed at 0.005 uU/mL (0.400 – 5.500 uU/mL), free thyroxine level was elevated at 2.9 ng/dL (0.7 – 1.8 ng/dL), and a RAI uptake and scan showed an increase uptake of 55% at 22 hours. She was retreated with 10.44 mCi of I-131 therapy, and was rendered hypothyroid again.

Conclusion: We report the longest time-to-relapse of GD post-hypothyroidism induced by RAI so far published, i.e. 32 years. Further research is needed to discern the pathophysiology underlying this relapse.

Key words

Graves’ disease; Hyperthyroidism; Relapse; Radioactive iodine; Thyroid regeneration

Introduction

Graves’ disease (GD) is the most common cause of hyperthyroidism with an annual incidence of up to 50 cases per 100,000 persons. Radioactive iodine (RAI) therapy has been used in GD for several decades [1]. Persistence or recurrence of GD is occasionally encountered in the immediate post-RAI therapy phase and is likely due to incomplete thyroid tissue ablation. Relapse of GD after a prolonged period of RAI-induced hypothyroidism or achievement of long-term euthyroidism, however, is very unusual. Here, we report the longest case so far published of relapsed GD 32 years post-RAI treatment with a hypothyroid phase in the interim necessitating levothyroxine therapy.

Case Presentation

A 69-year-old woman was referred in June 2016 for evaluation of hyperthyroidism. She presented with complaints of weight loss, heat intolerance, hair loss and insomnia for several months. Her past medical history was significant for GD diagnosed in 1984. Back then, she had presented with a few months’ history of heat intolerance, diarrhea and palpitations. Her exam was notable for a diffusely enlarged thyroid gland with an estimated weight of more than 30 grams. Her work-up in 1984 was remarkable for elevated free thyroxine index (FTI) at 17.7 ug/dL (reference range: 6.0 – 11.0 ug/dL). A radioactive iodine uptake and scan showed an uptake of 72% at 24 hours and the gland was diffusely enlarged with no focal lesions consistent with GD. She was treated with 9 mCi of I-131 and was rendered hypothyroid requiring over 10 years of levothyroxine therapy. Her thyroid-stimulating hormone (TSH) was documented at levels as high as 10.05 uU/mL (reference range: 0.400 – 5.500 uU/mL) in the setting of intermittent non-adherence to levothyroxine therapy between 1985 and 2016.

Her family history was significant for hyperthyroidism in mother, daughter and maternal aunt. She denied any history of tobacco use. Physical examination upon evaluation in June 2016 showed normal vital signs with a BMI of 34.77 kg/m2. She had mild right eye proptosis that was not previously noted. Her thyroid gland was slightly enlarged, with an estimated weight of 25 grams.

Her lab work in June 2016 was significant for suppressed TSH of 0.005 uU/mL and elevated free thyroxine (FT4) of 2.9 ng/dL (reference range: 0.7 – 1.8 ng/dL). She had been off her thyroid hormone therapy for several months at this point. A thyroid ultrasound demonstrated a heterogeneous, slightly enlarged gland with an inferior right lobar cystic nodule, measuring 9 x 9 x 6 mm (L x W x AP diameter). A thyroid uptake and scan showed homogenous increased uptake of 55% at 22 hours (normal 10-30% at 24-hours) consistent with GD. The patient received a second dose of 10.44 mCi of I-131 therapy in 2016. Subsequent blood work 2 weeks after I-131 therapy demonstrated a decline in FT4 levels from a pre-treatment level of 2.9 ng/dL to a post-treatment level of 1.8 ng/dL. Thyroid function testing two months after RAI treatment revealed an elevated TSH of 16.380 uU/mL, and a low FT4 of 0.2 ng/dL. The patient was restarted on levothyroxine therapy.

Discussion

Relapse of Graves’ hyperthyroidism after RAI-induced hypothyroidism is unusual. Here we report a case of GD with the longest time interval-to-relapse so far published, i.e. 32 years after therapy with I-131, with a hypothyroid phase in the interim that necessitated levothyroxine therapy. In this case, thyroid-stimulating immunoglobulins (TSI) were not obtained as the radioactive iodine uptake and scans were consistent with GD meeting 2016 American Thyroid Association (ATA) guidelines for diagnosis of GD [2,3]. Previously, the longest reported cases of relapsed GD post-RAI induced hypothyroidism were published by Hegele et al. and Tan et al. in 1985 and 1995 respectively. The former presented a case with persistently high levels of TSI who had a relapse of his GD after 23 years of 1-thyroxine therapy post-I-131 hypothyroidism [4]. The latter reported a recurrence of GD in a 90-year-old woman after 22 years of levothyroxine therapy post a 9 mCi ablative dose of I-131 [5]. While relapses of hyperthyroidism shortly after RAI therapy is likely due to incomplete thyroid ablation, this mechanism would be unlikely to explain the relapse of disease several decades later. In this report, we explore a few novel mechanisms underlying the pathogenesis of these rare long-term relapses.

The mechanisms underlying long-term relapses of GD after thyroid ablation have not been fully elucidated, primarily due to scarcity of such cases. A few animal models that simulated the process of thyroid regeneration after its destruction has shed some light on possible pathogenesis of relapses of GD. In the first animal model, experimental mice underwent semi-total partial thyroidectomies (one lobe and 2/5 of the other lobe were resected). In response to a marked decrease in thyroid function, there was an increase in the number of clear, immature cytoplasmic cells expressing 5-bromo-2’-deoxyuridine (BrdU), a synthetic nucleoside incorporated into dividing cells and marker of active cell proliferation, in the residual thyroid tissue. Investigators hypothesized that, in response to the stress of surgery, the residual follicular thyrocytes transform into less differentiated clear cytoplasmic cells and subsequently proliferate and differentiate into mature, functional thyrocytes [6]. Whether a similar mechanism exists for tissue regeneration post-RAI ablation is yet unknown.

In another thyroid regeneration mouse model, Experimental Autoimmune Thyroiditis (EAT) mice underwent complete destruction of their follicular architecture by injection of thyroglobulin (Tg) and subsequent induction of endogenous Tg antibodies. A stem cell marker, Oct-4 mRNA, was detected at baseline suggesting the presence of thyroid stem cells among the mature thyrocytes. After an acute destructive period induced by Tg antibodies, the murine thyroid gland demonstrated a remarkable capacity for tissue regeneration. There was an increase in BrdU expression suggesting active cell proliferation and concomitant decrease in Oct-4 as the follicles regenerated, elucidating an essential role of pre-existing thyroid stem cells in the regeneration of the thyroid [7].

Although both models emphasize the roles of dedifferentiated thyrocytes and pre-existing stem cells in the regeneration of the thyroid, there may also be a role for differentiated, remnant thyrocytes in the relapse of GD. Remnant thyrocytes that remained viable, but at concentrations insufficient to present clinically with euthyroidism or hyperthyroidism, may have hypertrophied under the influence of chronically elevated TSH levels, in the setting of intermittent non-compliance to levothyroxine therapy. It is well known that in patients with chronic autoimmune thyroiditis, increase in circulating TSH plays a significant role in the development of their goiter. Suppression of TSH by administration of thyroid hormone, on the other hand, has been shown to decrease goiter size [8]. Therefore, it is plausible that the elevation in TSH levels overtime may have stimulated remnant follicular cells, resulting in gland hypertrophy and overproduction of endogenous thyroid hormones.

It is also possible that an elevated TSI level may have also contributed to the hypertrophy of gland overtime. Additionally, a change in the conformational specificity of TSI overtime, resulting in higher affinity to the thyroid-stimulating hormone receptor (TSHR), may also play an important role in the overgrowth of residual differentiated thyrocytes and subsequent relapse of GD [9]. Finally, an acquired gain-of-function mutation within TSHR gene or adenylate cyclase-stimulating G α protein gene, a downstream signal in the TSHR pathway, could also explain relapse of GD. Although plausible, such mutations would more likely result in patchy, nodular overgrowth, similar to what has been described in development of toxic thyroid nodules, rather than a diffusely-enlarged gland [10]. Whether these mutations could develop as long-term sequelae of radioiodine exposure is unknown.

Conclusions

We reported a case of GD that relapsed 32 years after initial treatment with RAI-therapy with a documented hypothyroid phase in the interim that necessitated levothyroxine therapy. The mechanism behind this relapse is not fully understood. Further basic science and clinical research is needed to discern the pathophysiology underlying this rare relapse.

Acknowledgements

An abstract on this case was accepted for a poster presentation at the national meeting of The Endocrine Society; April 3rd, 2017; Orlando, FL, USA.

Disclosure Statement: The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Smith TJ, Hegedus L. Graves’ disease. N Engl J Med 375: 1552–1565. [Crossref]
  2. Wartofsky L, Glinoer D, Solomon B, Nagataki S, Lagasse R, et al. (1991) Differences and similarities in the diagnosis and treatment of Graves’ disease in Europe, Japan, and the United States. Thyroid. 1(2): 129–35. [Crossref]
  3. Ross DS, Burch HB, Cooper DS, Greenlee MC, Laurberg P, et al. (2016) 2016 American Thyroid Association Guidelines for Diagnosis and Management of Hyperthyroidism and Other Causes of Thyrotoxicosis. Thyroid 26(10): 1343–1421. [Crossref]
  4. Hegele RA, Volp&#0x00E9; R (1985) Relapse of Graves’ disease 23 years after treatment with radioactive iodine (131I). J Clin Lab Immunol 18(2): 103–105. [Crossref]
  5. Tan GH, Gharib H (1995) Recurrent hyperthyroidism after radioiodine-induced hypothyroidism: report of two cases and literature review. Endocr Pract 1(3): 158–160. [Crossref]
  6. Kimura S (2014) Thyroid Regeneration: How Stem Cells Play a Role? Frontiers in Endocrinology 5: 55.
  7. Chen CY, Kimura H, Landek-Salgado MA, Hagedorn J, Kimura M, et al. (2009) Regenerative potentials of the murine thyroid in experimental autoimmune thyroiditis: role of CD24. Endocrinology 150(1): 492–429. [Crossref]
  8. Berghout A, Wiersinga WM, Drexhage HA, Smits NJ, Touber JL (1990) Comparison of placebo with L-thyroxine alone or with carbimazole for treatment of sporadic non-toxic goitre. Lancet 28: 336(8709): 193–7. [Crossref]
  9. McLachlan SM, Rapoport B (2013) Thyrotropin-blocking autoantibodies and thyroid-stimulating autoantibodies: potential mechanisms involved in the pendulum swinging from hypothyroidism to hyperthyroidism or vice versa. Thyroid 23(1): 14–24. [Crossref]
  10. Liu C, Wu C, Wang F (2010) Mutations of GNAS and TSHR genes in subclinical toxic multinodular goiter. Ann Otol Rhinol Laryngol 119(2): 118–124.

Abbreviations

GD: Graves’ Disease
RAI: Radioactive iodine
FTI: Free Thyroxine Index
TSH: Thyroid-Stimulating Hormone
FT4: Free Thyroxine
TSI: Thyroid-Stimulating Immunoglobulin
EAT: Experimental Autoimmune Thyroiditis
Tg: Thyroglobulin
TSHR: Thyroid- Stimulating Hormone Receptor

Genetic Studies on Reproduction Performance of Raighar Goat in its Native Tract

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DOI: 10.31038/IJVB.2017112

Introduction

There are around 65 lakh goats comprising of 32% of total livestock in Odisha (19th livestock census, India, 2012) [1]. Goats in Odisha are meat type and are famous for their excellent meat and skin quality with high frequency of multiple births and less kidding interval (Dash, et al.,) [2]. Raighar goat is a lesser known meat type breed found in Nabarangapur district and part of Kalahandi and Nuapada district of Odisha along with a part of Chhatisgarh in India, adjacent to Nabarangapur district and mostly reared by Bhatra and Gond tribals along with Gouda community. Knowledge on reproductive potential is the most important thing for developing strategy towards genetic improvement in meat type animals like goat. Evaluation of economic reproduction traits would help to know the extent of genetic diversity present, which may be explored for future use in breeding plans. Study of the reproduction traits of these populations in detail with regard to their inheritance and relationship with other economical traits would be very beneficial for devising improvement programmes. The findings on the inheritance and association among the important reproduction traits need to be incorporated in selection indices specific to the breed or type for improvement of the desired traits.

Age and weight at sexual maturity and at first kidding along with kidding interval are the most important economic factors, which influence the growth, production and reproduction pattern of any goat type. Consideration of many economic traits to improve at a particular time is the target by all the stakeholders. Hence, genetic and phenotypic association among such traits of importance needs to be kept in mind before designing the breeding plan.

Very limited published information on heritability estimates of some reproductive traits and degree of association among those in indigenous goats of Odisha (Patro et al., Bariha et al., Rao et al., and Dash et al.,) [2-5] have been accomplished.

Keeping in view the above facts, the present study was taken up on Raighar goat to assess the heritability of reproduction parameters and genetic association among those traits in its native tract.

Materials and methods

The present study was conducted in Nabarangapur district and part of Kalahandi and Nuapada district of Odisha situated between 19o 10’ N to 20o 16’ N latitude and between 81o 57’ E to 82o 45’ E longitude in India. The climate ranges from hot and humid to hot and moist-sub humid. The average maximum and minimum temperatures are 42.9° C and 11.5°C observed in the month of May and January, respectively. The average relative humidity ranges between 39.6 and 89.4 per cent over the months during the year. The average annual rainfall is around 1550 mm.

Indigenous goats in the present study were reared only for meat purpose. Data on the reproductive parameters of 746 does, sired by 32 bucks, born during the period of 2 years (2013 to 2015) in 32 villages of three districts viz. Nabarangapur Kalahandi and Nuapada of Odisha were included in the present study. Kids were naturally weaned at 90 days of age. Goats were maintained exclusively under extensive system of management; mostly were allowed to graze for the entire day (around 8 hours). Dams were never milked and kids were allowed to suckle fully throughout the lactation length of around 90 days.

Data on age at sexual maturity, weight at sexual maturity, age at first kidding, weight at first kidding and kidding interval were collected directly from farmers’ flocks and further grouped according to season of birth, flock size, parity and type of birth. The entire year was divided to three seasons as S1 for summer (March to June), S2 for rainy (July to October) and S3 for winter (November to February). Flock size was taken as F1 for small flock (<10 heads), F2 for medium flock (>10 to <20) and F3 for large flock (>20 heads).Parity as P1 for first parity, P2 for second parity and P3 for third parity. Type of birth as T1 for single birth, T2 for twin birth and T3 for triple birth.

The heritability of economic reproduction parameters was estimated by half sib correlation method, which is based on phenotypic resemblance between relatives as compared to unrelated individuals. The recorded data were analysed for the estimation of variation between and within sire groups and various components of variance were worked out (Kempthorne) [6]. As there were unequal numbers of off springs in individual sire groups, the average number of progeny per sire (K) was worked out by formula:

 

IJVB 2017-101eq1

Where,

S = Number of sires
N = Total number of progenies used in the study
∑ = Sum of
ni = Number of observations on the ith sire.

The heritability was estimated by multiplying intraclass correlation (t) with the factor 4.

 

IJVB 2017-101eq2

where,

t = intraclass correlation
h2 = heritability
σ2s = genetic variation
σ2w = environmental variation

The data were subjected to least squares analysis using model Least-Squares and Maximum Likelihood Program (Harvey) [7] for estimation of means along with standard errors, analysis of variance, heritability and degree of association among reproductive parameters.

Results and discussion

Reproductive performance as age and weight at sexual maturity and at first kidding along with kidding interval influenced by season, flock size, parity and type of birth is presented in Table 1.

Table 1. Least squares means along with standard errors for reproduction traits.

Particulars Age at sexualmaturity
(Day)		  Weight at sexual maturity
(Kg)		 Age at first kidding
(Day)		 Weight at firstkidding
(Kg)		 Kidding interval
(Day)
Overall 286.13 ± 1.02(746) 13.92 ± 0.16(746) 453.85 ± 1.14(720) 21.07 ± 0.26(720) 226.42 ± 2.56(712)
S1 285.49 ± 1.68
(198)		 13.84 ± 0.36
(198)		 451.03 ± 1.74
(192)		 20.88 ± 0.42(192) 224.84 ± 3.11(189)
S2 283.92 ± 1.51
(252)		 13.72 ± 0.29
(252)		 454.39 ± 1.66
(240)		 21.11 ± 0.37
(240)		 227.56 ± 2.96(238)
S3 288.44 ± 1.40
(296)		 14.14 ± 0.25
(296)		 455.28 ± 1.48
(288)		 21.16 ± 0.31
(288)		 226.52 ± 2.88(285)
F1 288.25a ± 1.26
(456)		 14.08a ± 0.21
(456)		 458.41a ± 1.33
(438)		 21.24a ± 0.29
(438)		 228.17a ± 2.75(432)
F2 285.19a ± 1.68
(209)		 13.72b ± 0.31
(209)		 449.82b ± 1.69
(204)		 20.87b ± 0.39
(204)		 225.36a ± 2.98
(203)
F3 276.64b ± 1.93
(81)		 13.53c ± 0.46
(81)		 438.78c ± 1.97
(78)		 20.64c ± 0.46
(78)		 219.40b ± 3.31 (77)
P1 287.28 ± 1.42(394) 14.01 ± 0.20
(394)		 455.07 ± 1.43
(378)		 21.10 ± 0.30
(378)		 226.68 ± 2.92
(374)
P2 285.34 ± 1.53(228) 13.84 ± 0.23
(228)		 453.14 ± 1.54
(221)		 21.05 ± 0.34
(221)		 225.49 ± 3.23
(218)
P3 283.94 ± 1.71(124) 13.78 ± 0.28
(124)		 451.36 ± 1.69
(121)		 21.01 ± 0.41
(121)		 227.29 ± 3.48
(120)
T1 286.74 ± 1.34 (493) 13.96 ± 0.18
(493)		 454.43 ± 1.24
(477)		 21.09 ± 0.28
(477)		 226.93 ± 2.85
(472)
T2 285.01 ± 1.55 (226) 13.85 ± 0.22
(226)		 452.77 ± 1.48
(218)		 21.03 ± 0.33
(218)		 225.19 ± 3.16
(215)
T3 284.37 ± 1.94
(27)		 13.78 ± 0.36
(27)		 452.20 ± 1.81
(25)		 20.98 ± 0.48
(25)		 227.37 ± 4.12
(25)

*Figures in parenthesis indicate number of observations.

Different superscripts along the column (for a factor) indicate significantly (P < 0.05) different values.

Age at sexual maturity: Overall age at sexual maturity was 286.13 ± 1.02 days, which corroborates with the report of Fahim et al. [8] for Rohilkhand goats in UP and Kharkar et al. [9] for Berari goats of Maharashtra. However, comparatively higher estimate was reported by Rao et al. [5] for Ganjam goats. Lower estimate was reported by Bariha et al. [4] and Mohanty et al. [2] for indigenous goats in Odisha.

Weight at sexual maturity: Average weight at sexual maturity in the present study was 13.92 ± 0.16 kg. Lower estimate was reported by Bariha et al. [4] and Paul et al. [10]. However, higher estimate was reported by Rao et al. [5] in Ganjam goats and Yadav and Khada (2009) [11] in non-descript goats of Rajasthan.

Age at first kidding: Overall age at first kidding was found to be 453.85 ± 1.14 days in the present study. The present finding is in agreement with the report of Kharkar et al. [9] However; higher estimate was reported by, Fahim et al. [8], Patel and Pandey [12] in Mehsana goats and Kumar et al. [13] in Sirohi goats. Lower values compared to the present finding was reported by Bariha et al. [4] and Faruque et al. [14] in Black Bengal goats.

Weight at first kidding: The average weight at first kiddingwas found to be 21.07 ± 0.26 kg in the present study. The present finding is in close agreement with the report of Fahim et al. [8] Lower estimates were observed by Bariha et al. [4] and Haque et al. [19]. However, higher values were reported by Rao et al. [5] and Patel and Pandey [12] in Ganjam and Mehsana goats, respectively.

Kidding interval: The present study revealed that the kidding interval in Raighar goats was 226.42 ± 2.56 days. The present finding corroborates with the earlier report of Das et al. [15] in Bengal goats of West Bengal. But, higher estimates were reported by Haque et al. [16] and Singh et al. [17].

Season of birth, parity and type of birth had no significant effect on any of the reproduction traits in the present study with Raighar goats. Similar finding was observed for season of birth by Paul et al. [10] for weight and age at sexual maturity in Black Bengal goats. However, the flock size showed significant effect on all the reproductive parameters. Large flocks were found to be significantly better than medium and smaller flocks in all the parameters but medium flocks were significantly better than smaller flocks only in weight at sexual maturity, age at first kidding and weight at first kidding. This may be due to the fact that, the small flocks with less than 10 heads usually do not have a breeding buck to move in the flock. In contrary the larger flocks often own a buck, hence, the female goat in estrus gets successful service at grazing, which the female in smaller flock usually miss, resulting in delayed days open [Figures 1 to 3].

The heritability estimates of all the reproductive parameters in the present study were found to be moderate ranging from 0.192 ± 0.081 to 0.334 ± 0.211, indicating existence of substantial additive genetic variance in the population and can be utilized for improvement of the sought traits (Table 2). The low heritability estimate observed for age at sexual maturity may be explained by the differential nutritional level of the does, resulting in a large environmental variation. The heritability estimate of age at first kidding was 0.276 ± 0.119. This result is in close agreement with the findings of Haque et al. (2013) in Black Bengal goats, Kebede et al. [18] in indigenous Arsi-Bale goats of Ethiopia, Bariha et al. (2008) in indigenous goats of Keonjhar and Rao et al. (2002) in Ganjam goats. The heritability estimate of kidding interval in the present study was 0.256 ± 0.176. Lower estimate of 0.06 and 0.14 ± 0.96 was reported by Kebede et al. (2012) in their study with indigenous Arsi-Bale goats and Bariha et al. [4] in indigenous goats of Keonjhar district in Odisha, respectively.

IJVB 2017-101_Figure 1

IJVB 2017-101_Figure 2

IJVB 2017-101_Figure 3

Table 2. Heritability, genetic and phenotypic correlations among reproductive traits.

Trait Age at sexual maturity Weight at sexual maturity Age at first kidding Weight at first kidding Kidding interval
Age at sexual maturity 0.192 ± 0.08 1 0.687 ± 0.156 0.557 ± 0. 214 0.189 ± 0.1 72 0.094 ± 0.1 31
Weight at sexual maturity 0.395 ± 0. 122 0.334 ± 0.211 0.557 ± 0.181 0.298 ± 0. 135 0.147 ± 0.1 36
Age at first kidding 0.484 ± 0. 212 0.297 ± 0.109 0.276 ± 0.119 0.443 ± 0. 096 0.214 ± 0. 118
Weight at first kidding 0.263 ± 0. 211 0.397 ± 0.213 0.447 ± 0.116 0.331 ± 0.0 98 0.079 ± 0. 106
Kidding interval 0.224 ± 0. 209 0.207 ± 0.156 0.187 ± 0.065 0.175 ± 0.127 0.256 ± 0.176

*Values of diagonal are heritability estimates, above diagonal are genetic correlations and below diagonal are phenotypic correlations.

It was observed that the genetic correlations between age at sexual maturity with weight at sexual maturity and with age at first kidding were 0.687 ± 0.156 and 0.557 ± 0.214, respectively. The genetic correlations of kidding interval with all other reproductive traits were found to be very low. The phenotypic correlations of age at sexual maturity with weight at sexual maturity, age at first kidding and weight at first kidding were found to be medium and positive.

Conclusion

As flocks with more than 10 heads were better in reproduction, recommendation may be made to rear at least 10 females and a buck to obtain optimum efficiency in reproduction. The heritability estimates, being moderate, indicated that selection for reproduction performance may be done on the basis of individual selection with proper nutrition and other managemental care. A positive response could be expected in almost all traits owing to the moderate to high and positive genetic correlations among the economic reproductive traits which gives a scope for simultaneous selection of more than one trait at a time.

Acknowledgements

The authors are thankful to Chief District Veterinary Officers of Nabarangapur, Nuapada and Kalahandi district in Odisha for their cooperation in conducting this research work.

References

  1. Government of India, Livestock Census (2012) Available on www.http: //dahd.nic.in/LStock.htm.
  2. Dash SK, Mohanty GP, Kanungo S, Palai TK and Sahu S (2011). Factors influencing body weight of indigenous goats of Mayurbhanj district in Orissa. J Res 1: 196-198.
  3. Patro BN, Nayak S, Rao PK and Panda P. (2007) Genetic studies of Ghumusar goats of Orissa. Indian J Anim Breed. & Genetics 27: 12–16.
  4. Bariah SP, Rao PK, Patro BN, Dash SK and Panda P (2008) Genetic analysis of indigenous goats of Keonjhar district of Orissa. Indian Vet J 85: 843–845.
  5. Rao PK, Dash SK, Singh MK, Rai B and Singh NP (2009) Ganjam goat of Orissa and its management practices. Indian J of Small Ruminants 1: 44–50.
  6. Kempthorne O (1957) An introduction to genetic statistics. John Wiley and Sons Inc. New York. USA.
  7. Harvey WR (1990) User’s Guide for LSMLMW, PC-2 Version, Mixed Model Least Squares and Maximum Likelihood Computer Program, Mimeograph, Columbus, Ohio, USA.
  8. Fahim A, Singh M, Patel BHM, Mondal SK and Dutt T. (2013). Reproductive performance and milk yield of Rohilkhand goat. The Indian J Small Ruminants 1: 32–35
  9. Kharkar K, Kuralkar SV and Kuralkar P (2014) Growth, production and reproduction performance of Berari goats in their native tract. Indian Journal of Small Ruminants 1: 12–15.
  10. Paul RC, Rahman ANMI, Debnath S, Khandoker MAMY (2014) Evaluation of productive and reproductive performance of Black Bengal goat. Bang. J Anim Sci 2: 104–111.
  11. Yadav CM and Khada BS (2009) Management practices and performance of goats in tribal belt of Dungarpur district in Rajasthan. Indian Journal of Small Ruminants 1: 131–133.
  12. Patel AC and Pandey DP (2013) Growth, Production and Reproduction Performance of Mehsana Goat. J Livestock Sci 4: 17–21.
  13. Kumar MU, Nagda RK and Sharma SK (2012) Reproductive status of Sirohi goats under field conditions. Indian Journal of Small Ruminants 1: 143–144.
  14. Faruque S, Chowdhury SA, Siddiquee NU and Afroz MA (2010). Performance and genetic parameters of economically important traits of Black Bengal goat. Journal of Bangladesh Agricultural University. 8: 67–78.
  15. Dash SK, Rao PK and Patro BN (2010). Selection index for improvement of production performance in goats. Indian Vet J 87: 359–362.
  16. Haque MN, Husain SS, Khandoker MAMY, Mia MM and Apu AS (2013) Selection of Black Bengal Buck Based on Some Reproductive Performance of Their Progeny at Semi-Intensive Rearing System. Journal of Agricultural Science 8: 142–152.
  17. Singh KP, Dixit SP, Singh PK, Pandey DP and Ahlawat SPS (2009) A note on growth and reproduction traits of Mehsana goats under farmer’s flocks. Indian J of Small Ruminants 2: 271–273.
  18. Kebede T, Haile A, Hailu D and Alemu T (2012) Genetic and phenotypic parameter estimates for reproduction traits in indigenous Arsi-Bale goats. Trop Anim Health Prod 44: 1007–1015.

A Survey of Quantum Lyapunov Control Methods on COMPUTER-assisted Identified Ii-Key/HER-2/ neu(776-790) Hybrid poly-mimic peptide mimotopic vaccine-like chemostructure with active pharmacophore sites as a future in silico promising novel inhibitor trans-activator in Prostate Cancer Patients generated by the BiogenetoligandorolTM and ChemMine tools

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Abstract

Active immunotherapy is emerging as a potential therapeutic approach for prostate cancer. First phase I trials of an Ii-Key/HER-2/neu(776–790) hybrid peptide vaccine (AE37) with recombinant granulocyte macrophage colony-stimulating factor as adjuvant in patients with HER-2/neu+prostate cancer have shown positive resutls. The primary functionalities of ChemMine Tools fall into five major application areas: data visualization, structure comparisons, similarity searching, compound clustering and prediction of chemical properties. First, users can upload compound data sets to the online Compound Workbench. Numerous utilities are provided for compound viewing, structure drawing and format interconversion. Second, pairwise structural similarities among compounds can be quantified. Third, interfaces to ultra-fast structure similarity search algorithms are available to efficiently mine the chemical space in the public domain. These include fingerprint and embedding/ indexing algorithms. Fourth, the service includes a Clustering Toolbox that integrates cheminformatic algorithms with data mining utilities to enable systematic structure and activity based analyses of custom compound sets. Fifth, physicochemical property descriptors of custom compound sets can be calculated. These descriptors are important for assessing the bioactivity profile of compounds in silico and quantitative structure—activity relationship (QSAR) analyses. ChemMine Tools is available at: http://chemmine.ucr.ed. The condition of a quantum Lyapunov-based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed. Here, in Biogenea we for the first time discovered a COMPUTER-assisted Identified Ii-Key/HER-2/ neu (776-790) Hybrid Peptide-mimotopic poly-mimic chemostructure with vaccine-like active pharmacophore sites as a novel inhibitor trans-activator in Prostate Cancer Patients using the ChemMine tools. An online service for analyzing and clustering small molecules.

Keywords

Survey of Quantum Lyapunov, Control Methods, COMPUTER-assisted, Ii-Key/HER-2/ neu(776-790), Hybrid poly-mimic, peptide mimotopic, vaccine-like ,chemostructure, active pharmacophore sites, in silico, novel inhibitor, trans-activator, Prostate Cancer Patients.