Tumor metabolism, the ketogenic diet and beta-hydroxybutyrate: novel approaches to adjuvant brain tumor therapy

Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately ~18 months. It is clear that increased survival of brain tumor patients requires the design of new therapeutic modalities, especially those that enhance currently available treatments and/or limit tumor growth. One novel therapeutic arena is the metabolic dysregulation that results in an increased need for glucose in tumor cells. This phenomenon suggests that a reduction in tumor growth could be achieved by decreasing glucose availability, which can be accomplished through pharmacological means or through the use of a high-fat, low-carbohydrate ketogenic diet (KD). The KD, as the name implies, also provides increased blood ketones to support the energy needs of normal tissues. Preclinical work from a number of laboratories has shown that the KD does indeed reduce tumor growth in vivo. In addition, the KD has been shown to reduce angiogenesis, inflammation, peri-tumoral edema, migration and invasion. Furthermore, this diet can enhance the activity of radiation and chemotherapy in a mouse model of glioma, thus increasing survival. Additional studies in vitro have indicated that increasing ketones such as β-hydroxybutyrate (βHB) in the absence of glucose reduction can also inhibit cell growth and potentiate the effects of chemotherapy and radiation. Thus, while we are only beginning to understand the pluripotent mechanisms through which the KD affects tumor growth and response to conventional therapies, the emerging data provide strong support for the use of a KD in the treatment of malignant gliomas. This has led to a limited number of clinical trials investigating the use of a KD in patients with primary and recurrent glioma

Translational models for vascular cognitive impairment: a review including larger species.

BACKGROUND: Disease models are useful for prospective studies of pathology, identification of molecular and cellular mechanisms, pre-clinical testing of interventions, and validation of clinical biomarkers. Here, we review animal models relevant to vascular cognitive impairment (VCI). A synopsis of each model was initially presented by expert practitioners. Synopses were refined by the authors, and subsequently by the scientific committee of a recent conference (International Conference on Vascular Dementia 2015). Only peer-reviewed sources were cited. METHODS: We included models that mimic VCI-related brain lesions (white matter hypoperfusion injury, focal ischaemia, cerebral amyloid angiopathy) or reproduce VCI risk factors (old age, hypertension, hyperhomocysteinemia, high-salt/high-fat diet) or reproduce genetic causes of VCI (CADASIL-causing Notch3 mutations). CONCLUSIONS: We concluded that (1) translational models may reflect a VCI-relevant pathological process, while not fully replicating a human disease spectrum; (2) rodent models of VCI are limited by paucity of white matter; and (3) further translational models, and improved cognitive testing instruments, are required

Formalization of the classification pattern: Survey of classification modeling in information systems engineering

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move towards formalization in part because it illustrates some of the barriers to formalization; including the formal complexity of the pattern and the ontological issues surrounding the ‘one and the many’. Powersets are a way of characterizing the (complex) formal structure of the classification pattern and their formalization has been extensively studied in mathematics since Cantor’s work in the late 19th century. One can use this formalization to develop a useful benchmark. There are various communities within Information Systems Engineering (ISE) that are gradually working towards a formalization of the classification pattern. However, for most of these communities this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other Information Systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature; starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE’s understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.The UK Engineering and Physical Sciences Research Council (grant EP/K009923/1)

Neural cytoskeleton capabilities for learning and memory

This paper proposes a physical model involving the key structures within the neural cytoskeleton as major players in molecular-level processing of information required for learning and memory storage. In particular, actin filaments and microtubules are macromolecules having highly charged surfaces that enable them to conduct electric signals. The biophysical properties of these filaments relevant to the conduction of ionic current include a condensation of counterions on the filament surface and a nonlinear complex physical structure conducive to the generation of modulated waves. Cytoskeletal filaments are often directly connected with both ionotropic and metabotropic types of membrane-embedded receptors, thereby linking synaptic inputs to intracellular functions. Possible roles for cable-like, conductive filaments in neurons include intracellular information processing, regulating developmental plasticity, and mediating transport. The cytoskeletal proteins form a complex network capable of emergent information processing, and they stand to intervene between inputs to and outputs from neurons. In this manner, the cytoskeletal matrix is proposed to work with neuronal membrane and its intrinsic components (e.g., ion channels, scaffolding proteins, and adaptor proteins), especially at sites of synaptic contacts and spines. An information processing model based on cytoskeletal networks is proposed that may underlie certain types of learning and memory

HLA-A and -B alleles and haplotypes in hemochromatosis probands with HFE C282Y homozygosity in central Alabama

BACKGROUND: We wanted to quantify HLA-A and -B allele and haplotype frequencies in Alabama hemochromatosis probands with HFE C282Y homozygosity and controls, and to compare results to those in other populations. METHODS: Alleles were detected using DNA-based typing (probands) and microlymphocytotoxicity (controls). RESULTS: Alleles were determined in 139 probands (1,321 controls) and haplotypes in 118 probands (605 controls). In probands, A*03 positivity was 0.7482 (0.2739 controls; p =< 0.0001; odds ratio (OR) 7.9); positivity for B*07, B*14, and B*56 was also increased. In probands, haplotypes A*03-B*07 and A*03-B*14 were more frequent (p < 0.0001, respectively; OR = 12.3 and 11.1, respectively). The haplotypes A*01-B*60, A*02-B*39, A*02-B*62, A*03-B*13, A*03-B*15, A*03-B*27, A*03-B*35, A*03-B*44, A*03-B*47, and A*03-B*57 were also significantly more frequent in probands. 37.3% of probands were HLA-haploidentical with other proband(s). CONCLUSIONS: A*03 and A*03-B*07 frequencies are increased in Alabama probands, as in other hemochromatosis cohorts. Increased absolute frequencies of A*03-B*35 have been reported only in the present Alabama probands and in hemochromatosis patients in Italy. Increased absolute frequencies of A*01-B*60, A*02-B*39, A*02-B*62, A*03-B*13, A*03-B*15, A*03-B*27, A*03-B*44, A*03-B*47, and A*03-B*57 in hemochromatosis cohorts have not been reported previously

Representing Kidney Development Using the Gene Ontology

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease

The European Society of Human Reproduction and Embryology guideline for the diagnosis and treatment of endometriosis: an electronic guideline implementability appraisal

<p>Abstract</p> <p>Background</p> <p>Clinical guidelines are intended to improve healthcare. However, even if guidelines are excellent, their implementation is not assured. In subfertility care, the European Society of Human Reproduction and Embryology (ESHRE) guidelines have been inventoried, and their methodological quality has been assessed. To improve the impact of the ESHRE guidelines and to improve European subfertility care, it is important to optimise the implementability of guidelines. We therefore investigated the implementation barriers of the ESHRE guideline with the best methodological quality and evaluated the used instrument for usability and feasibility.</p> <p>Methods</p> <p>We reviewed the ESHRE guideline for the diagnosis and treatment of endometriosis to assess its implementability. We used an electronic version of the guideline implementability appraisal (eGLIA) instrument. This eGLIA tool consists of 31 questions grouped into 10 dimensions. Seven items address the guideline as a whole, and 24 items assess the individual recommendations in the guideline. The eGLIA instrument identifies factors that influence the implementability of the guideline recommendations. These factors can be divided into facilitators that promote implementation and barriers that oppose implementation. A panel of 10 experts from three European countries appraised all 36 recommendations of the guideline. They discussed discrepancies in a teleconference and completed a questionnaire to evaluate the ease of use and overall utility of the eGLIA instrument.</p> <p>Results</p> <p>Two of the 36 guideline recommendations were straightforward to implement. Five recommendations were considered simply statements because they contained no actions. The remaining 29 recommendations were implementable with some adjustments. We found facilitators of the guideline implementability in the quality of decidability, presentation and formatting, apparent validity, and novelty or innovation of the recommendations. Vaguely defined actions, lack of facilities, immeasurable outcomes, and inflexibility within the recommendations formed barriers to implementation. The eGLIA instrument was generally useful and easy to use. However, assessment with the eGLIA instrument is very time-consuming.</p> <p>Conclusions</p> <p>The ESHRE guideline for the diagnosis and treatment of endometriosis could be improved to facilitate its implementation in daily practice. The eGLIA instrument is a helpful tool for identifying obstacles to implementation of a guideline. However, we recommend a concise version of this instrument.</p

Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28