The cellular dynamics of bone remodeling: a mathematical model

The mechanical properties of vertebrate bone are largely determined by a process which involves the complex interplay of three different cell types. This process is called {\it bone remodeling}, and occurs asynchronously at multiple sites in the mature skeleton. The cells involved are bone resorbing osteoclasts, bone matrix producing osteoblasts and mechanosensing osteocytes. These cells communicate with each other by means of autocrine and paracrine signaling factors and operate in complex entities, the so-called bone multicellular units (BMU). To investigate the BMU dynamics in silico, we develop a novel mathematical model resulting in a system of nonlinear partial differential equations with time delays. The model describes the osteoblast and osteoclast populations together with the dynamics of the key messenger molecule RANKL and its decoy receptor OPG. Scaling theory is used to address parameter sensitivity and predict the emergence of pathological remodeling regimes. The model is studied numerically in one and two space dimensions using finite difference schemes in space and explicit delay equation solvers in time. The computational results are in agreement with in vivo observations and provide new insights into the role of the RANKL/OPG pathway in the spatial regulation of bone remodeling

Critical Appraisal of Baby Friendly Hospital Initiatives among the beneficiaries of Indore Urban - A Comparative Study

Background: Majority of the women in M.P who breastfeed their babies throw away the colostrum before they begin actual feed. Only 14.9% females feed their babies with in 1/2 an hour (National Family Health Survey-3). In Indore 2 hospitals have been given designation of baby friendly hospital (BFH) namely M.Y. hospital (MYH) which is a tertiary care center, attached to Medical College and District hospital (DH). Objectives: (1) To assess the process of implementation of baby friendly hospital initiative programme. (2) Knowledge, Attitude, Practice regarding breast feeding amongst the beneficiaries. Methods: A comparative study was conducted between two BFH and two NBFH (Non Baby Friendly Hospitals) of Indore. The respondents were categorized into health care providers and beneficiaries. A pretested questionnaire was used to interview the respondents which was made taking into consideration the essential 10 Criteria of BFHI Programme. Results: 89% in BFH and 97% in NBFH were having positive attitude towards colostrum feeding. 81% postnatal mothers in BFH and 96% in NBFH were having knowledge about benefits of exclusive breast feeding. 21% postnatal mothers in BFH and 39% in NBFH initiated breast feeding within ½ an hour. 64 % of mothers in BFH and 62 % mothers in NBFH were practicing exclusive breast Feeding. Conclusion: The above findings show that although designated as BFH, the breast feeding practices remained poor as compared to NBFH. There is an utmost need to reinforce training and constant monitoring of health care providers regarding BFHI

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Network analysis of unstructured EHR data for clinical research.

In biomedical research, network analysis provides a conceptual framework for interpreting data from high-throughput experiments. For example, protein-protein interaction networks have been successfully used to identify candidate disease genes. Recently, advances in clinical text processing and the increasing availability of clinical data have enabled analogous analyses on data from electronic medical records. We constructed networks of diseases, drugs, medical devices and procedures using concepts recognized in clinical notes from the Stanford clinical data warehouse. We demonstrate the use of the resulting networks for clinical research informatics in two ways-cohort construction and outcomes analysis-by examining the safety of cilostazol in peripheral artery disease patients as a use case. We show that the network-based approaches can be used for constructing patient cohorts as well as for analyzing differences in outcomes by comparing with standard methods, and discuss the advantages offered by network-based approaches

Computation in Focused Intuitionistic Logic

International audienceWe investigate the control of evaluation strategies in a variant of the λ-calculus derived through the Curry-Howard correspondence from LJF, a sequent calculus for intuitionistic logic implementing the focusing technique. The proof theory of focused intuitionistic logic yields a single calculus in which a number of known λ-calculi appear as subsystems obtained by restricting types to a certain fragment of LJF. In particular, standard λ-calculi as well as the call-by-push-value calculus are analysed using this framework, and we relate cut elimination for LJF to a new abstract machine subsuming well-known machines for these different strategies