The mobius strip, the cell, and soft logic mathematics.

The cell-cell signaling mechanisms that are the basis for all of physiology have been used to trace evolution back to the unicellular state, and beyond, to the "First Principles of Physiology". And since our physiology derives from the Cosmos based on Symbiogenesis, it has been hypothesized that the cell behaves like a functional Mobius Strip, having no 'inside or outside' cell membrane surface - it is continuous with the Cosmos, its history being codified from Quantum Entanglement to Newtonian Mechanics, affording the cell consciousness and unconsciousness/subconsciousness as a continuum for the first time. Similarly, Klein and Maimon have concluded that their 'Soft Logic' mathematics also constitutes a Mobius Strip, using both a real number axis, combined with a zero axis, numerically representing cognition. This is congruent with the cell as 'two-tiered' consciousness, the first tier being the real-time interface between the cell membrane and its environment; the second tier constituting integrated physiology, referencing the consciousness of the Cosmos. Thus, there is coherence between physiology, consciousness and mathematics for the first time.

Does Care Transition Matter? Exploring the Newly Published HCAHPS Measure.

In recent years, health care systems have undergone a consumer revolution-putting patients at the center. The study aim was to explore the association between care transition-the new measure proposed by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)-and hospital patients' overall rating based on their experience, along with hospitals' internal characteristics and operational attributes. Using HCAHPS and American Hospital Association published databases, the authors examined the association between hospital characteristics and measures of patient experience, focusing on the care transition measure, in 2350 US hospitals. Positive significant association was found between care transition and overall rating (P < .0001). An interaction regression model revealed that each of the following moderators-teaching affiliation, location, and membership in a health system-significantly (all P < .05) strengthens the association between care transition and overall rating in a positive direction. These findings may help improve hospital rating, value-based payments, and patient-centered outcomes.

Measuring the academic value of academic medical centers: describing a methodology for developing an evaluation model at one Academic Medical Center.

BACKGROUND: Academic Medical Centers (AMCs) must simultaneously serve different purposes: Delivery of high quality healthcare services to patients, as the main mission, supported by other core missions such as academic activities, i.e., researching, teaching and tutoring, while maintaining solvency. This study aims to develop a methodology for constructing models evaluating the academic value provided by AMCs and implementing it at the largest AMC in Israel. METHODS: Thirty five practiced educators and researchers, academic experts, faculty members and executives, all employed by a metropolitan 1500-bed AMC, were involved in developing academic quality indicators. First, an initial list of AMCs' academic quality indicators was drafted, using a literature review and consulting scholars. Afterwards, additional data and preferences were collected by conducting semi-structured interviews, complemented by a three-round Delphi Panel. Finally, the methodology for constructing a model evaluating the academic value provided by the AMC was developed. RESULTS: The composite academic quality indicators methodology consists of nine indicators (relative weight in parentheses): 'Scientific Publications Value' (18.7%), 'Completed Studies' (13.5%), 'Authors Value' (13.0%), 'Residents Quality' (11.3%), 'Competitive Grants Budget' (10.2%), 'Academic Training' (8.7%), 'Academic Positions' (8.3%), 'Number of Studies' (8.3%) and 'Academic Supervision' (8.0%). These indicators were grouped into three core categories: 'Education', 'Research' and 'Publications', having almost the same importance on a scale from zero to one (0-1), i.e., 0.363, 0.320, and 0.317, respectively. The results demonstrated a high level of internal consistency (Cronbach-alpha range: 0.79-0.86). CONCLUSIONS: We have found a gap in the ability to measure academic value provided by AMCs. The main contribution of this research is the development of methodology for constructing evaluation models for AMCs academic performance. Further studies are needed to further test the validity and reliability of the proposed methodology at other sites.

Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data.

BACKGROUND: Cobalt-ferrite nanoparticles (Co-Fe NPs) are attractive for nanotechnology-based therapies. Thus, exploring their effect on viability of seven different cell lines representing different organs of the human body is highly important. METHODS: The toxicological effects of Co-Fe NPs were studied by in-vitro exposure of A549 and NCIH441 cell-lines (lung), precision-cut lung slices from rat, HepG2 cell-line (liver), MDCK cell-line (kidney), Caco-2 TC7 cell-line (intestine), TK6 (lymphoblasts) and primary mouse dendritic-cells. Toxicity was examined following exposure to Co-Fe NPs in the concentration range of 0.05 -1.2 mM for 24 and 72 h, using Alamar blue, MTT and neutral red assays. Changes in oxidative stress were determined by a dichlorodihydrofluorescein diacetate based assay. Data analysis and predictive modeling of the obtained data sets were executed by employing methods of Knowledge Discovery from Data with emphasis on a decision tree model (J48). RESULTS: Different dose-response curves of cell viability were obtained for each of the seven cell lines upon exposure to Co-Fe NPs. Increase of oxidative stress was induced by Co-Fe NPs and found to be dependent on the cell type. A high linear correlation (R2=0.97) was found between the toxicity of Co-Fe NPs and the extent of ROS generation following their exposure to Co-Fe NPs. The algorithm we applied to model the observed toxicity belongs to a type of supervised classifier. The decision tree model yielded the following order with decrease of the ranking parameter: NP concentrations (as the most influencing parameter), cell type (possessing the following hierarchy of cell sensitivity towards viability decrease: TK6 > Lung slices > NCIH441 > Caco-2 = MDCK > A549 > HepG2 = Dendritic) and time of exposure, where the highest-ranking parameter (NP concentration) provides the highest information gain with respect to toxicity. The validity of the chosen decision tree model J48 was established by yielding a higher accuracy than that of the well-known "naive bayes" classifier. CONCLUSIONS: The observed correlation between the oxidative stress, caused by the presence of the Co-Fe NPs, with the hierarchy of sensitivity of the different cell types towards toxicity, suggests that oxidative stress is one possible mechanism for the toxicity of Co-Fe NPs.

Predictive toxicology of cobalt nanoparticles and ions: comparative in vitro study of different cellular models using methods of knowledge discovery from data.

The toxicological effects of cobalt nanoparticles (Co-NPs) aggregates were examined and compared with those of cobalt ions (Co-ions) using six different cell lines representing lung, liver, kidney, intestine, and the immune system. Dose-response curves were studied in the concentration range of 0.05-1.0 mM, employing 3-(4,5-dimethylthiazol-2-Yl)-2,5-diphenyltetrazolium bromide test, neutral red, and Alamar blue as end point assays following exposures for 48 and 72 h. Data analysis and predictive modeling of the obtained data sets were executed by employing a decision tree model (J48), where training and validation were carried out by an iterative process. It was established, as expected, that concentration is the highest rank parameter. This is because concentration parameter provides the highest information gain with respect to toxicity. The second-rank parameter emerged to be either the compound type (Co-ions or Co-NPs) or the cell model, depending on the concentration range. The third and the lowest rank in the model was exposure duration. The hierarchy of cell sensitivity toward cobalt ions was found to obey the following sequence of cell lines: A549 > MDCK > NCIH441 > Caco-2 > HepG2 > dendritic cells (DCs), with A549 being the most sensitive cell line and primary DCs were the least sensitive ones. However, a different hierarchy pattern emerged for Co-NPs: A549 = MDCK = NCIH441 = Caco-2 > DCs > HepG2. The overall findings are in line with the hypothesis that the toxic effects of aggregated cobalt NPs are mainly due to cobalt ion dissolution from the aggregated NPs.

Evaluation of gene-expression clustering via mutual information distance measure.

BACKGROUND: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI) measure versus the use of the well known Euclidean distance and Pearson correlation coefficient. RESULTS: Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions. It was found that the use of the MI measure yields a more significant differentiation among erroneous clustering solutions. The proposed measure was also used to analyze the performance of several known clustering algorithms. A comparative study of these algorithms reveals that their "best solutions" are ranked almost oppositely when using different distance measures, despite the found correspondence between these measures when analysing the averaged scores of groups of solutions. CONCLUSION: In view of the results, further attention should be paid to the selection of a proper distance measure for analyzing the clustering of gene expression data.

Context-sensitive medical information retrieval.

Substantial medical data such as pathology reports, operative reports, discharge summaries, and radiology reports are stored in textual form. Databases containing free-text medical narratives often need to be searched to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The con-text of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently "ruled out." Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the retrieved documents will be irrelevant. The purpose of this work is to develop a methodology for automated learning of negative context patterns in medical narratives and test the effect of context identification on the performance of medical information retrieval. The algorithm presented significantly improves the performance of information retrieval done on medical narratives. The precision im-proves from about 60%, when using context-insensitive retrieval, to nearly 100%. The impact on recall is only minor. In addition, context-sensitive queries enable the user to search for terms in ways not otherwise available