Optical imaging of postsynaptic odor representation in the glomerular layer of the mouse olfactory bulb.

Olfactory glomeruli are the loci where the first odor-representation map emerges. The glomerular layer comprises exquisite local synaptic circuits for the processing of olfactory coding patterns immediately after their emergence. To understand how an odor map is transferred from afferent terminals to postsynaptic dendrites, it is essential to directly monitor the odor-evoked glomerular postsynaptic activity patterns. Here we report the use of a transgenic mouse expressing a Ca(2+)-sensitive green fluorescence protein (GCaMP2) under a Kv3.1 potassium-channel promoter. Immunostaining revealed that GCaMP2 was specifically expressed in mitral and tufted cells and a subpopulation of juxtaglomerular cells but not in olfactory nerve terminals. Both in vitro and in vivo imaging combined with glutamate receptor pharmacology confirmed that odor maps reported by GCaMP2 were of a postsynaptic origin. These mice thus provided an unprecedented opportunity to analyze the spatial activity pattern reflecting purely postsynaptic olfactory codes. The odor-evoked GCaMP2 signal had both focal and diffuse spatial components. The focalized hot spots corresponded to individually activated glomeruli. In GCaMP2-reported postsynaptic odor maps, different odorants activated distinct but overlapping sets of glomeruli. Increasing odor concentration increased both individual glomerular response amplitude and the total number of activated glomeruli. Furthermore, the GCaMP2 response displayed a fast time course that enabled us to analyze the temporal dynamics of odor maps over consecutive sniff cycles. In summary, with cell-specific targeting of a genetically encoded Ca(2+) indicator, we have successfully isolated and characterized an intermediate level of odor representation between olfactory nerve input and principal mitral/tufted cell output.

The type-A horizontal cell: GABAergic characteristics and role in development of the outer plexiform layer

Morphological analysis of neonatal rabbit retina suggests that the type-A horizontal cell acts as the pioneer cell for development of the OPL. It is the first mature element of the OPL, and it forms the infrastructure upon which the OPL accrues. The role of type-A horizontal cells in influencing postnatal development of the OPL was examined.^ GABAergic characteristics of the type-A horizontal cell were defined. The type-A horizontal cell was found to possess two more GABAergic characteristics in addition to those previously demonstrated, during a short period in early postnatal development: endogenous stores of GABA and the GABA precursor, glutamate. Lesioning the type-A horizontal cell resulted in their permanent loss in addition to the disappearance of cone terminals and a dramatic increase in rod terminals within the OPL. Thus the type-A cells are not a necessary prerequisite for positioning the OPL in postnatal development, but may be necessary for establishment of the normal photoreceptor mosaic.^ Since type-A horizontal cells possess a number of GABAergic qualities during the period of cone photoreceptor cell differentiation, and there are reports of GABA's trophic action in other developing neuronal systems; the role that GABAergic type-A horizontal cells play in directing photoreceptor differentiation was examined.^ Disrupting effects of GABA-A receptor antagonists indicate that type-A horizontal cells act as postsynaptic targets for the growing cone terminals of photoreceptor cells. These trophic or synaptic interactions may involve GABA-A receptors activated by GABA released from horizontal cells. These findings are consistent with the hypothesis that type-A horizontal cells act as pioneering cells in directing the postnatal development of the OPL.^ These studies offer an in depth analysis of the structural and chemical relationship between type-A horizontal cells and other elements of the OPL from which the roles of type-A horizontal cells and the GABA system in development can be defined. They contribute to our knowledge of both structural and GABAergic mechanisms involved in central nervous system development. ^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.