Changes in Visual Function in the Elderly Population in the United States: 1995–2010

<p><i><b>Purpose</b></i>: To document recent trends in visual function among the United States population aged 70+ years and investigate how the trends can be explained by inter-temporal changes in: (1) population sociodemographic characteristics, and chronic disease prevalence, including eye diseases (compositional changes); and (2) effects of the above factors on visual function (structural changes).</p> <p><i><b>Methods</b></i>: Data from the 1995 Asset and Health Dynamics among the Oldest Old (AHEAD) and the 2010 Health and Retirement Study (HRS) were merged with Medicare Part B claims in the interview years and the 2 previous years. Decomposition analysis was performed. Respondents from both studies were aged 70+ years. The outcome measure was respondent self-reported visual function on a 6-point scale (from 6 = blind to 1 = excellent).</p> <p><i><b>Results</b></i>: Overall, visual function improved from slightly worse than good (3.14) in 1995 to slightly better than good (2.98) in 2010. A decline in adverse effects of aging on vision was found. Among the compositional changes were higher educational attainment leading to improved vision, and higher prevalence of such diseases as diabetes mellitus, which tended to lower visual function. However, compared to compositional changes, structural changes were far more important, including decreased adverse effects of aging, diabetes mellitus (when not controlling for eye diseases), and diagnosed glaucoma.</p> <p><i><b>Conclusion</b></i>: Although the US population has aged and is expected to age further, visual function improved among elderly persons, especially among persons 80+ years, likely reflecting a favorable role of structural changes identified in this study in mitigating the adverse effect of ongoing aging on vision.</p

Oxidation of amitriptyline and nortriptyline by ferrate(VI): efficiency and reaction pathways

<p>The oxidation of amitriptyline (AMI) and nortriptyline (NOR), two typical tricyclic antidepressants, has been studied in ferrate(VI) (Fe(VI)) solution. The removal rate of AMI and NOR increased with increasing Fe(VI) dosage and was seen to be pH dependent in the order pH 7.0 < 10.0 < 8.0 < 9.0. UV irradiation at 254 nm was found to exert a synergistic effect on the Fe(VI) oxidation of AMI and NOR. By LC–ESI-MS/MS analysis, the main oxidation products of AMI and NOR by Fe(VI) have been identified. The exocyclic double bond is first oxidized to give the <i>exo</i>-epoxide, which is then hydrolyzed and finally oxidized to give dibenzosuberenone and 3-dimethylamino-propionaldehyde. The results suggest that Fe(VI) has a good ability to oxidize AMI and NOR in aqueous solution and could be an effective treatment method for the purification of waters containing these particular antidepressants.</p

Additional file 1 of A tempo-spatial controllable microfluidic shear-stress generator for in-vitro mimicking of the thrombus

Additional file 1. Membrane deformation simulation. Flow resistance simulation. Theoretical study of the flow. Numerical study of the flow. Cell maintenance. Figure S1. The hydraulic computational design of microfluidic chips. Figure S2. Membrane deformation simulation. Figure S3. Flow resistance simulation. Figure S4. The velocity field at the entrance of the branch channel without the constriction. Figure S5. The velocity field at the exit of the branch channel without the constriction. Figure S6. The velocity field behind the constriction in the branch channel. Figure S7. The orientation angle distribution of cells under distinct shear stress. Figure S8. The growth curve adherent cells under various shear stress. Figure S9. The simulation results depict the flow field within the channel under various membrane deformation conditions, all subject to the same inlet flow conditions. Table S1. Mesh Independence Validation for membrane deformation simulation. Table S2. The relationship of membrane deformation and pressure. Table S3. Parameters in the mesh independence validation for flow resistance simulation. Table S4. The flow resistance of each part of the microfluidic chip. Table S5. The combination of membrane deformations and related shear stress range. Table S6. The same membrane deformation combination generates both maximum and minimum shear stress gradients. Table S7. The deformation of the membrane corresponding to different shear stress profiles. Table S8. The fitted model (y=ax2+bx+c) parameters for velocity in 5 channels (acquired by µPIV) without membrane deformation. Table S9. The Shear stress values generated by microfluidic chips in cell experiments

DataSheet_1_The white blood cell count to mean platelet volume ratio for ischemic stroke patients after intravenous thrombolysis.pdf

Background and PurposeWhite blood cell count to mean platelet volume ratio (WMR) is increasingly recognized as a promising biomarker. However, its predictive capability for acute ischemic stroke (AIS) patients is relatively less researched. The primary aim of this study is to explore its prognostic value in AIS patients after reperfusion regarding 3-month poor functional outcome.MethodsA total of 549 AIS patients who had undergone vascular reperfusion procedure with complete 3-month follow-up were retrospectively recruited in this study. White blood cell count, mean platelet volume at 24 h of admission were recorded. Stroke severity had been estimated using the National Institutes of Health Stroke Scale (NIHSS) and poor outcome was defined as modified Rankin Scale (mRS) 3–6 at 3 months.ResultsAIS patients with poor functional outcome at 3 months displayed higher WMR. A positive correlation between WMR and NIHSS score was found (r = 0.334, p ConclusionsWMR is significantly correlated with stroke severity on admission and is proved to be an important prognostic indicator for AIS outcomes, especially in high NIHSS on admission group. Additionally, the developed nomogram that includes high WMR for predicting 1-year survival provides us with an effective visualization tool.</p

AdditionalDataS5

This file contains the complete list of yeast strains present on the diagnostic array, which was used for genetic interaction screens in this study

Legislative Documents

Also, variously referred to as: Senate bills; Senate documents; Senate legislative documents; legislative documents; and General Court documents

AdditionalDataS6

This file contains the trigenic interactions list of MDY2-MTC1 and digenic interaction list of MDY2 and MTC1 corresponding to Fig. 3. The ‘Tetrad Analysis’ tab contains confirmations results obtained from tetrad analysis: SS is synthetic sick, SL is synthetic lethal. The ‘Genetic interactions’ tab contains columns that are annotated with ‘CellMap’ since they contain genetic interactions from (7) downloaded from theCellMap.org (26) as well as scores derived in this study

Data File S9. High and low interaction degree genes

This file lists the negative and positive interaction degree associated with every nonessential deletion (sn#), essential TS (tsq#), and DAmP (damp#) query mutant strain screened against the DMA (“query degree X DMA” tab) and/or TSA (“query degree X TSA” tab). A subset of strains were found to carry a second, spontaneous suppressor mutation that affected fitness of the query mutant strain. Strains carrying a suppressor mutation mapped through SGA analysis are indicated (“-supp”). Query mutants comprising the 20% highest and lowest degree groups of strains are indicated. Furthermore, a “Co-batch signal” rank is provided for every query (see “Co-batch filtering of query mutant strains”). Low ranks correspond to evidence for lingering batch effects. Another column, “ Gene with correlated GI profiles that are co-annotated with the query gene (%)", provides the percent of correlated gene pairs that are co-annotated to the particular query. A low negative interaction degree (e.g. 20% lowest negative interaction degree) coupled with a low co-batch rank (e.g. < ~0.2) and a low fraction of correlated pairs that share a similar functional annotation with a given query strain (e.g. < ~0.15) may be indicative of a low confidence screen. However, these criteria should be considered as loose indicators and not definitive metrics of screen quality and thus, should not be used as strict filters on the global interaction dataset. Another list (“Queries removed - batch effects” tab) indicates ~300 query strains that exhibited severe systematic batch effects and thus were removed from the indicated data set. Finally, two additional tabs provide the negative and positive interaction degree associated with every nonessential (“nonessential array degree” tab) and essential (“essential array degree” tab) array mutant, respectively

Data File S6. Genetic profile similarity-based hierarchy analysis

The first tab (“Gene to hierarchy cluster mapping”) lists the clusters identified at each level of the genetic interaction-based hierarchy and the deletion and TS allele array mutants assigned to each cluster. Examples of clusters described in the main text are highlighted. The subsequent 9 tabs indicate enrichment of clusters resolved at the specified profile similarity range for specific cell compartments (Cyclops_enrich), biological processes (GO BP_enrich), protein complexes (complex_enrich) and KEGG pathways (KEGG_enrich). The final tab in the file indicates the clusters used to map the functional distribution of negative and positive interactions shown in Fig. 5D

Data File S10. Correlation analysis of query strain GI degree

As a complement to analysis of array strains (fig. S11-S12), GI degrees were calculated for query strains by counting negative interactions (tab 1, interactions with DMA strains; tab 2, interactions with TSA strains) and by counting positive interactions (tab 3, interactions with DMA strains; tab 4, interactions with TSA strains). Essential and nonessential queries were analyzed separately and results are labeled by grouped column headers. Wilcoxon rank-sum tests compared the GI degree in paired gene sets defined by absence and presence of each binary feature tested (top table). If the P-value is significant (< 0.05), the “Test result” column describes the degree of the set of genes for which the listed binary feature is true (compared to the set for which the feature is false). Tests were not performed, indicated by “N/A”, if data were present for fewer than 50 strains; strains with missing data were excluded from the tests. Pearson’s correlation (column labeled “r”) was used to measure associations between GI degree and features that are continuous or counts (bottom table). Uncorrected P-values are shown. The features examined in this analysis are described above (see Methods section entitled, “ Genetic interaction degree and frequency analysis”). Given that analysis of different features required using different statistical tests and some features are not expected to be independent of each other, no multiple hypotheses correction procedures were used. We do note that 31 gene features were tested