Interrelationships between Cellular Density,Mosaic Patterning,and Dendritic Coverage of VGluT3 Amacrine Cells

Amacrine cells of the retina are conspicuously variable in their morphologies, their population demographics, and their ensuing functions. Vesicular glutamate transporter 3 (VGluT3) amacrine cells are a recently characterized type of amacrine cell exhibiting local dendritic autonomy. The present analysis has examined three features of this VGluT3 population, including their density, local distribution, and dendritic spread, to discern the extent to which these are interrelated, using male and female mice. We first demonstrate that Bax-mediated cell death transforms the mosaic of VGluT3 cells from a random distribution into a regular mosaic. We subsequently examine the relationship between cell density and mosaic regularity across recombinant inbred strains of mice, finding that, although both traits vary across the strains, they exhibit minimal covariation. Other genetic determinants must therefore contribute independently to final cell number and to mosaic order. Using a conditional KO approach, we further demonstrate that Bax acts via the bipolar cell population, rather than cell-intrinsically, to control VGluT3 cell number. Finally, we consider the relationship between the dendritic arbors of single VGluT3 cells and the distribution of their homotypic neighbors. Dendritic field area was found to be independent of Voronoi domain area, while dendritic coverage of single cells was not conserved, simply increasing with the size of the dendritic field. Bax-KO retinas exhibited a threefold increase in dendritic coverage. Each cell, however, contributed less dendrites at each depth within the plexus, intermingling their processes with those of neighboring cells to approximate a constant volumetric density, yielding a uniformity in process coverage across the population.SIGNIFICANCE STATEMENT Different types of retinal neuron spread their processes across the surface of the retina to achieve a degree of dendritic coverage that is characteristic of each type. Many of these types achieve a constant coverage by varying their dendritic field area inversely with the local density of like-type neighbors. Here we report a population of retinal amacrine cells that do not develop dendritic arbors in relation to the spatial positioning of such homotypic neighbors; rather, this cell type modulates the extent of its dendritic branching when faced with a variable number of overlapping dendritic fields to approximate a uniformity in dendritic density across the retina.     

Correction: Thrombotic and bleeding events,mortality, and anticoagulant use among 546,656 hospitalized patients with COVID‑19 in the United States: a retrospective cohort study

Journal of Thrombosis and Thrombolysis -     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.