ABSTRACT
Background: Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. Methods: We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Results: Mean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.54 (0.52-0.56) for WT and 0.61 (0.56-0.66) for ILK cKO TEM images. Automated and corresponding manual measured PFP widths differed significantly for both WT (p<0.05) and ILK cKO (p<0.05), while automated and manual GBM widths differed only for ILK cKO (p<0.05) but not WT (p=0.49) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p=0.06) more than PFP (p=0.20) widths. Conclusions: These results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis.
ABSTRACT
BACKGROUND: Firearm homicides (FH) are a major cause of mortality in the United States. Firearm law implementation is variable across states, and legislative gaps may represent opportunities for FH prevention. For each state, we sought to identify which firearm law category would have been most effective if implemented and how effective it would have been. METHODS: We conducted a retrospective cohort study examining the effects of firearm laws on FH rates in the 48 contiguous US states 2010 to 2019. Data were obtained from the CDC WONDER and FBI UCR databases, State Firearm Law Database, and US Census. Firearm laws were grouped into 14 categories. We assessed the association between the presence of each law category and FH rate as an incidence rate ratio (IRR) using a Poisson regression accounting for state population characteristics and laws of surrounding states. We estimated the IRR for each state that did not have a given law category present and determined which of these missing law categories would have been associated with the greatest reduction in FH rate. RESULTS: FH rates varied widely across states and increased from a mean of 3.2 (SD = 1.7) to 4.2 (SD = 2.9) FH per 100,000. All law categories were significantly associated with decreased FH rate ( p < 0.05), with IRR ranging from 0.25 to 0.85. The most effective missing law category differed between states but was most commonly child access prevention (34.09% of states), assault weapons and large-capacity magazines (15.91%), preemption (15.91%), and concealed carry permitting (13.64%). In total across 2010 to 2019, we estimated that 129,599 fewer FH would have occurred with enactment of the most effective missing law category in each state. CONCLUSION: Modeling firearm law prevention of FH with regard to state legislative and population characteristics can identify the highest impact missing law categories in each state. These results can be used to inform efforts to reduce FH. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.
