Rickie P. Voland

PURPOSE: To investigate the relationship between serum 25-hydroxyvitamin D (25[OH]D) levels and nuclear cataract among participants of the Carotenoids in Age-Related Eye Disease Study (CAREDS), an ancillary study of the Women's Health Initiative (WHI) Observational Study (OS). METHODS: Nuclear cataract was assessed from slit lamp photographs (2001-2004) taken 6 years after collecting serum analyzed for 25(OH)D levels at WHI baseline (1994-1998) in 1278 CAREDS participants age 50 to 79 years. Multivariate (age, iris color, smoking, pulse pressure) odds ratios (ORs) for nuclear cataract (nuclear opacities > level 4 or cataract extraction) by quintiles of serum 25(OH)D were estimated using logistic regression. RESULTS: No significant association was observed between serum 25(OH)D and nuclear cataract among women of all ages (age-adjusted OR [95% confidence interval (CI)] 0.97 [0.65-1.45]). However, there was a significant age interaction (P for interaction = 0.04). There were no significant associations in the women 70 years or older. In women younger than 70 years, we observed an inverse association between serum 25(OH)D and nuclear cataract (multivariate adjusted ORs [95% CI] 0.54 [0.29-0.99] and 0.66 [0.36-1.20] for quintiles 4 and 5 vs. 1, respectively; P = 0.03). Further adjustment for 25(OH)D determinants (body mass index, vitamin D intake, and UVB exposure) attenuated this association. CONCLUSIONS: Serum 25(OH)D levels were unrelated to nuclear opacities in this study sample. However, exploratory analyses suggest a protective association in women younger than 70 years. Further investigations of the relationship between vitamin D and nuclear lens opacities are warranted.

Background: Little is understood regarding the inter-relation between economic, marital, and racial/ethnic differences in presentation and survival of surgically-resected lung cancer patients. Our investigation will assess these differences in addition to known therapeutic, patient, and histopathologic factors. Methods: A retrospective review of the SEER database was conducted through the years 2007-2012.The population was split into 9 different ethnic groups. Population differences were assessed via chi-square testing. Multivariable analysis(MVA) were used to detect overall survival(OS) differences in the total surgical population (TS, N = 35,689) in an early-stage (T1-T2 < 4cm N0) surgically-resectable population(ESR, N= 17,931). Lung cancer specific survival (LCSS) was assessed in the ESR. Results: In the TS population, as compared to Whites, Blacks and Hispanics presented with younger age, more adenocarcinomas, lower rates of marriage, lower rates of insurance, less stage I tumors, and had less nodes examined, but their type of surgical procedures and OS/LCSS were the same. MVA demonstrated that lower OS and LCSS were associated with males, single/divorced/widowed partnership, lower income(TS only), and Medicaid insurance. MVA also found that Blacks and Hispanics had a similar OS/LCSS to Whites and that all ethnic groups were associated with a similar or better outcomes. 90-day mortality and positive nodes were correlated with not having insurance and not being married, but they were not associated with ethnicity. Conclusions: In TS and ESR groups, OS was not different in the two largest ethnic groups (Black, Hispanic) as compared to Whites, but was related to single/widowed/divorced status, Medicaid insurance, and income (TS group only). Nodal positivity was associated with patients who did not have a married partner or insurance suggesting that these factors may impact disease biology. Economic and psycho-social variables may play a role in survival of early-stage lung cancer in addition to standard histopathologic and treatment variables.

Purpose: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation. Methods: We investigated various AI architectures, each with distinct combinations of encoders and decoders. The architectures included three decoders-FPN (Feature Pyramid Network), UNet, and PSPNet (Pyramid Scene Parsing Network)-and serve as the foundation framework for segmentation task. Encoders including EfficientNet, ResNet (Residual Networks), VGG (Visual Geometry Group) and Mix Vision Transformer (mViT) have a role in extracting optimum latent features for accurate GA segmentation. Performance was measured through comparison of GA areas between human and AI predictions and Dice Coefficient (DC). Results: The training dataset included 601 FAF images from AREDS2 study and validation included 156 FAF images from the GlaxoSmithKline study. The mean absolute difference between grader measured and AI predicted areas ranged from -0.08 (95% CI = -1.35, 1.19) to 0.73 mm2 (95% CI = -5.75,4.29) and DC between 0.884-0.993. The best-performing models were UNet and FPN frameworks with mViT, and the least-performing models were PSPNet framework. Conclusions: The choice of AI architecture impacts GA segmentation performance. Vision transformers with FPN and UNet architectures demonstrate stronger suitability for this task compared to Convolutional Neural Network- and PSPNet-based models. Selecting an AI architecture must be tailored to the specific goals of the project, and developers should consider which architecture is ideal for their project.

Use of manure and compost as soil amendments has been proposed as a way to reduce farm input costs, waste disposal problems, and groundwater pollution, and at the same time control plant disease. The objective of this research was to compare the ability of fresh and composted manure to induce suppression of Rhizoctonia solani Kuhn;Soil in microplots was amended with urea, dairy manure, and composted dairy manure, infested with sclerotia of R. solani, and planted with beans. Infestation and planting were repeated two more times. Seedling emergence was greatest in soil amended with manure, and least with urea. Freedom from visible lesions and yield of plant tops were also greatest for plants grown with manure. Soil media in the greenhouse were amended with urea, urea and straw, manure, and compost. All four amendment rates were chosen to provide 75 ppm nitrogen; the latter three amendments added 0.3% organic matter to the soil media. After a 1-week incubation, soil mixtures were infested with 0, 10, 20, or 30 Colony Forming Units (CFU)/g R. solani sclerotia and then planted with radish four times. Radish Health Index (RHI) was used to compare treatment effects on disease. A large value for RHI indicated high levels of seedling emergence and a small size for any lesions. RHI was greatest for seedlings planted with urea and straw, less with manure or compost, and least with urea alone. RHI with urea and straw was greater at all infestation levels than with other amendments, but at 20 and 30 CFU/g infestation, RHI did not differ among the other three amendments. Despite the differences in RHI, the recovery of R. solani in heat-killed beet seed baits did not differ among treatments;In conclusion, all soils became suppressive, but amendments affect the rate at which suppressiveness develops and disease suppression does not imply pathogen suppression. Manure is more effective than compost in suppressing disease at low inoculum levels, but neither amendment is effective at high inoculum levels. Because manure can reduce plant disease better than urea, the value of amending soil with manure exceeds the fertilizer benefit.