Mary Frances Cotch

Bio: Mary Frances Cotch is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Population & Retinopathy. The author has an hindex of 56, co-authored 132 publications receiving 13626 citations.

Association between Bacterial Vaginosis and Preterm Delivery of a Low-Birth-Weight Infant

Abstract: Background Bacterial vaginosis is believed to be a risk factor for preterm delivery. We undertook a study of the association between bacterial vaginosis and the preterm delivery of infants with low birth weight after accounting for other known risk factors. Methods In this cohort study, we enrolled 10,397 pregnant women from seven medical centers who had no known medical risk factors for preterm delivery. At 23 to 26 weeks' gestation, bacterial vaginosis was determined to be present or absent on the basis of the vaginal pH and the results of Gram's staining. The principal outcome variable was the delivery at less than 37 weeks' gestation of an infant with a birth weight below 2500 g. Results Bacterial vaginosis was detected in 16 percent of the 10,397 women. The women with bacterial vaginosis were more likely to be unmarried, to be black, to have low incomes, and to have previously delivered low-birth-weight infants. In a multivariate analysis, the presence of bacterial vaginosis was related to preterm de...

Prevalence of diabetic retinopathy in the United States, 2005-2008

Abstract: Context The prevalence of diabetes in the United States has increased. People with diabetes are at risk for diabetic retinopathy. No recent national population-based estimate of the prevalence and severity of diabetic retinopathy exists. Objectives To describe the prevalence and risk factors of diabetic retinopathy among US adults with diabetes aged 40 years and older. Design, Setting, and Participants Analysis of a cross-sectional, nationally representative sample of the National Health and Nutrition Examination Survey 2005-2008 (N = 1006). Diabetes was defined as a self-report of a previous diagnosis of the disease (excluding gestational diabetes mellitus) or glycated hemoglobin A1c of 6.5% or greater. Two fundus photographs were taken of each eye with a digital nonmydriatic camera and were graded using the Airlie House classification scheme and the Early Treatment Diabetic Retinopathy Study severity scale. Prevalence estimates were weighted to represent the civilian, noninstitutionalized US population aged 40 years and older. Main Outcome Measurements Diabetic retinopathy and vision-threatening diabetic retinopathy. Results The estimated prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy was 28.5% (95% confidence interval [CI], 24.9%-32.5%) and 4.4% (95% CI, 3.5%-5.7%) among US adults with diabetes, respectively. Diabetic retinopathy was slightly more prevalent among men than women with diabetes (31.6%; 95% CI, 26.8%-36.8%; vs 25.7%; 95% CI, 21.7%-30.1%; P = .04). Non-Hispanic black individuals had a higher crude prevalence than non-Hispanic white individuals of diabetic retinopathy (38.8%; 95% CI, 31.9%-46.1%; vs 26.4%; 95% CI, 21.4%-32.2%; P = .01) and vision-threatening diabetic retinopathy (9.3%; 95% CI, 5.9%-14.4%; vs 3.2%; 95% CI, 2.0%-5.1%; P = .01). Male sex was independently associated with the presence of diabetic retinopathy (odds ratio [OR], 2.07; 95% CI, 1.39-3.10), as well as higher hemoglobin A1c level (OR, 1.45; 95% CI, 1.20-1.75), longer duration of diabetes (OR, 1.06 per year duration; 95% CI, 1.03-1.10), insulin use (OR, 3.23; 95% CI, 1.99-5.26), and higher systolic blood pressure (OR, 1.03 per mm Hg; 95% CI, 1.02-1.03). Conclusion In a nationally representative sample of US adults with diabetes aged 40 years and older, the prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy was high, especially among Non-Hispanic black individuals.

Trichomonas vaginalis associated with low birth weight and preterm delivery. The Vaginal Infections and Prematurity Study Group.

Abstract: Background:Several studies have suggested that pregnant women infected withTrichomonas vaginalismay be at increased risk of an adverse outcome.Goal:To evaluate prospectively the association betweenT. vaginalisand risk of adverse pregnancy outcome in a large cohort of ethnically diverse women.Study D

Genome-Wide Association Study of Retinopathy in Individuals Without Diabetes

Abstract: Background Mild retinopathy (microaneurysms or dot-blot hemorrhages) is observed in persons without diabetes or hypertension and may reflect microvascular disease in other organs. We conducted a genome-wide association study (GWAS) of mild retinopathy in persons without diabetes.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Abstract: Importance Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Exposure Deep learning–trained algorithm. Main Outcomes and Measures The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. Results The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%. Conclusions and Relevance In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

Vitamin D deficiency.

Abstract: admission. This proportion could already be greater in some parts of the country and may increase if referrals of cases of self-poisoning increase faster than the facilities for their assessment and management. The provision of social work and psychiatric expertise in casualty departments may be one means of preventing unnecessary medical admissions without risk to the patients. Dr Blake's and Dr Bramble's figures do not demonstrate, however, that any advantage would attach to medical teams taking over assessment from psychiatrists except that, by implication, assessments would be completed sooner by staff working on the ward full time. What the figures actually suggest is that if assessment of overdoses were left to house doctors there would be an increase in admissions to psychiatric units (by 19°U), outpatients (by 5O°'), and referrals to social services (by 140o). So for house doctors to assess overdoses would provide no economy for the psychiatric or social services. The study does not tell us what the consequences would have been for the six patients who the psychiatrists would have admitted but to whom the house doctors would have offered outpatient appointments. E J SALTER

Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease

Abstract: Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.