The impact of reducing the frequency of night feeding on infant BMI.

BACKGROUND: Teaching caregivers to respond to normal infant night awakenings in ways other than feeding is a common obesity prevention effort. Models can simulate caregiver feeding behavior while controlling for variables that are difficult to manipulate or measure in real life. METHODS: We developed a virtual infant model representing an infant with an embedded metabolism and his/her daily sleep, awakenings, and feeds from their caregiver each day as the infant aged from 6 to 12 months (recommended age to introduce solids). We then simulated different night feeding interventions and their impact on infant body mass index (BMI). RESULTS: Reducing the likelihood of feeding during normal night wakings from 79% to 50% to 10% lowered infant BMI from the 84th to the 75th to the 62nd percentile by 12 months, respectively, among caregivers who did not adaptively feed (e.g., adjust portion sizes of solid foods with infant growth). Among caregivers who adaptively feed, all scenarios resulted in relatively stable BMI percentiles, and progressively reducing feeding probability by 10% each month showed the least fluctuations. CONCLUSIONS: Reducing night feeding has the potential to impact infant BMI, (e.g., 10% lower probability can reduce BMI by 20 percentile points) especially among caregivers who do not adaptively feed. IMPACT: Teaching caregivers to respond to infant night waking with other soothing behaviors besides feeding has the potential to reduce infant BMI. When reducing the likelihood of feeding during night wakings from 79% to 50% to 10%, infants dropped from the 84th BMI percentile to the 75th to the 62nd by 12 months, respectively, among caregivers who do not adaptively feed. Night-feeding interventions have a greater impact when caregivers do not adaptively feed their infant based on their growth compared to caregivers who do adaptively feed. Night-feeding interventions should be one of the several tools in a multi-component intervention for childhood obesity prevention.

The Potential Clinical and Economic Value of a Human Papillomavirus Primary Screening Test That Additionally Identifies Genotypes 31, 45, 51, and 52 Individually.

BACKGROUND: Although current human papillomavirus (HPV) genotype screening tests identify genotypes 16 and 18 and do not specifically identify other high-risk types, a new extended genotyping test identifies additional individual (31, 45, 51, and 52) and groups (33/58, 35/39/68, and 56/59/66) of high-risk genotypes. METHODS: We developed a Markov model of the HPV disease course and evaluated the clinical and economic value of HPV primary screening with Onclarity (BD Diagnostics, Franklin Lakes, NJ) capable of extended genotyping in a cohort of women 30 years or older. Women with certain genotypes were later rescreened instead of undergoing immediate colposcopy and varied which genotypes were rescreened, disease progression rate, and test cost. RESULTS: Assuming 100% compliance with screening, HPV primary screening using current tests resulted in 25,194 invasive procedures and 48 invasive cervical cancer (ICC) cases per 100,000 women. Screening with extended genotyping (100% compliance) and later rescreening women with certain genotypes averted 903 to 3163 invasive procedures and resulted in 0 to 3 more ICC cases compared with current HPV primary screening tests. Extended genotyping was cost-effective ($2298-$7236/quality-adjusted life year) when costing $75 and cost saving (median, $0.3-$1.0 million) when costing $43. When the probabilities of disease progression increased (2-4 times), extended genotyping was not cost-effective because it resulted in more ICC cases and accrued fewer quality-adjusted life years. CONCLUSIONS: Our study identified the conditions under which extended genotyping was cost-effective and even cost saving compared with current tests. A key driver of cost-effectiveness is the risk of disease progression, which emphasizes the need to better understand such risks in different populations.

Modeling binary and graded cone cell fate patterning in the mouse retina.

Nervous systems are incredibly diverse, with myriad neuronal subtypes defined by gene expression. How binary and graded fate characteristics are patterned across tissues is poorly understood. Expression of opsin photopigments in the cone photoreceptors of the mouse retina provides an excellent model to address this question. Individual cones express S-opsin only, M-opsin only, or both S-opsin and M-opsin. These cell populations are patterned along the dorsal-ventral axis, with greater M-opsin expression in the dorsal region and greater S-opsin expression in the ventral region. Thyroid hormone signaling plays a critical role in activating M-opsin and repressing S-opsin. Here, we developed an image analysis approach to identify individual cone cells and evaluate their opsin expression from immunofluorescence imaging tiles spanning roughly 6 mm along the D-V axis of the mouse retina. From analyzing the opsin expression of ~250,000 cells, we found that cones make a binary decision between S-opsin only and co-expression competent fates. Co-expression competent cells express graded levels of S- and M-opsins, depending nonlinearly on their position in the dorsal-ventral axis. M- and S-opsin expression display differential, inverse patterns. Using these single-cell data, we developed a quantitative, probabilistic model of cone cell decisions in the retinal tissue based on thyroid hormone signaling activity. The model recovers the probability distribution for cone fate patterning in the mouse retina and describes a minimal set of interactions that are necessary to reproduce the observed cell fates. Our study provides a paradigm describing how differential responses to regulatory inputs generate complex patterns of binary and graded cell fates.

The economic value of identifying and treating Chagas disease patients earlier and the impact on Trypanosoma cruzi transmission.

BACKGROUND: The World Health Organization's 2020 Goals for Chagas disease include access to antiparasitic treatment and care of all infected/ill patients. Policy makers need to know the economic value of identifying and treating patients earlier. However, the economic value of earlier treatment to cure and prevent the Chagas' spread remains unknown. METHODS: We expanded our existing Chagas disease transmission model to include identification and treatment of Chagas disease patients. We linked this to a clinical and economic model that translated chronic Chagas disease cases into health and economic outcomes. We evaluated the impact and economic outcomes (costs, cost-effectiveness, cost-benefit) of identifying and treating different percentages of patients in the acute and indeterminate disease states in a 2,000-person village in Yucatan, Mexico. RESULTS: In the absence of early treatment, 50 acute and 22 new chronic cases occurred over 50 years. Identifying and treating patients in the acute stage averted 0.5-5.4 acute cases, 0.6-5.5 chronic cases, and 0.6-10.8 disability-adjusted life years (DALYs), saving $694-$7,419 and $6,976-$79,950 from the third-party payer and societal perspectives, respectively. Treating in the indeterminate stage averted 2.2-4.9 acute cases, 6.1-12.8 chronic cases, and 11.7-31.1 DALYs, saving $7,666-$21,938 from the third-party payer perspective and $90,530-$243,068 from the societal perspective. Treating patients in both stages averted ≤9 acute cases and ≤15 chronic cases. Identifying and treating patients early was always economically dominant compared to no treatment. Identifying and treating patients earlier resulted in a cumulative cost-benefit of $7,273-$224,981 at the current cost of identification and treatment. CONCLUSIONS: Even when identifying and treating as little as 5% of cases annually, treating Chagas cases in the acute and indeterminate stages reduces transmission and provides economic and health benefits. This supports the need for improved diagnostics and access to safe and effective treatment.

Are the London Declaration's 2020 goals sufficient to control Chagas disease?: Modeling scenarios for the Yucatan Peninsula.

BACKGROUND: The 2020 Sustainable Development goals call for 100% certified interruption or control of the three main forms of Chagas disease transmission in Latin America. However, how much will achieving these goals to varying degrees control Chagas disease; what is the potential impact of missing these goals and if they are achieved, what may be left? METHODS: We developed a compartmental simulation model that represents the triatomine, human host, and non-human host populations and vector-borne, congenital, and transfusional T. cruzi transmission between them in the domestic and peridomestic settings to evaluate the impact of limiting transmission in a 2,000 person virtual village in Yucatan, Mexico. RESULTS: Interruption of domestic vectorial transmission had the largest impact on T. cruzi transmission and prevalence in all populations. Most of the gains were achieved within the first few years. Controlling vectorial transmission resulted in a 46.1-83.0% relative reduction in the number of new acute Chagas cases for a 50-100% interruption in domestic vector-host contact. Only controlling congenital transmission led to a 2.4-8.1% (30-100% interruption) relative reduction in the total number of new acute cases and reducing only transfusional transmission led to a 0.1-0.3% (30-100% reduction). Stopping all three forms of transmission resulted in 0.5 total transmission events over five years (compared to 5.0 with no interruption); interrupting all forms by 30% resulted in 3.4 events over five years per 2,000 persons. CONCLUSIONS: While reducing domestic vectorial, congenital, and transfusional transmission can successfully reduce transmission to humans (up to 82% in one year), achieving the 2020 goals would still result in 0.5 new acute cases per 2,000 over five years. Even if the goals are missed, major gains can be achieved within the first few years. Interrupting transmission should be combined with other efforts such as a vaccine or improved access to care, especially for the population of already infected individuals.

Natural variation in stochastic photoreceptor specification and color preference in Drosophila.

Each individual perceives the world in a unique way, but little is known about the genetic basis of variation in sensory perception. In the fly eye, the random mosaic of color-detecting R7 photoreceptor subtypes is determined by stochastic on/off expression of the transcription factor Spineless (Ss). In a genome-wide association study, we identified a naturally occurring insertion in a regulatory DNA element in ss that lowers the ratio of SsON to SsOFF cells. This change in photoreceptor fates shifts the innate color preference of flies from green to blue. The genetic variant increases the binding affinity for Klumpfuss (Klu), a zinc finger transcriptional repressor that regulates ss expression. Klu is expressed at intermediate levels to determine the normal ratio of SsON to SsOFF cells. Thus, binding site affinity and transcription factor levels are finely tuned to regulate stochastic expression, setting the ratio of alternative fates and ultimately determining color preference.

Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark.

Data-parallel programming techniques can dramatically decrease the time needed to analyze large datasets. While these methods have provided significant improvements for sequencing-based analyses, other areas of biological informatics have not yet adopted them. Here, we introduce Biospark, a new framework for performing data-parallel analysis on large numerical datasets. Biospark builds upon the open source Hadoop and Spark projects, bringing domain-specific features for biology. AVAILABILITY AND IMPLEMENTATION: Source code is licensed under the Apache 2.0 open source license and is available at the project website: https://www.assembla.com/spaces/roberts-lab-public/wiki/Biospark CONTACT: eroberts@jhu.eduSupplementary information: Supplementary data are available at Bioinformatics online.