Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people

While an estimated 14-20% of young adults experience mental health conditions worldwide, the best strategies for prevention and management are not fully understood. The ubiquity of smartphone use among young people makes them excellent candidates for collecting data about lived experiences and their relationships to mental health. However, not much is known about the factors affecting young peoples’ willingness to share information about their mental health. OBJECTIVE: We aim to understand the data governance and engagement strategies influencing young peoples’ (aged 16-24) participation in app-based studies of mental health. We hypothesize that willingness to participate in research is influenced by involvement  in how their data is collected, shared, and used. METHODS: Here, we describe the MindKind Study, which employs mixed methods to understand the feasibility of global, smartphone-based studies of youth mental health. A pilot 12-week app-based substudy will query participants’ willingness to engage with remote mental health studies. Participants will be randomized into one of four different data governance models designed to understand their preferences, as well as the acceptability of models that allow them more or less control over how their data are accessed and used. Enrolees will receive one of two different engagement strategies. A companion qualitative study will employ a deliberative democracy approach to examine the preferences, concerns and expectations of young people, with respect to remote mental health research. We also detail our engagement with young people as co-researchers in this study. This pilot study is being conducted in India, South Africa and the United Kingdom. CONCLUSION: This study is expected to generate new insights into the feasibility of, and best practices for, remote smartphone-based studies of mental health in youth and represents an important step toward understanding which approaches could help people better manage their mental health

Multidimensional needs of patients living and dying with heart failure in Kenya: a serial interview study

Abstract Background Heart failure is an emerging challenge for Sub Saharan Africa. However, research on patients’ needs and experiences of care is scarce with little evidence available to support and develop services. We aimed to explore the experiences of patients living and dying with heart failure in Kenya. Methods We purposively recruited 18 patients admitted with advanced heart failure at a rural district hospital in Kenya. We conducted serial in depth interviews with patients at 0, 3 and 6 months after recruitment, and conducted bereavement interviews with carers. Interviews were recorded, transcribed into English and analyzed using a thematic approach, assisted by Nvivo software package. Results Forty-four interviews were conducted. Patients experienced physical, psychosocial, spiritual and financial distress. They also had unmet needs for information about their illness, how it would affect them and how they could get better. Patients experience of and their interpretation of symptoms influenced health care seeking. Patients with acute symptoms sought care earlier than those with more gradual symptoms which tended to be normalised as part of daily life or assumed to be linked to common treatable conditions. Nearly all patients expected to be cured and were frustrated by a progressive illness poorly responsive to treatment. Accumulating costs was a barrier to continuity of care and caused tensions in social relationships. Patients valued information on the nature of their illness, prognosis, self-care, lifestyle changes and prevention strategies, but this was rarely available. Conclusions This is the first in-depth study to explore the experiences of people living with advanced heart failure in Kenya. This study suggests that patients would benefit from holistic care, such as a palliative approach that is aimed at providing multidimensional symptom management. A palliative approach to services should be provided alongside chronic disease management aimed at primary prevention of risk factors, and early identification and initiation of disease modifying therapy. Further research is needed to determine best practice for integrating palliative care for people living and dying with heart failure

Fruits and vegetables intake and bladder cancer risk: a pooled analysis from 11 case-control studies in the BLadder cancer Epidemiology and Nutritional Determinants (BLEND) consortium.

Purpose High consumption of fruits and vegetables decrease the risk of bladder cancer (BC). The evidence of specific fruits and vegetables and the BC risk is still limited. Methods Fruit and vegetable consumptions in relation to BC risk was examined by pooling individual participant data from case–control studies. Unconditional logistic regression was used to estimate study-specific odds ratio’s (ORs) with 95% confidence intervals (CIs) and combined using a random-effects model for intakes of total fruits, total vegetables, and subgroups of fruits and vegetables. Results A total of 11 case–control studies were included, comprising 5637 BC cases and 10,504 controls. Overall, participants with the highest intakes versus the lowest intakes of fruits in total (OR 0.79; 95% CI 0.68–0.91), citrus fruits (OR 0.81; 95% CI 0.65–0.98), pome fruits (OR 0.76; 95% CI 0.65–0.87), and tropical fruits (OR 0.84; 95% CI 0.73–0.94) reduced the BC risk. Greater consumption of vegetables in total, and specifically shoot vegetables, was associated with decreased BC risk (OR 0.82; 95% CI 0.68–0.96 and OR 0.87; 95% CI 0.78–0.96, respectively). Substantial heterogeneity was observed for the associations between citrus fruits and total vegetables and BC risk. Conclusion This comprehensive study provides compelling evidence that the consumption of fruits overall, citrus fruits, pome fruits and tropical fruits reduce the BC risk. Besides, evidence was found for an inverse association between total vegetables and shoot vegetables intake

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models. Copyright 2013 by the American Association for the Advancement of Science; all rights reserve