Brigham and Women's Hospital
US
Researchers
Public research profiles associated with Brigham and Women's Hospital.
Elizabeth Loder
Sarah Rae Easter
Valerie Baker
Atul A. Gawande
Paul E. Farmer
Aude Oliva
Nancy R. Cook
Myles Brown
Jérôme Eeckhoute
Keith L. Ligon
William C. Hahn
Ali Khademhosseini
Paul M. Ridker
Peter Libby
Vincent J. Carey
P M Ridker
Daniel I. Chasman
Research from this institution
Publications linked through researcher authorship records.
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
Model-based Analysis of ChIP-Seq (MACS)
We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
Bioconductor: open software development for computational biology and bioinformatics
The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Assessing the Performance of Prediction Models
The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
Hydrogels in Biology and Medicine: From Molecular Principles to Bionanotechnology
Abstract Hydrophilic polymers are the center of research emphasis in nanotechnology because of their perceived “intelligence”. They can be used as thin films, scaffolds, or nanoparticles in a wide range of biomedical and biological applications. Here we highlight recent developments in engineering uncrosslinked and crosslinked hydrophilic polymers for these applications. Natural, biohybrid, and synthetic hydrophilic polymers and hydrogels are analyzed and their thermodynamic responses are discussed. In addition, examples of the use of hydrogels for various therapeutic applications are given. We show how such systems' intelligent behavior can be used in sensors, microarrays, and imaging. Finally, we outline challenges for the future in integrating hydrogels into biomedical applications.
Progress and challenges in translating the biology of atherosclerosis
Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Malignant astrocytic glioma: genetics, biology, and paths to treatment
Malignant astrocytic gliomas such as glioblastoma are the most common and lethal intracranial tumors. These cancers exhibit a relentless malignant progression characterized by widespread invasion throughout the brain, resistance to traditional and newer targeted therapeutic approaches, destruction of normal brain tissue, and certain death. The recent confluence of advances in stem cell biology, cell signaling, genome and computational science and genetic model systems have revolutionized our understanding of the mechanisms underlying the genetics, biology and clinical behavior of glioblastoma. This progress is fueling new opportunities for understanding the fundamental basis for development of this devastating disease and also novel therapies that, for the first time, portend meaningful clinical responses.
Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
The International Glossary on Infertility and Fertility Care, 2017
STUDY QUESTION: What updates of the International Glossary on Infertility and Fertility Care are required, to reflect contemporary scientific knowledge, social needs, and inclusive definitions, while harmonizing international communication across clinical, research, policy, and public domains? SUMMARY ANSWER: This 4th edition presents 348 consensus-based terms and definitions, including numerous revisions from the previous edition and 79 newly introduced definitions reflecting advances in reproductive science, technology, and evolving social contexts. WHAT IS KNOWN ALREADY: Previous glossary editions (2006, 2009, 2017) established internationally recognized definitions related to clinical practice, research, and policy. The 2017 edition comprised 283 terms and, among many others, expanded the concept of infertility to include not only its recognition as a disease, but also as an impairment of function generating disability. The glossary has been extensively used worldwide and has contributed to international standardization of data collection, appropriate comparison of outcome measures, and provided a reference for all stakeholders including policy makers. STUDY DESIGN, SIZE, DURATION: Under guidance of the organizing committee, 21 professionals from across the world, and representing expertise in different sub-specialties, formed five working groups: clinical definitions; outcome measures; embryology laboratory; clinical and laboratory andrology; and epidemiology, public health and gender related definitions. The definitions from the previous glossary were evaluated and new terms identified. All definitions were then reviewed by an international advisory panel of nine experts that evaluated the glossary from scientific, ethical, cultural, and policy perspectives. PARTICIPANTS/MATERIALS, SETTING, METHODS: Between November 2024 and October 2025, periodical virtual meetings were held within and between working groups and the organizing committee. Following circulation of the first consensually agreed draft, a one-day in-person meeting with representatives of all working groups and members of the international advisory panel was held at ESHRE, June 2025. Most terms and definitions were discussed and agreed. In the absence of agreement, further discussions were held between the organizing committee, working group chairs and members of the advisory panel. It had been determined at the outset that final disagreement would be resolved via a two-third majority vote. All terms and definitions were, however, reached by consensus and adopted following a final round of review and approval by all authors. MAIN RESULTS AND THE ROLE OF CHANCE: The glossary now includes 348 terms. Compared to the previous edition, 14 terms were deleted, numerous terms modified and 79 new terms were added. Modifications reflect current scientific knowledge, technological advancements, and inclusivity related to gender and family structures. Chance does not play a role, as all definitions are consensus-based. LIMITATIONS, REASONS FOR CAUTION: Some terms may require future refinement as scientific knowledge evolves and societal contexts change. The glossary reflects consensus rather than empirical testing of all definitions. WIDER IMPLICATIONS OF THE FINDINGS: This glossary provides a global reference for standardized terminology, supporting clinical care, research, international comparisons, policy making, patient communication, and reproductive health literacy. STUDY FUNDING/COMPETING INTEREST(S): Neither ICMART, responsible for conducting this project, nor any of the participants received specific financial support for their activities in this project. Ferring provided ICMART with a fixed amount to cover venue costs and a one-day hotel accommodation for participants attending the in-person meeting held prior to the ESHRE Congress in June 2025. Disclosures were provided by all authors, and none reported any conflict of interest related to this manuscript. TRIAL REGISTRATION NUMBER: N/A.
Maternal and Neonatal Morbidity and Mortality Among Pregnant Women With and Without COVID-19 Infection
Importance: Detailed information about the association of COVID-19 with outcomes in pregnant individuals compared with not-infected pregnant individuals is much needed. Objective: To evaluate the risks associated with COVID-19 in pregnancy on maternal and neonatal outcomes compared with not-infected, concomitant pregnant individuals. Design, Setting, and Participants: In this cohort study that took place from March to October 2020, involving 43 institutions in 18 countries, 2 unmatched, consecutive, not-infected women were concomitantly enrolled immediately after each infected woman was identified, at any stage of pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed up until hospital discharge. Exposures: COVID-19 in pregnancy determined by laboratory confirmation of COVID-19 and/or radiological pulmonary findings or 2 or more predefined COVID-19 symptoms. Main Outcomes and Measures: The primary outcome measures were indices of (maternal and severe neonatal/perinatal) morbidity and mortality; the individual components of these indices were secondary outcomes. Models for these outcomes were adjusted for country, month entering study, maternal age, and history of morbidity. Results: A total of 706 pregnant women with COVID-19 diagnosis and 1424 pregnant women without COVID-19 diagnosis were enrolled, all with broadly similar demographic characteristics (mean [SD] age, 30.2 [6.1] years). Overweight early in pregnancy occurred in 323 women (48.6%) with COVID-19 diagnosis and 554 women (40.2%) without. Women with COVID-19 diagnosis were at higher risk for preeclampsia/eclampsia (relative risk [RR], 1.76; 95% CI, 1.27-2.43), severe infections (RR, 3.38; 95% CI, 1.63-7.01), intensive care unit admission (RR, 5.04; 95% CI, 3.13-8.10), maternal mortality (RR, 22.3; 95% CI, 2.88-172), preterm birth (RR, 1.59; 95% CI, 1.30-1.94), medically indicated preterm birth (RR, 1.97; 95% CI, 1.56-2.51), severe neonatal morbidity index (RR, 2.66; 95% CI, 1.69-4.18), and severe perinatal morbidity and mortality index (RR, 2.14; 95% CI, 1.66-2.75). Fever and shortness of breath for any duration was associated with increased risk of severe maternal complications (RR, 2.56; 95% CI, 1.92-3.40) and neonatal complications (RR, 4.97; 95% CI, 2.11-11.69). Asymptomatic women with COVID-19 diagnosis remained at higher risk only for maternal morbidity (RR, 1.24; 95% CI, 1.00-1.54) and preeclampsia (RR, 1.63; 95% CI, 1.01-2.63). Among women who tested positive (98.1% by real-time polymerase chain reaction), 54 (13%) of their neonates tested positive. Cesarean delivery (RR, 2.15; 95% CI, 1.18-3.91) but not breastfeeding (RR, 1.10; 95% CI, 0.66-1.85) was associated with increased risk for neonatal test positivity. Conclusions and Relevance: In this multinational cohort study, COVID-19 in pregnancy was associated with consistent and substantial increases in severe maternal morbidity and mortality and neonatal complications when pregnant women with and without COVID-19 diagnosis were compared. The findings should alert pregnant individuals and clinicians to implement strictly all the recommended COVID-19 preventive measures.