Search

ObjectiveTo summarize proceedings of a workshop convened to discuss the current state of science in the disease of osteoarthritis (OA), identify the knowledge gaps, and examine the developmental and regulatory challenges in bringing these products to market.DesignSummary of the one-day workshop held virtually on June 22nd, 2021.ResultsSpeakers selected by the Planning Committee presented data on the current approach to assessment of OA therapies, biomarkers in OA drug development, and the assessment of disease progression and long-term benefit.ConclusionsDemonstrated by numerous failed clinical trials, OA is a challenging disease for which to develop therapeutics. The challenge is magnified by the slow time of onset of disease and the need for clinical trials of long duration and/or large sample size to demonstrate the effect of an intervention. The OA science community, including academia, pharmaceutical companies, regulatory agencies, and patient communities, must continue to develop and test better clinical endpoints that meaningfully reflect disease modification related to long-term patient benefit.

Fine particulate air pollution

The microorganisms that inhabit hospitals may influence patient recovery and outcome, although the complexity and diversity of these bacterial communities can confound our ability to focus on potential pathogens in isolation. To develop a community-level understanding of how microorganisms colonize and move through the hospital environment, we characterized the bacterial dynamics among hospital surfaces, patients, and staff over the course of 1 year as a new hospital became operational. The bacteria in patient rooms, particularly on bedrails, consistently resembled the skin microbiota of the patient occupying the room. Bacterial communities on patients and room surfaces became increasingly similar over the course of a patient's stay. Temporal correlations in community structure demonstrated that patients initially acquired room-associated taxa that predated their stay but that their own microbial signatures began to influence the room community structure over time. The α- and β-diversity of patient skin samples were only weakly or nonsignificantly associated with clinical factors such as chemotherapy, antibiotic usage, and surgical recovery, and no factor except for ambulatory status affected microbial similarity between the microbiotas of a patient and their room. Metagenomic analyses revealed that genes conferring antimicrobial resistance were consistently more abundant on room surfaces than on the skin of the patients inhabiting those rooms. In addition, persistent unique genotypes of Staphylococcus and Propionibacterium were identified. Dynamic Bayesian network analysis suggested that hospital staff were more likely to be a source of bacteria on the skin of patients than the reverse but that there were no universal patterns of transmission across patient rooms.

Buildings represent habitats for microorganisms that can have direct or indirect effects on the quality of our living spaces, health, and well-being. Over the last ten years, new research has employed sophisticated tools, including DNA sequencing-based approaches, to study microbes found in buildings and the overall built environment. These investigations have catalyzed new insights into and questions about the microbes that surround us in our daily lives. The emergence of the “microbiology of the built environment” field has required bridging disciplines, including microbiology, ecology, building science, architecture, and engineering. Early insights have included a fuller characterization of sources of microbes within buildings, important processes that structure the distributions and abundances of microbes, and a greater appreciation of the role that occupants can have on indoor microbiology. This ongoing work has also demonstrated that traditional culture- and microscopy-based approaches for studying microbiology vastly underestimate the types and quantity of microbes present in environmental samples. We offer ten questions that highlight important lessons learned regarding the microbiology of buildings and suggest future areas of investigation.

In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.

ObjectiveEarly diffuse cutaneous systemic sclerosis (dcSSc) is characterized by rapid changes in the skin and internal organs. The objective of this study was to develop a composite response index in dcSSc (CRISS) for use in randomized controlled trials (RCTs).MethodsWe developed 150 paper patient profiles with standardized clinical outcome elements (core set items) using patients with dcSSc. Forty scleroderma experts rated 20 patient profiles each and assessed whether each patient had improved or not improved over a period of 1 year. Using the profiles for which raters had reached a consensus on whether the patients were improved versus not improved (79% of the profiles examined), we fit logistic regression models in which the binary outcome referred to whether the patient was improved or not, and the changes in the core set items from baseline to followup were entered as covariates. We tested the final index in a previously completed RCT.ResultsSixteen of 31 core items were included in the patient profiles after a consensus meeting and review of test characteristics of patient-level data. In the logistic regression model in which the included core set items were change over 1 year in the modified Rodnan skin thickness score, the forced vital capacity, the patient and physician global assessments, and the Health Assessment Questionnaire disability index, sensitivity was 0.982 (95% confidence interval 0.982-0.983) and specificity was 0.931 (95% confidence interval 0.930-0.932), and the model with these 5 items had the highest face validity. Subjects with a significant worsening of renal or cardiopulmonary involvement were classified as not improved, regardless of improvements in other core items. With use of the index, the effect of methotrexate could be differentiated from the effect of placebo in a 1-year RCT (P = 0.02).ConclusionWe have developed a CRISS that is appropriate for use as an outcome assessment in RCTs of early dcSSc.

We develop an ozone transport and reaction model to determine reaction probabilities and assess the importance of physical properties such as porosity, porediameter, and material thickness on reactive uptake of ozone to five materials. The one-dimensional model accounts formolecular diffusion from bulk air to the air−material interface, reaction at the interface, and diffusive transport and reaction through material pore volumes. Material-ozone reaction probabilities that account for internal transport and internalpore area, γipa, are determined by a minimization of residuals between predicted and experimentally derived ozone concentrations. Values of γipa are generally less than effective reaction probabilities (γeff) determined previously, likely because of the inclusion of diffusion into substrates and reaction with internal surface area (rather than the use of the horizontally projected external material areas). Estimates of γipa average 1 × 10−7, 2 × 10−7, 4 × 10−5, 2 × 10−5, and 4 × 10−7 for two types of cellulose paper, pervious pavement, Portland cement concrete, and an activated carbon cloth, respectively. The transport and reaction model developed here accounts for observed differences in ozone removal to varying thicknesses of the cellulose paper, and estimates a near constant γipa as material thickness increases from 0.02 to 0.16 cm.

The dynamics of indoor environmental conditions, human occupancy, and operational characteristics of buildings influence human comfort and indoor environmental quality, including the survival and progression of microbial communities. A suite of continuous, long-term environmental and operational parameters were measured in ten patient rooms and two nurse stations in a new hospital building in Chicago, IL to characterize the indoor environment in which microbial samples were taken for the Hospital Microbiome Project. Measurements included environmental conditions (indoor dry-bulb temperature, relative humidity, humidity ratio, and illuminance) in the patient rooms and nurse stations; differential pressure between the patient rooms and hallways; surrogate measures for human occupancy and activity in the patient rooms using both indoor air CO2 concentrations and infrared doorway beam-break counters; and outdoor air fractions in the heating, ventilating, and air-conditioning systems serving the sampled spaces. Measurements were made at 5-minute intervals over consecutive days for nearly one year, providing a total of ∼8×106 data points. Indoor temperature, illuminance, and human occupancy/activity were all weakly correlated between rooms, while relative humidity, humidity ratio, and outdoor air fractions showed strong temporal (seasonal) patterns and strong spatial correlations between rooms. Differential pressure measurements confirmed that all patient rooms were operated at neutral pressure. The patient rooms averaged about 100 combined entrances and exits per day, which suggests they were relatively lightly occupied compared to higher traffic environments (e.g., retail buildings) and more similar to lower traffic office environments. There were also clear differences in several environmental parameters before and after the hospital was occupied with patients and staff. Characterizing and understanding factors that influence these building dynamics is vital for hospital environments, where they can impact patient health and the survival and spread of healthcare associated infections.

This report details the outcome of the 1st Hospital Microbiome Project workshop held on June 7th-8th, 2012 at the University of Chicago, USA. The workshop was arranged to determine the most appropriate sampling strategy and approach to building science measurement to characterize the development of a microbial community within a new hospital pavilion being built at the University of Chicago Medical Center. The workshop made several recommendations and led to the development of a full proposal to the Alfred P. Sloan Foundation as well as to the creation of the Hospital Microbiome Consortium.

Our growing awareness of the microbial worlds importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earths microbial diversity., Author(s): Luke R. Thompson [1, 2, 3]; Jon G. Sanders [1]; Daniel McDonald [1]; Amnon Amir [1]; Joshua Ladau [4]; Kenneth J. Locey [5]; Robert J. Prill [6]; Anupriya Tripathi [...]

This letter responds to a study that questioned the evidence supporting surrogate endpoints used for drug approvals by the US Food and Drug Administration (FDA). The authors of the letter argue that the FDA does not solely rely on meta-analyses, but also considers a range of sources such as mechanistic evidence, nonclinical models, epidemiologic data, and clinical trial data. They provide examples of HIV, hypercholesterolemia, and hypertension to illustrate how the FDA uses various types of evidence to determine the validity of surrogate endpoints. The letter concludes that the FDA's acceptance of surrogate endpoints based on robust scientific evidence has led to the availability of therapies for many diseases. [Extracted from the article]