The NIOSH Agricultural Centers' YouTube Channel: Time Series Modeling of Viewership of Agricultural Health and Safety Videos.

We sought to understand the mechanism underlying the growth trajectory in the United States Agricultural Safety and Health Centers YouTube channel. We also explored the benefits and limitations of using YouTube analytics to evaluate the impacts of public health interventions involving YouTube. Time series analysis of total views, total watch hours, average duration of watch time, and number of subscribers were assessed to determine the monthly patterns of non-seasonal and seasonal components in the data from 2013 to 2020. Health, safety, and animal handling video views were summarized descriptively across time and season. Lastly, time series regressions were used to determine the type of video that best predicted growth in the channel viewership metrics. The time series were not random but could be explained by autoregressive and moving average correlation structures. Health videos were the strongest predictors of future growth but were not the most watched type of video. Strong seasonality components indicated that videos were most watched during periods of high agricultural activity, but less so during the winter months. Generally, growth in YouTube viewership metrics was explained by past month viewership predicting future viewership. Outreach and media content may produce spikes of increased interest, but in order to sustainably grow the channel over time, Ag Centers and other agricultural stakeholders should continue to focus on the value of particular content to potential viewers, how and when content is released, and strategic promotion of the channel and its videos.

Identifying determinants of noise in a medical intensive care unit.

Continuous and intermittent exposure to noise elevates stress, increases blood pressure, and disrupts sleep among patients in hospital intensive care units. The purpose of this study was to determine the effectiveness of a behavior-based intervention to reduce noise and to identify determinants of noise in a medical intensive care unit. Staff were trained for 6 weeks to reduce noise during their activities in an effort to keep noise levels below 55 dBA during the day and below 50 dBA at night. One-min noise levels were logged continuously in patient rooms 8 weeks before and after the intervention. Noise levels were compared by room position, occupancy status, and time of day. Noise levels from flagged days (>60 dBA for >10 hr) were correlated with activity logs. The intervention was ineffective, with noise frequently exceeding project goals during the day and night. Noise levels were higher in rooms with the oldest heating, ventilation, and air-conditioning system, even when patient rooms were unoccupied. Of the flagged days, the odds of noise over 60 dBA occurring was 5.3 dBA higher when high-flow respiratory support devices were in use compared to times with low-flow devices in use (OR = 5.3, 95% CI = 5.0-5.5). General sources, like the heating, ventilation, and air-conditioning system, contribute to high baseline noise and high-volume (>10 L/min) respiratory-support devices generate additional high noise (>60 dBA) in Intensive Care Unit patient rooms. This work suggests that engineering controls (e.g., ventilation changes or equipment shielding) may be more effective in reducing noise in hospital intensive care units than behavior modification alone.

Theratyping in cystic fibrosis.

PURPOSE OF REVIEW: The treatment of cystic fibrosis (CF) with CF transmembrane conductance regulator (CFTR) modulators continues to develop at a fast pace. These compounds are potentially disease modifying but are only available to certain patient subsets based on genotype. This review discusses the role of theratyping in CF and the potential to assess all patients' response to current and emerging therapies. RECENT FINDINGS: There are limitations to treatment determined by mutation, as variable clinical response to CFTR modulators has been observed within the same genotype. Patients with rare mutations not currently licensed for CFTR modulator therapy have demonstrated response to these medications. Patient-specific cellular models called organoids can be used to demonstrate response to different CFTR modulators in vitro prior to their clinical application and represent a method of theratyping. SUMMARY: Theratyping charts patients' clinical response to different treatments on an individual basis. This overcomes the limitations of genotype being used to predict response to individual therapies and includes all patients regardless of mutation. The use of organoids in high throughput screening allows numerous compounds to be tested on patient-specific tissue preclinically. This could lead to the extension of theratyping beyond CFTR modulators.

Rapid Analysis of the Size Distribution of Metal-Containing Aerosol.

Conventional methods to measure the metallic content of particles by size are time consuming and expensive, requiring collection of particles with a cascade impactor and subsequent metals analysis by inductively coupled plasma mass spectrometry (ICP-MS). In this work, we describe a rapid way to measure the size distribution of metal-containing particles from 10 nm to 20 µm, using a nano micro-orifice uniform-deposit impactor (nano-MOUDI) to size-selective and collect particles that are then analyzed with a field portable X-ray fluorescence (FP-XRF) to determine metal composition and concentration. The nano-MOUDI was used to sample a stainless-steel aerosol produced by a spark discharge system. The particle-laden substrates were then analyzed directly with FP-XRF and then with ICP-MS. Results from FP-XRF were linearly correlated with results from ICP-MS (R2 = 0.91 for Fe and R2 = 0.84 for Cr). Although the FP-XRF was unable to detect Fe particles at mass per substrate loadings less than 2.5 µg effectively, it produced results similar to those using the ICP-MS at a mass per substrate loading greater than 2.5 µg.