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This article examines 152 reports the use of robots explicitly due to the COVID-19 pandemic reported in the science, trade, and press from 24 Jan 2021 to 23 Jan 2022 (Year 2) and compares with the previously published uses from 24 Jan 2020 to 23 Jan 2021 (Year 1). Of these 152 reports, 80 were new unique instances documented in 25 countries, bringing the total to 420 instances in 52 countries since 2020. The instances did not add new work domains or use cases, though they changed the relative ranking of three use cases. The most notable trend in Year was the shift from a) government or institutional use of robots to protect healthcare workers and the Public to b) personal and business use to enable the continuity of work and education. In Year 1, Public Safety, Clinical Care, and Continuity of Work and Education were the three highest work domains but in Year 2, Continuity of Work and Education had the highest number of instances.
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The Epidemiology of Coccidioidomycosis (Valley fever) and the Disease Ecology of Coccidioides spp. in New Mexico (2006–2023)
Coccidioidomycosis (Valley fever), caused by Coccidioides spp., is a fungal infection endemic to semi-arid regions of the Americas. Despite 80 years of disease recognition in New Mexico, there is limited disease awareness. We incorporated clinical, epidemiological, and ecological datasets to summarize the knowledge of Valley fever in New Mexico. We analyzed 1541 human cases from 2006 to 2023. On average, 86 cases were reported each year (4.1 cases per 100,000 population per year). The highest levels of incidence were in southwestern New Mexico. American Indian or Alaska Natives in New Mexico had a 1.9 times higher incidence rate of coccidioidomycosis than White people, and among age groups, older populations in New Mexico had the highest incidence rates. We analyzed 300 soil samples near Las Cruces, New Mexico, for the presence of Coccidioides and reported the first known positive soil samples collected from the state, the majority of which were from grassland-dominated sites and from animal burrows. Sequence analyses in clinical specimens, wild animals, and soil samples confirmed that Coccidioides posadasii is the main causative species of coccidioidomycosis in New Mexico. Environmental surveillance validated that locally acquired infections could occur in, but are not limited to, Catron, Doña Ana, Sierra, and Socorro Counties.
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- Award ID(s):
- 2425143
- PAR ID:
- 10661704
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Pathogens
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 2076-0817
- Page Range / eLocation ID:
- 607
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Keim-Malpass, Jessica (Ed.)During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiological signatures and clinical outcomes. We created a longitudinal electronic health record dataset for 75,762 adult patient admissions to a tertiary care center in 2014–2016 lasting six hours or longer. Physiotypes were derived via unsupervised machine learning in a training cohort of 41,502 patients applying consensus k -means clustering to six vital signs measured within six hours of admission. Reproducibility and correlation with clinical biomarkers and outcomes were assessed in validation cohort of 17,415 patients and testing cohort of 16,845 patients. Training, validation, and testing cohorts had similar age (54–55 years) and sex (55% female), distributions. There were four distinct clusters. Physiotype A had physiologic signals consistent with early vasoplegia, hypothermia, and low-grade inflammation and favorable short-and long-term clinical outcomes despite early, severe illness. Physiotype B exhibited early tachycardia, tachypnea, and hypoxemia followed by the highest incidence of prolonged respiratory insufficiency, sepsis, acute kidney injury, and short- and long-term mortality. Physiotype C had minimal early physiological derangement and favorable clinical outcomes. Physiotype D had the greatest prevalence of chronic cardiovascular and kidney disease, presented with severely elevated blood pressure, and had good short-term outcomes but suffered increased 3-year mortality. Comparing sequential organ failure assessment (SOFA) scores across physiotypes demonstrated that clustering did not simply recapitulate previously established acuity assessments. In a heterogeneous cohort of hospitalized patients, unsupervised machine learning techniques applied to routine, early vital sign data identified physiotypes with unique disease categories and distinct clinical outcomes. This approach has the potential to augment understanding of pathophysiology by distilling thousands of disease states into a few physiological signatures.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/pathogens14060607
