Stemming the Rise of Antibiotic Use for Community-Acquired Acute Respiratory Infections during COVID-19 Pandemic.

At the start of the COVID-19 pandemic, there was an increase in the use of antibiotics for the treatment of community-acquired respiratory tract infection (CA-ARI) in patients admitted for suspected or confirmed COVID-19, raising concerns for misuse. These antibiotics are not under the usual purview of the antimicrobial stewardship unit (ASU). Serum procalcitonin, a biomarker to distinguish viral from bacterial infections, can be used to guide antibiotic recommendations in suspected lower respiratory tract infection. We modified our stewardship approach, and used a procalcitonin-guided strategy to identify "high yield" interventions for audits in patients admitted with CA-ARI. With this approach, there was an increase in the proportion of patients with antibiotics discontinued within 4 days (16.5% vs. 34.9%, p < 0.001), and the overall duration of antibiotic therapy was significantly shorter [7 (6−8) vs. 6 (3−8) days, p < 0.001]. There was a significant decrease in patients with intravenous-to-oral switch of antibiotics to "complete the course" (45.3% vs. 34.4%, p < 0.05). Of the patients who had antibiotics discontinued, none were restarted on antibiotics within 48 h, and there was no-30-day readmission or 30-day mortality attributed to respiratory infection. This study illustrates the importance of the antimicrobial stewardship during the pandemic and the need for ASU to remain attuned to prescriber's practices, and adapt accordingly to address antibiotic misuse to curb antimicrobial resistance.

MOrgAna: accessible quantitative analysis of organoids with machine learning.

Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.

Seasonal variation of a plant-pollinator network in the Brazilian Cerrado: Implications for community structure and robustness.

Seasonal variation in the availability of floral hosts or pollinators is a key factor influencing diversity in plant-pollinator communities. In seasonally dry Neotropical habitats, where month-long periods of extreme drought are followed by a long rainy season, flowering is often synchronized with the beginning of precipitation, when environmental conditions are most beneficial for plant reproduction. In the Brazilian Cerrado, a seasonally dry ecosystem considered one of the world's biodiversity hotspots for angiosperms, plants with shallow root systems flower predominantly during the rainy season. Foraging activity in social bees however, the major pollinators in this biome, is not restricted to any particular season because a constant supply of resources is necessary to sustain their perennial colonies. Despite the Cerrado's importance as a center of plant diversity, the influence of its extreme cycles of drought and precipitation on the dynamics and stability of plant-pollinator communities is not well understood. We sampled plant-pollinator interactions of a Cerrado community weekly for one year and used network analyses to characterize intra-annual seasonal variation in community structure. We also compared seasonal differences in community robustness to species loss by simulating extinctions of plants and pollinators. We find that the community shrinks significantly in size during the dry season, becoming more vulnerable to disturbance due to the smaller pool of floral hosts available to pollinators during this period. Major changes in plant species composition but not in pollinators has led to high levels of turnover in plant-pollinator associations across seasons, indicated by in interaction dissimilarity (<3% of shared interactions). Aseasonal pollinators, which mainly include social bees and some solitary specialized bees, functioned as keystone species, maintaining robustness during periods of drastic changes in climatic conditions.