Efficient Tracing of the SARS-CoV-2 Omicron Variants in Santa Barbara County Using a Rapid Quantitative Reverse Transcription PCR Assay

The emergence of the SARS-CoV-2 Omicron variant in 2021 is associated with a global surge of cases in late 2021 and early 2022. Identifying the introduction of novel SARS-CoV-2 variants to a population is imperative to inform decisions by clinicians and public health officials. Here, we describe a quantitative reverse transcription PCR-based assay (RT-qPCR) targeting unique mutations in the Omicron BA.1/BA1.1 and BA.2 viral genomes. This assay accurately and precisely detect the presence of these Omicron variants in patient samples in less than four hours. Using this assay, we tested 270 clinical samples and detected the introduction of Omicron BA.1/BA1.1 and BA.2 in the Santa Barbara County (SBC) population in December 2021 and February 2022, respectively. Identifying Omicron variants using this RT-qPCR assay showed complete concordance with whole viral genome sequencing; both assays indicated that Omicron was the dominant variant in SB County. Our data substantiate that RT-qPCR-based virus detection assays offer a fast and inexpensive alternative to NGS for virus variant-specific detection approach, which allows streamlining the detection of Omicron variants in patient samples

Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery

Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this study, we apply a novel and a state-of-the-art image segmentation machine learning technique (DeepLab) to two years of high-resolution drone-based imagery of a marine flowering plant species (eelgrass, a temperate seagrass). We apply the model to eelgrass (Zostera marina) meadows in the Morro Bay estuary, California, an estuary that has undergone large eelgrass declines and the subsequent recovery of seagrass meadows in the last decade. The model accurately classified eelgrass across a range of conditions and sizes from meadow-scale to small-scale patches that are less than a meter in size. The model recall, precision, and F1 scores were 0.954, 0.723, and 0.809, respectively, when using human-annotated training data and random assessment points. All our accuracy values were comparable to or demonstrated greater accuracy than other models for similar seagrass systems. This study demonstrates the potential for advanced image segmentation machine learning methods to accurately support the active monitoring and analysis of seagrass dynamics from drone-based images, a framework likely applicable to similar marine ecosystems globally, and one that can provide quantitative and accurate data for long-term management strategies that seek to protect these vital ecosystems

The 4∶1 Schedule: A Novel Template for Internal Medicine Residencies

BACKGROUND: It is widely acknowledged that there is need for redesign of internal medicine training. Duty hour restrictions, an increasing focus on patient safety, the possibility of inadequate training in ambulatory care, and a growing shortage of primary care physicians are some factors that fuel this redesign movement. INTERVENTION: We implemented a 4∶1 scheduling template that alternates traditional 4-week rotations with week-long ambulatory blocks. Annually, this provides 10 blocks of traditional rotations without continuity clinic sessions and 10 weeks of ambulatory experience without inpatient responsibilities. To ensure continuous resident presence in all areas, residents are divided into 5 groups, each staggered by 1 week. EVALUATION: We surveyed residents and faculty before and after the intervention, with questions focused on attitudes toward ambulatory medicine and training. We also conducted focus groups with independent groups of residents and faculty, designed to assess the benefits and drawbacks of the new scheduling template and to identify areas for future improvement. RESULTS: Overall, the scheduling template minimized the conflicts between inpatient and outpatient training, promoted a stronger emphasis on ambulatory education, allowed for focused practice during traditional rotations, and enhanced perceptions of team development. By creating an immersion experience in ambulatory training, the template allowed up to 180 continuity clinic sessions during 3 years of training and provided improved educational continuity and continuity of patient care. CONCLUSION: Separating inpatient and ambulatory education allows for enhanced modeling of the evolving practice of internists and removes some of the conflict inherent in the present system