Experiences and lessons learned from two virtual, hands-on microbiome bioinformatics workshops.

In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops.

Legionella pneumophila and Staphylococcus aureus co-infections in a patient with SARS-CoV-2.

A man fully mRNA-vaccinated against COVID-19 presented to our hospital with an acute febrile illness, respiratory symptoms and a positive test for SARS-CoV-2. He was later found early into hospitalisation to have two morbid bacterial co-infections: Legionella pneumophila serogroup 1 and methicillin-resistant Staphylococcus aureus (MRSA). Although this patient was initially admitted for COVID-19 management, his initial presentation was remarkable for lobar pneumonia, hyponatraemia and rhabdomyolysis more compatible with Legionnaire's disease than severe COVID-19. On discovery of MRSA pneumonia as a second bacterial infection, immunosuppressive COVID-19 therapies were discontinued and targeted antibiotics towards both bacterial co-infections were initiated. The patient's successful recovery highlighted the need to have high suspicion for bacterial co-infections in patients presenting with community-acquired pneumonia and a positive SARS-CoV-2 test, as patients with serious bacterial co-infections may have worse outcomes with use of immunosuppressive COVID-19 therapies.

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone. METHODS: 174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups. RESULTS: The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases. CONCLUSION: A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone.

Contour Variability in Thyroid Eye Disease with Compressive Optic Neuropathy Treated with Radiation Therapy.

PURPOSE: Few studies have evaluated the methodology by which radiation therapy (RT) for thyroid eye disease and compressive optic neuropathy is performed. The objective of this study was to retrospectively review our experience from a radiation planning standpoint and to determine whether current treatment methods provide adequate dose to target and collateral structures. METHODS: A retrospective review of 52 patients (104 orbits) with bilateral thyroid eye disease and compressive optic neuropathy treated with RT (20 Gy in 10 fractions) at our institution. RT plans were analyzed for target volumes and doses. Visual fields, color plates, and visual acuity were assessed pretreatment and at last available follow-up post RT. A standardized, anatomic contour of the retro-orbital space was applied to these retrospective plans to determine dose to the entire space, rather than the self-selected target structure. RESULTS: Compared with the anatomic retro-orbital space, the original contour overlapped by only 68%. Maximum and mean dose was 2134 cGy and 1910 cGy to the anatomic retro-orbital space. Consequently, 39.8% of the orbits had a mean dose <19 Gy (<17 Gy 16.4%, <18 Gy 27.6% <19 Gy 37.8%, <20 Gy 59.2%, 20-21 Gy 35.8%, >21 Gy 5%). There was no significant association of improvement in color plates (P = .07), visual fields (P = .77), and visual acuity (P = .62), based on these dose differences. When beam placement was retrospectively adjusted to include a space of 0.5 cm between the lens and the anterior beam edge, there was a 39.4% and 20.3% decrease in max and mean dose to the lens. CONCLUSIONS: Without a standardized protocol for contouring in thyroid eye disease, target delineation was found to be rather varied, even among the same practitioner. Differences in dose to the anatomic retro-orbital space did not affect outcomes in the follow-up period. Although precise contouring of the retro-orbital space may be of little clinical consequence overall, a >0.5 cm space from the lens may significantly reduce or delay cataractogenesis.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Attitudes toward management of decreased sexual desire in premenopausal women: a national survey of nurse practitioners and physician assistants.

Recent studies have determined that low sexual desire affects between one quarter to one third of adult premenopausal women, leading to distress. The prevalence of distress from low desire may suggest management gaps in the clinicians managing these patients, as recent studies have shown a reluctance to discuss sexual concerns. A survey based on the Theory of Planned Behavior, a predictive model linking attitudes and behavior, was designed to determine what factors influenced behavioral intent and behavior of nurse practitioners and physician assistants when managing decreased sexual desire and sexual concerns in premenopausal women. A path analysis found that attitudes and subjective norms were associated with behavioral intent in both groups and explained roughly one third of the intent to initiate a discussion about sexual health. In general, nurse practitioners and physician assistants had positive attitudes toward managing sexual concerns in female patients but were still at times reluctant to initiate sexual health discussions. This study shows that increased education is needed on female sexual dysfunction in the allied health professional community, as well as techniques for communicating with patients about their sexual concerns.