Background: Aerosol generating medical procedures (AGMPs) present risks to health care workers (HCW) due to airborne transmission of pathogens. During the COVID-19 pandemic, it is essential for HCWs to recognize which procedures are potentially aerosolizing so that appropriate infection prevention precautions can be taken. The aim of this literature review was to identify potential AGMPs in Otolaryngology -Head and Neck Surgery and provide evidence-based recommendations.Methods: A literature search was performed on Medline, Embase and Cochrane Review databases up to April 3, 2020. All titles and abstracts of retrieved studies were evaluated and all studies mentioning potential AGMPs were included for formal review. Full text of included studies were assessed by two reviewers and the quality of the studies was evaluated. Ten categories of potential AGMPs were developed and recommendations were provided for each category.Results: Direct evidence indicates that CO2 laser ablation, the use of high-speed rotating devices, electrocautery and endotracheal suctioning are AGMPs. Indirect evidence indicates that tracheostomy should be considered as potential AGMPs. Nasal endoscopy and nasal packing/epistaxis management can result in droplet transmission, but it is unknown if these procedures also carry the risk of airborne transmission. Conclusions: During the COVID-19 pandemic, special care should be taken when CO2 lasers, electrocautery and high-speed rotating devices are used in potentially infected tissue. Tracheal procedures like tracheostomy and endotracheal suctioning can also result in airborne transmission via small virus containing aerosols.

We introduce Digital microfluidic Isolation of Single Cells for -Omics (DISCO), a platform that allows users to select particular cells of interest from a limited initial sample size and connects single-cell sequencing data to their immunofluorescence-based phenotypes. Specifically, DISCO combines digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to collect the contents of single cells from heterogeneous populations, followed by analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanoflow liquid chromatography and tandem mass spectrometry. The results described herein confirm the utility of DISCO for sequencing at levels that are equivalent to or enhanced relative to the state of the art, capable of identifying features at the level of single nucleotide variations. The unique levels of selectivity, context, and accountability of DISCO suggest potential utility for deep analysis of any rare cell population with contextual dependencies.

Vestibular rehabilitation therapy is an established treatment for patients with vestibular dysfunction. Virtual reality (VR) and augmented reality (AR) can be utilised in vestibular rehabilitation. Evidence of the efficacy of VR and AR delivered rehabilitation in patients with peripheral vestibular disorders is reviewed. MEDLINE, EMBASE, CENTRAL, CINAHL, PsychInfo, PsychBITE, OTSeeker, Ei Compendex, IEE, Clinical trials.gov and WebofScience databases were searched. Reduction in vestibular dysfunction symptoms 0–3 months post-intervention was the primary outcome. Secondary outcomes included long-term symptom improvement and side effects. Risk of bias assessment and meta analyses were planned. Five studies meeting eligibility criteria were included. Dizziness Handicap Inventory (DHI) scores 0–3 months post-intervention were reported by four studies. Meta-analysis identified a 1.13 (95% CI, − 1.74, − 0.52) standardized mean difference reduction in DHI in VR and AR treated patients compared to controls. Side effects reported by two studies were reduced by week four of VR intervention. Bias assessment identified DHI scores and side effects to be at high risk or of some concern. Adjunct VR interventions reduced patient DHI significantly more than vestibular rehabilitation alone 0–3 months post-intervention in adult patients diagnosed with unilateral vestibular disease. High quality studies are needed.

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Failed experiments are a common occurrence in research, yet many undergraduate science laboratories rely on established protocols to ensure students are able to obtain results. While it is logistically challenging to facilitate students’ conducting their own experiments in the laboratory, allowing students to “fail” in a safe environment could help with the development of problem-solving skills. To allow students a safe place to fail and encourage them to think through a laboratory protocol, online decision trees were created to lead students through protocols and give them timely feedback. The online decision trees present students with a scenario, then students execute a protocol by selecting options that will lead them down different paths and result in various realistic results from their experiments. They receive feedback and instructional tutorials throughout the simulation that are dependent on their choices. The significance of this new resource for student learning is that it allows students to practice their problem-solving skills and gain theoretical knowledge about the purpose of various experimental steps. The purpose of this research study was to evaluate whether online decision trees affected students’ self-efficacy, metacognition, and motivation for completing a wet laboratory. A mixed-methods approach was used; three surveys were administered throughout the academic term. For survey 1, students completed the decision tree and survey before the wet laboratory. For survey 2, students completed the survey before the wet laboratory but completed the decision tree after the wet laboratory. Students’ reported self-efficacy and intrinsic motivation were increased with the administration of the online decision trees before the wet laboratory, but their extrinsic motivation and metacognitive scores were unchanged. For survey 3, students provided written feedback about the impact of the online decision trees, and their responses highlighted the importance of the visual components of the approach.

Obstructive sleep apnea may occur throughout the lifespan, with peak occurrences in early childhood and during middle and older age. Onset in childhood is overwhelmingly due to adeno-tonsillar hypertrophy, while in adulthood, contributors include risk factors, such as obesity, male sex, and aging. More recently, there has been a precipitous increase in the prevalence of obstructive sleep apnea in youth. Drivers of this phenomenon include both increasing obesity and the survival of children with complex medical conditions into adulthood. Appropriate treatment and long-term management of obstructive sleep apnea is critical to ensure that these youth maintain well-being unfettered by secondary comorbidities. To this end, patient engagement and seamless transition of care from pediatric to adult health care systems is of paramount importance. To date, this is an unacknowledged and unmet need in most sleep programs. This article highlights the need for guideline-driven sleep disorder transition processes and illustrates the authors’ experience with the development of a program for sleep apnea.

Background Chronic rhinosinusitis (CRS) is an inflammatory disease that may require biological therapy. Omalizumab is an anti-IgE biologic that was recently approved by the FDA and Health Canada for use in severe CRS with nasal polyps (CRSwNP) recalcitrant to intranasal corticosteroids. Dosing is based on weight and pre-treatment serum IgE, with elevated levels of the latter being an indication for biologic treatment according to EPOS and EUFOREA guidelines. The goal of this study was to identify variables that predict IgE-high type 2 inflammation and serve as indicators for biologic treatment in CRS. Methods Patients ≥ 19 yo diagnosed with CRS undergoing functional endoscopic sinus surgery were included retrospectively. Demographics, past medical history, preoperative blood work, Lund-Mackay (LM), Lund Kennedy (LK), and SNOT-22 scores were extracted. Descriptive statistics and binary logistic regression analyses were conducted. Model superiority was based on Nagelkerke R2 scores and receiver operating characteristic curves. Results Sixty-five patients, average age 49.96 ± 13.59 years, were included. Sixty-one binary logistic regression models for elevated serum IgE were created. Among the top 3 models, the best model had sensitivity, specificity, positive predictive value and negative predictive values of 82.1, 69.2, 80.0, and 72.0. All performance measures except sensitivity exceeded the Canadian Biologics Guideline model. Serum eosinophils ≥ 300 cell/uL, CRSwNP and LM ≥ 17 increased the odds of elevated IgE. Conclusions IgE-high type-2 inflammation can be predicted by a model that includes eosinophil ≥ 300 cell/uL, CRSwNP, LM ≥ 17, asthma diagnosis and SNOT-22 ≥ 40. Patients meeting these parameters have a high pretest probability for elevated IgE and would benefit from IgE serology to determine qualification for omalizumab. This could reduce unwarranted IgE serology in patients with CRSwNP but also target a patient population for further workup that will lead to optimization of resource allocation and improve healthcare equity in rural and remote areas within Canada.

Objective To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice. Data Sources Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science. Review Methods English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others. Conclusions An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45). Implications for Practice This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.