Cross-sectional airway area is the main determinant of resistance to airflow in the respiratory system. In paediatric patients (<18 years), previous evidence for sex differences in cross-sectional airway area was limited to patients with history of pulmonary disease or cadaveric studies with small numbers of subjects. These studies either only report tracheal data and do not include a range of ages or correct for height. Therefore, we sought to assess sex differences in airway luminal area utilizing paediatric patients of varying ages and no history of respiratory disease.Using three-dimensional reconstructions from high-resolution computed tomography scans, we retrospectively assessed the cross-sectional airway area in healthy paediatric females (n = 97) and males (n = 128) over a range of ages (1-17 years). The areas of the trachea, left main bronchus, left upper lobe, left lower lobe, right main bronchus, intermediate bronchus and right upper lobe were measured at three discrete points by a blinded investigator. No differences between the sexes were noted in the cross-sectional areas of the youngest (ages 1-12 years) patients (P > 0.05).However, in patients ≥14 years the cross-sectional areas were larger in the males compared to females in most airway sites. For instance, the cross-sectional size of the trachea was 25% (218 ± 44 vs. 163 ± 24 mm 2 , P < 0.01) larger in males vs. females among ages 13-17 years. When accounting for height, these sex differences in airway areas were attenuated, but persisted. Our results indicate that sex differences in paediatric airway cross-sectional area manifest after age ≥14 years and are independent of height. K E Y W O R D Sairway resistance, dysanapsis, exercise

Traumatic brain injuries (TBIs) are a leading cause of death and disability. Additionally, growing evidence suggests a link between TBI-induced neuroinflammation and neurodegenerative disorders. Treatments for TBI patients are limited, largely focused on rehabilitation therapy, and ultimately, fail to provide long-term neuroprotective or neurorestorative benefits. Because of the prevalence of TBI and lack of viable treatments, new therapies are needed which can promote neurological recovery. Cell-based treatments are a promising avenue because of their potential to provide multiple therapeutic benefits. Cell-based therapies can promote neuroprotection via modulation of inflammation and promote neurorestoration via induction of angiogenesis and neurogenesis. Neural stem/progenitor cell transplantations have been investigated in preclinical TBI models for their ability to directly contribute to neuroregeneration, form neural-like cells, and improve recovery. Mesenchymal stem cells (MSCs) have been investigated in clinical trials through multiple different routes of administration. Intravenous administration of MSCs appears most promising, demonstrating a robust safety profile, correlation with neurological improvements, and reductions in systemic inflammation following TBI. While still preliminary, evidence suggests cell-based therapies may become a viable treatment for TBI based on their ability to promote neuroregeneration and reduce inflammation.

Treatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at enrollment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.

Introduction Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR). Materials and Methods For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen’s kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies. Results Of the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. Of 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%). Conclusion Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations for how to improve for future iterations.

The rapidly increasing biomedical knowledge, derived from biological experiments or gained from clinical practice, has become the important treasure in the biomedical research. The emerging knowledge graphs (KGs) provide an efficient and effective way to organize and retrieval the huge and increasing volume of biomedical knowledge. A biomedical KG (BKG) typically stores and represents knowledge by constructing a semantic network describing entities and the relationships between them. Previous efforts have been conducted to construct and curate BKGs by comprehensively integrating various biomedical data resources. Though the resulting BKGs have made a significant progress in this filed in advancing biological and medical research, there remain a big gap to a perfect one that is comprehensive and fine-grained enough. To this end, in the present study, we collected and integrated data from diverse well-curated biomedical knowledge bases and BKGs to curate a more comprehensive one, named the Cornell Biomedical Knowledge Hub (CBKH). To enhance the usage in accelerating biomedical research, we deployed CBKH using the famous graph database, Neo4j. This is a continuing effort and we are adding in more and more contents in CBKH to support the various complex needs in biomedical data analysis. Please contact us if you have better ideas and suggestions.