Growth hormone-releasing hormone receptor antagonist MIA-602 attenuates cardiopulmonary injury induced by BSL-2 rVSV-SARS-CoV-2 in hACE2 mice.

COVID-19 pneumonia causes acute lung injury and acute respiratory distress syndrome (ALI/ARDS) characterized by early pulmonary endothelial and epithelial injuries with altered pulmonary diffusing capacity and obstructive or restrictive physiology. Growth hormone-releasing hormone receptor (GHRH-R) is expressed in the lung and heart. GHRH-R antagonist, MIA-602, has been reported to modulate immune responses to bleomycin lung injury and inflammation in granulomatous sarcoidosis. We hypothesized that MIA-602 would attenuate rVSV-SARS-CoV-2-induced pulmonary dysfunction and heart injury in a BSL-2 mouse model. Male and female K18-hACE2tg mice were inoculated with SARS-CoV-2/USA-WA1/2020, BSL-2-compliant recombinant VSV-eGFP-SARS-CoV-2-Spike (rVSV-SARS-CoV-2), or PBS, and lung viral load, weight loss, histopathology, and gene expression were compared. K18-hACE2tg mice infected with rVSV-SARS-CoV-2 were treated daily with subcutaneous MIA-602 or vehicle and conscious, unrestrained plethysmography performed on days 0, 3, and 5 (n = 7 to 8). Five days after infection mice were killed, and blood and tissues collected for histopathology and protein/gene expression. Both native SARS-CoV-2 and rVSV-SARS-CoV-2 presented similar patterns of weight loss, infectivity (~60%), and histopathologic changes. Daily treatment with MIA-602 conferred weight recovery, reduced lung perivascular inflammation/pneumonia, and decreased lung/heart ICAM-1 expression compared to vehicle. MIA-602 rescued altered respiratory rate, increased expiratory parameters (Te, PEF, EEP), and normalized airflow parameters (Penh and Rpef) compared to vehicle, consistent with decreased airway inflammation. RNASeq followed by protein analysis revealed heightened levels of inflammation and end-stage necroptosis markers, including ZBP1 and pMLKL induced by rVSV-SARS-CoV-2, that were normalized by MIA-602 treatment, consistent with an anti-inflammatory and pro-survival mechanism of action in this preclinical model of COVID-19 pneumonia.

Improving case duration accuracy of orthopedic surgery using bidirectional encoder representations from Transformers (BERT) on Radiology Reports.

PURPOSE: A major source of inefficiency in the operating room is the mismatch between scheduled versus actual surgical time. The purpose of this study was to demonstrate a proof-of-concept study for predicting case duration by applying natural language processing (NLP) and machine learning that interpret radiology reports for patients undergoing radius fracture repair. METHODS: Logistic regression, random forest, and feedforward neural networks were tested without NLP and with bag-of-words. Another NLP method tested used feedforward neural networks and Bidirectional Encoder Representations from Transformers specifically pre-trained on clinical notes (ClinicalBERT). A total of 201 cases were included. The data were split into 70% training and 30% test sets. The average root mean squared error (RMSE) were calculated (and 95% confidence interval [CI]) from 10-fold cross-validation on the training set. The models were then tested on the test set to determine proportion of times surgical cases would have scheduled accurately if ClinicalBERT was implemented versus historic averages. RESULTS: The average RMSE was lowest using feedforward neural networks using outputs from ClinicalBERT (25.6 min, 95% CI: 21.5-29.7), which was significantly (P < 0.001) lower than the baseline model (39.3 min, 95% CI: 30.9-47.7). Using the feedforward neural network and ClinicalBERT on the test set, the percentage of accurately predicted cases, which was defined by the actual surgical duration within 15% of the predicted surgical duration, increased from 26.8 to 58.9% (P < 0.001). CONCLUSION: This proof-of-concept study demonstrated the successful application of NLP and machine leaning to extract features from unstructured clinical data resulting in improved prediction accuracy for surgical case duration.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.

Co-expression IL-15 receptor alpha with IL-15 reduces toxicity via limiting IL-15 systemic exposure during CAR-T immunotherapy.

BACKGROUND: Chimeric antigen receptor (CAR)-T cell therapy is a powerful adoptive immunotherapy against both B-cell malignancies and some types of solid tumors. Interleukin (IL) -15 is an important immune stimulator that may provide ideal long-term persistent CAR-T cells. However, higher base line or peak serum IL-15 levels are also related to severe toxicity, such as cytokine release syndrome (CRS), graft-versus-host disease (GVHD), and neurotoxicity. METHODS: We successfully constructed CD19 specific armored CAR-T cells overexpressing IL-I5 and IL-15 receptor alpha (IL-15Ra). In vitro cell differentiation and viability were monitored by flow cytometry, and an in vivo xenograft mouse models was used to evaluate the anti-tumor efficiency and liver damage of CAR-T cells. RESULTS: CAR-T cells overexpressing IL-15 alone demonstrated enhanced viability, retarded exhaustion in vitro and superior tumor-inhibitory effects in vivo. However, these tumor-free mice had lower survival rates, with serious liver injuries, as a possible result of toxicity. As expected, CAR-T cells overexpressing IL-15 combined with IL-15Ra had reduced CD132 expression and released fewer cytokines (IFNγ, IL-2 and IL-15) in vitro, as well as had the tendency to improve mouse survival via repressing the growth of tumor cells and keeping livers healthier compared to CAR-IL-15 T cells. CONCLUSIONS: These results indicated the importance of IL-15 in enhancing T cells persistence and IL-15Ra in reducing the adverse effects of IL-15, with superior tumor retardation during CAR-T therapy. This study paves the way for the rapid exploitation of IL-15 in adoptive cell therapy in the future.

Antigen-display exosomes provide adjuvant-free protection against SARS-CoV-2 disease at nanogram levels of spike protein.

As the only bionormal nanovesicle, exosomes have high potential as a nanovesicle for delivering vaccines and therapeutics. We show here that the loading of type-1 membrane proteins into the exosome membrane is induced by exosome membrane anchor domains, EMADs, that maximize protein delivery to the plasma membrane, minimize protein sorting to other compartments, and direct proteins into exosome membranes. Using SARS-CoV-2 spike as an example and EMAD13 as our most effective exosome membrane anchor, we show that cells expressing a spike-EMAD13 fusion protein produced exosomes that carry dense arrays of spike trimers on 50% of all exosomes. Moreover, we find that immunization with spike-EMAD13 exosomes induced strong neutralizing antibody responses and protected hamsters against SARS-CoV-2 disease at doses of just 0.5-5 ng of spike protein, without adjuvant, demonstrating that antigen-display exosomes are particularly immunogenic, with important implications for both structural and expression-dependent vaccines.

A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation.

BACKGROUND: Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery. OBJECTIVE: The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery. METHODS: Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model. RESULTS: There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption. CONCLUSIONS: Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.

SARS-CoV-2 Variant Pathogenesis Following Primary Infection and Reinfection in Syrian Hamsters.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, has evolved into multiple variants. Animal models are important to understand variant pathogenesis, particularly for variants with mutations that have significant phenotypic or epidemiological effects. Here, cohorts of naive or previously infected Syrian hamsters (Mesocricetus auratus) were infected with variants to investigate viral pathogenesis and disease protection. Naive hamsters infected with SARS-CoV-2 variants had consistent clinical outcomes, tissue viral titers, and pathology, while hamsters that recovered from initial infection and were reinfected demonstrated less severe clinical disease and lung pathology than their naive counterparts. Males had more frequent clinical signs than females in most variant groups, but few sex variations in tissue viral titers and lung pathology were observed. These findings support the use of Syrian hamsters as a SARS-CoV-2 model and highlight the importance of considering sex differences when using this species. IMPORTANCE With the continued circulation and emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, understanding differences in the effects between the initial infection and a subsequent reinfection on disease pathogenesis is critical and highly relevant. This study characterizes Syrian hamsters as an animal model to study reinfection with SARS-CoV-2. Previous infection reduced the disease severity of reinfection with different SARS-CoV-2 variants.

Growth media affects susceptibility of air-lifted human nasal epithelial cell cultures to SARS-CoV2, but not Influenza A, virus infection.

Primary differentiated human epithelial cell cultures have been widely used by researchers to study viral fitness and virus-host interactions, especially during the COVID19 pandemic. These cultures recapitulate important characteristics of the respiratory epithelium such as diverse cell type composition, polarization, and innate immune responses. However, standardization and validation of these cultures remains an open issue. In this study, two different expansion medias were evaluated and the impact on the resulting differentiated culture was determined. Use of both Airway and Ex Plus media types resulted in high quality, consistent cultures that were able to be used for these studies. Upon histological evaluation, Airway-grown cultures were more organized and had a higher proportion of basal progenitor cells while Ex Plus- grown cultures had a higher proportion terminally differentiated cell types. In addition to having different cell type proportions and organization, the two different growth medias led to cultures with altered susceptibility to infection with SARS-CoV-2 but not Influenza A virus. RNAseq comparing cultures grown in different growth medias prior to differentiation uncovered a high degree of differentially expressed genes in cultures from the same donor. RNAseq on differentiated cultures showed less variation between growth medias but alterations in pathways that control the expression of human transmembrane proteases including TMPRSS11 and TMPRSS2 were documented. Enhanced susceptibility to SARS-CoV-2 cannot be explained by altered cell type proportions alone, rather serine protease cofactor expression also contributes to the enhanced replication of SARS-CoV-2 as inhibition with camostat affected replication of an early SARS-CoV-2 variant and a Delta, but not Omicron, variant showed difference in replication efficiency between culture types. Therefore, it is important for the research community to standardize cell culture protocols particularly when characterizing novel viruses.