Holistic View on the Structure of Immune Response: Petri Net Model.

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell-cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.

BACKGROUND: Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance. METHODS: We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles). RESULTS: Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events. CONCLUSIONS: Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic. TRIAL REGISTRATION NUMBER: NCT04659187.

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'.

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Prevention of COVID-19 in Thoracic Surgery Patients: Lessons Learned during the First Pandemic Wave.

BACKGROUND: The aim of this retrospective study was to investigate the implementation of measures to prevent perioperative COVID-19 in thoracic surgery during the first wave of the COVID-19 pandemic 2020 allowing a continued surgical treatment of patients. METHODS: The implemented preventive measures in patient management of the thoracic surgery department of the Asklepios Lung Clinic Munich-Gauting, Germany were retrospectively analyzed. Postoperative COVID-19 incidence before and after implementation of preventive measures was investigated. Patients admitted for thoracic surgical procedures between March and May 2020 were included in the study. Patient characteristics were analyzed. For the early detection of putative postoperative COVID-19 symptoms, typical post-discharge symptomatology of thoracic surgery patients was compared to non-surgical patients hospitalized for COVID-19. RESULTS: Thirty-five surgical procedures and fifty-seven surgical procedures were performed before and after implementation of the preventive measures, respectively. Three patients undergoing thoracic surgery before implementation of preventive measures developed a COVID-19 pneumonia post-discharge. After implementation of preventive measures, no postoperative COVID-19 cases were identified. Fever, dyspnea, dry cough and diarrhea were significantly more prevalent in COVID-19 patients compared to normally recovering thoracic surgery patients, while anosmia, phlegm, low energy levels, body ache and nausea were similarly frequent in both groups. CONCLUSIONS: Based on the lessons learned during the first pandemic wave, we here provide a blueprint for successful easily implementable preventive measures minimizing SARS-CoV-2 transmission to thoracic surgery patients perioperatively. While symptoms of COVID-19 and the normal postoperative course of thoracic surgery patients substantially overlap, we found dyspnea, fever, cough, and diarrhea significantly more prevalent in COVID-19 patients than in normally recovering thoracic surgery patients. These symptoms should trigger further diagnostic testing for postoperative COVID-19 in thoracic surgery patients.

Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients.

PURPOSE: This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. METHODS: Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. RESULTS: Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. CONCLUSION: Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.

Clinical Course of Three Postoperative Symptomatic COVID-19 Cases in Patients After Lung Lobectomy.

The novel coronavirus disease 2019 is a highly contagious viral infection caused by the severe acute respiratory syndrome coronavirus 2 virus. Its rapid spread and severe clinical presentation influence patient management in all specialties including thoracic surgery. We report 3 cases of coronavirus disease 2019 occurring in patients shortly after thoracotomy and thoracoscopy procedures, illustrating the imminent threat of severe acute respiratory syndrome coronavirus 2 infection for thoracic surgery patients.