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1.
Cytotherapy ; 25(6 Supplement):S245-S246, 2023.
Artigo em Inglês | EMBASE | ID: covidwho-20245241

RESUMO

Background & Aim: With larger accessibility and increased number of patients being treated with CART cell therapy, real-world toxicity continues to remain a significant challenge to its widespread adoption. We have previously shown that allogeneic umbilical cord blood derived (UCB) regulatory T cells (Tregs) can resolve uncontrolled inflammation and can treat acute and immune mediated lung injury in a xenogenic model as well as in patients suffering from COVID-19 acute respiratory distress syndrome. The unique properties of UCB Tregs including: i) lack of plasticity when exposed to inflammatory micro-environments;ii) no requirement for HLA matching;iii) long shelf life of cryopreserved Tregs;and iv) immediate product availability for on demand treatment, makes them an attractive source for treating acute inflammatory syndromes. Therefore, we hypothesized that add-on therapy with UCB derived Tregs may resolve uncontrolled inflammation responsible for CART cell therapy associated toxicity. Methods, Results & Conclusion(s): UCB Tregs were added in 1:1 ratio to CART cells, where no interference in their ability to kill CD19+ Raji cells, was detected at different ratios : 8:1 (80.4% vs. 81.5%);4:1 (62.0% vs. 66.2%);2:1 (50.1% vs. 54.7%);1:1 (35.4% vs. 44.1%) (Fig 1A). In a xenogenic B cell lymphoma model, multiple injections of Tregs were administered after CART injection (Fig 1B), which did not impact distribution of CD8+ T effector cells (Fig 1C) or CART cells cells (Fig 1D) in different organs. No decline in the CAR T levels was observed in the Tregs recipients (Fig 1E). Specifically, no difference in tumor burden was detected between the two arms (Fig 2A). No tumor was detected in CART+Tregs in liver (Fig 2B) or bone marrow (Fig 2C). A corresponding decrease in multiple inflammatory cytokines in peripheral blood was observed in CART+Tregs when compared to CART alone (Fig 2D). Here we show "proof of concept" for add-on therapy with Tregs to mitigate hyper-inflammatory state induced by CART cells without interference in their on-target anti-tumor activity. The timing of Tregs administration after CART cells have had sufficient time for forming synapse with tumor cells allows for preservation of their anti-tumor cytotoxicity, such that the infused Tregs home to the areas of tissue damage to bind to the resident antigen presenting cells which in turn collaborate with Tregs to resolve inflammation. Such differential distribution of cells allow for a Treg "cooling blanket" and lays ground for clinical study. [Figure presented]Copyright © 2023 International Society for Cell & Gene Therapy

3.
Ebiomedicine ; 87, 2023.
Artigo em Inglês | Web of Science | ID: covidwho-2310586

RESUMO

Background Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.Methods We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.Findings We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

4.
European Respiratory Journal Conference: European Respiratory Society International Congress, ERS ; 60(Supplement 66), 2022.
Artigo em Inglês | EMBASE | ID: covidwho-2250886

RESUMO

Background: Post-COVID Syndrome (PCS) is an important sequela of COVID-19, characterised by symptom persistence >3 months, subacute symptom onset, and worsening of pre-existing comorbidities. The causes and public health impact of PCS are still unclear, not least for the lack of efficient means to assess the presence and severity of PCS. Method(s): COVIDOM is a population-based cohort study of PCR-confirmed cases of SARS-CoV-2 infection, recruited through local public health authorities in three German regions. Standardised interviews and in-depth onsite examinations were scheduled 6-12 months post infection. Based upon 12 long-term symptom complexes, we developed a comprehensive PCS severity score in a training cohort and validated the score in two independent subcohorts. Result(s): In the training sub-cohort (n=667, 56% female), 90% of participants were treated as outpatients for acute COVID-19. Neurological ailments (61.5%) and fatigue (57.1%) persisted most frequently. Across all sub-cohorts, higher PCS scores were associated with lower health-related quality of life (EQ-5D-5L-VAS/-index, all p<0.001). Similarly, participants with a higher PCS score consistently showed increased blood inflammatory markers and Ddimer as well as lower diffusing capacity in lung function (all p<0.01). Significant early predictors of the PCS score included the number and intensity of acute symptoms, resilience, and general anxiousness. Conclusion(s): PCS severity can be quantified by an easy-to-use score summarising individual disease burden and reflecting pathological processes. The PCS score promises to facilitate diagnosis of PCS, studies of its natural course, and of therapeutic interventions.

5.
Open Forum Infectious Diseases ; 9(Supplement 2):S741-S742, 2022.
Artigo em Inglês | EMBASE | ID: covidwho-2189897

RESUMO

Background. Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods. A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1: Study flow chart with inclusion criteria and methodological workflow. Results. Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970);delta: AUC 0.769 (95%CI 0.657-0.882);omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1: Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV- 2. Conclusion. Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size.

7.
Psycho-Oncology ; 31:43-43, 2022.
Artigo em Inglês | Web of Science | ID: covidwho-1756005
8.
Infection ; 49(6): 1277-1287, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: covidwho-1465929

RESUMO

PURPOSE: Over the course of COVID-19 pandemic, evidence has accumulated that SARS-CoV-2 infections may affect multiple organs and have serious clinical sequelae, but on-site clinical examinations with non-hospitalized samples are rare. We, therefore, aimed to systematically assess the long-term health status of samples of hospitalized and non-hospitalized SARS-CoV-2 infected individuals from three regions in Germany. METHODS: The present paper describes the COVIDOM-study within the population-based cohort platform (POP) which has been established under the auspices of the NAPKON infrastructure (German National Pandemic Cohort Network) of the national Network University Medicine (NUM). Comprehensive health assessments among SARS-CoV-2 infected individuals are conducted at least 6 months after the acute infection at the study sites Kiel, Würzburg and Berlin. Potential participants were identified and contacted via the local public health authorities, irrespective of the severity of the initial infection. A harmonized examination protocol has been implemented, consisting of detailed assessments of medical history, physical examinations, and the collection of multiple biosamples (e.g., serum, plasma, saliva, urine) for future analyses. In addition, patient-reported perception of the impact of local pandemic-related measures and infection on quality-of-life are obtained. RESULTS: As of July 2021, in total 6813 individuals infected in 2020 have been invited into the COVIDOM-study. Of these, about 36% wished to participate and 1295 have already been examined at least once. CONCLUSION: NAPKON-POP COVIDOM-study complements other Long COVID studies assessing the long-term consequences of an infection with SARS-CoV-2 by providing detailed health data of population-based samples, including individuals with various degrees of disease severity. TRIAL REGISTRATION: Registered at the German registry for clinical studies (DRKS00023742).


Assuntos
COVID-19 , Qualidade de Vida , COVID-19/complicações , Humanos , Pandemias , SARS-CoV-2 , Resultado do Tratamento , Síndrome Pós-COVID-19 Aguda
9.
Patterns ; 2(1):100155, 2021.
Artigo em Inglês | MEDLINE | ID: covidwho-1209447

RESUMO

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks;the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

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