Limited Effect of Prevention Strategies on Incidence of Clinically Detectable Venous Thromboembolism After Lung Transplantation.

BACKGROUND: Thromboembolic complications are common post-lung transplant, leading to significant morbidity. We instituted multiple interventions because of an observed 36.8% incidence of venous thromboembolism (VTE) (Incidence rate (IR) 5.74/1000 pt days) in our recipients. METHODS: Our initiative commenced January 2015 with enoxaparin initiation within 6-8 hours of intensive care unit arrival and continuation for 4-6 weeks. We evaluated the IR of VTE in lung transplant recipients within 90 days of transplant. In 2017, the protocol was modified to extend the time to initiation of prophylaxis to within 72 hours of ICU arrival. In 2019, we further amended our intraoperative vascular access strategy. RESULTS: Eighteen of 26 lung transplant recipients (LTR) met inclusion criteria in the 2015 cohort. Six of 18 (33.3%) developed VTE, 50% of which were upper extremity (UE), line associated. Fifty two of 75 LTR were eligible for enoxaparin prophylaxis in the 2017 cohort. Fifteen of 52 subjects (28.8%) developed VTE, 77.8% of which were UE and line associated. Despite improved adherence in 2017, there was little change in VTE IR (3.90/1000 pt days compared with 3.85/1000 pt days). Twenty six of 43 LTR met protocol inclusion criteria in the 2019 cohort. Ten subjects (38.5%) developed VTE, 67% of which were UE and line associated (IR 5.18/1000 pt days). CONCLUSION: Our prospective study found that LTR remain at high risk for VTE despite aggressive prophylaxis with 4-6 weeks of enoxaparin and adjustment of vascular access approach. Alternative interventions should be investigated to minimize VTE development in this vulnerable population.

Adverse Outcomes Associated With Atrial Arrhythmias After Veno-Venous Extracorporeal Membrane Oxygenation.

Veno-venous extracorporeal membrane oxygenation (VV ECMO) is used as a treatment modality in those who fail to respond to conventional care. Hypoxia and medications used in the intensive care unit may increase risk for atrial arrhythmias (AA). This study aims to evaluate the impact of AA on post-VV ECMO outcome. A retrospective review of patients who were placed on VV ECMO between October 2016 and October 2021. One hundred forty-five patients were divided into two groups, AA and no AA. Baseline characteristic and potential risk factors were assessed. Uni- and multivariate analysis using logistic regression models were constructed to evaluate the predictors of mortality between groups. Survival between groups was estimated by the Kaplan-Meier method using the log-rank test. Advanced age with history of coronary artery disease and hypertension were associated with increased risk to develop AA post-VV ECMO placement ( p value < 0.05). Length on ECMO, time intubated, hospital length of stay, and sepsis were significantly increased in patients in the AA group ( p value < 0.05). There was no difference in the overall mortality between the two groups. AAs were associated with worse hospital course and complications but no difference in overall mortality rate. Age and cardiovascular disease seem to be predisposing risk factors for this. Further studies are needed to investigate potential strategies to prevent AAs development in this population.

A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.

Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.

Conserved cell types with divergent features in human versus mouse cortex.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.