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Background: Cerebral O2 saturation (ScO2) reflects cerebral perfusion and can be measured noninvasively by near-infrared spectroscopy (NIRS). Objectives: In this pilot study, we describe the dynamics of ScO2 during TAVI in nonventilated patients and its impact on procedural outcome. Methods and Results: We measured ScO2 of both frontal lobes continuously by NIRS in 50 consecutive analgo-sedated patients undergoing transfemoral TAVI (female 58%, mean age 80.8 years). Compared to baseline ScO2 dropped significantly during RVP (59.3% vs. 53.9%, p < .01). Five minutes after RVP ScO2 values normalized (post RVP 62.6% vs. 53.9% during RVP, p < .01; pre 61.6% vs. post RVP 62.6%, p = .53). Patients with an intraprocedural pathological ScO2 decline of >20% (n = 13) had higher EuroSCORE II (3.42% vs. 5.7%, p = .020) and experienced more often delirium (24% vs. 62%, p = .015) and stroke (0% vs. 23%, p < .01) after TAVI. Multivariable logistic regression revealed higher age and large ScO2 drops as independent risk factors for delirium. Conclusions: During RVP ScO2 significantly declined compared to baseline. A ScO2 decline of >20% is associated with a higher incidence of delirium and stroke and a valid cut-off value to screen for these complications. NIRS measurement during TAVI procedure may be an easy to implement diagnostic tool to detect patients at high risks for cerebrovascular complications and delirium.
Improved integration of single cell transcriptome data demonstrated on heart failure in mice and men
(2023)
Biomedical research frequently uses murine models to study disease mechanisms. However, the translation of these findings to human disease remains a significant challenge. In order to improve the comparability of mouse and human data, we present a cross-species integration pipeline for single-cell transcriptomic assays.
The pipeline merges expression matrices and assigns clear orthologous relationships. Starting from Ensembl ortholog assignments, we allocated 82% of mouse genes to unique orthologs by using additional publicly available resources such as Uniprot, and NCBI databases. For genes with multiple matches, we employed the Needleman-Wunsch global alignment based on either amino acid or nucleotide sequence to identify the ortholog with the highest degree of similarity.
The workflow was tested for its functionality and efficiency by integrating scRNA-seq datasets from heart failure patients with the corresponding mouse model. We were able to assign unique human orthologs to up to 80% of the mouse genes, utilizing the known 17,492 orthologous pairs. Curiously, the integration process enabled the identification of both common and unique regulatory pathways between species in heart failure.
In conclusion, our pipeline streamlines the integration process, enhances gene nomenclature alignment and simplifies the translation of mouse models to human disease. We have made the OrthoIntegrate R-package accessible on GitHub (https://github.com/MarianoRuzJurado/OrthoIntegrate), which includes the assignment of ortholog definitions for human and mouse, as well as the pipeline for integrating single cells.