Deep molecular learning of transcriptional control of a synthetic CRE enhancer and its variants.

Massively parallel reporter assay measures transcriptional activities of various cis-regulatory modules (CRMs) in a single experiment. We developed a thermodynamic computational model framework that calculates quantitative levels of gene expression directly from regulatory DNA sequences. Using the framework, we investigated the molecular mechanisms of cis-regulatory mutations of a synthetic enhancer that cause abnormal gene expression. We found that, in a human cell line, competitive binding between family transcription factors (TFs) with slightly different binding preferences significantly increases the accuracy of recapitulating the transcriptional effects of thousands of single- or multi-mutations. We also discovered that even if various harmful mutations occurred in an activator binding site, CRM could stably maintain or even increase gene expression through a certain form of competitive binding between family TFs. These findings enhance understanding the effect of SNPs and indels on CRMs and would help building robust custom-designed CRMs for biologics production and gene therapy.

B-lines by lung ultrasound as a predictor of re-intubation in mechanically ventilated patients with heart failure.

Introduction: There have been few studies on predictors of weaning failure from MV in patients with heart failure (HF). We sought to investigate the predictive value of B-lines measured by lung ultrasound (LUS) on the risk of weaning failure from mechanical ventilation (MV) and in-hospital outcomes. Methods: This was a single-center, prospective observational study that included HF patients who were on invasive MV. LUS was performed immediate before ventilator weaning. A positive LUS exam was defined as the observation of two or more regions that had three or more count of B-lines located bilaterally on the thorax. The primary outcome was early MV weaning failure, defined as re-intubation within 72 h. Results: A total of 146 consecutive patients (mean age 70 years; 65.8% male) were enrolled. The total count of B-lines was a median of 10 and correlated with NT-pro-BNP level (r2 = 0.132, p < 0.001). Early weaning failure was significantly higher in the positive LUS group (9 out of 64, 14.1%) than the negative LUS group (2 out of 82, 2.4%) (p = 0.011). The rate of total re-intubation during the hospital stay (p = 0.004), duration of intensive care unit stay (p = 0.004), and hospital stay (p = 0.010) were greater in the positive LUS group. The negative predictive value (NPV) of positive LUS was 97.6% for the primary outcome. Conclusion: B-lines measured by LUS can predict the risk of weaning failure. Considering the high NPV of positive LUS, it may help guide the decision of weaning in patients on invasive MV due to acute decompensated HF.

Validity and reliability of the Yonsei lifestyle profile-active, balance, connection, diversity.

INTRODUCTION: Lifestyle, defined as a way of living resulting from an individual's deliberate choices, is a crucial factor in improving and maintaining health. Consequently, the measurement and analysis of lifestyle are of significant importance. This study aims to validate the validity and reliability of the Yonsei Lifestyle Profile-Active, Balance, Connection, Diversity (YLP-ABCD) in measuring health-related lifestyles. METHODS: Data were collected from 300 participants aged 55 and older using the YLP-ABCD. To analyse the validity and reliability of the YLP-ABCD, analysed using frequency, descriptive statistics, confirmatory factor analysis, and Rasch model analysis. RESULTS: The results indicate that the YLP-ABCD, which consists of 35 items, demonstrates unidimensionality and local independence. The Rasch model confirmed the suitability of the 5-point Likert scale for the factors, excluding subfactors Balanced and Unbalanced. The item fit criteria (0.5 < MNSQ < 1.5) were met for all items. The distribution of the respondents' abilities suggests the need for additional items to measure their ability levels. Both the item and respondent separation indices and their reliabilities were satisfactory. CONCLUSION: This study confirmed the utility of the YLP-ABCD as a valuable tool for measuring and understanding the multifaceted diversity of lifestyles. Therefore, by utilising YLP-ABCD to quantitatively measure health lifestyles, we anticipate contributing to improvements in human health and quality of life.

Clinical implications of pleural effusion following left ventricular assist device implantation.

BACKGROUND: Studies on the association between pleural effusion (PE) and left ventricular assist devices (LVADs) are limited. This study aimed to examine the characteristics and the clinical impact of PE following LVAD implantation. METHODS: This study is a prospective analysis of patients who underwent LVAD implantation from June 2015 to December 2022. We investigated the prognostic impact of therapeutic drainage (TD) on clinical outcomes. We also compared the characteristics and clinical outcomes between early and late PE and examined the factors related to the development of late PE. RESULTS: A total of 71 patients was analyzed. The TD group (n=45) had a longer ward stay (days; median [interquartile range]: 31.0 [23.0-46.0] vs. 21.0 [16.0-34.0], P=0.006) and total hospital stay (47.0 [36.0-82.0] vs. 31.0 [22.0-48.0], P=0.002) compared to the no TD group (n=26). Early PE was mostly exudate, left-sided, and neutrophil-dominant even though predominance of lymphocytes was the most common finding in late PE. Patients with late PE had a higher rate of reintubation within 14 days (31.8% vs. 4.1%, P=0.004) and longer hospital stays than those without late PE (67.0 [43.0-104.0] vs. 36.0 [28.0-48.0], P<0.001). Subgroup analysis indicated that female sex, low body mass index, cardiac resynchronization therapy, and hypoalbuminemia were associated with late PE. CONCLUSIONS: Compared to patients not undergoing TD, those undergoing TD had a longer hospital stay but not a higher 90-day mortality. Patients with late PE had poor clinical outcomes. Therefore, the correction of risk factors, like hypoalbuminemia, may be required.

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila.

Individual tissues perform highly specialized metabolic functions to maintain whole-body homeostasis. Although Drosophila serves as a powerful model for studying human metabolic diseases, a lack of tissue-specific metabolic models makes it challenging to quantitatively assess the metabolic processes of individual tissues and disease models in this organism. To address this issue, we reconstructed 32 tissue-specific genome-scale metabolic models (GEMs) using pseudo-bulk single cell transcriptomics data, revealing distinct metabolic network structures across tissues. Leveraging enzyme kinetics and flux analyses, we predicted tissue-dependent metabolic pathway activities, recapitulating known tissue functions and identifying tissue-specific metabolic signatures, as supported by metabolite profiling. Moreover, to demonstrate the utility of tissue-specific GEMs in a disease context, we examined the effect of a high sugar diet (HSD) on muscle metabolism. Together with 13C-glucose isotopic tracer studies, we identified glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a rate-limiting enzyme in response to HSD. Mechanistically, the decreased GAPDH activity was linked to elevated NADH/NAD+ ratio, caused by disturbed NAD+ regeneration rates, and oxidation of GAPDH. Furthermore, we introduced a pathway flux index to predict and validate additionally perturbed pathways, including fructose and butanoate metabolism. Altogether, our results represent a significant advance in generating quantitative tissue-specific GEMs and flux analyses in Drosophila, highlighting their use for identifying dysregulated metabolic pathways and their regulation in a human disease model.

Smartphone application-based rehabilitation in patients with chronic respiratory and cardiovascular diseases.

Rehabilitation improves symptoms, quality of life, and survival in patients with chronic respiratory or cardiovascular disease. We evaluated smartphone application-based rehabilitation programs for patients with chronic respiratory or cardiovascular diseases. This was a single-center prospective single arm study. Participants underwent smartphone application-based pulmonary or cardiac rehabilitation for 12 weeks. A total of 93 participants were recruited, and 75 visited after rehabilitation. Their median age was 67.0 (interquartile range, 60.0-70.8) years, and 60 (80.0%) were men. For patients with chronic respiratory disease (n = 41), VO2peak (median 13.7 to 15.4 ml/kg/min, P = 0.049), chronic obstructive pulmonary disease assessment test (median 14 to 6, P < 0.001), Euro-QoL 5-Dimension 5-Level (EQ-5D-5L) index (median 0.795 to 0.862, P = 0.001), and Health-related Quality of Life Instrument with 8 Items (HINT-8) index (median 0.784 to 0.855, P < 0.001) were significantly improved. For patients with chronic cardiovascular disease (n = 34), VO2peak (median 21.8 to 23.3, P = 0.007), EQ-5D-5L index (median 0.871 to 1.000, P = 0.037), and HINT-8 index (median 0.890 to 0.903, P < 0.001) were significantly improved. The smartphone application-based rehabilitation program improved exercise capacity and quality of life in patients with chronic respiratory or cardiovascular disease.Trial registration: https://clinicaltrials.gov/ct2/show/NCT05383950 (20/05/2022).

BMI1 regulates human erythroid self-renewal through both gene repression and gene activation.

The limited proliferative capacity of erythroid precursors is a major obstacle to generate sufficient numbers of in vitro-derived red blood cells (RBC) for clinical purposes. We and others have determined that BMI1, a member of the polycomb repressive complex 1 (PRC1), is both necessary and sufficient to drive extensive proliferation of self-renewing erythroblasts (SREs). However, the mechanisms of BMI1 action remain poorly understood. BMI1 overexpression led to 10 billion-fold increase BMI1-induced (i)SRE self-renewal. Despite prolonged culture and BMI1 overexpression, human iSREs can terminally mature and agglutinate with typing reagent monoclonal antibodies against conventional RBC antigens. BMI1 and RING1B occupancy, along with repressive histone marks, were identified at known BMI1 target genes, including the INK-ARF locus, consistent with an altered cell cycle following BMI1 inhibition. We also identified upregulated BMI1 target genes with low repressive histone modifications, including key regulator of cholesterol homeostasis. Functional studies suggest that both cholesterol import and synthesis are essential for BMI1-associated self-renewal. These findings support the hypothesis that BMI1 regulates erythroid self-renewal not only through gene repression but also through gene activation and offer a strategy to expand the pool of immature erythroid precursors for eventual clinical uses.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.