Prohibitin1 maintains mitochondrial quality in isoproterenol-induced cardiac hypertrophy in H9C2 cells.

BACKGROUND INFORMATION: Various types of stress initially induce a state of cardiac hypertrophy (CH) in the heart. But, persistent escalation of cardiac stress leads to progression from an adaptive physiological to a maladaptive pathological state. So, elucidating molecular mechanisms that can attenuate CH is imperative in developing cardiac therapies. Previously, we showed that Prohibitin1 (PHB1) has a protective role in CH-induced oxidative stress. Nevertheless, it is unclear how PHB1, a mitochondrial protein, has a protective role in CH. Therefore, we hypothesized that PHB1 maintains mitochondrial quality in CH. To test this hypothesis, we used Isoproterenol (ISO) to induce CH in H9C2 cells overexpressing PHB1 and elucidated mitochondrial quality control pathways. RESULTS: We found that overexpressing PHB1 attenuates ISO-induced CH and restores mitochondrial morphology in H9C2 cells. In addition, PHB1 blocks the pro-hypertrophic IGF1R/AKT pathway and restores the mitochondrial membrane polarization in ISO-treated cells. We observed that overexpressing PHB1 promotes mitochondrial biogenesis, improves mitochondrial respiratory capacity, and triggers mitophagy. CONCLUSION: We conclude that PHB1 maintains mitochondrial quality in ISO-induced CH in H9C2 cells. SIGNIFICANCE: Based on our results, we suggest that small molecules that induce PHB1 in cardiac cells may prove beneficial in developing cardiac therapies.

Anticancer effect and apoptosis induction by azaflavanone derivative in human prostate cancer cells.

Polyphenols are naturally occurring organic compounds with varying structures represented by four major groups: flavonoids, phenolic acids, lignans and stilbenes. Several studies suggested that these secondary metabolites have health benefits due to its anti-tumorigenic effect. Therefore, substantial effort has been put forward to isolate and characterize these natural compounds and synthesize analogues that may serve as potential anti-cancer therapeutics. This present study is aimed at designing and synthesis of azaflavanone derivative and in understanding its mechanism of action in vitro and in vivo. Molecular docking studies predicted that the compound can potentially bind strongly to the Cyclin E1-Cdk2 complex which is a key mediator of the cell cycle progression indicating a biological interference in aggressive prostate cancer. Further downstream studies to understand its cytotoxicity and mechanism of action showed this azaflavanone derivative markedly inhibits viability of prostate cancer cells (DU145) showing an IC50 value of 0.4 µM compared to other cancer cells. The pharmacological ROS insult using the azaflavanone derivative increases the oxidative damage leading to high expression of apoptotic markers with increasing concentration. On compound treatment, the cells lose the metabolic flexibility accompanied by mitochondrial dysfunction leading to cell cycle arrest and apoptosis. Further, no compound mediated toxicity was observed in xenograft mouse model of prostate cancer at a concentration as high as 5 mg/kg. The tumor burden was reduced to 60% rendering the azaflavanone derivative a potential candidate in cancer therapeutics. Collectively, the compound triggers cell cycle arrest and ROS mediated oxidative stress sensitizing the cancerous cells towards apoptosis.

Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room.

BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. METHODS: We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. RESULTS: The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). CONCLUSIONS: The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.

p38MAPKα Stromal Reprogramming Sensitizes Metastatic Breast Cancer to Immunotherapy.

Metastatic breast cancer is an intractable disease that responds poorly to immunotherapy. We show that p38MAPKα inhibition (p38i) limits tumor growth by reprogramming the metastatic tumor microenvironment in a CD4+ T cell-, IFNγ-, and macrophage-dependent manner. To identify targets that further increased p38i efficacy, we utilized a stromal labeling approach and single-cell RNA sequencing. Thus, we combined p38i and an OX40 agonist that synergistically reduced metastatic growth and increased overall survival. Intriguingly, patients with a p38i metastatic stromal signature had better overall survival that was further improved by the presence of an increased mutational load, leading us to ask if our approach would be effective in antigenic breast cancer. The combination of p38i, anti-OX40, and cytotoxic T-cell engagement cured mice of metastatic disease and produced long-term immunologic memory. Our findings demonstrate that a detailed understanding of the stromal compartment can be used to design effective antimetastatic therapies. SIGNIFICANCE: Immunotherapy is rarely effective in breast cancer. We dissected the metastatic tumor stroma, which revealed a novel therapeutic approach that targets the stromal p38MAPK pathway and creates an opportunity to unleash an immunologic response. Our work underscores the importance of understanding the tumor stromal compartment in therapeutic design. This article is highlighted in the In This Issue feature, p. 1275.

Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model.

BACKGROUND AND AIM: We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point. METHODS: This retrospective multisite study utilized ED data from the Mount Sinai Health System, NY, during 2015-2019. Data included tabular clinical features and free-text triage notes represented using bag-of-words. A full gradient boosting model, trained on data available at different time points (30, 60, 90, 120, and 150 min), was compared to single models trained exclusively at data available at each time point. This was conducted by concatenating the rows of data available at each time point to one data matrix for the full model, where each row is considered a separate case. RESULTS: The cohort included 1,043,345 ED visits. The full model showed comparable results to the single models at all time points (AUCs 0.84-0.88 for different time points for both the full and single models). CONCLUSION: A full model trained on data concatenated from different time points showed similar results to single models trained at each time point. An ML-based prediction model can use used for identifying hospital admission.

Imine stilbene analog ameliorate isoproterenol-induced cardiac hypertrophy and hydrogen peroxide-induced apoptosis.

Cardiac hypertrophy is an adaptive response to stress, in order to maintain proper cardiac function. However, sustained stress leads to pathological hypertrophy accompanied by maladaptive responses and ultimately heart failure. At the cellular level, cardiomyocyte hypertrophy is characterized by an increase in myocyte size, reactivation of the fetal gene markers, disassembly of the sarcomere and transcriptional remodelling which are regulated by heart-specific transcription factors like MEF2, GATA4 and immediate early genes like c-jun and c-fos.2. It has been explored and established that the hypertrophic process is associated by oxidative stress and mediated by pathways involving several terminal stress kinases like P38, JNK and ERK1/2. Stilbenoids are bioactive polyphenols and earlier studies have shown that imine stilbene exert cardioprotective and anti aging effects by acting as modulators of Sirt1. The present study was aimed at designing and synthesizing a series of imine stilbene analogs and investigate its anti hypertrophic effects and regulatory mechanism in cardiac hypertrophy and apoptosis. Interestingly one of the analog, compound 3e (10 µM) alleviated isoproterenol (ISO, 25 µM) induced hypertrophy in rat cardiomyocyte (H9c2) cells by showing a marked decrease in the myocyte size. Further, compound 3e also restored the cardiac function by activating the metabolic stress sensor, AMPK. Moreover, molecular docking studies showed stable binding between compound 3e and GSK3ß suggesting that compound 3e may directly regulate GSK3ß activity and ameliorate ISO-induced cardiac hypertrophy. In agreement with this, compound 3e also modulated the crosstalk of all the hypertrophy inducing terminal Kinases by bringing down the expression to near control conditions. The compound also relieved H2O2 (100 µM) mediated ROS and normalized abnormal mitochondrial oxygen demand in hypertrophic conditions indicating the possibility of the compound to show promise in playing a role in cardiac hypertrophy.

Glucose starvation-induced oxidative stress causes mitochondrial dysfunction and apoptosis via Prohibitin 1 upregulation in human breast cancer cells.

In recent years there has been an upsurge in research focusing on reprogramming cancer cells through understanding of their metabolic signatures. Alterations in mitochondrial bioenergetics and impaired mitochondrial function may serve as effective targeting strategies especially in triple-negative breast cancers (TNBCs) where hormone receptors and endocrine therapy are absent. Glucose starvation (GS) of MDA-MB-231 and MCF-7 breast cancer cells showed decrease in mitochondrial Oxygen Consumption Rate (OCR), which was rescuable to control level through addition of exogenous antioxidant N-Acetyl Cysteine (NAC). Mechanistically, GS led to increase in mitochondrial ROS and upregulation of the pleiotropic protein, Prohibitin 1 (PHB1), leading to its dissociation from Dynamin-related protein 1 (DRP1), perturbance of mitochondrial membrane potential (MMP) and triggering of the apoptosis cascade. PHB1 also reduced the invasive and migratory potential of both cell lines. We emphasize that glucose starvation remarkably sensitized the highly glycolytic metastatic TNBC cell line, MDA-MB-231 to apoptosis and decreased its migratory potential. Based on our findings, additional TNBC cell lines can be evaluated and a nutritional paradigm be proposed for anticancer therapy.