The metabolic myocardium is an omnivore and utilizes various carbon substrates to meet its energetic demand. While the adult heart preferentially consumes fatty acids (FAs) over carbohydrates, myocardial fuel plasticity is essential for organismal survival. This metabolic plasticity governing fuel utilization is under robust transcriptional control and studies over the past decade have illuminated members of the nuclear receptor family of factors (e.g., PPARα) as important regulators of myocardial lipid metabolism. However, given the complexity of myocardial metabolism in health and disease, it is likely that other molecular pathways are likely operative and elucidation of such pathways may provide the foundation for novel therapeutic approaches. We previously demonstrated that Kruppel-like factor 15 (KLF15) is an independent regulator of cardiac lipid metabolism thus raising the possibility that KLF15 and PPARα operate in a coordinated fashion to regulate myocardial gene expression requisite for lipid oxidation. In the current study, we show that KLF15 binds to, cooperates with, and is required for the induction of canonical PPARα-mediated gene expression and lipid oxidation in cardiomyocytes. As such, this study establishes a molecular module involving KLF15 and PPARα and provides fundamental insights into the molecular regulation of cardiac lipid metabolism.

Summary:Purpose: To determine whether epidural pentobarbital (PB) delivery can prevent and/or terminate neocortical seizures induced by locally administered acetylcholine (Ach) in freely moving rats.Methods: Rats were implanted permanently with an epidural cup placed over the right parietal cortex with intact dura mater. Epidural screw-electrodes, secured to the cup, recorded local neocortical EEG activity. In the seizure-termination study, Ach was delivered into the epidural cup, and after the development of electrographic and behavioral seizures, the Ach solution was replaced with either PB or artificial cerebrospinal fluid (aCSF; control solution). In the seizure-prevention study, the epidural Ach delivery was preceded by a 10-min exposure of the delivery site to PB or aCSF. Raw EEG recordings, EEG power spectra, and behavioral events were analyzed.Results: Ach-induced EEG seizures associated with convulsions, which were unaffected by epidural aCSF applications, were terminated by epidurally delivered PB within 2-2.5 min. Epidural deliveries of PB before Ach applications completely prevented the development of electrographic and behavioral seizures, whereas similar deliveries of aCSF exerted no influence on the seizure-generating potential of Ach.Conclusions: This study showed for the first time that epidural AED delivery can prevent, as well as terminate, locally induced neocortical seizures. The findings support the viability of transmeningeal pharmacotherapy for the treatment of intractable neocortical epilepsy.

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Background: Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning–based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts. Methods: Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence. Results: We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88–0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79–0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90–0.96 and 0.90–0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram). Conclusions: Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.