Search

The burden of adverse cardiorenal outcomes among patients with the trifecta of diabetes, heart failure (HF) and chronic kidney disease (CKD) remains high. Steroidal mineralocorticoid receptor antagonists (MRAs) have been shown to improve clinical outcomes in patients with HF, however, there is significant underutilization of these agents, especially in patients with advanced CKD. Non-steroidal MRAs are an emerging therapeutic option for patients with diabetic kidney disease and are now guideline-supported in this population. Non-steroidal MRAs have a unique pharmacological profile distinct from their steroidal counterparts that retains the class-specific cardiorenal benefits but may help mitigate adverse effects, especially hyperkalaemia, in patients with CKD. In this review we summarize the current evidence on the use of non-steroidal MRAs for improving cardiorenal outcomes in patients with CKD and diabetes, as well as for combination use alongside other foundational medical therapies used in HF and CKD. [ABSTRACT FROM AUTHOR]

We report the first known case of a 42-year-old man diagnosed with spinocerebellar ataxia type 3, also known as Machado–Joseph disease (MJD), who presented with recurrent spontaneous pneumothorax. Six other family members affected with MJD died of the same pulmonary complication. To date, there has been no direct genetic linkage between MJD and familial spontaneous pneumothorax. However, the mutated ataxin-3 (ATXN3) gene in MJD and Serpin Family A Member 1 (SERPINA1) gene in hereditary emphysema share the same loci on chromosome 14q32.1, which is suggestive of genetic proclivity of patients with MJD to develop familial pneumothorax. Furthermore, the abnormal ataxin protein encoded by ATXN3 and the patient's smoking history could have potentiated the dysregulation of the ubiquitin-proteasome system further aggravating his genetic predisposition to develop recurrent pneumothorax. These unexplored areas of inquiry invoke further molecular characterization to give an accent to medical knowledge as well as guide novel therapies in the future. [ABSTRACT FROM AUTHOR]

We present a random forest (RF) framework for predicting circumgalactic medium (CGM) physical conditions from quasar absorption line observables, trained on a sample of Voigt profile-fit synthetic absorbers from the simba cosmological simulation. Traditionally, extracting physical conditions from CGM absorber observations involves simplifying assumptions such as uniform single-phase clouds, but by using a cosmological simulation we bypass such assumptions to better capture the complex relationship between CGM observables and underlying gas conditions. We train RF models on synthetic spectra for H  i and selected metal lines around galaxies across a range of star formation rates, stellar masses, and impact parameters, to predict absorber overdensities, temperatures, and metallicities. The models reproduce the true values from simba well, with normalized transverse standard deviations of 0.50–0.54 dex in overdensity, 0.32–0.54 dex in temperature, and 0.49–0.53 dex in metallicity predicted from metal lines (not H  i), across all ions. Examining the feature importance, the RF indicates that the overdensity is most informed by the absorber column density, the temperature is driven by the line width, and the metallicity is most sensitive to the specific star formation rate. Alternatively examining feature importance by removing one observable at a time, the overdensity and metallicity appear to be more driven by the impact parameter. We introduce a normalizing flow approach in order to ensure the scatter in the true physical conditions is accurately spanned by the network. The trained models are available online. [ABSTRACT FROM AUTHOR]