Cardio-sarcopenia: A syndrome of concern in aging.

Cardiac alterations in structure and function, namely, the left ventricle, have been intensely studied for decades, in association with aging. In recent times, there has been keen interest in describing myocardial changes that accompany skeletal muscle changes in older adults. Initially described as a cardio-sarcopenia syndrome where alterations in myocardial structure were observed particularly among older adults with skeletal muscle sarcopenia, investigations into this syndrome have spurred a fresh level of interest in the cardiac-skeletal muscle axis. The purpose of this perspective is to summarize the background for this "syndrome of concern," review the body of work generated by various human aging cohorts, and to explore future directions and opportunities for understanding this syndrome.

Explainable machine learning predictions to support personalized cardiology strategies.

Aims: A widely practiced intervention to modify cardiac health, the effect of physical activity on older adults is likely heterogeneous. While machine learning (ML) models that combine various systemic signals may aid in predictive modelling, the inability to rationalize predictions at a patient personalized level is a major shortcoming in the current field of ML. Methods and results: We applied a novel methodology, SHapley Additive exPlanations (SHAP), on a dataset of older adults n = 86 (mean age 72 ± 4 years) whose physical activity levels were studied alongside changes in their left ventricular (LV) structure. SHAP was tested to provide intelligible visualization on the magnitude of the impact of the features in their physical activity levels on their LV structure. As proof of concept, using repeated K-cross-validation on the train set (n = 68), we found the Random Forest Regressor with the most optimal hyperparameters, which achieved the lowest mean squared error. With the trained model, we evaluated its performance by reporting its mean absolute error and plotting the correlation on the test set (n = 18). Based on collective force plot, individually numbered patients are indicated on the horizontal axis, and each bandwidth implies the magnitude (i.e. effect) of physical parameters (higher in red; lower in blue) towards prediction of their LV structure. Conclusions: As a tool that identified specific features in physical activity that predicted cardiac structure on a per patient level, our findings support a role for explainable ML to be incorporated into personalized cardiology strategies.

A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN.

Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several computer vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost.

Discovery, Identification, and Functional Characterization of Plant Long Intergenic Noncoding RNAs After Virus Infection.

Long intergenic noncoding RNAs (lincRNAs), which possess diverse features such as remodeling chromatin and genome architecture, RNA stabilization, and genome architecture, are important regulatory factors in plant genomes. They serve to fine-tune the expression of neighboring genes. Here, we describe a procedure of discovery, identification, and functional characterization of plant lincRNAs after virus infection. From high-throughput RNA-Seq transcriptome analysis, the noncoding RNA transcripts with significant fold changes (upregulation or downregulation) will be discovered and identified. The lincRNA of interest will be further confirmed and validated using rapid amplification of cDNA ends (RACE). In addition, functional characterization of the lincRNA will be followed up through overexpression and knockdown strategies.

Upregulation of LINC-AP2 is negatively correlated with AP2 gene expression with Turnip crinkle virus infection in Arabidopsis thaliana.

KEY MESSAGE: A long intergenic noncoding RNA LINC - AP2 is upregulated and negatively correlated with AP2 gene expression with Turnip crinkle virus infection in Arabidopsis. Plant vegetative growth and floral reproductive structure were severely retarded and distorted in Turnip crinkle virus (TCV)-infected Arabidopsis thaliana. Compared to mock-inoculated plants, the stamen filaments were shorter in flowers of TCV-infected plants. However, TCV-infected plants can still produce normal seeds through artificial pollination, indicating both its pollen and stigma were biologically functional. From our high-throughput RNA-Seq transcriptome analysis, a floral structure-related APETALA2 (AP2) gene was found to be downregulated and its neighboring long intergenic noncoding RNAs (lincRNA), At4NC069370 (named LINC-AP2 in this study), were upregulated significantly in TCV-infected plants. This LINC-AP2 was further confirmed for its existence using 5'RACE technology. LINC-AP2 overexpression (LINC-AP2 OE) transgenic Arabidopsis plants were generated to compare with TCV-infected WT plants. TCV-infected LINC-AP2 OE plants which contained lower AP2 gene expression displayed more severe symptoms (including floral structure distortion) and higher TCV-CP gene transcript and coat protein levels. Furthermore, compared to TCV-infected WT plants, TCV-infected ap2 mutant plants failed to open their flower buds and displayed more severe viral symptoms. In conclusion, upregulation of LINC-AP2 is negatively correlated with AP2 gene expression with TCV infection in Arabidopsis.