LEF1 isoforms regulate cellular senescence and aging.

The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high-throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. For the purpose of identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Our analysis identified lymphoid enhancer binding factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with idiopathic pulmonary fibrosis, an age-related disease with strong ties to cellular senescence, revealed a stark dysregulation of LEF1. Collectively, our results suggest that LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Remote Health Monitoring Systems for Elderly People: A Survey.

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

LEF1 isoforms regulate cellular senescence and aging.

Background: The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. Methods: With the focus on identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Results: We identified Lymphoid Enhancer Binding Factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with Idiopathic Pulmonary Fibrosis (IPF), an age-related disease with strong ties to cellular senescence, we demonstrated a stark dysregulation of LEF1. Conclusions: Collectively, our results suggest that the LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Evidence for conservation of primordial ~12-hour ultradian gene programs in humans under free-living conditions.

While circadian rhythms are entrained to the once daily light-dark cycle of the sun, many marine organisms exhibit ~12h ultradian rhythms corresponding to the twice daily movement of the tides. Although human ancestors emerged from circatidal environment millions of years ago, direct evidence of ~12h ultradian rhythms in humans is lacking. Here, we performed prospective, temporal transcriptome profiling of peripheral white blood cells and identified robust ~12h transcriptional rhythms from three healthy participants. Pathway analysis implicated ~12h rhythms in RNA and protein metabolism, with strong homology to the circatidal gene programs previously identified in Cnidarian marine species. We further observed ~12h rhythms of intron retention events of genes involved in MHC class I antigen presentation, synchronized to expression of mRNA splicing genes in all three participants. Gene regulatory network inference revealed XBP1, and GABP and KLF transcription factor family members as potential transcriptional regulators of human ~12h rhythms. These results suggest that human ~12h biological rhythms have a primordial evolutionary origin with important implications for human health and disease.

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.

Four-dimensional nuclear speckle phase separation dynamics regulate proteostasis.

Phase separation and biorhythms control biological processes in the spatial and temporal dimensions, respectively, but mechanisms of four-dimensional integration remain elusive. Here, we identified an evolutionarily conserved XBP1s-SON axis that establishes a cell-autonomous mammalian 12-hour ultradian rhythm of nuclear speckle liquid-liquid phase separation (LLPS) dynamics, separate from both the 24-hour circadian clock and the cell cycle. Higher expression of nuclear speckle scaffolding protein SON, observed at early morning/early afternoon, generates diffuse and fluid nuclear speckles, increases their interactions with chromatin proactively, transcriptionally amplifies the unfolded protein response, and protects against proteome stress, whereas the opposites are observed following reduced SON level at early evening/late morning. Correlative Son and proteostasis gene expression dynamics are further observed across the entire mouse life span. Our results suggest that by modulating the temporal dynamics of proteostasis, the nuclear speckle LLPS may represent a previously unidentified (chrono)-therapeutic target for pathologies associated with dysregulated proteostasis.

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.