Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

Abstract: Background: Insulin resistance is a condition in which the body produces insulin but is unable to use it effectively. Aberrations in insulin signaling are known to play a crucial role in pathogenesis of this disease state. Eventually patients will have glucose build up in their blood instead of being absorbed by the cells, leading to type 2 diabetes. Objective: In the current study we focus on understanding the role of rSNP mediated miRNA:mRNA dysregulation and its impact on the above metabolic condition. Methods: More than 30 genes involved in insulin signaling pathway were found using KEGG database. The 3’UTR end of genes was studied by using RegRNA and Ensembl, whereas TargetScan along with miRbase were used to identify their target miRNAs.Binding free energy was used as a parameter to analyze the affect of polymorphism on the miRNA:mRNA duplex formation.Further, UNA fold was used to determine the heat capacity changes. Results: The following genes INSR, INS, GLUT4, FOXO1, IL6, TRIB3 and SREBF1 were selected for analysis. Multiple miRNAs, hsa-miR-16-5p, hsa-miR-15a-15p were identified in the SNP occurring region for INSR. INS too showed similar results.INSR, INS and TRIB3 were found to have the maximum change in their binding free energy due to rSNP variation. A destabilisation in the heat capacity values was observed too, contributed due to rSNP induction. Conclusion: A direct relationship between miRNA target polymorphism and the stability of the miRNA:mRNA duplex was observed. The current methodology used to study insulin resistance pathogenesis could elaborate on our existing knowledge of miRNA mediated disease states.

Beetles (order Coleoptera) are a diverse insect group whose associated microbial communities remain understudied despite their ecological, commercial, and biotechnological importance. Larvae of the darkling beetle species Tenebrio molitor (mealworms) and Zophobas atratus (superworms) have gained attention for their reported ability to ingest and possibly degrade plastics such as polyethylene and polystyrene. This degradation is believed to be mediated by enzymes from their gut microbiota, yet the underlying microbial mechanisms remain unclear. Using deep shotgun metagenomics, we generated comprehensive gut metagenomes for mealworms and superworms, recovering 53 and 100 high-quality metagenome-assembled genomes (MAGs), respectively. We found that the gut microbiomes of both insect species are dominated by bacteria from the phyla Bacillota and Pseudomonadota, with superworms trending towards greater bacterial diversity than mealworms. Comparing gut bacterial communities of insects fed polyethylene or polystyrene versus controls revealed no clear plastic ingestion effects beyond those attributable to starvation. On the other hand, insect cohort contributed substantially to variation in bacterial community composition, explaining 23% and 33% of the total variation in mealworms and superworms, respectively. Finally, we computationally identified numerous secreted proteins with sequence similarity to enzymes previously implicated in polyethylene and polystyrene degradation, and we provide support for dye decolorizing peroxidase (DyP)-type peroxidases as being the enzyme class mostly likely to initiate plastic oxidation in the digestive tracts of mealworms and superworms. Overall, this study provides high-quality metagenomes and MAGs for mealworms and superworms, revealing substantial under-explored microbial biodiversity, and highlighting DyP‑type peroxidases as promising targets for future plastic biodegradation studies.