Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study.

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application. The cloud triggers a pipeline for feature extraction and subsequently feeds the ML model with these extracted features. METHODS: The pilot study included 23 participants. Eleven patients with diabetes and suspected diabetic neuropathy were included in the experimental group. In the control group, 8 patients had suspected radiculopathy, and 4 participants were healthy. All participants underwent an electrodiagnostic examination (EDx) and a Moveo examination, which consists of sensors placed on the feet and back of the participant's hands and use of the mobile application. The participant performs six tests that are part of a standard neurological examination, and a ML algorithm calculates the probability of diabetic neuropathy. A user experience questionnaire was used to compare participant experiences with regard to both methods. RESULTS: The total accuracy of the algorithm is 82.1%, with 78% sensitivity and 87% specificity. A high linear correlation up to 0.722 was observed between Moveo and EDx features, which underpins the model's adequacy. The user experience questionnaire revealed that the majority of patients preferred the less painful method. CONCLUSIONS: Moveo represents an accurate, easy-to-use device suitable for home environments, showing promising results and potential for future usage.

Modulation of Ferroptosis by microRNAs in Human Cancer.

Ferroptosis is a cell death pathway triggered by an imbalance between the production of oxidants and antioxidants, which plays an emerging role in tumorigenesis. It is mainly regulated at three different levels including iron metabolism, the antioxidant response, and lipid metabolism. Epigenetic dysregulation is a "hallmark" of human cancer, with nearly half of all human cancers harboring mutations in epigenetic regulators such as microRNA. While being the crucial player in controlling gene expression at the mRNA level, microRNAs have recently been shown to modulate cancer growth and development via the ferroptosis pathway. In this scenario, some miRNAs have a function in upregulating, while others play a role in inhibiting ferroptosis activity. The investigation of validated targets using the miRBase, miRTarBase, and miRecords platforms identified 13 genes that appeared enriched for iron metabolism, lipid peroxidation, and antioxidant defense; all are recognized contributors of tumoral suppression or progression phenotypes. This review summarizes and discuss the mechanism by which ferroptosis is initiated through an imbalance in the three pathways, the potential function of microRNAs in the control of this process, and a description of the treatments that have been shown to have an impact on the ferroptosis in cancer along with potential novel effects.

Mutant p53K120R expression enables a partial capacity to modulate metabolism.

The TP53 tumor suppressor gene is one of the most studied gene in virtue of its ability to prevent cancer development by regulating apoptosis, cell cycle arrest, DNA repair, autophagy and senescence. Furthermore, the modulation of metabolism by P53 is fundamental for tumor suppressor activity. Studies in mouse models showed that mice carrying TP53 mutations affecting the acetylation in the DNA binding domain still retain the ability to transactivate genes involved in metabolism. Noteworthy, mice expressing the triple 3KR or the single K117R mutant do not show early on-set tumor development in contrast to TP53 -/- mice. Interestingly, the mouse K117R mutation corresponds to the human tumor-derived K120R modification, which abrogates P53-dependent activation of apoptosis without affecting growth arrest. In this study, we investigated the property of the human P53 K120R mutant in the regulation of metabolism by analyzing the transcriptional specificity in yeast- and mammalian-based reporter assays, the metabolic phenotype associated to its expression in colon cancer HCT116 TP53-/- cells and the induction of P53 targets and proteins involved in the antioxidant response. These properties were analyzed in comparison to wild type P53 protein, the human triple mutant corresponding to mouse 3KR and the cancer hot-spot R273H mutant. We confirm the selective functionality of P53 K120R mutant, which shows a transcriptional activity on cell cycle arrest but not on apoptotic targets. Interestingly, this mutant shows a partial transactivation activity on p53 response element belonging to the metabolic target TIGAR. Moreover, we observe a significant uncoupling between oxygen consumption and ATP production associated with higher lipid peroxidation level in all P53 mutants carrying cells with respect to wild type P53 expressing cells. Noteworthy, in the absence of a pro-oxidative challenge, cells expressing K120R mutant retain a partial capacity to modulate glucose metabolism, limiting lipid peroxidation with respect to the other P53 mutants carrying cells. Lastly, especially in presence of human 3KR mutant, a high expression of proteins involved in the antioxidant response is found. However, this response does not avoid the increased lipid peroxidation, confirming that only wild type P53 is able to completely counteract the oxidative stress and relative damages.