Chewing-Activated TRPV4/PIEZO1--Zn Axes in a Rat Periodontal Complex.

The upstream mechanobiological pathways that regulate the downstream mineralization rates in periodontal tissues are limitedly understood. Herein, we spatially colocalized and correlated compression and tension strain profiles with the expressions of mechanosensory ion channels (MS-ion) TRPV4 and PIEZO1, biometal zinc, mitochondrial function marker (), cell senescence indicator (), and oxygen status marker hypoxia-inducible factor-1α () in rats fed hard and soft foods. The observed zinc and related cellular homeostasis in vivo were ascertained by TRPV4 and PIEZO1 agonists and antagonists on human periodontal ligament fibroblasts ex vivo.

Unraveling the phenotypic and metabolic responses induced by urea-encapsulated hydrogel beads on (L.) Czern & Coss.

Hydrogels, three-dimensional polymeric networks capable of absorbing and retaining significant amounts of aqueous solution, offer a promising platform for controlled release of desired compounds. In this study, we explored the effects of urea delivery through galactoxyloglucan-sodium alginate hydrogels on the phenotypic and metabolic responses of , a vital oilseed and vegetable crop. The experiments were conducted with four treatments: control (without hydrogel beads and urea), direct urea supplementation (U), hydrogel beads with urea (HBWU), and hydrogel beads without urea (HBWOU).

Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.

Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.

Global metabolomic alterations associated with endocrine-disrupting chemicals among pregnant individuals and newborns.

Background: Gestational exposure to non-persistent endocrine-disrupting chemicals (EDCs) may be associated with adverse pregnancy outcomes. While many EDCs affect the endocrine system, their effects on endocrine-related metabolic pathways remain unclear. This study aims to explore the global metabolome changes associated with EDC biomarkers at delivery.

Adaptive neural observer-based output feedback anti-actuator fault control of a nonlinear electro-hydraulic system with full state constraints.

This paper proposes an adaptive output feedback full state constrain (FSC) controller based on the adaptive neural disturbance observer (ANDO) for a nonlinear electro-hydraulic system (NEHS) with unmodeled dynamics. The Barrier Lyapunov Functions (BLFs) are utilized to ensure that all states of the system are specified within the constraints, and the approximation ability of radial basis function neural networks (RBFNNs) is used to cope with the unknown nonlinear functions. An adaptive neural compensation disturbance observer is elaborated to estimate the compound disturbance and oil leakage fault, effectively addressing these negative effects.