Echinatin mitigates sevoflurane-induced neurotoxicity through regulation of ferroptosis and iron homeostasis.

Surgery and anesthesia are vital medical interventions, but concerns over their potential cognitive side effects, particularly with the use of inhalational anesthetics like sevoflurane, have surfaced. This study delves into the neuroprotective potential of Echinatin against sevoflurane-induced neurotoxicity and the underlying mechanisms. Echinatin, a natural compound, has exhibited anti-inflammatory, antioxidant, and anticancer properties. Sevoflurane, while a popular anesthetic, is associated with perioperative neurocognitive disorders (PND) and neurotoxicity. Our investigation began with cellular models, where Echinatin demonstrated a significant reduction in sevoflurane-induced apoptosis. Mechanistically, we identified ferroptosis, a novel form of programmed cell death characterized by iron accumulation and lipid peroxidation, as a key player in sevoflurane-induced neuronal injury. Echinatin notably suppressed ferroptosis in sevoflurane-exposed cells, suggesting a pivotal role in neuroprotection. Expanding our research to a murine model, we observed perturbations in iron homeostasis, inflammatory cytokines, and antioxidants due to sevoflurane exposure. Echinatin treatment effectively restored iron balance, mitigated inflammation, and preserved antioxidant levels in vivo. Behavioral assessments using the Morris water maze further confirmed Echinatin's neuroprotective potential, as it ameliorated sevoflurane-induced spatial learning and memory impairments. In conclusion, our study unveils Echinatin as a promising candidate for mitigating sevoflurane-induced neurotoxicity. Through the regulation of ferroptosis, iron homeostasis, and inflammation, Echinatin demonstrates significant neuroprotection both in vitro and in vivo. These findings illuminate the potential for Echinatin to enhance the safety of surgical procedures involving sevoflurane anesthesia, minimizing the risk of cognitive deficits and neurotoxicity.

High-Sucrose Diet Exposure on Larvae Contributes to Adult Fecundity and Insecticide Tolerance in the Oriental Fruit Fly, Bactrocera dorsalis (Hendel).

Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is one of the broad host ranges and economically-important insect pests in tropical and subtropical areas. A wide range of hosts means they have strong adaptation ability to changes in dietary macronutrients (e.g., sucrose and protein). However, the effects of dietary conditions on the phenotypes and genotypes of B. dorsalis are still unclear. In this study, we aimed to investigate the effects of larval dietary sucrose on the life history traits and stress tolerance of B. dorsalis, and its defense response at the molecular level. The results showed that low-sucrose (LS) induced decreased body size, shortened developmental duration, and enhanced sensitivity to beta-cypermethrin. Otherwise, high-sucrose (HS) diet increased developmental duration, adult fecundity, and tolerance to malathion. Based on transcriptome data, 258 and 904 differentially expressed genes (DEGs) were identified in the NS (control) versus LS groups, and NS versus HS groups, respectively. These yielded DEGs were relevant to multiple specific metabolisms, hormone synthesis and signaling, and immune-related pathways. Our study will provide biological and molecular perspective to understand phenotypic adjustments to diets and the strong host adaptability in oriental fruit flies.

Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network.

Visual attention is an important mechanism in the human visual system (HVS) and there have been numerous saliency detection algorithms designed for 2D images/video recently. However, the research for fixation detection of stereoscopic video is still limited and challenging due to the complicated depth and motion information. In this paper, we design a novel multi-module fully convolutional network (MM-FCN) for fixation detection of stereoscopic video. Specifically, we design a fully convolutional network for spatial saliency prediction (S-FCN), where the initial spatial saliency map of stereoscopic video is learned by image database of object detection. Furthermore, the fully convolutional network for temporal saliency prediction (T-FCN) is constructed by combining saliency results from S-FCN and motion information from video frames. Finally, the fully convolutional network for depth fixation prediction (D-FCN) is designed to compute the final fixation map of stereoscopic video by learning depth features with spatiotemporal features from T-FCN. The experimental results show that the proposed MM-FCN can predict fixation results for stereoscopic video more effectively and efficiently than other related fixation prediction methods.