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Fucoxanthin (Fx), a xanthophyll carotenoid abundant in brown algae, possesses several biological functions, such as antioxidant, anti‐inflammatory, and cardiac‐protective activities. However, the role of Fx in myocardial ischemia/reperfusion (MI/R) is still unclear. Thus, the aim of this study was to investigate the effect of Fx on MI/R‐induced injury and explore the underlying mechanisms. Our results showed that in vitro, Fx treatment significantly suppressed inflammatory response, oxidative stress, and apoptosis in rat cardiomyocytes exposed to hypoxia/reoxygenation (H/R). In addition, Fx led to increased phosphorylation of AMPK, AKT, and GSK‐3β, and enhanced activation of Nrf2 in cardiomyocytes under H/R conditions. Notably, pretreatment with Compound C (AMPK inhibitor), partially reduced the beneficial effects of Fx in cardiomyocytes exposed to H/R. In vivo, Fx ameliorated myocardial damage, inhibited inflammatory response, oxidative stress, and apoptosis, and activated the AMPK/GSK‐3β/Nrf2 signaling in myocardial tissues in MI/R rat model. Taken together, these findings indicated that Fx attenuates MI/R‐induced injury by inhibiting oxidative stress, inflammatory response, and apoptosis. The AMPK/GSK‐3β/Nrf2 pathway is involved in the cardioprotective effect of Fx in MI/R injury. Thus, Fx may be a promising drug for the treatment of MI/R. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model's effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization. [ABSTRACT FROM AUTHOR]

There has been increased interest in recognizing the dynamic and flexible changes in shipborne multi-function radar (MFR) working modes. The working modes determine the distribution of pulse descriptor words (PDWs). However, building the mapping relationship from PDWs to working modes in reconnaissance systems presents many challenges, such as the duration of the working modes not being fixed, incomplete temporal features in short PDW slices, and delayed feedback of the reconnaissance information in long PDW slices. This paper recommends an MFR working mode recognition method based on the ShakeDrop regularization dual-path attention temporal convolutional network (DP-ATCN) with prolonged temporal feature preservation. The method uses a temporal feature extraction network with the Convolutional Block Attention Module (CBAM) and ShakeDrop regularization to acquire a high-dimensional space mapping of temporal features of the PDWs in a short time slice. Additionally, with prolonged PDW accumulation, an enhanced TCN is introduced to attain the temporal variation of long-term dependence. This way, secondary correction of MFR working mode recognition results is achieved with both promptness and accuracy. Experimental results and analysis confirm that, despite the presence of missing and spurious pulses, the recommended method performs effectively and consistently in shipborne MFR working mode recognition tasks. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of our study was to highlight the conventional and contrast‐enhanced ultrasound (US) features of mummified thyroid nodules, which should help differentiate them from histologically proven papillary thyroid carcinomas (PTCs). Methods: Thirty‐one patients with 33 mummified thyroid nodules, which showed suspicious US findings that were suggestive of malignancy, as well as 33 patients with 38 surgically confirmed PTCs were enrolled in this study. We evaluated the size, shape, margin, echogenicity, presence of shadowing and halo, presence of punctate echogenic foci, vascularity, and contrast enhancement parameters for each nodule. The final diagnosis of mummified thyroid nodules was confirmed via fine‐needle aspiration (FNA) or surgery. Results: Of the 33 mummified thyroid nodules, 9 (27.3%) were confirmed by surgery to be benign, and 24 (72.7%) were proven by FNA to be benign. A univariate analysis indicated that the mummified thyroid nodules more frequently showed wider‐than‐tall shapes, marked hypoechogenicity, the presence of posterior shadowing, the absence of nodular vascularity, hypoenhancement or no enhancement, and peak index and area under the curve indices of less than 1 in the findings of preoperative US and contrast‐enhanced US compared to PTCs. A multivariate analysis showed that marked hypoechogenicity and an area under the curve index of less than 1 were independent characteristics related to mummified nodules for discriminating from PTCs (all P < .05). Conclusions: Benign thyroid nodules may display shrinkage over time and may reveal malignant US features. Awareness of these findings and their connection with initial and follow‐up US examinations should help identify mummified thyroid nodules and to avoid surgical excision or unnecessary FNA. [ABSTRACT FROM AUTHOR]