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Curcumin (CUR) is a lipophilic natural polyphenol that can be isolated from the rhizome of turmeric. Studies have proposed that CUR possesses a variety of biological activities. Due to its anti-inflammatory and antioxidant properties, CUR shows promise in the treatment of inflammatory bowel disease, while its anti-obesity effects make it a potential therapeutic agent in the management of obesity. In addition, curcumin’s ability to prevent atherosclerosis and its cardiovascular benefits further expand its potential application in the treatment of cardiovascular disease. Nevertheless, owing to the limited bioavailability of CUR, it is difficult to validate its specific mechanism of action in the treatment of diseases. However, the restricted bioavailability of CUR makes it challenging to confirm its precise mode of action in disease treatment. Recent research indicates that the oral intake of curcumin may lead to elevated levels of residual curcumin in the gastrointestinal system, hinting at curcumin’s potential to directly influence gut microbiota. Furthermore, the ecological dysregulation of the gut microbiota has been shown to be critical in the pathogenesis of human diseases. This review summarizes the impact of gut dysbiosis on host health and the various ways in which curcumin modulates dysbiosis and ameliorates various diseases caused by it through the administration of curcumin.

Ulcerative colitis (UC) is a chronic, nonspecific inflammatory bowel disease (IBD) characterized by complicated and relapsing inflammation in the gastrointestinal tract. SM934 is a water-soluble artemisinin analogue that shows anti-inflammatory and immuno-regulatory effects. In this study, we investigated the effects of SM934 on UC both in vivo and in vitro . A mouse model of colitis was established in mice by oral administration of 5% dextran sulfate sodium (DSS). SM934 (3, 10 mg/kg per day, ig) was administered to the mice for 10 days. After the mice were sacrificed, colons, spleens and mesenteric lymph nodes (MLNs) were collected for analyses. We showed that SM934 administration restored DSS-induced body weight loss, colon shortening, injury and inflammation scores. Furthermore, SM934 administration significantly decreased the disease activity index (DAI), histopathological scores, and myeloperoxidase (MPO) activities in colonic tissues. Moreover, SM934 administration dose-dependently decreased the mRNA and protein levels of DSS-induced pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α), and the percentage of macrophages and neutrophils in colon tissues. The effects of SM934 on LPS-stimulated RAW 264.7 cells and THP-1-derived macrophages were examined in vitro. Treatment with SM934 (0.8, 8, 80 μmol/L) dose-dependently decreased the production of pro-inflammatory mediators in LPS-stimulated RAW264.7 cells and THP-1-derived macrophages via inhibiting activation of the NF-κB signaling. Our results reveal the protective effects of SM934 on DSS-induced colitis can be attributed to its suppressing effects on neutrophils and macrophages and its inhibitory role in the NF-κB signaling, suggests that SM934 might be a potential effective drug for ulcerative colitis.

The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) remains unclear. 240 patients with SPNs (malignant, n = 180; benign, n = 60) underwent non-contrast CT (NECT) and contrast-enhanced CT (CECT) which were reconstructed with different slice thickness and convolution kernel. 150 radiomics features were extracted separately from each set of CT and diagnostic performance of each feature were assessed. After feature selection and radiomics signature construction, diagnostic performance of radiomics signature for discriminating benign and malignant SPN was also assessed with respect to the discrimination and classification and compared with net reclassification improvement (NRI). Our results showed NECT-based radiomics signature demonstrated better discrimination and classification capability than CECT in both primary (AUC: 0.862 vs. 0.829, p = 0.032; NRI = 0.578) and validation cohort (AUC: 0.750 vs. 0.735, p = 0.014; NRI = 0.023). Thin-slice (1.25 mm) CT-based radiomics signature had better diagnostic performance than thick-slice CT (5 mm) in both primary (AUC: 0.862 vs. 0.785, p  = 0.015; NRI = 0.867) and validation cohort (AUC: 0.750 vs. 0.725, p  = 0.025; NRI = 0.467). Standard convolution kernel-based radiomics signature had better diagnostic performance than lung convolution kernel-based CT in both primary (AUC: 0.785 vs. 0.770, p  = 0.015; NRI = 0.156) and validation cohort (AUC: 0.725 vs.0.686, p  = 0.039; NRI = 0.467). Therefore, this study indicates that the contrast-enhancement, reconstruction slice thickness and convolution kernel can affect the diagnostic performance of radiomics signature in SPN, of which non-contrast, thin-slice and standard convolution kernel-based CT is more informative.

Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

Alzheimer's disease (AD) is a neurodegenerative disease associated with senile plaques (SP) and neurofibrillary tangles (NFTs) in the brain. With aging of the population, AD has become the most common form of dementia. However, the mechanisms leading to AD are still under investigation, and there are currently no specific drugs for its treatment. Therefore, further study on the pathogenesis of AD to develop new drugs for AD treatment remains a top priority. Several studies have suggested that intracellular calcium homeostasis is dysregulated in AD, and this has been implicated in the deposition of amyloid β (Aβ), hyperphosphorylation of tau protein, abnormal synaptic plasticity, and apoptosis, all of which are involved in the occurrence and development of AD. In addition, some based on pathways linking calcium homeostasis and AD have achieved results in AD treatment. This review comprehensively explores the relationship between calcium homeostasis and the pathogenesis of AD to provide a theoretical basis for the future exploration of AD and the development of novel therapeutic drugs.

Terrestrial evapotranspiration (ET) is crucial to the exchange of global carbon, water, and energy cycles and links the hydrological and ecological processes. The frequency and intensity of extreme droughts are expected to increase due to ongoing climate change, strongly impacting terrestrial ET with implications for ecosystems, societies, and climate systems. However, the response of terrestrial ET to extreme drought and the underlying mechanism of terrestrial ET change during droughts are still unclear. Here, we review previous studies on terrestrial ET’s responses to extreme drought and investigate the control factors of ET change in response to extreme drought under different situations. The response of terrestrial ET to extreme drought is affected by various factors including the duration and intensity of the drought, the original climate conditions, as well as the plant species. Terrestrial ET change during droughts is controlled by complex biological and physical processes that can be divided into four parts including supply, energy, demand, and vegetation activities. The response of terrestrial ET to elevate CO2 may offset the effects of drought because CO2 fertilization tends to increase water use efficiency through stomatal regulation. We found that large uncertainties remain in the terrestrial ET response to drought due to the discrepancies among different ET products and simulations. This work highlights the requirement for accurate estimates of ET changes in ET products and models. This review provides a systematic investigation of the terrestrial ET response to extreme drought and the underlying mechanism of terrestrial ET changes during droughts and will significantly improve the development of water management strategies under climate change.

A parking sharing strategy is proposed to solve the problems of parking difficulty caused by the imbalance between parking spaces and parking demand. The vacant parking spaces of residential area can be efficiently utilized to meet the parking demands of those who are working at nearby or come for other activities based on the parking sharing strategy. The paper analyzes the distribution of vehicle arrival numbers and parking durations, then establishes a shared parking allocation model aiming to maximize the parking benefit considering the overtime-parking behavior of the parking users. Simulation methods are used to the analyze the relationship among the parking benefit, proportion of reserved parking, numbers of parking demand, acceptance rate of parking demand and utilization of shared parking spaces. Then, based on the principle of maximum parking benefit, we can determine the optimal proportion of reserved parking, number of shared parking spaces that should be purchased from the residents. Taking the utilization of shared parking spaces as an indicator, the validity of the static allocation principle is proved to be effective. Some allocation rules for parking demand are proposed to guarantees the maximum parking revenue and minimum impact on residents simultaneously.

Curcumin is the most important active component in turmeric extracts. Curcumin, a natural monomer from plants has received a considerable attention as a dietary supplement, exhibiting evident activity in a wide range of human pathological conditions. In general, curcumin is beneficial to human health, demonstrating pharmacological activities of anti-inflammation and antioxidation, as well as antitumor and immune regulation activities. Curcumin also presents therapeutic potential in neurodegenerative, cardiovascular and cerebrovascular diseases. In this review article, we summarize the advancements made in recent years with respect to curcumin as a biologically active agent in malignant tumors, Alzheimer’s disease (AD), hematological diseases and viral infectious diseases. We also focus on problems associated with curcumin from basic research to clinical translation, such as its low solubility, leading to poor bioavailability, as well as the controversy surrounding the association between curcumin purity and effect. Through a review and summary of the clinical research on curcumin and case reports of adverse effects, we found that the clinical transformation of curcumin is not successful, and excessive intake of curcumin may have adverse effects on the kidneys, heart, liver, blood and immune system, which leads us to warn that curcumin has a long way to go from basic research to application transformation.

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer. Secondly, the GSConv convolution is used to replace traditional convolution to reduce the computational cost of the model and effectively aggregate global information. Finally, the Efficient Intersection over Union (EIoU) loss function is improved to enhance its sensitivity to positional errors and optimize the error distribution, thereby improving the model’s accuracy in capturing the position at the center of the typhoon. The experimental results show that the proposed TGE-YOLO model outperforms Faster R-CNN, YOLOv5s, YOLOv9s, and YOLOv11n, with the typhoon identification mean average precision (mAP) reaching 87.8%, the mean square error (MSE) of typhoon center localization at 0.115, and the detection speed at 416.7 frames per second (FPS). The model’s rapid inference capability and efficient performance provide technical support for typhoon monitoring, which is expected to improve the timeliness and accuracy of typhoon warnings in practical applications.