An ellagitannin-derived metabolite Urolithin A (UA) has emerged as a potential therapeutic agent for metabolic disorders due to its antioxidant, anti-inflammatory, and mitochondrial function-improving properties, but its efficacy in protecting against ER stress remains underexplored. The endoplasmic reticulum (ER) is a cellular organelle involved in protein folding, lipid synthesis, and calcium regulation. Perturbations in these functions can lead to ER stress, which contributes to the development and progression of metabolic disorders such as metabolic-associated fatty liver disease (MAFLD). In this study, we identified a novel target protein of UA and elucidated its mechanism for alleviating palmitic acid (PA)-induced ER stress. CETSA-LC-MS/MS analysis revealed that UA binds directly to the sarcoplasmic/endoplasmic reticulum Ca2+-ATPase (SERCA), an important regulator of calcium homeostasis in mitochondria-associated ER membranes (MAMs). As an agonist of SERCA, UA attenuates abnormal calcium fluctuations and ER stress in PA-treated liver cells, thereby contributing to cell survival. In addition, UA inhibits PA-induced mitochondrial ROS and lipid accumulation, further supporting its protective role. The lack of UA activity in SERCA knockdown cells suggests that UA regulates cellular homeostasis through its interaction with SERCA. Collectively, our results demonstrate that UA protects against PA-induced ER stress and enhances cell survival by regulating calcium homeostasis in MAM through SERCA. This study highlights the potential of UA as a therapeutic agent for metabolic disorders associated with ER stress.

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in early-stage of cancer, from whom all survived cancer. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually that leads to increase the risk of wrong decisions for cancer detection. Thus, utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and availability of datasets obtained from various imaging modalities have opened an opportunity to surpass limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Background: Evaluation of nutrition programs is essential to guarantee the effectiveness of community-based management of acute malnutrition (CMAM). Methods: The Rainbow Project Supplementary Feeding Programs (SFPs) in Zambia were evaluated between years 2015-17, following implementation of new recommendations based on previous evaluations (years 2012-14). Outcomes of the program were compared with International Standards and with those of 2012-14. Cox proportional risk regression analysis was performed to identify predictors of mortality and defaulting. Results: Data for 900 under age 5 years malnourished children (48.8% male; mean age 19.7months ±9.9) were analyzed. Rainbow 2015-17 program outcomes met International Standards, for general malnutrition or stratified moderate acute malnutrition (MAM) and severe acute malnutrition (SAM). When comparing with 2012-14 outcomes, better performance was noted: mortality rates were reduced by half (5.6% vs 3.1%, p = 0.01; for SAM: 12.4% vs 6.7%, p = 0.006), with significant improvement in average weight gain and mean length of stay (p<0.001), and increased awareness of HIV status (+30%; p < 0.001). HIV infection (5.5; 1.9–15.9), WAZ < −3 at baseline (4.6; 1.3–16.1) and kwashiorkor (3.5; 1.2–9.5) remained the major predictors of mortality. Conclusion: The effectiveness of the Rainbow SFPs for child malnutrition treatment and prevention in Zambia has significantly improved after evaluation and implementation activities, with impressive outcomes which resulted in a 50% reduction in mortality.