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Breast cancer (BC) is the most frequent malignant cancer diagnosis and is a primary factor for cancer deaths in women. The clinical subtypes of BC include estrogen receptor (ER) positive, progesterone receptor (PR) positive, human epidermal growth factor receptor 2 (HER2) positive, and triple-negative BC (TNBC). Based on the stages and subtypes of BC, various treatment methods are available with variations in the rates of progression-free disease and overall survival of patients. However, the treatment of BC still faces challenges, particularly in terms of drug resistance and recurrence. The study of epigenetics has provided new ideas for treating BC. Targeting aberrant epigenetic factors with inhibitors represents a promising anticancer strategy. The KDM5 family includes four members, KDM5A, KDM5B, KDM5C, and KDMD, all of which are Jumonji C domain-containing histone H3K4me2/3 demethylases. KDM5 proteins have been extensively studied in BC, where they are involved in suppressing or promoting BC depending on their specific upstream and downstream pathways. Several KDM5 inhibitors have shown potent BC inhibitory activity in vitro and in vivo, but challenges still exist in developing KDM5 inhibitors. In this review, we introduce the subtypes of BC and their current therapeutic options, summarize KDM5 family context-specific functions in the pathobiology of BC, and discuss the outlook and pitfalls of KDM5 inhibitors in this disease.

Acute kidney injury (AKI) is increasing in prevalence and causes a global health burden. AKI is associated with significant mortality and can subsequently develop into chronic kidney disease (CKD). The kidney is one of the most energy-demanding organs in the human body and has a role in active solute transport, maintenance of electrochemical gradients, and regulation of fluid balance. Renal proximal tubular cells (PTCs) are the primary segment to reabsorb and secrete various solutes and take part in AKI initiation. Mitochondria, which are enriched in PTCs, are the main source of adenosine triphosphate (ATP) in cells as generated through oxidative phosphorylation. Mitochondrial dysfunction may result in reactive oxygen species (ROS) production, impaired biogenesis, oxidative stress multiplication, and ultimately leading to cell death. Even though mitochondrial damage and malfunction have been observed in both human kidney disease and animal models of AKI and CKD, the mechanism of mitochondrial signaling in PTC for AKI-to-CKD transition remains unknown. We review the recent findings of the development of AKI-to-CKD transition with a focus on mitochondrial disorders in PTCs. We propose that mitochondrial signaling is a key mechanism of the progression of AKI to CKD and potential targeting for treatment.

Leukocyte cell-derived chemotaxin-2 (LECT2, also named ChM-II), initially identified as a chemokine mediating neutrophil migration, is a multifunctional secreted factor involved in diverse physiological and pathological processes. The high sequence similarity of LECT2 among different vertebrates makes it possible to explore its functions by using comparative biology. LECT2 is associated with many immune processes and immune-related diseases via its binding to cell surface receptors such as CD209a, Tie1, and Met in various cell types. In addition, the misfolding LECT2 leads to the amyloidosis of several crucial tissues (kidney, liver, and lung, etc.) by inducing the formation of insoluble fibrils. However, the mechanisms of LECT2-mediated diverse immune pathogenic conditions in various tissues remain to be fully elucidated due to the functional and signaling heterogeneity. Here, we provide a comprehensive summary of the structure, the “double-edged sword” function, and the extensive signaling pathways of LECT2 in immune diseases, as well as the potential applications of LECT2 in therapeutic interventions in preclinical or clinical trials. This review provides an integrated perspective on the current understanding of how LECT2 is associated with immune diseases, with the aim of facilitating the development of drugs or probes against LECT2 for the theranostics of immune-related diseases.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high mortality and drug resistance and no targeted drug available at present. Compound 4, a staurosporine alkaloid derived from Streptomyces sp. NBU3142 in a marine sponge, exhibits potent anti-TNBC activity. This research investigated its impact on MDA-MB-231 cells and their drug-resistant variants. The findings highlighted that compound 4 inhibits breast cancer cell migration, induces apoptosis, arrests the cell cycle, and promotes cellular senescence in both regular and paclitaxel-resistant MDA-MB-231 cells. Additionally, this study identified mitogen-activated protein kinase kinase kinase 11 (MAP3K11) as a target of compound 4, implicating its role in breast tumorigenesis by affecting cell proliferation, migration, and cell cycle progression.

Long-term memory (LTM) formation depends on the conversed cAMP response element-binding protein (CREB)-dependent gene transcription followed by de novo protein synthesis. Thirsty fruit flies can be trained to associate an odor with water reward to form water-reward LTM (wLTM), which can last for over 24 hours without a significant decline. The role of de novo protein synthesis and CREB-regulated gene expression changes in neural circuits that contribute to wLTM remains unclear. Here, we show that acute inhibition of protein synthesis in the mushroom body (MB) [alpha][beta] or [gamma] neurons during memory formation using a cold-sensitive ribosome-inactivating toxin disrupts wLTM. Furthermore, adult stage-specific expression of dCREB2b in [alpha][beta] or [gamma] neurons also disrupts wLTM. The MB [alpha][beta] and [gamma] neurons can be further classified into five different neuronal subsets including [alpha][beta] core, [alpha][beta] surface, [alpha][beta] posterior, [gamma] main, and [gamma] dorsal. We observed that the neurotransmission from [alpha][beta] surface and [gamma] dorsal neuron subsets is required for wLTM retrieval, whereas the [alpha][beta] core, [alpha][beta] posterior, and [gamma] main are dispensable. Adult stage-specific expression of dCREB2b in [alpha][beta] surface and [gamma] dorsal neurons inhibits wLTM formation. In vivo calcium imaging revealed that [alpha][beta] surface and [gamma] dorsal neurons form wLTM traces with different dynamic properties, and these memory traces are abolished by dCREB2b expression. Our results suggest that a small population of neurons within the MB circuits support long-term storage of water-reward memory in Drosophila.

Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced from the ISIC platform and CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks and vision transformers, were fine-tuned. The three best-performing models were combined into an ensemble model, which underwent multiple random experiments to ensure stability. To improve diagnostic accuracy and reduce false negatives, a two-stage classification strategy was employed: a three-class model for initial classification, followed by a binary model for secondary prediction of benign cases. Results: In the ISIC dataset, the false negative rate for malignant lesions was significantly reduced, and the number of malignant cases misclassified as benign dropped from 124 to 45. In the CSMUH dataset, false negatives for malignant cases were completely eliminated, reducing the number of misclassified malignant cases to zero, resulting in a notable improvement in diagnostic precision and a reduction in the false negative rate. Conclusions: Through the proposed method, the study demonstrated clear success in both datasets. First, a three-class AI model can assist doctors in distinguishing between melanoma patients who require urgent treatment, non-melanoma skin cancer patients who can be treated later, and benign cases that do not require intervention. Subsequently, a two-stage classification strategy effectively reduces false negatives in malignant lesions. These findings highlight the potential of AI technology in skin cancer diagnosis, particularly in resource-limited medical settings, where it could become a valuable clinical tool to improve diagnostic accuracy, reduce skin cancer mortality, and reduce healthcare costs.

To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

Chronic kidney disease (CKD), as a worldwide threat to public health, is a key determinant of poor health outcomes, but the severity of the problem is probably not fully appreciated [...].Chronic kidney disease (CKD), as a worldwide threat to public health, is a key determinant of poor health outcomes, but the severity of the problem is probably not fully appreciated [...].

The logographic nature of the Chinese writing system creates a huge hurdle for Chinese as a foreign language (CFL) learners. Existing literature suggests that radical knowledge facilitates character learning. In this project, the authors selected 48 compound characters in eight radical groups and examined how grouping characters based on their radicals affected the form, sound, and meaning representations of characters and radical knowledge development. They found that for beginning learners, learning radical-sharing characters in groups consistently led to better recall and better radical generalization than learning in distribution. For intermediate level learners, the grouping factor did not lead to significant differences, while participants in both conditions made improvement in radical perception and radical semantic awareness generalization. The authors concluded that there is a benefit to presenting learners with recurring radicals in compound characters in groups in character learning and in the autonomous generalization of radical knowledge. They also noted the differences between beginning and intermediate learners in their character perception and learning, and put forward implications for CFL pedagogy. (Verlag, adapt.).

The balance between ubiquitination and deubiquitination is instrumental in the regulation of protein stability and maintenance of cellular homeostasis. The deubiquitinating enzyme, ubiquitin-specific protease 36 (USP36), a member of the USP family, plays a crucial role in this dynamic equilibrium by hydrolyzing and removing ubiquitin chains from target proteins and facilitating their proteasome-dependent degradation. The multifaceted functions of USP36 have been implicated in various disease processes, including cancer, infections, and inflammation, via the modulation of numerous cellular events, including gene transcription regulation, cell cycle regulation, immune responses, signal transduction, tumor growth, and inflammatory processes. The objective of this review is to provide a comprehensive summary of the current state of research on the roles of USP36 in different pathological conditions. By synthesizing the findings from previous studies, we have aimed to increase our understanding of the mechanisms underlying these diseases and identify potential therapeutic targets for their treatment.