Pancreatic cancer is a highly lethal disease characterized by rapid onset, aggressive progression, and limited treatment options. The involvement of FoxM1 in the TGF-β/Smad signaling pathway has been linked to pancreatic cancer progression; however, the mechanisms behind the cooperative regulation of TGF-β signaling by FoxM1 and Smad4 remain poorly understood. In this study, we utilized molecular cytology techniques, animal models, and human pancreatic cancer tissues to investigate the role of FoxM1 in Smad4 stabilization and its regulation of TGF-β signaling. Our findings reveal that FoxM1 inhibits ubiquitin-proteasome-mediated degradation of Smad4, resulting in its stabilization. Once translocated into the nucleus, Smad4 binds to the FoxM1 promoter region, inducing FoxM1 expression and forming a positive feedback loop. Furthermore, we observed significantly higher expression of this feedback loop in pancreatic cancer tissues compared to adjacent normal tissues, with markedly elevated levels in poorly differentiated tissues compared to well-differentiated ones. Therefore, the loop aberrantly activates the TGF-β pathway, driving pancreatic cancer progression. These findings uncover a novel mechanism of TGF-β pathway activation and provide potential new targets for the prevention and treatment of pancreatic cancer. This study elucidates that FoxM1 functions to impede the ubiquitin proteasome-mediated degradation of Smad4, consequently stabilizing it. Following nuclear translocation, Smad4 binds to the FoxM1 promoter region, initiating FoxM1 expression and establishing a positive feedback loop. This loop plays a pivotal role in promoting pancreatic cancer development and migration by aberrantly activating the TGF-β pathway.

Pyroptosis, a recently identified cellular demise regulated by gasdermin family proteins, is emerging as a promising avenue in cancer immunotherapy. However, the realm of light-controlled pyroptosis in cancer cells remains largely unexplored. In this study, we took a deliberate approach devoid of any chemical alterations to develop a novel photosensitizer called “pharmaceutical-dots (pharm-dots)” by combining nonemissive polymers (Poly (lactic-co-glycolic acid), PLGA) with nonfluorescent invisible molecules like curcumin, berberine, oridonin into PLGA nanoparticles (PLGA-NPs). Initially, our research commenced with a comprehensive mechanistic comparison study, consolidating fragmented information on optical mechanisms. This exploration revealed that surface passivation atoms play a dominant role in governing the fluorescence emission of PLGA-NPs. Remarkably, these new luminophores, composed of two non-inherently luminous components, exhibit a remarkable synergistic boost in photoluminescence through a “0 + 0 > 2” phenomenon. In-depth investigations uncovered that these luminous PLGA-NPs, capable of generating 1O2, induce pyroptosis under photoexcitation conditions through the caspase-3/gasdermin E (GSDME) pathway. Simultaneously, our findings highlight PLGA-NPs as a novel optical formulation suitable for imaging, displaying substantial biological activity when paired with photoirradiation. This discovery holds the potential to facilitate the application of light-controlled pyroptosis in antitumor therapy, marking a promising stride toward innovative approaches in cancer treatment.

To fully leverage contextual information for the precise segmentation of objects in remote sensing images, while addressing the challenges associated with substantial object scale variations and complex backgrounds, we propose a lightweight cross-domain coupling network (LCCN) tailored for the semantic segmentation of high-resolution remote sensing images (HRSIs). To standardize feature selection and fusion procedures, the LCCN incorporates an innovative encoder-coupler-decoder architecture designed to facilitate key feature extraction and optimization. A cross-domain coupling module (CDCM) is created in the coupler to conduct preliminary feature screening of spaces and dimensions based on channel and spatial attention. It performs multiscale feature extraction and global information modeling through feature grouping and loop aggregation. This helps to extract key features while reducing the computational overhead. To further decrease the interference from complex backgrounds, a secondary optimization of the key features is carried out: a lightweight fully featured mapping attention module (LFMAM) is designed within the decoder. LFMAM utilizes an interactive fusion strategy and a lightweight linear self-attention (SA) mechanism, comprehensively considering all interactions between global-to-global, global-to-local, local-to-local, and local-to-global processes. By capturing the effective correlations and variances among features to further refine them, it enables the network to further optimize the crucial information while ensuring lightweight. We have conducted extensive comparison experiments and ablation experiments on the ISPRS Vaihingen and ISPRS Potsdam datasets. The extensive experimental results demonstrate that our proposed LCCN can obtain superior performance compared to other advanced semantic segmentation models.

Applications of remote sensing images in both defense and civilian sectors have spurred substantial research interest. In the field of remote sensing, object detection confronts challenges such as complex backgrounds, scale diversity, and the presence of dense small objects. To address these issues, we propose an improved deep learning-based model, the Global Multi-scale Fusion Self-calibration Network, which is expected to contribute to alleviating the challenges. It consists of three main components: the hierarchical feature aggregation backbone, which uses improved modules such as the receptive field context-aware feature extraction module, the global information acquisition module, and the simple parameter-free attention module to extract key features and minimize the background interference. To couple multi-scale features, we enhanced the fusing component and designed the multi-scale enhanced pyramid structure integrating the proposed new modules. During the detection phase, especially when focusing on small object detection, we designed a novel convolutional attention feature fusion head. This head is constructed to integrate local and global branches for feature extraction by leveraging channel shuffling and multi-head attention mechanisms for efficient and accurate detection. Experiments on the Detection in Optical Remote Sensing Images (DIOR), Northwestern Polytechnical University Very High-Resolution-10 (NWPU VHR-10), remote sensing object detection (RSOD), and DOTAv1.0 data sets show that our method achieves mAP50(mean average precision at 50% intersection over union) of 69.7%, 91.3%, 94.2%, and 70.0%, respectively, Aoutperforming existing comparative methods. The proposed network is expected to provide new perspectives for remote sensing tasks and possible solutions for relevant applications in the image domain.

The study aimed to explore the association between the ZJU (Zhejiang University) index and the prevalence of kidney stones in Chinese adults. Electronic health records of individuals undergoing routine physical examinations at Wuhu Second People’s Hospital between January 2021 and June 2024 were retrospectively analyzed. Participants were divided into kidney stone recurrence and non-recurrence groups. Data on biochemical parameters, hypertension, and diabetes history were collected, and group differences were assessed using the chi-square test or Kruskal-Wallis rank-sum test. Logistic regression, propensity score matching, and dose-response curve modeling were used to evaluate the relationship between the ZJU index and kidney stone prevalence. Among 5,104 participants aged over 18 years, 462 were diagnosed with kidney stones. After adjusting for confounders, a higher ZJU index was identified as an independent risk factor for kidney stone prevalence (odds ratio [OR] = 1.05, 95

Background: Radiotherapy is a major treatment option for non-small cell lung cancer (NSCLC); however, irradiated tumor cells can damage non-irradiated cells through radiation-induced bystander effects (RIBE), which can affect the therapeutic efficacy. The study aimed to investigate the mechanism underlying RIBS and the protective effects of vanillic acid (VA) on human bone marrow mesenchymal stem cells (BMSCs). Methods: We established two irradiation models to investigate RIBE. First, we established the A549 cell irradiation model alone, and tested the expression of cathepsin B (CTSB) and transforming growth factor- beta 1 (TGF-(31) by western blot and immunofluorescence staining. Next, we established a co-culture model of A549 cells and BMSCs. After 2 Gy X-rays irradiation of A549 cells, BMSCs cell viability was detected using Cell Counting Kit-8 (CCK-8), reactive oxygen species (ROS) level was detected using flow cytometry, and CTSB, TGF-(3 type I receptor (TGF(3RI), p62 (sequestosome 1), BECLIN1, microtubule-associated protein light chain 3 (LC3), etc., were detected using western blot. Phosphorylated histone H2AX (pH2AX), CTSB, lysosomal-associated membrane protein 1 (LAMP1), and TGF(3RI expression levels were detected by immunofluorescence staining. Molecular docking and molecular dynamics simulation, and a CCK-8 assay were used to screen for molecules from Astragalus membranceus that inhibited TGF(3RI activity, to protect BMSCs from RIBE. Lastly, we validated VA activity in vivo. Results: In this study, 2 Gy X-rays radiation on A549 cells was found to result in an increase in CTSB and TGF-(31, while CTSB inhibitor CA074Me reduced the radiation-induced TGF-(31 increase. In the co- culture model of A549 cells and BMSCs, 2 Gy X-rays radiation on A549 cells resulted in increase of TGF(3RI expression in BMSCs, which led to an increase in ROS, and resulted in DNA damage and the inhibition of BMSCs proliferation. The small molecule VA from Astragalus membranaceus inhibited TGF(3RI activity and restored the proliferation of BMSCs. Conclusions: Our findings reveal that radiation causes CTSB overexpression in A549 cells, which further promotes TGF-(31 expression. TGF-(31 activates its receptors on BMSCs to increase ROS levels in BMSCs, while reducing lysosomal double-chain CTSB (dc-CTSB), which results in decreased BMSCs autophagy and an inability to clear ROS, and thus inhibits proliferation. VA inhibits TGF(3RI to restore the proliferation of BMSCs, and in vivo, VA can enhance the killing effect of radiation on tumors.

Sleep disorders, such as insomnia, sleep terrors, sleep apnea, and sleep-wake schedule disorders, pose a significant public health challenge worldwide, yet their underlying pathophysiological mechanisms are not fully understood. Lipids, beyond being structural membrane components, actively regulate neuroinflammation, circadian rhythms, and neuronal signaling, all implicated in sleep disorder pathophysiology. This study employed two-sample Mendelian randomization (TSMR) to explore the causal relationships between the lipidome and these sleep disorders, analyzing a comprehensive GWAS dataset with 179 lipid species. Heterogeneity and pleiotropy were assessed using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test, and sensitivity analyses were done to check the influence of individual single nucleotide polymorphisms. The analysis revealed significant causal associations between specific lipid species and sleep disorders. For insomnia, several lipid species, including sterol ester (27:1/20:3), ceramides (d40:1, d42:1, d42:2), phosphatidylcholine (15:0_18:2), and sphingomyelin (d40:1), demonstrated potential protective effects (OR < 1). In contrast, for sleep terrors, phosphatidylcholines (16:0_22:4, O-16:0_16:1, O-16:0_18:2) and sphingomyelin (d34:0) were associated with increased risk (OR > 1), while triacylglycerol (46:2) showed a protective effect. For sleep apnea, cholesterol levels exhibited a protective effect (OR = 0.96), whereas specific phosphatidylcholines (16:1_18:0) and triacylglycerols (52:2, 52:3, 58:8) were associated with increased risk. Circadian rhythm disturbances were influenced by various lipid species, with diacylglycerol (18:1_18:3) and phosphatidylcholine (16:1_18:0) posing risk-increasing effects, while phosphatidylethanolamines (O-16:1_20:4, O-18:1_20:4) demonstrated protective roles. This study elucidates the complex interplay between lipid metabolism and sleep regulation, identifying specific lipid species that may serve as potential biomarkers or therapeutic targets for sleep disorders.

BackgroundKidney stones are a common benign condition of the urinary system, characterized by high incidence and recurrence rates. Our previous studies revealed an increased prevalence of kidney stones among diabetic patients, suggesting potential underlying mechanisms linking these two conditions. This study aims to identify key genes, pathways, and immune cells that may connect diabetes and kidney stones.MethodsWe conducted bulk transcriptome differential analysis using our sequencing data, in conjunction with the AS dataset (GSE231569). After eliminating batch effects, we performed differential expression analysis and applied weighted gene co-expression network analysis (WGCNA) to investigate associations with 18 forms of cell death. Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. Functional enrichment analysis was performed, alongside the construction of protein-protein interaction (PPI) networks and transcription factor (TF)-gene interaction networks.ResultsFor the first time, bioinformatics tools were utilized to investigate the close genetic relationship between diabetes and kidney stones. Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. The diagnostic potential of these biomarkers was validated in both training and test datasets.ConclusionWe identified three biomarkers—S100A4, ARPC1B, and CEBPD—that may play critical roles in the shared pathogenesis of diabetes and kidney stones. These findings open new avenues for the diagnosis and treatment of these comorbid conditions.

The involvement of B lymphocytes in the pathogenesis of rheumatoid arthritis (RA) is well-established, with their early and aberrant activation being a crucial factor. However, the mechanisms underlying this abnormal activation in RA remain incompletely understood. In this study, we identified a significant reduction in MAPK4 expression in both RA patients and collagen-induced arthritis (CIA) mouse models, which correlates with disrupted B cell activation. Using MAPK4 knockout (KO) mice, we demonstrated that MAPK4 intrinsically promotes the differentiation of marginal zone (MZ) B cells. Loss of MAPK4 in KO mice enhances proximal BCR signaling and activates the PI3K-AKT-mTOR pathway, leading to heightened B cell proliferation. Notably, B cells from MAPK4 KO mice produce significantly higher levels of IL-6, a key pro-inflammatory cytokine in RA. Furthermore, MAPK4 KO mice exhibit impaired T cell-independent humoral immune responses. Mechanistically, MAPK4 inhibits the activation of the PI3K signaling pathway in B cells by activating the IRF4-SHIP1 pathway. Treatment with the MAPK4 agonist Vacquinol-1 enhances MZ B cell differentiation in WT mice and reduces IL-6 secretion in CIA mouse models. In summary, this study reveals the diverse roles of MAPK4 in regulating of B cell functions, with potential implications for developing therapeutic strategies for RA and related autoimmune diseases.

Acute pancreatitis (AP) is a common gastrointestinal disease that can cause systemic inflammation and lead to dysfunction of multiple organs. In pancreatitis, ferroptosis promotes disease progression and organ damage by regulating oxidative stress and inflammatory response. Here, ferroptosis was significantly elevated in the AP rat model and participated in regulating disease progression. Meanwhile, the expression of S100A11 was significantly upregulated in the pancreatic tissue of rats with AP, as determined by tandem mass spectrometry (TMT) proteomics. This phenomenon was also confirmed in pancreatic acinar cells. To reveal whether S100A11 participates in regulating ferroptosis, an S100A11 knockdown lentivirus was transfected into caerulein-treated pancreatic acinar cells AR42J. Functional results revealed that S100A11 knockdown significantly increased cell viability and GSH levels, while decreasing reactive oxygen species (ROS), lipid ROS, and Fe2+ levels in pancreatic acinar cells compared to the control group. In vivo, S100A11 knockdown via adeno-associated virus inhibited caerulein-induced ferroptosis. These findings suggest that S100A11 promotes AP by upregulating ferroptosis, which exacerbates oxidative stress and inflammation in pancreatic tissue.

Complicated background and small object issues are the primary challenges currently confronting remote sensing object detection (RSOD). To tackle the aforementioned issues in RSOD, we propose an axis-squeeze and multirouting scale-adaptive fusion network (AMSFNet). In this network, a unidirectional multiscale coupling module (UMCM) is designed to enhance the object feature extraction capabilities and improve the accuracy of small object detection. Furthermore, utilizing the axis-squeeze and detail enhancement approach, we construct a squeeze-enhanced axial attention module that specifically targets background disruption, which can efficiently aggregate global and local information and reduce the impact of intricate background noise on the object. To improve the synergy between features of varying sizes and ensure that the model can allow for a compromise between detecting small and large targets simultaneously, a multirouting SAF approach is proposed, which combines fine-grained features with high-level features using multiple feed-forward connections. The proposed approach has been shown to be efficient by ablation and comparison experiments performed on three public benchmark datasets: RSOD, VisDrone, and DIOR. AMSFNet achieves a mean average precision (mAP) of 95.4%, 88.7%, and 36.6% on the RSOD, DIOR, and VisDrone datasets, respectively.

Prostate cancer is the second most common malignancy among men worldwide, with its incidence and mortality rates steadily increasing. Although androgen deprivation therapy (ADT) combined with androgen receptor inhibitors has shown significant efficacy in treating prostate cancer, resistance to treatment remains a major challenge, particularly in patients with metastatic prostate cancer. Reactive oxygen species (ROS), a class of highly reactive molecules, can induce oxidative stress within cells, thereby affecting cellular survival and function. In cancer cells, elevated ROS levels not only promote proliferation and invasion but also contribute to the malignancy of tumors by modulating the tumor microenvironment, enhancing angiogenesis, and facilitating extracellular matrix remodeling. This review systematically explores the pathways of ROS generation in prostate cancer, their interaction with the androgen receptor signaling pathway, and the role of external factors such as obesity and aging in promoting ROS production. The findings highlight that ROS drive prostate cancer progression through multiple mechanisms, including altering the tumor microenvironment, activating the unfolded protein response (UPR), and regulating miRNA expression. By providing a comprehensive analysis of ROS-mediated mechanisms in prostate cancer, this review offers new insights into the development of targeted antioxidant therapeutic strategies.