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Purpose In recent years, research interest in the potential link between female infertility (FI) and gynecological cancer (GC), including ovarian cancer (OC), endometrial cancer (EC), cervical cancer (CC), and breast cancer (BC), has grown, yet findings remain inconclusive. This study aims to explore the causal relationship between FI and GC using bidirectional two-sample Mendelian randomization (MR) analyses, thereby informing future strategies for FI and GC prevention. Methods We utilized SNPs identified from genome-wide association studies (GWAS) on FI and GC. The inverse variance weighted (IVW) method served as the primary approach to assess the causal association between FI and GC. Additionally, five other MR methods—Weighted median, Weighted mode, MR-Egger, Simple mode, and Robust-Adjusted Profile Score—were employed to enhance result robustness and credibility. Results In the forward MR analysis, our IVW results indicated no significant association between FI and GC (FI-BC: OR = 0.95, 95% CI: 0.83–1.09, P  = 0.47, P-FDR = 0.775; FI-OC: OR = 1.01, 95% CI: 0.84–1.24, P  = 0.789, P-FDR = 0.896; FI-CC: OR = 0.80, 95% CI: 0.61–1.06, P  = 0.118, P-FDR = 0.775; FI-EC: OR = 1.07, 95% CI: 0.88–1.30, P  = 0.490, P-FDR = 0.775).In the reverse MR analysis, we found a marginal association between BC and FI. However, after adjusting for multiple testing using the FDR method, no significant causal relationship was found between BC and FI, suggesting a marginal association (OR = 1.054, 95% CI: 1.001–1.108, P  = 0.043, P-FDR = 0.331). For other cancers, no significant causal relationships were observed between OC, CC and EC with FI(OC-FI: OR = 1.043, 95% CI: 0.999–1.087, P  = 0.051, P-FDR = 0.331;CC-FI: OR = 0.992, 95% CI: 0.956–1.028, P  = 0.654, P-FDR = 0.836; EC-FI: OR = 1.006, 95% CI: 0.956–1.055, P  = 0.809, P-FDR = 0.885). Conclusions Our study found no significant causal relationship between FI and GC. However, a potential marginal association between BC and FI was observed. These findings underscore the need for further research to confirm this association and emphasize the importance of reproductive protection for young breast cancer patients to preserve fertility.

Background/Aims: Recently, microRNAs (miRNA) have been identified as novel regulators in Chondrosarcoma (CHS). This study was aimed to identify the roles of miR-129-5p-5p in regulation of SOX4 and Wnt/β-catenin signaling pathway, as well as cell proliferation and apoptosis in chondrosarcomas. Materials and Methods: Tissue samples were obtained from chondrosarcoma patients. Immunohistochemistry, real-time quantitative RT-PCR (RT-qPCR) and western blot analysis were performed to detect the expressions of miR-129-5p and SOX4. Luciferase assay was conducted to confirm that miR-129-5p directly targeted SOX4 mRNA. Manipulations of miR-129-5p and SOX4 expression were achieved through cell transfection. Cell proliferation, migration and apoptosis were evaluated by CCK-8 assay, colony forming assay, wound healing assay and flow cytometry in vitro. For in vivo experiment, the tumor xenograft model was established to evaluate the effects of miR-129-5p and SOX4 on chondrosarcomas. Results: The expression of miR-129-5p was significantly down-regulated in chondrosarcoma tissues as well as cells in comparison with normal ones, while SOX4 was over-activated. Further studies suggested that miR-129-5p suppressed cell proliferation, migration and promoted apoptosis by inhibiting SOX4 and Wnt/β-catenin pathway. Conclusion: MiR-129-5p inhibits the Wnt/β-catenin signaling pathway by targeting SOX4 and further suppresses cell proliferation, migration and promotes apoptosis in chondrosarcomas.

Methane (CH4) is a potent greenhouse gas, with livestock rumination being a significant contributor to global emissions. This study developed a real-time monitoring system utilizing Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) to simultaneously track rumination behavior and CH4 concentrations in cattle breath. By optimizing the off-axis integrated cavity structure and implementing a specialized environmental control system, we enhanced stability and detection accuracy, achieving a rapid 3 s response time to dynamic concentration changes. Laboratory stability tests and Allan deviation analysis demonstrated a minimum detection limit of 0.07 ppm. Continuous field monitoring of Simmental cattle revealed a daily methane production of approximately 311.83 L. The emission rates exhibited a distinct double-peak pattern heavily influenced by feeding schedules. Furthermore, a positive correlation was observed between the time elapsed post feeding and both the frequency and intensity of methane emission peaks. This method enables highly dynamic, stable, long-term monitoring of greenhouse gas emissions from ruminants, providing a robust tool for quantifying emissions and informing scientific feeding practices.

To facilitate the effective assessment of respiratory entropy during poultry breeding, a novel oxygen (O2) and carbon dioxide (CO2) sensor was developed based on the off−axis integrated cavity output spectroscopy technique, featuring effective absorption optical paths of 15.5 m and 8.5 m, respectively. The sensor employs integrated environmental control technology, substantially enhancing detection precision. To improve the instrument’s response speed, the miniaturization of the cavity and structural optimization were implemented, achieving a rapid response time of merely 6.22 s, addressing the stringent requirements for quick responsiveness in poultry respiration thermometry research. A signal processing model tailored for on−site applications was designed, boosting the system’s signal−to−noise ratio by 4.7 times under complex environmental noise conditions. Utilizing Allan variance analysis, the sensor’s detection limits for O2 and CO2 were ascertained to be 2.9 ppm and 7.4 ppb, respectively. A 24−h field application test conducted in Gongzhuling demonstrated that the sensor’s results align with the respiratory characteristics of poultry under normal physiological conditions, validating its extensive potential for application in respiratory analysis, environmental monitoring, and industrial sectors.

Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and simulation programs are typically involved, desiring an integrated platform that can effectively manage the collaboration of such software resources and the execution of the underlying simulation workflow for efficiency purpose. Such a platform could be practically built with a scientific computing workflow engine that focuses on the automatic scheduling and execution of a batch of interrelated computing tasks. In this work, the main procedures on construction of a specialized integrated simulation platform for material research based on a general purpose scientific computing workflow engine named HSWAP is introduced in detail, and its application to molecule screening process of energetic materials is demonstrated. Due to the flexibility and the extensibility of the platform, the work could be handily extended to the screening of other materials such as protein to find optimized protein structures or high entropy alloys to find the best configuration of component contents, as well as other application scenarios such as geometry optimizations of complex structures.

To overcome the disadvantages of small and random samples in static detection, this paper presents a study on dynamic measurements of solid particles in jet fuel using large samples. In this paper, the Mie scattering theory and Lambert-Beer law are used to analyze the scattering characteristics of copper particles in jet fuel. We have presented a prototype for multi-angle scattered and transmitted light intensity measurements of particle swarms in jet fuel which is used to test the scattering characteristics of the jet fuel mixture with 0.5–10 μm particle sizes and 0–1 mg/L concentrations of copper particles. The vortex flow rate was converted to an equivalent pipe flow rate using the equivalent flow method. Tests were conducted at equivalent flow rates of 187, 250 and 310 L/min. Through numerical calculations and experiments, it has been discovered that the intensity of the scattering signal decreases as the scattering angle increases. Meanwhile, both the scattered light intensity and transmitted light intensity would vary with the particle size and mass concentration. Finally, the relationship equation between light intensity and particle parameters has also been summarized in the prototype based on the experimental results, which proves its detection capability.

The successful recognition of pathogen-associated molecular patterns (PAMPs) as a danger signal is crucial for plants to fend off numerous potential pathogenic microbes. The signal is relayed through mitogen-activated protein kinase (MPK) cascades to activate defenses. Here, we show that the Pseudomonas syringae type III effector HopF2 can interact with Arabidopsis thaliana MAP KINASE KINASE5 (MKK5) and likely other MKKs to inhibit MPKs and PAMP-triggered immunity. Inhibition of PAMP-induced MPK phosphorylation was observed when HopF2 was delivered naturally by the bacterial type III secretion system. In addition, HopF2 Arg-71 and Asp-175 residues that are required for the interaction with MKK5 are also necessary for blocking MAP kinase activation, PAMP-triggered defenses, and virulence function in plants. HopF2 can inactivate MKK5 and ADP-ribosylate the C terminus of MKK5 in vitro. Arg-313 of MKK5 is required for ADP-ribosylation by HopF2 and MKK5 function in the plant cell. Together, these results indicate that MKKs are important targets of HopF2.

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks.

This study aimed to explore the diagnostic value of the two cytology techniques, including liquid-based cytology of mammary ductal lavage fluid and nipple discharge smear cytology, in the intraductal lesions in patients with pathological nipple discharge (PND). This retrospective analysis included 119 patients with PND who underwent surgical treatment. At the same time, they all underwent fiberoptic ductoscopy (FDS), nipple discharge smear cytology and liquid-based cytology of ductal lavage fluid before surgery. With postoperative pathological diagnosis as the gold standard, we compared the clinical diagnostic efficacy of the two cytology techniques applied independently and combined with FDS to evaluate their diagnostic value in intraductal lesions. Finally, the receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the diagnostic value of each examination method. There were 22 breast malignant tumors, 75 intraductal papillomas and 22 non-tumorous lesions among the 119 PND patients. The cell types of liquid-based cytology of ductal lavage fluid was significantly more abundant than that of smear cytology, and the detection rate of tumor cells, atypia cells and atypical hyperplasia cells was significantly increased. The diagnostic accuracy and sensitivity of liquid-based cytology of ductal lavage fluid were significantly higher than that of smear cytology ( P  < 0.05). At the same time, the accuracy of liquid-based cytology was superior to that of smear cytology when combined with FDS ( P  < 0.05), and the diagnostic efficiency was excellent. FDS combined with liquid-based cytology of ductal lavage fluid was more effective than the other methods (AUC = 0.8182). When diagnosing the intraductal space-occupying lesions in PND patients, liquid-based cytology of ductal lavage fluid can obtain more abundant intraductal shed cells. This advantage can compensate for the limitation of FDS for diagnosing terminal ductal unit lesions and significantly improve the early diagnosis rate of intraductal lesions, especially intraductal malignant tumors.

Background Identification and validation of potential biomarkers could facilitate the identification of pulmonary arterial hypertension (PAH) and thus aid to study their roles in the disease process. Methods We used the isobaric tag for relative and absolute quantitation approaches to compare the protein profiles between the serum of PAH patients and the controls. Bioinformatics analyses and enzyme-linked immunosorbent assay (ELISA) identification of PAH patients and the controls were performed to identify the potential biomarkers. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these potential biomarkers. Mendelian randomization (MR) analysis further clarified the relationship between the potential biomarkers and PAH. Additionally, the expression levels of the potential biomarkers were further validated in two PAH animal models (monocrotaline-PH and Sugen5416 plus hypoxia-PH) using ELISA and reverse transcription-quantitative PCR (RT-qPCR). Results We identified significant changes in three proteins including heparanase (HPSE), gelsolin (GSN), and hepatocyte growth factor activator (HGFA) in PAH patients. The ROC analysis showed that the areas under the curve of HPSE, GSN, and HGFA in differentiating PAH patients from controls were 0.769, 0.777, and 0.964, respectively. HGFA was correlated with multiple parameters of right ventricular functions in PAH patients. Besides proteomic analysis, we also used MR method to demonstrate the causal link between genetically reduced HGFA levels and an increased risk of PAH. In subsequent validation study in PAH animal models, the mRNA expression levels of HGFA in the lung tissues were significantly lower in PAH rat models than in controls. In the rat models, serum levels of HGFA were lower compared to the control group and showed a negative correlation with right ventricular systolic pressure. Conclusion The study demonstrated that HGFA might be a promising biomarker for noninvasive detection of PAH.