Enhanced endoplasmic reticulum stress signaling disrupts porcine sertoli cell function in response to Bisphenol A exposure.

Bisphenol A (BPA), a pervasive substance in our daily lives and livestock excreta, poses significant threats due to its infiltration into foods and water sources. BPA has adverse impacts on male reproductive function, particularly affecting the critical Sertoli (ST) cells that play a pivotal role in the process of spermatogonia differentiating into spermatozoa. In this study, we examined the prevalence of BPA within the pig industry and delved into the impact of BPA exposure on the motility of boar sperm, the function of pig ST cells, as well as the underlying molecular mechanisms involved. This study revealed spatial disparities in the global distribution of BPA and its analogue contamination, utilizing data compiled from 130 comprehensive studies. The average concentration of BPA found in pig feed ranges from 9.7 to 47.9 µg/kg, while in serum, it averages between 55.1 and 75.6 ng/L. The BPA concentration in feed exhibits a negative correlation with sperm viability and the percentage of progressive motile spermatozoa. Exposure to BPA reduced sperm motility in boar and ST cell activity at both 6 and 24 h. The transcriptome analysis revealed that, compared to untreated control cells, endoplasmic reticulum stress (ERS)-related genes were upregulated in ST cells exposed to BPA at 6 and 24 h. This activation of ERS in ST cells was mediated by receptor protein kinase RNA-like endoplasmic reticulum kinase (PERK), inositol-requiring protein-1α (IRE1α), and activating transcription factor 6 (ATF6). Additionally, BPA exposure triggered oxidative stress and a proinflammatory response mediated by the transcription factor NF-κB, accompanied by an increase in downstream proinflammatory cytokines. BPA exposure also led to apoptosis in ST cells and upregulated the expression levels of pro-apoptosis proteins. However, inhibiting ERS activity with 4-PBA attenuated the BPA-induced inflammatory response and apoptosis in ST cells. Our findings suggest that BPA induced apoptosis and inflammatory response in porcine ST cells through persistent activation of ERS, thereby compromising the normal function of these cells.

Machine Learning-Aided Discovery of Low-Pt High Entropy Intermetallic Compounds for Electrochemical Oxygen Reduction Reaction.

Advancing the design of cathode catalysts to significantly maximize platinum utilization and augment the longevity has emerged as a formidable challenge in the field of fuel cells. Herein, we rationally design a high entropy intermetallic compound (HEIC, Pt(FeCoNiCu)3) for catalyzing oxygen reduction reaction (ORR) by an efficient machine learning stategy, where crystal graph convolutional neural networks are employed to expedite the multicomponent design. Based on a dataset generated from first-principles calculations, the model can achieve a high prediction accuracy with mean absolute errors of 0.003 for surface strain and 0.011 eV atom-1 for formation energy. In addition, we identify two chemical features (atomic size difference and mixing enthalpy) as new descriptors to explore advanced ORR catalysts. The carbon supported Pt(FeCoNiCu)3 catalyst with small particle size is successfully synthesized by a freeze-drying-annealing technology, and exhibits ultrahigh mass activity (4.09 A mgPt-1) and specific activity (7.92 mA cm-2). Meanwhile, The catalyst also shows significantly enhanced electrochemical stability which can be ascribed to the sluggish difussion effect in the HEIC structure. Beyond offering a promising low-Pt electrocatalysts for fuel cell cathode, this work offers a new paradigm to rationally design advanced catalysts for energy storage and conversion devices.

The Pivotal Role of Preclinical Animal Models in Anti-Cancer Drug Discovery and Personalized Cancer Therapy Strategies.

The establishment and utilization of preclinical animal models constitute a pivotal aspect across all facets of cancer research, indispensably contributing to the comprehension of disease initiation and progression mechanisms, as well as facilitating the development of innovative anti-cancer therapeutic approaches. These models have emerged as crucial bridges between basic and clinical research, offering multifaceted support to clinical investigations. This study initially focuses on the importance and benefits of establishing preclinical animal models, discussing the different types of preclinical animal models and recent advancements in cancer research. It then delves into cancer treatment, studying the characteristics of different stages of tumor development and the development of anti-cancer drugs. By integrating tumor hallmarks and preclinical research, we elaborate on the path of anti-cancer drug development and provide guidance on personalized cancer therapy strategies, including synthetic lethality approaches and novel drugs widely adopted in the field. Ultimately, we summarize a strategic framework for selecting preclinical safety experiments, tailored to experimental modalities and preclinical animal species, and present an outlook on the prospects and challenges associated with preclinical animal models. These models undoubtedly offer new avenues for cancer research, encompassing drug development and personalized anti-cancer protocols. Nevertheless, the road ahead continues to be lengthy and fraught with obstacles. Hence, we encourage researchers to persist in harnessing advanced technologies to refine preclinical animal models, thereby empowering these emerging paradigms to positively impact cancer patient outcomes.

KHSRP ameliorates acute liver failure by regulating pre-mRNA splicing through its interaction with SF3B1.

Acute liver failure (ALF) is characterized by the rapidly progressive deterioration of hepatic function, which, without effective medical intervention, results in high mortality and morbidity. Here, using proteomic and transcriptomic analyses in murine ALF models, we found that the expression of multiple splicing factors was downregulated in ALF. Notably, we found that KH-type splicing regulatory protein (KHSRP) has a protective effect in ALF. Knockdown of KHSRP resulted in dramatic splicing defects, such as intron retention, and led to the exacerbation of liver injury in ALF. Moreover, we demonstrated that KHSRP directly interacts with splicing factor 3b subunit 1 (SF3B1) and enhances the binding of SF3B1 to the intronic branch sites, thereby promoting pre-mRNA splicing. Using splicing inhibitors, we found that Khsrp protects against ALF by regulating pre-mRNA splicing in vivo. Overall, our findings demonstrate that KHSRP is an important splicing activator and promotes the expression of genes associated with ALF progression by interacting with SF3B1; thus, KHSRP could be a possible target for therapeutic intervention in ALF.

Modulated synthesis of hcp MOFs for preferential CO2 capture.

We herein show that the µ2-OH bridging groups in the double M12O8(OH)14 clusters of hcp UiO-66 could act as a preferential CO2 sorption site, compared to fcu UiO-66. As such, hcp-UiO-66-0.015 shows a high binary CO2 sorption capacity of 0.31 mmol g-1 and CO2/N2 (15/85) mixture selectivity of 87 at 298 K and 1 bar, which holds great potential for post-combustion CO2 capture.

Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer.

Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

Aerobic Se(IV) reducing bacteria and their reducing characteristics in estuarine sediment.

Microorganisms play a critical role in the biogeochemical cycling of selenium in natural ecosystems, particularly in reducing selenite (Se(IV)) to element selenium (Se(0)) which reduces its mobility and bioavailability. However, Se(IV)-reducing bacteria and their reducing characteristics in estuarine sediments remain inadequately understood. In this study, the reduction of Se(IV) was confirmed to be microbially driven through the cultivation of a mixture of estuarine sediment and Se(IV) under aerobic conditions. Community analysis indicates that Bacillus was primarily involved in the reduction of Se(IV). A strain with high salt tolerance (7.5 % NaCl) and Se(IV) resistance (up to 200 mM), Bacillus cereus SD1, was isolated from an estuarine sediment. The reduction of Se(IV) occurred concomitantly with the onset of microbial growth, and reduction capacity increased approximately 5-fold by adjusting the pH. In addition, Se(IV) reduction in Bacillus cereus SD1 was significantly inhibited by sulfite, and the key enzyme activity tests revealed the possible presence of a sulfite reductase-mediated Se(IV) reduction pathway. These research findings provide new insights into the bioreducing characteristics and the biogeochemical cycling of selenium in estuarine environments.

Tuning Molecular Packing and Boosting Self-Assembling Properties via Ring Fusion Strategy in Naphthalimide-Based A-D-A Conjugated Systems.

Two fully fused acceptor-donor-acceptor (A-D-A) architecture conjugated derivatives (NPF and NCF) comprising an electron-withdrawing naphthalimide (NMI) and two different electron-donating cores, phenanthrene and carbazole, respectively, were conveniently synthesized by bismuth(III)-catalyzed selective cyclization of vinyl ethers. Compared with their corresponding single bond-linked A-D-A molecules NPS and NCS, both having a moderately twisted aromatic configuration, the ring fusion strategy leads to fully coplanar conjugated skeletons and greatly changes the electronic structures, photophysical properties, self-assembling behaviors, and molecular packing motifs. In particular, the naphthalimide/carbazole derivative NCF exhibits intriguing 2D brickwork packing and significantly enhanced self-assembling properties.

VOx Matrix Confinement Approach to Generate Sub-3 nm L10-Pt-Based Intermetallic Catalysts for Fuel Cell Cathode.

Pt-based intermetallic compounds (IMCs) are considered as a class of promising fuel cell electrocatalysts, owing to their outstanding intrinsic activity and durability. However, the synthesis of uniformly dispersed IMCs with small sizes presents a formidable challenge during the essential high-temperature annealing process. Herein, a facile and generally applicable VOx matrix confinement strategy is demonstrated for the controllable synthesis of ordered L10-PtM (M = Fe, Co, and Mn) nanoparticles, which not only enhances the dispersion of intermetallic nanocrystals, even at high loading (40 wt%), but also simplifies the oxide removal and acid-washing procedures. Taking intermetallic PtCo as an example, the as-prepared catalyst displays a high-performance oxygen reduction activity (mass activity of 1.52 A mgPt -1) and excellent stability in the membrane electrode assemblies (MEAs) (the ECSA has just 7% decay after durability test). This strategy provides an economical and scalable route for the controlled synthesis of Pt-based intermetallic catalysts, which can pave a way for the commercialization of fuel cell technologies.

In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.

The root system plays a vital role in plants' ability to absorb water and nutrients. In situ root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for in situ root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors. Compared to traditional threshold segmentation methods, this approach demonstrates superior speed, accuracy, and stability. Moreover, through time-division soil image acquisition, diverse culture medium can be replaced in in situ root images, thereby enhancing dataset versatility. After validating the performance of the Improved_UNet network on the augmented dataset, the optimal results show a 0.63% increase in mean intersection over union, 0.41% in F1, and 0.04% in accuracy. In terms of generalization performance, the optimal results show a 33.6% increase in mean intersection over union, 28.11% in F1, and 2.62% in accuracy. The experimental results confirm the feasibility and practicality of the proposed dataset augmentation strategy. In the future, we plan to combine normal mapping with rendering software to achieve more accurate shading simulations of in situ roots. In addition, we aim to create a broader range of images that encompass various crop varieties and soil types.

Improved Transformer for Time Series Senescence Root Recognition.

The root is an important organ for plants to obtain nutrients and water, and its phenotypic characteristics are closely related to its functions. Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published. In light of this, this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties. High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation. By comparing the semantic segmentation of the root system by general convolutional neural networks and transformer neural networks, SegFormer-UN (large) achieves the optimal evaluation metrics with mIoU, mRecall, mPrecision, and mF1 metric values of 81.52%, 86.87%, 90.98%, and 88.81%, respectively. The segmentation results indicate more accurate predictions at the connections of root systems in the segmented images. In contrast to 2 algorithms for cotton root senescence extraction based on deep learning and image processing, the in situ root senescence recognition algorithm using the SegFormer-UN model has a parameter count of 5.81 million and operates at a fast speed, approximately 4 min per image. It can accurately identify senescence roots in the image. We propose that the SegFormer-UN model can rapidly and nondestructively identify senescence root in in situ root images, providing important methodological support for efficient crop senescence research.

Thin flexible photoacoustic endoscopic probe with a distal-driven micro-step motor for pump-probe-based high-specific molecular imaging.

Conventional photoacoustic endoscopy (PAE) is mostly for structural imaging, and its molecular imaging ability is quite limited. In this work, we address this issue and present the development of a flexible acoustic-resolution-based photoacoustic endoscopic (AR-PAE) probe with an outer diameter of 8 mm. This probe is driven by a micro-step motor at the distal end, enabling flexible and precise angular step control to synchronize with the optical parametric oscillator (OPO) lasers. This probe retains the high spatial resolution, high penetration depth, and spectroscopic imaging ability of conventional AR-PAE. Moreover, it is capable for background-free high-specific photoacoustic molecular imaging with a novel pump-probe detection technique, as demonstrated by the distribution visualizing of the FDA approved contrast agent methylene blue (MB) in an ex-vivo pig ileum. This proposed method represents an important technical advancement in multimodal PAE, and can potentially make considerable contributions across various biomedical fields.

Stabilizing Highly Active Ru Sites by Electron Reservoir in Acidic Oxygen Evolution.

Proton exchange membrane water electrolysis is hindered by the sluggish kinetics of the anodic oxygen evolution reaction. RuO2 is regarded as a promising alternative to IrO2 for the anode catalyst of proton exchange membrane water electrolyzers due to its superior activity and relatively lower cost compared to IrO2. However, the dissolution of Ru induced by its overoxidation under acidic oxygen evolution reaction (OER) conditions greatly hinders its durability. Herein, we developed a strategy for stabilizing RuO2 in acidic OER by the incorporation of high-valence metals with suitable ionic electronegativity. A molten salt method was employed to synthesize a series of high-valence metal-substituted RuO2 with large specific surface areas. The experimental results revealed that a high content of surface Ru4+ species promoted the OER intrinsic activity of high-valence doped RuO2. It was found that there was a linear relationship between the ratio of surface Ru4+/Ru3+ species and the ionic electronegativity of the dopant metals. By regulating the ratio of surface Ru4+/Ru3+ species, incorporating Re, with the highest ionic electronegativity, endowed Re0.1Ru0.9O2 with exceptional OER activity, exhibiting a low overpotential of 199 mV to reach 10 mA cm-2. More importantly, Re0.1Ru0.9O2 demonstrated outstanding stability at both 10 mA cm-2 (over 300 h) and 100 mA cm-2 (over 25 h). The characterization of post-stability Re0.1Ru0.9O2 revealed that Re promoted electron transfer to Ru, serving as an electron reservoir to mitigate excessive oxidation of Ru sites during the OER process and thus enhancing OER stability. We conclude that Re, with the highest ionic electronegativity, attracted a mass of electrons from Ru in the pre-catalyst and replenished electrons to Ru under the operating potential. This work spotlights an effective strategy for stabilizing cost-effective Ru-based catalysts for acidic OER.

Ammonia-Induced FCC Ru Nanocrystals for Efficient Alkaline Hydrogen Electrocatalysis.

The urgent development of effective electrocatalysts for hydrogen evolution and hydrogen oxidation reaction (HER/HOR) is needed due to the sluggish alkaline hydrogen electrocatalysis. Here, an unusual face-centered cubic (fcc) Ru nanocrystal with favorable HER/HOR performance is offered. Guided by the lower calculated surface energy of fcc Ru than that of hcp Ru in NH3, the carbon-supported fcc Ru electrocatalyst is facilely synthesized in the NH3 reducing atmosphere. The specific HOR kinetic current density of fcc Ru can reach 23.4 mA cmPGM -2, which is around 20 and 21 times greater than that of hexagonal close-packed (hcp) Ru and Pt/C, respectively. Additionally, the HER specific activity is enhanced more than six times in fcc Ru electrocatalyst when compared to Pt/C. Experimental and theoretical analysis indicate that the phase transition from hcp Ru to fcc Ru can negatively shift the d band center, weaken the interaction between catalysts and key intermediates and therefore enhances the HER/HOR kinetics.

Regulating Ir-O Covalency to Boost Acidic Oxygen Evolution Reaction.

The unsatisfactory oxygen evolution reaction (OER) activity of IrO2 has intensively raised the cost and energy consumption of hydrogen generation from proton exchange membrane water electrolyzers. Here, the acidic OER activity of the rutile IrO2 is significantly enhanced by the incorporation of trivalent metals (e.g., Gd, Nd, and Pr) to increase the Ir-O covalency, while the high-valence (pentavalent or higher) metal incorporation decreases the Ir-O covalency resulting in worse OER activity. Experimental and theoretical analyses indicate that enhanced Ir-O covalency activates lattice oxygen and triggers lattice oxygen-mediated mechanism to enhance OER kinetics, which is verified by the finding of a linear relationship between the natural logarithm of intrinsic activity and Ir-O covalency described by charge transfer energy. By regulating the Ir-O covalency, the obtained Gd-IrO2-δ merely needs 260 mV of overpotential to reach 10 mA cm-2 and shows impressive stability during a 200-h test in 0.5 м H2SO4. This work provides an effective strategy for significantly enhancing the OER activity of the widely used IrO2 electrocatalysts through the rational regulation of Ir-O covalency.

Development and validation of a nomogram for predicting overall survival in patients with early-onset endometrial cancer.

BACKGROUND: This study aimed to investigate the differences in the clinicopathological characteristics of younger and older patients with endometrial cancer (EC) and develop a nomogram to assess the prognosis of early onset EC in terms of overall survival. METHODS: Patients diagnosed with EC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were selected. Clinicopathological characteristics were compared between younger and older patients, and survival analysis was performed for both groups. Prognostic factors affecting overall survival in young patients with EC were identified using Cox regression. A nomogram was created and internal validation was performed using the consistency index, decision curve analysis, receiver operating characteristic curves, and calibration curves. External validation used data from 70 patients with early onset EC. Finally, Kaplan-Meier curves were plotted to compare survival outcomes across the risk subgroups. RESULTS: A total of 1042 young patients and 12,991 older patients were included in this study. Younger patients were divided into training (732) and validation (310) cohorts in a 7:3 ratio. Cox regression analysis identified age, tumorsize, grade, FIGO stage(International Federation of Gynecology and Obstetrics) and surgery as independent risk factors for overall survival, and a nomogram was constructed based on these factors. Internal and external validations demonstrated the good predictive power of the nomogram. In particular, the C-index for the overall survival nomogram was 0.832 [95% confidence interval (0.797-0.844)] in the training cohort and 0.839 (0.810-0.868) in the internal validation cohort. The differences in the Kaplan-Meier curves between the different risk subgroups were statistically significant. CONCLUSIONS: In this study, a nomogram for predicting overall survival of patients with early onset endometrial cancer based on the SEER database was developed to help assess the prognosis of patients and guide clinical treatment.

Effects of low level laser on periodontal tissue remodeling in hPDLCs under tensile stress.

This study aimed to investigate the effect of Low-Level Laser Therapy (LLLT) on human Periodontal Ligament Cells (hPDLCs) under tension stress. Primary hPDLCs were obtained using the tissue culture method, and P3 cells were utilized for the subsequent experiments. The study comprised four groups: a blank control group (Group B), a laser irradiation group (Group L), a tension stress group (Group T), and a laser + tension stress group (Group LT). Mechanical loading was applied using an in-vitro cell stress loading device at a frequency of 0.5 Hz and deformation of 2% for two hours per day for two days. Laser irradiation at 808 nm GaAlAs laser was administered 1 h after force loading. Cell samples were collected after the experiment. Bone and fiber remodeling factors were analyzed using PCR and Western blot. Flow cytometry was employed to assess the cell cycle, while ROS and Ca2+ levels were measured using a multifunctional enzyme labeling instrument. The results revealed that laser intervention under tension stress inhibited the expression of osteogenic differentiation factors, promoted the expression of osteoclast differentiation factors, and significantly increased the production of collagen factors, MMPs, and TIMPs. The LT group exhibited the most active cell cycle (P < 0.05). LLLT not only enhanced Ca2+ expression in hPDLCs under tension stress, but also stimulated the production of ROS. Overall, our findings demonstrate that LLLT effectively accelerated the proliferation of hPDLCs and the remodeling of periodontal tissue, possibly through the regulation of ROS and Ca2+ levels in hPDLCs.

The effect of training on sensitivity and stability of double fusion in Panum's limiting case.

Panum's limiting case is a phenomenon of monocular occlusion in binocular vision. This occurs when one object is occluded by the other object for one eye, but the two objects are both visible for the other eye. Although previous studies have found that vertical gradient of horizontal disparity and cue conflict are two important factors for double fusion, the effect of training on the sensitivity and stability of Panum's limiting case remains unknown. The current study trained 26 participants for 5 days with several of Panum's configurations (Gilliam, Frisby, and Wang series). The latency and duration of double fusion were recorded to examine the effects of training on sensitivity and stability of double fusion in Panum's limiting case. For each level of vertical gradient of horizontal disparity and cue conflict, the latency of double fusion decreased and the duration of double fusion increased with each additional training session. The results showed that vertical gradient of horizontal disparity and cue conflict interacted, and the duration of high cue conflict was significantly shorter than that of medium and low cue conflict for each level of vertical gradient of horizontal disparity. The findings suggest that there is an effect of training for vertical gradient of horizontal disparity and cue conflict in Panum's limiting case, and that the three factors jointly affect the sensitivity and stability of double fusion.

Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots.

The root is an important organ for crops to absorb water and nutrients. Complete and accurate acquisition of root phenotype information is important in root phenomics research. The in situ root research method can obtain root images without destroying the roots. In the image, some of the roots are vulnerable to soil shading, which severely fractures the root system and diminishes its structural integrity. The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored. Therefore, based on the in situ root image of cotton, this study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation. It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two. The research results show that the improved UNet model has an accuracy of 99.2%, mIOU of 87.03%, and F1 of 92.63%. The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%. This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network. It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems, also realizes the restoration of the integrity of the in situ root image, and provides a new method for in situ root phenotype study.