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The development of advanced in vitro models for assessing liver toxicity and drug responses is crucial for personalized medicine and preclinical drug development. 3D bioprinting technology provides opportunities to create human liver models that are suitable for conducting high-throughput screening for liver toxicity. In this study, we fabricated a humanized liver model using human-induced hepatocytes (hiHeps) derived from human fibroblasts via a rapid and efficient reprogramming process. These hiHeps were then employed in 3D bioprinted liver models with bioink materials that closely mimic the natural extracellular matrix. The constructed humanized 3D bioprinted livers (h3DPLs) exhibited mature hepatocyte functions, including albumin expression, glycogen storage, and uptake/release of indocyanine green and acetylated low-density lipoprotein. Notably, h3DPLs demonstrated increased sensitivity to hepatotoxic agents such as acetaminophen (APAP), making them a promising platform for studying drug-induced liver injury. Furthermore, our model accurately reflected the impact of rifampin, a cytochrome P450 inducer, on CYP2E1 levels and APAP hepatotoxicity. These results highlight the potential of hiHep-based h3DPLs as a cost-effective and high-performance alternative for personalized liver toxicity screening and preclinical drug testing, paving the way for improved drug development strategies and personalized therapeutic interventions.

The construction of bioartificial livers, such as liver organoids, offers significant promise for disease modeling, drug development, and regenerative medicine. However, existing methods for generating liver organoids have limitations, including lengthy and complex processes (taking 6–8 weeks or longer), safety concerns associated with pluripotency, limited functionality of pluripotent stem cell‐derived hepatocytes, and small, highly variable sizes (typically ≈50–500 µm in diameter). Prolonged culture also leads to the formation of necrotic cores, further restricting size and function. In this study, a straightforward and time‐efficient approach is developed for creating rapid self‐assembly mini‐livers (RSALs) within 12 h. Additionally, primary hepatocytes are significantly expanded in vitro for use as seeding cells. RSALs exhibit consistent larger sizes (5.5 mm in diameter), improved cell viability (99%), and enhanced liver functionality. Notably, RSALs are functionally vascularized within 2 weeks post‐transplantation into the mesentery of mice. These authentic hepatocyte‐based RSALs effectively protect mice from 90%‐hepatectomy‐induced liver failure, demonstrating the potential of bioartificial liver‐based therapy. A straightforward and time‐efficient approach is developed for creating rapid self‐assembly mini‐livers (RSALs), essentially scaled‐up liver organoids, using primary hepatocytes within 12 h. RSALs are functionally vascularized within 2 weeks post‐transplantation into the mesentery of mice and effectively protect mice from 90%‐hepatectomy‐induced liver failure, demonstrating the potential of bioartificial liver‐based therapy.

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness.

Key messageThis article attempts to provide comprehensive review of plant deubiquitinases, paying special attention to recent advances in their biochemical activities and biological functions.Proteins in eukaryotes are subjected to post-translational modifications, in which ubiquitination is regarded as a reversible process. Cellular deubiquitinases (DUBs) are a key component of the ubiquitin (Ub)–proteasome system responsible for cellular protein homeostasis. DUBs recycle Ub by hydrolyzing poly-Ub chains on target proteins, and maintain a balance of the cellular Ub pool. In addition, some DUBs prefer to cleave poly-Ub chains not linked through the conventional K48 residue, which often alter the substrate activity instead of its stability. In plants, all seven known DUB subfamilies have been identified, namely Ub-binding protease/Ub-specific protease (UBP/USP), Ub C-terminal hydrolase (UCH), Machado–Joseph domain-containing protease (MJD), ovarian-tumor domain-containing protease (OTU), zinc finger with UFM1-specific peptidase domain protease (ZUFSP), motif interacting with Ub-containing novel DUB family (MINDY), and JAB1/MPN/MOV34 protease (JAMM). This review focuses on recent advances in the structure, activity, and biological functions of plant DUBs, particularly in the model plant Arabidopsis.

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.

The liver plays a vital role in lipid synthesis and metabolism in poultry. To study the functional genes more effectively, it is essential to screen of reliable reference genes in the chicken liver, including females, males, embryos, as well as the Leghorn Male Hepatoma (LMH) cell line. Traditional reference gene screening involves selecting commonly used housekeeping genes (HKGs) for RT-qPCR experiments and using different algorithms to identify the most stable ones. However, this approach is limited in selecting the best reference gene from a small pool of HKGs. High-throughput sequencing technology may offer a solution to this limitation. This study aimed to identify the most consistently expressed genes by utilizing multiple published RNA-seq data of chicken liver and LMH cells. Subsequently, the stability of the newly identified reference genes was assessed in comparison to previously validated stable poultry liver expressed reference genes and the commonly employed HKGs using RT-qPCR. The findings indicated that there is a higher degree of similarity in stable expression genes between female and male liver (such as LSM14A and CDC40 ). In embryonic liver, the optimal new reference genes were SUDS3 , TRIM33 , and ERAL1 . For LMH cells, the optimal new reference genes were ALDH9A1 , UGGT1 , and C21H1orf174 . However, it is noteworthy that most HKGs did not exhibit stable expression across multiple samples, indicating potential instability under diverse conditions. Furthermore, RT-qPCR experiments proved that the stable expression genes identified from RNA-seq data outperformed commonly used HKGs and certain validated reference genes specific to poultry liver. Over all, this study successfully identified new stable reference genes in chicken liver and LMH cells using RNA-seq data, offering researchers a wider range of reference gene options for RT-qPCR in diverse situations.

In this study, the photovoltaic performances of dye-sensitized solar cells (DSSCs) have been studied for four different configurations involving two dyes (indoline-based, D205 dye, and ruthenium-based, N719 dye, respectively) and two types of electrolyte (volatile and low viscosity-based electrolyte, AN-50, and low-volatility and high-viscosity based electrolyte, Z-50, respectively). The electron transport and recombination properties in DSSCs have been investigated by electrochemical impedance spectroscopy, intensity-modulated photo-voltage spectroscopy and charge extraction methods. The D205 dye outperformed the N719 counterpart when the low-volatility electrolyte, Z-50, was employed, and similar performances between the two dyes were shown for the AN-50 electrolyte employment. The electron diffusion length was found to be positively correlated with and mainly responsible for the cells’ performances. Different dyes and/or electrolytes employment which was likely altered TiO 2 nanoparticle’s surface properties were found to give influence on the electron transport properties inside the nanostructured film in this study.

Akkermansia muciniphila (A. muciniphila) is a mucin-degrading bacterium that resides in the mucus layer, but its potential in intestinal inflammatory diseases has sparked controversy. It is well known that both the consumption of fructose-containing beverages and psychological stress increase the risk of intestinal disease. Our results revealed that a high-fructose diet aggravated the damage to the jejunal mucosal barrier caused by restraint stress, reduced tight junction protein expression and the intestinal digestion and absorption capacity, disrupted the ability of Paneth cells to secrete antimicrobial peptides, and promoted the expression of inflammatory cytokines. A. muciniphila colonization enhanced the defense function of the mucosal barrier by enhancing the function of the NLRP6, promoting autophagy, maintaining the normal secretion of antimicrobial peptides in Paneth cells, promoting the expression of tight junction proteins, negatively regulating the NF-kB signaling pathway and inhibiting the expression of inflammatory cytokines. Our work indicates that A. muciniphila ameliorates the disruption of the intestinal mucosal barrier under high fructose and restraint stress. These results provided a rationale for the development of probiotic colonization for the prevention or treatment of intestinal diseases.

Objective To assess the prognostic relevance of androgen receptor (AR) expression in patients following modified radical surgery for invasive breast cancer. Methods A cohort of 515 patients who underwent modified radical mastectomy for breast cancer from July 2016 to November 2017 was analyzed. Immunohistochemistry was employed to determine the expression levels of AR, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), cell proliferation nuclear antigen (Ki-67), oncogene (P-53), cytokeratin 5/6 (CK5/6), topoisomerase-2 (TOPO-2), and epidermal growth factor receptor (EGFR). The correlation between AR expression and clinicopathological features as well as prognosis was examined. Multifactorial analysis using Cox proportional risk regression identified independent prognostic factors for disease-free survival (DFS), and a nomogram model was developed based on these factors. Results Patients in the AR-positive group demonstrated a significantly higher frequency of low histologic grade (grade 1–2), ER positive, PR positive, TOPO-2-negative, CK5/6-negative, and EGFR-negative as compared to the AR-negative group ( P  < 0.05). Among ER( +)/HER-2(−) and ER( +)/HER-2( +) patients with breast cancer, AR-positive individuals exhibited prolonged DFS ( P  < 0.05). Conversely, in ER(−)/HER-2( +) and ER(−)/HER-2(−) patients with breast cancer, AR expression did not significantly influence disease-free survival ( P  > 0.05). Multifactorial regression analysis identified AR/ER ratio, histological classification, and lymph node metastasis as independent prognostic factors for DFS (all P  < 0.05). The developed nomogram model underscored the significance of histological classification as the primary predictive factor for patient outcomes, followed by AR/ER ratio and lymph node metastasis Conclusion AR expression holds varying prognostic implications across different breast cancer subtypes, with AR positivity indicating a favorable prognosis, particularly in ER-positive tumors.

Research on hepatic steatosis in animal husbandry has been a prominent area of study. Developing an appropriate in vitro cellular steatosis model is crucial for comprehensively investigating the mechanisms involved in liver lipid deposition in poultry and for identifying potential interventions to address abnormalities in lipid metabolism. The research on the methods of in vitro liver steatosis in chickens, particularly the effects of different fat mixtures, is still lacking. In this study, LMH cells were utilized to investigate the effects of OA, SO, PA, SP, and their pairwise combinations on steatosis development, with the aim of identifying the optimal conditions for inducing steatosis. Analysis of triglyceride (TG) content in LMH cells revealed that OA and SP had limited efficacy in increasing TG content, while a combination of SO and PA in a 1:2 ratio exhibited the highest TG content. Moreover, Oil Red O staining results in LMH cells demonstrated that the combination treatment had a more pronounced induction effect compared to 0.375 mM SO. Additionally, RNA-seq analysis showed that 0.375 mM SO significantly influenced the expression of genes associated with fatty acid metabolism compared to the control group, whereas the combination of SO and PA led to an enrichment of key GO terms associated with programmed cell death. These findings suggest that varying conditions of cellular steatosis could lead to distinct disruptions in gene expression. The optimal conditions for inducing steatosis in LMH cells were also tested on chicken embryonic liver cells and embryos. TG detection and Oil Red O staining assays showed that the combination of SO and PA successfully induced steatosis. However, the gene expression pattern differed from that of LMH cells. This study lays the foundations for further investigations into avian hepatic steatosis.