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Rice (Oryza sativa), the staple food for almost half of the world's population, prefers ammonium (NH4 +) as the major nitrogen resource, and while NH4 + has profound effects on rice growth and yields, the underlying regulatory mechanisms remain largely unknown. Brassinosteroids (BRs) are a class of steroidal hormones playing key roles in plant growth and development. In this study, we show that NH4 + promotes BR biosynthesis through miR444 to regulate rice root growth. miR444 targeted five homologous MADS-box transcription repressors potentially forming homologous or heterogeneous complexes in rice. miR444 positively regulated BR biosynthesis through its MADS-box targets, which directly repress the transcription of BR-deficient dwarf 1 (OsBRD1), a key BR biosynthetic gene. NH4 + induced the miR444-OsBRD1 signaling cascade in roots, thereby increasing the amount of BRs, whose biosynthesis and signaling were required for NH4 +-dependent root elongation inhibition. Consistently, miR444-overexpressing rice roots were hypersensitive to NH4 + depending on BR biosynthesis, and overexpression of miR444's target, OsMADS57, resulted in rice hyposensitivity to NH4 + in root elongation, which was associated with a reduction of BR content. In summary, our findings reveal a cross talk mechanism between NH4 + and BR in which NH4 + activates miR444-OsBRD1, an undescribed BR biosynthesis-promoting signaling cascade, to increase BR content, inhibiting root elongation in rice.

Pancreatic cancer (PC) is a highly aggressive malignancy characterized by insidious onset, rapid progression, and poor prognosis, predominantly affecting middle-aged and elderly men. Biomarker detection represents the most widely utilized approach for early PC diagnosis, encompassing both traditional and novel categories. Although traditional biomarkers like CA 19 − 9 are widely employed clinically, they carry a risk of false negatives in Lewis antigen-negative individuals, underscoring the urgent need for multi-parameter detection strategies to enhance accuracy. Integrating core indicators such as CEA and CA 242 can significantly improve the diagnostic effect of early PC and optimize diagnosis and treatment decisions. In recent years, breakthroughs have been made in biomarkers. Novel detection methods such as liquid biopsy and exosomes are entering the scope of clinical application. Due to their non-invasive nature, good sensitivity and specificity, these innovative biomarkers are gradually replacing traditional biomarkers and have become a new research focus. At present, single traditional biomarkers have limitations in diagnosing PC, while novel biomarkers lack extensive clinical verification. Meanwhile, there is no systematic review article specifically for early screening of PC to comprehensively evaluate the value and application prospects of these biomarkers. Therefore, this article provides a narrative review and comprehensive overview of the research progress on biomarkers related to the early diagnosis of PC in recent years.

Background The mitogenomes of 12 teleost fish of the bothid family (order Pleuronectiformes) indicated that the genomic-scale rearrangements characterized in previous work. A novel mechanism of genomic rearrangement called the Dimer-Mitogenome and Non-Random Loss (DMNL) model was used to account for the rearrangement found in one of these bothids, Crossorhombus azureus . Results The 18,170 bp mitogenome of G. polyophthalmus contains 37 genes, two control regions (CRs), and the origin of replication of the L-strand (O L ). This mitogenome is characterized by genomic-scale rearrangements: genes located on the L-strand are grouped in an 8-gene cluster ( Q - A - C - Y - S 1 - ND6 - E - P ) that does not include tRNA - N ; genes found on the H-strand are grouped together ( F - 12S … CytB - T ) except for tRNA - D that was translocated inside the 8-gene L-strand cluster. Compared to non-rearranged mitogenomes of teleost fishes, gene organization in the mitogenome of G. polyophthalmus and in that of the other 12 bothids characterized thus far is very similar. These rearrangements could be sorted into four types (Type I, II, III and IV), differing in the particular combination of the CR, tRNA - D gene and 8-gene cluster and the shuffling of tRNA - V . The DMNL model was used to account for all but one gene rearrangement found in all 13 bothid mitogenomes. Translocation of tRNA - D most likely occurred after the DMNL process in 10 bothid mitogenomes and could have occurred either before or after DMNL in the three other species. During the DMNL process, the tRNA - N gene was retained rather than the expected tRNA - N′ gene. tRNA - N appears to assist in or act as O L function when the O L secondary structure could not be formed from intergenic sequences. A striking finding was that each of the non-transcribed genes has degenerated to a shorter intergenic spacer during the DMNL process. These findings highlight a rare phenomenon in teleost fish. Conclusions This result provides significant evidence to support the existence of dynamic dimeric mitogenomes and the DMNL model as the mechanism of gene rearrangement in bothid mitogenomes, which not only promotes the understanding of mitogenome structural diversity, but also sheds light on mechanisms of mitochondrial genome rearrangement and replication.

Background The monophyly of flatfishes has not been supported in many molecular phylogenetic studies. The monophyly of Pleuronectoidei, which comprises all but one family of flatfishes, is broadly supported. However, the Psettodoidei, comprising the single family Psettodidae, is often found to be most closely related to other carangimorphs based on substantial sequencing efforts and diversely analytical methods. In this study, we examined why this particular result is often obtained. Results The mitogenomes of five flatfishes were determined. Select mitogenomes of representative carangimorph species were further employed for phylogenetic and molecular clock analyses. Our phylogenetic results do not fully support Psettodes as a sister group to pleuronectoids or other carangimorphs. And results also supported the evidence of long-branch attraction between Psettodes and the adjacent clades . Two chronograms, derived from Bayesian relaxed-clock methods, suggest that over a short period in the early Paleocene, a series of important evolutionary events occurred in carangimorphs. Conclusion Based on insights provided by the molecular clock, we propose the following evolutionary explanation for the difficulty in determining the phylogenetic position of Psettodes : The initial diversification of Psettodes was very close in time to the initial diversification of carangimorphs, and the primary diversification time of pleuronectoids, the other suborder of flatfishes, occurred later than that of some percomorph taxa. Additionally, the clade of Psettodes is long and naked branch, which supports the uncertainty of its phylogenetic placement. Finally, we confirmed the monophyly of flatfishes, which was accepted by most ichthyologists.

Tumor formation is closely associated with disulfidptosis, a new form of cell death induced by disulfide stress-induced. The exact mechanism of action of disulfidptosis in pancreatic cancer (PCa) is not clear. This study analyzed the impact of disulfidptosis-related genes (DRGs) on the prognosis of PCa and identified clusters of DRGs, and based on this, a risk score (RS) signature was developed to assess the impact of RS on the prognosis, immune and chemotherapeutic response of PCa patients. Based on transcriptomic data and clinical information from PCa tissue and normal pancreatic tissue samples obtained from the TCGA and GTEx databases, differentially expressed and differentially surviving DRGs in PCa were identified from among 15 DRGs. Two DRGs clusters were identified by consensus clustering by merging the PCa samples in the GSE183795 dataset. Analysis of DRGs clusters about the PCa tumor microenvironment and differential analysis to obtain differential genes between the two DRG clusters. Patients were then randomized into the training and testing sets, and a prognostic prediction signature associated with disulfidptosis was constructed in the training set. Then all samples were divided into high-disulfidptosis-risk (HDR) and low-disulfidptosis-risk (LDR) subgroups based on the RS calculated from the signature. The predictive efficacy of the signature was assessed by survival analysis, nomograms, correlation analysis of clinicopathological characteristics, and the receiver operating characteristic (ROC) curves. To assess differences between different risk subgroups in immune cell infiltration, expression of immune checkpoint molecules, somatic gene mutations, and effectiveness of immunotherapy and chemotherapy. The GSE57495 dataset was used as external validation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of DRGs. A total of 12 DRGs with differential expression and prognosis in PCa were identified, based on which a risk-prognosis signature containing five differentially expressed genes (DEGs) was developed. The signature was a good predictor and an independent risk factor. The nomogram and calibration curve shows the signature's excellent clinical applicability. Functional enrichment analysis showed that RS was associated with tumor and immune-related pathways. RS was strongly associated with the tumor microenvironment, and analysis of response to immunotherapy and chemotherapy suggests that the signature can be used to assess the sensitivity of treatments. External validation further demonstrated the model's efficacy in predicting the prognosis of PCa patients, with RT-qPCR and immunohistochemical maps visualizing the expression of each gene in PCa cell lines and the tissue. Our study is the first to apply the subtyping model of disulfidptosis to PCa and construct a signature based on the disulfidptosis subtype, which can provide an accurate assessment of prognosis, immunotherapy, and chemotherapy response in PCa patients, providing new targets and directions for the prognosis and treatment of PCa.

Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people’s emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.

In this work, the generalized finite difference method (GFDM), a popular meshless numerical method, is employed for predicting the thermal and mechanical behavior of an electrothermal micro-actuator. Based on the concept of GFDM and discretization on the computational domain, the discrete forms of the thermal and mechanical governing equations are derived, respectively. With the help of the incremental load method, the discrete form from the electrothermal analysis is solved precisely and the temperature distribution is obtained. Meanwhile, combining this approach with the discrete control equation derived from the natural boundary condition, its displacement is also evaluated. The convergence of the temperature by different iterative methods is tested and compared. The computational stability and efficiency (CPU time) in these two analyses are also given in this study. To further investigate the accuracy of the solutions, experiments to capture temperature and FEM analysis are conducted. Regardless of the imperfect boundary condition, the temperature distribution calculated by the GFDM shows great agreement with that obtained by experiment and FEM. A similar phenomenon can be also found in the comparison between the displacements evaluated by the GFDM and FEM, respectively.

For several decades, both concerted evolution and non-concerted evolution of rRNA genes have been discovered in a wide variety of species. To explore the evolutionary patterns and to evaluate the variability at the intra-individual and interspecific levels in Soleidae, a total of 233 complete 18S-ITS1-5.8S rDNA sequences from 11 representative species were generated. The results indicated that six species had little variation, suggesting a concerted evolutionary pattern. However, in the other five species, much variation was observed. Two or three types of 18S and ITS1-5.8S, or even the entire 18S-ITS1-5.8S rDNA sequence, were identified, suggesting a non-concerted evolutionary pattern. According to the pseudogene identification criteria, Type B and C in the five species that underwent non-concerted evolution were postulated as pseudogenes. The phylogenetic analysis based on these rDNA sequences showed that some of the pseudogenes diverged from the corresponding species or even clustered with other species, and the potential causes for this are discussed. Further analyses of the pseudogenes revealed that they could also provide particular evolutionary information, suggesting that pseudogenes should be taken into consideration rather than being discarded arbitrarily. Moreover, the results provided molecular support for the inclusion of Pseudaesopia japonica in the genus Pseudaesopia, and not in Zebrias or other genera.

Patients diagnosed with advanced cervical cancer (CC) have poor prognosis after primary treatment, and there is a lack of biomarkers for predicting patients with an increased risk of recurrence of CC. Cuproptosis is reported to play a role in tumorigenesis and progression. However, the clinical impacts of cuproptosis-related lncRNAs (CRLs) in CC remain largely unclear. Our study attempted to identify new potential biomarkers to predict prognosis and response to immunotherapy with the aim of improving this situation. The transcriptome data, MAF files, and clinical information for CC cases were obtained from the cancer genome atlas, and Pearson correlation analysis was utilized to identify CRLs. In total, 304 eligible patients with CC were randomly assigned to training and test groups. LASSO regression and multivariate Cox regression were performed to construct a cervical cancer prognostic signature based on cuproptosis-related lncRNAs. Afterwards, we generated Kaplan–Meier curves, receiver operating characteristic curves and nomograms to verify the ability to predict prognosis of patients with CC. Genes for assessing differential expression among risk subgroups were also evaluated by functional enrichment analysis. Immune cell infiltration and the tumour mutation burden were analysed to explore the underlying mechanisms of the signature. Furthermore, the potential value of the prognostic signature to predict response to immunotherapy and sensitivity to chemotherapy drugs was examined. In our study, a risk signature containing eight cuproptosis-related lncRNAs (AL441992.1, SOX21-AS1, AC011468.3, AC012306.2, FZD4-DT, AP001922.5, RUSC1-AS1, AP001453.2) to predict the survival outcome of CC patients was developed, and the reliability of the risk signature was appraised. Cox regression analyses indicated that the comprehensive risk score is an independent prognostic factor. Moreover, significant differences were found in progression-free survival, immune cell infiltration, therapeutic response to immune checkpoint inhibitors, and IC50 for chemotherapeutic agents between risk subgroups, suggesting that our model can be well employed to assess the clinical efficacy of immunotherapy and chemotherapy. Based on our 8-CRLs risk signature, we were able to independently assess the outcome and response to immunotherapy of CC patients, and this signature might benefit clinical decision-making for individualized treatment.

Background: Previous studies reported that N-myc downstream-regulated gene 1 (NDRG1) was upregulated in various cancer tissues and decreased expression of miR-188-3p and miR-133b could suppress cell proliferation, metastasis, and invasion and induce apoptosis of cancer cells. However, the molecular mechanism of NRDG1 involved in hepatocellular carcinoma (HCC) tumorigenesis is still unknown. Methods: The expressions of miR-188-3p, miR-133b, and NRDG1 in HCC tissues and cells were quantified by qRT-PCR and Western blot. MTT assay and transwell invasion assay were performed to evaluate cell growth and cell migration, respectively. Luciferase reporter assay were performed to determine whether miR-188-3p and miR-133b could directly bind to NRDG1 in HCC cells. Results: The results showed that NRDG1 was upregulated and these 2 microRNAs were downregulated in HCC tissues. NRDG1 was negatively correlated with miR-188-3p and miR-133b in HCC tissues. MiR-188-3p and miR-133b were demonstrated to directly bind to 3′UTR of NRDG1 and inhibit its expression. Upregulation of miR-188-3p and miR-133b reduced NRDG1 expression in hepatocellular carcinoma cell lines, which consequently inhibited cell growth and cell migration. Conclusions: Our finding suggested that miR-188-3p and miR-133b exert a suppressive effect on hepatocellular carcinoma proliferation, invasion, and migration through downregulation of NDRG1.