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Dalbergia odorifera, a traditional Chinese medicine, has been used to treat cardio- and cerebrovascular diseases in China for thousands of years. Flavonoids are major active compounds in D. odorifera. In this paper, a rapid and sensitive ultra-high performance liquid chromatography-triple quadrupole mass spectrometry method was developed and validated for simultaneous determination of 17 flavonoids in D. odorifera. Quantification was performed by multiple reaction monitoring using electrospray ionization in negative ion mode. Under the optimum conditions, calibration curves for the 17 analytes displayed good linearity (r2 > 0.9980). The intra- and inter-day precisions (relative standard deviations) were lower than 5.0%. The limit of quantitation ranged from 0.256 to 18.840 ng/mL. The mean recovery range at three spiked concentrations was 94.18–101.97%. The validated approach was successfully applied to 18 samples of D. odorifera. Large variation was observed for the contents of the 17 analytes. Sativanone and 3′-O-methylviolanone were the dominant compounds. The fragmentation behaviors of six flavonoids were investigated using UPLC with quadrupole time-of-flight tandem mass spectrometry. In negative ion electrospray ionization mass spectrometry, all the flavonoids yielded prominent [M − H]− ions. Fragments for losses of CH3, CO, and CO2 were observed in the mass spectra. Formononetin, liquiritigenin, isoliquiritigenin, sativanone, and alpinetin underwent retro-Diels–Alder fragmentations. The proposed method will be helpful for quality control of D. odorifera.

Soil organic matter (SOM) governs critical soil functions, including carbon storage, nutrient cycling, and microbial activity; yet the specific fractions responsible for its spectral predictability remain poorly understood. This study addresses a fundamental research gap by comparing visible–near-infrared (vis–NIR), mid-infrared (MIR), and fused spectroscopy for predicting SOM and its components: humic acid (HA), fulvic acid (FA), and Humin. Using 93 soil samples from subtropical croplands in southeastern China, we employed partial least squares regression with full spectra and LASSO-selected wavelengths to build predictive models. Results demonstrated that both vis–NIR and MIR individually provided moderately strong predictive performance for SOM and Humin (R2 = 0.79–0.90, CCC = 0.85–0.93), while FA remained unpredictable (R2 < 0.24) due to weak, overlapping spectral features. The strong predictability of SOM was primarily attributed to the Humin fraction, which comprises approximately 50 percent of total SOM and exhibits abundant spectrally active functional groups. Contrary to expectations, spectral fusion did not improve predictions because both spectral regions already contained complementary information, and fusion introduced redundancy and scale imbalance rather than increasing effective dimensionality. This study establishes that accurate SOM estimation depends fundamentally on the predictability and abundance of the Humin fraction, providing new mechanistic insights for spectroscopic soil carbon monitoring and highlighting the need for component-specific modeling approaches in soil organic matter research.

Background Increasing evidence has revealed that circular RNAs (circRNAs) play crucial roles in cancer biology. However, the role and underlying regulatory mechanisms of circFNDC3B in bladder cancer (BC) remain unknown. Methods A cell invasion model was established by repeated transwell assays, and invasion-related circRNAs in BC were identified through an invasion model. The expression of circFNDC3B was detected in 82 BC tissues and cell lines by quantitative real-time PCR. Functional assays were performed to evaluate the effects of circFNDC3B on proliferation, migration and invasion in vitro-, and on tumorigenesis and metastasis in vivo. The relationship between circFNDC3B and miR-1178-3p was confirmed by fluorescence in situ hybridization, pull-down assay and luciferase reporter assay. Results In the present study, we identified a novel circRNA (circFNDC3B) through our established BC cell invasion model. We found that circFNDC3B was dramatically downregulated in BC tissues and correlated with pathological T stage, grade, lymphatic invasion and patients’ overall survival rate. Functionally, overexpression of circFNDC3B significantly inhibited proliferation, migration and invasion both in vitro and in vivo. Mechanistically, circFNDC3B could directly bind to miR-1178-3p, which targeted the 5′UTR of the oncogene G3BP2. Moreover, circFNDC3B acted as a miR-1178-3p sponge to suppress G3BP2, thereby inhibiting the downstream SRC/FAK signaling pathway. Conclusions CircFNDC3B may serve as a novel tumor suppressive factor and potential target for new therapies in human BC.

Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500–640 nm and 740–1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R2) of 0.70 and a low mean bias error (MBE) of 0.04 mg g−1. The Cubist model performed even better, demonstrating an R2 of 0.77 and an exceptionally low MBE of 0.01 mg g−1. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

Background Circular RNAs (circRNAs) have been considered to mediate occurrence and development of human cancers, generally acting as microRNA (miRNA) sponges to regulate downstream genes expression. However, the aberrant expression profile and dysfunction of circRNAs in human bladder cancer remain to be investigated. The present study aims to elucidate the potential role and molecular mechanism of circACVR2A in regulating the proliferation and metastasis of bladder cancer. Methods circACVR2A (hsa_circ_0001073) was identified by RNA-sequencing and validated by quantitative real-time polymerase chain reaction and agarose gel electrophoresis. The role of circACVR2A in bladder cancer was assessed both in vitro and in vivo. Biotin-coupled probe pull down assay, biotin-coupled microRNA capture, dual-luciferase reporter assay, and fluorescence in situ hybridization were conducted to evaluate the interaction between circACVR2A and microRNAs. Results The expression of circACVR2A was lower in bladder cancer tissues and cell lines. The down-regulation of circACVR2A was positively correlated with aggressive clinicopathological characteristics, and circACVR2A served as an independent risk factor for overall survival in bladder cancer patients after cystectomy. Our in vivo and in vitro data indicated that circACVR2A suppressed the proliferation, migration and invasion of bladder cancer cells. Mechanistically, we found that circACVR2A could directly interact with miR-626 and act as a miRNA sponge to regulate EYA4 expression. Conclusions circACVR2A functions as a tumor suppressor to inhibit bladder cancer cell proliferation and metastasis through miR-626/EYA4 axis, suggesting that circACVR2A is a potential prognostic biomarker and therapeutic target for bladder cancer.

Castration-resistant prostate cancer (CRPC) that occurs after the failure of androgen deprivation therapy is the leading cause of deaths in prostate cancer patients. Thus, there is an obvious and urgent need to fully understand the mechanism of CRPC and discover novel therapeutic targets. Long noncoding RNAs (lncRNAs) are crucial regulators in many human cancers, yet their potential roles and molecular mechanisms in CRPC are poorly understood. In this study, we discovered that an lncRNA HOXD-AS1 is highly expressed in CRPC cells and correlated closely with Gleason score, T stage, lymph nodes metastasis, and progression-free survival. Knockdown of HOXD-AS1 inhibited the proliferation and chemo-resistance of CRPC cells in vitro and in vivo. Furthermore, we identified several cell cycle, chemo-resistance, and castration-resistance-related genes, including PLK1, AURKA, CDC25C, FOXM1, and UBE2C, that were activated transcriptionally by HOXD-AS1. Further investigation revealed that HOXD-AS1 recruited WDR5 to directly regulate the expression of target genes by mediating histone H3 lysine 4 tri-methylation (H3K4me3). In conclusion, our findings indicate that HOXD-AS1 promotes proliferation, castration resistance, and chemo-resistance in prostate cancer by recruiting WDR5. This sheds a new insight into the regulation of CRPC by lncRNA and provides a potential approach for the treatment of CRPC. [Display omitted] Huang, Lin, and colleagues show that long noncoding RNA HOXD-AS1 is upregulated in castration-resistant prostate cancer (CRPC) and correlated with disease progression. HOXD-AS1 promotes proliferation, castration resistance, and chemo-resistance of prostate cancer cells via interacting with WDR5, which in turn activates the transcription of downstream genes.

Background The Aconitum genus is a crucial member of the Ranunculaceae family. There are 350 Aconitum species worldwide, with about 170 species found in China. These species are known for their various pharmacological effects and are commonly used to treat joint pain, cold abdominal pain, and other ailments. Codon usage bias (CUB) analysis contributes to evolutionary relationships and phylogeny. Based on protein-coding sequences (PCGs), we selected 48 species of Aconitum for CUB analysis. Results The results revealed that Aconitum species had less than 50% GC content. Furthermore, the distribution of GC content was irregular and followed a trend of GC 1  > GC 2  > GC 3 , indicating a bias towards A/T bases. The relative synonymous codon usage (RSCU) heat map revealed the presence of conservative codons with slight variations within the genus. The effective number of codons (ENC)-Plot and the parity rule 2 (PR2)-bias plot analysis indicate that natural selection is the primary factor influencing the variation in codon usage. As a result, we screened various optimal codons and found that A/T bases were preferred as the last codon. Furthermore, our Maximum Likelihood (ML) analysis based on PCGs among 48 Aconitum species yielded results consistent with those obtained from complete chloroplast (cp.) genome data. This suggests that analyzing mutation in PCGs is an efficient method for demonstrating the phylogeny of species at the genus level. Conclusions The CUB analysis of 48 species of Aconitum was mainly influenced by natural selection. This study reveals the CUB pattern of Aconitum and lays the foundation for future genetic modification and phylogenetic analyses.

We used metagenomic analysis to identify Tacheng tick virus 2 infection in a patient with a history of tick bite in northwestern China. We confirmed the virus with reverse transcription-PCR, virus isolation, and genomic analysis. We detected viral RNA in 9.6% of ticks collected from the same region.

Polygonatum kingianum Collett & Hemsl., is one of the most important traditional Chinese medicines in China. The purpose of this study is to investigate the relationship between herb quality and microbial-soil variables, while also examining the composition and structure of the rhizosphere microbial community in Polygonatum kingianum, the ultimate goal is to provide a scientific approach to enhancing the quality of P. kingianum. Illumina NovaSeq technology unlocks comprehensive genetic variation and biological functionality through high-throughput sequencing. And in this study it was used to analyze the rhizosphere microbial communities in the soils of five P. kingianum planting areas. Conventional techniques were used to measure the organic elements, pH, and organic matter content. The active ingredient content of P. kingianum was identified by High Performance Liquid Chromatography (HPLC) and Colorimetry. A total of 12,715 bacterial and 5487 fungal Operational Taxonomic Units (OTU) were obtained and taxonomically categorized into 81 and 7 different phyla. Proteobacteria, Bacteroidetes, and Acidobacteriae were the dominant bacterial phyla Ascomycota and Basidiomycota were the dominat fungal phyla. The key predictors for bacterial community structure included hydrolysable nitrogen and available potassium, while for altering fungal community structure, soil organic carbon content (OCC), total nitrogen content (TNC), and total potassium content (TPOC) were the main influencing factors. Bryobacter and Candidatus Solibacter may indirectly increase the polysaccharide content of P. kingianum , and can be developed as potential Plant Growth Promoting Rhizobacteria (PGPR). This study has confirmed the differences in the soil and microorganisms of different origins of P. kingianum , and their close association with its active ingredients. And it also broadens the idea of studying the link between plants and microorganisms.