Background: Colon adenocarcinoma (COAD) is the most common subtype of colon cancer, and cuproptosis is a recently newly defined form of cell death that plays an important role in the development of several malignant cancers. However, studies of cuproptosis-related lncRNAs (CRLs) involved in regulating colon adenocarcinoma are limited. The purpose of this study is to develop a new prognostic CRLs signature of colon adenocarcinoma and explore its underlying biological mechanism. Methods: In this study, we downloaded RNA-seq profiles, clinical data and tumor mutational burden (TMB) data from the TCGA database, identified cuproptosis-associated lncRNAs using univariate Cox, lasso regression analysis and multivariate Cox analysis, and constructed a prognostic model with risk score based on these lncRNAs. COAD patients were divided into high- and low-risk subgroups based on the risk score. Cox regression was also used to test whether they were independent prognostic factors. The accuracy of this prognostic model was further validated by receiver operating characteristic curve (ROC), C-index and Nomogram. In addition, the lncRNA/miRNA/mRNA competing endogenous RNA (ceRNA) network and protein–protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). Results: We constructed a prognostic model based on 15 cuproptosis-associated lncRNAs. The validation results showed that the risk score of the model (HR = 1.003, 95% CI = 1.001–1.004; p < 0.001) could serve as an independent prognostic factor with accurate and credible predictive power. The risk score had the highest AUC (0.793) among various factors such as risk score, stage, gender and age, also indicating that the model we constructed to predict patient survival was better than other clinical characteristics. Meanwhile, the possible biological mechanisms of colon adenocarcinoma were explored based on the lncRNA/miRNA/mRNA ceRNA network and PPI network constructed by WGCNA. Conclusion: The prognostic model based on 15 cuproptosis-related lncRNAs has accurate and reliable predictive power to effectively predict clinical outcomes in colon adenocarcinoma patients.

Plant leaf disease classification and severity grading are essential for precision agriculture, enabling timely intervention and optimized management. Existing models often fail to recognize previously unseen disease categories due to rigid label spaces and limited representation of plant phenotypes. To address these challenges, we propose a knowledge-driven unified framework for plant disease classification and severity grading. A Common Knowledge Learner consolidates fundamental features of plant species, disease categories, and severity levels from labeled data, forming a transferable representation space. It employs a multi-level contrastive learning strategy to capture both global semantic representations and fine-grained lesion patterns. Building on these representations, a Cross-Domain Adaptation module leverages a teacher–student framework with Low-Rank Adaptation (LoRA) bridges in-domain and out-of-domain feature spaces using large-scale unlabeled data. Meanwhile, a contrastive feature library enables similarity-based reasoning and supports flexible label space expansion during inference without retraining. We evaluate our approach on Leaf-CG, a large-scale dataset comprising 441,448 images from 59 plant species, 373 disease categories, and four severity levels. Experiments demonstrate that our framework outperforms existing baselines, achieving 94.9% disease classification accuracy and 90.6% severity grading accuracy in-domain. Under out-of-domain conditions, the method achieves 82.1% true positive rate (TPR) in open-set settings, highlighting its strong generalization ability and potential for practical plant disease management. Code and dataset are available at https://www.uniplantcg.samlab.cn.

Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet syst

Background: Myocardial ischemia/reperfusion injury (MIRI) remains a major limitation to effective cardioprotection. Chikusetsusaponin IVa (CS-IVa) has shown promising cardioprotective activity; however, its pharmacokinetic behavior and exposure–response relationship under MIRI pathological conditions remain insufficiently characterized. This study aimed to evaluate the disease-state-related pharmacokinetics of CS-IVa in MIRI rats and to explore its concentration–effect relationship using a revised descriptive PK framework. Methods: A rat MIRI model was established by ligation and reperfusion of the left anterior descending coronary artery. The cardioprotective effects of CS-IVa were evaluated using echocardiography, hemodynamic parameters, myocardial infarct size, histopathological examination, and biochemical markers of myocardial injury and oxidative stress. Plasma CS-IVa concentrations were quantified by UHPLC-MS/MS over 0–24 h after administration. Non-compartmental pharmacokinetic parameters were statistically compared between normal and MIRI rats. To address model reliability and parameter identifiability, candidate PK models with different structural assumptions and weighting schemes were systematically re-evaluated. The selected descriptive PK model was further assessed using the leave-one-rat-out robustness analysis. An exploratory exposure–response analysis was performed using CK-MB as the longitudinal PD endpoint, and a Ke0 sensitivity analysis was conducted to evaluate the robustness of the downstream effect-compartment interpretation. Data-driven models were retained only as supplementary exploratory predictive analyses. Results: CS-IVa improved cardiac function; reduced myocardial infarct size; attenuated histopathological injury; decreased serum CK-MB, cTnI, LDH and plasma MDA levels; and restored SOD activity in MIRI rats. In normal rats, systemic exposure to CS-IVa increased with dose escalation. Compared with normal rats at 15 mg/kg, MIRI rats showed markedly altered pharmacokinetic behavior, including reduced C_max and AUC, delayed T_max, shortened apparent half-life, and increased apparent volume of distribution. After systematic model re-evaluation, a one-compartment model with first-order absorption, no lag time, and unweighted fitting was selected as the revised working descriptive PK model, providing a better balance between model fit, parameter stability, and parsimony. The leave-one-rat-out analysis supported the robustness of this revised model. The exploratory concentration–effect analysis revealed a temporal dissociation between plasma CS-IVa exposure and CK-MB response, suggesting a delayed pharmacodynamic response. Ke0 sensitivity analysis indicated that effect-compartment-based PD fitting was sensitive to Ke0 selection; accordingly, the exposure–response analysis is interpreted as exploratory rather than as a definitive mechanistic PK/PD model. Conclusions: CS-IVa exerted cardioprotective effects in MIRI rats, while MIRI markedly altered its overall pharmacokinetic behavior. The revised analysis supports disease-state-related PK changes and an exploratory exposure–response delay between plasma CS-IVa exposure and CK-MB response. These findings provide a pharmacokinetic basis for understanding CS-IVa under MIRI pathological conditions; however, further studies incorporating individual-level PD endpoints, tissue distribution data, and clinically relevant formulations are needed before translational dosing recommendations can be made.