ObjectiveTo explore the possible mechanisms by which ligustilide （LIG） exerts neuroprotective effects on ischemic stroke （IS） by inhibiting the release of neutrophil extracellular traps （NETs）， promoting blood-brain barrier repair， and alleviating post-ischemic neuroinflammation， thereby providing a new direction for IS treatment. MethodsA middle cerebral artery occlusion （MCAO） model was established in rats. The rats were divided into the sham operation （Sham） group， model （Model） group， low- and high-dose LIG groups （20， 40 mg·kg^-1）， and the NET inhibitor CI-amidine group （CI-amidine， 10 mg·kg^-1）. Drug treatments were administered for 3 days. Neurological injury after ischemia was evaluated by 2，3，5-triphenyltetrazolium chloride （TTC） staining， neurological deficit scoring， and brain index measurement. Flow cytometry and Western blot were used to analyze changes in neutrophil expression. Immunofluorescence was used to observe the fluorescence intensity of the NET marker citrullinated histone H3 （H3Cit）. Western blot was performed to detect the expression of blood-brain barrier tight junction-related proteins and inflammatory factors， including interleukin-18 （IL-18） and interleukin-1β （IL-1β）. ResultsCompared with the Sham group， the Model group exhibited significant brain tissue injury （P<0.05）， significantly increased neutrophil numbers and NET expression （P<0.05）， significantly impaired blood-brain barrier permeability （P<0.05）， and significantly increased expression of inflammatory factors （P<0.05）. Compared with the Model group， both low- and high-dose LIG significantly alleviated brain tissue injury in rats （P<0.01）， inhibited neutrophil numbers and NET expression （P<0.01）， reduced blood-brain barrier damage （P<0.01）， and suppressed the expression of inflammatory factors IL-18 and IL-1β （P<0.01）， thereby ultimately exerting a neuroprotective effect. ConclusionThe neuroprotective effect of LIG in rats with cerebral ischemia-reperfusion injury may be related to inhibition of neutrophils and the NETs induced by them.

ObjectiveTo explore the possible mechanisms by which ligustilide （LIG） exerts neuroprotective effects on ischemic stroke （IS） by inhibiting the release of neutrophil extracellular traps （NETs）， promoting blood-brain barrier repair， and alleviating post-ischemic neuroinflammation， thereby providing a new direction for IS treatment. MethodsA middle cerebral artery occlusion （MCAO） model was established in rats. The rats were divided into the sham operation （Sham） group， model （Model） group， low- and high-dose LIG groups （20， 40 mg·kg^-1）， and the NET inhibitor CI-amidine group （CI-amidine， 10 mg·kg^-1）. Drug treatments were administered for 3 days. Neurological injury after ischemia was evaluated by 2，3，5-triphenyltetrazolium chloride （TTC） staining， neurological deficit scoring， and brain index measurement. Flow cytometry and Western blot were used to analyze changes in neutrophil expression. Immunofluorescence was used to observe the fluorescence intensity of the NET marker citrullinated histone H3 （H3Cit）. Western blot was performed to detect the expression of blood-brain barrier tight junction-related proteins and inflammatory factors， including interleukin-18 （IL-18） and interleukin-1β （IL-1β）. ResultsCompared with the Sham group， the Model group exhibited significant brain tissue injury （P<0.05）， significantly increased neutrophil numbers and NET expression （P<0.05）， significantly impaired blood-brain barrier permeability （P<0.05）， and significantly increased expression of inflammatory factors （P<0.05）. Compared with the Model group， both low- and high-dose LIG significantly alleviated brain tissue injury in rats （P<0.01）， inhibited neutrophil numbers and NET expression （P<0.01）， reduced blood-brain barrier damage （P<0.01）， and suppressed the expression of inflammatory factors IL-18 and IL-1β （P<0.01）， thereby ultimately exerting a neuroprotective effect. ConclusionThe neuroprotective effect of LIG in rats with cerebral ischemia-reperfusion injury may be related to inhibition of neutrophils and the NETs induced by them.

Blood transfusion is an important life-support measure in modern medicine. As clinical demand for transfusion continues to rise, blood supply remains under persistent strain, making the efficient utilization of this precious medical resource a critical component of blood management. Accurate prediction of transfusion needs is of great significance for optimizing blood resource allocation and conserving blood products. Traditional transfusion decision-making relies on clinical experience and simplified scoring systems, which are insufficient to meet the demands of precision medicine. Machine learning (ML) technology can integrate multidimensional data—including demographic characteristics, laboratory indicators, surgical information, and dynamic physiological waveforms—to construct high-performance predictive models, offering a new approach to transfusion prediction. This article systematically reviews the application progress of ML in transfusion prediction across clinical scenarios such as trauma, the perioperative period, and obstetrics. In trauma, ML has been applied to early massive transfusion prediction, prehospital transfusion decision-making, and precise prediction in pediatric trauma. In the perioperative field, applications span specialties including traumatic brain injury, orthopedic surgery, cardiovascular and major vascular surgery, and hepatic surgery. In obstetrics, models can effectively predict postpartum hemorrhage and transfusion requirements associated with cesarean section. Studies have shown that algorithms such as random forest, gradient boosting machines, and deep neural networks achieve area under the receiver operating characteristic curve (AUC) values of 0.83-0.98 in massive transfusion prediction, 0.75-0.97 in perioperative scenarios, and 0.80-0.89 in obstetric transfusion prediction—significantly outperforming traditional scoring tools. However, current studies are generally limited by single-center retrospective designs, insufficient external validation, and inadequate reporting standards; limited model interpretability and challenges in clinical integration further constrain practical translation. Future research should focus on constructing multicenter data cohorts, integrating multimodal data, conducting prospective implementation studies, and deeply embedding models within electronic health record systems, thereby advancing the application of ML to optimize clinical transfusion decision-making.

This article provides a systematic review of the research progress in the mechanisms related to lung cancer and ferroptosis， ferroptosis-related lung cancer biomarkers and gene mutation targets， and ferroptosis-targeted regulation of Chinese medicine in treating lung cancer in the past five years， providing a feasible and effective basis for the prevention and treatment of lung cancer with Chinese medicine and the development of new drugs. According to the available studies， ferroptosis is widely suppressed in lung cancer， while the specific regulatory mechanisms have not been fully elucidated. The suppression is related to lipid metabolism， iron metabolism， cystine/glutamate antiporter system Xc^- （System Xc^-）/glutathione （GSH）/glutathione peroxidase 4 （GPX4）， ferroptosis suppressor protein 1 （FSP1）/coenzyme Q10 （CoQ10）/nicotinamide adenine dinucleotide phosphate ［NAD（P）H］， long non-coding RNA （lncRNA）， nuclear factor E₂-related factor 2 （Nrf2）， and p53. In modern times， traditional Chinese medicine is widely used in the comprehensive treatment of lung cancer， and it has gradually become a hot research topic due to its obvious advantages of anti-tumor activity， high efficacy， and low toxicity. Traditional Chinese medicine plays an important role in the treatment of lung cancer. Studies have shown that the active components， extracts， and prescriptions of Chinese medicine can induce ferroptosis in lung cancer cells through targeted regulation of iron metabolism， lipid metabolism， and p53， Nrf2， LncRNA， and GPX4 pathways to inhibit the growth and proliferation of lung cancer， thus exerting anti-tumor effects. Therefore， regulating ferroptosis is expected to become a new direction for preventing lung cancer. Basic research has shown that Chinese medicine can regulate ferroptosis via multiple targets and pathways in the treatment of lung cancer. At present， Chinese medicine demonstrates great research prospects in regulating ferroptosis to treat lung cancer， which， howeve， still faces challenges to achieve clinical transformation.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Psoriasis is an incurable chronic inflammatory disease that requires new interventions. Here, we found that fibroblasts exacerbate psoriasis progression by promoting macrophage recruitment via CCL2 secretion by single-cell multi-omics analysis. The natural small molecule celastrol was screened to interfere with the secretion of CCL2 by fibroblasts and improve the psoriasis-like symptoms in both murine and cynomolgus monkey models. Mechanistically, celastrol directly bound to the low-density lipoprotein receptor-related protein 1 (LRP1) β-chain and abolished its binding to the transcription factor c-Jun in the nucleus, which in turn inhibited CCL2 production by skin fibroblasts, blocked fibroblast-macrophage crosstalk, and ameliorated psoriasis progression. Notably, fibroblast-specific LRP1 knockout mice exhibited a significant reduction in psoriasis like inflammation. Taken together, from clinical samples and combined with various mouse models, we revealed the pathogenesis of psoriasis from the perspective of fibroblast-macrophage crosstalk, and provided a foundation for LRP1 as a novel potential target for psoriasis treatment.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.