ObjectivePost-ischemic acute inflammation and the subsequent persistent dysregulation of the immune microenvironment represent major pathological drivers that aggravate neuronal injury and severely restrict functional recovery following ischemic stroke. Although current reperfusion therapies partially restore blood flow, they fail to effectively modulate the secondary inflammatory cascade and oxidative stress, which remain critical barriers to neurological restoration. To address this challenge, this study aimed to engineer and systematically evaluate a biomimetic nanosystem composed of transforming growth factor-β1 (TGF-β1)-loaded platelet membrane-camouflaged lipid nanoparticles (PLP). This nanosystem was designed to achieve dual lesion-targeted delivery and immune microenvironment remodeling. By verifying its spatiotemporal accumulation, anti-inflammatory activity, and neuroprotective efficacy, we sought to establish an integrated therapeutic strategy that simultaneously enables lesion targeting, immune regulation, and functional recovery after ischemic injury. MethodsThe physicochemical properties of PLP, including hydrodynamic particle size, zeta potential, structural stability, and morphology, were characterized using dynamic light scattering, zeta potential analysis, and transmission electron microscopy. The preservation of platelet membrane-derived adhesion and immunoregulatory proteins was confirmed by SDS-PAGE through comparative analysis of protein band profiles between PLP and native platelet membranes. The in vitro biological activities of PLP were evaluated using two complementary cellular models. LPS-induced M1-polarized RAW264.7 macrophages were employed to assess inflammatory modulation, while oxygen glucose deprivation/reperfusion (OGD/R)-induced BV2 microglial cells and SH-SY5Y neuronal cells were utilized to investigate neuroinflammatory regulation and neuronal protection. For in vivo validation, a transient middle cerebral artery occlusion (tMCAO) mouse model was established to mimic ischemia-reperfusion injury. The spatiotemporal biodistribution and lesion-targeting capability of the PLP were monitored through live fluorescence imaging. Therapeutic efficacy was comprehensively evaluated by triphenyltetrazolium chloride (TTC) staining, glial fibrillary acidic protein (GFAP) immunofluorescence analysis, body weight monitoring, and neurological severity score (NSS) assessment. ResultsPLP nanoparticles displayed a uniform spherical morphology, nanoscale particle size distribution, and stable negative surface charge, indicating favorable colloidal stability and circulation potential. SDS-PAGE results confirmed the effective retention of key platelet membrane proteins associated with endothelial adhesion, immune evasion, and inflammatory regulation, demonstrating the successful biomimetic construction. Optimal therapeutic concentrations were determined in OGD/R-induced BV2 cells, where PLP exhibited excellent cytocompatibility and anti-inflammatory activity.In vitro experiments demonstrated that PLP significantly inhibited the polarization of RAW264.7 macrophages toward the pro-inflammatory M1 phenotype and markedly reduced neuronal apoptosis under ischemia-reperfusion conditions. In vivo fluorescence imaging revealed that PLP rapidly accumulated in the ischemic brain hemisphere and maintained prolonged retention for up to 7 d, suggesting enhanced lesion-specific targeting and sustained drug release. Compared with control group, PLP treatment significantly reduced cerebral infarct volume, attenuated reactive astrogliosis, improved weight recovery, and accelerated neurological functional restoration, as reflected by significantly improved NSS scores. ConclusionThis study establishes a multifunctional biomimetic nanoplatform that integrates platelet membrane-mediated active targeting with the anti-inflammatory, antioxidative, and neuroprotective properties of TGF-β1. The PLP system enables rapid lesion homing and long-term retention while synergistically regulating the post-stroke inflammatory microenvironment by suppressing pro-inflammatory immune activation, reducing neuronal apoptosis, and limiting excessive astrocyte reactivity. Importantly, this study proposes a conceptually therapeutic paradigm that combines targeted delivery with immune microenvironment remodeling to achieve comprehensive neurovascular protection. These findings provide strong experimental evidence supporting the translational potential of biomimetic nanotherapeutics as next-generation precision interventions for ischemic stroke.

Objective:To investigate the role of aging-related genes(ARGs)in the prognosis of lung squamous cell carcinoma(LUSC)and establish a novel prognostic prediction model.Methods:Transcriptomic data and clinical information of LUSC patients were obtained from TCGA,combined with ARGs from Aging Atlas.Key genes were screened through differential expression analysis,survival analysis,and Cox regression to construct a prognostic model.Model performance was validated in clinical subgroups,and biological pathway enrichment(GSEA)and immune microenvironment analyses were performed.Results:Five ARGs(ERFFI1,MDH1,SENP2,SNAI1,TP63)were identified to build the model.Significant survival differences were observed between high-and low-risk groups(P＜0.001),with 1-,3-,and 5-year AUC values of 0.610,0.668,and 0.665,respectively.The risk score was an independent prognostic factor(HR=11.261,95％CI:3.654-34.701,P＜0.001)and showed predictive efficacy in both early-stage(Ⅰ-Ⅱ,P=0.022)and advanced-stage(Ⅲ-Ⅳ,P=0.004)patients.GSEA revealed significant enrichment of Alzheimer's disease(P=0.003)and cell adhesion pathways(P=0.008)in high-risk groups.SNAI1 correlated positively with M1/M2 macrophage infiltration(r=0.45,P＜0.001),MDH1 associated with 12 immune cell types(|r|＞0.3,P＜0.05),and the risk score linked to CD8+T cells(r=0.38)and M2 macrophages(r=0.32)(both P＜0.001).Twenty-three immune checkpoints(e.g.,TNFRSF14,CD200R1)were differentially expressed between groups and survival-related(P＜0.05).High-risk patients exhibited elevated TIDE scores(P＜0.001),indicating enhanced immune suppression.Conclusion:This model provides a novel tool for LUSC prognosis assessment,but further clinical validation is required.

Objective To propose a ventilator failure probability prediction method based on kernel ridge regression(KRR).Methods Firstly,the failure interval data of ventilators was collected and preprocessed to remove outliers.Secondly,the median rank method was used to estimate the failure probability.Finally,using the time data as the feature variable and the failure probability value as the target variable,a KRR model was established and trained by selecting the optimal kernel function and hyperparameter combination from radial basis kernel function,linear kernel function,polynomial kernel function,and S-type kernel function through grid search and cross-validation methods to predict ventilator failures.To verify the performance of the KRR model in predicting ventilator failure probability,it was compared with Weibull and its extended models.Results KRR achieved a coefficient of determination of 0.993 5,a mean squared error of 5.399 5×10-4,a root mean squared error of 0.023 2 and a mean absolute error of 0.018 3,outperforming Weibull and its extended models in prediction accuracy and error control.Conclusion The failure probability prediction method for ventilators based on KRR demonstrates exceptional performance in prediction accuracy and error control,and thus holds great potential for application.[Chinese Medical Equipment Journal,2025,46(5):73-77]

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.

Objective To propose and validate a decomposition method based on half-projection reconstruction for dual-energy cone beam CT(DE CBCT),thereby providing a potentially feasible low-dose imaging solution for anatomical monitoring and dose reconstruction optimization in adaptive radiotherapy.Methods Dual-energy scans were performed on a Gammex phantom using the on-board kilovoltage CBCT system of a VitalBeam accelerator at acquisition frame rates of 15 and 7 frames per second(f/s).Images were reconstructed from the projection data,and dual-energy decomposition was applied to the 7 f/s dual-energy images to derive relative electron density(RED)and stopping power ratio(SPR)using weighted formulas and empirical functions,followed by accuracy evaluation.Additionally,the weighted CT dose index was calculated for different scanning parameters.Results Dual-energy decomposition effectively suppressed image artifacts,with RED and SPR errors remaining below 2.82%and 2.56%,respectively.Compared with the traditional dual-scan method which required high-and low-energy acquisitions,the weighted CT dose index of the half-projection DE CBCT was reduced by 11.60 mGy(a 52.90%reduction).Furthermore,it was 2.58 mGy lower than the dose of the full-projection high-energy CBCT alone(a 19.98%reduction)and only 1.31 mGy higher than that of the low-energy CBCT(a 14.52%increase).Conclusion The proposed method effectively suppresses image artifacts while maintaining high accuracy in RED and SPR under low radiation dose conditions,demonstrating its potential value for scenarios requiring frequent image guidance,such as adaptive radiotherapy.

Objective To propose a ventilator failure probability prediction method based on kernel ridge regression(KRR).Methods Firstly,the failure interval data of ventilators was collected and preprocessed to remove outliers.Secondly,the median rank method was used to estimate the failure probability.Finally,using the time data as the feature variable and the failure probability value as the target variable,a KRR model was established and trained by selecting the optimal kernel function and hyperparameter combination from radial basis kernel function,linear kernel function,polynomial kernel function,and S-type kernel function through grid search and cross-validation methods to predict ventilator failures.To verify the performance of the KRR model in predicting ventilator failure probability,it was compared with Weibull and its extended models.Results KRR achieved a coefficient of determination of 0.993 5,a mean squared error of 5.399 5×10-4,a root mean squared error of 0.023 2 and a mean absolute error of 0.018 3,outperforming Weibull and its extended models in prediction accuracy and error control.Conclusion The failure probability prediction method for ventilators based on KRR demonstrates exceptional performance in prediction accuracy and error control,and thus holds great potential for application.[Chinese Medical Equipment Journal,2025,46(5):73-77]

Objective To propose and validate a decomposition method based on half-projection reconstruction for dual-energy cone beam CT(DE CBCT),thereby providing a potentially feasible low-dose imaging solution for anatomical monitoring and dose reconstruction optimization in adaptive radiotherapy.Methods Dual-energy scans were performed on a Gammex phantom using the on-board kilovoltage CBCT system of a VitalBeam accelerator at acquisition frame rates of 15 and 7 frames per second(f/s).Images were reconstructed from the projection data,and dual-energy decomposition was applied to the 7 f/s dual-energy images to derive relative electron density(RED)and stopping power ratio(SPR)using weighted formulas and empirical functions,followed by accuracy evaluation.Additionally,the weighted CT dose index was calculated for different scanning parameters.Results Dual-energy decomposition effectively suppressed image artifacts,with RED and SPR errors remaining below 2.82%and 2.56%,respectively.Compared with the traditional dual-scan method which required high-and low-energy acquisitions,the weighted CT dose index of the half-projection DE CBCT was reduced by 11.60 mGy(a 52.90%reduction).Furthermore,it was 2.58 mGy lower than the dose of the full-projection high-energy CBCT alone(a 19.98%reduction)and only 1.31 mGy higher than that of the low-energy CBCT(a 14.52%increase).Conclusion The proposed method effectively suppresses image artifacts while maintaining high accuracy in RED and SPR under low radiation dose conditions,demonstrating its potential value for scenarios requiring frequent image guidance,such as adaptive radiotherapy.

Objective:To investigate the role of aging-related genes(ARGs)in the prognosis of lung squamous cell carcinoma(LUSC)and establish a novel prognostic prediction model.Methods:Transcriptomic data and clinical information of LUSC patients were obtained from TCGA,combined with ARGs from Aging Atlas.Key genes were screened through differential expression analysis,survival analysis,and Cox regression to construct a prognostic model.Model performance was validated in clinical subgroups,and biological pathway enrichment(GSEA)and immune microenvironment analyses were performed.Results:Five ARGs(ERFFI1,MDH1,SENP2,SNAI1,TP63)were identified to build the model.Significant survival differences were observed between high-and low-risk groups(P＜0.001),with 1-,3-,and 5-year AUC values of 0.610,0.668,and 0.665,respectively.The risk score was an independent prognostic factor(HR=11.261,95％CI:3.654-34.701,P＜0.001)and showed predictive efficacy in both early-stage(Ⅰ-Ⅱ,P=0.022)and advanced-stage(Ⅲ-Ⅳ,P=0.004)patients.GSEA revealed significant enrichment of Alzheimer's disease(P=0.003)and cell adhesion pathways(P=0.008)in high-risk groups.SNAI1 correlated positively with M1/M2 macrophage infiltration(r=0.45,P＜0.001),MDH1 associated with 12 immune cell types(|r|＞0.3,P＜0.05),and the risk score linked to CD8+T cells(r=0.38)and M2 macrophages(r=0.32)(both P＜0.001).Twenty-three immune checkpoints(e.g.,TNFRSF14,CD200R1)were differentially expressed between groups and survival-related(P＜0.05).High-risk patients exhibited elevated TIDE scores(P＜0.001),indicating enhanced immune suppression.Conclusion:This model provides a novel tool for LUSC prognosis assessment,but further clinical validation is required.

Numerous studies have shown that depression is main-ly associated with the abnormal expression of connexin 43(Cx43)in astrocytes(Astro)and its mediated dysfunction of gap junction(GJ).However,the molecular mechanism of post-translational modifications targeting Cx43 to regulate neuroin-flammation-associated depression is still unclear.Post-transla-tional modifications of Cx43 mainly include phosphorylation of specific amino acid sites by PKC,PKA,PKG,MAPK and PTK,and protein degradation of Cx43 through the K48/K63 polyubiq-uitylation and deubiquitination pathways,which ultimately lead to protein degradation through K48/K63 polyubiquitination and deubiquitination.These modifications are ultimately involved in the regulation of neuroinflammatory responses through the associ-ation of GJ function.In this paper,we systematically review the role of Cx43 post-translational modifications in neuroinflamma-tion,with the aim of further exploring the potential application of targeting these modifications to modulate the inflammatory re-sponse mechanism in improving depressive symptoms.

ObjectiveNeuroinflammation plays a crucial role in both the onset and progression of ischemic stroke, exerting a significant impact on the recovery of the central nervous system. Excessive neuroinflammation can lead to secondary neuronal damage, further exacerbating brain injury and impairing functional recovery. As a result, effectively modulating and reducing neuroinflammation in the brain has become a key therapeutic strategy for improving outcomes in ischemic stroke patients. Among various approaches, targeting immune regulation to control inflammation has gained increasing attention. This study aims to investigate the role of in vitro induced regulatory T cells (Treg cells) in suppressing neuroinflammation after ischemic stroke, as well as their potential therapeutic effects. By exploring the mechanisms through which Tregs exert their immunomodulatory functions, this research is expected to provide new insights into stroke treatment strategies. MethodsNaive CD4⁺ T cells were isolated from mouse spleens using a negative selection method to ensure high purity, and then they were induced in vitro to differentiate into Treg cells by adding specific cytokines. The anti-inflammatory effects and therapeutic potential of Treg cells transplantation in a mouse model of ischemic stroke was evaluated. In the middle cerebral artery occlusion (MCAO) model, after Treg cells transplantation, their ability to successfully migrate to the infarcted brain region and their impact on neuroinflammation levels were examined. To further investigate the role of Treg cells in stroke recovery, the changes in cytokine expression and their effects on immune cell interactions was analyzed. Additionally, infarct size and behavioral scores were measured to assess the neuroprotective effects of Treg cells. By integrating multiple indicators, the comprehensive evaluation of potential benefits of Treg cells in the treatment of ischemic stroke was performed. ResultsTreg cells significantly regulated the expression levels of both pro-inflammatory and anti-inflammatory cytokines in vitro and in vivo, effectively balancing the immune response and suppressing excessive inflammation. Additionally, Treg cells inhibited the activation and activity of inflammatory cells, thereby reducing neuroinflammation. In the MCAO mouse model, Treg cells were observed to accumulate in the infarcted brain region, where they significantly reduced the infarct size, demonstrating their neuroprotective effects. Furthermore, Treg cell therapy notably improved behavioral scores, suggesting its role in promoting functional recovery, and increased the survival rate of ischemic stroke mice, highlighting its potential as a promising therapeutic strategy for stroke treatment. ConclusionIn vitro induced Treg cells can effectively suppress neuroinflammation caused by ischemic stroke, demonstrating promising clinical application potential. By regulating the balance between pro-inflammatory and anti-inflammatory cytokines, Treg cells can inhibit immune responses in the nervous system, thereby reducing neuronal damage. Additionally, they can modulate the immune microenvironment, suppress the activation of inflammatory cells, and promote tissue repair. The therapeutic effects of Treg cells also include enhancing post-stroke recovery, improving behavioral outcomes, and increasing the survival rate of ischemic stroke mice. With their ability to suppress neuroinflammation, Treg cell therapy provides a novel and effective strategy for the treatment of ischemic stroke, offering broad application prospects in clinical immunotherapy and regenerative medicine.