Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the selective loss of dopaminergic neurons in the substantia nigra pars compacta and the pathological accumulation ofα‑synuclein. Although extensive progress has been made in elucidating its pathogenesis, current therapeutic approaches remain largely symptomatic, and effective disease-modifying treatments are still unavailable. Increasing evidence indicates that PD is driven by the interaction of multiple pathological processes, including neuroinflammation, iron homeostasis dysregulation and ferroptosis, endoplasmic reticulum (ER) stress, mitochondrial dysfunction, oxidative stress, and impaired protein homeostasis, which together contribute to neuronal vulnerability and degeneration. Fibroblast growth factors (FGFs) comprise a family of 22 ligands that play important roles in neural development, stress responses, metabolic regulation, and the maintenance of nervous system homeostasis. Recent studies have shown that several FGF family members, such as FGF1, FGF2, FGF9, and FGF21, exert neuroprotective effects in cellular and animal models of PD. These effects include the regulation of inflammatory responses, oxidative stress, iron homeostasis, cellular stress adaptation, and neuronal survival. Compared with therapeutic strategies targeting a single pathogenic pathway, FGFs appear to influence multiple disease-related processes, suggesting their potential relevance to the complex pathophysiology of PD. Experimental evidence indicates that altered FGF signaling may contribute to dopaminergic neuron dysfunction through the coordinated regulation of several interconnected mechanisms. FGFs have been reported to modulate neuroinflammation by affecting the activation of microglia and astrocytes, thereby influencing the inflammatory environment in the central nervous system. In addition, FGFs are involved in the regulation of iron homeostasis and ferroptosis, partly through antioxidant signaling pathways associated with NRF2, SLC7A11, and GPX4. Moreover, FGFs can alleviate ER stress and mitochondrial dysfunction by activating intracellular signaling pathways such as PI3K/AKT, AMPK-PGC-1α, as well as SIRT1-dependent programs, which support cellular energy metabolism and redox balance. Recent advances in single-cell and spatial transcriptomic studies further suggest that FGF signaling is not limited to neuron-intrinsic mechanisms but also involves interactions among different glial cell types. Altered FGF ligand-receptor communication between astrocytes and oligodendrocytes has been observed in PD models and is associated with increased susceptibility of dopaminergic neurons to oxidative stress and ferroptosis. These findings indicate that the biological effects of FGFs are influenced by cell type and disease stage and may vary under different pathological conditions. In this review, we summarize recent progress in understanding the roles of FGF family members in PD, with a focus on their involvement in iron homeostasis dysregulation and ferroptosis, neuroinflammation, cellular stress responses, and neuronal protection and regeneration. By integrating current evidence, this review aims to provide a clearer understanding of how FGFs participate in PD pathogenesis and to offer a theoretical basis for future studies exploring their potential value in disease-modifying therapeutic strategies.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To explore the mechanism of Gano-derma lucidum polysaccharides(GLPS)promoting my-elin repair and regeneration in mice with chronic demy-elination induced by cuprizone(CPZ).Methods A total of 40 C57BL/6 mice were randomly divided into four groups:Normal+NS,Normal+GLPS,CPZ+NS and CPZ+GLPS.A chronic demyelination model was established using 0.2％CPZ.Open field and elevated plus maze tests were performed to observe the behavior-al changes in the mice.Immunofluorescence staining and Western blot were used to detect changes in myelin basic protein expression in the corpus callosum.ELISA was performed to measure the levels of TNF-α,IL-6,IL-1β and IL-10 in brain homogenates.Immunofluo-rescence staining was also used to observe the expres-sion of ionized calcium binding adapter molecule 1(Iba1)and neural-glial antigen 2(NG2).RT-qPCR and Western blot were conducted to assess the mRNA and protein expression levels of MBP,iNOS,COX-2,JAK2 and STAT3.Results Mice in the CPZ+NS group showed a significant decrease in body weight,cognitive behavior abnormalities,and impaired myelin regeneration.The expression of pro-inflammatory fac-tors increased,while anti-inflammatory factors de-creased.Additionally,Iba1 and NG2 expression in-creased,and the JAK2/STAT3 signaling pathway was activated.After GLPS intervention,the mouse body weight increased,myelin regeneration occurred,cogni-tive behavior was improved,the expression of inflamma-tory factors decreased,anti-inflammatory factors in-creased,NG2 expression was further elevated,and the proliferation of microglia as well as the activation of the JAK2/STAT3 signaling pathway was inhibited.Conclu-sions GLPS can improve cognitive behavior abnormali-ties and inflammatory responses in chronic demyelinated mice by inhibiting the JAK2/STAT3 signaling pathway,thereby promoting myelin repair and regeneration.

Aim To mine the competing ceRNA net-works associated with programmed cell death in the pathophysiological mechanisms of atherosclerosis(AS)based on bioinformatics,in order to identify new targets for the diagnosis and treatment of AS.Methods Firstly,the GSE97210 and GSE28858 datasets were screened from the GEO database.Differentially ex-pressed lncRNA,mRNA and miRNA were identified,following which a IncRNA-miRNA-mRNA regulatory network was constructed in Cytoscape 3.7.2 software based on ceRNA theory.Second,GO and KEGG en-richment analysis of mRNA in the ceRNA network was performed.Finally,the mRNAS within the ceRNA net-work were compared with genes related to autophagy,pyroptosis and ferroptosis to establish a ceRNA network related to programmed cell death.Results A total of 1208 DElncRNAS,4723 DEmRNAS and 139 DEmiR-NAS were identified.A ceRNA network was estab-lished,comprising 64 lncRNAS,8 miRNAS and 167 mRNAS.The mRNAS within the CeRNA network were mainly enriched in biological processes such as positive regulation of transcription and migration,protein bind-ing,and signaling pathways including PI3K-Akt signa-ling pathway,and mTOR signaling pathway.Finally,this study established 7 lncRNA-mediated ceRNA regu-latory pathways associated with pyroptosis and 23 ln-cRNA-mediated regulatory pathways for ferroptosis and autophagy.Conclusion This study has successfully constructed a ceRNA network related to programmed cell death,which helps us understand the mechanism by which programmed cell death leads to AS.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To explore the mechanism of Gano-derma lucidum polysaccharides(GLPS)promoting my-elin repair and regeneration in mice with chronic demy-elination induced by cuprizone(CPZ).Methods A total of 40 C57BL/6 mice were randomly divided into four groups:Normal+NS,Normal+GLPS,CPZ+NS and CPZ+GLPS.A chronic demyelination model was established using 0.2％CPZ.Open field and elevated plus maze tests were performed to observe the behavior-al changes in the mice.Immunofluorescence staining and Western blot were used to detect changes in myelin basic protein expression in the corpus callosum.ELISA was performed to measure the levels of TNF-α,IL-6,IL-1β and IL-10 in brain homogenates.Immunofluo-rescence staining was also used to observe the expres-sion of ionized calcium binding adapter molecule 1(Iba1)and neural-glial antigen 2(NG2).RT-qPCR and Western blot were conducted to assess the mRNA and protein expression levels of MBP,iNOS,COX-2,JAK2 and STAT3.Results Mice in the CPZ+NS group showed a significant decrease in body weight,cognitive behavior abnormalities,and impaired myelin regeneration.The expression of pro-inflammatory fac-tors increased,while anti-inflammatory factors de-creased.Additionally,Iba1 and NG2 expression in-creased,and the JAK2/STAT3 signaling pathway was activated.After GLPS intervention,the mouse body weight increased,myelin regeneration occurred,cogni-tive behavior was improved,the expression of inflamma-tory factors decreased,anti-inflammatory factors in-creased,NG2 expression was further elevated,and the proliferation of microglia as well as the activation of the JAK2/STAT3 signaling pathway was inhibited.Conclu-sions GLPS can improve cognitive behavior abnormali-ties and inflammatory responses in chronic demyelinated mice by inhibiting the JAK2/STAT3 signaling pathway,thereby promoting myelin repair and regeneration.

Aim To mine the competing ceRNA net-works associated with programmed cell death in the pathophysiological mechanisms of atherosclerosis(AS)based on bioinformatics,in order to identify new targets for the diagnosis and treatment of AS.Methods Firstly,the GSE97210 and GSE28858 datasets were screened from the GEO database.Differentially ex-pressed lncRNA,mRNA and miRNA were identified,following which a IncRNA-miRNA-mRNA regulatory network was constructed in Cytoscape 3.7.2 software based on ceRNA theory.Second,GO and KEGG en-richment analysis of mRNA in the ceRNA network was performed.Finally,the mRNAS within the ceRNA net-work were compared with genes related to autophagy,pyroptosis and ferroptosis to establish a ceRNA network related to programmed cell death.Results A total of 1208 DElncRNAS,4723 DEmRNAS and 139 DEmiR-NAS were identified.A ceRNA network was estab-lished,comprising 64 lncRNAS,8 miRNAS and 167 mRNAS.The mRNAS within the CeRNA network were mainly enriched in biological processes such as positive regulation of transcription and migration,protein bind-ing,and signaling pathways including PI3K-Akt signa-ling pathway,and mTOR signaling pathway.Finally,this study established 7 lncRNA-mediated ceRNA regu-latory pathways associated with pyroptosis and 23 ln-cRNA-mediated regulatory pathways for ferroptosis and autophagy.Conclusion This study has successfully constructed a ceRNA network related to programmed cell death,which helps us understand the mechanism by which programmed cell death leads to AS.

Purpose To evaluate the predictive value of multifunctional parameters of single photon emission computed tomography gated myocardial perfusion imaging(SPECT G-MPI)for major adverse cardiovascular events(MACE)in chronic kidney disease(CKD)with abnormal stress myocardial perfusion.Materials and Methods A total of 99 patients diagnosed with CKD from June 2017 to March 2024 who underwent stress and rest G-MPI indicating abnormal myocardial perfusion in Beijing Anzhen Hospital,Capital Medical University.The American Heart Association 17-segment 5-point method and PHASE software were used to obtain the left ventricular myocardial perfusion,functional and synchronization parameters.According to the occurrence of MACE,the patients were divided into MACE group and non-MACE group.Cox regression was used to analyze the predictors related to MACE.The receiver operator characteristic curve was used to analyze the performance of predictors,the survival curves were obtained by the Kaplan-Meier method,Log-rank test was used to compare the differences in different groups.Results Finally,we enrolled 99 CKD patients with abnormal stress myocardial perfusion.35 patients(35.35%)developed MACE during the follow-up period.Cox regression analysis showed that stress phase bandwidth(SPBW)(HR=1.015,95%CI 1.002-1.028)and sum difference score(SDS)(HR=1.105,95%CI 1.008-1.211)were independent risk factors for predicting MACE(both P<0.05).The optimal cut-off value of SPBW and SDS for predicting MACE were 69° and 6 points,the area under the curve was 0.801 and 0.778,respectively.The incidence of MACE in the SPBW≥69° group and SDS≥6 points group was higher than that in SPBW<69° group and SDS<6 points group(66.6%vs.13.2%,53.3%vs.20.4%,both P<0.05).Conclusion SPECT G-MPI multifunctional parameters can be used to predict the prognosis of CKD patients with abnormal stress myocardial perfusion.SPBW and SDS are independent risk factors for MACE in these patients.

Purpose To evaluate the predictive value of multifunctional parameters of single photon emission computed tomography gated myocardial perfusion imaging(SPECT G-MPI)for major adverse cardiovascular events(MACE)in chronic kidney disease(CKD)with abnormal stress myocardial perfusion.Materials and Methods A total of 99 patients diagnosed with CKD from June 2017 to March 2024 who underwent stress and rest G-MPI indicating abnormal myocardial perfusion in Beijing Anzhen Hospital,Capital Medical University.The American Heart Association 17-segment 5-point method and PHASE software were used to obtain the left ventricular myocardial perfusion,functional and synchronization parameters.According to the occurrence of MACE,the patients were divided into MACE group and non-MACE group.Cox regression was used to analyze the predictors related to MACE.The receiver operator characteristic curve was used to analyze the performance of predictors,the survival curves were obtained by the Kaplan-Meier method,Log-rank test was used to compare the differences in different groups.Results Finally,we enrolled 99 CKD patients with abnormal stress myocardial perfusion.35 patients(35.35%)developed MACE during the follow-up period.Cox regression analysis showed that stress phase bandwidth(SPBW)(HR=1.015,95%CI 1.002-1.028)and sum difference score(SDS)(HR=1.105,95%CI 1.008-1.211)were independent risk factors for predicting MACE(both P<0.05).The optimal cut-off value of SPBW and SDS for predicting MACE were 69° and 6 points,the area under the curve was 0.801 and 0.778,respectively.The incidence of MACE in the SPBW≥69° group and SDS≥6 points group was higher than that in SPBW<69° group and SDS<6 points group(66.6%vs.13.2%,53.3%vs.20.4%,both P<0.05).Conclusion SPECT G-MPI multifunctional parameters can be used to predict the prognosis of CKD patients with abnormal stress myocardial perfusion.SPBW and SDS are independent risk factors for MACE in these patients.