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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

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.

As a cheap and effective antibiotic,sulfonamides are often used in animal husbandry.However,their residues in animal-derived foodstuffs will threaten human health.Consequently,a high-performance liquid chromatography(HPLC)method integrated with supramolecular solvent microextraction was successfully established for simultaneous quantification of sulfonamide residues sulfachlorpyridazine,sulfamethoxazole,sulfamethoxypyridazine and sulfadimethoxine in milk matrices.This approach exhibited prominent characteristics of operational simplicity,environmental sustainability,and high extraction efficiency.The supramolecular solvents prepared by tributyl octylphosphine tetrafluoroborate and tetrahydrofuran were employed as extraction solvents.The analytes underwent isolation and concentration via dispersive liquid-liquid microextraction(DLLME)prior to quantitative determination using high-performance liquid chromatography(HPLC).The composition and microscopic morphology of the supramolecular solvent were characterized through a series of analytical techniques,including phase diagram,Fourier transform infrared spectroscopy,scanning electron microscopy,and inverted fluorescence microscopy and so on.The density and pH value of supramolecular solvents were determined.The extraction conditions were optimized through the one-factor experiments.The experimental results demonstrated that under the optimal extraction conditions,the four kinds of sulfonamide antibiotics exhibited excellent linearity within respective detection range(R2 ≥ 0.9998)and the limits of detection(LOD)were 0.67-1.45 μg/L.Compared with literature methods,this approach offered some advantages such as simplicity of operation and less reagent consumption,and could be used for analysis and detection of sulfonamide antibiotic residues in milk samples.The present method provided technical support for food safety regulation and paved a new way for the application of supramolecular solvents in the field of extraction and separation.

Foodborne illnesses caused by Salmonella pose significant threats to worldwide public health safety.In this study,a rapid method for screening viable Salmonella in oyster sauce and milk was developed by utilizing an electronic microbial growth analyzer(EMGA).Target food samples were diluted 10-fold with RVS broth and loaded into test tubes.Test tubes were positioned in the EMGA to determine the bacterial growth curves and the time required to reach the maximum growth rate(Tmgr).Using Salmonella typhimurium(S.typhimurium)asan model species,there was linear relationship between the logarithmic value of viable bacterial concentration(lgC)and Tmgr over the range of 5×101-5×106 CFU/mL,with a detection limit of 10 CFU/mL.For oyster sauce,the regression equation was Tmgr(min)=-80.775lg[C/(CFU/mL)]+754.96(R2=0.9907),and the recovery rates of S.typhimurium ranged from 95.2%to 119.8%,with relative standard deviations(RSD)ranging from 3.5%to 16.3%.For milk,the regression equation was Tmgr(min)=-71.922 lg[C/(CFU/mL)]+618.65(R2=0.9985),with recovery rates ranging from 98.4%to 110.6%and RSD ranging from 6.4%to 12.8%.The EMGA method required only one portable instrument,and involving only three manual steps,i.e.,dilution,transfer,and insertion.When S.typhimurium contamination reached 106 CFU/mL,the total time consumption,from the unwrapping of samples to the readout of bacterial concentration,was no more than 7 h.When applied to detection of actual oyster sauce and milk samples,the new method demonstrated strong consistency with plate counting results in positive detection rates.This method was superior to the plate counting method,which was generally considered as a gold standard,in terms of accuracy,precision,simplicity and efficiency,representing a promising alternative for the on-site screening and quantification of viable Salmonella in oyster sauce and milk products.

AIM To investigate the effects of Jisuishang Formula on neurological function and ferroptosis in a rat model of cervical spondylotic myelopathy(CSM).METHODS The CSM rat models were established and randomly assigned to the model group,the Fer-1 group(2 g/kg Ferrostatin-1 via intraperitoneal injection),the low-dose(9.7 g/kg,intragastrically),medium-dose(19.4 g/kg,intragastrically)and high-dose(38.8 g/kg,intragastrically)Jisuishang Formula groups,and the sham operation group,with 6 rats in each group.Following 4 weeks of treatment administration,BBB locomotor scores and oblique plate test result were recorded to assess their neurological function in rats.Histopathological evaluation utilized HE staining for spinal cord tissue pathology,Nissl staining for Nissl body visualization,and Prussian blue staining for iron ion deposition analysis.Protein expressions of Nrf2,SLC7A11,GPX4,HO-1,TFRC and Cox2 in spinal cord tissues was detected by immunofluorescence and Western blot,while mRNA expressions were quantified using RT-qPCR.RESULTS Compared to the sham group,the CSM model group exhibited significantly reduced BBB locomotor scores and inclined plane test performance at 1,2 and 4 weeks post-operation(P＜0.05);obvious tissue cavitation,cellular edema and Prussian blue positive iron deposition in spinal cord tissues;downregulated protein and mRNA expressions of Nrf2,SLC7A11,GPX4,HO-1(P＜0.05);and upregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).Compared to the model group,the Jisuishang Formula and Fer-1 intervention groups showed significantly improved BBB scores and inclined plane test result at 1,2 and 4 weeks post-operation(P＜0.05);reduced tissue cavitation,attenuated cellular edema and decreased Prussian blue positive iron deposition in spinal cord tissues;upregulated protein and mRNA expression of Nrf2,SLC7A11,GPX4 and HO-1 in spinal cord tissues(P＜0.05);and downregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).CONCLUSION Targeting the Nrf2/SLC7A11/GPX4 signaling pathway,Jisuishang Formula potentially suppresses ferroptosis and alleviates iron accumulation in spinal cord neurons,thereby improving neurological recovery in CSM rats.

Objective:To investigate the risk factors and their predictive efficacy for early postoperative complications in elderly patients with hip fracture.Methods:A retrospective cohort study was conducted on the clinical data of 203 elderly patients with hip fracture admitted to the 908th Hospital of the Joint Logistics Support Force of the PLA and the First Affiliated Hospital of Nanchang University from January 2022 to December 2023, including 54 males and 149 females, aged 65-100 years [(80.5±7.7)years]. There were 96 patients with femoral neck fracture and 107 patients with intertrochanteric fracture. According to the AO/OTA classification, the fracture was classified as type 31A in 107 patients and type 31B in 96. Among them, 81 patients were treated with proximal femoral nail antirotation (PFNA), 65 with semi-hip arthroplasty, 52 with total hip arthroplasty (THA), and 5 with closed reduction and cannulated nail internal fixation. The patients were divided into complication group ( n=65) and non-complication group ( n=138) according to whether complications (mainly including delirium, lung infection, stress ulcer, and deep vein thrombosis of the lower limbs) occurred within 15 days after surgery. The gender, age, age stage, educational level, cause of injury, associated underlying diseases before surgery, AO/OTA classification, American Society of Anesthesiologists (ASA) classification, 5-factor modified frailty index (mFI-5) score, prognostic nutritional index (PNI), anesthesia method, operation method, operation time, intraoperative blood loss, length of hospital stay, etc., were recorded in the two groups. Univariate analysis and multivariate binary logistic regression analysis were used to evaluate the correlation between the above indexes and the occurrence of early postoperative complications in elderly patients with hip fracture and to determine their independent risk factors. The receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of each risk factor for the occurrence of early postoperative complications in elderly patients with hip fracture. Results:Univariate analysis showed a certain correlation between age, age stage, associated underlying diseases before surgery, AO/OTA classification, ASA classification, mFI-5 score, PNI, operation method, and length of hospital stay and the occurrence of early postoperative complications in elderly patients with hip fracture ( P<0.05), while gender, educational level, cause of injury, anesthesia method, operation time, and intraoperative blood loss were not correlated with the occurrence of early postoperative complications in elderly patients with hip fracture ( P>0.05). The results of multivariate binary logistic regression analysis showed that the associated underlying diseases before surgery ( OR=5.46, 95% CI 1.33, 22.39, P<0.05), mFI-5 score ( OR=15.90, 95% CI 5.36, 47.15, P<0.01), and PNI ( OR=0.70, 95% CI 0.60, 0.81, P<0.01) were significantly correlated with the occurrence of early postoperative complications in elderly patients with hip fracture. The results of ROC curve analysis showed that mFI-5 score (AUC=0.85, 95% CI 0.80, 0.91) and PNI (AUC=0.87, 95% CI 0.82, 0.93) had moderate predictive efficacy, while the early warning efficacy of associated underlying diseases was low (AUC=0.54, 95% CI 0.45, 0.62). The combination of the above risk factors was more effective in predicting early postoperative complications in elderly patients with hip fracture (AUC=0.95, 95% CI 0.92, 0.98). Conclusions:The mFI-5 score, PNI, and associated underlying diseases before surgery are independent risk factors for early postoperative complications in elderly patients with hip fracture. The mFI-5 score and PNI have a higher predictive efficacy than associated diseases before surgery on the occurrence of early postoperative complications in elderly patients with hip fracture, while the combination of the above risk factors provides a significantly better predictive performance.

Objective:To establish a fluorescent quantitative RT-PCR assay based on N_sgRNA of SARS-CoV-2 and preliminarily apply it on real samples.Methods:Recombinant plasmid, specific primers and probes of N_sgRNA were designed and synthesized based on Wuhan-Hu-1/2019_MN908947 and synthesis mechanism of subgenomic RNA (sgRNA). Using recombinant plasmid as amplification templates, the optimal reaction conditions and reaction system were screened according to the Ct value, fluorescence intensity, and shape of amplification curve and was evaluated for sensitivity, reproducibility, and specificity. Meanwhile, the possibility of practical application of the method was explored by testing 172 clinical samples and 256 municipal wastewater samples. Results:A qRT-PCR assay for N_sgRNA in SARS-CoV-2 was initially established. The detection limit of the assay was 20 copies/mL, and the variation coefficients of in-batch (0.002%~0.767%) and batch to batch repetition (0.016%~0.752%) were less than 1%. Only N_sgRNA recombinant plasmid was detected in the specificity assay. So the method is more highly sensitive, specific and reproducible. The RatiosgRNA/ gRNA of clinical samples HK.3, EG.5.1, JN.1 and their sub-lineages and their corresponding urban sewage samples in epidemic period were significantly different ( P<0.05). There is a strong correlation between the median of RatiosgRNA/ gRNA in clinical samples and sewage samples in the same period (correlation coefficient r=1.000, P=0.010). Conclusions:In this study, a qRT-PCR method for detecting N_sgRNA of SARS-CoV-2 was established and the method has the characteristics of higher sensitivity, stronger specificity and better repeatability, and it can be used to detect SARS-CoV-2 infectivity.