As an extension of the central nervous system, the retina is the only tissue in the body that allows direct in vivo observation of both neural and vascular structures, offering a unique non-invasive window for the assessment of cerebral neurovascular diseases. Recent studies have shown that multimodal imaging techniques—such as fundus photography, optical coherence tomography(OCT), and OCT angiography(OCTA)—enable quantitative analysis of retinal indicators including vascular caliber, fractal dimension, retinal nerve fiber layer thickness, and microvascular density. These metrics can, to some extent, reflect the severity and progression risk of cerebrovascular and neurodegenerative diseases such as stroke, Alzheimer's disease, and Parkinson's disease. Consequently, retinal imaging holds promise as a powerful tool for early screening, risk stratification, and treatment monitoring. This article systematically reviews the application of retinal imaging technologies in the diagnosis and management of cerebral neurovascular disorders, with a particular focus on the important role of artificial intelligence(AI)-driven image analysis in enhancing screening efficiency, improving risk assessment accuracy, and promoting multidisciplinary collaborative care, thereby providing insights for clinical translation.

Objective To establish a rapid and accurate droplet digital PCR(ddPCR)detection method for detecting Staphylococcus aureus(SA)in laboratory animals and the environment.Methods Using the heat-stable nuclease gene(nuc)of SA as the target gene,a pair of specific primers and probes are designed within its conserved region.Optimize the reaction conditions,test the dynamic range,and evaluate the specificity and stability of the method.Using the same template,test reactions were performed with both ddPCR and real-time quantitative PCR(qPCR)method to assess the interchangeability between the two approaches.Finally,the method is applied to the detection of various clinical samples.Results The kinetic range of the established SA ddPCR method is 100～15 000 copies/μL,with a detection limit of 2.5 copies and a quantification limit of 10 copies;The specificity of this method was tested,and only SA showed positive droplets,while no positive droplets were found for other pathogens;After measuring three parallel samples,the standard deviation and relative standard deviation were calculated.It was found that within the dynamic detection interval of ddPCR,as the target copy number gradually decreased,the relative standard deviation showed an upward trend,but remained below 25％.This result indicates that the detection method has good stability.Conclusions The established ddPCR method for detecting SA has the advantages of high sensitivity,strong specificity,good stability,and good reproducibility.This method can be applied for the detection of SA in laboratory animals.

Objective:To establish and evaluated a logistic regression model for predicting the acute biliary pancreatitis (ABP) based on liver-injury related indexes.Methods:Clinical data of 210 patients diagnosed with acute pancreatitis (AP) at the Affiliated Hospital of Jiangnan University from October 2020 to December 2022 were retrospectively analyzed, including 113 males and 97 females, with a median age of 52 years (range, 43 to 58). Among these, 88 were diagnosed with ABP and 122 with acute non-biliary pancreatitis (ANBP). Additionally, a test cohort was created using data from 101 AP patients diagnosed between January and December 2023, including 60 males and 41 females, with a median age of 53 years (range, 43 to 63). Based on the original dataset, univariate and multivariate logistic regression analyses were conducted to identify the factors influencing ABP. A prediction probability formula (Pre) was then established based on the multivariate results. The effectiveness of each indicator in predicting ABP was evaluated using the receiver operating characteristic (ROC) curve. The ROC curve analysis determined the optimal cutoff value of Pre, which was subsequently used to diagnose ABP and ANBP in the test cohort.Results:Multivariate logistic regression analysis showed the factors influencing ABP include direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), and fibrinogen (FIB). Based on the multivariate analysis results, the prediction probability formula (Pre) for ABP was established as follows: P=1/{1+ exp[-(4.807+ 0.134×DBIL-1.859×AST/ALT-0.0003×CHE-0.387×FIB)]}. ROC curve analysis revealed that the area under the curve (AUC) for Pre in predicting ABP was 0.858, with an optimal cutoff value of 0.56, at which the sensitivity was 69.3% and the specificity was 91.0%. Using the cutoff value of 0.56 for Pre, ABP was diagnosed when Pre≥0.56 and ANBP was diagnosed when Pre<0.56. This criterion was applied to diagnose patients in the test cohort, where the sensitivity and specificity of Pre for diagnosing ABP were 86.1% and 92.3%, respectively.Conclusion:The logistic regression model based on liver injury-related indicators is a valuable tool for clinically assessing the incidence of ABP.

Chronic obstructive pulmonary disease (COPD), which predominantly affects middle-aged and elderly individuals, is associated with a significantly reduced quality of life and often triggers various other pulmonary conditions. Lung cancer, as one of the most prevalent and deadly pulmonary malignancies worldwide, poses a severe threat to global public health. The risk of developing lung cancer is markedly higher in COPD patients compared to the general population, indicating numerous associations between the two conditions that warrant in-depth investigation. Although a substantial body of research has explored the relationship between COPD and lung cancer, studies focusing on the molecular mechanisms underlying their connection remain limited. This article reviews the latest research progress on the mechanisms of COPD complicated by lung cancer from four perspectives: the role of chronic pulmonary inflammation, programmed cell death, genetic and molecular interactions, and dysbiosis of the pulmonary microbiome. The aim of this article is to provide new insights and references for the prevention and therapeutic strategies of COPD complicated with lung cancer.

OBJECTIVE: To observe the effect of electroacupuncture at "Sishencong" (EX-HN1) and "Fengchi" (GB20) on hippocampal neuronal ferritinophagy mediated by the nuclear receptor coactivator 4 (NCOA4)/ferritin heavy chain 1 (FTH1) signaling pathway in vascular dementia (VD) rats, and to explore the potential mechanisms of electroacupuncture for VD. METHODS: A total of 60 male rats of SPF grade were randomly divided into a blank group (12 rats), a sham surgery group (12 rats) and a modeling group (36 rats). In the modeling group, the modified 4-vessel occlusion method was used to establish the VD model. The 24 successfully modeled rats were randomly divided into a model group and an electroacupuncture group, with 12 rats in each group. In the electroacupuncture group, electroacupuncture was applied at left and right "Sishencong" (EX-HN1), and bilateral "Fengchi" (GB20), with continuous wave, in frequency of 2 Hz and current intensity of 1 mA, 30 min a time, once daily for 21 consecutive days. The learning and memory abilities were assessed using the Morris water maze test before modeling, after modeling and after intervention, as well as the novel object recognition test after intervention. After intervention, the neuronal morphology in the hippocampus was observed by Nissl staining; the iron deposition was observed by Prussian blue staining; the reactive oxygen species (ROS) level was detected by dihydroethidium (DHE) fluorescence staining; the levels of iron, malondialdehyde (MDA) and superoxide dismutase (SOD) in the hippocampal tissue were measured by the colorimetric assay, TBA method, and WST-1 method, respectively; the positive expression of NCOA4, FTH1 and glutathione peroxidase 4 (GPX4) was detected by immunohistochemistry; the protein expression of NCOA4, FTH1, GPX4, and the ratio of microtubule-associated protein 1 light chain 3B (LC3B) Ⅱ/Ⅰ in the hippocampus were detected by Western blot. RESULTS: Compared with the sham surgery group, in the model group, the escape latency was prolonged, and the number of platform crossings reduced (P<0.01), the recognition index (RI) was decreased (P<0.01); the hippocampal neurons displayed a blurred laminar structure, disorganized cellular arrangement, and the number of Nissl bodies was decreased (P<0.01); the percentage of iron deposition area in the hippocampus was increased (P<0.01); in the hippocampus, the levels of ROS, iron, MDA, and the protein expression of NCOA4, as well as the LC3B Ⅱ/Ⅰ ratio were increased (P<0.01), the SOD level, and the protein expression of FTH1 and GPX4 were decreased (P<0.01). Compared with the model group, in the electroacupuncture group, the escape latency was shortened and the number of platform crossings was increased (P<0.01), the RI was increased (P<0.01); the hippocampal neurons exhibited more regular morphology, better-organized cellular structure, and the number of Nissl bodies was increased (P<0.05); the percentage of iron deposition area in the hippocampus reduced (P<0.01); in the hippocampus, the levels of ROS, iron, MDA, and the protein expression of NCOA4, as well as the LC3B Ⅱ/Ⅰ ratio were decreased (P<0.01, P<0.05), the SOD level, and the protein expression of FTH1 and GPX4 were increased (P<0.01). CONCLUSION Electroacupuncture at "Sishencong" (EX-HN1) and "Fengchi" (GB20) can improve learning and memory abilities in VD rats, and its mechanism may be associated with the regulation of the hippocampal NCOA4/FTH1 signaling pathway, inhibition of ferritinophagy, and alleviation of oxidative stress damage.
Animals ; Electroacupuncture ; Dementia, Vascular/genetics* ; Male ; Rats ; Signal Transduction ; Humans ; Memory ; Rats, Sprague-Dawley ; Nuclear Receptor Coactivators/genetics* ; Ferritins/genetics* ; Learning ; Hippocampus/metabolism* ; Acupuncture Points

Objective:To establish and evaluated a logistic regression model for predicting the acute biliary pancreatitis (ABP) based on liver-injury related indexes.Methods:Clinical data of 210 patients diagnosed with acute pancreatitis (AP) at the Affiliated Hospital of Jiangnan University from October 2020 to December 2022 were retrospectively analyzed, including 113 males and 97 females, with a median age of 52 years (range, 43 to 58). Among these, 88 were diagnosed with ABP and 122 with acute non-biliary pancreatitis (ANBP). Additionally, a test cohort was created using data from 101 AP patients diagnosed between January and December 2023, including 60 males and 41 females, with a median age of 53 years (range, 43 to 63). Based on the original dataset, univariate and multivariate logistic regression analyses were conducted to identify the factors influencing ABP. A prediction probability formula (Pre) was then established based on the multivariate results. The effectiveness of each indicator in predicting ABP was evaluated using the receiver operating characteristic (ROC) curve. The ROC curve analysis determined the optimal cutoff value of Pre, which was subsequently used to diagnose ABP and ANBP in the test cohort.Results:Multivariate logistic regression analysis showed the factors influencing ABP include direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), and fibrinogen (FIB). Based on the multivariate analysis results, the prediction probability formula (Pre) for ABP was established as follows: P=1/{1+ exp[-(4.807+ 0.134×DBIL-1.859×AST/ALT-0.0003×CHE-0.387×FIB)]}. ROC curve analysis revealed that the area under the curve (AUC) for Pre in predicting ABP was 0.858, with an optimal cutoff value of 0.56, at which the sensitivity was 69.3% and the specificity was 91.0%. Using the cutoff value of 0.56 for Pre, ABP was diagnosed when Pre≥0.56 and ANBP was diagnosed when Pre<0.56. This criterion was applied to diagnose patients in the test cohort, where the sensitivity and specificity of Pre for diagnosing ABP were 86.1% and 92.3%, respectively.Conclusion:The logistic regression model based on liver injury-related indicators is a valuable tool for clinically assessing the incidence of ABP.

Objective To establish a rapid and accurate droplet digital PCR(ddPCR)detection method for detecting Staphylococcus aureus(SA)in laboratory animals and the environment.Methods Using the heat-stable nuclease gene(nuc)of SA as the target gene,a pair of specific primers and probes are designed within its conserved region.Optimize the reaction conditions,test the dynamic range,and evaluate the specificity and stability of the method.Using the same template,test reactions were performed with both ddPCR and real-time quantitative PCR(qPCR)method to assess the interchangeability between the two approaches.Finally,the method is applied to the detection of various clinical samples.Results The kinetic range of the established SA ddPCR method is 100～15 000 copies/μL,with a detection limit of 2.5 copies and a quantification limit of 10 copies;The specificity of this method was tested,and only SA showed positive droplets,while no positive droplets were found for other pathogens;After measuring three parallel samples,the standard deviation and relative standard deviation were calculated.It was found that within the dynamic detection interval of ddPCR,as the target copy number gradually decreased,the relative standard deviation showed an upward trend,but remained below 25％.This result indicates that the detection method has good stability.Conclusions The established ddPCR method for detecting SA has the advantages of high sensitivity,strong specificity,good stability,and good reproducibility.This method can be applied for the detection of SA in laboratory animals.

OBJECTIVE To explore the lipid-regulating mechanism of the classic prescription Zexie Decoction on hyperlipidemia model mice.METHODS ELISA method was used to detect the four blood lipid indexes,liver function indicators and cholesterol acyltransferase levels in serum.HE and Oil Red O staining were used to determine the pathology of liver tissue.Network pharmacology was used to predict the lipid-lowering related targets of Zexie Decoction,and the GO function and KEGG pathway enrichment analyses of the intersection targets were realized.PCR chip technology was used to detect the target genes for network pharmacology screening,and qPCR and Western blot were used to detect gene and protein expression levels.RESULTS Zexie Decoction significantly regula-ted the four blood lipid indexes in hyperlipidemia model mice,improved the increase in liver damage indicators caused by high lipids,and had a reverse regulatory effect on the key enzymes HMGR and CYP7A1 of lipid metabolism and the lipid transporters ABCA1 and Apo-A1 in liver tissue.HE and Oil Red O staining showed that Zexie Decoction improved the pathological morphology of liver tissue,reduced lipid deposition in liver tissue,and significantly decreased the positive area ratio(P<0.01).The PCR chip obtained 44 re-verse-regulated genes,GO functional enrichment analysis obtained 266 entries,and KEGG pathway enrichment analysis screened 99 signaling pathways.The results of qPCR and Western blot showed that Zexie Decoction significantly downregulated the expression of PIK3CG,AKT1,and IL-6 genes(P<0.05,P<0.01),upregulated the expression of ABCG1 gene(P<0.05),downregulated PI3Kinase p110β,p-AKT(Ser473)and SREBP-1c protein expression levels(P<0.01),and reversely regulated miR21-5p(P<0.01).CONCLUSION Zexie Decoction has a significant regulatory effect on lipid metabolism in hyperlipidemia model mice and can improve liver damage caused by hyperlipidemia.Its lipid-regulating effect may be related to regulating cholesterol metabolism and transport in the body,and is closely linked to the miR21/PI3K-Akt/SREBP pathway.The lipid-regulating effect of the whole formula of Zexie Decoction is better than that of a single herb.

Objective     To investigate the accuracy of 18F-FDG positron emission tomography/computed tomography (PET/CT) combined with CT three-dimensional reconstruction (CT-3D) in the differential diagnosis of benign and malignant pulmonary nodules. Methods     The clinical data of patients who underwent pulmonary nodule surgery in the Department of Thoracic Surgery, Northern Jiangsu People's Hospital from July 2020 to August 2021 were retrospectively analyzed. The preoperative 18F-FDG PET/CT and chest enhanced CT-3D and other imaging data were extracted. The parameters with diagnostic significance were screened by the area under the receiver operating characteristic (ROC) curve (AUC). Three prediction models, including PET/CT prediction model (MOD PET), CT-3D prediction model (MOD CT-3D), and PET/CT combined CT-3D prediction model (MOD combination), were established through binary logistic regression, and the diagnostic performance of the models were validated by ROC curve. Results     A total of 125 patients were enrolled, including 57 males and 68 females, with an average age of 61.16±8.57 years. There were 46 patients with benign nodules, and 79 patients with malignant nodules. A total of 2 PET/CT parameters and 5 CT-3D parameters were extracted. Two PET/CT parameters, SUVmax≥1.5 (AUC=0.688) and abnormal uptake of hilar/mediastinal lymph node metabolism (AUC=0.671), were included in the regression model. Among the CT-3D parameters, CT value histogram peaks (AUC=0.694) and CT-3D morphology (AUC=0.652) were included in the regression model. Finally, the AUC of the MOD PET was verified to be 0.738 [95%CI (0.651, 0.824)], the sensitivity was 74.7%, and the specificity was 60.9%; the AUC of the MOD CT-3D was 0.762 [95%CI (0.677, 0.848)], the sensitivity was 51.9%, and the specificity was 87.0%; the AUC of the MOD combination was 0.857 [95%CI (0.789, 0.925)], the sensitivity was 77.2%, the specificity was 82.6%, and the differences were statistically significant (P<0.001). Conclusion     18F-FDG PET/CT combined with CT-3D can improve the diagnostic performance of pulmonary nodules, and its specificity and sensitivity are better than those of single imaging diagnosis method. The combined prediction model is of great significance for the selection of surgical timing and surgical methods for pulmonary nodules, and provides a theoretical basis for the application of artificial intelligence in the pulmonary nodule diagnosis.