Diabetic retinopathy (DR) is one of the most prevalent and vision-threatening microvascular complications of diabetes mellitus, yet its pathogenesis extends far beyond vascular injury alone. As the retina is among the most energy-demanding tissues in the body, its neurons, glial cells, pigment epithelial cells, pericytes, and endothelial cells are highly dependent on mitochondrial oxidative phosphorylation to maintain visual signal transduction, ionic homeostasis, and neurovascular integrity. This review summarizes current evidence indicating that mitochondrial dysfunction is not merely a downstream consequence of chronic hyperglycemia, but a central pathogenic hub that initiates, amplifies, and perpetuates retinal neurovascular degeneration in DR. Persistent hyperglycemia activates multiple abnormal metabolic pathways, including the polyol pathway, hexosamine pathway, protein kinase C signaling, advanced glycation end-product formation, and angiotensin II-related responses. Although these pathways differ mechanistically, they converge on excessive reactive oxygen species (ROS) generation, antioxidant depletion, and mitochondrial injury. Under diabetic stress, electron transport chain overload promotes mitochondrial ROS leakage, damages mitochondrial DNA, disrupts membrane potential, and impairs the transcription of key respiratory chain components. In parallel, mitochondrial quality-control systems become progressively compromised. The balance between fusion and fission shifts toward pathological fragmentation through reduced MFN1/2 and OPA1 activity and enhanced DRP1-mediated fission. Mitochondrial biogenesis is suppressed through inhibition of the AMPK/SIRT1/PGC-1α/NRF1/TFAM axis, while mitophagy changes from an early compensatory response to a later state of autophagic flux blockade and accumulation of dysfunctional mitochondria. Importantly, damaged mitochondria serve as signal amplifiers linking metabolic stress to inflammation and programmed cell death. Mitochondrial ROS, oxidized mitochondrial DNA, calcium overload, cardiolipin exposure, and membrane permeabilization activate interrelated death pathways, including intrinsic apoptosis, ferroptosis, and pyroptosis. Cytochrome C and apoptosis-inducing factor promote caspase-dependent and caspase-independent apoptosis; iron dyshomeostasis, glutathione depletion, GPX4 dysfunction, and lipid peroxidation drive ferroptosis; and mitochondrial danger signals activate the NLRP3 inflammasome and gasdermin-dependent pyroptosis. These pathways jointly damage the retinal neurovascular unit and contribute to pericyte loss, endothelial barrier breakdown, Müller cell dysfunction, retinal ganglion cell apoptosis, retinal pigment epithelial injury, and photoreceptor degeneration. This review also emphasizes the role of epigenetic regulation in stabilizing mitochondrial pathology. DNA methylation, histone modifications, and non-coding RNAs interact to silence mitochondrial protective genes, alter antioxidant responses, and maintain the “metabolic memory” of DR even after glycemic normalization. Therefore, mitochondrial dysfunction should be understood as a dynamic, multidimensional network rather than a single pathological event. Current clinical approaches, such as laser photocoagulation, intravitreal anti-VEGF therapy, and vitrectomy, mainly target advanced vascular lesions and are limited by invasiveness, incomplete responsiveness, recurrence, and potential adverse effects. Therapeutically, strategies targeting mitochondrial ROS, restoring mitochondrial dynamics, enhancing biogenesis, regulating mitophagy, inhibiting inflammasome activation, correcting epigenetic abnormalities, and improving targeted delivery systems show promising potential. However, major translational barriers remain, including retinal cell heterogeneity, stage-specific mitochondrial responses, insufficient organelle-specific drug delivery, and long-term safety concerns. A deeper understanding of mitochondrial regulatory networks may support earlier, more precise, and multi-target interventions for preventing or slowing DR progression.

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.

NAFLD is the most prevalent chronic liver disease,which has become a world public health issue and the incidence rate is also showing an increasing trend.A series of liver disea-ses,such as simple fatty liver disease,NASH,liver cirrhosis,liv-er failure and liver cancer,can be collectively referred to as NAFLD.Through the study of numerous factors that influence the production of NAFLD,it has been found that the main patho-logical mechanism is excessive synthesis of fat,which is difficult to be transported into the blood,causing massive lipid accumula-tion.Alcohol has a direct damaging effect on liver and will in-hibit the breakdown of liver fat,eventually forming AFLD.How-ever,it is still controversial whether alcohol has a synergistic effect on NAFLD onset.This article provides a review on the effect of alcohol intake on NAFLD and its potential mechanisms of action.

Objective To investigate whether successful percutaneous coronary intervention(PCI)could improve symptoms and quality of life(QOL)in left main disease and/or 3-vessel disease patients with diabetes.Methods Patients with left main disease and/or 3-vessel disease who underwent PCI in the First Affiliated Hospital of Air Force Medical University from April 2018 to May 2021 were consecutively enrolled and subdivided into 2 groups:diabetes and no diabetes.Detailed baseline characteristics,symptoms,including dyspnea and angina,assessed with the Rose dyspnea scale(RDS),Seattle angina questionnaire(SAQ),the European quality of life-5 dimensions(EQ-5D)and 12-item short-form health survey(SF-12)questionnaire respectively,procedural details,and 1 month and 1 year follow-up data were collected.Results Among 440 left main disease and/or 3-vessel disease patients,disease was present in 176(40.00％),who had more hypertension,peripheral artery disease,and LCX lesion(all P＜0.05).The incidence of major adverse cardiovascular events(MACE)and all-cause mortality were similar between the two groups(both P＞0.05)at 1 month follow-up,while all-cause mortality in diabetes patients was significantly higher than those without diabetes at 1 year follow-up(P=0.013).Low left ventricular ejection fraction was an independent risk factor for MACE and all-cause mortality at 1 month and 1 year follow-up after successful revascularization(all P＜0.05).Most importantly,symptoms,including dyspnea and angina,and QOL were markedly improved regardless of diabetes both at 1 month and 1 year follow-up(all P＜0.05).Diabetes patients showed improved dyspnea and QOL at similar degree to the non-diabetes patients(all P＞0.05)and a more significantly relieved angina(P=0.013).Additionally,the number of chronic total occlusion(CTO)per patient was identified as an independent risk factor of dyspnea(OR 0.723,95％CI 0.525～0.997,P=0.048)and angina relief(OR 0.686,95％CI 0.473～0.995,P=0.047),and the contrast volume(OR 0.995,95％CI 0.992～0.999,P=0.008)as an independent risk factor of QOL improvement in diabetic patients.Conclusions Successful PCI is beneficial for relieving symptoms and improving quality of life in patients with diabetes who have left main disease and/or 3-vessel disease.

Objective To obtain measurements of fetal four-chamber view and left and right ventricular shapes using two-dimensional speckle tracking,and to explore the clinical quantification of cardiac shape remodeling in small for gestational age(SGA)fetuses.Methods In this study,we prospectively collected data on singleton pregnancies from 28 to 39 weeks that were established in the archives of Obstetrics and Gynecology Hospital,Fudan University from May 2020 to Jul 2021.Fetuses eligible for inclusion criteria were randomly matched according to the ratio of estimated fetal weight(EFW)≥10th percentile(P10)∶EFWP90 in the apical segments of the left ventricle was greater in normal UAPI fetuses than that in low-risk fetuses(24.2%vs.14.3%,P=0.005);the SI of left ventricle in UAPI-normal SGA fetuses was greater in segments S1 to S22 than that of right ventricle with statistically significant difference(all P<0.05),while no significant difference was found between the SI of left and right ventricles in segments S23 and S24.Conclusion About one-fourth of SGA fetuses with normal UAPI have abnormally increased apical segments of the left ventricular SI,suggesting that cardiac remodeling with a predominantly"elongated"apical segments of the left ventricle precedes Doppler anomalies.

Objective To analyze the applicability of Berg Balance Scale(BBS)in the balance assessment of Parkinson's disease(PD)patients with balancing instrument test as the standard,so as to formulate targeted intervention programs accordingly.Methods A total of 186 patients with PD admitted to Department of Neurology and Department of Rehabilitation Medicine,Huashan Hospital,Fudan University from Jan 2020 to Sep 2022 were selected as the research objects.The static balance function of the patients was evaluated by balancing instrument.The balance function parameters were analyzed according to BBS and levodopa equivalent dose.Results The drug dose did not affect the score of balance instrument.There was a good correlation between BBS and balance instrument.The higher the scale score,the lower the balance instrument score(r=-0.333,P<0.001).There was a positive correlation between levodopa equivalent dose and item 11 of BBS in subjects with BBS total score of 21-40(r=0.715,P=0.046).There was a significant negative correlation between levodopa equivalent dose and item 6 of BBS in subjects with BBS total score of 51-56(r=-0.300,P=0.001).The number of people who lost the most points in BBS from large to small was item 14 of standing on one leg(134 cases),item 13 of standing without support with one foot in front(122 cases),item 11 of turning 360 degrees(75 cases),item 8 of reaching forward with outstretched arms(70 cases),and item 12 of placing one feet alternately on a step or a stool(56 cases).Conclusion In the absence of balancing instrument,BBS has good clinical applicability in the evaluation of PD patients,and some items can be focused on according to the lost points.

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.

Objective To obtain measurements of fetal four-chamber view and left and right ventricular shapes using two-dimensional speckle tracking,and to explore the clinical quantification of cardiac shape remodeling in small for gestational age(SGA)fetuses.Methods In this study,we prospectively collected data on singleton pregnancies from 28 to 39 weeks that were established in the archives of Obstetrics and Gynecology Hospital,Fudan University from May 2020 to Jul 2021.Fetuses eligible for inclusion criteria were randomly matched according to the ratio of estimated fetal weight(EFW)≥10th percentile(P10)∶EFWP90 in the apical segments of the left ventricle was greater in normal UAPI fetuses than that in low-risk fetuses(24.2%vs.14.3%,P=0.005);the SI of left ventricle in UAPI-normal SGA fetuses was greater in segments S1 to S22 than that of right ventricle with statistically significant difference(all P<0.05),while no significant difference was found between the SI of left and right ventricles in segments S23 and S24.Conclusion About one-fourth of SGA fetuses with normal UAPI have abnormally increased apical segments of the left ventricular SI,suggesting that cardiac remodeling with a predominantly"elongated"apical segments of the left ventricle precedes Doppler anomalies.

NAFLD is the most prevalent chronic liver disease,which has become a world public health issue and the incidence rate is also showing an increasing trend.A series of liver disea-ses,such as simple fatty liver disease,NASH,liver cirrhosis,liv-er failure and liver cancer,can be collectively referred to as NAFLD.Through the study of numerous factors that influence the production of NAFLD,it has been found that the main patho-logical mechanism is excessive synthesis of fat,which is difficult to be transported into the blood,causing massive lipid accumula-tion.Alcohol has a direct damaging effect on liver and will in-hibit the breakdown of liver fat,eventually forming AFLD.How-ever,it is still controversial whether alcohol has a synergistic effect on NAFLD onset.This article provides a review on the effect of alcohol intake on NAFLD and its potential mechanisms of action.

Objective To analyze the applicability of Berg Balance Scale(BBS)in the balance assessment of Parkinson's disease(PD)patients with balancing instrument test as the standard,so as to formulate targeted intervention programs accordingly.Methods A total of 186 patients with PD admitted to Department of Neurology and Department of Rehabilitation Medicine,Huashan Hospital,Fudan University from Jan 2020 to Sep 2022 were selected as the research objects.The static balance function of the patients was evaluated by balancing instrument.The balance function parameters were analyzed according to BBS and levodopa equivalent dose.Results The drug dose did not affect the score of balance instrument.There was a good correlation between BBS and balance instrument.The higher the scale score,the lower the balance instrument score(r=-0.333,P<0.001).There was a positive correlation between levodopa equivalent dose and item 11 of BBS in subjects with BBS total score of 21-40(r=0.715,P=0.046).There was a significant negative correlation between levodopa equivalent dose and item 6 of BBS in subjects with BBS total score of 51-56(r=-0.300,P=0.001).The number of people who lost the most points in BBS from large to small was item 14 of standing on one leg(134 cases),item 13 of standing without support with one foot in front(122 cases),item 11 of turning 360 degrees(75 cases),item 8 of reaching forward with outstretched arms(70 cases),and item 12 of placing one feet alternately on a step or a stool(56 cases).Conclusion In the absence of balancing instrument,BBS has good clinical applicability in the evaluation of PD patients,and some items can be focused on according to the lost points.