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.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver, and their activation plays a crucial role in the development and progression of hepatic fibrosis. Here, we report that retinoid X receptor-alpha (RXRα), a unique member of the nuclear receptor superfamily, is a key modulator of HSC activation and liver fibrosis. RXRα exerts its effects by modulating calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)-mediated activation of AMP-activated protein kinase-alpha (AMPKα). In addition, we demonstrate that K-80003, which binds RXRα by a unique mechanism, effectively suppresses HSC activation, proliferation, and migration, thereby inhibiting liver fibrosis in the CCl₄ and amylin liver NASH (AMLN) diet animal models. The effect is mediated by AMPKα activation, promoting mitophagy in HSCs. Mechanistically, K-80003 activates AMPKα by inducing RXRα to form condensates with CaMKKβ and AMPKα via a two-phase process. The formation of RXRα condensates is driven by its N-terminal intrinsic disorder region and requires phosphorylation by CaMKKβ. Our results reveal a crucial role of RXRα in liver fibrosis regulation through modulating mitochondrial activities in HSCs. Furthermore, they suggest that K-80003 and related RXRα modulators hold promise as therapeutic agents for fibrosis-related diseases.

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

Objective: To investigate the Alzheimer's disease (AD) influencing factors among the elderly, so as to provide a basis for early prevention and intervention. Methods: From March to June 2024, participants aged 60 years and above from a sub-district in Haishu District, Ningbo City, Zhejiang Province were selected using a convenience sampling method. Data on demographics, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were evaluated using the short-form Geriatric Depression Scale. The Chinese Mini-Mental State Examination (MMSE) was used for the initial screening of AD, and individuals who screened positive were further diagnosed by psychiatrists. Factors affecting AD among the elderly were analyzed using a multivariable logistic regression model. Results: A total of 3 644 individuals were surveyed, comprising 1 526 males (41.88%) and 2 118 females (58.12%). The mean age was (71.85±7.44) years. AD was detected in 200 cases, with a detection rate of 5.49%. Multivariable logistic regression analysis showed that individuals aged ≥65 years (65-<70 years, OR=3.012, 95%CI: 1.007-9.012; 70-<75 years, OR=3.131, 95%CI: 1.059-9.260; 75-<80 years, OR=5.779, 95%CI: 1.989-16.784; ≥80 years, OR=16.810, 95%CI: 5.926-47.685), those who were unmarried, divorced, or widowed (OR=1.973, 95%CI: 1.383-2.815), those with hearing loss (OR=1.573, 95%CI: 1.128-2.193), those with diabetes mellitus (OR=1.958, 95%CI: 1.362-2.814), and those with depressive symptoms (OR=4.143, 95%CI: 2.997-5.728) had a higher risk of AD. Conversely, individuals with an educational level of primary school or above (primary school, OR=0.579, 95%CI: 0.401-0.835; junior high school or above, OR=0.438, 95%CI: 0.259-0.741), and those who engaged in regular physical exercise (OR=0.414, 95%CI: 0.264-0.649) had a lower risk of AD. Conclusions The detection rate of AD was relatively high among the elderly in Haishu District. AD among the elderly was related to age, educational level, marital status, physical exercise, hearing loss, diabetes mellitus, and depressive symptoms.

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

OBJECTIVE To explore the effects of meropenem exposure and degradation levels on clinical efficacy in patients with purulent meningitis （PM）. METHODS A total of 131 PM patients treated with meropenem at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2022 to June 2025 were prospectively included. Relevant data were collected and divided into a cured group （91 cases） and a non-cured group （40 cases） based on the efficacy. High-performance liquid chromatography-tandem mass spectrometry was used to determine the concentration of meropenem and its open-loop metabolites. Risk factors that affect efficacy were screened， and their predictive power and correlation were evaluated by univariate analysis， and multivariate Logistic regression analysis， receiver operating characteristic （ROC） curves， and correlation analysis. RESULTS Univariate analysis showed that serum creatinine， creatinine clearance rate， minimum inhibitory concentration of meropenem ≥16 μg/mL， cerebrospinal fluid red blood cell count， cerebrospinal fluid white blood cell count， cerebrospinal fluid glucose content， blood trough concentration， blood open-loop metabolite concentration/trough concentration ratio， and intrathecal injection were all correlated with efficacy （P＜0.05）. The results of multiple Logistic regression analysis showed that serum creatinine blood open-loop metabolite concentration/trough concentration ratio， intrathecal injection， and cerebrospinal fluid glucose content were influencing factors for suboptimal anti-infective ltt efficacy （P＜0.05）. ROC curve analysis showed that when the blood open-loop metabolite concentration/trough concentration ratio was greater than 2.854 （AUC＝0.647）， serum creatinine was less than 59.5 μmol/L （AUC＝0.647）， and cerebrospinal fluid glucose content was less than 3.37 mmol/L （AUC＝0.709）， the risk of treatment failure significantly increased （P＜0.05）. Correlation analysis showed that the blood trough concentration of meropenem was positively correlated with the concentration of its open-loop metabolites （R 2＝0.134 5， P＜0.000 1）. CONCLUSIONS Insufficient exposure level and rapid degradation of meropenem are key mechanisms affecting the anti-infective efficacy of PM. Elevated blood open-loop metabolite concentration/ trough concentration ratio， low serum creatinine level， lack of intrathecal injection， and low cerebrospinal fluid glucose content are independent risk factors for poor efficacy.

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

Hematologic malignancies,such as lymphoma,myeloma,and myeloid neoplasms,can occur in extramedullary tissues.Traditional histopathological morphology and immunohistochemical staining have lim-itations,including time-consuming specimen processing,prolonged reporting cycles,and relatively low sensi-tivity in cases of limited cell numbers.Flow cytometry offers significant advantages in detecting tissue sam-ples,such as rapid processing,shorter reporting cycles,and high accuracy and sensitivity,making it an effective complement to histopathological and immunohistochemical methods.However,the application of flow cytome-try in tissue sample detection currently lacks standardized protocols for sample collection and preservation,single-cell suspension preparation,antibody panel design for limited samples,data analysis,and result repor-ting.To promote the standardized application of flow cytometry in detecting hematologic tumor cells in tissue samples,the Cell Analysis Professional Committee of the Chinese Society of Biotechnology organized experts to develop the Chinese Expert Consensus on Flow Cytometry for Detecting Hematologic Tumor Cells in Tis-sue Samples(hereinafter referred to as the Consensus).This Consensus elaborates on the technical aspects of flow cytometry for tissue sample detection,covering sample processing,antibody panel design,data analysis,reporting content,and quality management.It particularly emphasizes recommended antibody panels and data analysis methods for flow cytometry when tissue sample cell counts are low.This article aims to interpret the key points of the Consensus to facilitate its better application in clinical practice.

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.