Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

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.

This study examines the effects of zearalenone(ZEA)on the survival and function of lu-teinized granulosa cells,and studies the role of activating transcription factor 6(ATF6)in regula-ting apoptosis and functional abnormalities of luteinized granulosa cells induced by ZEA.An in vitro model of luteinized granulosa cells was utilized to examine the effects of ZEA treatment on apoptosis,hormone secretion,and the expression of relevant proteins.Furthermore,the expression of ATF6 was manipulated using siRNA to elucidate its regulatory function in the ZEA-induced damage of luteinized granulosa cells in mice.Our findings revealed that ZEA inhibited the activity of luteinized granulosa cells and reduced the secretion of estradiol(E2)and progesterone(P4)in a dose-dependent manner.The expression levels of p-IRE1,ATF6 and StAR in both low(20 pmol/L)and high(40 μmol/L)ZEA groups were significantly increased after 24 h(P＜0.05).GRP78 had no significant change at low concentration treatment(P＞0.05),but significantly increased at high concentration treatment(P＜0.05).Similarly,ATF4 and p-EIF2α had no significant change at low concentration treatment(P＞0.05),but significantly decreased at high concentration treat-ment(P＜0.05).HSD3B2 and CYP19A1 were significantly decreased in both low and high concentration treatments(P＜0.05).After 48 h of treatment,ATF6 and GRP78 were significantly increased in both low and high concentration treatments(P＜0.05).p-IRE1 was significantly de-creased at low concentration treatment(P＜0.05),but remained unchanged at high concentration treatment(P＞0.05).ATF4,p-EIF2α,HSD3B2 and CYP19A1 were significantly decreased in both low and high concentration treatments(P＜0.05).St AR was significantly increased in both low and high concentration treatments(P＜0.05).Interference with the expression of ATF6 could sig-nificantly reduce the apoptosis induced by low concentration group(P＜0.05),and enhanced the hormone secretion in both high and low concentration groups(P＜0.05).In conclusion,ZEA can cause damage to luteinized granulosa cells and activate ATF6 signaling pathway.Interference with ATF6 can alleviate apoptosis and hormone secretion disturbance induced by low concentration ZEA,but has limited effect on damage caused by high concentration ZEA.

Medical therapy and surgical intervention are the two primary approaches for treating myocardial bridge.However,there remains controversy regarding the use of coronary artery bypass grafting(CABG)and myocardial bridge unroofing.Here,we report a case of myocardial infarction following CABG in a patient with a myocardial bridge.The patient was admitted to Lanzhou First Peopie's Hospital with persistent chest pain,chest tightness,and shortness of breath lasting 2 hours.Physical examination revealed no significant abnormalities.Electrocardiography(ECG)indicated extensive anterior wall myocardial infarction.Laboratory findings showed myoglobin levels of 140.1 ng/ml and troponin Ⅰ levels of 2.59 ng/ml,with no other significant abnormalities.The initial diagnosis was acute extensive anterior wall myocardial infarction.Emergency coronary angiography revealed a myocardial bridge in the mid-segment of the left anterior descending artery(LAD).Emergency CABG using the left internal mammary artery to the LAD was performed,leading to symptomatic improvement,and the patient was discharged in stable condition.However,the patient experienced a recurrent myocardial infarction seven years post-surgery and received secondary preventive medical therapy.The patient is currently under ongoing follow-up care.CABG is an effective treatment for myocardial bridge.However,based on the case reported in this study,we recommend careful evaluation of whether a patient may benefit from CABG.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim Mouse model of myocardial hypertro-phy was established via intraperitoneal injection of iso-proterenol(ISO)in mice.This approach allows for an in-depth investigation into the pharmacological effects and mechanisms of action of emodin,offering novel in-sights and directions for the improvement of myocardial hypertrophy.Methods The mice were randomly di-vided into the following groups:control group(CON),emodin group(EMO),MAPK activator control group(EMO+Ani),model group(ISO),treatment group(ISO+EMO),and activator intervention group(ISO+EMO+Ani).After treatment with emodin and inter-vention with MAPK activator,the heart weight ratio and cardiac size of each group were observed.Hematoxy-lin-eosin(HE)staining was used to observe the patho-logical changes in cardiac tissue,and kits were utilized to measure the levels of GSH,LDH,and MDA in the serum.Western blot was employed to detect the protein expression levels of inflammatory and oxidative factors,as well as p-p38,p-ERK,p38,and ERK in cardiac tis-sue.Results Emodin can significantly inhibit the production of myocardial inflammatory and oxidative factors induced by ISO,thereby effectively alleviating the degree of myocardial hypertrophy and fibrosis.Af-ter the p38/ERK signaling pathway was specifically ac-tivated by farnesol,the improvement effect of emodin on myocardial hypertrophy was weakened.Further comparison revealed that,compared with the myocardi-al hypertrophy pathological model group,the pathologi-cal protein expression levels in the farnesol-treated group showed no significant difference,and were even higher in some indicators.Conclusion Emodin can effectively inhibit the release of inflammatory factors and improve the state of oxidative stress by modulating the p38/ERK signaling pathway,thereby exerting an ameliorative effect on myocardial hypertrophy.

Objective To retrospectively analyze the association between metabolic factors and high-risk colorectal adenoma(CRA).Methods The medical records of patients aged 18-75 years who underwent their initial colonoscopy at Karamay Central Hospital of Xinjiang Uygur Autonomous Region from Jul 2000 to Mar 2017 were collected.The comparison between normal colonoscopy(NC)and high-risk CRA patients was conducted using an unpaired t-test,while chi-square test was used for categorical variables.Least absolute shrinkage and selection operator(LASSO)regression and Logistic regression were utilized to analyze the association between metabolic factors and high-risk CRA.Results A total of 1 798 patients meeting the inclusion and exclusion criteria were enrolled and divided into normal colonoscopy(NC)findings group(n=972)and high-risk CRA group(n=826).The high-risk CRA group exhibited significantly lower levels of high-density lipoprotein cholesterol(HDL-C)in comparison to the NC group,while uric acid and fibrosis 4(FIB-4)index levels were significantly higher than those observed in the NC group(all P<0.05).Based on LASSO regression analysis,we identified 12 variables that potentially influence the occurrence of high-risk CRA,including age,gender,smoking history,alcohol consumption history,non-alcoholic fatty liver disease(NAFLD),hypertension,coronary artery disease,hyperglycemia,hypercholesterolemia,low levels of HDL-C,elevated alanine aminotransferase,and elevated gamma-glutamyl transferase.Multivariate analysis revealed that individuals aged over 50 years,male gender,cigarette and alcohol consumption,low HDL-C levels,history of NAFLD and hypertension were identified as independent risk factors associated with high-risk CRA(P<0.05).In addition,without or with adjusting for age,sex,smoking,and drinking history,patients with a high TG/HDL-C ratio(the ratio≥2.68)had a significantly higher risk of high-risk CRA than those with a low TG/HDL-C ratio(the ratio<2.68)[odds ratios(ORs)were1.430 and 1.235 respectively,all P<0.05)].Without or with adjusting variables,the ORs for NAFLD patients with FIB-4 index>2.67 were 1.849(P=0.466)and 1.435(P=0.707),respectively.Conclusion A significant association exists between metabolic factors and high-risk CRA.Independent risk factors for high-risk CRA include older age(≥50 years),male,smoking history,alcohol consumption history,low levels of HDL-C,and a history of NAFLD and hypertension.Individuals exhibiting a TG/HDL-C ratio exceeding 2.68 manifest a significantly heightened susceptibility to the development of high-risk CRA.Therefore,elderly males with one or more aforementioned metabolic abnormalities should be considered a priority population for colorectal screening.

Fe/Mn/Gd polymetallic nanooxide(FMGN)were prepared by one-step solvent thermal reaction by using Fe(acac)3,Mn(acac)2 and Gd(acac)3 as reaction precursors.Next,hyaluronic acid(HA)was used to modify FMGN to fabricate tumor-targeting T 1-T 2 dual-mode magnetic resonance imaging(MRI)contrast agent(HA-FMGN)for accurate diagnosis of prostate cancer.The structure and morphology of FMGN were observed by transmission electron microscope(TEM).It was found that FMGN exhibited a uniform nanocluster spherical structure when the feeding ratio of iron acetylacetonate,manganese acetylacetonate,and gadolinium acetylacetonate was 3:2:1.X-ray diffraction(XRD)analysis showed that FMGN had a typical inverse spinel structure of Mn doped Fe 3O 4,with Gd existing in the form of amorphous gadolinium oxide.The longitudinal relaxivity(r 1)and transverse relaxivity(r 2)of FMGN were 13.395 and 428.535 L/(mmol·s),respectively,measured by 0.5 T MRI analyzer,which proved that FMGN had excellent T 1-T 2 dual-mode MRI contrast capability.The cytotoxicity and hemolysis test found that HA-FMGN didn't damage red cells and induce toxicity for normal cells,indicating that HA-FMGN had excellent cell biocompatibility.The internalization efficacy of HA-FMGN was observed by CLSM,and the results showed that HA-FMGN possessed excellent prostate tumor-targeting ability.In vivo MRI experiment showed that HA-FMGN significantly enhanced T 1 and T 2 weighted MRI signal to noise ratio(SNR)of prostate tumor,which promoted the accurate diagnosis of orthotopic prostate cancer.

Risks of food safety induced by small molecule drug residues in animal food and environment have become an increasing public concern,so it is necessary to develop highly sensitive and easy-to-operate techniques to detect small molecules.Herein,a metasurface plasma resonance(MetaSPR)sensor chip coupled with gold nanoparticles(AuNPs)was developed for detection of enrofloxacin(ENR)based on competitive immunoassay.The detection range of the sensor for ENR was 0.025-3.2 ng/mL,and the detection limit(3σ)was 20 pg/mL.The biosensor showed excellent performance including high selectivity,good stability,ease to operate and high throughput,etc.The developed method was applied to detection of ENR residues in real samples,with recoveies of 96.0% -105.0%.The proposed sensing strategy provided new technique reference for detection of other small molecules in the field of residue analysis in food safety and environment monitoring.