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.

To address the constrains imposed by insufficient hydrogen peroxide (H2O2) and high glutathione (GSH) expression in tumor cells on the efficacy of chemodynamic therapy (CDT), zeolitic imidazolate framework-8 (ZIF-8) loaded with disulfiram (DSF) and 3-amino-1,2,4-triazole (3-AT) was synthesized via a one-pot approach. Subsequently, hyaluronic acid (HA)-modified cupric peroxide (CuO2) was in-situ grown on its surface through biomineralization to construct a multifunctional copper-based nanozyme ADZCH (3-AT/DSF@ZIF-8@CuO2-HA). This nanoplatform disrupts the intratumoral H2O2 homeostasis, depletes GSH, and synchronously delivers DSF and Cu2+ via cascade catalysis, thereby enhancing CDT and sensitizing tumors to DSF-based chemotherapy. The results of physicochemical characterization indicated that ADZCH presented a uniform core-shell structure with favorable dispersibility. Its particle size and Zeta potential were 196.5 nm and −19.5 mV, respectively. It possessed a microporous structure with a specific surface area of 81.8600 m2/g, and demonstrated efficient loading capacity for DSF and 3-AT, achieving drug loading efficiencies of 5.91% and 45.07%, respectively. Moreover, ADZCH can continuously and slowly release drugs in an acidic environment and maintain good stability under diverse physiological conditions. In vitro functional assays verified that ADZCH catalytically generated H2O2 and hydroxyl radicals while concurrently depleting GSH in a concentration- and incubation time-dependent manner. Cellular uptake experiments showed that HA modification significantly improved the uptake of nanoparticles by 4T1 cells. Cytotoxicity tests showed that 80 μg/mL ADZCH had a significant cytotoxic effect on 4T1 cells but no significant toxicity on L929 cells. DCFH-DA probe detection indicated that ADZCH could significantly induce intracellular reactive oxygen species (ROS) generation, thereby enhancing CDT efficacy. Live/dead staining experiments showed that ADZCH efficiently induced apoptosis, with the proportion of dead cells reaching 94.74%, demonstrating its promising potential for anti-tumor applications.This study provides new research ideas and experimental basis for overcoming the tumor microenvironment barrier and enhancing the anti-tumor effect of CDT combined with chemotherapy.

Objective:To investigate the prevalence and influencing factors of kinesiophobia in chronic pain patients undergoing conservative treatment for lumbar disc herniation (LDH), providing reference and basis for reducing the occurrence of kinesiophobia in this patient population.Methods:A cross-sectional study was conducted from May 2023 to January 2024. A convenience sample of chronic pain patients undergoing conservative treatment for LDH in Guangdong Second Provincial General Hospital was selected as the study population. Data were collected by the general information questionnaire, Numerical Rating Scale, Tampa Scale of Kinesiophobia-11, Psychological Inflexibility in Pain Scale, and Chronic Pain Self-efficacy Scale. A random forest model was constructed to rank variable importance, and binary Logistic regression was used to analyze the influencing factors of kinesiophobia.Results:The effective response rate of the questionnaire was 96.8% (270/279). Among 270 patients, there were 139 males and 131 females, 92 patients aged 18-40 years, 132 patients aged 41-60 years, and 46 patients aged >60 years old. The total kinesiophobia score for chronic pain patients undergoing conservative treatment for LDH was (31.00 ± 5.09) points, with a kinesiophobia prevalence of 79.6% (215/270). The random forest algorithm identified eight influencing factors: pain self-efficacy, psychological flexibility, physical exercise after low back pain onset, duration of pain, pain status, understanding of LDH, marital status, and pain intensity. Binary Logistic regression analysis showed that physical exercise after low back pain onset ( OR=0.583, 95% CI 0.344-0.986), pain status ( OR=0.424, 95% CI 0.206-0.873), psychological flexibility ( OR=1.102, 95% CI 1.052-1.155), pain self-efficacy ( OR=0.923, 95% CI 0.895-0.953) were significant influencing factors of kinesiophobia in chronic pain patients undergoing conservative treatment for LDH (all P<0.05). Conclusions:The prevalence of kinesiophobia is high among chronic pain patients undergoing conservative treatment for LDH. Physical exercise after low back pain onset, pain status, psychological flexibility, and pain self-efficacy are significant influencing factors. Healthcare professionals should pay increased attention to kinesiophobia in chronic pain patients undergoing conservative treatment for LDH and implement targeted early interventions to reduce its occurrence.

Objective To investigate the mediating effect of psychological flexibility on the relationship between pain self-efficacy and kinesiophobia in patients with lumbar disc herniation(LDH),so as to provide references for relief from kinesiophobia of the patients.Methods Convenience sampling was used to select 256 patients with LDH as the research subjects from the Outpatient Department of Spinal Orthopaedics of a Grade IIIA hospital in Guangdong Province between May and December 2023.The subjects were surveyed with a general information questionnaire,the kinesiophobia assessment scale,psychological flexibility inventory for pain patients,and chronic pain self-efficacy scale.The mediation effect of psychological flexibility on pain self-efficacy and kinesiophobia was analysed using SPSS 26.0 and the PROCESS 3.5 macro.Results The scores for kinesiophobia,psychological flexibility and pain self-efficacy among the LDH patients were 31.66±4.73,55.26±11.06 and 68.14±17.48,respectively.Kinesiophobia was positively correlated with the psychological flexibility(r=0.545,P<0.001)and negatively correlated with the pain self-efficacy(r=-0.599,P<0.001).The psychological flexibility was negatively correlated with the pain self-efficacy(r=-0.510,P<0.001).Psychological flexibility partially mediated the relationship between pain self-efficacy and kinesiophobia,with a mediating effect of-0.045,accounting for 27.78%of the total effect.Conclusion The patients who have LDH and under conservative treatment exhibit a high level of kinesiophobia and with a moderate levels of pain self-efficacy and psychological flexibility.The medical staff can improve the self-efficacy and psychological flexibility of patients,so as to reduce kinesiophobia level and its incidence.

Brain age prediction is of great significance to the in-depth understanding of individual neurodevelopment,early diagnosis of neuropsychiatric disorders,and formulation of personalized treatment plans. With the continuous advancement of deep learning,more and more researches focus on using such algorithms to predict brain age. Compared with traditional regression algorithms,deep learning which has the advantages of complex pattern learning,end-to-end learning and high adaptability can more accurately reveal the neuropathological mechanisms of neuropsychiatric disorders,and provide more precise tools for clinical assessment,assisted diagnosis and prognosis prediction. Herein the study reviews the recent advances in the application of deep learning algorithms in brain age prediction,introduces the achievements in deep learning model optimization,multimodal data inputs and interpretability studies for brain age prediction,discusses the methods for the establishment of integrated deep learning architectures and the future challenges of developing unified benchmarking,and provides an outlook on the application of deep learning in brain age prediction.

Objective:To analyze the association between standardized management of clinical research, initiated by investigators and guided by clinical research management policies in healthcare institutions, and changes in the research behavior of graduate students.Methods:Theses related to cardiovascular health published by graduate students in the Sichuan-Chongqing region of China between January 2019 and June 2024 were retrieved from the China National Knowledge Infrastructure database. Multilevel models were used to analyze changes in ethical compliance awareness, research methodology standardization, and academic collaboration of graduate students before and after policy implementation. Using Shapiro Wilk test and percentage representation.Results:Among the 712 theses included in this study, the proportion of studies with ethical review reports increased from 44.50% to 55.32% following the implementation of standardized management [odds ratio ( OR)=1.80, P=0.017]. Standardized management significantly improved the quality scores of cross-sectional studies and randomized controlled trials ( P<0.001), as well as significantly increased the frequencies of multi-center collaboration ( OR=2.84, P=0.001) and intra-provincial collaboration ( OR=2.80, P=0.001). Conclusions:Standardized clinical research management shows significant association with positive changes in the research behavior of graduate students. Further optimization of management measures is recommended to comprehensively enhance the clinical research capabilities of graduate students.

Aim To investigate the protective effect of Shengmai Yin on myocardial ischemia/reperfusion in-jury(MI/RI)in vitro and in vivo and to unravel the underlying mechanism.Methods SD rats were divid-ed into the sham group,model group,and Shengmai Yin group(SM).Rat MI/RI model was established.Cardiac function,infarct area,pathological changes,cardiomyocyte apoptosis,macrophage infiltration,and serum cTnT and CK-MB levels were measured.The mRNA and protein expressions of Calpain-1 and Cal-pain-2 were assessed.The hypoxia/reoxygenation(H/R)model was constructed in H9c2 cells.The active ingredients of Shengmai Yin were screened using net-work pharmacology and verified by CCK-8.In the car-diomyocytes H/R model,Fluo-4 AM staining was used to detect the changes of Ca2+levels.Results Com-pared with model group,LVEF and LVFS of Shengmai Yin-treated rats increased,myocardial infarction area was reduced,while myocardial tissue injury was allevi-ated.Myocardial apoptosis rate and the number of macrophages were reduced.Similarly,cTnT and CK-MB levels decreased.In addition,the expression lev-els of Calpain-1 and Calpain-2 mRNA and protein de-creased in the SM treatment group.Under the H/R model,all the active ingredients of Shengmai decoction had protective effects on cardiomyocytes,and the treat-ment could reduce the level of Ca2+in cardiomyocytes.Conclusions Shengmai Yin has protective effects on MI/RI in rats.This effect may be related to the de-crease in Ca2+levels,as well as Calpain-1 and Calap-in-2 mRNA and protein expression.

Brain age prediction is of great significance to the in-depth understanding of individual neurodevelopment,early diagnosis of neuropsychiatric disorders,and formulation of personalized treatment plans. With the continuous advancement of deep learning,more and more researches focus on using such algorithms to predict brain age. Compared with traditional regression algorithms,deep learning which has the advantages of complex pattern learning,end-to-end learning and high adaptability can more accurately reveal the neuropathological mechanisms of neuropsychiatric disorders,and provide more precise tools for clinical assessment,assisted diagnosis and prognosis prediction. Herein the study reviews the recent advances in the application of deep learning algorithms in brain age prediction,introduces the achievements in deep learning model optimization,multimodal data inputs and interpretability studies for brain age prediction,discusses the methods for the establishment of integrated deep learning architectures and the future challenges of developing unified benchmarking,and provides an outlook on the application of deep learning in brain age prediction.

The MGF300-4L protein of African swine fever virus(ASFV),a virulence-associated fac-tor,degrades IKKβ through the chaperone-mediated autophagy and enhances the stability of IKBαto suppress the generation of IL-1β and TNF-α regulated by the NF-κB signaling pathway.To iden-tify the host proteins interacting with MGF300-4L,PK-15 cells were transfected with the eukary-otic plasmid expressing MGF300-4L and analyzed using immunoprecipitation-mass spectrometry(IP-MS)to identify the host proteins that interact with MGF300-4L.Additionally,gene ontology(GO)and KEGG pathway enrichment analyses were conducted.Furthermore,molecular docking a-nalysis,co-immunoprecipitation,and laser confocal microscopy assays were performed to validate the host proteins interacting with MGF300-4L.The IP-MS analysis identified 145 host proteins that potentially interact with MGF300-4L.Subsequent GO and KEGG pathway enrichment analy-ses revealed that these proteins are predominantly involved in metabolic,cellular,and innate immune responses.Through molecular docking prediction,co-immunoprecipitation assay,and laser confocal microscopy,we identified the interaction between MGF300-4L and STAT1.This study provides critical insights into the mechanisms underlying the interactions between MGF300-4L and the host proteins.