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.

Objective To construct a competency evaluation model for forensic practitioners,providing a reference for their training and assessment.Methods Based on the iceberg and onion models of com-petency,and with reference to Spencer's Competency Dictionary,literature research was conducted and focus group interviews were employed to preliminarily construct core indices and measurement items for evaluating the competency of forensic practitioners.The Delphi method was applied for two rounds of expert consultation to further refine the competency evaluation index system.The analytic hierarchy process(AHP)was used to calculate the weights of the indices.Results A competency evaluation model for forensic practitioners was constructed,consisting of 7 core indices,encompassing forensic skills,identification service capabilities,and the ability to apply relevant legal knowledge and 49 mea-surement items.The weights of the core indices and measurement items were determined.Conclusion The constructed competency evaluation model for forensic practitioners is scientifically sound and inno-vative,and has unique characteristics of forensic medicine compared with other medical models.

Objective:To analyze the prevalence and risk factors of blindness and moderate to severe visual impairment among Han and Kazakh residents aged 50 years and older in Tacheng City, Xinjiang Uygur Autonomous Region.Methods:This study is a cross-sectional survey conducted using cluster random sampling from October 2015 to June 2018 in Emin County, Tacheng City, Xinjiang Uygur Autonomous Region. The study included individuals aged 50 years and older to survey blindness and moderate to severe visual impairment. Ophthalmological examinations combined with questionnaires were conducted to gather basic information. The data collected from the questionnaires included general demographic information and health conditions. The results of the eye examinations were used to diagnose a total of 12 risk factors including cataracts, glaucoma, pterygium, suspected glaucoma, glaucoma, and refractive errors. These risk factors were analyzed in relation to blindness and moderate to severe visual impairment. Univariate analysis was conducted first, followed by logistic regression to identify the significant factors.Results:A total of 2 114 patients were included in the final analysis, among which the prevalence of moderate to severe visual impairment was 18.54% (392/2 114), and the prevalence of blindness was 2.74% (58/2 114). Univariate analysis showed that blindness and moderate to severe visual impairment were associated with age ( χ2 = 32.97, P < 0.05), hypertension ( χ2 = 3.48, P < 0.05), age-related cataract ( χ2 = 17.43, P < 0.05), glaucoma ( χ2 = 3.90, P < 0.05), macular degeneration ( χ2 = 16.04, P < 0.05), diabetes ( χ2 = 3.09, P < 0.05), pterygium ( χ2 = 2.57, P < 0.05), and fundus arteriosclerosis ( χ2 = 2.31, P < 0.05). Multivariate logistic regression analysis indicated that moderate to severe visual impairment was correlated with age (50 to < 60 years: OR = 2.91, 95% CI: 0.44-13.45; 60 to < 70 years: OR = 3.52, 95% CI: 0.73-8.77; 70 to < 80 years: OR = 4.31, 95% CI: 0.85-8.96), ethnicity ( OR = 4.45, 95% CI: 0.56-5.95), sex ( OR = 0.47, 95% CI: 0.34-0.64), age-related cataract ( OR = 1.67, 95% CI: 1.05-2.65), glaucoma ( OR = 2.97, 95% CI: 1.67-5.30), and coronary heart disease ( OR = 2.56, P < 0.05). Blindness was correlated with age (70-79 years: OR = 1.54, 95% CI: 1.12-2.11), sex ( OR = 0.67, 95% CI: 0.34-0.64), glaucoma ( OR = 1.65, 95% CI: 0.42-6.49), diabetes ( OR = 2.05, 95% CI: 1.35-3.09), and coronary heart disease ( OR = 1.92, 95% CI: 1.07-3.43). Among these, age (70-79 years), glaucoma, diabetes, and coronary heart disease were identified as risk factors for blindness, while sex was observed as a protective factor against blindness in this region. Based on univariate and multivariate analyses as well as clinical practice, it was concluded that age (50 to < 60 years: OR = 4.42, 95% CI: 1.31-14.92; 60 to < 70 years: OR = 4.49, 95% CI: 1.70-11.84; 70 to < 80 years: OR = 3.19, 95% CI: 1.29-7.87), age-related cataract ( OR = 1.67, 95% CI: 1.05-2.65), and glaucoma ( OR = 2.97, 95% CI: 1.67-5.30) were identified as significant risk factors for moderate to severe visual impairment. Glaucoma ( OR = 1.65, 95% CI: 0.42-6.49) and diabetes ( OR = 2.05, 95% CI: 1.35-3.09) were identified as the main risk factors for blindness in this region (both P < 0.05). Conclusions:In Tacheng City, Xinjiang Uygur Autonomous Region, the prevalence rates of moderate to severe visual impairment and blindness among Han and Kazakh residents are relatively high. Age-related cataracts and glaucoma are the primary causes, while age and diabetes are the main risk factors.

Objective:To compare minimally invasive surgery with traditional open surgery, analyze the current application status of health economic evaluations in the treatment of digestive tract cancers, such as esophageal cancer, gastric cancer, and colorectal cancer by minimally invasive surgery and provide evidence for the rational selection of clinical treatment, alleviation of disease-related economic burdens, and rational allocation of healthcare resources.Methods:By using five databases, i.e. China National Knowledge Infrastructure, Wanfang data, Chinese Biomedical Literature Database, PubMed, and Embase, a database was established to retrieve all the papers about health economic studies of minimally invasive surgery for esophageal cancer, gastric cancer, and colorectal cancer published until December 31, 2023. Literature was analyzed by using software NoteExpress 3.8, and data were processed using Excel 2021. The quality of included papers was evaluated using the CHEERS 2022 checklist, and Meta-analysis was conducted by using software Stata 17.0.Results:A total of 10 919 relevant papers were retrieved, and 59 studies were included. Only 14 studies (23.7%) used standard health economic evaluation methods. Meta-analysis results revealed no significant differences in direct medical expenditure and total expenditure between minimally invasive surgery and open surgery. However, the expenditure for minimally invasive surgery exhibited a significant increase [mean difference ( MD)=5 973.12 yuan, P<0.001], while hospital stay and indirect expenditure significantly decreased ( MD: -4.85 days and -733.79 yuan, P<0.001). In China, for gastric cancer, the direct medical expenditure of endoscopic surgery was lower than that of open surgery ( MD=-33 000.00 yuan) with no significant difference ( P<0.001). In colorectal cancer cases, the direct medical and surgical expenditures for laparoscopic surgery were higher than those for open surgery ( MD: 4 277.94 yuan and 4 267.80 yuan, P<0.001), while the indirect and total medical expenditures decreased ( MD: -768.34 yuan and -159.10 yuan). Hospital stays in patients who had minimally invasive surgery for all three types of cancer were shorter than those who had open surgery ( P<0.001). Conclusions:In the treatment of gastrointestinal cancer, compared with open surgery, minimally invasive surgery shows higher expenditure, but has advantages, such as shorter hospital stay and lower indirect expenditure, and there were no significant differences in direct medical and total expenditures between the two approaches. When conducting health economic evaluation, factors such as postoperative complications, hospital stay, and patient's economic status should be considered for their impact on total medical expenditure. It is necessary to pay attention to the application of health economic evaluations in healthcare decision-making.

BACKGROUND: Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated. METHODS: Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups. RESULTS: A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes. CONCLUSIONS The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.

OBJECTIVES: Hypertriglyceridemic acute pancreatitis (HTG-AP) has a rapid onset and is associated with a high risk of progression and recurrence. Early identification of patients at risk of severe disease can help reduce the likelihood of multiple organ failure and mortality. This study aims to investigate the changes in inflammatory composite markers and D-dimer (D-D) levels in young and middle-aged/elderly patients with HTG-AP and to evaluate their predictive value for disease progression. METHODS: A total of 230 patients with HTG-AP admitted to Chongqing University Jiangjin Hospital (Jiangjin Central Hospital) between 2017 and 2023 were retrospectively enrolled. Patients were first divided into a young group (≤45 years) and a middle-aged/elderly group (>45 years), and then stratified into mild and severe groups based on disease severity. Inflammatory composite markers, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), C-reactive protein-to-lymphocyte ratio (CLR), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), as well as D-D levels, were compared among groups. Least absolute shrinkage and selection operator (LASSO) regression and Logistic regression were used to identify independent risk factors for disease progression in each age group. Receiver operating characteristic (ROC) curves and the DeLong test were used to assess and compare the predictive performance (area under the curve, AUC) of risk factors. Internal validation was performed using the bootstrap method (n=1 000). RESULTS: No significant differences in NLR, PLR, MLR, SIRI, SII, CLR, or D-D levels were observed between the young (n=127) and middle-aged/elderly (n=103) groups (all P>0.05). Among young patients, the severe group (n=59) had significantly higher NLR, SIRI, SII, CLR, and D-D levels compared to the mild group (n=68) (all P<0.05). Among middle-aged/elderly patients, CLR and D-D levels were significantly higher in the severe group (n=49) than in the mild group (n=54) (P<0.05). LASSO and Logistic regression analyses identified elevated D-D as an independent risk factor for disease progression in young patients (P=0.007, OR=1.458, 95% CI 1.107 to 1.920), while both D-D (P=0.001, OR=2.267, 95% CI 1.413 to 3.637) and CLR (P=0.003, OR=1.007, 95% CI 1.003 to 1.012) were independent risk factors in middle-aged/elderly patients. ROC analysis showed that D-D predicted disease progression in young and middle-aged/elderly patients with AUCs of 0.653 and 0.741, sensitivities of 67.8% and 57.1%, and specificities of 72.1% and 88.9%, respectively. CLR predicted progression in middle-aged/elderly patients with an AUC of 0.687, sensitivity of 63.3%, and specificity of 70.4%. DeLong test showed no significant difference in AUC between D-D and CLR for middle-aged/elderly patients (Z=0.993, P=0.321). Internal validation via bootstrap analysis yielded a D-D AUC of 0.732, with sensitivity and specificity of 68.1% and 91.0%, respectively. CONCLUSIONS Differences in inflammatory response and coagulation function exist across age groups and disease severities in HTG-AP patients. Elevated D-D is an independent predictor of disease progression in both young and middle-aged/elderly patients, while CLR also predicts progression in the latter group. D-D, in particular, demonstrates strong predictive value for severe disease in middle-aged/elderly patients with HTG-AP.
Humans ; Fibrin Fibrinogen Degradation Products/metabolism* ; Disease Progression ; Middle Aged ; Pancreatitis/etiology* ; Male ; Female ; Retrospective Studies ; Adult ; Biomarkers/blood* ; Hypertriglyceridemia/blood* ; Acute Disease ; Predictive Value of Tests ; Aged ; Inflammation ; C-Reactive Protein/analysis* ; Neutrophils ; Age Factors

Clinical management of atopic dermatitis (AD) is challenged by its susceptibility to recurrence, side effects, and high costs. We found that Portulaca oleracea L.-derived nanovesicles (PDNV) exert anti-inflammatory effects by modulating macrophage M1/M2 polarization. These effects were achieved through pathways including inhibition of nuclear factor-κB (NF-κB) and stimulator of interferon genes (STING) protein expression in diseased tissues, demonstrating their potential to ameliorate AD symptoms. To increase the transdermal permeation of PDNV, dissolvable microneedles composed primarily of hyaluronic acid (HA) were developed as an adjunctive means of delivery. Meanwhile, polysaccharides of Portulaca oleracea L., which were synergistic with PDNV, were used as microneedle constituent materials to enhance the mechanical properties and physical stability of HA. This new means of delivery significantly improves the treatment of AD and also provides new options for the efficient utilization of plant extracellular vesicles and the treatment of AD. In addition, transcriptomic analysis of PDNV showed that the mRNAs of Portulaca oleracea L. are closest to those of ferns, which may shed light on related evolutionary and plant species identification studies.

The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue's pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.
Humans ; Spectrum Analysis, Raman/methods* ; Tongue Neoplasms/diagnostic imaging* ; Carcinoma, Squamous Cell/diagnostic imaging* ; Artificial Intelligence ; Margins of Excision