Objective: To verify the inhibitory effect of a carbon monoxide hemoglobin-based oxygen carrier (CO-HBOC) on the fibrotic process in mice with idiopathic pulmonary fibrosis (IPF), clarify its efficacy difference compared with hemoglobin-based oxygen carriers (HBOCs), and elucidate its mechanism of action via proteomic analysis. Methods: CO-HBOC was prepared using gas loading technology. An IPF mouse model was established and the mice were randomly divided into a normal saline control group, an HBOC treatment group, and a CO-HBOC treatment group. The fibrotic area percentage was analyzed using Micro-CT; the degree of inflammatory infiltration and fibrosis in lung tissue was assessed by pathological section staining (e.g., HE and Masson staining); and differentially expressed proteins in lung tissue of IPF mice after CO-HBOC treatment were screened using proteomic technology. Results: Micro-CT results showed that the mean fibrotic area percentage in the CO-HBOC treatment group on day 21 was (8.89±0.98)%, which was better than that of the HBOC group (16.5±1.732)% and the normal saline group (30.75±6.45)% (P<0.05). HE and Masson staining results showed that the CO-HBOC group had reduced inflammatory cell infiltration and significantly decreased collagen fiber deposition in lung tissue, with a mean pathological score of 3.33±0.58, which was lower than that of the normal saline control group (8.33±1.53)(P<0.05); the mean collagen-positive area percentage was (3.33±1.53)%, significantly lower than that of the normal saline control group (14.00±3.61)% (P<0.05). Proteomic analysis identified 330 differentially expressed proteins, which were mainly enriched in inflammatory response regulatory pathways (such as the complement and coagulation cascades), and the expression changes of complement proteins may be the core target of CO-HBOC's anti-fibrotic effects. Conclusion: CO-HBOC can inhibit inflammatory responses and regulate fibrosis-related signaling pathways, there-by effectively inhibiting the fibrotic process in IPF mice, with superior efficacy to HBOC. Its mechanism of action involves the regulation of complement cascade-related signaling pathways and complement protein expression, providing an experimental and theoretical basis for targeted therapy of IPF.

Objective: To enhance the ability of nanoparticles to target and bind tumor cells by constructing a neutrophil hitchhiking system based on hyaluronic acid (HA)-modified dual-drug loaded lipid nanoparticles. Methods: Lipid nanoparticles (LNPs) were prepared using microfluidic technology, and the nitrogen/phosphate (N/P) ratio, flow rate ratio, and drug-to-lipid ratio were optimized. HA-modified LNPs (HA-LNPs) were prepared and characterized. The interaction between the nanoparticles and tumor cells was evaluated through in vitro cell experiments. Results: The optimal preparation conditions for LNPs are N/P=8, flow rate ratio=5, and drug-to-lipid ratio=1∶30 (w∶w). HA-LNPs has a particle size of (177.28±2.41) nm, a polydispersity index (PDI) of 0.198±0.10, and an siRNA encapsulation efficiency of (91.37±0.47)%. The optimal binding rate with neutrophils was (98.64±2.34)%. Conclusion: An HA-modified dual-drug loaded lipid nanoparticle-neutrophil hitchhiking system was successfully constructed, enhancing the synergistic anti-tumor activity of the nanomedicine and the uptake of nanoparticles by tumor cells, providing a novel delivery strategy for targeted therapy of bone marrow tumors.

Objective To explore the effectiveness of the cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) for early warning of influenza epidemic using two datasets of reported influenza cases and influenza-like illness (ILI) cases. Methods Using the reported cases of influenza and ILI in Daxing District, Beijing, from week 23 of 2018 to week 22 of 2024 as data sets, the CUSUM and EWMA models were established, respectively. The positive rate of influenza etiology was used as the ^“gold standard^”, and the Youden index was used as the evaluation index to compare the early warning effect of the two models under different data sets and different parameters. Results In CUSUM, the optimal Youden indices of the reported influenza cases set and the ILI cases set were 0.751 and 0.635, respectively. In EWMA, the optimal Youden indices of the reported influenza cases set and the ILI cases set were 0.544 and 0.464, respectively. The optimal EWMA and CUSUM models could both issue early warning signals in advance of the ^“gold standard^”. Conclusion In the influenza epidemic early warning in Daxing District, Beijing, the CUSUM model established with the reported cases of influenza can achieve good early warning effects, but the model parameters need to be dynamically adjusted according to the local epidemic characteristics.

Objective To quantitatively assess the exposure-response association between exposure to heatwave and sudden death, estimate the attributable excess deaths, and identify potential vulnerable subgroups. Methods A time-stratified case-crossover study was conducted among residents who died from sudden death in Jiangsu Province, China between 2015 and 2021. Heatwave events in Jiangsu Province, defined using varying relative temperature thresholds and durations, were identified using temperature data from the China Meteorological Administration Land Data Assimilation System (CLDAS V2.0). Individual heatwave exposure was assessed based on each subject's residential address. The exposure-response association between heatwave and sudden death was evaluated using conditional logistic regression model combined with a Distributed Lag Nonlinear Model(DLNM). Heatwave-attributable excess deaths were estimated. Stratified analyses by sex and age were performed to assess potential effect modifications. Results Under all definitions, exposure to heatwave was significantly associated with an increased risk of sudden death, and the risk increased with the intensity of heatwave. Using the P95_3d definition (temperature exceeding the 95th percentile for ≥3 consecutive days), heatwave was significantlyassociated with a 56% increased risk of sudden death (95% CI: 31%, 86%). The population-attributable fraction of sudden death due to heatwave exposure was 1.45% (95% CI: 0.97%, 1.90%). Stratified analyses indicated no statistically significant differences in the association between heatwave exposure and sudden death across age or sex subgroups. Conclusion Heatwave exposure was associated with an increased risk of sudden death. Reducing heatwave exposure during summer may help lower the occurrence of sudden death.

Objective: To prepare the erythrocyte-liposome drug delivery system to enhance the therapeutic effect of drugs on tumors and inhibit tumor metastasis. Methods: This study prepared and characterized paclitaxel (PTX)-plerixafor (AMD3100) liposomes (Lips), developed the erythrocyte-liposome drug delivery system, and evaluated its targeting efficiency and therapeutic efficacy through a series of in vitro cellular and in vivo animal experiments. Results: The particle size of PTX-AMD-Lips was (186.4±0.83) nm. Drug encapsulation efficiency of PTX-AMD-Lips was (75.50±5.27)% for PTX and (88.31±2.45)% for AMD. The Binding efficiency between RBC and liposomes in the drug delivery system was (69.93±2.55)%. Vitro cellular experiments revealed that PTX-AMD-Lips significantly inhibited tumor cell migration. In vivo animal experiments, the erythrocyte-liposome drug delivery system significantly increased drug accumulation in the lungs. At the experimental endpoint, the quantitative fluorescence signal of tumor size measured (4.04±0.44)×10 for the PTX-Lips group, and (5.14±3.40)×10 for the RBC-PTX-AMD-Lips group. Conclusion: The erythrocyte-liposome drug delivery system could enhance the lung-specific targeting capability of liposomes, kill tumor cells and suppress further metastasis effectively.

Objective: Patients with atherosclerotic cardiovascular disease (ASCVD) following percutaneous coronary intervention (PCI) are classified as very-high-risk individuals in cardiovascular disease (CVD) risk stratification. The distribution pattern of traditional Chinese medicine (TCM) syndromes in this patient population, as well as its association with blood lipid profiles and clinical prognosis, remains unclear. The present prospective cohort study aims to investigate these correlations, thereby providing insights to enrich the research fields. Methods: We enrolled consecutive patients with ASCVD who underwent PCI at the Integrated Cardiology Unit of China-Japan Friendship Hospital between September 1, 2020 and December 31, 2022. Demographics and clinical characteristics, signs and symptoms defining each TCM syndrome, and fasting venous blood samples were collected at baseline and follow up or upon major adverse cardiovascular events (MACEs). We analyzed the correlation between TCM syndromes, blood lipid profiles, and MACEs, and developed a new joint prognostic model incorporating both TCM syndromes and blood lipids using logistic regression. The analyses were based on detailed baseline and one-year follow-up data. Results: A per-protocol analysis was performed on 586 patients with complete data ultimately. During the one-year follow-up, 174 patients (29.69%) experienced a MACE. We performed statistical analyses on comorbidities, medication, and biochemical indicators across groups defined by TCM syndrome differentiation. When comparing different TCM syndromes, no significant differences were found in age, body mass index (BMI), history of revascularization, comorbidities, family history of CVD, smoking or drinking, or statin intensity (P > 0.05). Patients with intertwined phlegm and blood stasis syndrome exhibited significantly higher levels of total cholesterol (TC, 5.27 ± 1.18 mmol/L, P < 0.001), triglyceride (TG, 1.96 ± 1.33 mmol/L, P = 0.008), low-density lipoprotein cholesterol (LDL-C, 3.35 ± 0.79 mmol/L, P < 0.001), and high-density lipoprotein cholesterol (HDL-C, 1.24 ± 0.81 mmol/L, P < 0.001) compared with those with other TCM syndromes combined. A multivariable logistic regression model was constructed to predict MACEs. The model included TCM syndrome type [with intertwined phlegm and blood stasis as a predictor, adjusted odds ratio (OR) = 1.413, 95% confidence interval (CI): 0.517 – 3.864, P = 0.501], age (adjusted OR = 0.97, 95% CI: 0.955 – 1.001, P = 0.057), male gender (adjusted OR = 0.698, 95% CI: 0.416 – 1.170, P = 0.173), TC (adjusted OR = 1.004, 95% CI: 0.513 – 1.965, P = 0.990), and LDL-C (adjusted OR = 5.825, 95% CI: 2.214 – 15.326, P < 0.001). This model demonstrated good discriminatory ability for MACEs in post-PCI ASCVD patients [the area under the receiver operating characteristic (ROC) curve (AUC) = 0.865, 95% CI: 0.816 – 0.914]. Conclusion The intertwined phlegm and blood stasis TCM syndrome is associated with a distinct atherogenic lipid profile characterized by elevated levels of TC and LDL-C. The prognostic model that incorporates this TCM syndrome type along with conventional lipid parameters (TC and LDL-C) shows good discriminatory ability for predicting MACEs in ASCVD patients after PCI, underscoring the potential clinical utility of integrating TCM syndrome differentiation into CVD risk assessment.

ObjectiveTo investigate the epidemiological characteristics and influencing factors of depressive symptoms among middle school (junior, senior, and vocational high school) students in Yunnan Province, China, and to inform evidence-based intervention strategies for adolescent mental health. MethodsA cross-sectional survey was conducted between October and November 2023 using stratified random cluster sampling. Students from eight counties (districts) across four prefectures (cities) in Yunnan Province were included. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D). Multivariate logistic regression analysis was performed to examine factors associated with depressive symptoms, with stratified analyses conducted by gender, educational stage, and ethnicity. ResultsA total of 4 672 questionnaires were distributed, with 4 670 valid questionnaires retrieved, yielding a valid response rate of 99.96%. The surveyed participants were predominantly female students (50.81%), junior high school students (49.13%), ethnic minorities (52.78%), and urban residents (79.29%). The mean CES-D score for middle school students in Yunnan Province was (15.31±10.83). Female students had a significantly higher mean score (16.63±11.41) than male students (13.95±10.02) (P<0.001). Senior high school students had a significantly higher mean score (16.61±10.61) compared to both junior high school students (14.74±11.45) and vocational high school students (13.10±7.71) (all pairwise comparisons P<0.001). The prevalence of depressive symptoms among middle school students in Yunnan Province was 28.18%. The prevalence was significantly higher in females (34.09%) than in males (22.07%). By school type, the detection rate was highest among senior high school students (33.39%), followed by junior high school students (26.29%) and vocational high school students (17.27%) (P<0.05). Multivariate logistic regression analysis showed that female gender (OR=2.16, 95%CI: 1.86‒2.50), being in junior high school (OR=2.43, 95%CI: 1.84‒3.20) or senior high school (OR=2.27, 95%CI: 1.73‒2.98), not living with parents (OR=1.24, 95%CI: 1.07‒1.44), irregular breakfast consumption (OR=1.52, 95%CI: 1.33‒1.75), lack of moderate-to-vigorous physical activity (MVPA) (OR=1.69, 95%CI: 1.37‒2.09), sleep duration ≤5 h per night (OR=2.52, 95%CI: 2.02‒3.14) or 6‒7 h per night (OR=1.47, 95%CI: 1.25‒1.73), smoking (OR=1.86, 95%CI: 1.56‒2.23), and alcohol consumption (OR=1.81, 95%CI: 1.54‒2.13) were positively associated with depressive symptoms. In contrast, screen time ≤1 h (OR=0.71, 95%CI: 0.59‒0.86) was negatively associated with depressive symptoms. Stratified analyses showed that female students not living with parents (OR=1.29, 95%CI: 1.06‒1.58), senior high school students (OR=1.51, 95%CI: 1.21‒1.88), and Han Chinese students (OR=1.37, 95%CI: 1.11‒1.69) were more likely to experience depressive symptoms. Han Chinese students who smoked were also more likely to have depressive symptoms (OR=1.72, 95%CI: 1.34‒2.21). In contrast, male students with screen time ≤1 h (OR=0.71, 95%CI: 0.53‒0.95) and ethnic minority students (OR=0.74, 95%CI: 0.58‒0.95) were less likely to experience depressive symptoms. Regardless of gender, irregular breakfast consumption, lack of MVPA, sleep duration less than 8 h per night, smoking, and alcohol consumption were all positively associated with depressive symptoms (P<0.05). Among both junior and senior high school students, irregular breakfast consumption, lack of MVPA, smoking, and alcohol consumption were positively associated with depressive symptoms (all P<0.05), while screen time ≤1 h was negatively associated with depressive symptoms (all P<0.05). For junior high school students, engaging in 1‒2 days of MVPA per week, screen time more than 2 h per day, and sleep duration 6‒7 h per night were all positively associated with depressive symptoms (all P<0.05). Among junior high, senior high, and vocational high school students, sleep duration ≤5 h per night was positively associated with depressive symptoms (P<0.05). For both Han Chinese and ethnic minority students, irregular breakfast consumption, lack of MVPA, sleep duration less than 8 h per night, and alcohol consumption were all positively associated with depressive symptoms (all P<0.05). ConclusionThe prevalence of depressive symptoms among middle school students in Yunnan Province is comparable to that in central China and higher than that in northern regions. Prevention and control efforts should prioritize female students and those in junior and senior high school stages. Universal improvements in lifestyle behaviors among middle school students, such as regular breakfast consumption, MVPA, sufficient sleep (≥8 h), and abstinence from smoking and alcohol. Particular attention should be given to limiting excessive screen time among junior high school students and addressing the mental health needs of females not living with their parents, senior high school students, and Han Chinese students.

From the patient’s perspective， taking the control right of artificial intelligence （AI） in the analysis of pregnant and postpartum women’s health data as the entry point， this paper constructed a patient-centered governance framework for AI medical data control. It defined the different stages and scenarios of applications related to the health data analysis of pregnant and postpartum women， conducted a comparative analysis that combines existing ethical issues concerning AI data control in the medical field， and discussed autonomy issues in health management scenarios from three dimensions， namely， voluntariness， purposiveness， and steadfastness. Based on these discussions， this paper highlighted the reflection on autonomy under the behavioral research paradigm. Starting from the basic actions such as provision， deletion， analysis， triggering， presentation， revision， and response， it regarded technical behaviors as a form of instrumental communication and emphasized ensuring patients’ ample opportunities to participate in interactive links such as “information provision and adoption” “analysis and judgment” “presentation and summarization，” and “feedback and revision.” This will lay a foundation for expanding the existing discussion on medical AI autonomy to the ethical category of information usage.

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.