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.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Methods: Cervical radiographs were analyzed. EOP hyperplasia was classified into three subtypes with standardized length measurements. Variables encompassed demographics, ONL-related indices, and sagittal parameters. Subtype comparisons and multivariate regression analyses (with EOP length as dependent variable) were conducted. Results: Analysis of 187 CSM patients (64.2% male) identified gender-specific patterns: males exhibited greater EOP length (9.4±6.8 mm vs. 4.6±3.4 mm, p<0.001). Type III EOP demonstrated male predominance (82.4% vs. type I 31.8%, type II 51.4%; p<0.001), with associated longer hyperplasia length (11.6±6.6 mm vs. type II 5.1±1.9 mm, p<0.001). Type III EOP was associated with higher ONL prevalence (type III 64.8% vs. type I 45.5%, type II 41.9%; p=0.010) and longer ONL osteophyte length (type III 18.8±9.8 mm vs. type I 14.2±8.1 mm, type II 14.2±9.4 mm; p=0.046). Multivariate regression confirmed male sex (β=–3.82, p=0.009), ONL osteophyte length (β=0.16, p=0.017), T1 slope (β=0.27, p=0.041), and spino-cranial angle (β=–0.19, p=0.009) as factors independently associated with EOP length (adjusted R²=0.382). Conclusions Severe EOP hyperplasia exhibits a male-predominant distribution pattern and demonstrates significant radiological associations with ONL and cervical sagittal imbalance in CSM patients. These findings advocate for EOP evaluation in clinical evaluations to identify high-risk biomechanical profiles.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Objective:To analyze the epidemiological characteristics and influencing factors of single-cigarette use and dual cigarette-cigar use in China.Methods:This study was a cross-sectional study that selected 85 638 urban and rural residents who met the inclusion criteria from the 2018 China Health Literacy Survey as research subjects. An analysis was conducted on 21 849 users of cigarettes and cigars among them. Due to the small number of individuals who exclusively used cigars (247 cases), the research subjects were divided into two categories: exclusive cigarette users and dual users of cigarettes and cigars. The groups were categorized by age (18-34 years, 35-54 years, ≥55 years), gender (male, female), education level (primary school and below, junior high school and high school, university and above) and annual household income (<20 000 yuan, 20 000-<80 000 yuan, ≥80 000 yuan) to compare the tobacco usage rate and conduct subgroup analyses for each subgroup. Multivariate logistic regression analysis was employed, incorporating general demographic characteristic information to explore the influencing factors of exclusive cigarette use and dual use of cigarettes and cigars, respectively.Results:The rate of exclusive cigarette use in our country was 24.3%, while the dual use rate of cigarettes and cigars was 0.9%. The exclusive cigarette use rate and the dual use rate of cigarettes and cigars among males were significantly higher than those among females (48.25% vs 2.48%, and 1.84% vs 0.06%) (both P<0.001). For males, the high-risk factors for exclusive cigarette use included living in urban areas ( OR: 1.37, 95% CI: 1.23-1.54), being Han ethnicity ( OR: 1.73, 95% CI: 1.51-1.98), and having an annual household income ≥20 000 yuan ( OR: 1.54, 95% CI: 1.38-1.82) while having a junior high school education or higher was a protective factor ( OR: 0.68, 95% CI: 0.52-0.90). Age≥35 years ( OR: 3.36, 95% CI: 2.62-4.32) and having a junior high school education or higher ( OR: 1.30, 95% CI: 1.02-1.67) were risk factors for dual use of cigarettes and cigars in males. Among females, living in urban areas ( OR: 1.53, 95% CI: 1.19-1.97) and being Han ethnicity ( OR: 5.96, 95% CI: 4.47-7.96) were risk factors for exclusive cigarette use, while having a university education or higher was a protective factor ( OR: 0.28, 95% CI: 0.18-0.42). However, for female dual use of cigarettes and cigars, no significant effects were observed for any demographic characteristics. Conclusions:The use rate of cigarettes alone in China is significantly higher than that of cigarette-cigar dual use, and the rates of cigarette use alone and cigarette-cigar dual use in men are significantly higher than those in women. Tobacco use is being affected by sociodemographic factors, among which place of residence, ethnicity and education level are the main influencing factors of cigarette use alone, and gender, age and education level are the main influencing factors of cigarette-cigar dual use.

Objective To evaluate the predictive value of dose-surface histogram(DSH)for radiation proctitis(RP)in prostate cancer(PCa)patients undergoing radiotherapy.Methods This prospective randomized controlled clinical trial included 380 PCa patients who underwent image-guided radiotherapy in the First Affiliated Hospital of Hebei Northern University from January 2018 to January 2023.Patients were randomly divided into observation group(n=200)and control group(n=180).The rectal dose distribution of patients in the two groups was evaluated by using DSH and dose-volume histogram(DVH),respectively.The receiver operating characteristic(ROC)curve was utilized to evaluate the predictive value of DSH for acute RP,with DVH serving as a reference.Results The difference was not statistically significant in clinical information such as age,KPS score,and body mass index(BMI)between the observation and control groups(P＞0.05),as well as in acute RP incidence(P＞0.05).There were significant differences in S40 and V40,S50 and V50,S60 and V60,S70 and V70,and S78 and V78 between the two groups(P＜0.05).S40,S50,V40,and V50 showed low efficacy(P＜0.001)in predicting acute RP at each level,with AUC≤0.700.S60 and V60 showed moderate efficacy(P＜0.001)in predicting acute RP at each level,with AUC 0.700-0.900.S70,S78,V70 and V78 showed high efficacy(P＜0.001)in predicting acute RP at each level,with AUC＞0.900.Conclusions The predictive value of DSH for rectal toxicity in patients with PCa is basically consistent with that of DVH.It is expected to become a novel and valuable tool for evaluating radiotherapy plans in the future.

Objective: To construct a deep learning model based on convolutional neural network(CNN)-long short term memory network(LSTM)-Attention to explore the correlation between meteorological and clinical factors and the incidence of ischemic stroke. Methods: A fusion model CNN-LSTM-Attention based on CNN, LSTM, and Attention was constructed by incorporating clinical data and meteorological data of ischemic stroke inpatients. The predictive performance of the model was evaluated by maximum prediction error and root mean square error(RMSE). The impact of different lag days on prediction performance was investigated by selecting lag periods ranging from 1 to 7 days. Results: In both short-term and long-term predictions, the CNN-LSTM-Attention fusion model(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5) showed superior maximum prediction bias and RMSE compared to the LSTM model(short-term: 2.8 and 1.2; long-term: 19.5 and 5.5) and the CNN-LSTM model(short-term: 2.0 and 0.8; long-term: 11.2 and 3.3). After incorporating lag days, the maximum prediction deviation and RMSE for lags of 3 days(short-term: 0.7 and 0.4; long-term: 5.5 and 1.9) and 5 days(short-term: 0.8 and 0.3; long-term: 6.5 and 2.0) in both short-term and long-term forecasts were smaller than lags of 0 days(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5). The maximum prediction deviation and RMSE in the short-term forecast were greater than lag 0 days for both lag 1 days(1.5 and 0.8) and lag 7 days(1.9 and 0.9). In the long-term forecast, the two indicators for lag 1 days(6.8 and 2.4) were lower than those for lag 0 days but higher than those for lag 3 days and 5 days. The maximum prediction deviation for lag 7 days(7.5) was lower than that for lag 0 days, but the RMSE(2.7) is higher than that for lag 0 days. Conclusion The established CNN-LSTM-Attention model demonstrates significant predictive value for the onset of ischemic stroke and can provide reference for the rational allocation of medical resources.

Objective:To explore the relationship between international collaboration papers and academic impact in global health,using the member universities of the Chinese Consortium of Universities for Global Health(CCUGH)as a case study.Methods:The study focuses on journal articles in global health field published by 31 CCUGH member universities between 2014 and 2024.Descriptive statistical analysis of international and non-international collaboration publication volumes was conducted using Excel.Regression analysis and chi-square tests were performed using R to examine the relationship between international collaboration papers and academic impact,and the correlation between the breadth of collaboration and the academic impact of the papers.Results:From 2014 to 2023,the total number of publications,the number of non-international collaborationpublications,and the number of internationally collaborated publications all showed a consistent annual increase,with average annual growth rates of 56.7%,68.3%,and 41.4%,respectively.By the first half of 2024,the total number of publications had increased to 1.5 times that of the corresponding period in 2023.International collaboration positively influenced academic impact,with broader collaborative networks correlating with higher academic influence.Conclusion:The global health publication output of CCUGH member universities has steadily increased,but the volume of international collaboration papers and their proportion remain relatively low.Therefore,it is necessary for CCUGH member universities to strengthen international collaboration papers in global health.

An implantable cardiac monitor(ICM)is a subcutaneous electronic device for recording and remotely transmitting electrocardiographic events.New-generation ICM,like the Reveal LINQ,have extended arrhythmia monitoring duration and improved diagnostic yield through miniaturization,large storage capacity,and easy implantation.They provide comprehensive monitoring data for cardiovascular disease diagnosis and treatment,optimize individualized treatment plans,and enhance patient prognosis.In recent years,research on ICM in cardiovascular fields has deepened,and their clinical application scope has expanded.They show significant advantages and unique value and are widely used in early cardiovascular disease diagnosis and prognostic management.In the future,ICM are expected to play a bigger role in boosting diagnostic and prognostic capabilities for cardiovascular disease patients,offering stronger support for early disease detection,accurate diagnosis,and effective management.