Objective:To investigate the association between small vulnerable newborn (SVN) phenotypes and the risk of neurodevelopmental delay at the age of 1 year.Methods:A prospective cohort study was conducted. A total of 25 860 singleton infants from "The Born in Guangzhou Cohort Study" who completed the Gesell developmental scale assessment at 1 year of age between January 2013 and June 2025 were included. Maternal sociodemographic characteristics, and other information were collected using a self-administered questionnaire, and maternal pregnancy-related information and neonatal birth data were extracted from medical records. Global developmental delay (GDD) was defined as a developmental quotient below 86 in ≥3 domains of the Gesell developmental scale, which assesses the adaptive, gross motor, fine motor, language, and personal-social domains. The random forest algorithm was employed for missing data imputation. Based on prematurity, small for gestational age (SGA), and low birth weight (LBW), newborns were categorized into 6 phenotypes: preterm-SGA-LBW, preterm-appropriate for gestational age (AGA)-LBW, preterm-AGA-nonLBW, term-SGA-LBW, term-LBW-only or term-SGA-only, and term-AGA-nonLBW phenotype. Among these, the first 5 were classified as SVN phenotypes, and the last one served as the reference group. Inter-group comparisons were performed using analysis of variance (ANOVA), χ2 tests, or Kruskal-Wallis test, as appropriate.?? Multivariable robust Poisson regression models were applied to analyze the association of different SVN phenotypes with the risks of GDD and developmental delays in specific domains, with stratified analyses by sex. Results:Among the 25 860 infants, 13 719 (53.1%) were male and 12 141 (46.9%) were female. The gestational age at birth was 39.4 (38.6, 40.0) weeks. The overall detection rate of GDD at 1 year of age was 3.7% (962/25 860). The rates of delay across developmental domains, in descending order, language in 8 134 cases (31.5%), gross motor in 4 488 cases (17.4%), personal-social in 1 271 cases (4.9%), adaptive in 1 262 cases (4.9%), and fine motor in 621 cases (2.4%). Compared with the reference group, preterm-AGA-LBW, preterm-SGA-LBW, preterm-AGA-noneLBW, and term-SGA-LBW phenotypes were all associated with an increased risk of GDD, with the adjusted RR (95% CI) of 6.07(5.01-7.35), 4.81(3.11-7.46), 2.10(1.54-2.88) and 1.89(1.29-2.76) respectively.The preterm-AGA-noneLBW phenotype was all associated with an increased risk of delay in gross motor, language and personal-social functional domains (all P<0.05). The term-SGA-LBW phenotype was associated with an increased risk of delay in gross motor, fine motor and personal-social functional domains (all P<0.01). Whereas the term-LBW-only or term-SGA-only phenotype showed no statistically association with developmental delay in any functional domain (all P≥0.05). Conclusion:The combined classification based on gestational age and birth weight helps identify infants at high risk for neurodevelopmental delay at 1 year of age, suggesting that it may offer a reference for the rational allocation of clinical resources.

The linear ubiquitin chain assembly complex (LUBAC), composed of heme-oxidized IRP2 ubiquitin ligase 1-interacting protein (HOIP), haem-oxidized IRP2 ubiquitin ligase-1(HOIL-1), and SHANK-associated RBCK1 homology-domain-interacting protein (SHARPIN), and its specific deubiquitinating enzyme OTULIN, regulates the dynamic balance of Met1-linked linear ubiquitination and maintains ubiquitin signaling homeostasis. Their precise interaction plays a central role in the regulation of nuclear factor-κB (NF-κB) and type I interferon signaling, inflammatory response, and cell survival and death. Dysregulation of the LUBAC-OTULIN axis can lead to aberrant immune and inflammatory signaling, immunodeficiency, and dysregulation of receptor-interacting protein kinase 1-mediated apoptosis or necroptosis. Genetic defects in OTULIN and LUBAC are associated with rare autoinflammatory diseases such as OTULIN-related autoinflammatory syndrome (ORAS) and HOIP/HOIL-1/SHARPIN deficiency, respectively, with complex clinical phenotypes and gene dose correlation. Current treatments primarily rely on glucocorticoids and tumor necrosis factor inhibitors, and hematopoietic stem cell transplantation has shown potential curative value in some patients with severe ORAS. This review summarizes the molecular composition, interaction mechanisms, and pathogenic roles of the LUBAC-OTULIN axis in rare autoinflammatory diseases, providing reference for the development of targeted therapeutic strategies.

Objective: To identify research priorities on physical and mental comorbidity among children and adolescents in Zhejiang Province, so as to provide a theoretical base for improving their physical and mental health. Methods: In May 2025, 77 experts in the fields of health and education from 11 cities in Zhejiang Province were selected by convenient sampling method to participate in the first round of expert consultation. In June, 2025, snowball sampling was used to expand to 194 experts for the second round of consultation, and it was convenient to select 21 students from primary schools to high schools in Zhejiang Province and 29 parents to empower the evaluation criteria. It applied the Child Health and Nutrition Research Initiative (CHNRI) method in a structured process, which encompassed the definition of the research field, generation of research ideas, scoring, and quantitative ranking of priorities. Research ideas were evaluated against 6 predefined criteria: effectiveness, safety, answerability, feasibility, sustainability, and scientific significance. Results: After 2 rounds of structured consultations, 81 research ideas on physical and mental comorbidity among children and adolescents were established and classified into 7 subthemes: epidemiological characteristics and influencing factors, optimization of primary service systems and policies, comprehensive intervention strategies for physical and mental comorbidity, biological mechanisms and clinical research, the impact of education and environment on physical and mental health, special populations and social support, and digital and technology driven disease prevention and intervention. The top 10 research priorities primarily centered on the subdomain of "epidemiological characteristics and influencing factors" (5 items). The top 3 research priorities were "the association between outdoor activity duration and the incidence of common diseases (including mental disorders) among children and adolescents" "the impact of outdoor activity duration on the physical and mental health of adolescents" "comprehensive intervention strategies for myopia, obesity, and their comorbidities among children and adolescents". Conclusion The framework of priority issues in the field of psychosomatic comorbidity of children and adolescents in Zhejiang Province based on CHNRI method provides a reference for optimizing the allocation of provincial research resources.

Background The prevalence of chronic diseases among the Chinese occupational population is rising steadily, with hypertension and diabetes becoming important health concerns. Occupational health examinations (OHE) provide stable population coverage, standardized protocols, and fixed follow-up intervals, offering a robust data foundation for risk assessment. However, most existing hypertension prediction studies rely on cross-sectional data and mainly focus on clinic onset, failing to capture the dynamic progression and cumulation of individual risk. Objective To construct a machine learning-based risk prediction model for blood pressure abnormality in occupational populations, providing a reference for health risk stratification and targeted health interventions. Methods Longitudinal data from 2020 to 2023 were extracted from the occupational health examination database of an institution in Shanghai. After excluding individuals with hypertension in any of the first three years, 3710 workers who participated 4 consecutive years of OHE were included. Six categories of blood pressure-related indicators from 2020 to 2022 were selected, including basic information, lifestyles, occupational exposures, cardiovascular indicators, routine blood tests, and biochemical markers, comprising 30 variables in total. For feature engineering, continuous variables were processed to reflect three-year dynamic characteristics: level (mean), variability (variance), and temporal trend (slope). Categorical variables were incorporated using 2022 values. Least absolute shrinkage and selection operator regression was applied to identify variables for inclusion in the machine learning models. Five models (decision tree, logistic regression, random forest, support vector machine, and XGBoost) were employed to predict blood pressure abnormality risk in 2023. And then sensitive analysis was conducted. The optimal model was selected based on the area under the curve (AUC) of receiver operating characteristic. Feature importance and SHAP analyses were applied to interpret the final model. Results Feature engineering transformed 17 continuous variables into 51 secondary variables, combined with 13 baseline variables, resulted in 24 variables screened out by LASSO regression. The logistic regression model achieved the best performance. Feature importance analysis indicated that the dynamic trajectory of blood pressure played a central role in the model, complemented by other biochemical and lifestyle indicators. SHAP analysis further showed that the model’s ability to not only identify risks of high-normal blood pressure and hypertension but also quantify the specific contribution of individual examination indicators to predicted risk, supporting risk-stratified population management in occupational health management. Conclusion Based on an existing occupational health examination database and routinely collected data, this study has developed a risk prediction model for blood pressure abnormalities in occupational populations. By incorporating dynamic change characteristics from longitudinal examinations, the study demonstrates the feasibility of using routine health data for early risk identification and stratified management. However, its applicability is limited by the availability of repeated examination data and the focus on specific occupational groups, requiring further validation across broader populations and diverse clinic scenarios.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

Objective To prepare Zanthoxylum alkaloid thermosensitive hydrogel, optimize the preparation process and conduct related performance studies. Methods Zanthoxylum alkaloids were obtained by reflux extraction, followed by enrichment and purification using macroporous adsorption resin. Poloxamer 407 and Poloxamer 188 were used as substrates to prepare the thermosensitive hydrogel of Zanthoxylum alkaloids, and the preparation process was optimized by orthogonal design. The quality of the hydrogel was systematically evaluated based on its gelation temperature, gelation time, Fourier transform infrared （FTIR） spectroscopy, scanning electron microscopy （SEM） images, mechanical properties, and in vitro release profile. Results The optimal preparation conditions for the Zanthoxylum alkaloid thermosensitive hydrogel were: 20% （g/ml） poloxamer 407, 2% （g/ml） poloxamer 188 and 100 μg/ml Zanthoxylum alkaloid. The gelation temperature was 32.6℃, and the average gelling time was 143.3 s. The hydrogel appeared as a transparent liquid at room temperature and was transformed into a semi-solid gel state when the temperature exceeded 33℃. Experimental results confirmed the successful preparation of poloxamer 407 and poloxamer 188 thermosensitive hydrogel loaded with Zanthoxylum alkaloids, which exhibited good bio adhesion, self-healing properties, and tensile strength. Conclusion The Zanthoxylum alkaloid thermosensitive hydrogel demonstrated favorable mechanical properties and a sustained-release effect, showing promising potential for further development and application.

ObjectiveTo investigate the epidemiological and clinical characteristics of human bocavirus (HBoV) in hospitalized children with acute lower respiratory tract infection (ALRTI) at a single-center children’s hospital in Shanghai, thereby providing evidence for the diagnosis, treatment, and prevention of HBoV infection. MethodsA retrospective study was conducted on 19 537 hospitalized children with ALRTI at Shanghai Children’s Hospital from January 2021 to December 2023. Multiplex polymerase chain reaction (PCR) combined with capillary electrophoresis was used to detect HBoV and 12 other common respiratory viruses /atypical pathogens. The positive detection rate, demographic characteristics (sex, age), temporal distribution (year, season) of HBoV, as well as the clinical characteristics of severe and non-severe pneumonia were analyzed. ResultsThe overall HBoV-positive rate was 2.57% (503/19 537), with 59.44% (299/503) being single infections and 40.56% (204/503) being co-infections. The positive detection rate was significantly higher in boys than that in girls (2.78% vs 2.33%, χ²=3.88, P=0.049). The highest infection rate was observed in toddlers, followed by infants (χ²=379.57, P<0.001). The positive rate peaked in 2021 and reached its lowest point in 2023 (χ²=45.49, P<0.001), with epidemics mainly prevalent in summer and autumn. The main clinical symptoms were cough (90.06%, 453/503), fever (75.94%, 382/503), and wheezing (39.96%, 201/503). Children with severe pneumonia showed a higher incidence of wheezing compared with the non-severe group (P<0.001), while underlying diseases and co-infections had no significant association with disease severity (P>0.05). ConclusionHBoV was an important pathogen of ALRTI in children, predominantly affecting infants and toddlers, with higher susceptibility in boys and seasonal peaks in autumn and summer. The main clinical manifestations included cough, fever, and wheezing, with wheezing being more prevalent in children with severe pneumonia.

OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

ObjectiveTo observe the clinical efficacy of Anshen Buxin Gao in patients with coronary artery disease （CAD） complicated with somatization disorder after percutaneous coronary intervention （PCI）， as well as its effect on heart rate variability （HRV）. MethodsA total of 96 patients with somatization disorder after PCI were selected and randomized into control and treatment groups （48 cases）. On the basis of standardized Western medical treatment for CAD， the control group received Dailixin， while the treatment group received Dailixin combined with Anshen Buxin Gao. The somatic symptom scale （SSS）， generalized anxiety disorder 7-item scale （GAD-7）， patient health questionnaire-9 （PHQ-9）， and self-rating scale of sleep （SRSS） scores in both groups were recorded before and after treatment. The traditional Chinese medicine symptom efficacy， HRV， and incidence of adverse drug reactions were observed. ResultsA total of 90 patients completed the trial， encompassing 45 patients in the control group and 45 patients in the treatment group. Baseline characteristics between the two groups showed no statistically significant differences， indicating comparability. After treatment， both groups exhibited reductions in SSS， GAD-7， and PHQ-9 scores （P<0.05）， and the treatment group outperformed the control group in alleviating somatic symptoms and anxiety-depression symptoms （P<0.05）. The control group did not achieve a significant reduction in SRSS score， whereas the treatment group effectively lowered the SRSS score （P<0.05）. Regarding traditional Chinese medicine symptom efficacy， the total response rate in the treatment group was 91.1% （41/45）， which was higher than that （71.1%， 32/45） in the control group （Z=-2.663， P<0.05）. Both groups improved HRV in patients with somatization disorder， and the treatment group showed greater improvement （P<0.05）. There were no serious clinical adverse events during the study period. The incidence of adverse reactions in the treatment group was 6.7% （3/45）， which was lower than that （14/45， 31.10%） in the control group （χ²=7.252， P<0.05）. ConclusionThe addition of Anshen Buxin Gao to Dailixin therapy significantly alleviates the clinical symptoms and improves the sleep quality， treatment efficacy， and HRV in patients with CAD complicated with somatization disorder after PCI， while reducing the adverse effects associated with Dailixin alone. This approach demonstrates considerable clinical value and warrants further promotion.