ObjectivePost-ischemic acute inflammation and the subsequent persistent dysregulation of the immune microenvironment represent major pathological drivers that aggravate neuronal injury and severely restrict functional recovery following ischemic stroke. Although current reperfusion therapies partially restore blood flow, they fail to effectively modulate the secondary inflammatory cascade and oxidative stress, which remain critical barriers to neurological restoration. To address this challenge, this study aimed to engineer and systematically evaluate a biomimetic nanosystem composed of transforming growth factor-β1 (TGF-β1)-loaded platelet membrane-camouflaged lipid nanoparticles (PLP). This nanosystem was designed to achieve dual lesion-targeted delivery and immune microenvironment remodeling. By verifying its spatiotemporal accumulation, anti-inflammatory activity, and neuroprotective efficacy, we sought to establish an integrated therapeutic strategy that simultaneously enables lesion targeting, immune regulation, and functional recovery after ischemic injury. MethodsThe physicochemical properties of PLP, including hydrodynamic particle size, zeta potential, structural stability, and morphology, were characterized using dynamic light scattering, zeta potential analysis, and transmission electron microscopy. The preservation of platelet membrane-derived adhesion and immunoregulatory proteins was confirmed by SDS-PAGE through comparative analysis of protein band profiles between PLP and native platelet membranes. The in vitro biological activities of PLP were evaluated using two complementary cellular models. LPS-induced M1-polarized RAW264.7 macrophages were employed to assess inflammatory modulation, while oxygen glucose deprivation/reperfusion (OGD/R)-induced BV2 microglial cells and SH-SY5Y neuronal cells were utilized to investigate neuroinflammatory regulation and neuronal protection. For in vivo validation, a transient middle cerebral artery occlusion (tMCAO) mouse model was established to mimic ischemia-reperfusion injury. The spatiotemporal biodistribution and lesion-targeting capability of the PLP were monitored through live fluorescence imaging. Therapeutic efficacy was comprehensively evaluated by triphenyltetrazolium chloride (TTC) staining, glial fibrillary acidic protein (GFAP) immunofluorescence analysis, body weight monitoring, and neurological severity score (NSS) assessment. ResultsPLP nanoparticles displayed a uniform spherical morphology, nanoscale particle size distribution, and stable negative surface charge, indicating favorable colloidal stability and circulation potential. SDS-PAGE results confirmed the effective retention of key platelet membrane proteins associated with endothelial adhesion, immune evasion, and inflammatory regulation, demonstrating the successful biomimetic construction. Optimal therapeutic concentrations were determined in OGD/R-induced BV2 cells, where PLP exhibited excellent cytocompatibility and anti-inflammatory activity.In vitro experiments demonstrated that PLP significantly inhibited the polarization of RAW264.7 macrophages toward the pro-inflammatory M1 phenotype and markedly reduced neuronal apoptosis under ischemia-reperfusion conditions. In vivo fluorescence imaging revealed that PLP rapidly accumulated in the ischemic brain hemisphere and maintained prolonged retention for up to 7 d, suggesting enhanced lesion-specific targeting and sustained drug release. Compared with control group, PLP treatment significantly reduced cerebral infarct volume, attenuated reactive astrogliosis, improved weight recovery, and accelerated neurological functional restoration, as reflected by significantly improved NSS scores. ConclusionThis study establishes a multifunctional biomimetic nanoplatform that integrates platelet membrane-mediated active targeting with the anti-inflammatory, antioxidative, and neuroprotective properties of TGF-β1. The PLP system enables rapid lesion homing and long-term retention while synergistically regulating the post-stroke inflammatory microenvironment by suppressing pro-inflammatory immune activation, reducing neuronal apoptosis, and limiting excessive astrocyte reactivity. Importantly, this study proposes a conceptually therapeutic paradigm that combines targeted delivery with immune microenvironment remodeling to achieve comprehensive neurovascular protection. These findings provide strong experimental evidence supporting the translational potential of biomimetic nanotherapeutics as next-generation precision interventions for ischemic stroke.

Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

ObjectiveTo investigate the therapeutic effect of Astragali Radix-mediated changes in gut microbiota on treating type 2 diabetes （T2DM）. MethodsA 12-week randomized， placebo-controlled clinical trial enrolled eighty patients with newly diagnosed type 2 diabetes and poor glycemic control in the Qi deficiency type. All patients received insulin therapy. The observation group （40 cases） was administered with Astragali Radix Granules， while the control group （40 cases） received a placebo. Both treamtents were taken orally twice daily. Changes in gut microbiota were assessed by 16s rDNA sequencing. Serum glucagon-like peptide-1 （GLP-1） levels were measured using enzyme-linked immunosorbent assay （ELISA）. Glucose metabolism indicators including fasting blood glucose （FPG）， 2-hour postprandial blood glucose （2 h PG），glycated albumin（GA）， and glycated hemoglobin （HbA1c） were evaluated. Pancreatic function was evaluated using fasting C-peptide （FCP）， 2-hour postprandial C-peptide （2 h CP）， and C-peptide area under the curve （AUC_cp）. Traditional Chinese medicine （TCM） syndrome scores， clinical efficacy， and safety indicators were also observed. ResultsIn terms of glucose metabolism indicators， compared with the baseline， both groups exhibited significantly lower FPG， 2 h PG， GA and HbA1C （P<0.01），while FCP， 2 h CP and AUC_cp were significantly higher （P<0.01）. Compared with the control group after the treatment， the observation group showed significantly lower FPG， 2 h PG， GA and HbA1C（P<0.05， P<0.01），and significantly higher FCP， 2 h CP and AUC_cp （P<0.05， P<0.01）， indicating that Astragali Radix can improve glucose metabolism. In terms of the diversity of gut microbiota， no significant differences were detected in the Chao1， Shannon and Simpson indexes of the two groups compared with their respective baselines. However， compared with the post-treatment control group， the observation group demonstrated significant increases in the Chao1， Shannon and Simpson indexes （P<0.05， P<0.01）. The β-diversity analysis showed significant separation in gut microbiota composition before and after treatment in both groups， indicating that Astragali Radix can significantly alter the structure and improve the diversity of gut microbiota. At the phylum level， compared with the baseline， both groups showed a significant increase in the relative abundance of Bacteroidota（P<0.01）. The relative abundance of the potentially harmful phylum Proteobacteria was significantly lower in the observation Group after treatment （P<0.01）. Compared with the post-treatment control group， the observation group had a significantly higher relative abundance of Bacteroidota（P<0.01）. No significant difference was found in Firmicutes/Bacteroidota （F/B） ratio between the two groups after treatment， and other phyla showed no significant differences. At the genus level， compared with the baseline， the observation group exhibited a significant increase in Bacteroides （P<0.01） and a significant decrease in Escherichia-Shigella （P<0.01）， whereas no significant difference was seen in the control group . Compared with the control group after treatment， the observation group after treatment had a significantly higher relative abundance of Bacteroides （P<0.01）. No significant differences were seen in other genera. Linear discriminant analysis （LDA） identified potential characteristics taxa： in the observation group， Bacteroidota at the phylum level and Bacteroides and Dubosiella at the genus level， in the control group， Proteobacteria at the phylum level as well as Barnesiella and Staphylococcus at the genus level. Correlation analysis based on a heatmap revealed that GLP-1 levels were positively correlated with Firmicutes， F/B ratio and Fusobacterium， and negatively correlated with Bacteroidota， Proteobacteria， Bacteroides and Escherichia-Shigella. In terms of clinical efficacy， compared with the control group， the total effective rate of the observation group was significantly higher （P<0.05）. Compared with the baseline， the scores for shortness of breath， fatigue， weakness， spontaneous sweating and reluctance to speak significantly decreased in both groups （P<0.01）. Compared with the control group after treatment， the score for weakness was significantly lower in the observation group （P<0.01），indicating that Astragali Radix could improve clinical symptoms and alleviate weakness symptoms. In terms of safety， compared with the baseline， alanine aminotransferase （ALT） levels significantly decreased in both groups （P<0.05，P<0.01），indicating that Astragali Radix did not induce any significant abnormalities in liver and kidney functions. ConclusionAstragali Radix demonstrates the potential to significantly improve the gut microbiota environment in patients of newly diagnosed type 2 diabetes with Qi deficiency. The therapeutic effect may contribute to glycemic control， possibly mediated by an elevation in GLP-1 level. These findings may support its further clinical investigations and potential applications.

Objectives: . This study aimed to explore the role of the Pcdh15 gene in hearing maintenance and to examine the effects of its deficiency on cochlear structure, cochlear function, and hearing loss in mice. Methods: . CRISPR/Cas9 technology was used to generate Pcdh15 knockout (KO) mice. Auditory function was evaluated using hearing tests, while histological approaches were employed to assess morphological changes in cochlear hair cells and spiral ganglion neurons (SGNs). RNA sequencing (RNA-seq) was performed on cochlear tissue from postnatal day 18 (P18) wild-type (WT) and Pcdh15 KO mice to identify differentially expressed genes (DEGs). These DEGs were subjected to functional annotation and pathway analysis, with particular emphasis on oxidative phosphorylation (OXPHOS). Reactive oxygen species (ROS) levels were quantified using flow cytometry and DCFH-DA assays. Results: . Pcdh15 KO mice exhibited progressive sensorineural hearing loss, which worsened to profound deafness by P18. Notably, cochlear hair cells and SGNs showed substantial loss and pronounced morphological abnormalities, particularly within the basal high-frequency region. RNA-seq analysis identified 1,359 DEGs, including 1,024 downregulated and 335 upregulated genes. Pathway analysis indicated that Pcdh15 deficiency was associated with inhibition of the OXPHOS pathway, potentially disrupting mitochondrial respiratory chain complexes I, III, IV, and V and thereby impairing energy production and ATP metabolism. In addition, early-stage cochleae from KO mice demonstrated elevated ROS levels and increased oxidative stress, suggesting that redox imbalance may contribute to hearing damage. Conclusion . Pcdh15 plays a critical role in hearing maintenance. Its deficiency is associated with severe hearing loss and marked cochlear abnormalities, which are linked to inhibition of OXPHOS, disruption of energy metabolism, and exacerbation of oxidative stress. Together, these findings provide new insights into the mechanisms underlying hearing loss and suggest potential targets for therapeutic intervention.

Metal-organic frameworks(MOFs)are a class of organic-inorganic hybrid materials formed by the self-assembly of metal ions or metal clusters with organic ligands through coordination,and possess high specific surface area,tunable pore size and diverse structures.In recent years,MOFs and their composites have shown great application potential in the field of drug detection,especially in selective recognition,enhancing detection sensitivity and on-site rapid detection.This paper summarized the structural characteristics,synthesis methods and detection principles of MOFs and their composites,and reviewed the latest research progresses in detection of various drugs such as opioids,amphetamines,cannabinoids,cathinones,cocaine,ketamine,fentanyls and psychotropic drugs.The advantages and challenges of MOFs materials in the pretreatment of complex biological samples,sensor construction and on-site rapid detection were discussed,and the prospects for future development were analyzed,with the aim of providing theoretical support and technical references for promoting the applications of MOFs in anti-drug practice.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

Objective::To report a fetus with ARCN1-related syndrome caused by a novel de novo heterozygous variant, highlighting the importance of early genetic diagnosis in prenatal care. Methods::The clinical and genetic data of a fetus with a complex combination of clinical signs and a novel de novo heterozygous variant were collected and have been summarized in this study. The potential pathogenic variant was identified throughout the whole exome sequencing and the effects of candidate variants were further validated by a minigene splicing assay. Results::Prenatal systematic ultrasound detected fetal growth restriction. Genetic analysis identified a novel de novo heterozygous variant within the ARCN1 gene—c.1241 +5G>A-located in intron 8. In vitro minigene splicing assays demonstrated that the variant led to two abnormal transcripts. The longer transcript retained 189 base pairs of intron 8, resulting in a truncated protein of 414 amino acids (p.Ser415*). The shorter transcript involved exon 8 skippings, producing a truncated protein of 407 amino acids (p.Ile378Serfs*31). Conclusion::A novel de novo heterozygous variant of the ARCN1 gene, namely NM_001655.5: c.1241 +5G>A, was discovered and identified in a fetus with rhizomelic short stature, microretrognathia, and developmental delays.

Objective::To evaluate the diagnostic efficacy and clinical application of the Obstetrical Chinese Disseminated Intravascular Coagulation (DIC) Scoring System (OCDSS).Methods::This study is a retrospective study that collected 1063 cases from Wuhan Union Hospital, Yichang Central People’s Hospital, and the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture between July 2017 and June 2024. These cases were divided into DIC and non-DIC groups based on score standard. Diagnosis of DIC, the rate of hysterectomy, neonatal mortality, and severe asphyxia are the main outcome measures. All the laboratory indicators are all determined by clinical laboratory department of the hospital. Data were expressed as mean ± standard deviation or median (interquartile range) and frequencies. Independent sample t-test or non-parametric test were used to compare measurement data, while the chi-square test was used for count data. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to test the predictive accuracy. Using univariate and multivariate logistic regression analysis to study the high-risk factors. P < 0.050 indicates a statistical significance. Results::Of 1063 participants in this study, 29 participants (2.73%) were diagnosed with obstetrical DIC by OCDSS score standard, and all the participants were diagnosed as DIC with underlying disease. When the Takao, Clark, and Erez score standard is the "gold standard", the OCDSS score standard always shows good sensitivity and specificity, with all the AUC over 0.75. OCDSS score standard also has better predictive of hysterectomy (68.18%, 91.07%, 0.872), severe neonatal asphyxia and death (79.17%, 75.07%, 0.842) than the other three score standards. All the indicators included in the OCDSS score standard contributed to the DIC diagnosis (all the P < 0.001). The indicators in the DIC group were more abnormal than the non-DIC group (all the P < 0.001). Conclusion::OCDSS is a first score standard, especially for pregnancies, it considers the underlying disease, clinical symptoms, and laboratory results. This score system shared a good diagnosis performance for DIC in the Chinese population and may help clinicians make timely decisions.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]