BACKGROUND:Nanogels can combine the properties of nanoparticles and hydrogels,which have multiple advantages and can play a great role in clinical translation.OJECTIVE:To summarize the self-and cross-linking properties of nanogels,the diagnostic and therapeutic integration strategies they played in a variety of imaging processes,in conjunction with their functional visualization and application properties.METHODS:By searching PubMed,Web of Science,CNKI,and Wanfang databases,English and Chinese search terms were"nanogel,nanohydrogel,imaging,magnetic resonance imaging,fluorescence imaging,ultrasound imaging,photoacoustic imaging,multimodal imaging,therapy,treatment."According to the inclusion criteria,78 articles were finally selected for review.RESULTS AND CONCLUSION:The preparation process of nanogels is significantly affected by their multiple properties and cross-linking mechanisms.Optimization of these properties and mechanisms can help to improve their structural performance and accelerate the translation of their clinical applications.With the trend of integrated diagnosis and treatment,functional visualization of nanogels can effectively image tumors with the help of magnetic resonance imaging,fluorescence imaging,ultrasonography,photoacoustic imaging,and multimodal imaging,which can be of key value in the treatment strategies of various diseases.These findings not only pave the way for improving the preparation methods of nanogels,but also create a robust groundwork for hastening their clinical translation.

BACKGROUND:Autologous bone,allogeneic bone or artificial bone has been used to promote bone defect repair in the clinic,but the rate of non-healing is still high.The key is to ignore the importance of periosteum in the bone healing process.In the early stage of the project,the project team constructed an electrospinning membrane loaded with vascular endothelial growth factor to highly simulate the intramembranous osteogenesis of natural periosteum at the bone defect site,which promoted bone regeneration to a certain extent.However,the injured area often faces the dilemma of severe inflammatory response mediated by macrophages and lack of seed cells,resulting in the risk of inactivation or diffusion of delivered biological factors.Therefore,it is necessary to further optimize and coordinate the immune regulation and angiogenesis functions of biomimetic periosteum to promote bone repair.OBJECTIVE:To investigate the physicochemical properties of stem cell-engineered bionic periosteum and its role in regulating the inflammatory microenvironment to promote bone repair.METHODS:By combining L-polylactic acid-based microsol electrospinning,type Ⅰ collagen self-assembly and gel stem cell transplantation technology,a bionic periosteum(M@C-B)was constructed,in which the core layer loaded with vascular endothelial growth factor and the shell layer delivered bone marrow mesenchymal stem cells to regulate the immune microenvironment of bone defects.The physicochemical properties of the periosteum were characterized by scanning electron microscopy,transmission electron microscopy,and Fourier transform infrared spectroscopy.A co-culture system was established between the bionic periosteum and macrophages,bone marrow mesenchymal stem cells and human umbilical vein endothelial cells to explore immune regulation and in vitro osteogenic and angiogenic abilities.Finally,the osteogenic properties of the stem cell engineered bionic periosteum were further verified in a rat femoral condyle defect model.RESULTS AND CONCLUSION:(1)Transmission electron microscopy results showed that the micro-sol electrospinning(MS)formed a distinct core-shell structure.Scanning electron microscopy indicated that after the assembly of the collagen-l artificial periosteum(M@C)on the surface of the vascular endothelial growth factor-loaded micro-sol,a distinct"spider web-like"fibrous structure was deposited.Infrared spectroscopy further confirmed the successful self-assembly of collagen-l.Release experiments demonstrated that the M@C group mitigated the burst release phenomenon compared to the MS group,maintaining internal vascular endothelial growth factor activity and sustained release.(2)Live/dead cell staining and CCK-8 assay showed that bone marrow mesenchymal stem cells proliferated well and survived on three types of artificial periosteum:MS,purely aligned poly(L-lactic acid)(PLLA)surface self-assembled collagen-l artificial periosteum(PLLA@C),and vascular endothelial growth factor-loaded micro-sol fiber surface self-assembled collagen-l-bone marrow mesenchymal stem cells artificial periosteum(M@C-B).Among them,the M@C-B group had the highest number of live cells and the fastest proliferation rate.(3)Alkaline phosphatase staining,alizarin red staining,and osteopontin immunofluorescence staining showed that the PLLA@C and M@C-B groups significantly promoted osteogenic differentiation of bone marrow mesenchymal stem cells.Angiogenesis experiments demonstrated that the vascular endothelial growth factor-loaded groups(MS and M@C-B)had longer blood vessel lengths and more reticular vascular-like structures with more cross-linked nodes,with the M@C-B group being the most prominent.(4)Immunofluorescence and flow cytometry showed that artificial periosteum in the M@C-B group significantly inhibited the pro-inflammatory macrophage phenotype and promoted the polarization of macrophages towards the anti-inflammatory M2 phenotype.(5)In vivo studies further confirmed that the M@C-B group showed superior bone mineral density,trabecular thickness,relative bone volume,and trabecular spacing compared to other groups.(6)These results indicate that bone marrow mesenchymal stem cell-engineered artificial periosteum,through the rapid regulation of the bone defect immune microenvironment by the collagen-l-bone marrow mesenchymal stem cells outer phase and the sustained release of vascular endothelial growth factor by the micro-sol electrospinning core-shell structure of the inner phase,synergistically promotes bone healing.

BACKGROUND:Some studies have confirmed the changes in the function of immune cell subsets such as monocytes,T cells,B cells,and natural killer cells(NK cells)in patients with osteoarthritis,but the specific regulatory mechanisms are unclear.OBJECTIVE:To explore the causal relationship between plasma metabolite-mediated immune cells and hip osteoarthritis.METHODS:The Genome-Wide Association Studies(GWAS)data of 731 immune cells were used as the exposure,the GWAS data of hip osteoarthritis were used as the outcome,and 1 400 plasma metabolites were selected as mediating factors.The GWAS database is an important database for genetic association studies,maintained by international organizations with no country-specific affiliation.The inverse variance weighting method in the two-sample Mendelian randomization method was the main method,and the Bayesian weighted Mendelian randomization method was used to analyze the prior distribution,sample data and weights,which were then used to calculate the posterior distribution.The accuracy and reliability of the inverse variance weighting results were evaluated according to the posterior distribution,supplemented by MR-Egger,weighted median,simple model,and weighted mode methods.The pliotropy test and heterogeneity test were used to ensure the robustness of the process.The results of the inverse variance weighting method were used for subsequent mediating effect analysis.RESULTS AND CONCLUSION:(1)The inverse variance weighting method identified 4 immune cells strongly correlated with hip osteoarthritis,and 20 metabolites strongly associated with hip osteoarthritis,all of which had no reverse causal relationship.At the same time,the validation results of Bayesian weighted Mendelian randomization method showed that the posterior mean value was similar to the estimated value of the inverse variance weighting,and the posterior variance was relatively lower.One monocyte subtype(PDL-1 on CD14-CD16+)was finally screened out to have a causal relationship with hip osteoarthritis,with a total effect of-0.047(odds ratio=0.954,95％confidence interval:0.926-0.983),and a mediating effect of-0.004(odds ratio=0.939,95％confidence interval:0.902-0.978)mediated by alliin levels,accounting for 8.5％of the total effect.It was concluded that alliin is a protective factor in the progression of hip osteoarthritis,in which this metabolite plays a mediating role.(2)The large amount of data from international databases and European population analysis is of great significance to Chinese biomedicine,which can provide clues for research on the genetic susceptibility to similar diseases in the Chinese population,aiding in discovering the unique associations.The pharmacogenomic approaches used can be adapted to screen for drug response genes in the Chinese population,enhancing the precision of personalized medicine.Additionally,the advanced high-throughput technologies and statistical methods employed can be learned and applied to disease prevention and treatment research.

Community-acquired pneumonia （CAP） refers to an infectious inflammation of the lung parenchyma （including the alveolar wall，that is，the broad pulmonary interstitium） acquired outside the hospital. Its common pathogens include streptococcus pneumoniae，respiratory viruses， mycoplasma pneumoniae， and so on. The related factors for the occurrence and development of CAP include patient characteristics （immune function，mucus production and clearance function，coagulation function，physical condition， and comorbidity） and pathogen characteristics （susceptibility，virulence，and antibiotic resistance）. The pathogenesis of CAP lies in immune deficiency，pathogen invasion，inflammatory response disorder，mucus production and clearance disorder， coagulation disorder， and so on. The pathogenesis of CAP in traditional Chinese medicine can be described as "vital Qi deficiency and toxic stasis". Vital Qi deficiency （lack of immunity） is the potential pathogenesis of the disease and easy to be invaded by external pathogens （respiratory pathogens）. Toxic stasis （inflammatory disorder，mucus production and clearance disorder，and coagulation dysfunction） is the key pathogenic factor. Vital Qi deficiency and toxic stasis are intermingled in a state of deficiency and excess，which suggests that the treatment of CAP lies in strengthening vital Qi and eliminating pathogenic factors. This involves strengthening vital Qi in the whole process to consolidate body resistance and nourish promordial Qi. It also involves clearing heat，eliminating phlegm，removing dampness，and dispelling stasis to dispel pathogenic toxins based on the syndrome differentiation. Its action mechanism is to regulate immune and inflammatory responses，resist pathogens，and improve mucus production and clearance， as well as coagulation disorders. Starting from the key pathogenesis of CAP，"vital Qi deficiency and toxic stasis"， this paper discussed the pathogenesis of CAP and summarized the action mechanism of vital Qi strengthening for detoxification in its treatment. It is intended to complement the theoretical system by identifying "vital Qi deficiency and toxic stasis" as the key pathogenesis underlying CAP and the scientific connotation of treating CAP with vital Qi strengthening for detoxification，thereby providing insights for its clinical application.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

Objective To explore scientific and accurate prediction methods for the incidence of hand, foot, and mouth disease in the post-pandemic era, and to address modeling challenges caused by abnormal fluctuations in case numbers from 2020 to 2023. Methods The seasonal index was used to pre-process the data. The traditional seasonal autoregressive integrated moving average (SARIMA) model, singular spectrum analysis (SSA)-ARIMA model, ARIMA-Long short-term memory (LSTM) model, and SSA-ARIMA-LSTM model were used to fit the incidence from 2013 to 2023, and the incidence of hand, foot and mouth disease in 2024 was predicted. The real data collected in 2024 were used as the test set to compare the prediction performance of the models. Results The fitting performance of the constructed models was as follows: the ARIMA model had MAE=107.50 and RMSE=144.53, the SSA-ARIMA model showed MAE=2.84 and RMSE=4.33, the ARIMA-LSTM model achieved MAE=99.46 and RMSE=131.59, and the SSA-ARIMA-LSTM model had MAE=96.35 and RMSE=132.13. In terms of prediction performance, the ARIMA model resulted in MAE=151.64 and RMSE=146.70, the SSA-ARIMA model demonstrated MAE=41.22 and RMSE=57.01, the ARIMA-LSTM model yielded MAE=220.75 and RMSE=257.89, and the SSA-ARIMA-LSTM model recorded MAE=58.83 and RMSE=72.06. Conclusion The SSA-ARIMA model has the best fitting degree and the highest prediction accuracy, and is suitable for predicting the incidence trend of hand, foot and mouth disease.

OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.

BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

ObjectiveTo construct a multifunctional liposomal delivery system by replacing cholesterol（Chol） in conventional liposomes with saikosaponin D（SSD） and modifying with poloxamer 407（P407） for co-delivery of curcumin（Cur）. The system was evaluated for in vivo tumor targeting and inhibitory effects on mouse subcutaneous solid tumors. MethodsSingle-factor and orthogonal tests combined with information entropy weighting were used to optimize the formulation process of the liposome with encapsulation efficiency and absolute Zeta potential as indexes， and validation studies and liposomal characterization were performed. A subcutaneous solid tumor model was established by injecting H22 hepatocellular carcinoma cells subcutaneously into the dorsal surface of the right forelimb of mice. DiR-loaded traditional Chol liposomes（P407-DiR-Chol-LPs， PDCL） and novel SSD-based liposomes（P407-DiR-SSD-LPs， PDSL） were prepared by the optimized formulation process， and tail vein injection was performed to investigate the impact of SSD on liposome tumor targeting with small animal in vivo imaging. Mice were randomly divided into eight groups， including blank group， model group， free doxorubicin（DOX） group（2 mg·kg^-1）， free Cur group（8 mg·kg^-1）， free SSD group（10 mg·kg^-1）， P407-Cur-Chol-LPs（PCCL） group， P407-SSD-LPs（PSL） group， and P407-Cur-SSD-Lps（PCSL） group. Treatments were administered intraperitoneally every other day for seven doses. Antitumor efficacy and biocompatibility were evaluated by monitoring body weight change， organ indices， tumor volume and mass， relative tumor proliferation rate（T/C）， and tumor growth inhibition rate（TGI）. Histopathological analysis of liver， kidney， and tumor tissues was performed using hematoxylin-eosin（HE） staining. Serum levels of aspartate aminotransferase（AST）， alanine aminotransferase （ALT）， blood urea nitrogen（BUN）， and creatinine（Crea）in mice were quantified by fully automated biochemical analyzer. ResultsOrthogonal test yielded optimal ratios of Cur， SSD， and P407 to soybean phosphatidylcholine（SPC） as 1∶25， 1∶20， and 1∶4. The optimized PCSL exhibited spherical morphology with a particle size of 179.15 nm， a Zeta potential of -47.25 mV， and an encapsulation efficiency of 96.40%. Its in vitro release profile conformed to first-order kinetics， demonstrating excellent storage stability and hemocompatibility. In vivo imaging revealed that the fluorescence signal in tumor tissues and the fluorescence intensity ratio between tumors and organs were significantly higher in the PDSL group than in the PDCL group（P<0.05， P<0.01）. Among the treatment groups， PCSL group showed superior efficacy over free Cur group， free SSD group， PCCL group， and PSL group， with TGI>40% and T/C<60%， indicating pronounced anti-hepatocellular carcinoma effects（P<0.05， P<0.01）. Histopathology and serum biochemistry indicated minimal hepatorenal toxicity and improved hepatic and renal function in PCSL-treated mice. ConclusionReplacing Chol with SSD in preparing multifunctional drug delivery systems not only stabilizes liposomes but also yields superior anti-hepatocellular carcinoma efficacy， achieving the effect of drug-excipient integration. Co-delivery of Cur via this system can be used for treating subcutaneous solid tumors in hepatocellular carcinoma， providing new insights and technical approaches for anti-hepatocellular carcinoma research and the meridian-guiding and messenger-directing theory in traditional Chinese medicine.