OBJECTIVE To explore the influencing factors for immune-related endocrine toxicity in the treatment of malignant solid tumors with sintilimab， aiming to provide a reference for rational drug use. METHODS Case data were collected from patients with malignant solid tumors， who were treated with sintilimab at the First Affiliated Hospital of Jinzhou Medical University from January 1， 2020 to December 31， 2024， using the electronic medical record system. The patients were divided into an endocrine immune-related adverse events （irAEs） group and a non-endocrine irAEs group based on whether they developed immune-related endocrine toxicity after sintilimab administration. The statistical significance of predictive variables was examined through univariate and multivariate Logistic regression methods. RESULTS A total of 224 patients were enrolled， including 138 cases （61.6%） in the non-endocrine irAEs group and 86 cases （38.4%） in the endocrine irAEs group. After univariate and multivariate Logistic regression analysis， a treatment period of 1-12 cycles was identified as an independent influencing factor for immune-related endocrine toxicity [OR＝7.175， 95%CI （1.239， 41.563）， P ＜0.05 ] ， immune-related hyperglycemia [OR＝6.600， 95%CI （1.053， 41.359）， P ＜0.05 ] ， and immune-related subclinical hypothyroidism [OR＝20.200， 95%CI （3.224， 126.558）， P ＜0.05 ] . The combination with paclitaxel-based drugs was identified as an independent influencing factor for immune-related subclinical hyperthyroidism [OR＝6.410， 95%CI （1.790， 22.955）， P ＜0.05 ] . CONCLUSIONS Among patients treated with sintilimab， the treatment cycle is a risk factor for immune-related endocrine toxicity， immune-related hyperglycemia and immune-related subclinical hypothyroidism. The combination of paclitaxel-based drugs is a risk factor for immune-related subclinical hyperthyroidism. It is recommended that when applying sintilimab in clinical practice， especially during the first few treatment cycles， the relevant endocrine indicators should be dynamically monitored in a standardized manner. In addition， special attention should be paid to patients treated with the combination of paclitaxel-based drugs to be vigilant against the occurrence of endocrine adverse events.

To address issues such as loss of detailed information, blurred target boundaries, and unclear structural hierarchy in medical image fusion, this paper proposes an adaptive feature medical image fusion network based on a full-scale diffusion model. First, a region-level feature map is generated using a kernel-based saliency map to enhance local features and boundary details. Then, a full-scale diffusion feature extraction network is employed for global feature extraction, alongside a multi-scale denoising U-shaped network designed to fully capture cross-layer information. A multi-scale feature integration module is introduced to reinforce texture details and structural information extracted by the encoder. Finally, an adaptive fusion scheme is applied to progressively fuse region-level features, global features, and source images layer by layer, enhancing the preservation of detail information. To validate the effectiveness of the proposed method, this paper validates the proposed model on the publicly available Harvard dataset and an abdominal dataset. By comparing with nine other representative image fusion methods, the proposed approach achieved improvements across seven evaluation metrics. The results demonstrate that the proposed method effectively extracts both global and local features of medical images, enhances texture details and target boundary clarity, and generates fusion image with high contrast and rich information, providing more reliable support for subsequent clinical diagnosis.
Humans ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Diagnostic Imaging/methods* ; Image Interpretation, Computer-Assisted/methods*

The vasculature plays a critical role in homeostasis and health as well as in the development and progression of a wide range of diseases, including cancer, cardiovascular diseases, metabolic diseases (and their complications), chronic inflammatory diseases, ophthalmic diseases, and neurodegenerative diseases. As such, the growth of the vasculature mediates normal development and physiology, as well as disease, when pathologically induced vessels are morphologically and functionally altered owing to an imbalance of angiogenesis-stimulating and angiogenesis-inhibiting factors. This review offers an overview of the angiogenic process and discusses recent findings that provide additional interesting nuances to this process, including the roles of intussusception and angiovasculogenesis, which may hold promise for future therapeutic interventions. In addition, we review the methodology, including those of in vitro and in vivo assays, which has helped build the vast amount of knowledge on angiogenesis available today and identify important remaining knowledge gaps that should be bridged through future research.
Animals ; Signal Transduction/physiology* ; Humans ; Neovascularization, Pathologic/physiopathology* ; Neovascularization, Physiologic/physiology* ; Models, Animal ; Angiogenesis

Objective:To investigate the effect of nuclide distribution with time from the in vivo metabolism based on measurement of radioactive contamination using lung counting method. Methods:The distribution of nuclides in the body with time was calculated on a basis of a single inhalation of aerosols containing 235U and the International Commission of Radiological Protection(ICRP) nuclide metabolism compartment model. A passive efficiency calibration of the lung counter, was performed using the simulation and calculation software Geant4 to obtain the contribution of the tissue or organs of interest to the lung counter, and to investigate the effect of the nuclide distribution on the lung counting method. Results:The time elapsed after inhalation of radionuclides, as well as their physicochemical state, has the effect on their distribution in the body and on the detection efficiency of the lung counter. Radionuclides with smaller particulate sizes have a higher initial retention in the lungs, and those with an activity median aerodynamic diameter (ADAM) of 1 μm contributed more fraction to the lung counter than those with an ADAM of 5 μm. F-type compounds were metabolized more rapidly by the respiratory system, and after 8 h of ingestion, nuclides were distributed in the lungs. F-type compounds were metabolized in the respiratory system at a relatively fast rate, and 8 h after inhalation, the fraction of nuclides retained in the lung contributed no more than 30% to the lung counter. Within 3 d after ingestion of M-type and S-type compounds, radioactive particulats largely deposites in the nasopharyngeal region. With biological metabolization and clearance, the fraction contributed by lung to counter is in rising, and the fraction to the lung counter typically remained larger than 80% after 3 d.Conclusions:Radionuclide metabolization in the body varies with their physicochemical properties and measurement time and site. For estimating internal contamination, consideration should be given to the distribution of nuclides, in order to avoid the overestimation.

Objective:To investigate the effect of nuclide distribution with time from the in vivo metabolism based on measurement of radioactive contamination using lung counting method. Methods:The distribution of nuclides in the body with time was calculated on a basis of a single inhalation of aerosols containing 235U and the International Commission of Radiological Protection(ICRP) nuclide metabolism compartment model. A passive efficiency calibration of the lung counter, was performed using the simulation and calculation software Geant4 to obtain the contribution of the tissue or organs of interest to the lung counter, and to investigate the effect of the nuclide distribution on the lung counting method. Results:The time elapsed after inhalation of radionuclides, as well as their physicochemical state, has the effect on their distribution in the body and on the detection efficiency of the lung counter. Radionuclides with smaller particulate sizes have a higher initial retention in the lungs, and those with an activity median aerodynamic diameter (ADAM) of 1 μm contributed more fraction to the lung counter than those with an ADAM of 5 μm. F-type compounds were metabolized more rapidly by the respiratory system, and after 8 h of ingestion, nuclides were distributed in the lungs. F-type compounds were metabolized in the respiratory system at a relatively fast rate, and 8 h after inhalation, the fraction of nuclides retained in the lung contributed no more than 30% to the lung counter. Within 3 d after ingestion of M-type and S-type compounds, radioactive particulats largely deposites in the nasopharyngeal region. With biological metabolization and clearance, the fraction contributed by lung to counter is in rising, and the fraction to the lung counter typically remained larger than 80% after 3 d.Conclusions:Radionuclide metabolization in the body varies with their physicochemical properties and measurement time and site. For estimating internal contamination, consideration should be given to the distribution of nuclides, in order to avoid the overestimation.

Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.

Objective:To explore the association between cognitive function and the level of gamma-amino-butyric acid(GABA)in anterior cingulate cortex(ACC)before and after treatment in patients with major depres-sion disorder.Methods:Totally 31 medication-naive patients with major depression disorder meeting the criteria of the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)and 33 normal controls were col-lected.Each eligible patient received treatment with selective serotonin reuptake inhibitor agents for 8 weeks.The MATRICS Consensus Cognitive Battery(MCCB)was used to evaluate the cognitive function.By means of 1H magnetic resonance spectroscopy,anterior cingulate cortex GABA concentrations were measured.Results:At base-line,the concentration of ACC GABA relative to water(GABA+/W)was lower in the patient group than in the control group(P＜0.05)and increased after treatment(P＜0.05).ACC GABA+/W was negatively associated with verbal learning and visual memory score in patient group at baseline(correlation coefficient and P value were r=-0.40,P＜0.05;r=-0.42,P＜0.05,respectively).The ACC GABA+/W difference resulted of treatment in patient group was positively associated with the difference of working memory score and the difference of reasoning and problem-solving score(correlation coefficient and P value were r=0.58,P＜0.05;r=0.66,P＜0.05,respec-tively).Conclusion:The cognitive dysfunction of patients with major depression disorder may not be related to the degree of depression and anxiety.And improvement of cognitive function may be associated with increase of ACC GABA concentrations.

Anti-angiogenic drugs (AADs), which mainly target the vascular endothelial growth factor-A signaling pathway, have become a therapeutic option for cancer patients for two decades. During this period, tremendous clinical experience of anti-angiogenic therapy has been acquired, new AADs have been developed, and the clinical indications for AAD treatment of various cancers have been expanded using monotherapy and combination therapy. However, improvements in the therapeutic outcomes of clinically available AADs and the development of more effective next-generation AADs are still urgently required. This review aims to provide historical and perspective views on tumor angiogenesis to allow readers to gain mechanistic insights and learn new therapeutic development. We revisit the history of concept initiation and AAD discovery, and summarize the up-to-date clinical translation of anti-angiogenic cancer therapy in this field.