Large amount of data including chemical composition and size information of individual particles would be generated in the measurement of aerosol particles using atmospheric aerosol time-of-flight mass spectrometry ( ATOFMS ) . Our home-made ATOFMS was used to measure the indoor individual aerosol particles in real-time for 24 h, and the obtained mass spectrometric data were clustering analysis by self-organizing map ( SOM ) because of its ability of vector quantization and data dimensionality reduction. 20 classification results were got which includedCalcium-Containing,Salt+Secondary particles,Secondary particles,Organic Amines,K+-Rich Organics andSoil particles, etc. Compared with previous mass spectrometric methods, SOM is a natural visualization tool, more classification results can be obtained. This classification information would be useful to assess the response and toxicity of atmospheric aerosol particles and identify the origin of atmospheric aerosol particles.

Diabetic macular edema(DME)is a complication of diabetic retinopathy(DR), and is also the main cause of vision loss and blindness in DR patients. Optical coherence tomography(OCT)and optical coherence tomography angiography(OCTA)serve as the principal methods for the non-invasive assessment of microstructural and microvascular pathological changes in the retina. They are widely-used methods for detecting and evaluating DME. As OCT and OCTA technologies advance, various parameters have assumed the role of biomarkers, such as central subfield thickness(CST), cube average thickness(CAT), cube volume(CV), disorganization of retinal inner layers(DRIL), hyperreflective foci(HRF)and subfoveal neuroretinal detachment(SND). OCT and OCTA are widely used in clinical practice. OCT can visually show the layer changes and subtle structures of the retina and choroid in the macular area, while OCTA is more often used to detect microvascular changes. In this article, the role of OCT and OCTA-related biomarkers in prognosis and monitoring in DME is described, while the biomarkers visible in the test results can provide new ideas for monitoring and treatment strategies in DME, and provide new insights into the pathogenesis of DR and DME.

ObjectiveTo establish and validate a clinical prediction model for 1-year major adverse cardiovascular events（MACEs）risk after percutaneous coronary intervention （PCI） in coronary heart disease （CHD） patients with blood stasis syndrome. MethodThe consecutive CHD patients diagnosed with blood stasis syndrome in the Department of Integrative Cardiology at China-Japan Friendship Hospital from September 1， 2019 to March 31， 2021 were selected for a retrospective study， and basic clinical features and relevant indicators were collected. Eligible patients were classified into a derivation set and a validation set at a ratio of 7∶3， and each set was further divided into a MACEs group and a non-MACEs group. The factors affecting the outcomes were screened out by least absolute shrinkage and selection operator （Lasso） and used to establish a logistic regression model and identify independent prediction variables. The goodness-of-fit of the model was evaluated by the Hosmer-Lemeshow test， and the area under curve （AUC） of the receiver operating characteristic （ROC） curve， calibration curve， decision curve analysis （DCA）， and clinical impact curve （CIC） were employed to evaluate the discrimination， calibration， and clinical impact of the model. ResultA total of 731 consecutive patients were assessed and 404 eligible patients were enrolled， including 283 patients in the derivation set and 121 patients in the validation set. Lasso identified ten variables influencing outcomes， which included age， sex， fasting plasma glucose （FPG）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， homocysteine （Hcy）， brachial-ankle pulse wave velocity （baPWV）， flow-mediated dilatation （FMD）， left ventricular ejection fraction （LVEF）， and Gensini score. The multivariate Logistic regression preliminarily identified age， FPG， TG， Hcy， LDL-C， LVEF， and Gensini score as the independent variables that influenced the outcomes. Of these variables， male， high FMD and high LVEF were protective factors， and the rest were risk factors. The prediction model for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome showed χ²=12.371 （P=0.14） in Hosmer-Lemeshow test and the AUC of 0.90. With the threshold probability > 10%， the model showed better prediction performance for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome than for that in all the patients. With the threshold probability > 60%， the estimated value was much closer to the real number of patients. ConclusionThe established clinical prediction model facilitates the early prediction of 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome， which can provide ideas for the precise treatment of CHD patients after PCI and has guiding significance for improving the prognosis of the patients. Meanwhile， multi-center studies with larger sample sizes are expected to further validate， improve， and update the model.