An anaerobic sequencing batch reactor (ASBR) was used to treat thermal denitration tail liquid and microbial community was studied. Activated sludge was taken from the reactor for scanning electron microscope analysis. The images showed that the dominant cells in the flora were oval cocci. Its diameter was about 0.7 μm. Through a series of molecular biology methods such as extracting total DNA from the sludge, PCR amplification, positive clone authentication and sequencing, we obtained the 16S rDNA sequences of the flora. Phylogenetic tree and clone library were established. The universal bacteria primers of 27F-1492R PCR amplification system obtained 85 clones and could be divided into 21 OTUS. The proportions were as follows: Proteobacteria 61.18%; Acidobacteria 17.65%; Chlorobi 8.24%; Chlorofexi 5.88%; Gemmatimonadetes 3.53%; Nitrospirae 2.35% and Planctomycetes 1.18%. The specific anammox bacterial primers of pla46rc-630r and AMX368-AMX820 PCR amplification system obtained 45 clones. They were divided into 3 OTUS. Candidatus brocadia sp. occupied 95.6% and unknown strains occupied 4.4%.
Ammonia ; chemistry ; Bacteria ; Phylogeny ; Polymerase Chain Reaction ; RNA, Ribosomal, 16S ; Sewage ; microbiology

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.

ObjectiveTo explore the association between triglyceride-glucose （TyG） index and major adverse cardiovascular events （MACEs） risk in coronary heart disease （CHD） patients with blood stasis syndrome after percutaneous coronary intervention （PCI）. MethodsA total of 857 CHD patients with blood stasis syndrome after PCI were enrolled and divided into four groups according to the baseline TyG index quartiles， Q1 （TyG ＜ 8.51）， Q2 （8.51 ≤ TyG ＜ 8.88）， Q3 （8.88 ≤ TyG ＜ 9.22）， and Q4 （TyG ≥ 9.22）. The clinical outcome was defined as a compound endpoint of cardiovascular events including cardiac death， non-fatal myocardial infarction， unplanned revascularization， in-stent restenosis and stroke. The machine learning Boruta algorithm was used for feature selection related to MACEs risk. Kaplan-Meier survival analysis and Cox proportional hazards regression model were used to compare the differences in MACEs risk among the four groups. Restricted cubic spline （RCS） and subgroup analysis were performed to determine the relationship between the TyG index and MACEs risk. The area under the receiver operating characteristic （ROC） curve （AUC）， calibration curve and Hosmer-Lemeshow test， and decision curve analysis （DCA） were plotted to evaluate the predictive value of the TyG index for MACEs risk. ResultsThe median follow-up time of the included patients was 2.45 years. During the follow-up period， 313 cases （36.52%） of new MACEs occurred. The incidence of MACEs in Q1， Q2， Q3， Q4 group was 28.17% （60/213）， 29.05% （61/210）， 39.45% （86/218） and 49.07% （106/216）， respectively. Kaplan-Meier survival analysis suggested statistically significant differences in MACEs risk among the four groups （P＜0.001）. Cox proportional hazards regression model analysis found that the risk of MACEs in patients with high TyG index increased by 60.1% （P＜0.01）. Using Q1 as the reference， the MACEs risk in Q2， Q3 and Q4 groups gradually increased， and the trend was statistically significant （P＜0.05）. RCS model suggested that the TyG index was nonlinearly associated with the MACEs risk （P＜0.001）. The TyG index had a good predictive performance for MACEs risk according to ROC analysis （AUC=0.758， 0.724-0.792） and Hosmer-Lemeshow test （χ² = 4.319， P = 0.827）. Additionally， DCA analysis also suggested a good clinical efficacy of the TyG index for predicting MACEs. Subgroup analysis showed that different baseline TyG index was positively correlated with the MACEs risk in the stratification of age， male， BMI， history of diabetes and hypertension， and low-density lipoprotein cholesterol （LDL-C）≥1.8 mmol·L^-1（P＜0.05）. ConclusionThe elevated TyG index is closely corrected with an increased risk of MACEs in CHD patients with blood stasis syndrome after PCI， indicating a guiding significance for early risk stratification and clinical decisions.

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.

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.