Objective: To analyze the characteristics of fine particulate matter (PM2.5) pollution in Urumqi City, Xinjiang Uygur Autonomous Region from 2016 to 2023 and establish a prediction model, so as to provide the reference for air pollution prevention and control. Methods: PM2.5 monitoring data of Urumqi City from 2016 to 2023 were collected through the website of Ministry of Ecology and Environment of China. The changing trend of PM2.5 concentration was analyzed using temporal chart and seasonal index. PM2.5 monthly average concentrations from 2016 to 2023 were used to establish an autoregressive integrated moving average (ARIMA) model, and the data in 2023 was fitted and compared with the actual values, using mean absolute percentage error (MAPE) to evaluate the effectiveness of the model, and PM2.5 monthly average concentration from 2024 to 2025 was predicted. Results: PM2.5 daily average concentration in Urumqi City showed a decreasing trend from 2016 to 2023 (rs=-0.239, P<0.001), with high seasonal indexes in January, February and December, indicating certain seasonal characteristics. The optional model was ARIMA (1, 0, 0) (1, 1, 0)12, with the value of Akaike information criterion, corrected Akaike information criterion, and Bayesian information criterion being 727.38, 727.88 and 737.10, respectively. PM2.5 monthly average concentration in 2023 was fitted and compared with the actual values, with an absolute error range of 0.31-7.45 μg/m3, a relative error range of 0.01-0.53, and MAPE of 14.42%. PM2.5 monthly average concentration in Urumqi City from 2024 to 2025 was predicted to be consistent with the trend from 2016 to 2023. Conclusions PM2.5 concentration in Urumqi City showed a tendency towards a decline from 2016 to 2023, and was relatively high in winter. ARIMA (1, 0, 0) (1, 1, 0)12 can be used for short-term prediction of PM2.5 pollution in Urumqi City.

ObjectiveTo preliminarily establish and verify the quantitative diagnosis method of dampness syndrome of colorectal adenoma， so as to provide evidence for the diagnosis of colorectal adenoma syndrome. MethodsThis study included 334 patients with colorectal adenoma， who were grouped into 200 in the training group and 134 in the validation group by clinical visits chronologically. According to the data from the four examinations of traditional Chinese medicine， patients in training group were subgrouped into dampness syndrome subgroup and non-dampness syndrome subgroup. After eliminating items with response rate less than 3%， the factors showed statistical difference in frequency between the subgroups were screened as diagnostic items. The diagnostic items were assigned scores using the conditional probability formula conversion method， and the diagnostic thresholds and grading criteria were determined by the maximum likelihood discriminant method， so as to establish the quantitative criteria preliminarily. Retrospective and prospective tests were conducted respectively on patients in training group and validation group， including the sensitivity， specificity， accuracy， positive likelihood ratio and other indexes， to evaluate the quantitative criteria. ResultsThe training group included 176 participants as dampness syndrome subgroup and 24 participants as non-dampness syndrome subgroup， who applied 40 diagnostic items， and 19 related factors were identified as significant differences. After assigning the scores in turn， the quantitative diagnostic threshold was determined as 45， and the quantitative diagnostic criteria for colorectal adenoma with dampness syndrome were as follows： greasy coating （7 scores）， thick coating （8 scores）， heaviness of head （9 scores）， heaviness of whole body （6 scores）， heaviness of limbs （6 scores）， sticky and greasy stool （6 scores）， sticky and greasy mouth （10 scores）， obesity （6 scores）， sleepiness （12 scores）， laziness （13 scores）， epigastric fullness （8 scores）， abdominal distension and pain （11 scores）， lumbar and knee aches and heaviness （8 scores）， joint and muscle aches and pains （9 scores）， loose stools （12 scores）， fetid mouth odor （15 scores）， slippery pulse （8 scores）， overabundance of eye secretion （7 scores）， and large touge （10 scores）. Grading criteria： 45 ≤ points ＜ 61 as mild， 61 ≤ points ≤ 104 as moderate， points ＞ 104 as severe. In the test retrospective of the training group involving 200 patients， the sensitivity， specificity， accuracy， and positive likelihood ratio were 86.36%， 95.83%， 87.50%， and 20.73 respectively； In the test prospective of the verification group involving 134 patients， the sensitivity， specificity， accuracy， and positive likelihood ratio were 76.64%%， 96.30%， 80.60%， and 20.69， respectively. ConclusionIt is effective to diagnose and identify the dampness syndrome of colorectal adenoma by preliminarily establish a quantitative diagnostic method with a combined model of disease and evidence， and the method may provide support for future related studies.

ObjectiveTo evaluate the effectiveness and consistency of three commonly used early colorectal cancer screening models for advanced colorectal adenoma as a noninvasive means， and to assess the predictive value of traditional Chinese medicine （TCM） tongue images in the models. MethodsPatients diagnosed with colorectal adenoma who underwent colonoscopy and pathological examination were selected as the study participants. Basic clinical data and tongue image were collected. The prediction models of Asia-Pacific colorectal screening （APCS） model， its revision （M-APCS） and colorectal neoplasia predict （CNP） model were applied to compare the predictive effects of the three models on advanced stage adenomas of the colon， the differences in clinical data and traditional Chinese medicine tongue characteristics among patients with different degrees of adenomas， and the similarities and differences in tongue characteristics among the models. The discriminative ability of the three risk models was evaluated using the area under the curve （AUC） and receiver operating characteristic （ROC） curves. The calibration was assessed using the Kuder-Richardson coefficient and the Hosmer-Lemeshow test for consistency analysis. ResultsA total of 227 patients with adenoma were analyzed， including 104 patients （45.82%） with advanced adenoma. In the detection of advanced adenoma， those with greasy coating （70 cases， 67.3%） were higher than those without greasy coating （34 cases， 32.7%， P＜0.05）. After multivariate analysis， the odds ratio （OR） value of non-greasy coating was 0.371 （0.204~0.673， P＜0.01）， indicating that non-greasy coating was a protective factor for advanced adenomas. Among the three risk models， the detection rate of advanced adenoma in the high-risk group with APCS was the highest （63.3%）， which was 1.49 times and 2.04 times that of the medium-risk group （42.6%） and the low-risk group （31.1%， P＜0.01）. The detection rate of advanced adenomas in high-risk groups of M-APCS and CNP was slightly higher than that in moderate or low risk groups （P＞0.05）. The proportion of yellow and greasy coating in high-risk group was higher than that in the medium-risk or low-risk group （P＜0.05）. For the ability to distinguish advanced and non-advanced adenomas， the AUC of APCS was 0.629 （95% CI： 0.556~0.702） and was higher than that of M-APCS （0.591） and CNP （0.586）. In calibration evaluation， Cronbach's alpha was 0.919 （＞0.7）， which indicated that the three models were consistent. In the correlation matrix， the correlation coefficients between APCS model and M-APCS model， and CNP model were 0.794 and 0.717， respectively， and the correlation coefficients between M-APCS model and CNP model were 0.873， Hosmer-Lemeshow χ² =2.552， P＞0.05， which suggested that the three models had good calibration ability. ConclusionAll three models demonstrate the efficiency to identify advanced colorectal adenoma， and their calibration ability is considered to be good. Among the three models， the APCS exhibits the highest recognition efficiency， however， the recognition accuracy of the APCS model needs improvement. The presence of a greasy coating is identified as one of the potential predictors of advanced adenoma. Consequently， it can be considered for inclusion in the risk model of advanced colorectal adenoma to enhance the accuracy.