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Abstract Objective: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). Methods: Cross-sectional study involving 161 adolescents with a body mass index ≥ + 1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. Results: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. Conclusion: The adolescent at higher risk is younger with higher fasting glycemia levels.
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Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.
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Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.
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@#Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. Methods The patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.
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@#Objective To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1 389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7 163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.
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Objective To assess the risk of nosocomial infection in patients with multiple myeloma during their first hospitalization. Methods Totally 480 patients with multiple myeloma who were hospitalized for the first time in department of hematology of West China Hospital, Sichuan University from August 2021 to August 2022 were included, and the nosocomial infection during treatment was statistically analyzed. The patients were divided into infected group and uninfected group. The independent influencing factors of nosocomial infection were analyzed and a prediction model was established. The reliability of the prediction model was analyzed by receiver operating characteristic curve (ROC). Results The incidence rate of nosocomial infection was 31.2% among 480 patients hospitalized for the first time. There were statistically significant differences in age, ISS staging, controlling nutritional status (CONUT) score, agranulocytosis, hemoglobin, and albumin between the infected group and the uninfected group (P<0.05). Logistic multivariate regression analysis showed that age, ISS staging, CONUT score, agranulocytosis, hemoglobin level, and albumin level were all independent correlated factors of nosocomial infection in patients with multiple myeloma hospitalized for the first time (P<0.05). The area under the ROC curve (AUC), sensitivity and specificity of multivariate logistic regression prediction model were 0.88 (95%CI: 0.840-0.920), 85.00% and 76.36%, respectively. Conclusion The incidence rate of nosocomial infection is high among patients with multiple myeloma in the first hospitalization. The prediction model established according to independent correlated factors of nosocomial infection has high predictive value on the occurrence of nosocomial infection.
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Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.
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AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P&#x003C;0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P&#x003C;0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.
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Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.
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OBJECTIVE To construct a risk prediction model for bloodstream infection （BSI） induced by carbapenem-resistant Klebsiella pneumoniae （CRKP）. METHODS Retrospective analysis was conducted for clinical data from 253 patients with BSI induced by K. pneumoniae in the First Hospital of Qinhuangdao from January 2019 to June 2022. Patients admitted from January 2019 to December 2021 were selected as the model group （n＝223）， and patients admitted from January 2022 to June 2022 were selected as the validation group （n＝30）. The model group was divided into the CRKP subgroup （n＝56） and the carbapenem- sensitive K. pneumoniae （CSKP） subgroup （n＝167） based on whether CRKP was detected or not. The univariate and multivariate Logistic analyses were performed on basic information such as gender， age and comorbid underlying diseases in two subgroups of patients； independent risk factors were screened for CRKP-induced BSI， and a risk prediction model was constructed. The established model was verified with patients in the validation group as the target. RESULTS Admissioning to intensive care unit （ICU）， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus were independent risk factors of CRKP-induced BSI （ORs were 3.749， 3.074， 2.909， 9.419， 95%CIs were 1.639-8.572， 1.292- 7.312， 1.180-7.717， 2.877-30.840， P＜0.05）. Based on this， a risk prediction model was established with a P value of 0.365. The AUC of the receiver operating characteristic （ROC） curve of the model was 0.848 [95%CI （0.779， 0.916）， P＜0.001]， and the critical score was 6.5. In the validation group， the overall accuracy of the prediction under the model was 86.67%， and the AUC of ROC curve was 0.926 [95%CI （0.809， 1.000]， P＜0.001]. CONCLUSIONS Admission to ICU， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus are independent risk factors of CRKP- induced BSI. The CRKP-induced BSI risk prediction model based on the above factors has good prediction accuracy.
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OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.
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AIM:To establish a nomogram model to predict the effect of serum ferritin on diabetic retinopathy and evaluate the model.METHODS:A total of 21 variables, including ferritin, were screened by univariate and multivariate regression analysis to determine the risk factors of diabetic retinopathy. A nomogram prediction model was established for evaluation and calibration.RESULTS:Ferritin, duration of diabetes, hemoglobin, urine microalbumin, regularity of medication and body mass index were included in the nomogram model. The consistency index of the prediction model with serum ferritin was 0.813(95%CI: 0.748-0.879). The calibration curves of internal and external verification showed good performance, and the probability of the threshold suggested by the decision curve was in the range 10% to 90%. The model had a high net profit value.CONCLUSIONS:Serum ferritin is an important risk factor for diabetic retinopathy. A new nomogram model, which includes body mass index, duration of diabetes, ferritin, hemoglobin, urine microalbumin and regularity of medication, has a high predictive accuracy and could provide early prediction for clinicians.
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ObjectiveTo construct and validate a clinical prediction model for diabetic kidney disease （DKD） based on optical coherence tomography angiography （OCTA）. MethodsThis study enrolled 567 diabetes patients. The random forest algorithm as well as logistic regression analysis were applied to construct the prediction model. The model discrimination and clinical usefulness were evaluated by receiver operating characteristic curve （ROC） and decision curve analysis （DCA）， respectively. ResultsThe clinical prediction model for DKD based on OCTA was constructed with area under the curve （AUC） of 0.878 and Brier score of 0.11. ConclusionsThrough multidimensional verification， the clinical prediction nomogram model based on OCTA allowed for early warning and advanced intervention of DKD.
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Objective To analyze the epidemic characteristics of varicella in Chongqing from 2014 to 2020, and to provide evidence for the development of scientific and effective varicella control strategies. Methods Data on the outbreak of varicella and vaccination in Chongqing from 2014 to 2020 were collected through the China Disease Prevention and Control Information System, and descriptive epidemiological methods were used for statistical analysis. Results A total of 181 551 cases of varicella were reported in Chongqing from 2014 to 2020, with an average annual incidence rate of 83.79 per 100 000. The incidence rate of varicella increased from 39.95 per 100 000 in 2014 to 81.88 per 100 000 in 2020 (P < 0.001). The incidence of varicella was seasonal, with the peak periods occurring from May to June and from October to December each year. The average annual incidence rate in municipal districts was 88.90/100 000, higher than 67.42/100 000 in counties and 82.50/100 000 in autonomous counties. The average annual incidence rate of varicella in males (87.13/100 000) was higher than that in females (80.38/100 000). The incidence of varicella was mainly distributed in people under 15 years old, with 143 508 cases (79.10%) reported, and the highest incidence age was 5-9 years old (37.00%). Among the affected occupations , 133 733 cases (62.6%) were students , 39 274 cases (18.40%) were children in nursery care, and 17 963 cases (8.4%) were scattered children. The actual number of doses of varicella vaccine from 2014 to 2020 was 2 302 522 doses, with the coverage rates of one-dose and two-dose vaccines being 75.56% and 32.17%, respectively. ARIMA predicted that there would be 2 604, 811, 756, 1 226, 2 405, 3 904, 2 410, 1 211, 2 034, 6 878, 10 887, and 8 955 cases of varicella from January to December 2021. Conclusion The incidence of varicella in Chongqing is on the rise, with obvious seasonal, regional and population distribution characteristics. It is necessary to strengthen the prevention and control of varicella epidemic, strengthen the prevention and control measures of key groups and key institutions in the high incidence season, strengthen the publicity of varicella vaccine, and improve the vaccination rate of two-doses of varicella vaccine for eligible children.
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Objective To investigate the risk factors for re-fracture after percutaneous kyphoplasty (PKP) in elderly patients with osteoporotic thoracolumbar compression fractures and to construct a line graph prediction model. Methods One hundred and eighty-two elderly patients with osteoporotic thoracolumbar compression fractures treated with PKP from January 2016 to November 2019 were selected for the study‚ and the patients were continuously followed up for 3 years after surgery. Clinical data were collected from both groups; Receiver operating characteristic (ROC) curve analysis was performed on the measures; Logistic regression analysis was performed to determine the independent risk factors affecting postoperative re-fracture in PKP; the R language software 4. 0 “rms” package was used to construct a predictive model for the line graph‚ and the calibration and decision curves were used to internally validate the predictive model for the line graph and for clinical evaluation of predictive performance. Results The differences between the two groups were statistically significant (P0. 22‚ which could provide a net clinical benefit‚ and the net clinical benefit was higher than the independent predictors. Conclusion BMD‚ number of injured vertebrae‚ single-segment cement injection‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity angle change are independent risk factors affecting the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture‚ and this study constructs a column line graph model to predict the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture as a predictor for clinical. This study provides an important reference for clinical prevention and treatment‚ and has clinical application value.
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@#Objective To explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. Results A total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. Conclusion Cystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.
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OBJECTIVE To screen the quality biomarkers of Gnaphalium affine with anti-chronic obstructive pulmonary disease （COPD） effect and determine their contents. METHODS The effective components and targets of “G. affine” with anti- COPD effect were predicted by using network pharmacology as a search criterion. HPLC fingerprints for 10 batches of G. affine were established by using Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）； common peak identification and similarity evaluation were conducted； cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed to screen differential components as quality maker that affected the quality of G. affine using variable importance projection （VIP）＞1 as the standard. The same HPLC method was adopted to determine the contents of the differential components in 10 batches of samples. RESULTS A total of 10 flavonoids （such as quercetin， luteolin， and chlorogenic acid） and organic acid components， were identified through network pharmacology search， with 91 targets closely related to anti-COPD. A total of 9 common peaks were identified in 10 batches of samples， with similarity greater than 0.90. Among them， the differential components included chlorogenic acid， caffeic acid， 1，3-O- dicaffeoylquinic acid and apigenin 7-O-β-D-glucopyranoside； S3， S4， S6， S7 and S10 were clustered into one category， S2， S5， S8 and S9 clustered into one category， and S1 clustered into one category. The contents of chlorogenic acid， caffeic acid， 1，3-O- dicaffeoylquinic acid， and apigenin 7-O-β-D-glucopyranoside in 10 batches of G. affine ranged 0.070-7.653， 0.010-0.097， 0.001- 0.036， 0.508-6.627 mg/g， respectively. CONCLUSIONS Chlorogenic acid， caffeic acid， 1，3-O-dicaffeoylquinic acid， apigenin 7- O-β-D-glucopyranoside can serve as the potential quality marker for the anti-COPD effect of G. affine， with the highest content of chlorogenic acid in G. affine produced in Ji’an， Jiangxi province， and the highest content of caffeic acid in G. affine produced in Ji’an， Jiangxi province and Sanming， Fujian province. The contents of the last two components are highest in G. affine produced in Chaoshan， Guangdong province.
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AIM: To assess the accuracy of predicting intraocular lens(IOL)power after myopic refractive surgery using the Pentacam system's true net power(TNP)in the 3 mm zone combined with the SRK/T formula [i.e. TNP 3 mm(SRK/T)].METHODS: Retrospective study. This study enrolled 35 cases(50 eyes)of patients undergoing cataract surgery after laser assisted in situ keratomileusis(LASIK)or photorefractive keratectomy(PRK)from July 2019 to December 2021. Preoperatively, IOL power of 50 eyes, 34 eyes and 41 eyes was calculated by TNP 3 mm(SRK/T), Barrett True-K and Olsen 2 formulas, respectively, with at least 2 formulas used to calculate IOL power for each patient. The actual diopter was recorded 3 mo postoperatively. Prediction errors(PE)of IOL power were compared among the three calculation methods, and the proportion of eyes with PE within ±0.5 D and ±1.0 D was analyzed.RESULTS: The PE at 3 mo postoperatively for TNP 3 mm(SRK/T), Barrett True-K, and Olsen 2 was -0.02±0.63, -0.54±0.80, and 0.25±0.80 D, respectively(P<0.001). The proportions of PE within ±0.5 D were 66%(33/50), 44%(15/34)and 37%(15/41), respectively(P<0.05); the proportions of PE within ±1.0 D were 88%(44/50), 71%(24/34)and 80%(33/41), respectively(P>0.05).CONCLUSION: The Pentacam TNP 3 mm(SRK/T)method is simple to operate and provides accurate calculation of IOL power after corneal refractive surgery.
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@#Introduction: Prediction and identification of miRNAs target genes are crucial for understanding the biology of miRNAs. Amidst reported long-coding RNA (lncRNA), the microRNA 195-497 cluster host gene (MIR497HG) regulation is mediated by multiple non-coding RNAs (ncRNAs) such as microRNAs (miRNAs). MIR497HG has been implicated as a tumour suppressor in various cancers. However, the impact of MIR497HG and its derived miRNAs is largely unknown and still needs to be further explored. Employing an experimental approach is often challenging since some lncRNAs are difficult to identify and isolate by the current isolation technique. Thus, bioinformatic tools are introduced to aid these problems. This study sought to search and identify the miRNAs targeting the 3’untranslated region (3’UTR) of MIR497HG. Methods: Here, bioinformatic tools were adopted to identify a unique list of miRNAs that potentially target the 3’UTR of MIR497HG. Results: A total of 57 candidate miRNAs that target the 3’UTR of MIR497HG were extracted using the miRDB. Meanwhile, STarMir predicted 291 miRNAs that potentially target the 3’UTR of MIR497HG. A common list of 36 miRNAs was obtained using the Venny 2.1.0 and further narrowed down using the LogitProb score of StarMir. Finally, a total 4 miRNAs (hsa-miR-3182, hsa-miR-7156-5p, hsa-miR-452-3p and hsa-miR-2117) were identified. The mRNA target of identified miRNAs was identified by TargetScan. Finally, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of mRNA target was done using Enrichr. Conclusion: This finding could be useful in understanding the complex interaction between MIR497HG and its regulatory miRNA. In addition, a comparative analysis of computational miRNA-target predictions is provided in this study would potentially lay the foundations for miRNAs to be used for biomarkers in cancer research.
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OBJECTIVE To investigate the monitoring of tacrolimus blood concentration in patients with nephrotic syndrome （NS），and to establish a prediction model for tacrolimus blood concentration. METHODS Data from 509 concentration monitoring sessions of 166 NS patients using tacrolimus were collected from January 1， 2020 to August 31， 2023 in Zhongshan Hospital Affiliated to Xiamen University. The relationship of efficacy and adverse drug reaction（ADR） with blood concentration was analyzed. A multilayer perceptron （MLP） prediction model was established by using the blood concentration monitoring data of 302 times from 109 NS patients with genetic information， and then verified. RESULTS In terms of efficacy， the median blood concentration of tacrolimus in the non-remission group was 2.20 ng/mL， which was significantly lower than that in the partial remission group （4.00 ng/mL， P＜0.001） and the complete remission group （3.60 ng/mL， P＝0.002）. In terms of ADR， the median blood concentration of tacrolimus in the ADR group was 5.01 ng/mL， which was significantly higher than that in the non-ADR group （3.37 ng/mL） （P＝0.001）. According to the subgroup analysis of the receiver operating characteristic curve， when the blood concentration of tacrolimus was ≥6.65 ng/mL， patients were more likely to develop elevated blood creatinine [area under the curve （AUC） was 0.764， P＜0.001）； when the blood concentration of tacrolimus was ≥6.55 ng/mL， patients were more likely to develop blood glucose （AUC＝0.615， P＝ 0.005）. The established MLP prediction model has a loss function of 0.9， with an average absolute error of 0.279 5 ng/mL between the predicted and measured values. The determination coefficient of the validation scatter plot was 0.984， indicating an excellent predictive performance of the model. CONCLUSION Tacrolimus blood concentration has an impact on both efficacy and ADR in NS patients. The use of the MLP model for predicting blood concentration exhibits high accuracy with minimal error between predicted and measured values. The model can be used as an important tool in clinical individualized medication regimens.