AIM: To evaluate the relationship between the medial rectus cells counts in concomitant exotropia and surgical results. METHODS: A total of 32 pieces of medial rectus muscle were collected for HE staining in this study, of which 18 pieces were from patients with concomitant exotropia and 14 pieces were from healthy individuals. A method of strabismus score was used to assess the operative effect.RESULTS: The difference of strabismus score before and after the operation in the intermittent exotropia group was significantly higher than that in constant exotropic group (P<0.01). Under light microscope, the loosen muscle fibers and the increased stromal components in the cross sectional area of medial rectus were observed in strabismic group. The muscle cells counts was obviously lower in strabismic group than in control group (P<0.01), which was related to the difference of strabismus score before and after the operation (P<0.05).CONCLUSION: The decreased medial rectus cells counts induce concomitant exotropia directly. It is the crucial causes of the bad surgical results.

OBJECTIVE: To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. METHODS: A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. RESULTS: Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. CONCLUSION The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.
Critical Illness ; Humans ; Intensive Care Units ; Machine Learning ; Patient Readmission ; ROC Curve

OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and to compare the prediction perfor-mance of the three machine learning models with the customized simplified acute physiology score Ⅱ(SAPS-Ⅱ) model. METHODS: We used medical information mart for intensive care (MIMIC)-Ⅲ database for model development and validation. The primary outcome was prolonged LOS-ICU(pLOS-ICU), defined as longer than the third quartile of patients' LOS-ICU in the studied dataset. The recursive feature elimination method was used to do feature selection for three machine learning models. We utilized 5-fold cross validation to evaluate model prediction performance. The Brier value, area under the receiver operation characteristic curve (AUROC), and estimated calibration index (ECI) were used as perfor-mance measures. Performances of the four models were compared, and performance differences between the models were assessed using two-sided t test. The model with the best prediction performance was employed to generate variable importance ranking, and the identified top five important predictors were pre-sented. RESULTS: The final cohort in our study consisted of 40 200 eligible ICU patients, of whom 23.7% were with pLOS-ICU. The proportion of the male patients was 57.6%, and the age of all the ICU patients was (61.9±16.5) years.Results showed that the three machine learning models outperformed the customized SAPS-Ⅱ model in terms of all the performance measures with statistical significance (P < 0.01). Among the three machine learning models, the RF model achieved the best overall performance (Brier value, 0.145), discrimination (AUROC, 0.770) and calibration (ECI, 7.259). The calibration curve showed that the RF model slightly overestimated the risk of pLOS-ICU in high-risk ICU patients, but underestimated the risk of pLOS-ICU in low-risk ICU patients. Top five important predictors for pLOS-ICU identified by the RF model included age, heart rate, systolic blood pressure, body tempe-rature, and ratio of arterial oxygen tension to the fraction of inspired oxygen(PaO₂/FiO₂). CONCLUSION The RF algorithm-based pLOS-ICU prediction model had a best prediction performance in this study. It lays a foundation for future application of the RF-based pLOS-ICU prediction model in ICU clinical practice.
Aged ; Humans ; Intensive Care Units ; Machine Learning ; Male ; Middle Aged ; Research Design

OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) which is commonly used in the ICU. METHODS: We used Medical Information Mart for Intensive Care III (MIMIC-III) database as data source. The AKI patients in the MIMIC-III database were selected according to the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) definition of AKI. We employed the same predictor variable set as used in SAPS-II to construct an SVM model. Meanwhile, we also developed a customized SAPS-II model using MIMIC-III database, and compared performances between the SVM model and the customized SAPS-II model. The performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), root mean squared error (RMSE), sensitivity, specificity, Youden's index and accuracy based on 5-fold cross-validation. The agreement of the results between the SVM model and the customized SAPS-II model was illustrated using Bland-Altman plots. RESULTS: A total number of 19 044 patients with AKI were included. The observed in-hospital mortality of the AKI patients was 13.58% in MIMIC-III. The results based on the 5-fold cross validation showed that the average AUROC of the SVM model and the customized SAPS-II model was 0.86 and 0.81, respectively (The difference between the two models was statistically significant with t=13.0, P<0.001). The average RMSE of the SVM model and the customized SAPS-II model was 0.29 and 0.31, respectively (The difference was statistically significant with t=-9.6, P<0.001). The SVM model also outperformed the customized SAPS-II model in terms of sensitivity and Youden's index with significant statistical differences (P=0.002 and <0.001, respectively).The Bland-Altman plot showed that the SVM model and the customized SAPS-II model had similar mortality prediction results when the mortality of a patient was certain, but the consistency between the mortality prediction results of the two models was poor when the mortality of a patient was with high uncertainty. CONCLUSION Compared with the SAPS-II model, the SVM model has a better performance, especially when the mortality of a patient is with high uncertainty. The SVM model is more suitable for predicting the mortality of patients with AKI in ICU and early intervention in patients with AKI in ICU. The SVM model can effectively help ICU clinicians improve the quality of medical treatment, which has high clinical value.
Acute Kidney Injury/mortality* ; Critical Care ; Hospital Mortality ; Humans ; Intensive Care Units ; Prognosis ; ROC Curve ; Sensitivity and Specificity ; Support Vector Machine

OBJECTIVE: This study tests whether long-term intake of Allium tuberosum (AT) can alleviate pulmonary inflammation in ovalbumin (OVA)-induced asthmatic mice and evaluates its effect on the intestinal microbiota and innate lymphoid cells (ILCs). METHODS: BALB/c mice were divided into three groups: phosphate buffer saline, OVA and OVA + AT. The asthmatic murine model was established by sensitization and challenge of OVA in the OVA and OVA + AT groups. AT was given to the OVA + AT group by oral gavage from day 0 to day 27. On day 28, mice were sacrificed. Histopathological evaluation of lung tissue was performed using hematoxylin and eosin, and periodic acid-Schiff staining. The levels of IgE in serum, interleukin-5 (IL-5) and IL-13 from bronchoalveolar lavage fluid (BALF) were measured by enzyme-linked immunosorbent assay. The ILCs from the lung and gut were detected by flow cytometry. 16S ribosomal DNA sequencing was used to analyze the differences in colon microbiota among treatment groups. RESULTS: We found that long-term intake of AT decreased the number of inflammatory cells from BALF, reduced the levels of IL-5 and IL-13 in BALF, and IgE level in serum, and rescued pulmonary histopathology with less mucus secretion in asthmatic mice. 16S ribosomal DNA sequencing results showed that AT strongly affected the colonic bacteria community structure in asthmatic mice, although it had no significant effect on the abundance and diversity of the microbiota. Ruminococcaceae and Desulfovibrionaceae were identified as two biomarkers of the treatment effect of AT. Moreover, AT decreased the numbers of ILCs in both the lung and gut of asthmatic mice. CONCLUSION The results indicate that AT inhibits pulmonary inflammation, possibly by impeding the activation of ILCs and adjusting the homeostasis of gut microbiota in asthmatic mice.

This study explored whether Sagittaria sagittifolia polysaccharides(SSP) activates the nuclear factor erythroid-2-related factor2(Nrf2)/heme oxygenase-1(HO-1) signaling pathway to protect against liver damage jointly induced by multiple heavy metals. First, based on the proportion of dietary intake of six heavy metals in rice available in Beijing market, a heavy metal mixture was prepared for inducing mouse liver injury and HepG2 cell injury. Forty male Kunming mice were divided into five groups: control group, model group, glutathione positive control group, and low-and high-dose SSP groups, with eight mice in each group. After 30 days of intragastric administration, the liver injury in mice was observed by HE staining. In the in vitro experiment, MTT assay was conducted to detect the effects of SSP at 0.25, 0.5, 1, and 2 mg·mL~(-1) on HepG2 cell survival at different time points. The content of alanine transaminase(ALT) and aspartate aminotransferase(AST) in the 48-h cell culture fluid was measured using micro-plate cultivation method, followed by the detection of the change in reactive oxygen species(ROS) content by flow cytometry. The mRNA expression levels of Nrf2 and HO-1 in cells were determined by RT-PCR, and their protein expression by Western blot. HE staining results showed that compared with the model group, the SSP administration groups exhibited significantly alleviated inflammatory cell infiltration and fatty infiltration in the liver, with better outcomes observed in the high-dose SSP group. In the in vitro MTT assay, compared with the model group, SSP at four concentrations all significantly increased the cell survival rate, decreased the ALT, AST, and ROS content(P<0.05), and down-regulated Nrf2 and HO-1 mRNA and protein expression(P<0.05). SSP significantly improves inflammatory infiltration in the liver tissue of mice exposed to a variety of heavy metals and corrects the liver fat degeneration, which may be related to its regulation of the Nrf2/HO-1 signaling pathway, reduction of ROS, and alleviation of oxidative damage.
Animals ; Heme Oxygenase-1/metabolism* ; Liver ; Male ; Metals, Heavy/metabolism* ; Mice ; NF-E2-Related Factor 2/metabolism* ; Oxidative Stress ; Polysaccharides/pharmacology* ; RNA, Messenger/metabolism* ; Reactive Oxygen Species/metabolism* ; Sagittaria/metabolism*

Objective: To assess the prevalence, risk factors and treatment of anemia in patients with chronic kidney disease (CKD). Methods: A descriptive method was used to analyze the prevalence and treatment of anemia in CKD patients based on regional health data in Yinzhou District of Ningbo during 2012-2018. The multivariate logistic regression analysis was used to identify independent influence factors of anemia in the CKD patients. Results: In 52 619 CKD patients, 15 639 suffered from by anemia (29.72%), in whom 5 461 were men (26.41%) and 10 178 were women (31.87%), and anemia prevalence was higher in women than in men, the difference was significant (P<0.001). The prevalence of anemia increased with stage of CKD (24.77% in stage 1 vs. 69.42% in stage 5, trend χ² test P<0.001). Multivariate logistic regression analysis revealed that being women (aOR=1.57, 95%CI: 1.50-1.63), CKD stage (stage 2: aOR=1.10, 95%CI: 1.04-1.16;stage 3: aOR=2.28,95%CI: 2.12-2.44;stage 4: aOR=4.49,95%CI :3.79-5.32;stage 5: aOR=6.31,95%CI: 4.74-8.39), age (18-30 years old: aOR=2.40,95%CI: 2.24-2.57, 61-75 years old: aOR=1.35,95%CI:1.28-1.42, ≥76 years old: aOR=2.37,95%CI:2.20-2.55), BMI (<18.5 kg/m²:aOR=1.29,95%CI: 1.18-1.41;23.0-24.9 kg/m²:aOR=0.79,95%CI: 0.75-0.83;≥25.0 kg/m²:aOR=0.70,95%CI: 0.66-0.74), abdominal obesity (aOR=0.91, 95%CI: 0.86-0.96), chronic obstructive pulmonary disease (aOR=1.15, 95%CI: 1.09-1.22), cancer (aOR=3.03, 95%CI: 2.84-3.23), heart failure (aOR=1.44, 95%CI: 1.35-1.54) and myocardial infarction (aOR=1.54, 95%CI:1.16-2.04) were independent risk factors of anemia in CKD patients. Among stage 3-5 CKD patients with anemia, 12.03% received iron therapy, and 4.78% received treatment with erythropoiesis-stimulating agent (ESA) within 12 months after anemia was diagnosed. Conclusions: The prevalence of anemia in CKD patients was high in Yinzhou. However, the treatment rate of iron therapy and ESA were low. More attention should be paid to the anemia management and treatment in CKD patients.