Endo-beta-N-acetylglucosaminidase (ENGase) is widely distributed in various organisms. The first reported ENGase activity was detected in Diplococcus pneumoniae in 1971. The protein (Endo D) was purified and its peptide sequence was determined in 1974. Three ENGases (Endo F1-F3) were discovered in Flavobacterium meningosepticum from 1982 to 1993. After that, the activity was detected from different species of bacteria, yeast, fungal, plant, mice, human, etc. Multiple ENGases were detected in some species, such as Arabidopsis thaliana and Trichoderma atroviride. The first preliminary crystallographic analysis of ENGase was conducted in 1994. But to date, only a few ENGases structures have been obtained, and the structure of human ENGase is still missing. The currently identified ENGases were distributed in the GH18 or GH85 families in Carbohydrate-Active enZyme (CAZy) database. GH18 ENGase only has hydrolytic activity, but GH85 ENGase has both hydrolytic and transglycosylation activity. Although ENGases of the two families have similar (β/α)8-TIM barrel structures, the active sites are slightly different. ENGase is an effective tool for glycan detection andglycan editing. Biochemically, ENGase can specifically hydrolyze β‑1,4 glycosidic bond between the twoN-acetylglucosamines (GlcNAc) on core pentasaccharide presented on glycopeptides and/or glycoproteins. Different ENGases may have different substrate specificity. The hydrolysis products are oligosaccharide chains and a GlcNAc or glycopeptides or glycoproteins with a GlcNAc. Conditionally, it can use the two products to produce a new glycopeptides or glycoprotein. Although ENGase is a common presentation in cell, its biological function remains unclear. Accumulated evidences demonstrated that ENGase is a none essential gene for living and a key regulator for differentiation. No ENGase gene was detected in the genomes of Saccharomyces cerevisiae and three other yeast species. Its expression was extremely low in lung. As glycoproteins are not produced by prokaryotic cells, a role for nutrition and/or microbial-host interaction was predicted for bacterium produced enzymes. In the embryonic lethality phenotype of the Ngly1-deficient mice can be partially rescued by Engase knockout, suggesting down regulation of Engase might be a solution for stress induced adaptation. Potential impacts of ENGase regulation on health and disease were presented. Rabeprazole, a drug used for stomach pain as a proton inhibitor, was identified as an inhibitor for ENGase. ENGases have been applied in vitro to produce antibodies with a designated glycan. The two step reactions were achieved by a pair of ENGase dominated for hydrolysis of substrate glycoprotein and synthesis of new glycoprotein with a free glycan of designed structure, respectively. In addition, ENGase was also been used in cell surface glycan editing. New application scenarios and new detection methods for glycobiological engineering are quickly opened up by the two functions of ENGase, especially in antibody remodeling and antibody drug conjugates. The discovery, distribution, structure property, enzymatic characteristics and recent researches in topical model organisms of ENGase were reviewed in this paper. Possible biological functions and mechanisms of ENGase, including differentiation, digestion of glycoproteins for nutrition and stress responding were hypothesised. In addition, the role of ENGase in glycan editing and synthetic biology was discussed. We hope this paper may provide insights for ENGase research and lay a solid foundation for applied and translational glycomics.

Hip fracture in the elderly is characterized by high incidence, high disability rate, and high mortality and has been recognized as a public health issue threatening their health. Surgery is the preferred choice for the treatment of elderly patients with hip fracture. However, lower extremity deep venous thrombosis (DVT) has an extremely high incidence rate during the perioperative period, and may significantly increase the risk of patients′ death once it progresses to pulmonary embolism. In response to this issue, the clinical guidelines and expert consensuses all emphasize active application of comprehensive preventive measures, including basic prevention, physical prevention, and pharmacological prevention. In this prevention system, basic prevention is the basis of physical and pharmacological prevention. However,there is a lack of unified and definite recommendations for basic preventive measures in clinical practice. To this end, the Orthopedic Nursing Professional Committee of the Chinese Nursing Association and Nursing Department of the Orthopedic Branch of the China International Exchange and Promotive Association for Medical and Health Care organized relevant nursing experts to formulate Expert consensus on perioperative basic prevention for lower extremity deep venous thrombosis in elderly patients with hip fracture ( version 2024) . A total of 10 recommendations were proposed, aiming to standardize the basic preventive measures for lower extremity DVT in elderly patients with hip fractures during the perioperative period and promote their subsequent rehabilitation.

OBJECTIVE: To explore the possible effects and mechanism of Zhizhu Decoction (ZZD) on the pathophysiology of slow transit constipation (STC). METHODS: A total of 54 C57BL/6 mice was randomly divided into the following 6 groups by a random number table, including control, STC model (model), positive control, and low-, medium- and high-doses ZZD treatment groups (5, 10, 20 g/kg, namely L, M-, and H-ZZD, respectively), 9 mice in each group. Following 2-week treatment, intestinal transport rate (ITR) and fecal water content were determined, and blood and colon tissue samples were collected. Hematoxylin-eosin and periodic acid-Schiff staining were performed to evaluate the morphology of colon tissues and calculate the number of goblet cells. To determine intestinal permeability, serum levels of lipopolysaccharide (LPS), low-density lipoprotein (LDL) and mannose were measured using enzyme-linked immunosorbent assay (ELISA). Western blot analysis was carried out to detect the expression levels of intestinal tight junction proteins zona-occludens-1 (ZO-1), claudin-1, occludin and recombinant mucin 2 (MUC2). The mRNA expression levels of inflammatory cytokines including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, IL-4, IL-10 and IL-22 were determined using reverse transcription-quantitative reverse transcription reaction. Colon indexes of oxidative stress were measured by ELISA, and protein expression levels of colon silent information regulator 1/forkhead box O transcription factor 1 (SIRT1/FoxO1) antioxidant signaling pathway were detected by Western blot. RESULTS: Compared with the model group, ITR and fecal moisture were significantly enhanced in STC mice in the M-ZZD and H-ZZD groups (P<0.01). Additionally, ZZD treatment notably increased the thickness of mucosal and muscular tissue, elevated the number of goblet cells in the colon of STC mice, reduced the secretion levels of LPS, LDL and mannose, and upregulated ZO-1, claudin-1, occludin and MUC2 expressions in the colon in a dose-dependent manner, compared with the model group (P<0.05 or P<0.01). In addition, ZZD significantly attenuated intestinal inflammation and oxidative stress and activated the SIRT1/FoxO1 signaling pathway (P<0.05 or P<0.01). CONCLUSION ZZD exhibited beneficial effects on the intestinal system of STC mice and alleviated intestinal inflammation and oxidative stress via activating SIRT1/FoxO1 antioxidant signaling pathway in the colon.
Mice ; Animals ; Sirtuin 1/genetics* ; Antioxidants ; Occludin ; Lipopolysaccharides ; Claudin-1 ; Mannose ; Mice, Inbred C57BL ; Constipation/drug therapy* ; Inflammation ; Signal Transduction

Objective: To analyze the status of excess heart age and its risk factors among Chinese residents aged 35 to 64 years. Methods: The study subjects were Chinese residents aged 35 to 64 years who completed the heart age assessment by WeChat official account "Heart Strengthening Action" through the internet from January 2018 to April 2021. Information such as age, gender, body mass index (BMI), blood pressure, total cholesterol (TC), smoking history, and diabetes history was collected. The heart age and excess heart age were calculated according to the characteristics of individual cardiovascular risk factors and the heart aging was defined as excess heart age≥5 years and 10 years respectively. The heart age and standardization rate were calculated respectively based on the population standardization of the 7th census in 2021.CA trend test was used to analyze the changing trend of excess heart age rate and population attributable risk (PAR) was used to calculate the contribution of risk factors. Results: The mean age of 429 047 subjects was (49.25±8.66) years. The male accounted for 51.17% (219 558/429 047) and the excess heart age was 7.00 (0.00, 11.00) years. The excess heart age rate defined by excess heart age≥5 years and ≥10 years was 57.02% (the standardized rate was 56.83%) and 38.02% (the standardized rate was 37.88%) respectively. With the increase of the age and number of risk factors, the excess heart age rate of the two definitions showed an upward trend according to the result of the trend test analysis (P<0.001). The top two risk factors of the PAR for excess heart age were overweight or obese and smoking. Among them, the male was smoking and overweight or obese, while the female was overweight or obese and having hypercholesterolemia. Conclusion: The excess heart age rate is high in Chinese residents aged 35 to 64 years and the contribution of overweight or obese, smoking and having hypercholesterolemia ranks high.
Humans ; Male ; Female ; Overweight ; Hypercholesterolemia/epidemiology* ; Risk Factors ; Obesity/epidemiology* ; Body Mass Index ; China/epidemiology*

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.

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: 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.

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

Air Pollution ; China ; Humans ; Meteorological Concepts ; Outpatients ; Particulate Matter/analysis* ; Skin Diseases/epidemiology*