Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

OBJECTIVE To research the effect and mechanism of fermentation of Astragalus membranaceus on endogenous metabolites in hyperuricemia model rats using serum UHPLC-HRMS. METHODS The SD rats were randomly divided into different groups, including blank group, model group, benzbromarone group(20 mg·kg-1), as well as fermentation of Astragalus membranaceus high-dose(3 g·kg-1) and low-dose group(1.5 g·kg-1). Model group and each treatment group were disposed with 300 mg·kg-1 oxonic acid potassium to establish hyperuricemia models. At the time of 1 h after modeling, rats in each treatment group were given corresponding drugs for intervention. Collected rat serum after 14 d. The serum of different groups were collected for endogenous metabolites research using UHPLC-HRMS. After multivariate statistical analysis, the different metabolites and metabolic pathways were selected. RESULTS The hyperuricemia rat modes were successfully established by oxonic acid potassium 14 d, and fermentation of Astragalus membranaceus showed good uric acid reducing effect. Compared with the blank group, 17 potential biomarkers associated with hyperuricemia were found in the model group. Among them, 9 potential biomarkers were significantly recalled by fermentation of Astragalus membranaceus. It mainly involved sphingolipid metabolism, pyrimidine metabolism, tryptophan metabolism, pantothenic acid and CoA biosynthesis, glycine, serine and threonine metabolism and other pathway. CONCLUSION This study can provide a basis for revealing the mechanism of reducing uric acid by fermentation of Astragalus membranaceus, and lay a foundation for the further development and utilization of Astragalus.

Enzyme separation, purification, immobilization, and catalytic performance improvement have been the research hotspots and frontiers as well as the challenges in the field of biocatalysis. Thus, the development of novel methods for enzyme purification, immobilization, and improvement of their catalytic performance and storage are of great significance. Herein, ferritin was fused with the lichenase gene to achieve the purpose. The results showed that the fused gene was highly expressed in the cells of host strains, and that the resulted fusion proteins could self-aggregate into carrier-free active immobilized enzymes in vivo. Through low-speed centrifugation, the purity of the enzymes was up to > 90%, and the activity recovery was 61.1%. The activity of the enzymes after storage for 608 h was higher than the initial activity. After being used for 10 cycles, it still maintained 50.0% of the original activity. The insoluble active lichenase aggregates could spontaneously dissolve back into the buffer and formed the soluble polymeric lichenases with the diameter of about 12 nm. The specific activity of them was 12.09 times that of the free lichenase, while the catalytic efficiency was 7.11 times and the half-life at 50 ℃ was improved 11.09 folds. The results prove that the ferritin can be a versatile tag to trigger target enzyme self-aggregation and oligomerization in vivo, which can simplify the preparation of the target enzymes, improve their catalysis performance, and facilitate their storage.
Biocatalysis ; Enzymes, Immobilized/metabolism* ; Ferritins/metabolism* ; Glycoside Hydrolases/metabolism*

PEGylation is considered one of the most successful techniques to improve the characteristics of protein drugs including to increase the circulating half-life of proteins in blood and to decrease their immunogenicity and antigenicity. One known PEG modification method is to attach PEG to the free amino group, typically at lysine residues or at the N-terminal amino acid with no selectivity, resulting in a heterogeneous product mixture. This lack of selectivity can present problems when a therapeutic PEGylated protein is being developed, because predictability of activity and manufacturing reproducibility are needed for regulatory approval. Enzymatic PEGylation of proteins is one route to overcome this limitation. Transglutaminases (TGase) are enzyme candidates for site-specific PEGylation. We use human interferon alpha 2a (IFN α2a) as a test case, and predict that the potential modification residues are Gln101 by computational approach as it contains 12 potential PEGylation sites. IFN α2a was PEGylated by Y shaped PEG40k-NH2 mediated by microbial transglutaminase. Our results show that the microbial transglutaminase mediated PEGylation of IFN α2a was site-specific only at the site of Gln101 in IFN α2a, yielding the single mono-conjugate PEG-Gln101-IFN α2a with a mass of 59 374.66 Da. Circular dichroism studies showed that PEG-Gln101-IFN α2a preserved the same secondary structures as native IFN α2a. As expected, the bioactivity and pharmacokinetic profile in rats of PEG-Gln101-IFN α2a revealed a significant improvement to unmodified IFN α2a, and better than PEGASYS.
Animals ; Antiviral Agents ; Humans ; Interferon alpha-2 ; metabolism ; Interferon-alpha ; biosynthesis ; pharmacokinetics ; Polyethylene Glycols ; pharmacokinetics ; Protein Structure, Secondary ; Rats ; Recombinant Proteins ; biosynthesis ; pharmacokinetics ; pharmacology ; Reproducibility of Results ; Transglutaminases ; metabolism