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.

Purpose: The incidence rate of breast cancer (BC) has increased annually. Downstream neighbor of son (DONSON) critically affects cell cycle progression and maintains stable genomic properties; however, its relevant effects on BC growth and progression require indepth investigation. Methods: DONSON upregulation was validated in public databases. DONSON expression in matched BC and adjacent tissues and cell lines (MDA-MB-231, BT-549, and HS-578T) was determined using quantitative reverse transcription polymerase chain reaction. In vitro apoptosis, invasion, migration, and proliferation tests were performed to ascertain the functions of DONSON in BC cell lines. Then, using western blot analysis, the levels of DONSON downstream proteins were determined. Results: Compared to the control, DONSON was expressed at higher levels in BC tissues and cell lines. DONSON knockdown facilitated apoptosis and limited proliferation, migration, invasion, and S/G2 transition of BC cells In vitro. Furthermore, DONSON overexpression promoted BC cell proliferation and inhibited apoptosis In vitro. Moreover, DONSON knockdown reduced cyclin A1 and cyclin-dependent kinase 2 levels. Moreover, DONSON knockdown limited the progression of epithelial-mesenchymal transition. Conclusion DONSON critically affects BC growth and serves as a possible target and marker for the efficacy of subsequent therapies.

Objective:To explore the status and influencing factors of positive feelings of caregivers of patients with enterostomy, so as to provide a reference for targeted interventions.Methods:From May to October 2021, convenience sampling was used to select 130 caregivers of patients with enterostomy who received follow-up visits in the Stoma Clinic of the First Affiliated Hospital of Xi'an Jiaotong University as the research subject. The survey was carried out using the General Information Questionnaire, the Positive Aspects of Caregiving (PAC) , and the Care Preparedness Scale (CPS) . A total of 130 questionnaires were distributed, and 127 valid questionnaires were recovered, with a valid recovery rate of 97.69%.Results:Among 127 caregivers of patients with enterostomy, the total score of PAC was (32.69±4.99) , and the self-affirmation dimension score was (18.39±2.84) , and the life outlook dimension score was (14.36±2.53) , and the total score of CPS was (17.76±4.36) . Multiple linear regression analysis showed that caregiver gender, education level, work status, and readiness were the influencing factors of positive feelings of caregivers of patients with enterostomy ( P<0.05) , which explained 43.7% of the total variance. Conclusions:The positive perception of caregivers of patients with enterostomy is at an upper-middle level. Nurses should formulate targeted humanistic care strategies according to the influencing factors, increase the positive feelings of caregivers, help patients adapt to the stoma as soon as possible, and then improve the quality of life of patients.

y under uhrasonography is a more useful technique in diagnosing nonpalpable thyroid nodules.