Adipose-derived stem cells ameliorate radiation-induced lung injury by activating the DDAH1/ADMA/eNOS signaling pathway.

Background: Ionizing radiation-induced lung injury is caused by the initial inflammatory reaction and leads to advanced fibrosis of lung tissue. Adipose-derived stem cells (ASCs) are a type of mesenchymal stem cell that can differentiate into various functional cell types with broad application prospects in the treatment of tissue damage. The purpose of this study was to explore the protective effect of ASCs against radiation-induced lung injury and to provide a novel basis for prevention and treatment of radiation-induced lung injury. Materials and methods: Fifty mice were randomly divided into a control group (Ctrl), radiation exposure group (IR), radiation exposure plus ASC treatment group (IR + ASC), radiation exposure plus L-257 group (IR + L-257), and radiation exposure plus ASC treatment and L-257 group (IR + ASC + L-257). Mice in IR, IR + ASC, and IR + ASC + L-257 groups were exposed to a single whole-body dose of 5 Gy X-rays (160 kV/25 mA, 1.25 Gy/min). Within 2 h after irradiation, mice in IR + ASC and IR + ASC + L-257 groups were injected with 5 × 106 ASCs via the tail vein. Mice in IR + L-257 and IR + ASC + L-257 groups were intraperitoneally injected with 30 mg/kg L-257 in 0.5 mL saline. Results: The mice in the IR group exhibited lung hemorrhage, edema, pulmonary fibrosis, and inflammatory cell infiltration, increased release of proinflammatory cytokines, elevation of oxidative stress and apoptosis, and inhibition of the dimethylarginine dimethylamino hydratase 1 (DDAH1)/ADMA/eNOS signaling pathway. ASC treatment alleviated radiation-induced oxidative stress, apoptosis, and inflammation, and restored the DDAH1/ADMA/eNOS signaling pathway. However, L-257 pretreatment offset the protective effect of ASCs against lung inflammation, oxidative stress, and apoptosis. Conclusions: These data suggest that ASCs ameliorate radiation-induced lung injury, and the mechanism may be mediated through the DDAH1/ADMA/eNOS signaling pathway.

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study.

Objective: The prognostic factors for elderly patients with cervical cancer differ from those of younger patients. Competitive risk events could cause biases in the Cox proportional hazards (PH) model. This study aimed to construct a competitive risk model (CRM) nomogram for patients aged > 65 years with nonmetastatic cervical cancer. Methods: We retrospectively analyzed data extracted from the Surveillance, Epidemiology, and End Results (SEER) database and a total of 1856 patients from 18 cancer registries across the United States diagnosed between 2010 and 2015 were included. Kaplan-Meier analysis and log-rank tests were used to compare intergroup survival. Univariate and multivariate Cox proportional regression analyses were performed to identify independent prognostic factors. The cumulative incidence function (CIF) and Fine and Gray's test were used to determine the impact of competitive risk events on prognosis. The CRM nomogram was internally and externally validated using time-dependent receiver operator characteristic (ROC) curve (time-AUC), Brier scores, Harrell's concordance index (C-index), calibration curve, and decision curve analysis (DCA). Results: Analyses revealed that histology, age, the International Federation of Gynaecologists and Obstetricians (FIGO) stage, number of in situ malignancies, chemotherapy, radiotherapy (RT), and surgery were independent prognostic factors. The CRM nomogram accurately predicted 1-year, 3-year, and 5-year disease-specific survival (DSS). The C-indexes and Brier scores of the CRM nomogram were 0.641 and 0.094, respectively, at the 1-year cut-off in the training set. The time-AUC of the CRM nomogram at the 1-year, 3-year, and 5-year intervals in the training set were 77.6%, 77.3%, and 74.5%, respectively. The calibration curve demonstrated a favorable concordance. DCA suggested that the nomogram had a good net benefit. Therefore, the Cox model underestimated the weight of risk factors compared to CRM. Conclusions: This study presents the CRM nomogram to predict DSS in patients aged > 65 years with nonmetastatic cervical cancer. It can help clinicians implement more accurate personalized diagnostic and treatment modalities for elderly patients with cervical cancer.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

BACKGROUND: Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. OBJECTIVE: To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. METHODS: Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. RESULTS: After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. CONCLUSION: The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

Effects of nursing workload on medication administration errors: A quantitative study.

BACKGROUND: Medication administration errors by nurses form a high proportion of medical errors in medical institutions. Studies have shown that such errors are closely linked to nursing workload. OBJECTIVE: To quantitatively explore the effects of different types of nursing workloads on different medication administration errors. METHOD: Three medical institutions were selected as the objects of error data collection based on the following criteria: the medical institution experience in error data collection, the complete range of medical departments, and the institution size. Error cases were self-reported from all nurses in all medical departments. The relationship between the error types and nursing workload types were quantitatively examined using partial least squares and structural equation modeling. RESULTS: The study recorded 290 medication administration errors, and extracted four error types and nine nursing workload types. The workload type for each error type was also identified and the path coefficient was found to be between 0.087 to 0.416. CONCLUSION: This study confirmed the effect of workload on medication administration errors and determined a theoretical mechanism for this effect. Research results will provide the evidence for nursing managers to reduce workload and ensure quality in the nursing administration process.

Influence of different protection levels of PPE on nurses' physical fatigue during the COVID-19 pandemic.

BACKGROUND: The continuous spread of COVID-19 globally has led to busier medical practices and nurses are having to provide medical services to patients while suffering from high levels of fatigue. OBJECTIVE: This study experimentally investigated the influence of different levels of personal protection equipment (PPE) on nurses' physical fatigue. METHODS: We collected data from 12 participants by simulating a series of nursing tasks. The participants wore one of three different protection levels of PPE, and data recording their heart rate (HR), oral temperature, task completion time, and subjective fatigue were collected. Following this, relationship models between subjective fatigue and objective fatigue indices was established using multiple linear regression analysis. RESULTS: By analyzing the variation trends of single indices of HR, oral temperature, task completion time, and subjective fatigue, it was found that the higher the level of protection levels of PPE worn by the participants, the higher the degree of fatigue experienced by them. In addition, taking subjective fatigue variation as the dependent variable and objective fatigue indices as independent variables, relationship models of the three different protection levels of PPE were established. CONCLUSIONS: The results confirmed the influence of different protection levels of PPE on nurses' physical fatigue levels. They also provide a basis for medical administrators to monitor the degree of fatigue experienced by nurses, attempt to arrange nurses' work content accordingly, and schedule suitably to combat and manage recorded increases in fatigue.

Occupational risk factors of contracting COVID-19 among health workers: A systematic review.

BACKGROUND: With the spread of COVID-19 and the worsening global prevention and control situation, the risk of infection faced by health workers has been unprecedented. It is necessary to fully understand the occupational risks of health workers to protect them and reduce their risk of infection. OBJECTIVE: This study aimed to obtain comprehensive and detailed information on occupational risk factors of infectious diseases for HWs in different dimensions and to propose control strategies for three risk dimensions to protect HWs who are at high risk of infection during the pandemic. METHODS: A total number of 619 articles published from 2010 to 2021 were searched to conduct bibliometric analysis, which were retrieved in the Web of Science database with defined search terms. There were 26 articles met the criteria, and they were screened to identify occupational risk factors. RESULTS: We conducted an analysis of cited institutions, co-citation network analysis of journals, and references from bibliometric analysis. Nine risk factors were extracted, and they were classified and sorted into three dimensions. Infection control strategies for each dimension were proposed. CONCLUSIONS: The risk of infection faced by HWs is unprecedented. Medical institutions should pay more attention to the nine risk factors that we identified and use the three risk dimensions to carry out risk identification and infection control to reduce the infection risk of HWs and protect them better.