Zebrafish have become a popular animal model for studying various biological processes and human diseases. The metabolic pathways and players conserved among zebrafish and mammals facilitate the use of zebrafish to understand the pathological mechanisms underlying various metabolic disorders in humans. Adipocytes play an important role in metabolic homeostasis, and zebrafish adipocytes have been characterized. However, a versatile and reliable zebrafish model for long-term monitoring of adipose tissues has not been reported. In this study, we generated stable transgenic zebrafish expressing enhanced green fluorescent protein (EGFP) in adipocytes. The transgenic zebrafish harbored adipose tissues that could be detected using GFP fluorescence and the morphology of single adipocyte could be investigated in vivo. In addition, we demonstrated the applicability of this model to the long-term in vivo imaging of adipose tissue development and regulation based on nutrition. The transgenic zebrafish established in this study may serve as an excellent tool to advance the characterization of white adipose tissue in zebrafish, thereby aiding the development of therapeutic interventions to treat metabolic diseases in humans.

Background: Unplanned intensive care unit (ICU) transfers are associated with high mortality. Conventional early warning scores like modified Early Warning Score (MEWS) show limited predictive ability. Carbon dioxide combining power (CO2CP), a routine acid-base parameter, may enable earlier detection of clinical deterioration. Objective: To evaluate CO2CP as a predictor of mortality in unplanned ICU transfers and assess its additive value to traditional scoring systems. Methods: This single-center retrospective study analyzed 101 adults with unplanned ICU transfers (Feb 2024-Jun 2025). CO2CP levels within 8 h pre-transfer, MEWS, and APACHE II scores were collected. Primary outcome was ICU mortality. Analyses included ROC curves, survival analysis, multivariable logistic regression, decision curve analysis, and correlation assessment. Results: The area under the ROC curve (AUC) for CO2CP was 0.722 (95% CI: 0.615–0.829), higher than that of MEWS (AUC = 0.528, p = 0.040). Bootstrap validation yielded an AUC of 0.723 (95% CI: 0.581–0.849), indicating relative stability. Survival analysis showed poorer outcomes in patients with lower CO2CP (log-rank P = 0.003). In multivariable analysis, CO2CP remained independently associated with mortality (OR ≈ 0.87 per 1 mmol/L increase, p < 0.001), although the effect size was modest. The combined CO2CP-MEWS model demonstrated good discriminatory ability, although its clinical utility requires further confirmation. Conclusion: Carbon dioxide combining power may serve as an independent predictor of mortality risk in unplanned ICU transfers. Its integration with MEWS may enhance early risk stratification in general wards, though these findings require validation in larger prospective studies.

Objective: Accurate risk stratification of emergency department (ED) patients transferred to the intensive care unit (ICU) is essential for improving outcomes. The traditional Modified Early Warning Score (MEWS) alone has limited utility in predicting mortality risk. Combining MEWS with arterial blood gas parameters—lactate (Lac) and base excess (BE)—may enhance predictive performance. Methods: A retrospective cohort study was conducted involving 262 ED patients admitted to the ICU, who were divided into a non-survivor group ( n = 83, 28-day mortality rate: 31.68%) and a survivor group ( n = 179). Multivariable Cox regression analysis was used to identify independent predictors of 28-day mortality. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, DeLong test, net reclassification improvement (NRI)/integrated discrimination improvement (IDI) metrics, and stratified validation. Results: Multivariate Cox regression analysis showed that both Lac were independent risk factors for 28-day mortality (both p < 0.05). ROC curve analysis revealed that the Lac + BE + MEWS combined model achieved the highest area under the curve (AUC) of 0.819 (95% CI: 0.760–0.870), which was significantly superior to other models. The BE + MEWS model yielded an AUC of 0.805 (95% CI: 0.749–0.864), with no statistically significant difference from the Lac + BE + MEWS model ( p = 0.098). NRI and IDI analyses indicated that both the BE + MEWS and Lac + BE + MEWS models markedly improved predictive performance (NRI: 85.9 and 83.1%, respectively; IDI: 0.249 and 0.255, respectively; both p < 0.001). Notably, BE conferred greater incremental value to MEWS than Lac. Stratified validation confirmed that the Lac + BE + MEWS model exhibited the best stability and risk-stratification capacity, especially in the intermediate-risk stratum (5 ≤ MEWS ≤ 8, AUC = 0.812). Conclusion: Lac and BE are independent predictors of 28-day mortality in ED patients admitted to the ICU. BE adds significantly more predictive value to MEWS than Lac. The Lac + BE + MEWS combined model demonstrates the strongest stability and optimal risk-stratification performance, particularly for patients with MEWS scores of 5–8.