Hypothesis: This study hypothesized that augmented reality (AR) technology has comparable accuracy and safety to conventional CT localization in guiding percutaneous transthoracic lung puncture (PTLP) to localize small pulmonary nodules. Methods: This study was a prospective, non-inferiority randomized clinical trial. Patients were randomly assigned between 23 May 2023, and 26 September 2023. Patients with small peripheral lung nodules (≤2 cm) were recruited. Patients were randomly assigned to either the CT-guided PTLP group or the AR-guided PTLP group, with a 1:1 allocation ratio. The primary outcome was the accuracy of lung nodule localization measured by localization error. The secondary outcomes included procedure duration, radiation exposure dosage and complications. Results: A total of 70 patients underwent either CT- or AR-guided lung nodule localization and subsequent surgeries. Localization error was smaller in the AR-guided group than in the CT-guided group (mean ± SD, 3.1 ± 4.0 mm vs. 5.4 ± 4.2 mm, P = 0.026). The mean difference of localization errors was −2.3 mm (95% CI: − 4.2 to −0.3 mm, P < 0.001 for non-inferiority). Compared to the CT-guided group, the AR-guided group demonstrated significantly lower radiation exposure (mean ± SD, 421 ± 168 vs. 694 ± 229 mGy × cm, P < 0.001) and shorter localization procedure duration (mean ± SD, 8.8 ± 2.3 vs. 14.1 ± 1.8 minutes, P < 0.001), with no statistical difference in complications. Conclusions: The accuracy of the AR-guided approach is comparable to that of the CT-guided approach in localizing small lung nodules. Furthermore, the utilization of AR technology has been demonstrated to reduce procedural time and minimize radiation exposure for patients.

Introduction: Studies indicate that endometriosis (EMs) is associated with inflammation and metabolic abnormalities. The Remnant Cholesterol Inflammatory Index (RCII) concurrently reflects both inflammatory and metabolic status. However, the relationship between RCII and EMs remains unclear. Therefore, this study aims to investigate the association between RCII and EMs and explore the potential of RCII as a risk predictor for EMs. Methods: Making use of information from the 1999– 2006 NHANES database, we investigated the connection between natural logarithm-transformed RCII (lnRCII) and EMs employing logistic regression analysis, restricted cubic spline (RCS) models, and subgroup analysis approaches. Finally, a nomogram was constructed based on feature variables selected via LASSO regression to enhance the interpretability of the predictive model. Results: After adjusting for potential confounding factors, lnRCII demonstrated a substantial positive correlation with the risk of EMs. For each 1-standard-deviation increase in lnRCII, the EMs’ risk increased by 25% (fully adjusted model OR = 1.250, 95% CI: 1.266– 1.461, P = 0.006). Furthermore, a linear relationship between lnRCII and EMS was observed via RCS analysis. According to subgroup analysis, the relationship between lnRCII and EMS has a consistent association across the majority of the population. (P for interaction > 0.05). Conclusion: In the US population, RCII was substantially linked to a higher risk of EMs, suggesting that the lipid metabolism-inflammation axis may represent a potential target for intervention in the prevention or treatment of EMs.