Purpose: Tumor-associated macrophages (TAMs) originate from monocytes and differentiate into mature macrophages. The interaction between cancer cells and TAMs promotes tumor growth and suppresses immunosurveillance. However, this phenomenon has seldom been observed in ampullary cancer. Patients and Methods: TAMs in ampullary cancer were investigated using immunohistochemical (IHC) staining of cancer tissues. Bioinformatic analysis of data from the Gene Expression Omnibus (GEO) database revealed transforming growth factor-beta (TGF-β) signaling in ampullary cancer. The complementary DNA microarray of cancer was compared with adjacent normal duodenum and enzyme-linked immunosorbent assay of serum was used to verify TGF-β signaling in patients. The THP-1 cell line was activated in vitro to imitate M2 TAMs. ClueGo and CluePedia software were operated to simulate TGF-β-related networks in ampullary cancer. Results: The IHC study revealed that the majority of TAMs inside ampullary cancer were cluster of differentiation (CD)163⁺ cells and that the expression of mature CD68⁺ macrophages was correlated with advanced cancer stage. Bioinformatics analysis revealed that TGF-β and its downstream signaling were significantly upregulated. To verify our bioinformatics-derived predictions, we performed several experiments and demonstrated that increased TGF-β expression was detected in the cDNA microarray. Higher serum levels of TGF-β were correlated with fewer CD68⁺ and more inducible nitric oxide synthase macrophages in ampullary cancer. Treatment with TGF-β induced modulation of THP-1-derived macrophages. Conclusion: The present study demonstrates that TGF-β modulates macrophage activity in ampullary cancer. Targeting TGF-β could be an approach to activating immunosurveillance.

Background and objectives: High-flow nasal cannula (HFNC) can be used as a respiratory support strategy for patients with acute respiratory failure (ARF). However, no clear evidence exists to support or oppose HFNC use in immunocompromised patients. Thus, this meta-analysis aims to assess the effects of HFNC, compared to conventional oxygen therapy (COT) and noninvasive ventilation (NIV), on the outcomes in immunocompromised patients with ARF. The Pubmed, Embase and Cochrane databases were searched up to November 2018. Materials and Methods: Only clinical studies comparing the effect of HFNC with COT or NIV for immunocompromised patients with ARF were included. The outcome included the rate of intubation, mortality and length of stay (LOS). Results: A total of eight studies involving 1433 immunocompromised patients with ARF were enrolled. The pooled analysis showed that HFNC was significantly associated with a reduced intubation rate (risk ratio (RR), 0.83; 95% confidence interval (CI), 0.74–0.94, I² = 0%). Among subgroup analysis, HFNC was associated with a lower intubation rate than COT (RR, 0.86; 95% CI, 0.75–0.95, I² = 0%) and NIV (RR, 0.59; 95% CI, 0.40–0.86, I² = 0%), respectively. However, there was no significant difference between HFNC and control groups in terms of 28-day mortality (RR, 0.78; 95% CI, 0.58–1.04, I² = 48%), and intensive care unit (ICU) mortality (RR, 0.87; 95% CI, 0.73–1.05, I² = 57%). The ICU and hospital LOS were similar between HFNC and control groups (ICU LOS: mean difference, 0.49 days; 95% CI, −0.25–1.23, I² = 69%; hospital LOS: mean difference, −0.12 days; 95% CI, −1.86–1.61, I² = 64%). Conclusions: Use of HFNC may decrease the intubation rate in immunocompromised patients with ARF compared with the control group, including COT and NIV. However, HFNC could not provide additional survival benefit or shorten the LOS. Further large, randomized controlled trials are needed to confirm these findings.

Background and Objectives: Major trauma ranks among the leading causes of mortality and handicap in both developing and developed countries, consuming substantial healthcare resources. Its unpredictable nature and diverse clinical presentations often lead to rapid and challenging-to-predict changes in patient conditions. An increasing number of models have been developed to address this challenge. Given our access to extensive and relatively comprehensive data, we seek assistance in making a meaningful contribution to this topic. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in major trauma patients. Methods: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. Results: A total of 5521 major trauma patients were analyzed. Among five AI models tested, XGBoost showed the best performance (AUC 0.748), outperforming traditional clinical scores such as ISS and GCS. The model was deployed as a web-based application integrated into the hospital information system. Preliminary clinical use demonstrated improved efficiency, interpretability through SHAP analysis, and positive user feedback from healthcare professionals. Conclusions: This study presents a predictive model for estimating recovery probabilities in severe burn patients, effectively integrated into the hospital information system (HIS) without complex computations. Clinical use has shown improved efficiency and quality. Future efforts will expand predictions to include complications and treatment outcomes, aiming for broader applications as technology advances.