Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models. METHODS: Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix. RESULTS: Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy. CONCLUSIONS: Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment.

The causal effects of circulating cytokines on sepsis: a Mendelian randomization study.

Background: In observational studies, sepsis and circulating levels of cytokines have been associated with unclear causality. This study used Mendelian randomization (MR) to identify the causal direction between circulating cytokines and sepsis in a two-sample study. Methods: An MR analysis was performed to estimate the causal effect of 41 cytokines on sepsis risk. The inverse-variance weighted random-effects method, the weighted median-based method, and MR-Egger were used to analyze the data. Heterogeneity and pleiotropy were assessed using MR-Egger regression and Cochran's Q statistic. Results: Genetically predicted beta-nerve growth factor (OR = 1.12, 95% CI [1.037-1.211], P = 0.004) increased the risk of sepsis, while RANTES (OR = 0.92, 95% CI [0.849-0.997], P = 0.041) and fibroblast growth factor (OR = 0.869, 95% CI [0.766-0.986], P = 0.029) reduced the risk of sepsis. These findings were robust in extensive sensitivity analyses. There was no clear association between the other cytokines and sepsis risk. Conclusion: The findings of this study demonstrate that beta-nerve growth factor, RANTES, and fibroblast growth factor contribute to sepsis risk. Investigations into potential mechanisms are warranted.

Laparoscopic partial splenectomy for a giant splenic pseudocyst with elevated CA19-9: a case report.

Introduction and importance: Currently, there is a lack of reliable evidence on the management of splenic cysts, which are rare. Exploring the efficacy of laparoscopic partial splenectomy can aid in the accumulation of treatment-related evidence. Case presentation: Here, we report the case of a 31-year-old female who was diagnosed with a giant splenic cyst with elevated serum CA19-9 and subsequently underwent laparoscopic partial splenectomy. Clinical discussion: The effects of most treatment options for splenic cysts, including percutaneous aspiration and drainage, fenestration, and partial splenectomy, have not been confirmed by high-level evidence. With the development of minimally invasive surgery, laparoscopic partial splenectomy has drawn increasing attention. Additionally, the relationships between tumor markers and splenic cysts need to be further elucidated. Conclusions: Laparoscopic partial splenectomy might be recommended for patients with splenic cysts, especially when the cysts are not completely covered by the splenic parenchyma.

N6-methyladenosine modification of B7-H3 mRNA promotes the development and progression of colorectal cancer.

B7-H3 is a common oncogene found in various cancer types. However, the molecular mechanisms underlying abnormal B7-H3 expression and colorectal cancer (CRC) progression need to be extensively explored. B7-H3 was upregulated in human CRC tissues and its abnormal expression was correlated with a poor prognosis in CRC patients. Notably, gain- and loss-of-function experiments revealed that B7-H3 knockdown substantially inhibited cell proliferation, migration, and invasion in vitro, whereas exogenous B7-H3 expression yielded contrasting results. In addition, silencing of B7-H3 inhibited tumor growth in a xenograft mouse model. Mechanistically, our study demonstrated that the N6-methyladenosine (m6A) binding protein YTHDF1 augmented B7-H3 expression in an m6A-dependent manner. Furthermore, rescue experiments demonstrated that reintroduction of B7-H3 considerably abolished the inhibitory effects on cell proliferation and invasion induced by silencing YTHDF1. Our results suggest that the YTHDF1-m6A-B7-H3 axis is crucial for CRC development and progression and may represent a potential therapeutic target for CRC treatment.

Sulfur Dioxide Alleviates Organ Damage and Inflammatory Response in Cecal Ligation and Puncture-Induced Sepsis Rat.

The study aimed to elucidate the mechanisms by which sulfur dioxide (SO2) alleviates organ damage during sepsis using RNA-Seq technology. A cecal ligation and puncture (CLP) sepsis model was established in rats, and the effects of SO2 treatment on organ damage were assessed through histopathological examinations. RNA-Seq was performed to analyze differentially expressed genes (DEGs), and subsequent functional annotations and enrichment analyses were conducted. The CLP model successfully induced sepsis symptoms in rats. Histopathological evaluation revealed that SO2 treatment considerably reduced tissue damage across the heart, kidney, liver, and lungs. RNA-Seq identified 950 DEGs between treated and untreated groups, with significant enrichment in genes associated with ribosomal and translational activities, amino acid metabolism, and PI3K-Akt signaling. Furthermore, gene set enrichment analysis (GSEA) showcased enrichments in pathways related to transcriptional regulation, cellular migration, proliferation, and calcium-ion binding. In conclusion, SO2 effectively mitigates multi-organ damage induced by CLP sepsis, potentially through modulating gene expression patterns related to critical biological processes and signaling pathways. These findings highlight the therapeutic promise of SO2 in managing sepsis-induced organ damage.

High-Transmission Biomimetics Structural Surfaces Produced via Ultrafast Laser Manufacturing.

Inspired by periodically aligned micro/nanostructures on biological surfaces, researchers have been fabricating biomimetic structures with superior performance. As a promising and versatile tool, an ultrafast laser combined with other forms of processing technology has been utilized to manufacture functional structures, e.g., the biomimetic subwavelength structures to restrain the surface Fresnel reflectance. In this review paper, we interpret the biomimetic mechanism of antireflective subwavelength structures (ARSSs) for high-transmission windows. Recent advances in the fabrication of ARSSs with an ultrafast laser are summarized and introduced. The limitations and challenges of laser processing technology are discussed, and the future prospects for advancement are outlined, too.