目的 探討彌漫性軸索損傷（diffuse axonal injury，DAI）的發病機制、臨床特點、診斷及治療方法，以提高治愈率，降低致殘、致死率。 方法 回顧性分析200６年6月-2010年3月間65例臨床診斷為DAI患者的受傷機制、臨床特征、影像學表現、治療及預后。 結果 DAI最常見原因為車禍傷70.7%，其次為墜落、墜物傷（10.7%），其他（18.6%）。按格拉斯哥昏迷分級（GCS）評分結果3~5分18例，6~8分15例，9~12分32例；治愈43例，輕殘15例，中殘8例，重殘或植物生存7例，死亡7例。 結論 DAI具有診斷、治療困難，預后差等特點，交通事故是導致DAI的主要原因，格拉斯哥昏迷分級（GCS）評分、昏迷時間和瞳孔變化是判定預后的重要指標。目前尚無特效治療方法，由于80％以上患者往往是多發傷，故早期氣管切開、呼吸機輔助呼吸、促醒、亞低溫治療、防治并發癥、鈣離子拮抗劑應用等綜合治療可顯著改善預后。
ObjectiveTo explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). MethodsThe Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. GO, KEGG, and GSEA enrichment analyses were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients' 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. ResultsDifferentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. ConclusionThe prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.
Objective To develop and validate a composite model (PAH score) based on dual-center data, integrating logistic regression and machine learning approaches, to improve the preoperative differential diagnostic efficacy for appendiceal mucinous neoplasms (AMNs). MethodsA dual-center retrospective case-control design was adopted. The study included 108 AMNs patients and 230 healthy controls from The 900th Hospital of Joint Logistics Support Force (January 2014 to November 2024) and Sanming First Hospital Affiliated to Fujian Medical University (December 2018 to December 2023) for feature screening and model construction. Additionally, 258 patients with pathologically confirmed chronic appendicitis (CA) from the same period were included as the differential validation group. Predictors were screened using leastabsolute shrinkage and selection operator combined with traditional logistic regression, and four machine learning algorithms—random forest, support vector machine, gradient boosting, and decision tree—were applied to rank feature importance. Core variables consistently identified by both approaches were integrated to construct a logistic regression model. Based on the model results, the PAH score was formulated, and its performance in distinguishing AMNs from CA was validated. An online visualization platform for AMNs risk prediction was subsequently developed.ResultsBaseline characteristics were balanced between the AMNs group and healthy control group, as well as between the AMNs group and CA group (P>0.05). Multivariate logistic regression identified prognostic nutritional index (PNI, OR=0.81), albumin-to-globulin ratio (AGR, OR=0.37), and hemoglobin to red blood cell distribution width ratio (HRR, OR=0.36) as independent predictors of AMNs (all P<0.001). All four machine learning algorithms consistently ranked PNI, AGR, and HRR as the top three important features. Based on these findings, a PAH model was constructed, and the PAH score was calculated using the standardized regression coefficient weighting method as follows: PAH score=20.8–0.21×PNI–0.99×AGR–1.01×HRR. The model demonstrated excellent discriminative ability for AMNs, with an area under the curve (AUC) of 0.918. The Hosmer-Lemeshow test indicated good calibration between predicted and observed probabilities (P=0.925). Decision curve analysis (DCA) showed significant net clinical benefit within the risk threshold range of 0.15–0.25. Bootstrap internal validation confirmed robust model performance (AUC=0.911). The median PAH score was significantly higher in the AMNs group than that of the CA group (MD=1.78, P<0.001). For distinguishing AMNs from CA, the PAH score achieved an AUC of 0.758. At the optimal cutoff value (–1.00), sensitivity was 70%, specificity was 76%, and accuracy rate was 74%. The Hosmer-Lemeshow test again confirmed good calibration (P=0.106), and Bootstrap validation indicated stable performance (AUC=0.783). DCA further demonstrated considerable net benefit within the threshold range of 0.15–0.35. ConclusionsThe PAH score developed in this study effectively predicts the risk of AMNs and accurately differentiates AMNs from CA, showing promising clinical application potential. However, as an exploratory study, further validation through multicenter, large-sample, prospective studies with diverse control groups is needed to enhance the generalizability and stability of the scoring system.