The role of coagulopathy and subdural hematoma thickness at admission in predicting the prognoses of patients with severe traumatic brain injury: A multicenter retrospective cohort study from China.

BACKGROUND: Traumatic brain injury (TBI) is one of the diseases with high disability and mortality worldwide. Recent studies have shown that TBI-related factors may change the complex balance between bleeding and thrombosis, leading to coagulation disorders. The aim of this retrospective study was to investigate the prediction of coagulopathy and subdural hematoma thickness at admission using the Glasgow Outcome Scale (GOS) in patients with severe TBI at 6 months after discharge. METHODS: In this retrospective cohort study, a total of 1,006 patients with severe TBI in large medical centers in three different provinces of China from June 2015 to June 2021 were enrolled after the exclusion criteria, and 800 patients who met the enrollment criteria were included. A receiver operating characteristic (ROC) curve was used to determine the best cut-off values of platelet (PLT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and subdural hematoma (SDH) thickness. The ROC curve, nomogram, calibration curve, and the decision curve were used to evaluate the predictive effect of the coagulopathy and Coagulopathy-SDH(X1) models on the prognoses of patients with severe TBI, and the importance of predictive indicators was ranked by machine learning. RESULTS: Among the patients with severe TBI on admission, 576/800 (72%) had coagulopathy, 494/800 (61%) had SDH thickness ≥14.05 mm, and 385/800 (48%) had coagulopathy combined with SDH thickness ≥14.05 mm. Multivariate logistic regression analyses showed that age, pupil, brain herniation, WBC, CRP, SDH, coagulopathy, and X1 were independent prognostic factors for GOS after severe TBI. Compared with other single indicators, X1 as a predictor of the prognosis of severe TBI was more accurate. The GOS of patients with coagulopathy and thick SDH (X1, 1 point) at 6 months after discharge was significantly worse than that of patients with coagulopathy and thin SDH (X1, 2 points), patients without coagulopathy and thick SDH (X1, 3 point), and patients without coagulopathy and thin SDH (X1, 4 points). In the training group, the C-index based on the coagulopathy nomogram was 0.900. The C-index of the X1-based nomogram was 0.912. In the validation group, the C-index based on the coagulopathy nomogram was 0.858. The C-index of the X1-based nomogram was 0.877. Decision curve analysis also confirmed that the X1-based model had a higher clinical net benefit of GOS at 6 months after discharge than the coagulopathy-based model in most cases, both in the training and validation groups. In addition, compared with the calibration curve based on the coagulopathy model, the prediction of the X1 model-based calibration curve for the probability of GOS at 6 months after discharge showed better agreement with actual observations. Machine learning compared the importance of each independent influencing factor in the evaluation of GOS prediction after TBI, with results showing that the importance of X1 was better than that of coagulopathy alone. CONCLUSION: Coagulopathy combined with SDH thickness could be used as a new, accurate, and objective clinical predictor, and X1, based on combining coagulopathy with SDH thickness could be used to improve the accuracy of GOS prediction in patients with TBI, 6 months after discharge.

Construction of a prediction model for rebleeding in patients with acute upper gastrointestinal bleeding.

BACKGROUND: The incidence of rebleeding in patients with upper gastrointestinal bleeding (UGIB) remains despite advances in intervention approaches. Therefore, early prediction of the risk of rebleeding could help to greatly reduce the mortality rate in these patients. We aim to develop and validate a new prediction model to predict the probability of rebleeding in patients with AUGIB. METHODS: A total of 1170 AUGIB patients who completed the procedure of emergency gastroscopy within 48 h of admission were included. Logistic regression analyses were performed to construct a new prediction model. A receiver operating characteristic curve, a line graph, and a calibration and decision curve were used to assess the predictive performance of our new prediction model and compare its performance with that of the AIMS65 scoring system to determine the predictive value of our prediction model. RESULTS: A new prediction model was constructed based on Lactic acid (LAC), neutrophil percentage (NEUTP), platelet (PLT), albumin (ALB), and D-DIMER. The AUC values and their 95% confidence interval (CI) for the new prediction model and the AIMS65 score were 0.746 and 0.619, respectively, and 0.697-0.795 and 0.567-0.670, respectively. In the training group, the C index values based on the prediction model and the AIMS65 scoring system were 0.720 and 0.610, respectively. In the validation group, the C index values based on the prediction model and the AIMS65 scoring system were 0.828 and 0.667, respectively. The decision and calibration curve analysis also showed that the prediction model was superior to the AIMS65 scoring system in terms of accuracy of prediction, consistency, and net clinical benefit. CONCLUSION: The prediction model can predict the probability of rebleeding in AUGIB patients after endoscopic hemostasis therapy.

A lipid metabolism-based prognostic risk model for HBV-related hepatocellular carcinoma.

BACKGROUND: Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms. METHODS: Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis. RESULTS: The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups. CONCLUSIONS: This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.

Predictive Value of the Systemic Immune Inflammation Index for Adverse Outcomes in Patients With Acute Ischemic Stroke.

Background and Purpose: The systemic immune-inflammation index, a new index based on platelets, neutrophils and lymphocytes, has been shown to be associated with outcomes of patients with venous sinus thrombosis and cancer. However, its application in acute ischemic stroke has rarely been reported. Therefore, we examined the relationship between systemic immune-inflammation index levels at hospital admission and the outcomes of patients 3 months after onset, and plotted a nomogram to predict the probability of adverse outcomes in patients with acute ischemic stroke. Methods: We retrospectively analyzed a total of 208 patients with acute ischemic stroke who were admitted between January 2020 and December 2020, and recorded the modified Rankin score 3 months later. A modified Rankin score ≥ 3 was defined as an adverse outcome. Age, sex, NIHSS score, SII, hypertension and coronary heart disease were included in the binary logistic regression, and the nomogram was plotted with a regression equation. Results: Receiver operating characteristic (ROC) curve analysis indicated that the best cutoff value of the systemic immune-inflammation index was 802.8, with a sensitivity of 70.9% and specificity of 58.2% (area under the curve: 0.657, 95% confidence interval: 0.572-0.742). The nomogram had a C index of 0.802. The average error of the calibration curves of the training set and the validation set was 0.021 and 0.034, respectively. Conclusion: The systemic immune-inflammation index is associated with short-term adverse outcomes in patients with acute ischemic stroke, and the nomograms can predict the risk of adverse outcomes in patients with acute ischemic stroke.

Systemic immune inflammation index and peripheral blood carbon dioxide concentration at admission predict poor prognosis in patients with severe traumatic brain injury.

Background: Recent studies have shown that systemic inflammation responses and hyperventilation are associated with poor outcomes in patients with severe traumatic brain injury (TBI). The aim of this retrospective study was to investigate the relationships between the systemic immune inflammation index (SII = platelet × neutrophil/lymphocyte) and peripheral blood CO2 concentration at admission with the Glasgow Outcome Score (GOS) at 6 months after discharge in patients with severe TBI. Methods: We retrospectively analyzed the clinical data for 1266 patients with severe TBI at three large medical centers from January 2016 to December 2021, and recorded the GOS 6 months after discharge. The receiver operating characteristic (ROC) curve was used to determine the best cutoff values for SII, CO2, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), and chi-square tests were used to evaluate the relationships among SII, CO2 and the basic clinical characteristics of patients with TBI. Multivariate logistic regression analysis was used to determine the independent prognostic factors for GOS in patients with severe TBI. Finally, ROC curve, nomogram, calibration curve and decision curve analyses were used to evaluate the value of SII and coSII-CO2 in predicting the prognosis of patients with severe TBI. And we used the multifactor regression analysis method to build the CRASH model and the IMPACT model. The CRASH model included age, GCS score (GCS, Glasgow Coma Scale) and Pupillary reflex to light: one, both, none. The IMPACT model includes age, motor score and Pupillary reflex to light: one, both, none. Results: The ROC curves indicated that the best cutoff values of SII, CO2, PLR, NLR and LMR were 2651.43×109, 22.15mmol/L, 190.98×109, 9.66×109 and 1.5×109, respectively. The GOS at 6 months after discharge of patients with high SII and low CO2 were significantly poorer than those with low SII and high CO2. Multivariate logistic regression analysis revealed that age, systolic blood pressure (SBP), pupil size, subarachnoid hemorrhage (SAH), SII, PLR, serum potassium concentration [K+], serum calcium concentration [Ca2+], international normalized ratio (INR), C-reactive protein (CRP) and co-systemic immune inflammation index combined with carbon dioxide (coSII-CO2) (P < 0.001) were independent prognostic factors for GOS in patients with severe TBI. In the training group, the C-index was 0.837 with SII and 0.860 with coSII-CO2. In the external validation group, the C-index was 0.907 with SII and 0.916 with coSII-CO2. Decision curve analysis confirmed a superior net clinical benefit with coSII-CO2 rather than SII in most cases. Furthermore, the calibration curve for the probability of GOS 6 months after discharge showed better agreement with the observed results when based on the coSII-CO2 rather than the SII nomogram. According to machine learning, coSII-CO2 ranked first in importance and was followed by pupil size, then SII. Conclusions: SII and CO2 have better predictive performance than NLR, PLR and LMR. SII and CO2 can be used as new, accurate and objective clinical predictors, and coSII-CO2, based on combining SII with CO2, can be used to improve the accuracy of GOS prediction in patients with TBI 6 months after discharge.

GSK-3ß Regulates the Expression of P21 to Promote the Progression of Chordoma.

PURPOSE: Chordoma is a rare malignant bone tumor transformed from the remnants of notochord. It is characterized as highly aggressive and locally invasive, difficult to be completely removed by surgery, and has a poor clinical prognosis. Glycogen synthase kinase 3 beta (GSK-3ß) is involved in many cellular processes. GSK-3ß overexpression has been shown to promote the development of many cancers, according to previous studies. However, the role of GSK-3ß in chordoma remains unclear. METHODS: Immunohistochemistry (IHC) and Western blotting (WB) were performed on clinical specimens to measure GSK-3ß expression in chordoma, and immunofluorescence and quantitative real-time polymerase chain reaction (QRT-PCR) were performed to examine the expression of GSK-3ß and P21 in cell lines. Cell proliferation was detected by the CCK-8 assay and colony formation analysis, cell migration and invasion checked by Transwell experiments, and cell apoptosis was determined by Annexin V/propidium iodide staining. P21 was predicted as a downstream target gene of GSK-3ß using STRING and UNIHI databases. Moreover, we used immunoprecipitation to confirm that GSK-3ß and P21 interacted with each other. The double luciferase reporter gene assay showed that GSK-3ß could regulate the promoter activity of P21. Finally, the role of the GSK-3ß -P21 pathway in chordoma tumorigenesis was analyzed in vivo in nude mice. RESULTS: Our study showed that GSK-3ß was significantly higher in chordoma tissues than in paracancer tissues, and siRNA knockdown of GSK-3ß inhibited chordoma cell proliferation and promoted cell apoptosis. Additionally, our research found that GSK-3ß bound and downregulated the expression of the P21 gene, and the expression of silencing P21 partially reversed the inhibitory effect of knockdown GSK-3ß on chordoma. Furthermore, xenografts showed that knockdown GSK-3ß inhibited the formation of chordomas in vivo. CONCLUSION: Our results indicated that the GSK-3ß-P21 axis may be an important signaling pathway for the occurrence and development of chordoma, providing a new therapeutic target for the clinical treatment of this disorder.