Background: Tumor necrosis factor-alpha (TNF-α) is elevated early in injured brain after traumatic brain injury (TBI), in humans and in animals. Etanercept (a TNF-α antagonist with anti-inflammatory effects) attenuates TBI in rats by reducing both microglial and astrocytic activation and increased serum levels of TNF-α. However, it is not known whether etanercept improves outcomes of TBI by attenuating microglia-associated, astrocytes-associated, and/or neurons-associated TNF-α expression in ischemic brain. A well clinically relevant rat model, where a lateral fluid percussion is combined with systemic administration of etanercept immediately after TBI, was used. The neurological severity score and motor function was measured on all rats preinjury and on day 3 after etanercept administration. At the same time, the neuronal and glial production of TNF-α was measured by Immunofluorescence staining. In addition, TNFα contents of ischemic cerebral homogenates was measured using commercial enzyme-linked immunosorbent assay kits. Results: In addition to inducing brain ischemia as well as neurological and motor deficits, TBI caused significantly higher numbers of microglia-TNF-α double positive cells, but not neurons-TNF-α or astrocytes-TNF-α double positive cells in the injured brain areas than did the sham operated controls, when evaluated 3 days after TBI. The TBI-induced cerebral ischemia, neurological motor deficits, and increased numbers of microglia-TNF-α double positive cells and increased TNF-α levels in the injured brain were all significantly attenuated by etanercept therapy. Conclusion: This finding indicates that early microglia overproduction of TNF-α in the injured brain region after TBI contributes to cerebral ischemia and neurological motor deficits, which can be attenuated by etanercept therapy. Studies in this model could provide insight into the mechanisms underlying neurological motor disturbance in brain-injured patients.

Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient’s condition to family members.

Rosmarinic acid, a major component of rosemary, is a polyphenolic compound with potential neuroprotective effects. Asreducing the synaptic release of glutamate is crucial to achieving neuroprotectant’s pharmacotherapeutic effects, the effect of rosmarinic acid on glutamate release was investigated in rat cerebrocortical nerve terminals (synaptosomes). Rosmarinic acid depressed the 4-aminopyridine (4-AP)-induced glutamate release in a concentration-dependent manner. The removal of extracellular calcium and the blockade of vesicular transporters prevented the inhibition of glutamate release by rosmarinic acid. Rosmarinic acid reduced 4-AP-induced intrasynaptosomal Ca²⁺ elevation. The inhibition of N-, P/Q-type Ca²⁺ channels and the calcium/calmodulin-dependent kinase II (CaMKII) prevented rosmarinic acid from having effects on glutamate release. Rosmarinic acid also reduced the 4-AP-induced activation of CaMKII and the subsequent phosphorylation of synapsin I, the main presynaptic target of CaMKII. In addition, immunocytochemistry confirmed the presence of GABA_A receptors. GABA_A receptor agonist and antagonist blocked the inhibitory effect of rosmarinic acid on 4-AP-evoked glutamate release. Docking data also revealed that rosmarinic acid formed a hydrogen bond with the amino acid residues of GABA_A receptor. These results suggested that rosmarinic acid activates GABA_A receptors in cerebrocortical synaptosomes to decrease Ca²⁺ influx and CaMKII/synapsin I pathway to inhibit the evoked glutamate release.

Background: Ceftriaxone is a β-lactam antibiotic used to treat central nervous system infections. Whether the neuroprotective effects of ceftriaxone after TBI are mediated by attenuating neuroinflammation but not its antibacterial actions is not well established. Methods: Anesthetized male Sprague–Dawley rats were divided into sham-operated, TBI + vehicle, and TBI + ceftriaxone groups. Ceftriaxone was intraperitoneally injected at 0, 24, and 48 h with 50 or 250 mg/kg/day after TBI. During the first 120 min after TBI, we continuously measured heart rate, arterial pressure, intracranial pressure (ICP), and cerebral perfusion pressure. The infarct volume was measured by TTC staining. Motor function was measured using the inclined plane. Glutamate transporter 1 (GLT-1), neuronal apoptosis and TNF-α expression in the perilesioned cortex were investigated using an immunofluorescence assay. Bacterial evaluation was performed by Brown and Brenn’s Gram staining. These parameters above were measured at 72 h after TBI. Results: Compared with the TBI + vehicle group, the TBI + ceftriaxone 250 mg/kg group showed significantly lower ICP, improved motor dysfunction, reduced body weight loss, decreased infarct volume and neuronal apoptosis, decreased TBI-induced microglial activation and TNF-α expression in microglia, and increased GLT-1 expression in neurons and microglia. However, the grades of histopathological changes of antibacterial effects are zero. Conclusions: The intraperitoneal injection of ceftriaxone with 250 mg/kg/day for three days may attenuate TBI by increasing GLT-1 expression and reducing neuroinflammation and neuronal apoptosis, thereby resulting in an improvement in functional outcomes, and this neuroprotective effect is not related to its antibacterial effects.

Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

This study was aimed at determining the outcome and examining the association between comorbidities and mortality after intracerebral hemorrhage in chronic dialysis patients. We used the Taiwan National Health Insurance Research Database and enrolled patients who underwent maintenance dialysis between 2000 and 2007. Annual incidence of intracerebral hemorrhage in patients receiving dialysis from 2000 to 2007 was determined. To identify predictors of hemorrhagic stroke, we used logistic regression model to estimate the relative ratio of factors for intracerebral hemorrhage in the most recent cohort (2007). The cumulative survival rate and comorbid conditions associated with mortality after intracerebral hemorrhage among all dialysis patients between 2000 and 2007 was calculated using the Kaplan-Meier method and Cox regression analysis. We identified 57,261 patients on maintenance dialysis in the cohort of 2007, and 340 patients had history of intracerebral hemorrhage among them. Hypertension was the most common comorbidity of dialysis patients. The incidence rate of intracerebral hemorrhage among dialysis patients was about 0.6%. Adjusted logistic regression model showed that male gender, middle age (45–64 years), hypertension, and previous history of stroke were the independent predictors for the occurrence of intracerebral hemorrhage among chronic dialysis patients. 1,939 dialysis patients with development of intracerebral hemorrhage in the analysis period from 2000 to 2007 were identified. In-hospital mortality was high (36.15%) following intracerebral hemorrhage. They were followed up after intracerebral hemorrhage for a mean time of 41.56 months. Adjusted Cox regression analyses demonstrated that the factors independently associated with mortality after intracerebral hemorrhage among dialysis patients included diabetes mellitus, malignancy and a history of prior stroke. Dialysis patients who have history of prior stroke, diabetes and malignancy have worse survival than patients without these comorbidities. Attention must focus on providing optimal medical care after hemorrhagic stroke for these target groups to reduce mortality.