Low-intensity focused ultrasound attenuates early traumatic brain injury by OX-A/NF-κB/NLRP3 signaling pathway.

BACKGROUND: Traumatic brain injury (TBI) is a serious hazard to human health and is characterized by high rates of disability and mortality. It is necessary to explore new effective treatment methods to reduce the impact of TBI on individuals and society. As an emerging neuromodulation technique, ultrasound is used to treat some neurological diseases, but the neuroprotective mechanism of low-intensity focused ultrasound (LIFUS) in TBI remains unclear. We aimed to investigate the protective effects and potential mechanisms of LIFUS in TBI. METHODS: A rat model of TBI was established using the free-fall method. After establishing the TBI model, the hypothalamus region was covered with LIFUS radiation, and an orexin receptor 1 (OXR1) antagonist (SB334867) was injected intraperitoneally. Neurobehavioral examination, Nissl staining, hematoxylin and eosin staining of the brain tissue, and brain water content, were performed 3 days later. Western blotting, quantitative real-time polymerase chain reaction, immunofluorescence staining, and immunohistochemical staining, were used to evaluate the neuroprotective mechanisms of LIFUS. RESULTS: LIFUS improved tissue damage, neurological deficits, and brain edema. LIFUS can increase the expression of orexin-A (OX-A) and OXR1, significantly inhibit the activation of nuclear factor-κB (NF-κB) protein and nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome after TBI, and reduce the release of pro-inflammatory factors after TBI; however, SB334867 can reverse this effect. CONCLUSIONS: This study suggests that LIFUS may play a neuroprotective role by promoting the release of OX-A from the hypothalamus and inhibiting the inflammatory response after TBI through the OX-A /NF-κB/NLRP3 pathway.

Development and Validation of a Website to Guide Decision-Making for Disorders of Consciousness.

Background: This study aimed to develop and validate a nomogram and present it on a website to be used to predict the overall survival at 16, 32, and 48 months in patients with prolonged disorder of consciousness (pDOC). Methods: We retrospectively analyzed the data of 381 patients with pDOC at two centers. The data were randomly divided into training and validation sets using a ratio of 6:4. On the training set, Cox proportional hazard analyses were used to identify the predictive variables. In the training set, two models were screened by COX regression analysis, and based on clinical evidence, model 2 was eventually selected in the nomogram after comparing the receiver operating characteristic (ROC) of the two models. In the training and validation sets, ROC curves, calibration curves, and decision curve analysis (DCA) curves were utilized to measure discrimination, calibration, and clinical efficacy, respectively. Results: The final model included age, Glasgow coma scale (GCS) score, serum albumin level, and computed tomography (CT) midline shift, all of which had a significant effect on survival after DOCs. For the 16-, 32-, and 48-month survival on the training set, the model had good discriminative power, with areas under the curve (AUCs) of 0.791, 0.760, and 0.886, respectively. For the validation set, the AUCs for the 16-, 32-, and 48-month survival predictions were 0.806, 0.789, and 0.867, respectively. Model performance was good for both the training and validation sets according to calibration plots and DCA. Conclusion: We developed an accurate, efficient nomogram, and a corresponding website based on four correlated factors to help clinicians improve their assessment of patient outcomes and help personalize the treatment process and clinical decisions.

A dynamic model to predict long-term outcomes in patients with prolonged disorders of consciousness.

PURPOSE: It is important to predict the prognosis of patients with prolonged disorders of consciousness (DOC). This study established and validated a nomogram and corresponding web-based calculator to predict outcomes for patients with prolonged DOC. METHODS: All data were obtained from the First Affiliated Hospital of Nanchang University and the Shangrao Hospital of Traditional Chinese Medicine. Predictive variables were identified by univariate and multiple logistic regression analyses. Receiver operating characteristic curves, calibration curves, and a decision curve analysis (DCA) were utilized to assess the predictive accuracy, discriminative ability, and clinical utility of the model, respectively. RESULTS: Independent prognostic factors, such as age, Glasgow coma scale score, state of consciousness, and brainstem auditory-evoked potential grade were integrated into a nomogram. The model demonstrated good discrimination in the training and validation cohorts, with area-under-the-curve values of 0.815 (95% confidence interval [CI]: 0.748-0.882) and 0.805 (95% CI: 0.727-0.883), respectively. The calibration plots and DCA demonstrated good model performance and clear clinical benefits in both cohorts. CONCLUSIONS: Based on our nomogram, we developed an effective, simple, and accurate model of a web-based calculator that may help individualize healthcare decision-making. Further research is warranted to optimize the system and update the predictors.

Orexin-A Attenuates Inflammatory Responses in Lipopolysaccharide-Induced Neural Stem Cells by Regulating NF-KB and Phosphorylation of MAPK/P38/Erk Pathways.

BACKGROUND: Neuronal damage is the main cause of neurological diseases. Neural stem cells (NSCs) have the functions of cell repair and replacement of neurons, secretion of neurotrophic factors, and immune regulation of the neural microenvironment. OBJECTIVE: Previous study found that Orexin-A had a protective effect on neurons in the central nervous system, but it is lacking in making great efforts on the function of Orexin-A on NSCs. This study aimed to investigate the anti-inflammatory responses and signaling mechanisms of Orexin-A on lipopolysaccharide (LPS)-induced NSCs. METHODS: Quantitative real-time polymerase chain reaction was used to detect the mRNA level. Signaling pathway-related protein expression was detected by Western blot. The proliferation and migration of NSCs were investigated by Cell Counting Kit-8 (CCK-8) detection kit and transwell assay. Besides, the staining of hematoxylin and eosin (HE) was performed to study the morphology of cell. RESULTS: Orexin-A decreased the pro-inflammatory cytokines of IL-1ß, TNF-α, and IL-6 induced by LPS by regulating nuclear factor-k-gene binding (NF-kB) and phosphorylation of P38/Erk-mitogen-activated protein kinases (MAPKs) pathways, but not p-JNK signaling. CONCLUSION: Our findings indicate that Orexin-A can alleviate the inflammatory response of NSC. It can provide beneficial help in neural stem cell therapy applications.

A simple-to-use web-based calculator for survival prediction in Parkinson's disease.

BACKGROUND: To establish and validate a nomogram and corresponding web-based calculator to predict the survival of patients with Parkinson's disease (PD). METHODS: In this cohort study, we retrospectively evaluated patients (n=497) with PD using a two-stage design, from March 2004 to November 2007 and from July 2005 to July 2015. Predictive variables included in the model were identified by univariate and multiple Cox proportional hazard analyses in the training set. RESULTS: Independent prognostic factors including age, PD duration, and Hoehn and Yahr stage were determined and included in the model. The model showed good discrimination power with the area under the curve (AUC) values generated to predict 4-, 6-, and 8-year survival in the training set being 0.716, 0.783, and 0.814, respectively. In the validation set, the AUCs of 4- and 6-year survival predictions were 0.85 and 0.924, respectively. Calibration plots and decision curve analysis showed good model performance both in the training and validation sets. For convenient application, we established a web-based calculator (https://tangyl.shinyapps.io/PDprognosis/). CONCLUSIONS: We developed a satisfactory, simple-to-use nomogram and corresponding web-based calculator based on three relevant factors to predict prognosis and survival of patients with PD. This model can aid personalized treatment and clinical decision-making.

Predictive Value and Correlation of Neuron-Specific Enolase for Prognosis in Patients with Coma: A Systematic Review and Meta-Analysis.

OBJECTIVE: Coma is the most serious disturbance of consciousness, which affects the life quality of patients and increases the burden of their family. Studies to assess the prognostic value of neuron-specific enolase (NSE) in patients with coma have not led to precise, generally accepted prognostic rules. The study aims to assess the correlation between NSE and prognosis of coma and the predictive value of NSE for clinical prognosis. METHODS: A search was conducted using PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), and WanFang Data from the establishment time of databases to December 2019. This analysis included patients with coma, regardless of how long the coma was. In total, 26 articles were retrieved and included in the review. RESULTS: The meta-analysis revealed the NSE concentration of patients with coma is significantly higher than that of the control group (standard mean difference = 0.88, 95% confidence interval [CI]: 0.63-1.12, p < 0.05). The pooled sensitivity and specificity of NSE in coma diagnosis was 0.5 (95% CI: 0.39-0.61) and 0.86 (95% CI: 0.71-0.94). CONCLUSIONS: The NSE concentration of patients with poor coma prognosis is significantly higher than that of the control group. The high NSE concentration is not necessarily a poor prognosis for coma, but low NSE concentration indicates a high probability of a good prognosis for coma.

Vagus Nerve Stimulation Attenuates Early Traumatic Brain Injury by Regulating the NF-κB/NLRP3 Signaling Pathway.

BACKGROUND: Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Oxidative stress, inflammation, and apoptosis are vital pathophysiological features post-TBI. OBJECTIVES: Research has shown that vagus nerve stimulation (VNS) can attenuate oxidative stress in various diseases. However, the critical role of VNS in TBI is still not completely understood. This study investigated the protective effects and potential mechanism of VNS on TBI. METHODS: Male Sprague-Dawley rats were randomized into 3 groups: sham, TBI, and TBI + VNS. The TBI model was induced in rats by the free-fall drop method. The vagal nerve trunk was separated, and VNS was performed after establishing the TBI model. RESULTS: The results showed that VNS significantly ameliorated tissue damage, neurological deficits, and cerebral edema, compared with the sham VNS group. Additionally, VNS alleviated oxidative stress, inflammation, and apoptosis in the pericontusive cortex of rats after TBI. VNS also significantly suppressed expression of the nuclear factor-κB (NF-κB) protein in the nucleus and activation of the nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome. CONCLUSIONS: Taken together, the present study indicates that VNS may attenuate brain damage after TBI by inhibiting oxidative stress, inflammation, and apoptosis, possibly through the NF-κB/NLRP3 signaling pathway.