Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury.

AIM: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients. METHOD: We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models. RESULTS: A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model. CONCLUSION: The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.

Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury.

AIM: This study aims to develop prediction models for in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury (TBI). METHOD: We conducted a retrospective review of patients hospitalized for moderate-to-severe TBI in our department from 2011 to 2020. Five machine learning (ML) algorithms and the conventional logistic regression (LR) model were employed to predict in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes. These models utilized clinical and routine blood data collected upon admission. RESULTS: This study included a total of 196 patients who received only non-surgical treatment after moderate-to-severe TBI. When predicting mortality, ML models achieved area under the curve (AUC) values of 0.921 to 0.994 using clinical and routine blood data, and 0.877 to 0.982 using only clinical data. In comparison, LR models yielded AUCs of 0.762 and 0.730 respectively. When predicting the GOS outcome, ML models achieved AUCs of 0.870 to 0.915 using clinical and routine blood data, and 0.858 to 0.927 using only clinical data. In comparison, the LR model yielded AUCs of 0.798 and 0.787 respectively. Repeated internal validation showed that the contributions of routine blood data for prediction models may depend on different prediction algorithms and different outcome measurements. CONCLUSION: The study reported ML-based prediction models that provided rapid and accurate predictions on short-term outcomes after non-surgical treatment among patients with moderate-to-severe TBI. The study also highlighted the superiority of ML models over conventional LR models and proposed the complex contributions of routine blood data in such predictions.

Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81.

PURPOSES: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma-CpGs island methylator phenotype (G-CIMP) but have an unmethylated promoter of O-6-methylguanine-DNA methyltransferase (unMGMT). METHODS: The discovery-validation approach was planned incorporating a series of G-CIMP-/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi-CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration. RESULTS: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy-related CpGs, from which a 10-CpG risk signature was further constructed. Both the 64-CpG panel and the 10-CpG risk signature were validated showing significant correlations with overall survival of G-CIMP-/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10-CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression. CONCLUSIONS: The 64 TMZ efficacy-related CpGs and in particular the 10-CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G-CIMP-/unMGMT GBMs.

Biomimetic Macrophage Membrane-Camouflaged Nanoparticles Induce Ferroptosis by Promoting Mitochondrial Damage in Glioblastoma.

The increasing understanding of ferroptosis has indicated its role and therapeutic potential in cancer; however, this knowledge has yet to be translated into effective therapies. Glioblastoma (GBM) patients face a bleak prognosis and encounter challenges due to the limited treatment options available. In this study, we conducted a genome-wide CRISPR-Cas9 screening in the presence of a ferroptosis inducer (RSL3) to identify the key driver genes involved in ferroptosis. We identified ALOX15, a key lipoxygenase (LOX), as an essential driver of ferroptosis. Small activating RNA (saRNA) was used to mediate the expression of ALOX15 promoted ferroptosis in GBM cells. We then coated saALOX15-loaded mesoporous polydopamine (MPDA) with Angiopep-2-modified macrophage membranes (MMs) to reduce the clearance by the mononuclear phagocyte system (MPS) and increase the ability of the complex to cross the blood-brain barrier (BBB) during specific targeted therapy of orthotopic GBM. These generated hybrid nanoparticles (NPs) induced ferroptosis by mediating mitochondrial dysfunction and rendering mitochondrial morphology abnormal. In vivo, the modified MM enabled the NPs to target GBM cells, exert a marked inhibitory effect on GBM progression, and promote GBM radiosensitivity. Our results reveal ALOX15 to be a promising therapeutic target in GBM and suggest a biomimetic strategy that depends on the biological properties of MMs to enhance the in vivo performance of NPs for treating GBM.

Machine learning algorithms for improved prediction of in-hospital outcomes after moderate-to-severe traumatic brain injury: a Chinese retrospective cohort study.

AIM: Controversy remains high over the superiority of advanced machine learning (ML) algorithms to conventional logistic regression (LR) in the prediction of prognosis after traumatic brain injury (TBI). This study aimed to compare the performance of ML and LR models in predicting in-hospital prognosis after TBI. METHOD: In a single-center retrospective cohort of adult patients hospitalized for moderate-to-severe TBI (Glasgow coma score ≤12) in our hospital from 2011 to 2020, LR and three ML algorithms (XGboost, lightGBM, and FT-transformer) were run to build prediction models for in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes using either all 19 clinical and laboratory features or the 10 non-laboratory ones collected at admission to the neurological intensive care unit. The Shapley (SHAP) value was used for model interpretation. RESULT: In total, 482 patients had an in-hospital mortality rate of 11.0%. A total of 23.0% of the patients had good functional scores (GOS ≥ 4) at discharge. All ML models performed better than the LR model in predicting in-hospital prognosis after TBI, among which the lightGBM model showed the best performance: When predicting mortality, the lightGBM model yielded an area under the curve (AUC) of 0.953 using all 19 features (the LR model: 0.813) and an AUC of 0.935 using 10 non-laboratory features (the LR model: 0.803); when predicting GOS functional outcomes, it yielded an AUC of 0.913 using all 19 features (the LR model: 0.832) and an AUC of 0.889 using non-laboratory data (the LR model: 0.818). The SHAP method identified key contributors to explain the lightGBM models. Finally, the integration of the lightGBM models with different prediction purposes was found to provide refined prognostic information, particularly for patients who survived moderate-to-severe TBI. CONCLUSION: The study supported the superiority of ML to LR in predicting prognosis after moderate-to-severe TBI and highlighted its potential use for clinical application.

Potent predictive CpG signature for temozolomide response in non-glioma-CpG island methylator phenotype glioblastomas with methylated MGMT promoter.

Aim: We aimed to identify potent CpG signatures predicting temozolomide (TMZ) response in glioblastomas (GBMs) that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of MGMT (meMGMT). Materials & methods: Different datasets of non-G-CIMP meMGMT GBMs with molecular and clinical data were analyzed. Results: A panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature were identified and validated for distinguishing differential outcomes to radiotherapy plus TMZ versus radiotherapy alone in non-G-CIMP meMGMT GBMs. An integrated classification scheme was also proposed for refining a MGMT-based TMZ-guiding approach in all G-CIMP-GBMs. Conclusion: The CpG signatures may serve as promising predictive biomarker candidates for guiding optimal TMZ usage in non-G-CIMP meMGMT GBMs.

Glioblastomas that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of the MGMT gene (meMGMT) show considerable variability in their response to temozolomide (TMZ). Powerful biomarkers that provide predictive information on optimal TMZ decision-making can be clinically useful. This study has identified and validated a panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature for predicting TMZ usage in non-G-CIMP meMGMT glioblastomas. An integrated classification scheme is proposed for refining a MGMT-based TMZ-guiding approach in non-G-CIMP glioblastomas.

A Novel Pseudogene Methylation Signature to Predict Temozolomide Outcome in Non-G-CIMP Glioblastomas.

Objective: Alterations in the methylation state of pseudogenes may serve as clinically useful biomarkers of glioblastomas (GBMs) that do not have glioma-CpG island methylator phenotype (G-CIMP). Methods: Non-G-CIMP GBM datasets were included for evaluation, and a RISK-score signature was determined from the methylation state of pseudogene loci. Both bioinformatic and experimental analyses were performed for biological validation. Results: By integrating clinical information with DNA methylation microarray data, we screened a panel of eight CpGs from discovery cohorts of non-G-CIMP GBMs. Each CpG could accurately and independently predict the prognosis of patients under a treatment regime that combined radiotherapy (RT) and temozolomide (TMZ). The 8-CpG signature appeared to show opposite prognostic correlations between patients treated with RT/TMZ and those treated with RT monotherapy. The analyses further indicated that this signature had predictive value for TMZ efficacy because different survival benefits between RT/TMZ and RT therapies were observed in each risk subgroup. The incorporation of other risk factors, such as age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, with our pseudogene methylation signature could provide precise risk classification. In vitro experimental data revealed that two locus-specific pseudogenes (ZNF767P and CLEC4GP1) may modulate TMZ resistance via distinct mechanisms in GBM cells. Conclusion: The biologically and clinically relevant RISK-score signature, based on pseudogene methylation loci, may offer information for predicting TMZ responses of non-G-CIMP GBMs, that is independent from, but complementary to, MGMT-based approaches.

Differentiated embryonic chondrocyte-expressed gene 1 promotes temozolomide resistance by modulating the SP1-MGMT axis in glioblastoma.

Glioblastoma multiforme (GBM) is a malignant brain tumor with a high mortality rate and poor prognosis. Temozolomide (TMZ) is a first-line drug against GBM, but resistance limits its use. We previously reported that differentiated embryonic chondrocyte (DEC1) expression is associated with TMZ resistance and poor prognosis in GBM; however, the underlying mechanism remains unclear. By using glioma cell lines with stably overexpressed or silenced DEC1, we examined the effects of DEC1 on TMZ sensitivity using proliferation assays, Western blotting, and flow cytometry. We demonstrated that DEC1 overexpression suppressed, whereas DEC1 knockdown enhanced, TMZ-induced cell apoptosis in methylguanine methyltransferase (MGMT)-positive T98G and LN18 cells but not in MGMT-negative U251 cells. Mechanistically, DEC1 positively regulated MGMT through specificity protein 1 (SP1). MGMT silencing in DEC1-overexpressing cells or overexpression in DEC1-silenced cells abrogated DEC1's effects on TMZ sensitivity, and siRNA-mediated SP1 knockdown phenocopied TMZ sensitivity, which was rescued by MGMT overexpression. Thus, DEC1 may control TMZ resistance via the SP1-MGMT axis. Immunohistochemical staining of the human glioma tissue microarray revealed that the expression levels of DEC1 and MGMT were correlated. Therefore, DEC1 expression has a predictive value for TMZ resistance and poor outcome in glioma patients, and is a novel therapeutic target in TMZ-resistant glioma.

Integrative analysis identifies an immune-relevant epigenetic signature for prognostication of non-G-CIMP glioblastomas.

The clinical and molecular implications of DNA methylation alterations remain unclear among the majority of glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP); integrative multi-level molecular profiling may provide useful information. Independent cohorts of non-G-CIMP GBMs or IDH wild type (wt) lower-grade gliomas (LGGs) from local and public databases with DNA methylation and gene expression microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Bioinformatic and in vitro functional analyses were employed for biological validation. Using a strict multistep selection approach, we identified eight CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs, independent of age, the O-6-methylguanine-DNA methyltransferase (MGMT) methylation status, treatments and other identified CpGs. An epigenetic RISK signature of the 8 CpGs was developed and validated to robustly and independently prognosticate prognosis in different cohorts of not only non-G-GIMP GBMs, but also IDHwt LGGs. It also showed good discriminating value in stratified cohorts by current clinical and molecular factors. Bioinformatic analysis revealed consistent correlation of the epigenetic signature to distinct immune-relevant transcriptional profiles of GBM bulks. Functional experiments showed that S100A2 appeared to be epigenetically regulated by one identified CpG and was associated with GBM cell proliferation, apoptosis, invasion, migration and immunosuppression. The prognostic 8-CpGs RISK score signature may be of promising value for refining current glioma risk classification, and its potential links to distinct immune phenotypes make it a promising biomarker candidate for predicting response to anti-glioma immunotherapy.

Effectiveness of neuromuscular electrical stimulation combined with rehabilitation training for treatment of post-stroke limb spasticity.

BACKGROUND: This study aims to systematically explore the effectiveness of neuromuscular electrical stimulation (NMES) combined with rehabilitation training (RT) for the treatment of post-stroke limb spasticity (PSLS). METHODS: We will search Cochrane Library, MEDILINE, EMBASE, CINAHL, AMED, PsycINFO, WOS, Scopus, OpenGrey, and 4 Chinese databases from inception to the present without language restrictions. We will only consider randomized controlled trial on assessing the effectiveness and safety of NMES combined with RT for the treatment of PSLS. All included randomized controlled trials will be assessed using Cochrane risk of bias tool. Two researchers will independently perform study selection, risk of bias assessment, and data extraction, respectively. Any disagreements will be solved by a third researcher through discussion. RESULTS: Primary outcome is limb spasticity status. Secondary outcomes comprise of limb function, quality of life, and adverse events. CONCLUSION: This study will summarize the latest evidence of NMES combined with RT for the treatment of patients with PSLS. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019138900.

Novel predictive epigenetic signature for temozolomide in non-G-CIMP glioblastomas.

OBJECTIVE: To identify novel epigenetic signatures that could provide predictive information that is complementary to promoter methylation status of the O-6-methylguanine-DNA methyltransferase (MGMT) gene for predicting temozolomide (TMZ) response, among glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP) METHODS: Different cohorts of primary non-G-CIMP GBMs with genome-wide DNA methylation microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Different statistical analyses and functional experiments were performed for clinical and biological validation. RESULTS: By employing discovery cohorts with radiotherapy (RT) and TMZ versus RT alone and a strict multistep selection strategy, we identified seven CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs with RT/TMZ, independent of age, MGMT promoter methylation status, and other identified CpGs. A RISK score signature of the 7 CpGs was developed and validated to distinguish non-G-CIMP GBMs with differential survival outcomes to RT/TMZ, but not to RT alone. The interaction analyses also showed differential outcomes to RT/TMZ versus RT alone within the RISK score-based subgroups. The signature could also improve the risk classification by age and MGMT promoter methylation status. Functional experiments showed that HSBP2 appeared to be epigenetically regulated by one identified CpG and was associated with TMZ resistance, but it was not associated with cell proliferation or apoptosis in GBM cell lines. The predictive value of the single CpG methylation of HSBP2 by pyrosequencing was observed in a local cohort of isocitrate dehydrogenase 1 (IDH1) R132H wild-type GBMs. CONCLUSIONS: This novel epigenetic signature might be a promising predictive (but not a general prognostic) biomarker and be helpful for refining the MGMT-based guiding approach to TMZ usage in non-G-CIMP GBMs.

A five-CpG signature of microRNA methylation in non-G-CIMP glioblastoma.

AIMS: DNA methylation has been found to regulate microRNAs (miRNAs) expression, but the prognostic value of miRNA-related DNA methylation aberration remained largely elusive in cancers including glioblastomas (GBMs). This study aimed to investigate the clinical and biological feature of miRNA methylation in GBMs of non-glioma-CpG island methylator phenotype (non-G-CIMP). METHODS: Prognostic miRNA methylation loci were analyzed, with TCGA and Rennes cohort as training sets, and independent datasets of GBMs and low-grade gliomas (LGGs) were obtained as validation sets. Different statistical and bioinformatic analysis and experimental validations were performed to clinically and biologically characterize the signature. RESULTS: We identified and validated a risk score based on methylation status of five miRNA-associated CpGs which could predict survival of GBM patients in a series of training and validation sets. This signature was independent of age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The risk subgroup was associated with angiogenesis and accordingly differential responses to bevacizumab-contained therapy. MiRNA target analysis and in vitro experiments further confirmed the accuracy of this signature. CONCLUSION: The five-CpG signature of miRNA methylation was biologically relevant and was of potential prognostic and predictive value for GBMs. It might be of help for improving individualized treatment.

Ventilator for the treatment of acute respiratory distress syndrome: A protocol for systematic review and meta-analysis.

BACKGROUND: Ventilator has been reported to treat acute respiratory distress syndrome (ARDS). However, its efficacy is still inconclusive. This systematic review and meta-analysis study aims to evaluate its efficacy and safety for the treatment of patients with ARDS. METHODS: The electronic databases of Cochrane central register of controlled trials (CENTRAL), EMBASE, MEDILINE, CINAHL, allied and complementary medicine database (AMED) and 4 Chinese databases will be used to search relevant literature from their inception to the present to evaluate the efficacy and safety of ventilator for ARDS without the language restrictions. This study will only consider randomized controlled trials (RCTs) of ventilator for the treatment of ARDS. The Cochrane risk of bias tool will be utilized to assess the quality of the included RCTs studies. The primary outcomes include arterial blood gases values (recorded once a day) and ventilator settings. The secondary outcomes will include the Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score, quality of life, cost, death, and any other adverse events. The summary results will be performed by using the models of random-effects or fixed-effects based on the heterogeneity of the included RCTs. RESULTS: The results will be disseminated to peer-reviewed journals for publication. This study does not need ethics approval, because of no individual data will be involved. The results of this study will help clinicians and health policy-makers to refer for the policy or guideline making. CONCLUSION: The results of this systematic review and meta-analysis study may provide helpful evidence for the efficacy and safety of ventilator for ARDS. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018 115409.

The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis.

Background Quantitative evaluation of the effect of glioblastoma (GBM) heterogeneity on survival stratification would be critical for the diagnosis, treatment decision, and follow-up management. Purpose To evaluate the effect of GBM heterogeneity on survival stratification, using texture analysis on multimodal magnetic resonance (MR) imaging. Material and Methods A total of 119 GBM patients (65 in long-term and 54 in short-term survival group, separated by overall survival of 12 months) were selected from the Cancer Genome Atlas, who underwent the T1-weighted (T1W) contrast-enhanced (CE), T1W, T2-weighted (T2W), and FLAIR sequences. For each sequence, the co-occurrence matrix, run-length matrix, and histogram features were extracted to reflect GBM heterogeneity on different scale. The recursive feature elimination based support vector machine was adopted to find an optimal subset. Then the stratification performance of four MR sequences was evaluated, both alone and in combination. Results When each sequence used alone, the T1W-CE sequence performed best, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.7915, 80.67%, 78.45%, and 83.33%, respectively. When the four sequences combined, the stratification performance was basically equal to that of T1W-CE sequence. In the optimal subset of features extracted from multimodality, those from the T2W sequence weighted the most. Conclusion All the four sequences could reflect heterogeneous distribution of GBM and thereby affect the survival stratification, especially T1W-CE and T2W sequences. However, the stratification performance using only the T1W-CE sequence can be preserved with omission of other three sequences, when investigating the effect of GBM heterogeneity on survival stratification.

A novel prognostic six-CpG signature in glioblastomas.

AIMS: We aimed to identify a clinically useful biomarker using DNA methylation-based information to optimize individual treatment of patients with glioblastoma (GBM). METHODS: A six-CpG panel was identified by incorporating genome-wide DNA methylation data and clinical information of three distinct discovery sets and was combined using a risk-score model. Different validation sets of GBMs and lower-grade gliomas and different statistical methods were implemented for prognostic evaluation. An integrative analysis of multidimensional TCGA data was performed to molecularly characterize different risk tumors. RESULTS: The six-CpG risk-score signature robustly predicted overall survival (OS) in all discovery and validation cohorts and in a treatment-independent manner. It also predicted progression-free survival (PFS) in available patients. The multimarker epigenetic signature was demonstrated as an independent prognosticator and had better performance than known molecular indicators such as glioma-CpG island methylator phenotype (G-CIMP) and proneural subtype. The defined risk subgroups were molecularly distinct; high-risk tumors were biologically more aggressive with concordant activation of proangiogenic signaling at multimolecular levels. Accordingly, we observed better OS benefits of bevacizumab-contained therapy to high-risk patients in independent sets, supporting its implication in guiding usage of antiangiogenic therapy. Finally, the six-CpG signature refined the risk classification based on G-CIMP and MGMT methylation status. CONCLUSIONS: The novel six-CpG signature is a robust and independent prognostic indicator for GBMs and is of promising value to improve personalized management.

Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas.

Molecular and clinical heterogeneity critically hinders better treatment outcome for glioblastomas (GBMs); integrative analysis of genomic and epigenomic data may provide useful information for improving personalized medicine. By applying training-validation approach, we identified a novel hypomethylation signature comprising of three CpGs at non-CpG island (CGI) open sea regions for GBMs. The hypomethylation signature consistently predicted poor prognosis of GBMs in a series of discovery and validation datasets. It was demonstrated as an independent prognostic indicator, and showed interrelationships with known molecular marks such as MGMT promoter methylation status, and glioma CpG island methylator phenotype (G-CIMP) or IDH1 mutations. Bioinformatic analysis found that the hypomethylation signature was closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network. The integrative molecular analysis finally revealed that the gene network defined two distinct clinically relevant molecular subtypes reminiscent of different immature neuroglial lineages in GBMs. The novel hypomethylation signature and relevant gene network may provide new insights into prognostic classification, molecular characterization, and treatment development for GBMs.

Transcription factor RBP-J-mediated signalling regulates basophil immunoregulatory function in mouse asthma model.

Basophils (BA) play an important role in the promotion of aberrant T helper type 2 (Th2) immune responses in asthma. It is not only the effective cell, but also modulates the initiation of Th2 immune responses. We earlier demonstrated that Notch signalling regulates the biological function of BAin vitro. However, whether this pathway plays the same role in vivo is not clear. The purpose of the present study was to investigate the effect of Notch signalling on BA function in the regulation of allergic airway inflammation in a murine model of asthma. Bone marrow BA were prepared by bone marrow cell culture in the presence of recombinant interleukin-3 (rIL-3; 300 pg/ml) for 7 days, followed by isolation of the CD49b+ microbeads. The recombination signal binding protein J (RBP-J-/- ) BA were co-cultured with T cells, and the supernatant and the T-cell subtypes were examined. The results indicated disruption of the capacity of BA for antigen presentation alongside an up-regulation of the immunoregulatory function. This was possibly due to the low expression of OX40L in the RBP-J-/- BA. Basophils were adoptively transferred to ovalbumin-sensitized recipient mice, to establish an asthma model. Lung pathology, cytokine profiles of brobchoalveolar fluid, airway hyperactivity and the absolute number of Th1/Th2 cells in lungs were determined. Overall, our results indicate that the RBP-J-mediated Notch signalling is critical for BA-dependent immunoregulation. Deficiency of RBP-J influences the immunoregulatory functions of BA, which include activation of T cells and their differentiation into T helper cell subtypes. The Notch signalling pathway is a potential therapeutic target for BA-based immunotherapy against asthma.

Der p2 recombinant bacille Calmette-Guerin priming of bone marrow-derived dendritic cells suppresses Der p2-induced T helper17 function in a mouse model of asthma.

BACKGROUND AND OBJECTIVE: Previous studies have demonstrated that our recombinant bacille Calmette-Guerin (rBCG), which expresses Der p2 in house dust mite (Der p2 rBCG) suppresses asthmatic airway inflammation by regulating the phenotype and function of dendritic cells (DC) and reprogramming T helper (Th) 0 cell differentiation into different T cell (Th1/Th2/Treg) subtypes. However, the exact role of Der p2 rBCG in reprogramming Th17 differentiation and the relevant mechanisms are not known. The aim of this study was to examine whether Der p2 rBCG-mediated inhibition of allergic airway inflammation is mediated by regulating Th17 differentiation in a murine asthma model. METHODS: Primary mouse bone marrow-derived dendritic cells (BMDC) were infected with Der p2 rBCG and adoptively transferred to Der p2-intranasally sensitized mice. The role of Der p2 rBCG-BMDC on the regulation of airway inflammation and Th17 cell differentiation was assessed. RESULTS: Adoptive transfer of Der p2 rBCG-BMDC suppressed airway inflammation and mucin secretion. Der p2 rBCG-BMDC inhibited excessive Th17 immune responses but not BCG-BMDC. Furthermore, Der p2 rBCG decreased jagged-2 and increased delta-like-4 expressions on BMDC to a greater extent than BCG. CONCLUSIONS: These findings suggest that DC plays a key role in Der p2 rBCG-induced immunoregulation. Der p2 rBCG also displayed a potent inhibitory effect on Th17 differentiation, and these findings increase our understanding of the cellular basis of Der p2 BCG-mediated inhibition of asthma.

Antisense oligodeoxynucleotides targeting connexin43 reduce cerebral astrocytosis and edema in a rat model of traumatic brain injury.

OBJECTIVE: Brain injury induces an acute increase in the expression of gap junction protein connexin43 (Cx43). It also leads to cerebral edema, probably due to the swelling and proliferation of astrocytes reactive to the injury. Antisense oligodeoxynucleotides (AS-ODN) targeting Cx43 were tested for their ability to reduce reactive astrocytosis and cerebral edema in a rat model of traumatic brain injury (TBI). METHODS: The brains of experimental animals were intraventricularly injected with these AS-ODNs, while sham rats and normal controls were administered saline in the same way. Controlled cortical impact (CCI) injury was induced in both experimental and sham rats, then the damaged brain tissue was stained for glial fibrillary acidic protein (GFAP) immunofluorescein, measured for water content using the wet-dry weight method, and examined for Cx43 protein expression by western blotting. RESULTS: The brains of both experimental and sham groups were found to have a higher level of Cx43 expression, higher water content, and more swollen and proliferative astrocytes than the normal controls at 6 hours, 24 hours, and 48 hours post-trauma. But compared with the sham animals, brains of experimental rats showed less Cx43 expression, lower water content, and less swollen and proliferative astrocytes. These two brain-injured groups displayed a similar pattern of changes in these outcomes over the 48-hour time period studied. DISCUSSION: Antisense oligodeoxynucleotides targeting Cx43 reduced reactive astrocytosis and cerebral edema following TBI, indicating that Cx43 might be involved in regulating the water imbalance between brain cells.

Expression of candidate plasticity-related gene 15 is increased following traumatic brain injury.

OBJECTIVES: Candidate plasticity-related gene 15 (cpg15) is an activity-regulated gene that mediates synaptic plasticity; this study assesses the potential link between cpg15 expression levels and repair and regeneration following traumatic brain injury (TBI). METHODS: We investigated the expression of cpg15 in the frontal lobe of rats subjected to TBI and sham rats, using the following methods: immunohistochemical analysis, Western blotting, and reverse transcription-polymerase chain reaction. RESULTS: CPG15(+) neurons were present in coronal sections of the frontal lobe at Day 1, reached the highest level around Day 14, and maintained elevated levels through Day 21. CPG15 protein and mRNA levels were noticed to increase in a similar temporal pattern. In contrast, rats in the sham group had undetectable levels of CPG15. CONCLUSIONS: Elevated cpg15 expression in the frontal cortex suggests its possible involvement in regenerative and reparative processes that follow TBI.