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.

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.

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.

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.

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.

Der p 2 recombinant bacille Calmette-Guérin targets dendritic cells to inhibit allergic airway inflammation in a mouse model of asthma.

BACKGROUND: Previous studies showed that a recombinant bacille Calmette-Guérin (rBCG) which expressed the Der p 2 of house dust mites (Der p 2 rBCG) could suppress asthmatic airway inflammation. There are two possible mechanisms: (1) Der p 2 rBCG elicits immune deviation from Th2 to Th1, and (2) Der p 2 rBCG induces antigen-specific regulatory T cells. However, the role of dendritic cell (DC) Der p 2 rBCG in this protective effect and in reprogramming T-cell commitment still needs to be studied. OBJECTIVES: The aim of this study was to determine whether DCs play a central role in the Der p 2 rBCG-mediated inhibition of allergic airway inflammation. METHODS: DCs were collected from Der p 2 rBCG-immunized mice (Der p 2 rBCG-DCs) and adoptively transferred to Der p 2-sensitized mice. The effects of DCs on airway inflammation and immune regulation were analyzed. RESULTS: Adoptive transfer of DCs from Der p 2 rBCG-immunized mice suppressed asthmatic responses, including airway inflammation, mucin secretion and airway responsiveness. Der p 2 rBCG-DCs could effectively inhibit excessive Th2 immune responses and induced a subtype of CD4+CD25+Foxp3+ anti-specific regulatory T cells in this asthma model. Furthermore, Der p 2 rBCG immunization recruited more plasmacytoid DCs in abdominal draining lymph nodes. CONCLUSIONS: These findings suggest that DCs played a key role in Der p 2 rBCG-induced immunoregulation. Compared with BCG, Der p 2 rBCG displayed a more potent inhibitory effect on asthma responses, which may be related to the increase in plasmacytoid DC recruitment. These results improve our understanding of the cellular basis of Der p 2 BCG-mediated inhibition of asthma.

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.

Expression of Helicobacter pylori ggt gene in baculovirus expression system and activity analysis of its products.

The gamma-glutamyltranspeptidase (GGT) of Helicobacter pylori (HpGT) is a newly found virulence factor. In an approach to gain insight into the gene function, the four domains of the HpGT were cloned and expressed in baculovirus expression system. The results of a functional assay showed that the HpGT products acted as GGT, even when the N-terminal 380 amino acids were deleted. However, only the full length open reading frame (ORF) of the HpGT gene was apparently effective on cell growth. This result indicated that the products of the full length ORF might have an important role in gastric carcinogenesis. In this paper, we are the first to report that changes of mitochondrial membrane potential can be detected using 5, 5', 6, 6'-tetrachloro-1, 1', 3, 3'-tetraethylbenzimidazole carbocyanine iodide (JC-1) staining in insect cells.

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.

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.

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.

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.

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.

Glomus tumor of the stomach: a case treated by endoscopic submucosal dissection.

Glomus tumors, as a type of quite rare neoplasms, originate from modified smooth muscle cells of the glomus body whose function is to regulate blood flow within arteries according to the body temperature. Although these tumors most commonly occur in the peripheral soft tissues, especially in the distal parts of extremities, there have been rare reports of visceral involvement (Lorber et al., 2005) [1]. We report a case of gastric glomus tumor, which was preoperatively diagnosed by ultrasonic endoscopy as a gastric stromal tumor and treated by endoscopic submucosal dissection (ESD).