METHODS: All patients with traumatic brain injury (mild, moderate, and severe) who were admitted to Queen Elizabeth Hospital from November 1, 2017, to January 31, 2019, were prospectively analyzed through a data collection sheet. The discriminatory power of the models was assessed as area under the receiver operating characteristic curve and calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test and Cox calibration regression analysis.
RESULTS: We analyzed 281 patients with significant TBI treated in a single neurosurgical center in Malaysia over a 2-year period. The overall observed 14-day mortality was 9.6%, a 6-month unfavorable outcome of 23.5%, and a 6-month mortality of 13.2%. Overall, both the CRASH and IMPACT models showed good discrimination with AUCs ranging from 0.88 to 0.94 and both models calibrating satisfactorily H-L GoF P>0.05 and calibration slopes >1.0 although IMPACT seemed to be slightly more superior compared to the CRASH model.
CONCLUSIONS: The CRASH and IMPACT prognostic models displayed satisfactory overall performance in our cohort of TBI patients, but further investigations on factors contributing to TBI outcomes and continuous updating on both models remain crucial.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.
Materials and Methods: Genomic DNA was extracted from 21 fresh-frozen tumor tissues and blood samples of the same meningioma patients. The entire mtDNA D-loop region (positions 16024-576) was polymerase chain reaction amplified using designed primers, and then amplification products were purified before the direct DNA sequencing proceeds.
Results: Overall, 10 (47.6%) patients were detected to harbor a total of 27 somatic mtDNA D-loop mutations. Most of these mtDNA mutations were identified in the hypervariable segment II (40.7%), with 33.3% being located mainly in the conserved sequence block II of the D310 sequence. Furthermore, 58 different germline variations were observed at 21 nucleotide positions.
Conclusion: Our results suggest that mtDNA alterations in the D-loop region may be an important and early event in developing meningioma. Further studies are needed, including validation in a larger patient cohort, to verify the clinicopathological outcomes of mtDNA mutation biomarkers in meningiomas.