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Introduction: Low back pain (LBP) is a prevalent and complex condition that poses significant medical, social, and economic burdens worldwide. The accurate and timely assessment and diagnosis of LBP, particularly non-specific LBP (NSLBP), are crucial to developing effective interventions and treatments for LBP patients. In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of NSLBP patients. Methods: We recruited 52 subjects with NSLBP from the University of Hong Kong-Shenzhen Hospital and collected B-mode ultrasound images and SWE data from multiple sites. The Visual Analogue Scale (VAS) was used as the ground truth to classify NSLBP patients. We extracted and selected features from the data and employed a support vector machine (SVM) model to classify NSLBP patients. The performance of the SVM model was evaluated using five-fold cross-validation and the accuracy, precision, and sensitivity were calculated. Results: We obtained an optimal feature set of 48 features, among which the SWE elasticity feature had the most significant contribution to the classification task. The SVM model achieved an accuracy, precision, and sensitivity of 0.85, 0.89, and 0.86, respectively, which were higher than the previously reported values of MRI. Discussion: In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of non-specific low back pain (NSLBP) patients. Our results showed that combining B-mode ultrasound image features with SWE features and employing an SVM model can improve the automatic classification of NSLBP patients. Our findings also suggest that the SWE elasticity feature is a crucial factor in classifying NSLBP patients, and the proposed method can identify the important site and position of the muscle in the NSLBP classification task.
RESUMO
Sevoflurane is a commonly used inhalation anesthesia, which has been previously demonstrated to impair long-term emotional memory consolidation and induce learning dysfunction through inducing hippocampal dysfunction. However, the underlying molecular mechanisms remain largely unknown. MicroRNAs (miRNAs) play critical roles in multiple cells apoptosis, including hippocampal neural. The present study, therefore, was aimed to investigate the miR-145 function on the hippocampal neural apoptosis induced by sevoflurane exposure. A hippocampal neural cell line HT22 was used, and treated with 4.1% sevoflurane. Cell viability and apoptosis were determined by MTT assay and flow cytometry, respectively. microRNA microarray was used to select differentially expression miRNAs in cells with sevoflurane exposure and controls. Results showed that sevoflurane could significantly induce the hippocampal neural cell apoptosis via mitochondria apoptotic pathway. Then, miR-145 was selected as a significantly down expression microRNA in sevoflurane treated HT22 cell lines, by microarray analysis and real-time PCR verification. Furthermore, we found that miR-145 overexpression could protect HT22 cells against apoptosis caused by sevoflurane. Bioinformatics analysis and dual-luciferase reporter assays indicated that Bnip3, which has a key role in the mitochondrial dysfunction, is a novel target of miR-145. Finally, we found that over expression of Bnip3 by pcDNA-Bnip3 transfection significantly induced apoptosis in HT22 cells, which was inhibited by miR-145 mimic. Therefore, it concluded that miR-145 could protect against sevoflurane-induced hippocampal apoptosis through the mitochondrial pathway at least by directly inhibiting its target gene-Bnip3 expression in hippocampal neural cell lines.