RESUMEN
To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers' health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers' health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.
Asunto(s)
Comercio , Dispositivos Electrónicos Vestibles , Humanos , Recolección de Datos , Estado de SaludRESUMEN
Cancer-induced bone pain (CIBP) occurs frequently among advanced cancer patients. Voltage-gated sodium channels (VGSCs) have been associated with chronic pain, but how VGSCs function in CIBP is poorly understood. Here, we aimed to investigate the specific role of VGSCs in the dorsal root ganglia (DRGs) in CIBP. A CIBP rat model was generated by the intratibial inoculation of MRMT-1 breast carcinoma cells. Transcriptome sequencing was conducted to assess the gene expression profiles. The expression levels of key genes and differentiated genes related to activated pathways were measured by Western blotting and qPCR. We implanted a catheter intrathecally for the administration of lentivirus and drugs. Then, the changes in the mechanical withdrawal threshold (MWT) were measured. We identified 149 differentially expressed mRNAs (DEmRNAs) in the DRGs of CIBP model rats. The expression of Nav1.6, which was among these DEmRNAs, was significantly upregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEmRNAs showed that they were mainly enriched in the mitogen-activated protein kinase (MAPK) pathway. The decrease in MWT induced by bone cancer was attenuated by Nav1.6 knockdown. Western blot analysis revealed that a p38 inhibitor decreased the expression of Nav1.6 and attenuated pain behavior. Our study shows that the upregulation of Nav1.6 expression by p38 MAPK in the DRGs of rats contributes to CIBP.
Asunto(s)
Dolor en Cáncer , Canal de Sodio Activado por Voltaje NAV1.6 , Proteínas Quinasas p38 Activadas por Mitógenos , Animales , Ratas , Neoplasias Óseas/complicaciones , Neoplasias Óseas/metabolismo , Ganglios Espinales/metabolismo , Proteínas Quinasas p38 Activadas por Mitógenos/metabolismo , Dolor/genética , Dolor/metabolismo , Ratas Sprague-Dawley , Regulación hacia Arriba , Canales de Sodio Activados por Voltaje/metabolismo , Canal de Sodio Activado por Voltaje NAV1.6/genética , Canal de Sodio Activado por Voltaje NAV1.6/metabolismo , Dolor en Cáncer/genética , Dolor en Cáncer/metabolismoRESUMEN
Bone cancer pain (BCP)-depression comorbidity has become a complex clinical problem during cancer treatment; however, its underlying molecular mechanisms have not been clarified. Several long noncoding RNAs (lncRNAs) have been demonstrated to be promising therapeutic targets in depression, but research on the role of lncRNAs in BCP-depression comorbidity has been limited. Therefore, high-throughput RNA sequencing was performed to detect differentially expressed profiles in the amygdala of a BCP-depression rat model in this study. We detected 330 differentially expressed mRNAs (DEmRNAs) and 78 differentially expressed lncRNAs (DElncRNAs) in the BCP-depression comorbidity model and then verified the expression of six DEmRNAs and six DElncRNAs with the greatest degrees of difference by RT-qPCR. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that differentially expressed genes were strongly enriched in inflammatory and immunologic systemic responses. Then the nuclear factor kappa B (NF-κB) signaling pathway and the Th17 differentiation pathway showed significant differences, as determined by Western blot analysis. Finally, we constructed a protein-protein interaction (PPI) network to explore the potential regulatory mechanism of DEmRNAs. In conclusion, our study reveals a new resource for the understanding of dysregulated lncRNAs and mRNAs in BCP-depression comorbidity and provides novel potential therapeutic targets for further approaches.