Objective At present, the grading evaluation of patients with disorders of consciousness (DOC) is still a focus and difficulty in related fields. Electroencephalogram (EEG) can directly read and continuously reflect scalp electrical activity generated by brain tissue structure, with high temporal resolution. Auditory stimulation is easy to operate and has broad application prospects in clinical detection of DOC. The causal network can intuitively reflect the direction of information transmission through the causal relationship between time series, helping us better understand the information interaction between different regions of the brain of patients. This paper combines EEG and causal networks to explore the differences in brain functional connectivity between patients with unresponsive arousal syndrome (VS) and those with minimum state of consciousness (MCS) under auditory stimulation. MethodsA total of 23 DOC patients were included, including 11 MCS patients and 12 VS patients. Based on the Oddball paradigm, auditory naming stimulation was performed on DOC patients and EEG signals of DOC patients were synchronously collected. The brain functional networks were constructed using multivariate Granger causality method, and the differences in node degree, clustering coefficient, global efficiency, and causal flow of the brain networks between MCS patients and VS patients were calculated. The differences in network characteristics of patients with different levels of consciousness under auditory stimulation were compared from the perspective of cooperation between brain regions. ResultsThe causal connectivity between most brain regions in MCS patients was stronger than that in VS patients, and MCS patients had more brain network connectivity edges than VS patients. The average degree (P<0.05), average clustering coefficient, and global efficiency (P<0.05) of MCS patients under naming stimulation were higher than those of VS patients. The difference in out-degree between each node of VS patients was larger, and the difference in in-degree between each node of MCS patients was smaller. The difference in in-degree of MCS patients was more significant than that of VS patients, and the inflow and outflow of information in the brain functional network of MCS patients were stronger than those of VS patients. MCS and VS patients had differences of causal flow in the frontal and temporal lobes, the direction of information transmission in the parietal lobe and central region was not the same, and MCS patients had more electrodes as causal sources than VS patients. ConclusionThe information transmission ability of MCS patients is stronger than that of VS patients under auditory naming stimulation. Compared with VS patients, MCS patients have an increase in the number of electrode channels as the causal source, an increase in information output to other brain regions, and also an increase in the information output within brain regions, which may indicate a better state of consciousness in patients. MCS patients have more electrode channels for information output in the frontal lobe than VS patients, and the number of electrode channels for changing the direction of information transmission in the frontal lobe is the highest. The frontal lobe is closely related to the level of consciousness in patients with consciousness disorders. This study can provide a theoretical basis for the grading evaluation of consciousness levels in DOC patients.

The accumulation and spread of prion-like proteins is a key feature of neurodegenerative diseases (NDs) such as Alzheimer's disease, Parkinson's disease, or Amyotrophic Lateral Sclerosis. In a process known as 'seeding', prion-like proteins such as amyloid beta, microtubule-associated protein tau, α-synuclein, silence superoxide dismutase 1, or transactive response DNA-binding protein 43 kDa, propagate their misfolded conformations by transforming their respective soluble monomers into fibrils. Cellular and molecular evidence of prion-like propagation in NDs, the clinical relevance of their 'seeding' capacities, and their levels of contribution towards disease progression have been intensively studied over recent years. This review unpacks the cyclic prion-like propagation in cells including factors of aggregate internalization, endo-lysosomal leaking, aggregate degradation, and secretion. Debates on the importance of the role of prion-like protein aggregates in NDs, whether causal or consequent, are also discussed. Applications lead to a greater understanding of ND pathogenesis and increased potential for therapeutic strategies.
Humans ; Prions ; Neurodegenerative Diseases/pathology* ; Amyloid beta-Peptides ; Alzheimer Disease ; alpha-Synuclein ; tau Proteins ; Parkinson Disease

Objective Corruption is the most common cadaver phenomenon in forensic practice and an important basis for inferring time of death(PMI),but the definition of corruption degree and the construction of model inference models have always been difficult in the practice of forensic science.Methods In this study,the late postmortem phenomena were observed.Meanwhile,the microbial flora structure of gut and gravesoil and the nature of gravesoil were detected,for analyzing the changes before and after the key moment of abdominal rupture which naturally happened during the cadaver decay.Results The results found that from the macroscopic and microscopic levels,there were significant differences in cadaver decay,including microbial flora structure and gravesoil properties before and after the key moment of the natural abdominal rupture during cadaver decay.The phenomena are highly observable and can be accurately judged by forensic examinations,as well as related means in the field of biology and physiochemistry.In this study,this critical event was called Rupture Point.Conclusion The Rupture Point can be used as an important node for the assessment of cadaver decay degree in the practice of forensic medicine.It can be utilized for a cut-off point as well when constructing PMI inference models based on microbial flora structure changes.The accuracy of PMI inference models can be improved when the models were constructed in segments.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To observe the clinical efficacy of"hegu needling"combined with"joint needling"in the treatment of chronic lumbar muscle strain.Methods A total of 64 patients with chronic lumbar muscle strain were randomly divided into observation group and control group,32 cases in each group.The control group was treated with routine acupuncture,and the observation group was treated with"hegu needling"combined with"joint needling"on the basis of the control group.One week for a course of treatment,a total of two courses of treatment.After two weeks of treatment,the clinical efficacy of the two groups was evaluated,and the changes of Visual Analogue Scale(VAS)of pain score and simplified Oswestry Dysfunction Index questionnaire(simplified ODI)score were observed before and after treatment.The changes of spinal mobility were compared before and after treatment between the two groups.Results(1)The total effective rate was 93.75%(30/32)in the observation group and 78.13%(25/32)in the control group.The curative effect of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(2)After treatment,the simplified ODI score and spinal activity score of the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the simplified ODI score and spinal activity score,the differences were statistically significant(P＜0.05).(3)After two weeks of treatment,the VAS scores of the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the VAS score,the difference was statistically significant(P＜0.05).After one month of treatment,there was no significant difference in VAS score of the observation group when compared with that after two weeks of treatment(P＞0.05).Conclusion"Hegu needling"combined with"joint needling"in the treatment of chronic lumbar muscle strain can significantly improve the patients'pain symptoms,enhance the patient's waist function,and improve the patients'spinal mobility.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.