Objective To study the factors that affect the prognosis of status epilepticus (SE) and to improve the understanding of clinicians. Methods A retrospective analysis of 57 patients with SE witch from the General Hospital of Ningxia Medical University and Cardio-cerebrovascular Disease Hospital were carried out to collect their clinical data. The data were analyzed by SPSS 17.0 software. The prognosis of the patients was assessed by the Status epilepticus severity score (STESS) scale. Results A total of 57 patients were included, 53 cases improved, 4 cases were automatically discharged. Telephone follow-up showed that 4 cases of automatic discharge were dead. The mortality rate of SE was 7.02%. The most common cause of SE was acute cerebrovascular disease (17.54%), followed by intracranial infection (10.53%); The most common incidence were the occasional medication, self-medication, withdrawal (15.79%). Age, state of consciousness and concurrent infection were associated with prognosis (improvement/death) (P<0.05). STESS score of 0 to 2 points were 45 patients, all improved; score of 3 to 5 points were 12 patients, 8 patients improved, 4 patients died. There were significant differences in the prognosis between the two groups (P<0.05). Conclusions Age, state of consciousness, concurrent infection were related to prognosis, more than 65 years, the state of consciousness for the sleeping or coma had the poor prognosis. STESS scale can predict the prognosis of patients effectively.
Objective To evaluate proton MR spectroscopy (1H-MRS) for detection of the motor cortex and adjacent brain in amyotrophic lateralsclerosis (ALS) patients with apparent upper motor neuron involvement after olfactory ensheathing cells(OECs) transplantation. Methods From December 2004 to February 2005, 7 patients with clinically definite ALS who could safely undergo MRS were admitted into the perspective study. The neurological status, ALS functional rating scale (ALSFRS), EMG, and 1H-MRS taken before and 2 weeks after operationswere carefully analyzed. The NAA/Cr and Cho/Cr ratios were measured in the cerebral peduncle,genu and posterior limb of the internal capsule, corona radiata and precentral gyrus. Results The ALSFRS in 2 cases mproved obviously whose ALSFRS increased from 30 to 33 and from 29 to 34 respectively. And 5 cases remained stable 2 weeks after OECs transplantation. Statistical analyses for all seven cases showed both theNAA/Cr and Cho/Cr ratios decreased, but in the two cases with ALSFRS improvement the NAA/Cr increased in the certain anatomic position which confirmed the neurological and EMG findings. Conclusion The proton MR spectroscopy is a suitablenoninvasive measure for ALS evaluation. The preliminary study suggests that twoof the seven ALS cases improved apparently shortterm after OECs transplantation. More patients are required for the clinical study and longer followup duration is needed for future research.
In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions.
Objective To assess the correlation between bispectral index (BIS) and richmond agitation sedation scale (RASS) and sedation-agitation scale (SAS) through the spearman correlation coefficient by systematic review. Methods Databases including PubMed, EMbase, Web of Science, The Cochrane Library (Issue 7, 2016), CNKI, VIP, WanFang Data and CBM were searched from inception to July 2016 to collect literature on the correlation between BIS and RASS and SAS. The studies were screened according to the inclusion and exclusion criteria. After extracting data and assessing the quality of the included studies, meta-analysis was conducted using Comprehensive Meta Analysis 3.0 software. Results A total of 12 studies involving 397 patients were included. BIS was positively correlated with RASS score and SAS, and the summary correlation coefficient was 0.742 with 95% CI 0.678 to 0.795 and 0.605 with 95% CI 0.517 to 0.681, respectively. Conclusion BIS has a good correlation with RASS and SAS, which will provide more options for assessing sedation of patients with mechanical ventilation in ICU.
Objective To introduce a new functional self-assessment scale of amyotrophic lateral sclerosis (ALS). Methods By comparing current different ALS functional scales and combining relative cl inical experience and numeric pain intensity scale, ALS self-assessment scale was set down by International Association of Neural Restoration. Results ALS self-assessment scale included 3 categories with 18 items, adopting 10 points grading system, namely 10 was defined as the normal, 0 as the worst, and the total scores was 180. This scale included: ① Bulbus medullae function: speech, swallowing, sal ivation, and tongue extension. ② Limbs function: left arm movement, left hand movement, right arm movement, right hand movement, left leg movement, right leg movement, trunk movement, head-up, walking, and cl imbing stairs. ③ Others: breathing, muscular tone, pain, and muscle discomfort. Conclusion ALS self-assessment scale is specifically designed for ASL patients. It can evaluate patient’s function comprehensively and is simple and convenient, consuming less time.
Deformable image registration plays a crucial role in medical image analysis. Despite various advanced registration models having been proposed, achieving accurate and efficient deformable registration remains challenging. Leveraging the recent outstanding performance of Mamba in computer vision, we introduced a novel model called MCRDP-Net. MCRDP-Net adapted a dual-stream network architecture that combined Mamba blocks and convolutional blocks to simultaneously extract global and local information from fixed and moving images. In the decoding stage, we employed a pyramid network structure to obtain high-resolution deformation fields, achieving efficient and precise registration. The effectiveness of MCRDP-Net was validated on public brain registration datasets, OASIS and IXI. Experimental results demonstrated significant advantages of MCRDP-Net in medical image registration, with DSC, HD95, and ASD reaching 0.815, 8.123, and 0.521 on the OASIS dataset and 0.773, 7.786, and 0.871 on the IXI dataset. In summary, MCRDP-Net demonstrates superior performance in deformable image registration, proving its potential in medical image analysis. It effectively enhances the accuracy and efficiency of registration, providing strong support for subsequent medical research and applications.
Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.
The automatic classification of epileptic electroencephalogram (EEG) is significant in the diagnosis and therapy of epilepsy. A classification algorithm for epileptic EEG based on wavelet multiscale analysis and extreme learning machine (ELM) is proposed in this paper. Firstly, wavelet multiscale analysis is applied to the original EEG to extract its sub-bands. Then, two nonlinear methods, i.e. Hurst exponent (Hurst) and sample entropy (SamEn) are used to the feature extraction of EEG and its sub-bands. Finally, ELM algorithm is employed in epileptic EEG classification with the nonlinear features. The proposed method in this paper achieved 99.5% classification accuracy for the discrimination between epileptic ictal and interictal EEG. The result implies that this method has good prospects in the diagnosis and therapy of epilepsy.
Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.