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BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

Purpose – This paper endeavors to create a predictive model for the energy consumption associated with the multi-material fused deposition modeling (FDM) printing process. Design/methodology/approach – An online measurement system for monitoring power and temperature has been integrated into the dual-extruder FDM printer. This system enables a comprehensive study of energy consumption during the dual-material FDM printing process, achieved by breaking down the entire dual-material printing procedure into distinct operational modes. Concurrently, the analysis of the G-code related to the dual-material FDM printing process is carried out. Findings – This work involves an investigation of the execution instructions that delineate the tooling plan for FDM. We measure and simulate the nozzle temperature distributions with varying filament materials. In our work, we capture intricate details of energy consumption accurately, enabling us to predict fluctuations in power demand across different operational phases of multi-material FDM 3D printing processes. Originality/value – This work establishes a model for quantifying the energy consumption of the dual-material FDM printing process. This model carries significant implications for enhancing the design of 3D printers and advancing their sustainability in mobile manufacturing endeavors.

Abstract Objective We aimed to investigate the impact of sarcopenia and sarcopenic obesity (SO) on the clinical outcome in older patients with COVID-19 infection and chronic disease. Methods We prospectively collected data from patients admitted to Huadong Hospital for COVID-19 infection between November 1, 2022, and January 31, 2023. These patients were included from a previously established comprehensive geriatric assessment (CGA) cohort. We collected information on their pre-admission condition regarding sarcopenia, SO, and malnutrition, as well as their medical treatment. The primary endpoint was the incidence of intubation, while secondary endpoints included in-hospital mortality rates. We then utilized Kaplan-Meier (K-M) survival curves and the log-rank tests to compare the clinical outcomes related to intubation or death, assessing the impact of sarcopenia and SO on patient clinical outcomes. Results A total of 113 patients (age 89.6 ± 7.0 years) were included in the study. Among them, 51 patients had sarcopenia and 39 had SO prior to hospitalization. Intubation was required for 6 patients without sarcopenia (9.7%) and for 18 sarcopenia patients (35.3%), with 16 of these being SO patients (41%). Mortality occurred in 2 patients without sarcopenia (3.3%) and in 13 sarcopenia patients (25.5%), of which 11 were SO patients (28%). Upon further analysis, patients with SO exhibited significantly elevated risks for both intubation (Hazard Ratio [HR] 7.43, 95% Confidence Interval [CI] 1.26–43.90, P

Abstract Background Esophageal carcinoma is a type of cancer that occurs in the esophagus. For patients with locally advanced or metastatic esophageal squamous cell carcinoma who have either experienced disease progression following first-line standard chemotherapy or are intolerant to it, the prognosis is typically poor. Additionally, these patients often bear a substantial economic burden during the course of their treatment. Tislelizumab is a selective PD-1 inhibitor with efficacy proven in locally advanced or metastatic esophageal squamous cell carcinoma. The study aims to evaluate the cost-effectiveness of tislelizumab versus camrelizumab as the second-line treatment in locally advanced or metastatic esophageal squamous cell carcinoma (ESCC) patients in China. Methods From the perspective of China’s healthcare system, the partitioned survival model with three health states was established in a 3-week cycle and a lifetime horizon. Anchored matching adjusted indirect comparison was used for survival analyses based on individual patient data from RATIONALE 302 trial and the published ESCORT study due to the lack of head-to-head clinical trials. Only direct medical costs were included. Costs and utility values were derived from local charges, the published literature, and related databases. Sensitivity analyses and a scenario analysis were also performed to verify the robustness of the model results. Results Compared with camrelizumab monotherapy, tislelizumab monotherapy incurred a lower lifetime cost ($8,346 vs. $8,851) and yielded higher quality-adjusted life-years (QALYs) (0.87 vs. 0.63), which resulted in an incremental cost-effectiveness ratio (ICER) of -$2,051/QALY. Tislelizumab monotherapy is a dominant option over camrelizumab monotherapy in China. The three primary parameters upon which this result was most sensitive were the unit cost of camrelizumab, the unit cost of tislelizumab, and the duration of reactive cutaneous capillary endothelial proliferation (RCCEP). According to the probabilistic sensitivity analysis (PSA), tislelizumab monotherapy was 100% cost-effective when the WTP was 1–3 times GDP per capita in China($11,207/QALY∼$33,621/QALY). Scenario analysis showed that the result was consistent. Conclusion Tislelizumab monotherapy is a dominant option compared with camrelizumab monotherapy as the second-line treatment for locally advanced or metastatic ESCC in China.

Abstract Background Clinical trials have demonstrated the efficacy of edaravone dexborneol in the treatment of acute ischemic stroke. This study aims to determine the cost-effectiveness of edaravone dexborneol compared with human urinary kallidinogenase from China’s healthcare system perspective. Methods A combination of the decision tree and Markov model was constructed to evaluate the cost-effectiveness of edaravone dexborneol versus human urinary kallidinogenase in the treatment of acute ischemic stroke over a lifetime horizon. Efficacy data were derived from pivotal clinical trials of edaravone dexborneol and human urinary kallidinogenase (TASTE trial and RESK trial, respectively) and adjusted using matching-adjusted indirect comparison. Cost and health utility inputs were extracted from published literature and open databases. One-way deterministic sensitivity and probabilistic sensitivity analyses were performed to examine the robustness of the results. Results Compared with human urinary kallidinogenase, edaravone dexborneol generated 0.153 incremental quality-adjusted life years (QALYs) with an incremental cost of ¥856, yielding an incremental cost-effectiveness ratio of ¥5,608 per QALY gained under the willingness-to-pay threshold (one-time gross domestic product per capita). Both one-way deterministic sensitivity analysis and probabilistic sensitivity analysis demonstrated the robustness of the base case results. Conclusions Edaravone dexborneol is a cost-effective treatment choice for acute ischemic stroke patients compared with human urinary kallidinogenase in China.