During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
Brain-Computer Interfaces ; Humans ; Space Flight ; Astronauts/psychology* ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems

Dynamic navigation technology can "real-time guide" the implantologist to place the implant in the alveolar bone of the missing tooth area according to the preoperative design of the optimal site and path, making the whole implant surgery process more safe and precise. In order to further promote the standardized application of oral implant dynamic navigation technology, China Association of Gerontology and Geriatrics has convened distinguished experts to engage in deliberations and develop the standard. This standard covers the basic requirements, indications and contraindications, operation procedures, common complications and treatment measures, and accuracy verification. This standard can be used as a reference for the use of dynamic navigation technology in implant surgery.
Humans ; Dental Implantation, Endosseous/standards* ; Surgery, Computer-Assisted/standards* ; Dental Implants ; Surgical Navigation Systems/standards*

In order to understand the characteristics of adverse drug reaction/adverse event(ADR/AE) of Ginkgo biloba Dropping Pills and evaluate the safety of clinical use after marketing, 407 ADR/AE case report data of Ginkgo biloba Dropping Pills collected by National Center for ADR Monitoring System during 2009-2018 was systematically analyzed, and its general characteristics were analyzed using descriptive statistical methods. The results showed that among the 407 cases of spontaneous reporting system(SRS) data, 401 cases were general ADR/AE, accounting for 98.5%, and 6 cases were severe ADR/AE, accounting for 1.5%; there were more females than males(171/150) in ADR/AE, and they were mainly middle-aged and elderly people aged 45-64 years(152 cases, accounting for 37.35%); gastrointestinal system(23.89%) was mostly involved in ADR/AE. The top ten clinical symptoms were nausea(15.49%), dizziness(9.88%), vomiting(8.11%), rash(5.60%), chest tightness(5.46%), palpitations(5.31%), pruritus(4.72%), headache(4.57%), abdominal distension(3.83%), gastric dysfunction(3.54%); proportional reporting ratio(PRR) and Bayesian confidence progressive neural network method(BCPNN) were adopted to mine ADR/AE warning signals. Due to the small sample size, there were only 0-2 ADR/AE cases with various symptoms in many quarters, with no warning signal by PRR and BCPNN methods. The findings suggest that ADR/AE of Ginkgo biloba Dropping Pills based on SRS system was not recorded in the package insert, and further active monitoring studies shall be conducted to improve relevant ADR/AE information and pay attention to its clinical drug safety issues.
Adverse Drug Reaction Reporting Systems ; Aged ; Bayes Theorem ; Drug-Related Side Effects and Adverse Reactions ; Female ; Ginkgo biloba ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Pharmacovigilance

In recent years, the safety problems and events of traditional Chinese medicine represented by liver injury have occurred frequently. In particular, Polygonum multiflorum has been widely used and considered as a "non-toxic" tonic traditional Chinese medicine for thousands of years. However, in recent years, frequent reports of liver injury events have attracted widespread attention at home and abroad, which has made unfavorable impacts on traditional Chinese medicine and its international development. Some scho-lars have found that susceptible genes of P. multiflorum on liver injury lay a scientific foundation for formulating rational comprehensive prevention and control measures for liver injury risk of P. multiflorum and its relevant preparations. But what are the risk signals of adverse reactions of P. multiflorum in clinical application? Spontaneous reporting system is an important way to monitor and find adverse drug reaction(ADR) signals after the drug is launched in the market. It can find the ADR signals in time and effectively, and then effectively prevent and avoid the occurrence of adverse drug events. At present, the data mining technique has gradually become the main method of ADR/adverse event(AE) report analysis and evaluation at home and abroad. Specifically, Bayesian confidence propagation neural network in Bayesian method is a commonly used risk signal early warning analysis method. In this paper, BCPNN method was used to excavate the risk signals of adverse reactions of Xinyuan Capsules, a traditional Chinese medicine preparation containing P. multiflorum, such as nausea, diarrhea, rash, dizziness, vomiting, abdominal pain, headache, liver cell damage, so as to provide evidence-based evidence for clinical safe and rational use of drugs.
Adverse Drug Reaction Reporting Systems ; Bayes Theorem ; Capsules ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Neural Networks, Computer

The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
Action Potentials ; Computer Simulation ; Computer Systems ; Entorhinal Cortex ; Grid Cells ; Hippocampus ; Models, Neurological

OBJECTIVE: Reaction time is defined as the time from the start of a stimulus to the start of the voluntary movement. Time plays an important role in undertaking daily living activities. Reaction time is an important factor in respect of both quality of life and of capabilities demonstrated in the work environment. Alcohol and some addictive substances have effect on RT. The aim of this study was to compare the visual and auditory reaction times of patients with opioid use disorder with healthy control subjects. METHODS: The study was applied to two groups as the opioid use disorder group and the control group. A Sociodemographic and Clinical Data Form was prepared for each patient including age, gender, marital status and education level. Using a computer program the response to visual screen color change (red/blue) and to an auditory ‘beep’ sound of the computer system were recorded. The Student’s t-test was applied as a statistical method. RESULTS: The results showed longer reaction times in the patients with opioid use disorder. CONCLUSION: To add improving reaction time approaches in opioid use disorder treatment may contribute to treatment by increasing quality of life and work performance.
Activities of Daily Living ; Computer Systems ; Drug Users ; Education ; Heroin Dependence ; Humans ; Marital Status ; Methods ; Mortuary Practice ; Opioid-Related Disorders ; Quality of Life ; Reaction Time ; Work Performance

OBJECTIVES: In this paper, we present an efficient method to visualize computed tomography (CT) datasets using ambient occlusion, which is a global illumination technique that adds depth cues to the output image. We can change the transfer function (TF) for volume rendering and generate output images in real time. METHODS: In preprocessing, the mean and standard deviation of each local vicinity are calculated. During rendering, the ambient light intensity is calculated. The calculation is accelerated on the assumption that the CT value of the local vicinity of each point follows the normal distribution. We approximate complex TF forms with a smaller number of connected line segments to achieve additional acceleration. Ambient occlusion is combined with the existing local illumination technique to produce images with depth in real time. RESULTS: We tested the proposed method on various CT datasets using hand-drawn TFs. The proposed method enabled real-time rendering that was approximately 40 times faster than the previous method. As a result of comparing the output image quality with that of the conventional method, the average signal-to-noise ratio was approximately 40 dB, and the image quality did not significantly deteriorate. CONCLUSIONS: When rendering CT images with various TFs, the proposed method generated depth-sensing images in real time.
Acceleration ; Computer Systems ; Cues ; Dataset ; Lighting ; Mathematical Computing ; Methods ; Signal-To-Noise Ratio

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.
Computer Systems ; Decision Making ; Decision Support Systems, Clinical ; Diagnosis ; Emergencies ; Emergency Medical Services ; Humans ; Triage

BACKGROUND/AIMS: A risk prediction model for the development of hepatocellular carcinoma (HCC) from indeterminate nodules detected on computed tomography (CT) (Rad(CT) score) in patients with chronic hepatitis B (CHB)-related cirrhosis was proposed. We validated this model for indeterminate nodules on magnetic resonance imaging (MRI).METHODS: Between 2013 and 2016, Liver Imaging Reporting and Data System (LI-RADS) 2/3 nodules on MRI were detected in 99 patients with CHB. The Rad(CT) score was calculated.RESULTS: The median age of the 72 male and 27 female subjects was 58 years. HCC history and liver cirrhosis were found in 47 (47.5%) and 44 (44.4%) patients, respectively. The median Rad(CT) score was 112. The patients with HCC (n=41, 41.4%) showed significantly higher Rad(CT) scores than those without (median, 119 vs. 107; P=0.013); the Chinese university-HCC and risk estimation for HCC in CHB (REACH-B) scores were similar (both P>0.05). Arterial enhancement, T2 hyperintensity, and diffusion restriction on MRI were not significantly different in the univariate analysis (all P>0.05); only the Rad(CT) score significantly predicted HCC (hazard ratio [HR]=1.018; P=0.007). Multivariate analysis showed HCC history was the only independent HCC predictor (HR=2.374; P=0.012). When the subjects were stratified into three risk groups based on the Rad(CT) score (<60, 60–105, and >105), the cumulative HCC incidence was not significantly different among them (all P>0.05, log-rank test).CONCLUSIONS: HCC history, but not Rad(CT) score, predicted CHB-related HCC development from LI-RADS 2/3 nodules. New risk models optimized for MRI-defined indeterminate nodules are required.
Asian Continental Ancestry Group ; Carcinoma, Hepatocellular ; Diffusion ; Female ; Fibrosis ; Hepatitis B ; Hepatitis B, Chronic ; Hepatitis, Chronic ; Humans ; Incidence ; Information Systems ; Liver ; Liver Cirrhosis ; Liver Neoplasms ; Magnetic Resonance Imaging ; Male ; Multivariate Analysis ; Radiographic Image Interpretation, Computer-Assisted ; Risk Assessment

OBJECTIVES: This study aimed to investigate the user experience (UX) of the New Defense Medical Information System (N-DEMIS), which was introduced in 2012 as part of an effort to improve the old system of armed forces hospitals and ultimately bring their standards up to those of civilian hospitals. METHODS: In this study, the dependent variable was the UX of N-DEMIS and was composed of usability, affect, and user value. The questionnaire comprised 41 questions: nine on general characteristics, 20 on usability, four on affect, and eight on user value. The data collection period was from April 15 to April 30, 2018. Overall, 85 responses were received; of these, three insincere responses were excluded, and the remaining 82 responses were used in the analysis. RESULTS: The overall value of Cronbach's alpha was 0.917, indicating an overall high-reliability. There was a significant difference between user value and usability, but there was no significant differences between the other pairs. We observed a significant effect on UX for length of time working in an armed forces hospital and employment type. CONCLUSIONS: The results of our survey showed an even distribution of scores across the three elements of UX, showing that no particular aspect of N-DEMIS is superior to the others in terms of user satisfaction. However, the overall UX score of around 60% indicates the need for future improvements. Rather than focusing improvements on a specific area, improvements should be spread across usability, affect, and user value.
Arm ; Data Collection ; Electronic Health Records ; Employment ; Hospital Information Systems ; Information Systems ; Personal Satisfaction ; User-Computer Interface