The concept of "spirit" in traditional Chinese medicine （TCM） aligns closely with "the liver governs the designing of strategy". By exploring the relationship between the liver and executive function decline， it is proposed that prolonged liver constraint leads to indecisiveness in strategy designing， which is the initiating factor for executive function decline； liver blood deficiency causes difficulties in executing strategy， which forms an essential foundation for the progression of executive function decline； obstruction in the "liver-du mai-brain" pathway leads to unclear strategy designing， which accelerates executive function decline. This relationship is examined from the perspectives of TCM， modern medicine， and cognitive psychology， aiming to provide insights into addressing executive function decline through treatments focused on the liver.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective To construct a key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury, and provide a basis for the implementation of such treatment and nursing. Methods The draft of the key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury was determined by literature review, case study, and field investigation. The indicators of the system were determined through two rounds of Delphi consultation and using the precedence chart method. According to the criteria of indicator evaluation, the reliability of expert opinions, and the opinions of the research group, the indicators were refined and evaluated. Results Twenty experts were included for two rounds of consultation via mailed inquiries, with a 100% effective response rate in both rounds. The expert authority coefficients were both 0.945, and the Kendall’s W values were 0.347 and 0.448, respectively (P < 0.05). Following the expert consultations, 1 indicator was deleted, 12 indicators were added, and 6 indicators were modified. The key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury established in this study included 4 first-level indicators, 17 second-level indicators, and 73 third-level indicators. The means of importance assignment for all indicators were > 4.00, and the coefficients of variation were < 0.25. Conclusion The key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury established in this study is scientifically rigorous and practically grounded. The indicators demonstrate strong professional relevance and provide important guidance for in-hospital treatment and nursing of patients with nuclear radiation injury.

Objective To develop a risk assessment scale for immune checkpoint inhibitor (ICI)-associated myocarditis based on multidisciplinary collaboration, and to evaluate its diagnostic performance. Methods Based on multidisciplinary cooperation, integrating clinical experience from oncology and cardiology, literature data, and patient conditions, a risk assessment scale for ICI-associated myocarditis was developed. A total of 101 patients with malignancies who received immunotherapy at Zhongshan Hospital, Fudan University, from October 2020 to October 2024 were included as the validation cohort. Patients were stratified into low-risk (0-1 point), medium-risk (2-4 points), and high-risk (≥5 points) groups based on their scale scores. The association between pretictive risk stratifications and actual assessment results was assessed using the Cox proportional hazards regression model. The predictive value of the scale for ICI-associated myocarditis was evaluated using receiver operating characteristic (ROC) curve. Agreement between the scale scores and actual assessment results was assessed using Cohen’s Kappa coefficient. Results Based on the scale pretictive results, 28(27.7%), 8(7.9%), 65(64.4%) patients were at low risk, medium risk, and high risk for ICI-related myocarditis, respectively; however, 46(45.5%), 8(7.9%), 47(46.5%) were at low risk, medium risk, and high risk actually. Kaplan-Meier survival analysis showed that the cumulative incidence of ICI-related myocarditis in the high-risk group was significantly higher than that in the medium- and low-risk groups (P＜0.05). In the multivariable-adjusted Cox proportional hazards model, the ICI-related myocarditis risk in high-risk group was about 4 times that in the low-risk group. ROC curve analysis demonstrated that the average area under the curve (AUC) for predicting ICI-related myocarditis was 0.81, with an accuracy of 0.74. The Cohen’s Kappa coefficient was 0.55, indicating moderate agreement. In the actual high-risk group, no patient was predicted to be at low risk; in the actual low-risk group, 16 patients were predicted to be at high risk. Conclusions This risk assessment scale for ICI-associated myocarditis shows high predictive performance. It provides oncologists with a simple yet effective multidisciplinary diagnostic reference tool, potentially enhancing early identification of ICI-associated myocarditis.

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

Hemodynamic monitoring can reflect cardiac function and blood perfusion and is an indispensable monitoring method in clinical practice. Invasive hemodynamic monitoring methods represented by the thermodilution method are limited in their clinical application scope because they require vascular cannulation. Non-invasive hemodynamic monitoring has attracted extensive attention from medical companies and clinicians at home and abroad in recent years due to its advantages such as safety, non-invasiveness, continuous monitoring, simple operation, and low cost. This paper designs a non-invasive hemodynamic monitoring system based on the impedance cardiography, including hardware, algorithm, software design, and performance parameter evaluation. Among them, the hardware part mainly includes a differential high-frequency constant current source stimulation circuit, impedance cardiogram signal acquisition, and ECG signal acquisition circuit. Signal processing includes wave filtering, impedance cardiogram signal calibration, and ECG signal and impedance cardiogram signal feature point recognition. According to the collected impedance cardiogram and ECG signals, hemodynamic parameters such as heart rate (HR), stroke volume (SV), cardiac output (CO), stroke index (SI), cardiac index (CI), and cardiac contractility index (ICON) are calculated based on the Nyboer thoracic cylinder model. After testing, the key technical indicators of the system hardware are better than that of the relevant medical device standards. The system was used to collect impedance cardiogram and ECG signal data from 40 volunteers. The calculated HR, SV, and CO, three important hemodynamic indicators, were compared with the ICONCore non-invasive cardiac output monitor of OSYPKA Medical in Germany. Their Pearson correlation coefficients were 0.992 ( P<0.001), 0.948 ( P<0.001), and 0.933 ( P<0.001), respectively, verifying that the designed system has high accuracy and reliability.
Cardiography, Impedance/methods* ; Humans ; Hemodynamic Monitoring/methods* ; Equipment Design ; Signal Processing, Computer-Assisted ; Hemodynamics ; Algorithms ; Monitoring, Physiologic/methods* ; Electrocardiography

End-tidal carbon dioxide monitoring is an important means of evaluating human lung function and is widely used in fields such as clinical emergency treatment and cardiopulmonary resuscitation. This paper develops a microstream end-tidal carbon dioxide monitoring system. It adopts an integrated gas circuit design to further reduce the size of the equipment. The system uses the method of calculating the root mean square (RMS) of differential pressure signals to regulate the gas circuit flow, enabling the system to stably operate at a flow state of 30 mL/min. In addition, by simultaneously detecting multiple environmental parameters such as temperature and pressure, the system realizes system state monitoring and gas parameter compensation. The test results show that various indicators of the system meet the requirements of relevant standards, laying a good foundation for subsequent engineering applications.
Carbon Dioxide/analysis* ; Equipment Design ; Monitoring, Physiologic/methods* ; Humans

In order to improve the effect of transcranial electrical stimulation treatment and realize personalized treatment for patients with varying severity levels, this paper designed an integrated four-channel EEG recording multimodal transcranial electrical stimulation system. This system can conduct real-time monitoring on EEG and related characteristic analysis before stimulation, in stimulation, and after stimulation. This enables physicians and researchers to resolve real-time brain states, evaluate transcranial electrical stimulation effect, and then artificially adjust the stimulation parameters. After relevant testing and verification, the system can select four stimulation modes: TACS, TDCS, TPCS and TRNS, which can output the constant stimulation current of 0.03 mA accuracy in the range of ±2 mA and the stimulation frequency of low frequency of 0~4 kHz (precision of 0.01 Hz) and high frequency 50~100 kHz, which can obtain more accurate EEG signals under stimulation interference, demonstrating a good market application prospect.
Electroencephalography/methods* ; Transcranial Direct Current Stimulation/instrumentation* ; Humans ; Equipment Design

This paper introduces a 16-channel multimodal high-precision transcranial electrical stimulation system specifically for non-invasive brain stimulation. This system added TMCS mixed four traditional stimulation modes with TACS, TDCS, TPCS and TRNS. By designing a compensated high-precision constant current source, the constant stimulation current with an accuracy of 0.03 mA in the range of ±2 mA and the stimulation frequency of 50~200 kHz with low frequency of 0~4 kHz (high frequency of 0.1 Hz) are realized. In TACS stimulation mode, there are five adjustable wave forms: triangular wave, sine wave, sawtooth wave, square wave and mixed wave. The system has dual closed-loop control overcurrent detection and simultaneous real-time electrode contact impedance detection. After relevant tests and verification, the system has good stimulation accuracy, high safety and reliability. Compared with the existing products at home and abroad, it features lower cost, richer stimulation mode and waveforms, demonstrating a certain market application value.
Transcranial Direct Current Stimulation/instrumentation* ; Equipment Design ; Humans

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers