Latest ArticlesThe automation level of medical anesthesia machines is generally low. Therefore, this study independently developed an economical and practical fully automatic intelligent medical anesthesia machine. Based on the automatic regulation of fresh gas delivery requirements, a multi-channel switching gas path structure was designed. Through the electronic control system, intelligent fresh gas delivery and multiple mechanical ventilation modes can be achieved. The system also has an alarm function and a complete safety mechanism, and uses a touch screen for control parameter setting and patient status monitoring. The test results based on the GASMAN platform show that the system can achieve safe and effective anesthesia induction, with a maximum relative deviation of 8.86%. This design can effectively reduce the workload of anesthesiologists and the consumption of anesthetic drugs, and lay a solid foundation for more intelligent functions of anesthesia machines in the future, with significant application value.
Functional gastrointestinal disorders (FGIDs) are a group of chronic and recurrent digestive system conditions characterized by the absence of detectable organic lesions using current diagnostic technologies. Effective long-term treatments for alleviating FGIDs symptoms remain limited. Recent studies indicate that the pathogenesis of FGIDs is linked to visceral hypersensitivity, autonomic dysfunction in brain-gut interactions, and abnormal central nervous system processing. Consequently, neuromodulation techniques have been increasingly explored in clinical practice for FGIDs diagnosis and treatment, demonstrating initial success. This article reviews advances in the clinical application of neuromodulation technology for FGIDs management, aiming to propose a novel and effective therapeutic approach.
The ‘loop intervention’ for the hand function rehabilitation of stroke patients is based on the ‘central-peripheral-central’ closed loop. By organically integrating central intervention and peripheral intervention, it constructs a ‘closed-loop’ information feedback system, which precisely acts on the specific or functionally related brain regions of patients. This model is based on brain plasticity and the remodeling of neural pathways, giving full play to the complementary functions of ‘peripheral intervention’ and ‘central intervention’. It directly addresses the key issue of the change in cortical function after brain injury, effectively promoting central remodeling and peripheral control, and improving the hand function of patients. Functional electrical stimulation (FES) has various peripheral intervention modes, such as linked FES, task-oriented training FES, real-time FES, etc. The commonly combined central intervention methods include repetitive transcranial magnetic stimulation, transcranial direct current stimulation, brain-computer interface, etc. The combination of the two forms the ‘loop intervention’ model. The ‘loop intervention’ is committed to providing a more effective treatment plan for the hand function rehabilitation of clinical stroke patients. It has great application value in improving patients' ability to perform daily activities and their quality of life. It is expected to effectively improve the rehabilitation status of stroke patients and has far-reaching social value.
The aim of this study is to explore the impact of intermittent theta burst stimulation (iTBS) on upper limb motor function in subacute cerebral infarction patients.
This study is a randomized controlled trial conducted from January 2023 to July 2024 at the Department of Rehabilitation Medicine, Guangzhou First People's Hospital. A total of 50 individuals were recruited for the study. Participants were randomly assigned to either the iTBS group (n=25) or sham stimulation group (n=25), each receiving 10 sessions of intervention, along with conventional rehabilitation therapy. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), Wolf Motor Function Test (WMFT), Modified Barthel Index (MBI), and Motor Evoked Potential (MEP) were measured, and the results of these assessments were compared.
Before and after treatment, there was no significant difference in FMA-UE scores between the two groups (P>0.05). After treatment, the FMA-UE score of the two groups was significantly different from that of the group before treatment (P<0.05). Before and after treatment, there was no significant difference in movement scores between the two groups (P>0.05). After treatment, the ARAT score of iTBS group was higher than that before treatment (P<0.05), but there was no significant difference between false stimulation group and before treatment (P>0.05). Before and after treatment, there was no significant difference in WMFT scores between the two groups (P>0.05). After treatment, WMFT score in iTBS group was higher than that before treatment, the difference was statistically significant (P<0.001), while there was no statistically significant difference between false stimulation group and before treatment (P>0.05). Before treatment, there was no significant difference in MBI between the two groups (P>0.05). After treatment, the MBI in iTBS group was higher than that before treatment and in spurious stimulation group at the same time, and the difference was statistically significant (P<0.05). Before and after treatment, there was no significant difference in latency and amplitude between the two groups (P>0.05). After treatment, the latency and amplitude of MEP in iTBS group were lower than before treatment (P<0.05).
iTBS stimulation improves upper extremity motor function and enhances functional independence of patients with subacute stroke. This provides strong evidence for the potential inclusion of iTBS as a key adjunctive therapy in stroke rehabilitation protocols.
The legal metrological technical institutions need to carry out the inspection work of glass thermometer according to the metrological verification regulation ‘JJG111-2019 Glass thermometer’, and the workload is huge, so, it is urgent to study the automatic verification of glass thermometer.
According to the related researches of artificial intelligence in image recognition in recent years, the automatic recognition system of vitreous thermometer was constructed based on system design, image acquisition and algorithm, advantages and disadvantages of existing image recognition technology.
The fast and accurate automatic reading of the glass thermometer is realized, and the model recognition accuracy was as high as 0.9640.
The automatic verification with image recognition solved many problems in the traditional manual verification of thermometers, and greatly improved the verification efficiency and accuracy.
Ensuring the stable operation of medical equipment and systems is crucial for safeguarding the health and safety of patients. This article summarizes and analyzes the common faults and their handling methods that have a significant impact on the daily operation of the Radixact. Four levels of fault classification are proposed, and the quality control acceptance strategy after maintenance is discussed. These treatment plans have been proven to be effective and practical in clinical practice, able to quickly solve common faults encountered in work and perform quality control verification after fault recovery, thereby ensuring the safe and efficient treatment of patients.
This article introduces and discusses on analysis of the differences for Registration Submission Dossiers for Medical Devices (excluding innovation products) in China、European Union and Canada, Analysis and comparison of differences in Registration Submission Dossiers. Through comparison and analysis of differences, although the focus of medical device registration processes varies in different countries, but according to its medical device management system, submitting Registration Submission Dossiers strictly per relevant requirements which ensure the safety and effectiveness of the marketed products.
This study aims to explore the clinical features and CT imaging manifestations of spread through air spaces (STAS) in stage IA subsolid lung adenocarcinoma and to construct a predictive model based on machine learning algorithms.
We retrospectively analyzed patients with stage IA subsolid lung adenocarcinoma admitted to Xiangtan Central Hospital from January 2021 to June 2023. Patients were classified into STAS(+) and STAS(-) groups based on postoperative pathological results. We employed five machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (Xgboost), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF)—to develop a predictive model for STAS in stage IA subsolid lung adenocarcinoma.
Among the 106 patients with stage IA subsolid lung adenocarcinoma, 16 (15%) were diagnosed with STAS. After comparing the five machine learning algorithms, Xgboost demonstrated the highest diagnostic accuracy, achieving an area under the curve (AUC) of 0.969 (95% CI: 0.915-0.993). The Xgboost algorithm identified several key predictors of STAS in stage IA subsolid lung adenocarcinoma: higher proportion of solid component, CT value of the solid component, lesion size, degree of blurring in the surrounding ground-glass opacity (GGO) border, and volume of the solid component.
Through the analysis of clinical and imaging data, the Xgboost algorithm can effectively predict STAS in stage IA subsolid lung adenocarcinoma, providing valuable insights for clinical decision-making.
This study aims to use machine learning technology to construct an efficient fatty liver prediction model to help medical personnel accurately identify and classify high-risk populations for fatty liver, thereby providing support for early intervention and personalized treatment.
The research data was sourced from the National Health and Nutrition Examination Survey (NHANES) database in the United States from 2017 to 2020. Finally, 11 variables were included for analysis. Using 6 machine learning algorithms, Decision Tree (DT) Extreme Gradient Boosting (XGBoost), Adaptive Boosting (Adaboost), K-Nearest Neighbor (KNN), Logistic Regression, and Artificial Neural Network (ANN) models are used to construct a fatty liver prediction model. The randomly generated 70% is used as the training set, and the remaining 30% is used as the testing set. The model evaluation metrics include accuracy, precision, recall, F1 score, Receiver Operating Characteristic curve (ROC), and Area Under Curve (AUC).
A total of 3383 participants were included, including 1506 patients with fatty liver. The decision tree The AUC of XGBoost, AdaBoost, KNN, logistic regression, and ANN models are 0.98838, 0.94624, 0.88935, 0.99110, 0.90047, and 0.98990, respectively. Except for the KNN model, the calibration curves of all other models performed well, with the AdaBoost model showing the best predictive performance among the six models.
The AdaBoost model is considered the optimal predictive model for predicting fatty liver and can explain the interaction between feature variables. Body mass index, triglycerides, and low-density lipoprotein indicators can effectively identify high-risk populations for fatty liver.
This study aims to develop a polydopamine-based nanoplatform loaded with Mn2+ (Mn-PDA, MD) and explore its mechanism and efficacy in hepatocellular carcinoma treatment through the combination of photothermal therapy and chemodynamic therapy.
MD was synthesized by self-oxidative polymerization and characterized by transmission electron microscopy, dynamic light scattering, and X-ray photoelectron spectroscopy for its structure and properties. Its photothermal performance, Mn2+ release characteristics in response to stimuli, and Fenton-like reaction catalytic activity were further evaluated. In vitro experiments were conducted to assess the survival rate of tumor cells using CCK-8 and live/dead staining, and to detect the effects of photothermal effect and Mn2+ release on cytotoxicity.
The synthesized MD had a particle size of approximately 200 nm. After 7 minutes of near-infrared (NIR) laser irradiation (1 W/cm2), the temperature rapidly rose to 47.2℃. NIR irradiation triggered the release of Mn2+ from MD, which further catalyzed the generation of hydroxyl radicals. In vitro experiments showed that at a concentration of 100 μg/ml and under NIR irradiation (1 W/cm2, 10 min), MD reduced the viability of tumor cells to 40%. Live/dead staining further confirmed that a large number of tumor cells died. Compared with polydopamine, MD exhibited more significant killing ability, and the difference was statistically significant.
The developed MD achieved efficient killing of tumor cells through the combination of photothermal effect and Mn2+-mediated reactive oxygen species generation, demonstrating the advantages of synergistic therapy and providing a new strategy and direction for liver cancer treatment.
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