ObjectiveTo investigate the association between metabolic associated fatty liver disease （MAFLD） and the risk of atherosclerotic cardiovascular disease （ASCVD）， and to provide data support for the prevention and treatment of such metabolic-associated diseases in clinical practice. MethodsAn observation cohort was established for the workers of Kailuan who underwent physical examination for the first time from June 2006 to October 2007 and had complete liver assessment data， without the history of malignant tumor， MAFLD or ASCVD. According to the presence or absence of MAFLD， the patients were divided into non-MAFLD group with 67 565 patients and MAFLD group with 29 004 patients， and according to the presence or absence of ASCVD， the patients were divided into non-ASCVD group with 69 141 patients and ASCVD group with 481 patients. The group t-test or the Wilcoxon rank-sum test was used for comparison of continuous data between the two groups. The χ² test was used for comparison of categorical data between the two groups. The life-table method was used to calculate the cumulative incidence rates of new-onset ASCVD and MAFLD； the Kaplan-Meier method was used to plot the survival curves of the cumulative incidence rates of ASCVD in the MAFLD group and the non-MAFLD group and the cumulative incidence rates of MAFLD in the ASCVD group and the non-ASCVD group， and the log-rank test was used for comparison of cumulative incidence rates between two groups. A multivariate Cox proportional-hazards regression model analysis was used to investigate the impact of MAFLD on the risk of ASCVD and the impact of ASCVD on the risk of MAFLD. ResultsCompared with the non-MAFLD group， the MAFLD group had significantly higher levels of body mass index （BMI）， waist circumference， systolic blood pressure （SBP）， diastolic blood pressure （DBP）， resting heart rate， alanine aminotransferase， uric acid （UA）， fasting blood glucose （FBG）， triglyceride （TG）， total cholesterol， low-density lipoprotein cholesterol， and high sensitivity C-reactive protein （hs-CRP）， as well as significanty lower levels of estimated glomerular filtration rate （eGFR） and high-density lipoprotein cholesterol （all P <0.05）. Compared with the non-ASCVD group， the ASCVD group had significantly higher levels of BMI， waist circumference， SBP， DBP， UA， FBG， TG， and hs-CRP and a significantly lower level of eGFR （all P<0.05）. The incidence rate of new-onset ASCVD continued to increase over time in the MAFLD group and the non-MAFLD group， while the incidence rate of new-onset MAFLD firstly increased and then remained stable over time in the ASCVD group and the non-ASCVD group. There were 4 263 cases （14.70%） of new-osnet ASCVD in the MAFLD group， with an incidence density of 12.90 per 1 000 person-years， while there were 6 529 cases （9.66%） of new-osnet ASCVD in the non-MAFLD group， with an incidence density of 8.24 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of ASCVD between the two groups （χ²=519.09 and 531.80， both P<0.05）. There were 148 cases （30.77%） of new-onset MAFLD in the ASCVD group， with an incidence density of 40.10 per 1 000 person-years， while there were 32 194 cases （46.56%） of new-onset MAFLD in the non-ASCVD group， with an incidence density of 57.59 per 1 000 person-years， and there were significant differences in the incidence density and cumulative incidence rate of MAFLD between the two groups （χ²=19.29 and 30.78， both P<0.05）. The corrected multivariate Cox proportional-hazards regression model analysis showed that MAFLD was a risk factor for new-onset ASCVD （hazard ratio ［HR］=1.11， 95% confidence interval ［CI］： 1.06 — 1.16， P<0.001）， while ASCVD was a protective factor against new-onset MAFLD （HR=0.72， 95%CI： 0.61 — 0.85， P<0.001）. ConclusionThere is a significant association between MAFLD and ASCVD， with an increase in the risk of ASCVD in the MAFLD population and a reduction in the risk of MAFLD in the ASCVD population.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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 explore the role of lactate in Superoxide dismutase 2 (SOD2) lactylation in cerebral ischemia-reperfusion injury in mice.Methods:Male C57BL/6 mice were randomLy (random number) divided into 4 groups: sham operation group (Sham group), Middle Cerebral Artery Occlusion/Reperfusion group (MCAO/R group), Middle Cerebral Artery Occlusion/Reperfusion+2-Deoxy-D-glucose group (MCAO/R+2-DG group), Middle Cerebral Artery Occlusion/Reperfusion+sodium lactate group (MCAO/R+Nala group). Cerebral ischemia reperfusion injury model was established in the mice of MCAO/R group using the thread occlusion. In the MCAO/R+2-DG group, mice received an intraperitoneal injection of 250 mg/kg of 2-DG 90 min before ischemia. Mice in the MCAO/R+ Nala group was given an intraventricular injection of 2 μL of 100 mmol/L Nala 24 h before ischemia. Commercial kits was used to detect lactate levels, Hematoxylin & Eosin Staining (HE) was employed to observe cell morphology, Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) was performed to assess cell apoptosis, and immunofluorescence was utilized to detect reactive oxygen species (ROS). Western blot was conducted to measure SOD2, Superoxide Dismutase 2 Lysine 114 Lactylation(SOD2-K114la), Iron regulatory protein 2(IRP2) and transferrin receptor protein 1(TFR1) levels. The above indicators were analyzed and compared by one-way variance.Results:Compared with the Sham group, the MCAO/R group showed increased levels of lactate, SOD2-K114la, TUNEL positive rate, ROS, IRP2 and TFR1[lactate: (0.608±0.064) vs. (0.376±0.030), P<0.005; SOD2-K114la: (2.311±0.146) vs. (1.009±0.073), P<0.0005; TUNEL positive rate: (35.420±2.832) vs. (0.294±0.147), P<0.0001; ROS: (3.415±0.229) vs. (1.166±0.155), P<0.0001; IRP2: (1.735±0.125) vs. (1.000±0.000), P<0.0001; TFR1: (1.611±0.058) vs. (1.000±0.000), P<0.0001], while SOD2 decreased[(0.545±0.062) vs. (1.082±0.088), P<0.0001]. HE staining indicated brain damage. Compared with the MCAO/R group, the MCAO/R+2-DG group showed reduced levels of lactate, SOD2-K114la, TUNEL positive rate, ROS, IRP2, and TFR1[lactate: (0.453±0.047) vs. (0.608±0.064), P<0.05; SOD2-K114la: (1.764±0.188) vs. (2.311±0.146), P<0.05; TUNEL positive rate: (23.800±3.168) vs. (35.420±2.832), P<0.005; ROS: (2.640±0.213) vs. (3.415±0.229), P<0.005; IRP2: (1.463±0.055) vs. (1.735±0.125), P<0.05; TFR1: (1.252±0.081) vs. (1.611±0.058), P<0.005], with higher level of SOD2 [(0.727±0.026) vs. (0.545±0.062), P<0.05]. Meanwhile, HE staining indicated reduced damage. Compared with the MCAO/R group, the MCAO/R+Nala group showed increased levels of lactate, SOD2-K114la, TUNEL positive rate, ROS, IRP2 and TFR1[lactate: (1.021±0.051) vs. (0.608±0.064), P<0.0001; SOD2-K114la: (3.479±0.275) vs. (2.311±0.146), P<0.0005; TUNEL positive rate: (53.430±3.551) vs. (35.420±2.832), P<0.0001; ROS: (4.687±0.253) vs. (3.415±0.229), P<0.0001; IRP2: (2.463±0.117) vs. (1.735±0.125), P<0.0001; TFR1: (2.209±0.094) vs. (1.611±0.058), P<0.0001], with decreased levels of SOD2 [(0.286±0.040) vs. (0.545±0.062), P<0.0001]. And HE staining revealed worsened braindamage. Conclusions:Increased lactate levels can enhance the lactylation of SOD2, exacerbating brain damage after Cerebral ischemia reperfusion injury(CIRI). Inhibiting lactate production may alleviate brain injury by regulating iron Metabolism.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

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