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

OBJECTIVE To analyze and identify the metabolites of pirtobrutinib （PTN） in rats， and clarify the possible metabolic pathways of PTN in rats. METHODS Six rats were intragastrically administered with 10 mg/kg PTN suspension. Blood samples were collected from the rats 30 minutes before administration and at 0.25， 0.5， 1， 2， 4， 6， 8， 12， 24 hours after administration. Urine and feces samples were collected 12 hours before administration and 24 hours after administration. UHPLC- Orbitrap Exploris 240 system combined with Compound Discoverer 3.0 and Xcalibur 2.0 software were adopted for structural identification and metabolic pathway analysis of PTN metabolites in rat plasma， urine， and feces. RESULTS A total of 29 PTN metabolites were identified， including 17， 19 and 22 metabolites in plasma， urine and feces， respectively. The metabolic pathways of PTN mainly included oxidation， sulfation， glucuronidation， etc.， and its metabolites were mostly combination products of two or more different metabolic forms. In detail， a total of 26 metabolites were associated with phase Ⅰ metabolic reactions （14 oxidation metabolites， 9 reduction/dehydrogenation metabolites， 8 demethylation metabolites， and 5 hydrolysis metabolites）. Meanwhile， a total of 20 products were involved in phase Ⅱ metabolites （14 sulfation metabolites and 8 glucuronic acid binding metabolites）. CONCLUSIONS PTN exhibits a diverse range of metabolites in rat fecal samples， with the primary metabolic pathways being oxidation， sulfation， glucuronidation， and others.

Objective:To evaluate the diagnostic performance of resting echocardiography in detecting severe coronary artery stenosis.Methods:A total of 136 patients with suspected coronary artery disease（CAD）who presented to Sichuan Provincial People's Hospital between January 2021 and December 2024 were prospectively enrolled. All patients underwent both coronary computed tomography angiography（CCTA）and transthoracic echocardiography within one week. Based on CCTA results，the patients were divided into non-severe stenosis group（ n=78）and severe stenosis group（ n=58）. Echocardiographic parameters including left atrial maximum volume（LAVmax），left ventricular global longitudinal strain（GLS），left ventricular longitudinal strain of endo-myocardium，mid-myocardium，epi-myocardium（LSendo，LSmid，LSepi），early diastolic mitral inflow velocity（E），early diastolic mitral annular velocity of the lateral and septal walls（e'），and E/e' were measured. Predictive factors for severe coronary stenosis were identified using LASSO regression，and a nomogram model was developed via multivariate Logistic regression. Model performance was evaluated using ROC curves，calibration curves，and decision curve analysis. Results:Multivariate Logistic regression analysis revealed LSendo，LAVmax，and E/e' as independent predictors of severe coronary artery stenosis. The nomogram constructed based on these predictors achieved an area under the curve of 0.798（95% CI=0.723-0.873），with sensitivity and specificity of 0.756 and 0.759，respectively. Conclusions:The resting echocardiography-based nomogram model demonstrates good diagnostic efficacy for severe coronary artery stenosis. It may serve as a noninvasive tool to assist in risk stratification and clinical decision-making in patients with suspected CAD.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective:To evaluate the diagnostic performance of resting echocardiography in detecting severe coronary artery stenosis.Methods:A total of 136 patients with suspected coronary artery disease（CAD）who presented to Sichuan Provincial People's Hospital between January 2021 and December 2024 were prospectively enrolled. All patients underwent both coronary computed tomography angiography（CCTA）and transthoracic echocardiography within one week. Based on CCTA results，the patients were divided into non-severe stenosis group（ n=78）and severe stenosis group（ n=58）. Echocardiographic parameters including left atrial maximum volume（LAVmax），left ventricular global longitudinal strain（GLS），left ventricular longitudinal strain of endo-myocardium，mid-myocardium，epi-myocardium（LSendo，LSmid，LSepi），early diastolic mitral inflow velocity（E），early diastolic mitral annular velocity of the lateral and septal walls（e'），and E/e' were measured. Predictive factors for severe coronary stenosis were identified using LASSO regression，and a nomogram model was developed via multivariate Logistic regression. Model performance was evaluated using ROC curves，calibration curves，and decision curve analysis. Results:Multivariate Logistic regression analysis revealed LSendo，LAVmax，and E/e' as independent predictors of severe coronary artery stenosis. The nomogram constructed based on these predictors achieved an area under the curve of 0.798（95% CI=0.723-0.873），with sensitivity and specificity of 0.756 and 0.759，respectively. Conclusions:The resting echocardiography-based nomogram model demonstrates good diagnostic efficacy for severe coronary artery stenosis. It may serve as a noninvasive tool to assist in risk stratification and clinical decision-making in patients with suspected CAD.

Objective:To compare the perioperative outcomes of laparoscopic duodenal-preserving pancreatic head resection(LDPPHR) with laparoscopic pancreaticoduodenectomy(LPD) in the treatment of borderline and benign diseases of the pancreatic head.Methods:This is a retrospective cohort study. Perioperative data from 87 patients with non-malignant pancreatic head diseases who underwent LDPPHR or LPD were retrospectively collected in the Department of Biliary-Pancreatic Surgery,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology from January 2020 to December 2022. There were 49 male and 38 female patients with an age ( M(IQR)) of 57.0(16.5) years (range: 20 to 75 years). Forty patients underwent LDPPHR and 47 patients underwent LPD. Quantitative data following a normal distribution were compared using Student′s t-test, while quantitative data not following a normal distribution were compared using the Mann-Whitney U test. Comparisons of categorical or ordinal variables were made using χ2 test or Fisher′s exact test. Logistic regression analysis was used to estimate the risk factors associated with the rate of complications. Results:There were no statistically significant differences between the LDPPHR group and the LPD group in terms of reoperation rate,total hospital stay duration,postoperative hospital stay duration,90-day mortality rate,30-day and 90-day readmission rates,and 2-year tumor recurrence rate (all P>0.05). The complication rate was higher in the LDPPHR group compared to the LPD group (80.0%(32/40) vs. 51.1%(24/47), χ2=7.89, P=0.005),but there was no difference in the rate of Clavien-Dindo classification of surgical complications ≥Ⅲ between the two groups (10.0%(4/40) vs. 12.8%(6/47), χ2<0.01, P=0.947). Additionally,the rate of delayed gastric emptying (DGE) was higher in the LDPPHR group compared to the LPD group ( χ2=10.79, P=0.001),but there was no statistically significant difference in the rate of B,C grade DGE between the two groups ( χ2=0.48, P=0.487). There were no statistically significant differences in the rates of postoperative pancreatic fistula,bile leakage,post-pancreatectomy hemorrhage,intra-abdominal infection,and pulmonary infection between the two groups (all P>0.05). The results of the univariate logistic regression analysis showed that LDPPHR (compared to LPD, OR=3.83, 95% CI: 1.46 to 10.04, Z=2.73, P=0.006) and preoperative biliary stent placement (compared to non-use of biliary stent, OR=5.30, 95% CI: 1.13 to 25.00, Z=2.11, P=0.035) were risk factors for the complication rate,but neither was an independent risk factor for complication rate (all P>0.05). Conclusion:The preliminary results suggest that LDPPHR can achieve perioperative safety and effectiveness comparable to LPD.