ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective:To investigate the effectiveness of predictive nursing based on root cause analysis in patients undergoing cesarean section.Methods:A convenience sampling method was used to select 180 women who underwent cesarean section under combined spinal-epidural anesthesia in the Affiliated Hospital of Jining Medical University from September 2021 to October 2022. According to the random number table method, they were divided into a control group ( n=90) and an observation group ( n=90). The control group received routine nursing care, while the observation group received predictive nursing based on root cause analysis. Compared the pain intensity at 24 hours after cesarean section and the incidence of postoperative complications between the two groups of parturients. Results:The Visual Analog Scale scores at 24 hours post-cesarean section and the overall incidence of postoperative complications were lower in the observation group than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Predictive nursing based on root cause analysis can effectively relieve postoperative pain and reduce the incidence of complications in patients undergoing cesarean section with combined spinal-epidural anesthesia.

OBJECTIVE To study the characteristics,micro-traits and microscopic characteristics of Peucedani radix and seven kinds of its common varieties and summarize the key identification characteristics so as to provide a reference for the effective identifica-tion of Peucedani radix and its common varieties.METHODS The key identification features and high-definition images of Peucedani radix and its common varieties were obtained by using the identification methods of traits,microtraits and microscopy,combined with the techniques of depth-of-field extended imaging and image stitching,and some of the features were digitally extracted and statistical-ly analyzed by SPSS26.0 software.RESULTS The high-definition color image data of Peucedani radix and its common varieties were obtained.Its specific identification features were:root head length and annular sparseness,skin pore shape and area,root texture,and fracture surface oil spot density,etc.under the property identification;the diameter and number of oil chambers,the number of cathe-ters,the presence or absence of bast fibers and wood fibers,etc.under the microscopic identification.The results of statistical analysis showed that there were significant differences in skin pore area,oil chamber diameter and density,and conduit density among different varieties of Peucedani radix(P<0.01).CONCLUSION Micro-traits and microidentification methods can be comprehensively ap-plied to distinguish Peucedani radix and its common varieties.In particular,the microscopic features of polarized light holographic col-or images in cross section have significant distinguishing significance,and some of the features are digitally extracted and statistically analyzed,which makes up for the shortcomings of subjective factors in the traditional empirical identification research,and provides a reference for the circulation,testing,clinical medication,and standard drafting of Peucedani radix.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To investigate the effect and safety of pancreatic extracorporeal shock wave lithotripsy (P-ESWL) in treating the chronic pancreatitis (CP) patients with main pancreatic duct (MPD) stones and to analyze the influencing factors of success of lithotripsy.Methods:Clinical data of 132 patients with CP complicated with MPD stones treated with P-ESWL in the Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2022 to December 2023 were retrospectively analyzed, including 103 males and 29 females, aged (50.3±16.9) years. The times of P-ESWL, the success rate of stone fragmentation, the necessity of combining endoscopic retrograde cholangiopancreatograhy (ERCP), clearance rate of MPD stones, and incidence of post-P-ESWL complications (acute pancreatitis, abdominal hematoma, infection, steinstrasse, perforation, etc.) were evaluated. The factors influencing the success rate of lithotripsy were analyzed using univariate and multivariate logistic regression.Results:All patients underwent P-ESWL treatment, with (2.23±0.82) times of P-ESWL per person. The success rate of stone fragmentation was 87.1%(115/132). Of 107 CP patients (81.1%, 107/132) were treated with their first P-ESWL. Of 12 patients (9.1%, 12/132) underwent single P-ESWL, and 120 (90.9%, 120/132) underwent P-ESWL combined with ERCP. There were 95 cases (72.0%, 95/132) with effective removal of stones, and 62 (47.0%, 62/132) with complete removal of stones. Post-P-ESWL complications included eight cases (6.1%, 8/132) of acute pancreatitis, two (7.6%, 2/132) of steinstrasse complicated with acute pancreatitis and one (0.8%, 1/132) of abdominal hematoma. No infection or perforation occurred. Multivariate logistic regression analysis showed that the higher CT value of stones ( OR=1.239, 95% CI: 1.040-1.477, P=0.017) was associated with the lower success rate of stone fragmentation. Conclusion:P-ESWL is safe and effective in treating patients with CP complicated with MPD stones. The CT value of stones is a risk factor for the success rate of P-ESWL.

Objective:To investigate the effectiveness of predictive nursing based on root cause analysis in patients undergoing cesarean section.Methods:A convenience sampling method was used to select 180 women who underwent cesarean section under combined spinal-epidural anesthesia in the Affiliated Hospital of Jining Medical University from September 2021 to October 2022. According to the random number table method, they were divided into a control group ( n=90) and an observation group ( n=90). The control group received routine nursing care, while the observation group received predictive nursing based on root cause analysis. Compared the pain intensity at 24 hours after cesarean section and the incidence of postoperative complications between the two groups of parturients. Results:The Visual Analog Scale scores at 24 hours post-cesarean section and the overall incidence of postoperative complications were lower in the observation group than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Predictive nursing based on root cause analysis can effectively relieve postoperative pain and reduce the incidence of complications in patients undergoing cesarean section with combined spinal-epidural anesthesia.