To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Objective:To explore the correlation between serum ferritin(SF)levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Methods:Patients with malignant hematological diseases who received repeated transfusions of apheresis platelets in Department of Hematology of Aerospace Center Hospital in 2023 were selected.The platelet corrected count increment(CCI)was used to evaluate the efficacy of platelet transfusion.The correlations between sex,age,disease type,transplantation history,red blood cell transfusion history,and SF level and the efficacy of platelet transfusion were analyzed.Results:A total of 87 patients were included,with a cumulative 326 person-times platelet transfusions.As suggested by one-way analysis of variance,compared with the patients in the age groups of 24-45 years old and 46-66 years old,the patients in the age group of 2-23 years old had a better efficacy of platelet transfusion(P=0.004,P=0.004).There was no significant difference in the efficacy of platelet transfusion between the patients in the age group of 24-45 years old and those in the age group of 46-66 years old(P=0.876).Compared with the patients who had a history of red blood cell transfusion within 3 days,the patients without a history of red blood cell transfusion within 3 days had a better efficacy of platelet transfusion(P＜0.001).Compared with the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L,the group with SF levels＜1.44 ng/L had a better efficacy of platelet transfusion(P=0.028,P＜0.001).Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).After adjusting for age and the history of red blood cell transfusion,the transfusion efficacy of the group with SF levels＜1.44 ng/L was better than that of the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L(P=0.021,P＜0.001);Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).Both univariate and multivariate linear regression models showed that SF levels were negatively correlated with the efficacy of platelet transfusion(P＜0.001).Conclusion:There is a negative correlation between SF levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Detection of SF levels may provide guidance for predicting the efficacy of platelet transfusion.

Objective To explore the covariant network features of iron deposition in Parkinson's disease with mild cognitive impairment(PD-MCI)based on quantitative susceptibility mapping(QSM).Methods Seventy-one patients with Parkinson's disease(PD)were divided into PD-MCI group(n=37)and PD with normal cognition(PD-NC)group(n=34).The differences of edge-connection and topological property parameters of the iron deposition covariant network between the two groups were compared,respectively.Results The iron deposition covariant network edge-connection analysis showed that there were significant difference in r values of the right globus pallidus-right cerebellar dentate nucleus,right globus pallidus-left putamen,and left hippo-campus-left substantia nigra between the two groups(P＜0.05).The global topological property analysis showed that when the sparsity was 0.47 and 0.48,respectively,the Cp in PD-MCI group was significantly higher than that in PD-NC group(P＜0.05).The node topological property analysis showed that there were significant differences in the node network property parameters of the left substantia nigra,left entorhinal cortex,left precentral gyrus,left putamen,left dentate nucleus,and right orbital frontal cortex between the two groups(P＜0.05).Conclusion The changes of covariant network topological properties based on QSM may be the neuropathological mechanism in PD-MCI at the level of large-scale iron metabolism network.

Objective:To explore the correlation between serum ferritin(SF)levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Methods:Patients with malignant hematological diseases who received repeated transfusions of apheresis platelets in Department of Hematology of Aerospace Center Hospital in 2023 were selected.The platelet corrected count increment(CCI)was used to evaluate the efficacy of platelet transfusion.The correlations between sex,age,disease type,transplantation history,red blood cell transfusion history,and SF level and the efficacy of platelet transfusion were analyzed.Results:A total of 87 patients were included,with a cumulative 326 person-times platelet transfusions.As suggested by one-way analysis of variance,compared with the patients in the age groups of 24-45 years old and 46-66 years old,the patients in the age group of 2-23 years old had a better efficacy of platelet transfusion(P=0.004,P=0.004).There was no significant difference in the efficacy of platelet transfusion between the patients in the age group of 24-45 years old and those in the age group of 46-66 years old(P=0.876).Compared with the patients who had a history of red blood cell transfusion within 3 days,the patients without a history of red blood cell transfusion within 3 days had a better efficacy of platelet transfusion(P＜0.001).Compared with the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L,the group with SF levels＜1.44 ng/L had a better efficacy of platelet transfusion(P=0.028,P＜0.001).Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).After adjusting for age and the history of red blood cell transfusion,the transfusion efficacy of the group with SF levels＜1.44 ng/L was better than that of the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L(P=0.021,P＜0.001);Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).Both univariate and multivariate linear regression models showed that SF levels were negatively correlated with the efficacy of platelet transfusion(P＜0.001).Conclusion:There is a negative correlation between SF levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Detection of SF levels may provide guidance for predicting the efficacy of platelet transfusion.

Hypercholesterolemia is pathologically characterized by abnormal accumulation of low-density lipoprotein cholesterol,which is closely associated with metabolic dysfunction-associated fatty liver disease and increased cardiovascular risks.Hepatocytes maintain cholesterol homeostasis through LDL receptor-mediated uptake and esterification storage mechanisms.However,chronic cholesterol overload induces mitochondrial dysfunction,reactive oxygen species accumulation,and endoplasmic reticulum stress,leading to hepatocyte injury.Moreover,systemic hypercholesterolemia disrupts gut microbiota balance and impairs short-chain fatty acid and ketone metabolism,exacerbating metabolic disturbances and aggravating hepatic injury through enhanced metabolic stress.In this article,we review the advance of studies on hypercholesterolemia in recent years and summary its association with hepatic injury,which can provide theoretical support for further research.

Hypercholesterolemia is pathologically characterized by abnormal accumulation of low-density lipoprotein cholesterol,which is closely associated with metabolic dysfunction-associated fatty liver disease and increased cardiovascular risks.Hepatocytes maintain cholesterol homeostasis through LDL receptor-mediated uptake and esterification storage mechanisms.However,chronic cholesterol overload induces mitochondrial dysfunction,reactive oxygen species accumulation,and endoplasmic reticulum stress,leading to hepatocyte injury.Moreover,systemic hypercholesterolemia disrupts gut microbiota balance and impairs short-chain fatty acid and ketone metabolism,exacerbating metabolic disturbances and aggravating hepatic injury through enhanced metabolic stress.In this article,we review the advance of studies on hypercholesterolemia in recent years and summary its association with hepatic injury,which can provide theoretical support for further research.

Objective To explore the pathogenesis of Alzheimer's disease by examining the effects of dihydroartemisinin(DHA)on cognitive behavior,hippocampal,cerebral cortex and retinal cell morphology,β-amyloid(Aβ)and autophagy-related proteins in 5×FAD mice.Methods Twenty 5×FAD mice and 5 wild type(WT)mice were selected,all of which were female.The 5×FAD mice were randomly divided into model(M)group,donepezil(D)group,low-dose DHA(DHA-L)group,and high-dose DHA(DHA-H)group.The WT and M groups were not treated,and the D group was given donepezil 0.1 mg/kg per day.DHA-L group and DHA-H group were given 10 mg/kg and 20 mg/kg DHA per day,respectively.Group D,group DHA-L and group DHA-H were given intragastric administration once a day for 3 months.The changes of in cognitive behavior were measured by Morris experiment.HE staining was used to observe the arrangement and morphology of nerve cells in cerebral cortex,hippocampus and retina.The expressions of Aβ protein in cerebral cortex,hippocampus and retina were detected by immunohistochemistry.Western blotting detected the expression of autophagy related proteins(LC3-Ⅰ,LC3-Ⅱ,Beclin-1,P62,β-actin).Results The DHA-H group and the D group exhibited more frequent adoption of both linear and trending exploration routes.Compared to the model group,significant differences in the contents of Aβ in the hippocampal CA1,cerebral cortex S1,and retinal were observed(P＜0.0001)in the other four groups.The analysis also showed significant differences in autophagy-associated proteins between the DHA-L,DHA-H,and model groups(P＜0.01).Conclusion DHA improves cognitive function and increases the number of nerve cells in mice.It also reduces Aβ content in the cerebral cortex,hippocampus,and retina,along with improving autophagy-associated protein deposition in mice.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

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.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometr (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution (HR) mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition, and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.