ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

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 establish a method for determining the molecular weight and molecular weight distribution of Poly(Lactide-co-Glycolide Acid) (PLGA) using Size Exclusion Chromatography-Refractive Index-Multiangle Laser Light Scattering (SEC-RI-MALLS) and Size Exclusion Chromatography-Refractive Index (SEC-RID), and to compare the results obtained from these two methods.
Methods: For SEC-RI-MALLS, tetrahydrofuran was used as the mobile phase, Shodex GPC KF-803L was employed as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, and an injection volume of 100 μL. For SEC-RID, tetrahydrofuran was also used as the mobile phase, Agilent PLgel 5 μm MIXD-D was used as the chromatographic column with a flow rate of 1 mL·min^-1, column temperature at 30 ℃, differential detector temperature at 35 ℃, and an injection volume of 20 μL. The molecular weight and molecular weight distribution were calculated using Agilent’s GPC software. The newly established methods were validated methodologically, and the molecular weight and molecular weight distribution of 13 batches of samples were determined.
Results: The precision, accuracy, stability, and repeatability tests for SEC-RI-MALLS showed RSD values of 1.35%, 1.58%, 1.53%, and 1.26%, respectively. The SEC-RID method exhibited good linearity (r=0.999 9), with RSD values for precision, accuracy, stability, and repeatability tests (n=6) of 2.05%, 1.62%, 1.30%, and 2.97%, respectively. The results obtained from SEC-RI-MALLS were lower than those from SEC-RID, and the molecular weight distribution coefficient was smaller, but the results from the paired T-test performed with the value measured by SEC-RID method and the value measured by SEC-RI-MALLS method multiplied a conversion coefficient of 1.5 showed no significant difference between the two methods.
Conclusion: Both methods are stable and reliable, and can be used for the determination of PLGA molecular weight and molecular weight distribution based on the specific situations.

OBJECTIVE To establish a method for simultaneous determination of 7 anti-tumor drugs （irinotecan， capecitabine， paclitaxel， docetaxel， tamoxifen， letrozole and methotrexate） in human plasma and apply it to the clinic. METHODS After precipitating with a methanol-acetonitrile mixture （1∶ 1， V/V） containing 0.1% formic acid， liquid chromatography-tandem mass spectrometry （LC-MS/MS） was used to determine the plasma concentration， using deuterium isotopes of each analyte as internal standards. The chromatography was performed on the Agilent Eclipse Plus C18 column with a gradient elution of water （containing 0.1% formic acid+0.04% 5 mmol/L ammonium formate） as mobile phase A and acetonitrile （containing 0.1% formic acid） as mobile phase B. The flow rate was 0.6 mL/min， and the column temperature was set at 40 ℃ . The sample size was 10 μL， and the analysis lasted for 5.5 min. Electrospray ionization was used in positive and negative ion mode， and multiple reaction monitoring mode was used. The ion pairs used for quantitative analysis were m/z 587.1→167.1 （irinotecan）， m/z 360.1→244.1 （capecitabine）， m/z 876.4→308.0 （paclitaxel）， m/z 830.3→304.2 （docetaxel）， m/z 372.1→129.1 （tamoxifen）， m/z 284.1→242.1 （letrozole）， and m/z 455.0→ 308.0 （methotrexate）. A total of 97 patients with malignant tumors in our hospital were selected to measure the plasma concentrations of 7 anti-tumor drugs using the above method. RESULTS The linear ranges of irinotecan， capecitabine， paclitaxel， docetaxel， tamoxifen， letrozole and methotrexate were 2-1 000 ng/mL （r＝0.994 3）， 20-10 000 ng/mL （r＝0.997 5）， 2-1 000 ng/mL （r＝0.997 9）， 1-500 ng/mL （r＝0.995 8）， 1-500 ng/mL （r＝0.995 2）， 1-500 ng/mL （r＝0.996 4）， 10-5 000 （r＝0.997 7）， respectively. The quantitative lower limits were 2， 20， 2， 1， 1， 1 and 10 ng/mL； RSDs of intra-assay precision were 0.08%-14.86% （n＝6）. RSDs of inter-batch precision were 1.51%-11.55% （n＝3）， and the accuracies were 89.17%-114.93% （n＝6）. The matrix effects ranged from 89.89%-119.74% （n＝6）. RSDs of the stability tests were 1.98%-14.88% （n＝6）. The results of E-mail：hangpengzhou@163.com clinical application showed， the average plasma concentrations of irinotecan， capecitabine， paclitaxel and docetaxel were 704.09， 909.40， 36.45， 150.43 ng/mL， respectively. The values of the coefficient of variation were 25.24%， 62.65%， 122.69%， and 92.27%. CONCLUSIONS The established LC-MS/MS method is simple and rapid， and can be used for the simultaneous determination of 7 commonly used anti-tumor drugs in the plasma of patients with malignancy.

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.

Hypoxic-ischemic brain damage (HIBD) is one of the main causes of disability in middle-aged and elderly people, as well as high mortality rates and long-term physical impairments in newborns. The pathological manifestations of HIBD include neuronal damage and loss of myelin sheaths. Tau protein is an important microtubule-associated protein in brain, exists in neurons and oligodendrocytes, and regulates various cellular activities such as cell differentiation and maturation, axonal transport, and maintenance of cellular cytoskeleton structure. Phosphorylation is a common chemical modification of Tau. In physiological condition, it maintains normal cell cytoskeleton and biological functions by regulating Tau structure and function. In pathological conditions, it leads to abnormal Tau phosphorylation and influences its structure and functions, resulting in Tauopathies. Studies have shown that brain hypoxia-ischemia could cause abnormal alteration in Tau phosphorylation, then participating in the pathological process of HIBD. Meanwhile, brain hypoxia-ischemia can induce oxidative stress and inflammation, and multiple Tau protein kinases are activated and involved in Tau abnormal phosphorylation. Therefore, exploring specific molecular mechanisms by which HIBD activates Tau protein kinases, and elucidating their relationship with abnormal Tau phosphorylation are crucial for future researches on HIBD related treatments. This review aims to focus on the mechanisms of the role of Tau phosphorylation in HIBD, and the potential relationships between Tau protein kinases and Tau phosphorylation, providing a basis for intervention and treatment of HIBD.
Humans ; tau Proteins/physiology* ; Phosphorylation ; Hypoxia-Ischemia, Brain/physiopathology* ; Animals ; Oxidative Stress

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Nicotinamide adenine dinucleotide (NAD⁺) is a central and pleiotropic metabolite involved in multiple cellular energy metabolism, such as cell signaling, DNA repair, protein modifications, and so on. Evidence suggests that NAD⁺ levels decline with age, obesity, and hypertension, which are all significant CVD risk factors. In addition, the therapeutic elevation of NAD⁺ levels reduces chronic low-grade inflammation, reactivates autophagy and mitochondrial biogenesis, and enhances antioxidation and metabolism in vascular cells of humans with vascular disorders. In preclinical animal models, NAD⁺ boosting also extends the health span, prevents metabolic syndrome, and decreases blood pressure. Moreover, NAD⁺ storage by genetic, pharmacological, or natural dietary NAD⁺-increasing strategies has recently been shown to be effective in improving the pathophysiology of cardiac and vascular health in different animal models and humans. Here, we discuss NAD⁺-related mechanisms pivotal for vascular health and summarize recent research on NAD⁺ and its association with vascular health and disease, including hypertension, atherosclerosis, and coronary artery disease. This review also assesses various NAD⁺ precursors for their clinical efficacy and the efficiency of NAD⁺ elevation in the prevention or treatment of major CVDs, potentially guiding new therapeutic strategies.
Humans ; Cardiovascular Diseases/physiopathology* ; NAD/metabolism* ; Animals ; Hypertension/metabolism*

Selenoproteins, as the active form of selenium, play an important role in various physiological and pathological processes, such as anti-oxidation, anti-tumor, immune response, metabolic regulation, reproduction and aging. Although the expression level of selenoproteins in adipose tissue is significantly influenced by dietary selenium intake, it is closely related to the homeostasis of adipose tissue. In this review, we summarized the role of selenoproteins in the physiological function of adipose tissue and the pathogenesis of obesity in recent years, in order to provide a rationale for developing potential therapeutic agents for the treatment of obesity and related metabolic diseases.
Selenoproteins/metabolism* ; Adipose Tissue/physiology* ; Obesity/metabolism* ; Humans ; Animals ; Selenium