ObjectiveWith the epidermal growth factor receptor（EGFR）/Signal Transducers and Activators of Transcription（STAT3）/Hexokinase 2（HK2） signaling pathway in atherosclerosis （AS） and ovarian cancer （OC） as the entry point， this paper discusses the molecular mechanism of Gypenoside L （Gyp-L） treating AS and OC with different diseases， provides a new perspective and theoretical basis for TCM treating AS and OC with EGFR-STAT3-HK2 pathway， and enriches the scientific connotation of the theory of "cytoskeleton in the heart". MethodsCCK-8 was used to detect the proliferation of HUVEC and OVCAR-3 cells， in order to determine the intervention concentration for subsequent experiments. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. Western blot was used to detect the expression levels of relevant proteins. Furthermore， two cell models overexpressing EGFR were constructed and co treated with Gyp-L. HUVEC cells were divided into control， ox-LDL， OE-NC， OE-EGFR， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. OVCAR-3 cells were divided into control， OE-NC， OE-EGFR ， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Western blot was used to detect the expression levels of EGFR-STAT3-HK2 pathway related proteins. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. ResultsGyp-L can significantly reduce the NO content of HUVEC and the pyruvate and LDH content of two cell lines （P<0.05）； Inhibit the proliferation and migration ability of OVCAR-3 cells； Reduce the expression levels of EGFR/STAT3/HK2 pathway related proteins in HUVEC and OVCAR-3 cell lines （P<0.05）， and inhibit the glycolysis pathway. ConclusionGyp-L can inhibit glycolysis in HUVEC and OVCAR-3 cells through the EGFR/STAT3/HK2 pathway，thereby suppressing the occurrence and development of AS and OC.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

ObjectiveWith the epidermal growth factor receptor（EGFR）/Signal Transducers and Activators of Transcription（STAT3）/Hexokinase 2（HK2） signaling pathway in atherosclerosis （AS） and ovarian cancer （OC） as the entry point， this paper discusses the molecular mechanism of Gypenoside L （Gyp-L） treating AS and OC with different diseases， provides a new perspective and theoretical basis for TCM treating AS and OC with EGFR-STAT3-HK2 pathway， and enriches the scientific connotation of the theory of "cytoskeleton in the heart". MethodsCCK-8 was used to detect the proliferation of HUVEC and OVCAR-3 cells， in order to determine the intervention concentration for subsequent experiments. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. Western blot was used to detect the expression levels of relevant proteins. Furthermore， two cell models overexpressing EGFR were constructed and co treated with Gyp-L. HUVEC cells were divided into control， ox-LDL， OE-NC， OE-EGFR， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. OVCAR-3 cells were divided into control， OE-NC， OE-EGFR ， OE-NC+Gyp-L， and OE-EGFR+Gyp-L group. The colorimetric method was used to detect the NO content in HUVEC and the contents of pyruvate and LDH in two cell lines. Western blot was used to detect the expression levels of EGFR-STAT3-HK2 pathway related proteins. Cell cloning experiments and scratch experiments reflect the proliferation and migration ability of OVCAR-3 cells. ResultsGyp-L can significantly reduce the NO content of HUVEC and the pyruvate and LDH content of two cell lines （P<0.05）； Inhibit the proliferation and migration ability of OVCAR-3 cells； Reduce the expression levels of EGFR/STAT3/HK2 pathway related proteins in HUVEC and OVCAR-3 cell lines （P<0.05）， and inhibit the glycolysis pathway. ConclusionGyp-L can inhibit glycolysis in HUVEC and OVCAR-3 cells through the EGFR/STAT3/HK2 pathway，thereby suppressing the occurrence and development of AS and OC.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

To the present study aimed to investigate the protective effects of Erlong Zuoci Pills on oxidative stress induced by hydrogen peroxide (H2O2) in House Ear Institute-Organ of Corti 1 (HEI-OC1) and to explore the mechanism by cellular metabolomics. There were 6 groups in the experiment: the control group, model group, three dose groups of ELZC (low, medium, and high), and positive control ascorbic acid group. The oxidative stress injury model was established in the HEI-OC1 by inducing 0.9 mmol/L H2O2 for 12 h. The proliferation of HEI-OC1 cells was observed by CCK-8 assay; the contents and activity of lactate hydrogenase (LDH), reactive oxygen species (ROS), and superoxide dismutase (SOD) in HEI-OC1 cells were detected by corresponding kits. Finally, the endogenous substances of cells were analyzed from the perspective of metabolomics. Compared with the model group, ELZC groups could significantly increase the cell proliferation rate after administration. Moreover, they could also ameliorate the increase of ROS and LDH content and the decrease of antioxidant enzyme SOD caused by H2O2. Metabolomic results revealed significant differences among multiple groups in the scores of partial least squares discriminant analysis. The ELZC group could relocate the model group back to the control group. The metabolic regulation of ELZC on oxidative stress in HEI-OC1 cells mainly affects nucleotide metabolism and amino acid metabolism. In summary, the results indicate that ELZC exhibits protective effects on H2O2-induced oxidative stress in HEI-OC1 cells. Additionally, this protective effect may be produced by increasing the content of amino acids such as uridine and phenylalanine, thereby regulating pathways such as pyrimidine metabolism, phenylalanine metabolism, biosynthesis of phenylalanine, tyrosine, and tryptophan, and histidine metabolism.

Objective: To analyze the nucleic acid load of human parvovirus B19 in major commercially available blood products in China, including human albumin, human intravenous immunoglobulin, human rabies immunoglobulin and various coagulation factor products, aiming to provide evidence for improving blood product manufacturing processes and quality control of source plasma. Methods: A total of 98 batches of coagulation factor products were tested for human parvovirus B19 nucleic acid using real-time fluorescent quantitative PCR, including 42 batches of human prothrombin complex, 35 batches of human coagulation factor Ⅷ, and 21 batches of human fibrinogen. Additionally, 6 batches of human albumin, 6 batches of human intravenous immunoglobulin, and 38 batches of human rabies immunoglobulin were tested for human parvovirus B19 nucleic acid. Results: Human parvovirus B19 nucleic acid were undetectable in human albumin, human intravenous immunoglobulin and human rabies immunoglobulin. Among the 98 batches of coagulation factor products tested for human parvovirus B19 nucleic acid, B19 nucleic acid reactivity rate was 69.0% (29/42) for human prothrombin complex batches, but nucleic acid concentration were all significantly lower than 10 IU/mL. The reactivity rate of B19 nucleic acid in 35 batches of human coagulation factor Ⅷ was 48.6% (17/35), with nucleic acid concentration all below 10 IU/mL. The reactivity rate of B19 nucleic acid in 21 batches of human fibrinogen was 61.9% (13/21), with nucleic acid concentration all below 10 IU/mL. Conclusion: No human parvovirus B19 has been detected in human albumin, human intravenous immunoglobulin, or human rabies immunoglobulin. Human parvovirus B19 nucleic acid may exist in commercially available coagulation factor products, highlighting the need for enhanced screening of human parvovirus B19 nucleic acid in these products. It is also recommended that B19 viral nucleic acid testing be conducted on source plasma, particularly for coagulation factor products.