Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Objective To study the effects of diterpene ginkgolides meglumine injection ( DGMI) on memory impairment, activation of microglia and astrocytes and inflammatory cytokines in aged mice. Methods Twenty aged mice (22 months old) were randomly divided into two groups:aged mouse group(n=10) and DGMI group(n=10). Another 10 mice (2 months old) were selected as young mouse control group. The mice in DGMI group were received 5 mg/kg DGMI per day by tail vain injection for 4 weeks. The mice in the other two groups were received the same amount normal saline for 4 weeks. The Morris water maze was used to evaluate the function of spacial learning and memory after administration of drugs. The ex-pression of CD11b,GFAP,IL-1β,IL-6,TNF-α and NFκB in mice brain hippocampus were detected by West-ern blot. Results (1) The escape latency time of aged mouse group was significantly longer than that of young mouse control group from the 2nd day to the 7th day(P<0. 01). The times of platform crossing,time and distance in target quadrant of aged mouse group were significantly shorter than those of young mouse group (all P<0. 01). Compared with aged mouse group,DGMI significantly reduced the escape latency time of DGMI group (P<0. 01). DGMI increased the times of platform crossing,time and distance in target quad-rant of aged mouse group (P<0. 01). (2) The expressions of CD11b,GFAP in young mouse control group, aged mouse group and DGMI group were as follows respectively:CD11b:(1. 036±0. 023),(1. 757±0. 046), (1. 214±0. 024);GFAP:(1. 022±0. 071),(1. 344±0. 021),(1. 086±0. 073). DGMI reduced the expres-sion of CD11b and GFAP in hippocampus compared with aged mouse group ( t=5. 556,P<0. 01;t=5. 484, P<0. 01). (3) The expressions of IL-1β,IL-6,TNF-α and NFκB in young mouse control group,aged mouse group and DGMI group were as follows respectively:IL-1β:( 1. 003 ± 0. 057),( 2. 062± 0. 105),( 1. 182± 0. 084);IL-6:(1. 018±0. 024),(1. 583± 0. 052),( 1. 152± 0. 031); TNF-α:( 1. 021± 0. 054),(1. 449± 0. 053),(1. 211±0. 036);p-NFκB:(1. 052±0. 034),(1. 782± 0. 113),( 1. 158± 0. 066). DGMI reduced the expression of p-NFκB(t=6. 547,P<0. 01) and pro-inflammatory cytokines including IL-1β(t=8. 513,P<0. 01),IL-6(t=3. 421,P<0. 01) and TNF-α( t=5. 562,P<0. 01) in hippocampus compared with aged mouse group. Conclusion DGMI can improve the ability of learning and memory in aged mice. The mecha-nism may be related with inhibiting activity of microgliosis,astrocytosis,NFκB and neuroinflammaton.

A 63-year-old female patient received Ⅳ infusions of salvianolate,cattle encephalon glycoside,and pantoprazole and an intramuscular injection of diphenhydramine (only once) in Emergency Department for dizziness,nausea,vomiting,and weakness of lower limbs.Laboratory tests showed no abnormalities in liver function.Craniocerebral CT showed multiple lacunar ischemic demyelination and bilateral internal carotid atherosclerosis.The patient was diagnosed with lacunar cerebral infarction and admitted to hospital.On the night of admission,oral rosuvastatin calcium 10 mg/d and clopidogrel75 mg/d were given.Eleven hours later,laboratory tests showed aspartate aminotransferase (AST) 254 U/L and alanine aminotransferase (ALT) 157 U/L.Salvianolate and pantoprazole were discontinued and reduced glutathione was given.On day 3 of reduced glutathione treatment,laboratory tests showed AST 587 U/L and ALT 660 U/L.Rosuvastatin calcium-induced liver transaminase elevation was considered.Then rosuvastatin calcium was discontinued and compound glycyrrhizin was given.On day 9 of rosuvastatin calcium withdrawal,laboratory tests showed AST 112 U/L and ALT 201 U/L,and then reduced glutathione was discontinued.On day 15 of rosuvastatin calcium withdrawal,laboratory tests showed AST 42 U/L and ALT 63 U/L,and then compound glycyrrhizin was discontinued.The patient was discharged 4 days later.At 2 weeks of follow-up,no abnormalities in liver function were found in the patient.

A 63-year-old female patient received Ⅳ infusions of salvianolate,cattle encephalon glycoside,and pantoprazole and an intramuscular injection of diphenhydramine (only once) in Emergency Department for dizziness,nausea,vomiting,and weakness of lower limbs.Laboratory tests showed no abnormalities in liver function.Craniocerebral CT showed multiple lacunar ischemic demyelination and bilateral internal carotid atherosclerosis.The patient was diagnosed with lacunar cerebral infarction and admitted to hospital.On the night of admission,oral rosuvastatin calcium 10 mg/d and clopidogrel75 mg/d were given.Eleven hours later,laboratory tests showed aspartate aminotransferase (AST) 254 U/L and alanine aminotransferase (ALT) 157 U/L.Salvianolate and pantoprazole were discontinued and reduced glutathione was given.On day 3 of reduced glutathione treatment,laboratory tests showed AST 587 U/L and ALT 660 U/L.Rosuvastatin calcium-induced liver transaminase elevation was considered.Then rosuvastatin calcium was discontinued and compound glycyrrhizin was given.On day 9 of rosuvastatin calcium withdrawal,laboratory tests showed AST 112 U/L and ALT 201 U/L,and then reduced glutathione was discontinued.On day 15 of rosuvastatin calcium withdrawal,laboratory tests showed AST 42 U/L and ALT 63 U/L,and then compound glycyrrhizin was discontinued.The patient was discharged 4 days later.At 2 weeks of follow-up,no abnormalities in liver function were found in the patient.