AIM To study the chemical constituents from Citri reticulatae Pericarpium Viride and their anti-triple negative breast cancer activities in vitro.METHODS The ethanolic extract of Citri reticulatae Pericarpium Viride was isolated and purified by silica gel,polyamide,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-triple negative breast cancer activities were screened by SRB assay,and their effects on the proliferation of triple negative breast cancer cell lines HCC1806,HCC1937 and MDA-MB-231 in vitro were evaluated.RESULTS Twenty compounds were isolated and identified as nobiletin(1),tangeritin(2),5,4'-dihydroxy-7,8-dimethoxy flavonoid(3),naringenin(4),artemetin(5),5-demethynobiletin(6),3,5,6,7,8,3',4'-pentamethoxy flavonoid(7),5,4'-dihydroxy-3,6,7,8,3'-pentamethoxyflavone(8),xanthomicrol(9),p-hydroxycinnamic acid(10),5,4'-dihydroxy-6,7,8,3'-tetramethoxyflavone(11),pectolinarigenin(12),4'-dihydroxy-5,6,7-tetramethoxyflavone(13),hispidulin(14),4',5,6,7-tetramethoxy-flavone(15),1-methyl-4-(prop-1-en-2-yl)cyclohexane-1,2-diol(16),umbelliferone(17),5-hydroxymethyl furfural(18),hydroquinone(19),1H-indole-3-carbaldehyde(20).Compound 8 showed a significant inhibitory effect with the IC50 value of(5.36±0.24)μmol/L on HCC1806 cells.CONCLUSION Compound 20 is isolated from genus Citrus for the first time,8,12-13,16-17 are isolated from this plant for the first time.Compound 8 show inhibitory effects on the proliferation of HCC1806,HCC1937 and MDA-MB-231 cells in vitro and have the strongest activities.Compounds 3-4,11-12,15,17 and 19 show strong inhibitory effect on HCC1806 cells.Compounds 15,19 also inhibit the proliferation of HCC1937 cells in vitro.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

High-performance liquid chromatography(HPLC) was used to determine the content of three major components in Citri Reticulatae Semen(CRS), including limonin, nomilin, and obacunone. The chromaticity of the CRS sample during salt processing and stir-frying was measured using a color difference meter. Next, the relationship between the color and content of the salt-processed CRS sample was investigated through correlation analysis. By integrating the oil bath technique for processing simulation with HPLC, the changes in the relative content of nomilin and its transformation products were analyzed, with its structural transformation pattern during processing identified. Additionally, RAW264.7 cells were induced with lipopolysaccharides(LPSs) to establish an inflammatory model, and the anti-inflammatory activity of nomilin and its transformation product, namely obacunone was evaluated. The results indicated that as processing progressed, E~*ab and L~* values showed a downward trend; a~* values exhibited a slow increase over a certain period, followed by no significant changes, and b~* values remained stable with no significant changes over a certain period and then started to decrease. The limonin content remained barely unchanged; the nomilin content decreased, and the obacunone increased significantly. The changing trends in content and color parameters during salt-processing and stir-frying were basically consistent. The content of nomilin and obacunone was significantly correlated with the colorimetric values(L~*, a~*, b~*, and E~*ab), while limonin content showed no significant correlation with these values. By analyzing HPLC patterns of nomylin at different heating temperatures and time, it was found that under conditions of 200-250 ℃ for heating of 5-60 min, the content of nomilin significantly decreased, while the obacunone content increased pronouncedly. The in vitro anti-inflammatory activity results indicated that compared to the model group, the group with a high concentration of nomilin and the groups with varying concentrations of obacunone showed significantly reduced release of nitric oxide(NO)(P<0.01). When both were at the same concentration, obacunone showed better performance in inhibiting NO release. In this study, the obvious correlation between the color and content of major components during the processing of CRS samples was identified, and the dynamic patterns of quality change in CRS samples during processing were revealed. Additionally, the study revealed and confirmed the transformation of nomilin into obacunone during processing, with the in vitro anti-inflammatory activity of obacunone significantly greater than that of nomilin. These findings provided a scientific basis for CRS processing optimization, tablet quality control, and its clinical application.
Mice ; Animals ; Drugs, Chinese Herbal/pharmacology* ; RAW 264.7 Cells ; Limonins/chemistry* ; Chromatography, High Pressure Liquid ; Citrus/chemistry* ; Color ; Benzoxepins/chemistry* ; Anti-Inflammatory Agents/chemistry*

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.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

AIM To study the chemical constituents from salt-processed Litchi Semen and their antioxidant activities.METHODS The 85％ethanol extract from salt-processed Litchi Semen was isolated and purified by silica gel,Sephadex LH-20,MCI,ODS and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.DPPH and ABTS+free radical scavenging method were used to evaluate their antioxidant activities.RESULTS Fifteen compounds were isolated and identified as dehydrocostuslactone(1),ananosmoside A(2),funingensin A(3),(2S)-pinocembrin-7-O-(6-O-α-L-rhamnopyranosyl-β-D-glucopyranoside)(4),liquiritienin(5),quercetin(6),rutin(7),isorhamnetin-3-O-β-rutinoside(8),procyanidin A2(9),procyanidin A1(10),ethyl protocatechuate(11),5-hydroxymethylfurfural(12),di(2-ethyl-hexyl)phthalate(13),nicotinamide(14),(10E,15Z)-9,12,13-trihydroxyoctadeca-10,15-dienoic acid(15).Compounds 6-7,9-10 exhibited scavenging activities against DPPH radicals with IC50 values of(12.929±1.232),(14.104±0.946),(10.417±1.736),(6.944±0.030)μmol/L,respectively.Compounds 6-10 exhibited scavenging activities against ABTS+radicals with IC50 values of(21.952±0.577),(25.683±0.625),(22.970±1.336),(20.210±1.435),(18.725±0.324)μmol/L,respectively.CONCLUSION Compounds 1,5,14-15 are isolated from Litchi genus for the first time.Compounds 6-7,9-10 have strong in vitro antioxidant activities.

AIM To study the chemical constituents from salt-processed Litchi Semen and their antioxidant activities.METHODS The 85％ethanol extract from salt-processed Litchi Semen was isolated and purified by silica gel,Sephadex LH-20,MCI,ODS and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.DPPH and ABTS+free radical scavenging method were used to evaluate their antioxidant activities.RESULTS Fifteen compounds were isolated and identified as dehydrocostuslactone(1),ananosmoside A(2),funingensin A(3),(2S)-pinocembrin-7-O-(6-O-α-L-rhamnopyranosyl-β-D-glucopyranoside)(4),liquiritienin(5),quercetin(6),rutin(7),isorhamnetin-3-O-β-rutinoside(8),procyanidin A2(9),procyanidin A1(10),ethyl protocatechuate(11),5-hydroxymethylfurfural(12),di(2-ethyl-hexyl)phthalate(13),nicotinamide(14),(10E,15Z)-9,12,13-trihydroxyoctadeca-10,15-dienoic acid(15).Compounds 6-7,9-10 exhibited scavenging activities against DPPH radicals with IC50 values of(12.929±1.232),(14.104±0.946),(10.417±1.736),(6.944±0.030)μmol/L,respectively.Compounds 6-10 exhibited scavenging activities against ABTS+radicals with IC50 values of(21.952±0.577),(25.683±0.625),(22.970±1.336),(20.210±1.435),(18.725±0.324)μmol/L,respectively.CONCLUSION Compounds 1,5,14-15 are isolated from Litchi genus for the first time.Compounds 6-7,9-10 have strong in vitro antioxidant activities.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

AIM To study the chemical constituents from Citri reticulatae Pericarpium Viride and their anti-triple negative breast cancer activities in vitro.METHODS The ethanolic extract of Citri reticulatae Pericarpium Viride was isolated and purified by silica gel,polyamide,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-triple negative breast cancer activities were screened by SRB assay,and their effects on the proliferation of triple negative breast cancer cell lines HCC1806,HCC1937 and MDA-MB-231 in vitro were evaluated.RESULTS Twenty compounds were isolated and identified as nobiletin(1),tangeritin(2),5,4'-dihydroxy-7,8-dimethoxy flavonoid(3),naringenin(4),artemetin(5),5-demethynobiletin(6),3,5,6,7,8,3',4'-pentamethoxy flavonoid(7),5,4'-dihydroxy-3,6,7,8,3'-pentamethoxyflavone(8),xanthomicrol(9),p-hydroxycinnamic acid(10),5,4'-dihydroxy-6,7,8,3'-tetramethoxyflavone(11),pectolinarigenin(12),4'-dihydroxy-5,6,7-tetramethoxyflavone(13),hispidulin(14),4',5,6,7-tetramethoxy-flavone(15),1-methyl-4-(prop-1-en-2-yl)cyclohexane-1,2-diol(16),umbelliferone(17),5-hydroxymethyl furfural(18),hydroquinone(19),1H-indole-3-carbaldehyde(20).Compound 8 showed a significant inhibitory effect with the IC50 value of(5.36±0.24)μmol/L on HCC1806 cells.CONCLUSION Compound 20 is isolated from genus Citrus for the first time,8,12-13,16-17 are isolated from this plant for the first time.Compound 8 show inhibitory effects on the proliferation of HCC1806,HCC1937 and MDA-MB-231 cells in vitro and have the strongest activities.Compounds 3-4,11-12,15,17 and 19 show strong inhibitory effect on HCC1806 cells.Compounds 15,19 also inhibit the proliferation of HCC1937 cells in vitro.