GPT-4 can pass the Korean National Licensing Examination for Korean Medicine Doctors.

Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. GPT-4 answered 340 questions from the 2022 K-NLEKMD. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2), and acupuncture medicine which are highly localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge than those that did not. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A significant positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.

Functionally similar genes exhibit comparable/similar time-course expression kinetics in the UV-induced photoaged mouse model.

Skin photoaging induced by ultraviolet (UV) irradiation contributes to the formation of thick and coarse wrinkles. Humans are exposed to UV light throughout their lives. Therefore, it is crucial to determine the time-sequential effects of UV on the skin. In this study, we irradiated the mouse back skin with UV light for eight weeks and observed the changes in gene expressions via microarray analysis every week. There were more downregulated genes (514) than upregulated genes (123). The downregulated genes had more functional diversity than the upregulated genes. Additionally, the number of downregulated genes did not increase in a time-dependent manner. Instead, time-dependent kinetic patterns were observed. Interestingly, each kinetic cluster harbored functionally enriched gene sets. Since collagen changes in the dermis are considered to be a major cause of photoaging, we hypothesized that other gene sets contributing to photoaging would exhibit kinetics similar to those of the collagen-regulatory genes identified in this study. Accordingly, co-expression network analysis was conducted using 11 well-known collagen-regulatory seed genes to predict genes with similar kinetics. We ranked all downregulated genes from 1 to 504 based on their expression levels, and the top 50 genes were suggested to be involved in the photoaging process. Additionally, to validate and support our identified top 50 gene lists, we demonstrated that the genes (FN1, CCDC80, PRELP, and TGFBR3) we discovered are downregulated by UV irradiation in cultured human fibroblasts, leading to decreased collagen levels, which is indicative of photoaging processes. Overall, this study demonstrated the time-sequential genetic changes in chronically UV-irradiated skin and proposed 50 genes that are involved in the mechanisms of photoaging.

Integrative Approach to Identifying System-Level Mechanisms of Chung-Sang-Bo-Ha-Hwan's Influence on Respiratory Tract Diseases: A Network Pharmacological Analysis with Experimental Validation.

Chung-Sang-Bo-Ha-Hwan (CSBHH) is an herbal prescription widely used to treat various chronic respiratory diseases. To investigate the system-level treatment mechanisms of CSBHH in respiratory tract diseases, we identified 56 active ingredients of CSBHH and evaluated the degree of overlap between their targets and respiratory tract disease-associated proteins. We then investigated the respiratory tract disease-related signaling pathways associated with CSBHH targets. Enrichment analysis showed that the CSBHH targets were significantly associated with various signaling pathways related to inflammation, alveolar structure, and tissue fibrosis. Experimental validation was conducted using phorbol-12-myristate-13-acetate (PMA)-stimulated NCI-H292 cells by analyzing the mRNA expression levels of biomarkers (IL-1ß and TNF-α for inflammation; GSTP1, GSTM1, and PTEN for apoptosis) derived from network pharmacological analysis, in addition to the mucin genes MUC5AC and MUC2, to investigate the phlegm-expelling effect of CSBHH. The mRNA expression levels of these genes were consistent with network pharmacological predictions in a concentration-dependent manner. These results suggest that the therapeutic mechanisms of CSBHH in respiratory tract diseases could be attributed to the simultaneous action of multiple active ingredients in the herbal prescription.

Hair Growth Effect of DN106212 in C57BL/6 Mouse and Its Network Pharmacological Mechanism of Action.

Centipeda minima (CMX) has been widely investigated using network pharmacology and clinical studies for its effects on hair growth via the JAK/STAT signaling pathway. Human hair follicle papilla cells exhibit hair regrowth through the expression of Wnt signaling-related proteins. However, the mechanism of action of CMX in animals has not been elucidated fully. This study examined the effect of induced hair loss and its side-effects on the skin, and observed the mechanism of action of an alcoholic extract of CMX (DN106212) on C57BL/6 mice. Our results showed that DN106212 was more effective in promoting hair growth than dimethyl sulfoxide in the negative control and tofacitinib (TF) in the positive control when mice were treated with DN106212 for 16 days. We confirmed that DN106212 promotes the formation of mature hair follicles through hematoxylin and eosin staining. We also found that the expression of vascular endothelial growth factor (Vegfa), insulin-like growth factor 1 (Igf1), and transforming growth factor beta 1 (Tgfb1) is related to hair growth using PCR. DN106212-treated mice had significantly higher expression of Vegfa and Igf1 than TF-treated ones, and inhibiting the expression of Tgfb1 had similar effects as TF treatment. In conclusion, we propose that DN106212 increases the expression of hair growth factors, promotes the development of hair follicles, and promotes hair growth. Although additional experiments are needed, DN106212 may serve as an experimental basis for research on natural hair growth-promoting agents.

Predicting activatory and inhibitory drug-target interactions based on structural compound representations and genetically perturbed transcriptomes.

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.

Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach.

Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods: Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results: Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion: This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.

Network Pharmacological Analysis on the Herbal Combinations for Mitigating Inflammation in Respiratory Tracts and Experimental Evaluation.

The regulation of inflammatory mediators, such as TNF-α, IL-6, IL-1ß, and leukotriene B4, could play a crucial role in suppressing inflammatory diseases such as COVID-19. In this study, we investigated the potential mechanisms of drug combinations comprising Ephedrae Herba, Schisandra Fructus, Platycodonis Radix, and Ginseng Radix; validated the anti-inflammatory effects of these drugs; and determined the optimal dose of the drug combinations. By constructing a herb-compound-target network, associations were identified between the herbs and tissues (such as bronchial epithelial cells and lung) and pathways (such as the TNF, NF-κB, and calcium signaling pathways). The drug combinations exerted anti-inflammatory effects in the RAW264.7 cell line treated with lipopolysaccharide by inhibiting the production of nitric oxide and inflammatory mediators, including TNF-α, IL-6, IL-1ß, and leukotriene B4. Notably, the drug combinations inhibited PMA-induced MUC5AC mRNA expression in NCI-H292 cells. A design space analysis was carried out to determine the optimal herbal medicine combinations using the design of experiments and synergy score calculation. Consequently, a combination study of the herbal preparations confirmed their mitigating effect on inflammation in COVID-19.

Individualized Diagnosis and Prescription in Traditional Medicine: Decision-Making Process Analysis and Machine Learning-Based Analysis Tool Development.

While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge, and to observe the actual process by which this knowledge affects the choice of diagnosis and treatment in individual TM doctors. Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the doctors' explicit scoring and feature evaluations of ML models, respectively. There were many differences between the explicit and implicit importance scores in this study. Implicit importance is more closely related to explicit importance in prescription than in diagnosis. The analysis results for eight doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. This is the first study to evaluate the actual process by which explicit and implicit knowledge affect the choice of individual TM doctors and to identify assessment tools for the definition of the decision-making process in diagnosing PI and prescribing herbal treatments by TM clinicians. The assessment tool suggested in this study could be broadly used for the standardization of precision medicine, including TM therapeutics.

Regulation of appetite-related neuropeptides by Panax ginseng: A novel approach for obesity treatment.

Obesity is a primary factor provoking various chronic disorders, including cardiovascular disease, diabetes, and cancer, and causes the death of 2.8 million individuals each year. Diet, physical activity, medications, and surgery are the main therapies for overweightness and obesity. During weight loss therapy, a decrease in energy stores activates appetite signaling pathways under the regulation of neuropeptides, including anorexigenic [corticotropin-releasing hormone, proopiomelanocortin (POMC), cholecystokinin (CCK), and cocaine- and amphetamine-regulated transcript] and orexigenic [agouti-related protein (AgRP), neuropeptide Y (NPY), and melanin-concentrating hormone] neuropeptides, which increase food intake and lead to failure in attaining weight loss goals. Ginseng and ginsenosides reverse these signaling pathways by suppressing orexigenic neuropeptides (NPY and AgRP) and provoking anorexigenic neuropeptides (CCK and POMC), which prevent the increase in food intake. Moreover, the results of network pharmacology analysis have revealed that constituents of ginseng radix, including campesterol, beta-elemene, ginsenoside Rb1, biotin, and pantothenic acid, are highly correlated with neuropeptide genes that regulate energy balance and food intake, including ADIPOQ, NAMPT, UBL5, NUCB2, LEP, CCK, GAST, IGF1, RLN1, PENK, PDYN, and POMC. Based on previous studies and network pharmacology analysis data, ginseng and its compounds may be a potent source for obesity treatment by regulating neuropeptides associated with appetite.

Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy.

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD.

System-level investigation of anti-obesity effects and the potential pathways of Cordyceps militaris in ovariectomized rats.

BACKGROUND: Cordyceps species have been used as tonics to enhance energy, stamina, and libido in traditional Asian medicine for more than 1600 years, indicating their potential for improving reproductive hormone disorders and energy metabolic diseases. Among Cordyceps, Cordyceps militaris has been reported to prevent metabolic syndromes including obesity and benefit the reproductive hormone system, suggesting that Cordyceps militaris can also regulate obesity induced by the menopause. We investigated the effectiveness of Cordyceps militaris extraction (CME) on menopausal obesity and its mechanisms. METHODS: We applied an approach combining in vivo, in vitro, and in silico methods. Ovariectomized rats were administrated CME, and their body weight, area of adipocytes, liver and uterus weight, and lipid levels were measured. Next, after the exposure of MCF-7 human breast cancer cells to CME, cell proliferation and the phosphorylation of estrogen receptor and mitogen-activated protein kinases (MAPK) were measured. Finally, network pharmacological methods were applied to predict the anti-obesity mechanisms of CME. RESULTS: CME prevented overweight, fat accumulation, liver hypertrophy, and lowered triglyceride levels, some of which were improved in a dose-dependent manner. In MCF-7 cell lines, CME showed not only estrogen receptor agonistic activity through an increase in cell proliferation and the phosphorylation of estrogen receptors, but also phosphorylation of extracellular-signal-regulated kinase and p38. In the network pharmacological analysis, bioactive compounds of CME such as cordycepin, adenine, and guanosine were predicted to interact with non-overlapping genes. The targeted genes were related to the insulin signaling pathway, insulin resistance, the MARK signaling pathway, the PI3K-Akt signaling pathway, and the estrogen signaling pathway. CONCLUSIONS: These results suggest that CME has anti-obesity effects in menopause and estrogenic agonistic activity. Compounds in CME have the potential to regulate obesity-related and menopause-related pathways. This study will contribute to developing the understanding of anti-obesity effects and mechanisms of Cordyceps militaris.

Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layer.

Sensorimotor information provided by mossy fibers (MF) is mapped to high-dimensional space by a huge number of granule cells (GrC) in the cerebellar cortex's input layer. Significant studies have demonstrated the computational advantages and primary contributor of this expansion recoding. Here, we propose a novel perspective on the expansion recoding where each GrC serve as a kernel basis function, thereby the cerebellum can operate like a kernel machine that implicitly use high dimensional (even infinite) feature spaces. We highlight that the generation of kernel basis function is indeed biologically plausible scenario, considering that the key idea of kernel machine is to memorize important input patterns. We present potential regimes for developing kernels under constrained resources and discuss the advantages and disadvantages of each regime using various simulation settings.

A System-Level Mechanism of Anmyungambi Decoction for Obesity: A Network Pharmacological Approach.

Obesity is a low-grade systemic inflammatory disease involving adipocytokines. As though Anmyungambi decoction (AMGB) showed significant improvement on obesity in a clinical trial, the molecular mechanism of AMGB in obesity remains unknown. Therefore, we explored the potential mechanisms of action of AMGB on obesity through network pharmacological approaches. We revealed that targets of AMGB are significantly associated with obesity-related and adipocyte-elevated genes. Evodiamine, berberine, genipin, palmitic acid, genistein, and quercetin were shown to regulate adipocytokine signaling pathway proteins which mainly involved tumor necrosis factor receptor 1, leptin receptor. In terms of the regulatory pathway of lipolysis in adipocytes, norephedrine, pseudoephedrine, quercetin, and limonin were shown to affect adrenergic receptor-beta, protein kinase A, etc. We also found that AMGB has the potentials to enhance the insulin signaling pathway thereby preventing type II diabetes mellitus. Additionally, AMGB was discovered to be able to control not only insulin-related proteins but also inflammatory mediators and apoptotic regulators and caspases, hence reducing hepatocyte injury in nonalcoholic fatty liver disease. Our findings help develop a better understanding of how AMGB controls obesity.

Hair Growth Stimulation Effect of Centipeda minima Extract: Identification of Active Compounds and Anagen-Activating Signaling Pathways.

Centipeda minima (L.) A. Braun & Asch is a well-studied plant in Chinese medicine that is used for the treatment of several diseases. A recent study has revealed the effects of extract of Cetipeda minima (CMX) standardized by brevilin A in inducing hair growth. However, the mechanism of action of CMX in human hair follicle dermal papilla cells (HFDPCs) has not yet been identified. We aimed to investigate the molecular basis underlying the effect of CMX on hair growth in HFDPCs. CMX induced the proliferation of HFDPCs, and the transcript-level expression of Wnt family member 5a (Wnt5a), frizzled receptor (FZDR), and vascular endothelial growth factor (VEGF) was upregulated. These results correlated with an increase in the expression of growth-related factors, such as VEGF and IGF-1. Immunoblotting and immunocytochemistry further revealed that the phosphorylation of ERK and JNK was enhanced by CMX in HFDPCs, and ß-catenin accumulated significantly in a dose-dependent manner. Therefore, CMX substantially induced the expression of Wnt signaling-related proteins, such as GSK phosphorylation and ß-catenin. This study supports the hypothesis that CMX promotes hair growth and secretion of growth factors via the Wnt/ß-catenin, ERK, and JNK signaling pathways. In addition, computational predictions of drug-likeness, together with ADME property predictions, revealed the satisfactory bioavailability score of CMX compounds, exhibiting high gastrointestinal absorption. We suggest that CMX could be used as a promising treatment for hair regeneration and minimization of hair loss.

Diversity of Acupuncture Point Selections According to the Acupuncture Styles and Their Relations to Theoretical Elements in Traditional Asian Medicine: A Data-Mining-Based Literature Study.

Acupuncture point (AP) selections can vary depending on clinicians' acupuncture style, and therefore, acupuncture style is an important factor in determining the efficacy of acupuncture treatment. However, few studies have examined the differences in AP selections according to the acupuncture styles and theoretical backgrounds causing the differences. We compared the AP prescriptions used for 14 diseases in three classical medical textbooks, Dongeuibogam (DEBG), Saamdoinchimgooyogyeol (SADI), and Chimgoogyeongheombang (CGGHB), which represent unique acupuncture styles and have affected clinicians during this time. AP prescriptions showed more diversity between textbooks than between types of diseases. Among the three textbooks, AP prescriptions of SADI were most different compared to those of DEBG and CGGHB. Importantly, we found each style can be more clearly explained by AP attributes than by the APs per se. Specifically, SADI, DEBG, and CGGHB preferred five transport points located on the limbs, APs of the extra meridians, and source points, respectively. This suggests the possibility that the theoretical diversity of acupuncture styles results in the heterogeneity of AP selections.

Investigating the Biomarkers of the Sasang Constitution via Network Pharmacology Approach.

Sasang constitutional (SC) medicine classifies people into Soeum (SE), Soyang (SY), Taeeum (TE), and Taeyang (TY) types based on psychological and physical traits. However, biomarkers of these types are still unclear. We aimed to identify biomarkers among the SC types using network pharmacology methods. Target genes associated with the SC types were identified by grouping herb targets that preserve and strengthen the requisite energy (Bomyeongjiju). The herb targets were obtained by constructing an herb-compound-target network. We identified 371, 185, 146, and 89 target genes and their unique biological processes related to SE, SY, TE, and TY types, respectively. While the targets of SE and SY types were the most similar among the target pairs of the SC types, those of TY type overlapped with only a few other SC-type targets. Moreover, SE, SY, TE, and TY were related to "diseases of the digestive system," "diseases of the nervous system," "endocrine, nutritional, and metabolic diseases," and "congenital malformations, deformations, and chromosomal abnormalities," respectively. We successfully identified the target genes, biological processes, and diseases related to each SC type. We also demonstrated that a drug-centric approach using network pharmacology analysis provides a deeper understanding of the concept of Sasang constitutional medicine at a phenotypic level.

Identification of gallic acid as a active ingredient of Syzygium aromaticum against tacrolimus-induced damage in renal epithelial LLC-PK1 cells and rat kidney.

Tacrolimus (FK506), a calcineurin inhibitor, is an effective immunosuppressive agent mainly used to lower the risk of organ rejection after allogeneic organ transplant. However, FK506-associated adverse effects, such as nephrotoxicity, may limit its therapeutic use. In this study, we confirmed that epigallocatechin-3-gallate (EGCG), sanguiin H-6, and gallic acid increased cell survival following FK506-induced cytotoxicity in renal epithelial LLC-PK1. Among these compounds, gallic acid exerted the strongest protective effect, further confirmed in the FK506-induced nephrotoxicity rat model. Additionally, we identified supporting evidence for the nephroprotective function of gallic acid using molecular docking and bioavailability investigations.

Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis.

BACKGROUND: Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. METHODS: Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis. RESULTS: The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general information, and cold-heat were more decisive than others in classifying Sasang types. Costal angle was the most informative feature. In pairwise classification, we found Sasang type-dependent distinctions that body measurement features played a key role in TE-SE and TE-SY datasets, while personality and cold-heat features showed importance in SE-SY dataset. CONCLUSION: Current study investigated a comprehensive diagnostic model for Sasang type using machine learning and achieved better performance than previous studies. This study helps data-driven decision making in clinics by revealing key features contributing to the Sasang type diagnosis.

Effects of estrogen inhibition formula herbal mixture for danazol-induced precocious puberty in female rats: an experimental study with network pharmacology.

BACKGROUND: This study aimed at determining the effect of the herbal mixture estrogen inhibition formula (EIF) and its possible mechanisms by precocious puberty animal models and network pharmacology-based analysis. METHODS: Precocious puberty animal models were established by a single injection of 300 µg danazol, then female rats were administered EIF, vaginal openings were monitored, uterus and pituitary indices were determined. The levels of ALP, E2, LH, and FSH were measured using ELISA kits. Real-time PCR was performed to evaluate the mRNA expression of GnRH, UNC5C, and netrin-1 in hypothalamic tissues. We applied network pharmacological analysis to predict potential targets and pathways of EIF. RESULTS: EIF delayed danazol-induced early vaginal opening. In the onset model, EIF reduced the increased levels of serum ALP, E2, LH, and FSH; as well as mRNA expressions of GnRH, Netrin-1, and UNC5C. Moreover, long-term administration of EIF not only diminished all impaired factors but also had no effect on the normal development of the animals. The gene set enrichment analysis showed that the targets of EIF are mainly associated with the GnRH signaling and ovarian steroidogenesis pathways. CONCLUSION: EIF could be used in preclinical research for the treatment of precocious puberty by the inhibition of HPGA pre-maturation.

A Novel Framework for Understanding the Pattern Identification of Traditional Asian Medicine From the Machine Learning Perspective.

Pattern identification (PI), a unique diagnostic system of traditional Asian medicine, is the process of inferring the pathological nature or location of lesions based on observed symptoms. Despite its critical role in theory and practice, the information processing principles underlying PI systems are generally unclear. We present a novel framework for comprehending the PI system from a machine learning perspective. After a brief introduction to the dimensionality of the data, we propose that the PI system can be modeled as a dimensionality reduction process and discuss analytical issues that can be addressed using our framework. Our framework promotes a new approach in understanding the underlying mechanisms of the PI process with strong mathematical tools, thereby enriching the explanatory theories of traditional Asian medicine.