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.

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.

Absolute Configuration and Corrected NMR Assignment of 17-Hydroxycyclooctatin, a Fused 5-8-5 Tricyclic Diterpene.

The absolute configuration and corrected NMR assignment of 17-hydroxycyclooctatin isolated from Streptomyces sp. M56 recovered from a nest of South African Macrotermes natalensis termites are reported. 17-Hydroxycyclooctatin is a unique tricyclic diterpene (C20) consisting of a fused 5-8-5 ring system, and in this study, its structure was unambiguously determined by a combination of HR-ESIMS and 1D and 2D NMR spectroscopic experiments to produce corrected NMR assignments. The absolute configuration of 17-hydroxycyclooctatin is reported for the first time in the current study using chemical reactions and quantum chemical ECD calculations. The corrected NMR assignments were verified using a gauge-including atomic orbital NMR chemical shifts calculation, followed by DP4 probability. To understand the pharmacological properties of 17-hydroxycyclooctatin, a network pharmacological approach and molecular docking analyses were used, which also predicted its effects on human breast cancer cell lines. Cytotoxicity and antiestrogenic activity of 17-hydroxycyclooctatin were determined, and it was found this compound may be an ERα antagonist.

Multiple Targets of 3-Dehydroxyceanothetric Acid 2-Methyl Ester to Protect Against Cisplatin-Induced Cytotoxicity in Kidney Epithelial LLC-PK1 Cells.

Chronic exposure to cisplatin, a potent anticancer drug, causes irreversible kidney damage. In this study, we investigated the protective effect and mechanism of nine lupane- and ceanothane-type triterpenoids isolated from jujube (Ziziphus jujuba Mill., Rhamnaceae) on cisplatin-induced damage to kidney epithelial LLC-PK1 cells via mitogen-activated protein kinase (MAPK) and apoptosis pathways. Cisplatin-induced LLC-PK1 cell death was most significantly reduced following treatment with 3-dehydroxyceanothetric acid 2-methyl ester (3DC2ME). Additionally, apoptotic cell death was significantly reduced. Expression of c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 was markedly suppressed by 3DC2ME, indicating inhibition of the MAPK pathway. Treatment with 3DC2ME also significantly reduced expression of active caspase-8 and -3, Bcl-2-associated X protein (Bax), and B cell lymphoma 2 (Bcl-2), indicating the inhibition of apoptosis pathways in the kidneys. We also applied the network pharmacological analysis and identified multiple targets of 3DC2ME related to MAPK signaling pathway and apoptosis.

Protective Effect of Artemisia argyi and Its Flavonoid Constituents against Contrast-Induced Cytotoxicity by Iodixanol in LLC-PK1 Cells.

Preventive effects and corresponding molecular mechanisms of mugwort (Artemisia argyi) extract and its flavonoid constituents on contrast-induced nephrotoxicity were explored in the present study. We treated cultured LLC-PK1 cells with iodixanol to induce contrast-induced nephrotoxicity, and found that A. argyi extracts ameliorated the reduction in cellular viability following iodixanol treatment. The anti-apoptotic effect of A. argyi extracts on contrast-induced nephrotoxicity was mediated by the inhibition of mitogen-activated protein kinase (MAPK) phosphorylation and the activation of caspases. The flavonoid compounds isolated from A. argyi improved the viability of iodixanol-treated cells against contrast-induced nephrotoxicity. Seven compounds (1, 2, 3, 15, 16, 18, and 19) from 19 flavonoids exerted a significant protective effect. Based on the in silico oral-bioavailability and drug-likeness assessment, which evaluate the drug potential of these compounds, compound 2 (artemetin) showed the highest oral bioavailability (49.55%) and drug-likeness (0.48) values. We further investigated the compound⁻target⁻disease network of compound 2, and proliferator-activated receptor gamma (PPAR-γ) emerged as a predicted key marker for the treatment of contrast-induced nephrotoxicity. Consequently, compound 2 was the preferred candidate, and its protective effect was mediated by inhibiting the contrast-induced inflammatory response through activation of PPAR-γ and inhibition of MAPK phosphorylation and activation of caspases.

A Systems-Level Analysis of Mechanisms of Platycodon grandiflorum Based on A Network Pharmacological Approach.

Platycodon grandiflorum (PG) is widely used in Asia for its various beneficial effects. Although many studies were conducted to understand the molecular mechanisms of PG, it is still unclear how the combinations of multiple ingredients work together to exert its therapeutic effects. The aim of the present study was to provide a comprehensive review of the systems-level mechanisms of PG by adopting network pharmacological analysis. We constructed a compound⁻target⁻disease network for PG using experimentally validated and machine-leaning-based prediction results. Each target of the network was analyzed based on previously known pharmacological activities of PG. Gene ontology analysis revealed that the majority of targets were related to cellular and metabolic processes, responses to stimuli, and biological regulation. In pathway enrichment analyses of targets, the terms related to cancer showed the most significant enrichment and formed distinct clusters. Degree matrix analysis for target⁻disease associations of PG suggested the therapeutic potential of PG in various cancers including hepatocellular carcinoma, gastric cancer, prostate cancer, small-cell lung cancer, and renal cell carcinoma. We expect that network pharmacological approaches will provide an understanding of the systems-level mechanisms of medicinal herbs and further develop their therapeutic potentials.

The effect of µ-opioid receptor activation on GABAergic neurons in the spinal dorsal horn.

The superficial dorsal horn of the spinal cord plays an important role in pain transmission and opioid activity. Several studies have demonstrated that opioids modulate pain transmission, and the activation of µ-opioid receptors (MORs) by opioids contributes to analgesic effects in the spinal cord. However, the effect of the activation of MORs on GABAergic interneurons and the contribution to the analgesic effect are much less clear. In this study, using transgenic mice, which allow the identification of GABAergic interneurons, we investigated how the activation of MORs affects the excitability of GABAergic interneurons and synaptic transmission between primary nociceptive afferent and GABAergic interneurons. We found that a selective µ-opioid agonist, [D-Ala2, NMe-Phe4, Gly-ol]-enkephanlin (DAMGO), induced an outward current mediated by K+ channels in GABAergic interneurons. In addition, DAMGO reduced the amplitude of evoked excitatory postsynaptic currents (EPSCs) of GABAergic interneurons which receive monosynaptic inputs from primary nociceptive C fibers. Taken together, we found that DAMGO reduced the excitability of GABAergic interneurons and synaptic transmission between primary nociceptive C fibers and GABAergic interneurons. These results suggest one possibility that suppression of GABAergic interneurons by DMAGO may reduce the inhibition on secondary GABAergic interneurons, which increase the inhibition of the secondary GABAergic interneurons to excitatory neurons in the spinal dorsal horn. In this circumstance, the sum of excitation of the entire spinal network will control the pain transmission.

Large-scale plastic changes of the brain network in an animal model of neuropathic pain.

Pain is a multidimensional experience emerging from the flow of information in the brain. It is reasonable therefore to understand pathological pain in terms of plasticity of the distributed brain network. Recently, we demonstrated that multivariate pattern analysis of fluorodeoxyglucose micro-positron emission tomography (FDG micro-PET) imaging can successfully identify neuropathic pain animals at the individual level by capturing the distributed patterns of the resting-state brain activity (Kim et al., 2014). Here, we aimed to reveal the underlying plastic changes of the distributed brain network that enabled successful discrimination of neuropathic pain. We analyzed FDG micro-PET images in awake rats with spinal nerve ligation (SNL) (SNL group, n=13; sham group, n=10) that were acquired in our previous study. In order to investigate the altered functional connectivity pattern of the brain network, first, we developed a node set search algorithm that defines the optimal node set representing the whole brain in given brain images and constructed resting-state brain networks with defined nodes. Graph theoretical analysis revealed that SNL resulted in decreased small-worldness and more fragmented modular structure compared to sham group. Connectivity pattern analyses showed that regions in the brainstem, sensorimotor cortex, and some part of the prefrontal cortex became highly connected following SNL, whereas the cerebellum and some prefrontal regions showed decreased connections. In addition, we found close relationships between characteristics of connectivity and metabolic changes. Our findings suggest that neuropathic pain is associated with connectional plasticity of the resting-state brain.

Identifying neuropathic pain using (18)F-FDG micro-PET: a multivariate pattern analysis.

Pain is a multidimensional experience emerging from the flow of information between multiple brain regions. A growing body of evidence suggests that pathological pain causes plastic changes of various brain regions. Here, we hypothesized that the induction of neuropathic pain alters distributed patterns of the resting-state brain activity in animal models, and capturing the altered pattern would enable identification of neuropathic pain at the individual level. We acquired micro-positron emission tomography with [(18)F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31% sensitivity, 90.00% specificity, and 91.30% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). In contrast, predictive regions with decreased metabolism were observed in widespread cortical areas including secondary somatosensory cortex (S2), occipital cortex (OC), temporal cortex (TC), retrosplenial cortex (RSC), and the cerebellum (CBL). We also applied the univariate approach and obtained reduced prediction performance compared to MVPA. Our results suggest that developing neuroimaging-based diagnostic tools for pathological pain can be achieved by considering patterns of the resting-state brain activity.

Deciphering the Systemic Impact of Herbal Medicines on Allergic Rhinitis: A Network Pharmacological Approach.

Allergic rhinitis (AR) is a systemic allergic disease that has a considerable impact on patients' quality of life. Current treatments include antihistamines and nasal steroids; however, their long-term use often causes undesirable side effects. In this context, traditional Asian medicine (TAM), with its multi-compound, multi-target herbal medicines (medicinal plants), offers a promising alternative. However, the complexity of these multi-compound traits poses challenges in understanding the overall mechanisms and efficacy of herbal medicines. Here, we demonstrate the efficacy and underlying mechanisms of these multi-compound herbal medicines specifically used for AR at a systemic level. We utilized a modified term frequency-inverse document frequency method to select AR-specific herbs and constructed an herb-compound-target network using reliable databases and computational methods, such as the Quantitative Estimate of Drug-likeness for compound filtering, STITCH database for compound-target interaction prediction (with a high confidence score threshold of 0.7), and DisGeNET and CTD databases for disease-gene association analysis. Through this network, we conducted AR-related targets and pathway analyses, as well as clustering analysis based on target-level information of the herbs. Gene ontology enrichment analysis was conducted using a protein-protein interaction network. Our research identified 14 AR-specific herbs and analyzed whether AR-specific herbs are highly related to previously known AR-related genes and pathways. AR-specific herbs were found to target several genes related to inflammation and AR pathogenesis, such as PTGS2, HRH1, and TBXA2R. Pathway analysis revealed that AR-specific herbs were associated with multiple AR-related pathways, including cytokine signaling, immune response, and allergic inflammation. Additionally, clustering analysis based on target similarity identified three distinct subgroups of AR-specific herbs, corroborated by a protein-protein interaction network. Group 1 herbs were associated with the regulation of inflammatory responses to antigenic stimuli, while Group 2 herbs were related to the detection of chemical stimuli involved in the sensory perception of bitter taste. Group 3 herbs were distinctly associated with antigen processing and presentation and NIK/NF-kappa B signaling. This study decodes the principles of TAM herbal configurations for AR using a network pharmacological approach, providing a holistic understanding of drug effects beyond specific pathways.

Identifying Novel Subtypes of Functional Gastrointestinal Disorder by Analyzing Nonlinear Structure in Integrative Biopsychosocial Questionnaire Data.

Background/Objectives: Given the limited success in treating functional gastrointestinal disorders (FGIDs) through conventional methods, there is a pressing need for tailored treatments that account for the heterogeneity and biopsychosocial factors associated with FGIDs. Here, we considered the potential of novel subtypes of FGIDs based on biopsychosocial information. Methods: We collected data from 198 FGID patients utilizing an integrative approach that included the traditional Korean medicine diagnosis questionnaire for digestive symptoms (KM), as well as the 36-item Short Form Health Survey (SF-36), alongside the conventional Rome-criteria-based Korean Bowel Disease Questionnaire (K-BDQ). Multivariate analyses were conducted to assess whether KM or SF-36 provided additional information beyond the K-BDQ and its statistical relevance to symptom severity. Questions related to symptom severity were selected using an extremely randomized trees (ERT) regressor to develop an integrative questionnaire. For the identification of novel subtypes, Uniform Manifold Approximation and Projection and spectral clustering were used for nonlinear dimensionality reduction and clustering, respectively. The validity of the clusters was assessed using certain metrics, such as trustworthiness, silhouette coefficient, and accordance rate. An ERT classifier was employed to further validate the clustered result. Results: The multivariate analyses revealed that SF-36 and KM supplemented the psychosocial aspects lacking in K-BDQ. Through the application of nonlinear clustering using the integrative questionnaire data, four subtypes of FGID were identified: mild, severe, mind-symptom predominance, and body-symptom predominance. Conclusions: The identification of these subtypes offers a framework for personalized treatment strategies, thus potentially enhancing therapeutic outcomes by tailoring interventions to the unique biopsychosocial profiles of FGID patients.

Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches.

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

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.

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.

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.

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.

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.

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.

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.

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.