Intelligent Fusion of Multi-Source Senses Information for Identifying the Nature of Five Flavors in Chinese Medicine: A Comprehensive Study of Five Classifications.

Objective: To develop a classification model for the five flavors of Chinese medicine using advanced multi-source intelligent sensory information fusion technology. The primary aim is to investigate the feasibility of applying this model to classify and identify the flavors of various Chinese medicines effectively. Methods: We selected 122 representative Chinese medicines, each exhibiting a single distinct flavor (sour, pungent, salty, sweet, bitter), along with 14 common foods. Utilizing the nature and flavors of these decoction pieces specified in Chinese Pharmacopeia (ChP)2020 and the inherent attributes of food components, we obtained valuable data from various sensors, including the PEN3 electronic nose, ASTREE electronic tongue, and SA402B electronic tongue. We then collected single-source data matrices from these sample sensors and a multi-source data matrix that combined the data from all sensors. Using discriminant analysis (DA), principal component analysis-discriminant analysis (PCA-DA), and K-nearest neighbor algorithm (KNN) three kinds of chemometric methods were used to establish five flavors and five-category discrimination models. The results were comprehensively evaluated with the highest correct rate of the model of leave-one-out cross-validation as the index. Results: Upon leave-one-out cross-validation, the correct judgment rate of the five flavors, five-category two-source fusion DA discrimination model (83.8%; ASTREE + SA402B) was significantly higher than the correct judgment rate of the single-source optimal DA and KNN model (73.5%; ASTREE). Following full-sample modeling, the correct judgment rate of the five flavors, five-category three-source fusion DA discrimination model (94.9%; PEN3+ASTREE+SA402B) rose substantially. This was higher than the correct judgment rate of the single-source optimal DA model (77.9%; ASTREE) and slightly higher than the two-source optimal correct judgment rate (89.7%; PEN3 + ASTREE). Conclusions: Compared to single-source identification, multi-source intelligent senses information fusion (MISIF) significantly improved accuracy, providing a new outlook for identifying flavor in Chinese medicine.

Study on the taste-masking effect and mechanism of Acesulfame K on berberine hydrochloride.

OBJECTIVE: In our previous taste-masking study, we found that Acesulfame K (AK) had a better taste-masking effect than other high-efficiency sweeteners for several representative bitter natural drugs in aqueous decoction. Furthermore, we performed a preliminary taste-masking study of AK for representative bitter API Berberine Hydrochloride (BH) and found that it had a good taste-masking effect. We also found that flocculent precipitation was generated in the BH solution, but it was not clear whether it was related to the good taste-masking effect. This study was conducted to explore the taste-masking effect and mechanism of AK on BH. METHODS: The taste-masking effect of AK on BH was evaluated based on the Traditional Human Taste Panel Method and the electronic tongue evaluation method. DSC, XRD, and molecular simulation techniques were used to explore the mechanism of AK on BH, from the macro level and molecular level, respectively. RESULTS: When evaluating the taste-masking effect, we found that 0.1% AK had the best taste-masking effect on BH, while higher concentrations had a worse taste-masking effect. DSC and XRD revealed that the flocculent precipitation was a complex AK-BH. Finally, by simulating the binding of AK, BH, and TAS2R46 receptors, we found the unique taste-masking mechanism of AK. CONCLUSION: The sweet taste stimulus of AK can mask the bitter taste stimulus of BH, and AK can generate AK-BH with BH to reduce the contact between BH and bitter taste receptors. Additionally, it could block the expression of the TAS2R46 receptors.

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. CONCLUSION: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

A comparative study of consistency between Paeonia rubra hort traditional decoction and dispensing granule decoctions from different sources.

OBJECTIVE: To compare the consistency between the decoction of Paeonia rubra hort dispensing granules from different manufacturers and traditional decoction (TD), and to provide a reference for the clinical application of Paeonia rubra hort dispensing granules. METHODS: Nine batches of Paeonia rubra hort dispensing granules (from three manufacturers, A, B, and C) and 20 batches of Paeonia rubra hort decoction pieces were collected. The contents of four active components in vivo and in vitro were determined by HPLC and UPLC-MS/MS, respectively. The consistency of the Paeonia rubra hort decoction pieces and dispensing granules were compared based on the Criteria Importance Though Intercrieria Correlation (CRITIC) weighting method and the equivalent correction suggestions (1 g of dispensing granule equals the same amount of Chinese herbal samples) were put forward for the dispensing granules. RESULTS: The total content of active ingredients in vivo and in vitro of manufacturer A was significantly lower than that of TD (p < 0.05), and the total content of active ingredients in vivo of manufacturer C was significantly lower than that of TD (p < 0.05); The equivalent of manufacturer A and manufacturer C should be corrected from 1:11 and 1:5 to 1:5 and 1:4, respectively. CONCLUSION: The quality of Paeonia rubra hort dispensing granule decoction from some manufacturers aligns that of TD, but the other is slightly inferior to that of TD. After appropriate equivalent correction, quality consistency can be achieved with TD.

[Chinese medicine dispensing granules: history, status quo, and development prospect in "post-pilot era"].

Chinese medicine dispensing granules, the result of the efforts to transform Chinese medicinal decoction pieces in China, features portability and ease of storage. Thus, it is destined to be an indispensible dosage form in the modernization drive of Chinese medicine. The Announcement on Ending the Pilot Project of Chinese Medicine Dispensing Granules was released in February 2021 and relevant regulations went into force in November 2021, which marks the a new journey for the development of Chinese medicine dispensing granules and the beginning of the "post-pilot era". However, it faces the challenges in quality and standard. This study reviewed the history of Chinese medicine dispensing granules, analyzed the technical progress, market, and main problems in development, and proposed suggestions and prospects for its development in the "post-pilot era", which is expected to serve as a reference for its industry development and rational use.

[Main characteristics and practical discussions of national drug standards for 196 kinds of Chinese medicine formula granules].

As China is implementing the policy of "Announcement on Ending the Pilot Work of Chinese medicine formula gra-nules", the standard of Chinese medicine formula granules has gradually become the focus of industry development. Up to now, 196 national drug standards for Chinese medicine formula granules have been published by China, which guaranteed the production quality of Chinese medicine formula granules. However, there are still several challenges such as the rational application of national drug standards and the enrichment and improvement of varieties. The basic content of the issued national drug standards for Chinese medicine formula granules was analyzed and compared with the quality standard provisions of the corresponding decoction pieces in the Chinese Pharmacopoeia(2020 edition) in this paper. This paper discussed the main characteristics of paste-forming rate of each medicinal raw materials, "quantity-quality" transformation, equivalent ratio, and so on, and clarified the characteristics of the national standard for Chinese medicine formula granules. This paper provided references for achieving the unified quality control and meeting the overall quality requirements of Chinese medicine formula granules.

[Study on superposition law of drug bitterness based on tongue taste evaluation and electronic tongue evaluation].

Traditional Chinese medicine( TCM) decoction contains complex bitterness. In this paper,the simple mixing of TCM monomer bitter substances is used as the entry point to study the law of bitterness superposition. With berberine hydrochloride( alkaloids),geniposide( terpenoids),and arbutin( glycosides) as mother liquor,sophoridine( alkaloids),gentiopicroside( terpenoids),and puerarin( glycosides) as additive substances,these different additive substances were mixed with different mother liquor according to concentration gradients to form different liquid mixtures. The bitterness of the additive solution and the mixtures was evaluated by traditional human taste panel method( THTPM) and electronic tongue; the bitterness-concentration fitting model of the additive solution and the liquid mixtures was established by Weibull and logarithmic curves. By comparing and analyzing the bitterness-concentration model and the bitterness difference( ΔI_0/ΔI_e) of the additive solution and the mixture,the influence of mother liquor on the bitterness of the mixture was investigated. The results showed that both the additive solution bitterness model and the liquid mixture bitterness model were consistent with the Weibull model and the logarithmic model( THTPM: R~2≥0. 887 0,P<0. 01; electronic tongue test:R~2≥0. 753 2,P<0. 05). The fitting degree of the Weibull model was generally higher than that of the logarithmic model; the bitterness difference( ΔI_0) was monotonously decreasing; the fitting equation of tongue bitterness and electronic tongue bitterness: R~2≥0. 874 2,P<0. 01. In this article,through the superposition of different kinds of TCM bitter substances,THTPM and electronic tongue test was combined. It was found that the bitterness after superposition was still in Weibull or logarithmic relationship with the concentration of additive substances; THTPM showed that the effect of bitter mother liquor on the bitterness of the mixture decreased with the increase of the concentration of the additive; the bitterness of the electronic tongue was obviously related to the bitterness of THTPM. However,further verification is needed later by optimizing the concentration gradient and expanding the sample size.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method.

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb's test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

[Current understanding of sleep, dreaming and related memory consolidation].

Sleep is a naturally recurring state found throughout the animal kingdom and characterized by a reversible loss of consciousness. Although in humans the daily amount of sleep decreases with aging, the total amount of time spent for sleep is estimated as up to one-third of one's lifetime. In mammals, sleep shows a clear daily rhythmicity as well as nightly phases, which are strongly controlled by the circadian clock located in the hypothalamic suprachiasmatic nuclei and are also regulated by ambient light. While it is certain that sleep is critical for survival in general, the functional significance of sleep is still under investigation. Dreaming is a common psychological phenomenon occurring during human sleep, yet its content and natural function, if any, are still a matter of debate. In recent years, accumulated evidence strongly supports the notion that new information acquired during the day time is processed and transformed into long-term memory in a complicated and sophisticated way during sleeping. Such information processing is commonly referred to as memory consolidation.

Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose.

Amomi fructus is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available A. fructus varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased A. fructus quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of A. fructus using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of A. fructus. The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with R 2 = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of A. fructus, and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy.

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

Study on Masking the Bitterness of Chinese Medicine Decoction-Mate.

Background: Traditional Chinese medicine decoction (TCMD) is an oral liquid made by decocting crude medicinal compounds with water. It has complex compositions and diverse odor and taste, most of which have an unacceptable level of bitterness which seriously affects patients' medication compliance. To solve this problem, a variety of taste-masking pathways and different types of taste-masking excipients were combined, using the application of coffee-mate to mask the bitterness of coffee as an existing example. Three composite taste-masking adjuvants were developed to improve the taste of TCMD, referred to as the Chinese Medicine Decoction-Mate (CMD-M). However, whether CMD-M has a good taste-masking effect and whether it affects the chemical compositions and pharmacological effects of the medicine remain unclear. Method: The commonly used pediatric medicine Qingre Huazhi Decoction (QRHZD) and the personalized decoctions used in clinical practices were used as the masking research carriers. The taste-masking effect of CMD-M on QRHZD was evaluated by both healthy volunteers and an electronic tongue, and the personalized decoctions were evaluated by clinical subjects. The changes of chemical components of QRHZD before and after taste-masking were evaluated by HPLC. The changes in anti-inflammatory effects were evaluated by establishing mice as an acute inflammatory model. Results: The taste-masking effect evaluation results showed that the bitterness of QRHZD was significantly reduced after adding CMD-M. There was no significant difference in the relative peak areas change rate and total peak areas ratio of common peaks of QRHZD before and after taste-masking (P > 0.05), shown by HPLC analysis. The inhibitory rates of QRHZD on ear swelling in mice before and after taste-masking also showed no significant difference (P > 0.05). Conclusions: CMD-M can effectively mask the bitterness of decoctions while bringing no significant difference overall in chemical compositions and pharmacological effects before and after QRHZD masking.

Optimization and validation of the protocol used to analyze the taste of traditional Chinese medicines using an electronic tongue.

Tools to define the active ingredients and flavors of Traditional Chinese Medicines (TCMs) are limited by long analysis times, complex sample preparation and a lack of multiplexed analysis. The aim of the present study was to optimize and validate an electronic tongue (E-tongue) methodology to analyze the bitterness of TCMs. To test the protocol, 35 different TCM concoctions were measured using an E-tongue, and seven replicate measurements of each sample were taken to evaluate reproducibility and precision. E-tongue sensor information was identified and classified using analysis approaches including least squares support vector machine (LS-SVM), support vector machine (SVM), discriminant analysis (DA) and partial least squares (PLS). A benefit of this analytical protocol was that the analysis of a single sample took <15 min for all seven sensors. The results identified that the LS-SVM approach provided the best bitterness classification accuracy (binary classification accuracy, 100%; ternary classification accuracy, 89.66%). The E-tongue protocol developed showed good reproducibility and high precision within a 6 h measurement cycle. To the best of our knowledge, this is the first study of an E-tongue being applied to assay the bitterness of TCMs. This approach could be applied in the classification of the taste of TCMs, and serve important roles in other fields, including foods and beverages.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.