Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model.

Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng.

Improving the geographical origin classification of (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition.

(licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith).

A Nondestructive Methodology for Determining Chemical Composition of via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks.

The quality assurance of bulk medicinal materials, crucial for botanical drug production, necessitates advanced analytical methods. Conventional techniques, including high-performance liquid chromatography, require extensive pre-processing and rely on extensive solvent use, presenting both environmental and safety concerns. Accordingly, a non-destructive, expedited approach for assessing both the chemical and physical attributes of these materials is imperative for streamlined manufacturing.

An intelligent extraction approach of feature information from three-dimensional DAD chromatogram for integrated quality control of traditional Chinese medicines: root as an example.

Traditional Chinese medicine (TCM) fingerprinting, which has the characteristics of holism and ambiguity, is a conventional strategy for the holistic quality control of TCMs. However, the fingerprinting of TCMs at the current stage generally adopts a single wavelength or few wavelengths, lacking the effective utilization of diode-array detector (DAD) chromatogram data. This study proposes an intelligent extraction approach of feature information from a three-dimensional DAD chromatogram to establish a novel bar-form-diagram (BFD) for integrated quality control of TCMs.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information.

Deep residual network enabled smart hyperspectral image analysis and its application to monitoring moisture, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets.

Bed collapse is a serious problem in a fluid-bed granulation process of traditional Chinese medicine. Moisture content and size distribution are regarded as two pivotal influencing factors. Herein, a smart hyperspectral image analysis methodology was established via deep residual network (ResNet) algorithm, which was then applied to monitoring moisture content, size distribution and contents of four bioactive compounds of granules in the fluid-bed granulation process of Guanxinning tablets.

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets.

Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose.

An effective approach for assessing a drug's potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD.

Assessment of the Potential Adverse Events Related to Ribavirin-Interferon Combination for Novel Coronavirus Therapy.

Purpose: We aimed to analyze and evaluate the safety signals of ribavirin-interferon combination through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS), so as to provide reference for the rationale use of these agents in the management of relevant toxicities emerging in patients with novel coronavirus pneumonia (COVID-19).

Methods: Reports to the FAERS from 1 January 2004 to 8 March 2020 were analyzed. The proportion of report ratio (PRR), reporting odds ratio (ROR), and Bayesian confidence interval progressive neural network (BCPNN) method were used to detect the safety signals.

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers.

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient's specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens.

The landscape of hepatobiliary adverse reactions across 53 herbal and dietary supplements reveals immune-mediated injury as a common cause of hepatitis.

Recent evidence suggests herbal-induced liver injury (HILI) to account for 20% of cases among the U.S. Drug-Induced-Liver-Injury-Network.

The Development of a Database for Herbal and Dietary Supplement Induced Liver Toxicity.

The growing use of herbal dietary supplements (HDS) in the United States provides compelling evidence for risk of herbal-induced liver injury (HILI). Information on HDS products was retrieved from MedlinePlus of the U.S.

Investigating chemical features of based on integrating HPLC fingerprinting and determination of multiconstituents by single reference standard.

Background: is a highly valued medicine and functional food, whose quality is considered to be influenced by the size, botanical parts, and growth environments.

Methods: In this study, a HPLC method integrating fingerprinting and determination of multiconstituents by single reference standard was established and adopted to investigate the chemical profiles and active constituent contents of 215 notoginseng samples with different sizes, from different botanical parts and geographical regions.

Results: Chemical differences among main root, branch root, and rotten root were not distinct, while rhizome and fibrous root could be discriminated from other parts.

Q-marker based strategy for CMC research of Chinese medicine: A case study of Panax Notoginseng saponins.

Background: To ensure pharmaceutical quality, chemistry, manufacturing and control (CMC) research is essential. However, due to the inherent complexity of Chinese medicine (CM), CMC study of CM remains a great challenge for academia, industry, and regulatory agencies. Recently, quality-marker (Q-marker) was proposed to establish quality standards or quality analysis approaches of Chinese medicine, which sheds a light on Chinese medicine's CMC study.

[Network analysis of ethanol precipitation process for Schisandrae chinensis fructus].

A set of central composite design experiments were designed by using four factors which were ethanol amount, ethanol concentration, refrigeration temperature and refrigeration time. The relation between these factors with the target variables of the retention rate of schizandrol A, the soluble solids content, the removal rate of fructose and the removal rate of glucose were analyzed with Bayesian networks, and ethanol amount and ethanol concentration were found as the critical process parameters. Then a network model was built with 2 inputs and 4 outputs using back propagation artificial neural networks which was optimized by genetic algorithms.

[Multi-objective optimization of extraction process for red ginseng based upon extraction efficiency and cost control].

It is the objective of this study to optimize the extraction process of red ginseng to minimize the unit cost of extracting effective ingredients. The relation between the target variables of total quantity of ginsenosides and first extraction time, first extraction solution amount, second extraction time, second extract solution amount were studied with Box-Behnken experimental design method. At the same we also considered the cost of extraction solution and energy usage.

[Dynamic predictive modeling of extraction process for red ginseng using near-infrared spectroscopy].

It is the objective of this study to develop dynamic predictive model for the extraction process of red Ginseng using NIR spectroscopy. NIR spectroscopy was collected online and PLSR models were developed for total quantity of ginsenosides. The performance of NIR prediction model achieved R, RMSEC, RMSEP of 0.

Chemometric analysis for identification of botanical raw materials for pharmaceutical use: a case study using Panax notoginseng.

The overall control of the quality of botanical drugs starts from the botanical raw material, continues through preparation of the botanical drug substance and culminates with the botanical drug product. Chromatographic and spectroscopic fingerprinting has been widely used as a tool for the quality control of herbal/botanical medicines. However, discussions are still on-going on whether a single technique provides adequate information to control the quality of botanical drugs.

[Inhibitory effects of astragaloside IV on cytochrome P450 enzyme of rat liver microsomes].

Objective: To provide a scientific basis for the drug-combination and aim to examine whether astragaloside IV has the impact on the cytochrome P450 enzymes.

Method: Tolbutamide, chlorzoxazone, coumarin, nifedipine, and phenacetin were as probe substrates of rat CYP2C9, CYP2E1, CYP2A6, CYP3A4, and CYP1A2, and were incubated in rat liver microsomes with astragaloside IV. Triplicate samples were run to generate IC50 value by incubating P450 probe substrates in the presence of five concentrations of astragaloside IV in the incubation mixture.