Scanning Electron Microscopy and Liquid Chromatography for Physical and Chemical Inspection of Industrial Pharmaceutical Traditional Chinese Herbal Medicine

A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification

Introduction

Chinese Herbal Medicine (CHM), with its deep-rooted history and increasing global recognition, encounters significant challenges in automation for microscopic identification. These challenges stem from limitations in traditional microscopic methods, scarcity of publicly accessible datasets, imbalanced class distributions, and issues with small, unevenly distributed, incomplete, or blurred features in microscopic images.

Methods

To address these challenges, this study proposes a novel deep learning-based approach for Chinese Herbal Medicine Microscopic Identification (CHMMI). A segmentation-combination data augmentation strategy is employed to expand and balance datasets, capturing comprehensive feature sets. Additionally, a shallow-deep dual attention module enhances the model's ability to focus on relevant features across different layers. Multi-scale inference is integrated to process features at various scales effectively, improving the accuracy of object detection and identification.

Results

The CHMMI approach achieved an Average Precision (AP) of 0.841, a mean Average Precision at IoU=.50 (mAP@.5) of 0.887, a mean Average Precision at IoU from .50 to .95 (mAP@.5:.95) of 0.551, and a Matthews Correlation Coefficient of 0.898. These results demonstrate superior performance compared to state-of-the-art methods, including YOLOv5, SSD, Faster R-CNN, and ResNet.

Discussion

The proposed CHMMI approach addresses key limitations of traditional methods, offering a robust solution for automating CHM microscopic identification. Its high accuracy and effective feature processing capabilities underscore its potential to modernize and support the growth of the CHM industry.

Preclinical Pharmacokinetics and Pharmacodynamics of Coptidis Preparation in Combination with Lovastatin in High-Fat Diet-Induced Hyperlipidemic Rats

Lovastatin is a standard therapy for dyslipidemia. Alternatively, some ethnomedicines, such as Coptidis preparation, have been used for the treatment of dyslipidemia. Statins and complementary and alternative medicines may possess individual mechanisms of action against dyslipidemia. We hypothesize that the combination of Coptidis preparation and lovastatin may have synergistic effects for the treatment of dyslipidemia. To investigate this hypothesis, we developed a validated ultra-high-performance liquid chromatography-tandem mass spectrometry method to monitor lovastatin and its metabolites for pharmacokinetic studies in rats. This study was divided into four groups: lovastatin (10 mg/kg, p.o.) alone and lovastatin (10 mg/kg, p.o.) + Coptidis preparation (0.3, 1, or 3 g/kg, p.o.) for five consecutive days. In pharmacodynamic studies, a high-fat diet (HFD) was used to induce dyslipidemia in experimental rat models. The HFD rats were divided into four groups: treatment with HFD, HFD + lovastatin (100 mg/kg, p.o.), HFD + Coptidis preparation (1 g/kg, p.o.), and HFD + lovastatin (50 mg/kg, p.o.) + Coptidis preparation (1 g/kg, p.o.) for 28 consecutive days. The pharmacokinetic results demonstrated that Coptidis preparation significantly augmented the conversion of lovastatin into its main metabolite lovastatin acid in vivo. The pharmacodynamic results revealed that the Coptidis preparation and half-dose lovastatin group reduced the body weight, liver weight, and visceral fat in HFD rats. These findings provide constructive preclinical pharmacokinetic and pharmacodynamic applications of Coptidis preparation on the benefit of hyperlipidemia.