Publications by authors named "Haoshu Xiong"

Article Synopsis
  • Intelligent manufacturing technologies have significantly impacted traditional Chinese medicine (TCM) industries but struggle with unstructured data, like research reports and production records.
  • Generative artificial intelligence (AI) has shown promise in managing unstructured data, offering tools for information extraction, knowledge generation, and semantic retrieval within the TCM sector.
  • The study identifies four key applications of generative AI in TCM: a knowledge base, on-the-job training, production quality control, and supply chain management, while proposing the idea of a "smart industrial brain" to enhance AI integration in TCM manufacturing.
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Article Synopsis
  • * Currently, there are challenges in understanding TCM extraction methods and utilizing data effectively, which impede advancements in the industry.
  • * The article discusses major technological challenges in TCM extraction and reviews data-driven approaches for improving extraction processes, including analysis, optimization, online detection, control, and management.
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Owing to the advancement in pharmaceutical technology, traditional Chinese medicine industry has seen rapid development. Preferring conventional manufacturing mode, pharmaceutical enterprises of traditional Chinese medicine have no effective process detection tools and process control methods. As a result, the quality of the final products mainly depends on testing and the quality is inconsistent in the same batch.

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To realize the real-time monitoring of the production process of Yangxue Qingnao Granules and improve the inter-batch consistency of granule quality in the granulation process, this study established a near-infrared quantitative prediction model of moisture, particle size, bulk density, and angle of repose in the fluidized bed granulation process of Yangxue Qingnao Granules based on near-infrared spectroscopy(NIRS). The near-infrared spectra were collected from 355 samples in 12 batches in the granulation process by integrating the sphere detection module of the near-infrared spectrometer. In combination with the pretreatment methods such as the first derivative, multiplicative scatter correction(MSC), and standard normal variate(SNV), the model was established by partial least squares(PLS) regression.

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Physical attributes of Chinese herbal extracts are determined by their chemical components, and the physical and chemical attributes jointly affect the preparation process performance and the final product quality. Therefore, in order to improve the quality control of Chinese herbal extracts, we should comprehensively study the batch-to-batch consistency of physical and chemical attributes as well as the correlations between them. This paper first explored the physical attributes affecting the preparation process performance of the compound Danshen extract and developed a method for characterizing the texture attributes.

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The mixing process is one of the key operation units for solid preparation of traditional Chinese medicine. The physical properties such as particle size, density and viscosity of the mixture are key factors that need to be controlled, which will directly affect the performance of the preparation molding process and product quality. Subsequent dripping process performance and appearance qua-lity of dripping pills will be affected by dynamic viscosity of materials in the mixing process.

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Introduction: Charged aerosol detection (CAD) has the merits of high sensitivity, high universality and response uniformity. The strategy that combines the quantitative analysis of multi-components by single marker (QAMS) with CAD has certain advantages for the quantification of multi-components. However, relevant research was limited.

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China healthcare industry has gradually developed the consumer-centric integrated service model. To satisfy consumers' increasing demands on pluralistic, personalized and transparent healthcare services, pharmaceutical manufacturing enterprises must provide high-quality, precise and flexible medicines. This can be achieved by accelerating implementation of intelligent manufacturing, which is the core competitiveness of pharmaceutical manufacturing enterprises.

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Near-infrared (NIR) spectroscopy is one of the most successful pharmaceutical process analytical tools. For botanical drug products (BDPs), many studies have applied NIR spectroscopy for rapid analysis of botanical raw materials, extracts and formulations. However, the real-time process analysis reported for BDPs was still mainly conducted on lab- or pilot- scale equipment, where some essential conditions of the materials and process parameters can be easily controlled for NIR spectral measurement.

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Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products.

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Objective: To establish a method for monitoring the quality of intermediates generated in each working procedure during the production process of traditional Chinese medicine (TCM) , in order to ensure the batch-to-batch quality consistency of TCM products.

Method: The multistage multivariate statistic quality control (MMSQC) was proposed to monitor production quality of TCMs based on multivariate data analysis technique. Hotelling T2 and SPE were adopted for monitoring the quality of intermediates generated in each working procedure.

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Traditional Chinese medicine (TCM) products are usually manufactured through batch processes. To improve batch-to-batch reproducibility, the feasible approaches for real-time monitoring of batch evolution need to be developed. In-line near-infrared (NIR) spectroscopy combined with multivariate data analysis as an efficient process analytical technology (PAT) tool, is presented in this study for real-time batch process monitoring.

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The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models.

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