1.Tracing origin of "Qinggong Maidong" production area based on analysis of literature and historical materials and identification of characteristics of Qinggong medicinal materials and cultural relics.
Ao-Yu REN ; Ting YAO ; Feng-Yuan LI ; Hua-Sheng PENG
China Journal of Chinese Materia Medica 2025;50(11):2931-2937
Maidong products are categorized into "Hang Maidong" and "Chuan Maidong". Since the Ming and Qing Dynasties, "Hang Maidong" has been regarded as having superior quality, but currently, it remains in name only in the market. This article reviewed historical materia medica and local chronicles from the Ming and Qing Dynasties and analyzed the historical evolution of Maidong production areas. The history of Maidong production in Zhejiang can be traced back to the Song Dynasty, and cultivation had already developed by at least the Ming Dynasty. During the Ming and Qing Dynasties, it was consistently used as a tribute. Ming Dynasty chronicles record "Chuan Maidong", which had already been cultivated on a large scale by the Qing Dynasty. "Hang Maidong" and "Chuan Maidong" share the same origin, with the former identifiable by the "gourd waist" shape of its tuberous root. Based on this, it can be inferred that the "Maimendong" herb illustrated in the Origins of Materia Medica(Ben Cao Yuan Shi) and the Maidong stored in the Qing Palace Imperial Pharmacy were both "Hang Maidong". The protection and development of the authentic "Hang Maidong" medicinal herb are urgently needed.
China
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Drugs, Chinese Herbal/history*
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History, 17th Century
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History, Ancient
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Medicine, Chinese Traditional/history*
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History, Medieval
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History, 16th Century
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History, 18th Century
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History, 15th Century
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Plants, Medicinal/chemistry*
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History, 19th Century
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History, 20th Century
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Humans
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Materia Medica/history*
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History, 21st Century
2.Intelligent diagnostic model of keratoconus based on deep learning algorithm
Di-Hua AO ; Xi-Rui TIAN ; Ming-Xun MA ; Bo ZHANG ; Min CHEN ; Yan-Li PENG
International Eye Science 2023;23(2):299-304
AIM: To establish an intelligent diagnostic model of keratoconus for small-diameter corneas by data mining and analysis of patients' clinical data.METHODS: Diagnostic study. A total of 830 patients(830 eyes)were collected, including 338 male(338 eyes)and 492 female(492 eyes), with an average age of 14-36(23.19±5.71)years. Among them, 731 patients(731 eyes)had undergone corneal refractive surgery at Chongqing Nanping Aier Eye Hospital from January 2020 to March 2022, and 99 patients had a diagnosed keratoconus from January 2015 to March 2022. Corneal diameter ≤11.1 mm was measured by Pentacam in all patients. Two cornea specialists classified patients' data into normal corneas, suspect keratoconus, and keratoconus groups based on the Belin/Ambrósio enhanced ectasia display(BAD)system in Pentacam. The data of 665 patients were randomly selected as the training set and the other 165 patients as the validation set by computer random sampling method. Seven parametric corneal features were extracted by convolutional neural networks(CNN), and the models were built by Residual Network(ResNet), Vision Transformer(ViT), and CNN+Transformer, respectively. The diagnostic accuracy of models was verified by cross-entropy loss and cross-validation method. In addition, sensitivity and specificity were evaluated using receiver operating characteristic curve.RESULTS: The accuracy of ResNet, ViT, and CNN+Transfermer for the diagnosis of normal cornea and suspect keratoconus was 85.57%, 86.11%, and 86.54% respectively, and the area under the receiver operating characteristic curve(AUC)was 0.823, 0.830 and 0.842 respectively. The accuracy of models for the diagnosis of suspect keratoconus and keratoconus was 97.22%, 95.83%, and 98.61%, respectively, and the AUC was 0.951, 0.939, and 0.988 respectively.CONCLUSION: For corneas ≤11.1 mm in diameter, the data model established by CNN+Transformer has a high accuracy rate for classifying keratoconus, which provides real and effective guidance for early screening.
3.Analysis and evaluation of eight active ingredients in Lilium lancifolium from different regions.
Huang-Qin ZHANG ; Hui YAN ; Da-Wei QIAN ; Zhen-Hua ZHU ; Sheng GUO ; Lan-Ping GUO ; Zhi-Shu TANG ; Jin-Ao DUAN
China Journal of Chinese Materia Medica 2017;42(2):311-318
This study established a rapid UPLC-TQ-MS/MS method for determination of eight active ingredients in Lilium lancifolium. The contents range of regaloside E, F, C and B are as follows: 0.604 0×10⁻¹-18.62×10⁻¹, 0.680 0×10⁻²-44.75×10⁻², 0.700 0×10⁻³-29.65×10⁻¹, 0.170 0×10⁻¹-4.724 mg•g⁻¹; the contents of chlorogenic acid, caffeic acid, protocatechualdehyde and ferulic acid, within the range of 6.827×10⁻³-16.07×10⁻³, 0.011 1×10⁻³-79.71×10⁻³, 0.593 7×10⁻³-2.962×10⁻³, 2.606×10⁻²-45.89×10⁻² mg•g⁻¹, respectively. According to PCA (principal components analysis) plotting, 35 batches can be divided into two categories, namely Anhui Huoshan and Hunan Longshan. The main different elements between these two categories are caffeic acid and ferulic acid according to the VIP (variable importance in the projection) points figure. Based on comprehensive principal component values, there are eight batches of L. lancifolium from Huoshan among the comprehensive ranking of ten. The UPLC-TQ-MS method for simultaneous analysis of eight active ingredients is accurate, efficient and convenient. This result can provide scientific basis for quality control of L. lancifolium.

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