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Since the 16th century, Western countries have conducted extensive plant collections in Asia, particularly in China, driven by the need to collect botanical resources and foster academic development. These activities have not only significantly enriched the Western botanical specimen collections but have also had a profound impact on the development of related disciplines such as botany, ecology, and horticulture. During this process, a large number of renowned plant hunters emerged, whose discoveries and contributions are still remembered today. George Forrest (1873-1932) was one of these distinguished plant hunters. From 1904 to 1932, he visited China seven times to collect plants and became famous for the regional distinctiveness of the species he collected. However, due to the lack of systematic collection, organization, and analysis of specimens collected by Forrest, only a few species, such as the species Rhododendron, are well-known among the many species he introduced to the West. Furthermore, the personal collecting characteristics and the characteristic species collected by Forrest are also not clear. This limits a comprehensive understanding of the specimen collection history and impact of Forrest in China. Therefore, systematic organization and analysis of Forrest's plant specimens collected in China are crucial to understanding his impact on botanical classification, Rhododendrons introduction, global horticulture, and plant propagation. This study aims to systematically organize and analyze the plant specimens collected by George Forrest in China to investigate the family, genus, and species composition of the collected specimens and the seven collection expeditions of Forrest in China, as well as the time and altitude of these collections. Furthermore, it seeks to discuss Forrest's scientific contributions to the global spread of plants, the widespread application of the Rhododendron, and his impact on the development of modern gardens, providing a theoretical basis and data reference for related research and professional development. To this end, we extensively consulted important historical literature related to Forrest and systematically collected data from online specimen databases. The conclusions drawn from the available data include 38,603 specimens, with 26,079 collection numbers, belonging to 233 families, 1395 genera, and 5426 species, which account for 48.24%, 32.63%, and 14.17% of the plant families, genera, and species in China, respectively. Rhododendron specimens made up 17.20% of the specimens collected in this study. The collection locations cover three provinces or autonomous regions, 11 prefecture-level cities, and 25 counties. Furthermore, we found that Forrest's collections were concentrated in spring and summer, mainly in high-altitude areas, with 135 species found below 1500 m and 3754 species at 1500 m and above. Rhododendron specimens were mostly found above 3000 m.
RESUMO
Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS. Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC). Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc. Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.
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Application value of epidural anesthesia combined with epidural analgesia and general anesthesia combined with intravenous analgesia in ovarian cancer surgery was explored. In total 298 ASA I-III grade patients with ovarian cancer, undergoing extensive total hysterectomy and pelvic lymphotomy, were retrospectively analyzed. Patients were divided into two groups: the epidural anesthesia combined with epidural analgesia group (group A, 158 cases), and the general anesthesia combined with intravenous analgesia group (group B, 140 cases). The first exhaust time, incidence of adverse reactions, Aldrete score, and recovery were observed, and the visual analogue scale (VAS) scores during resting, exercise and cough at 24 h after surgery were recorded. Fasting venous blood (2 ml) was drawn at the same time before anesthesia and at 24 h after anesthesia in both groups to determinate cortisol (COR) and C-reactive protein (CRP) levels. The first exhaust time and incidence of adverse reactions in group A were significantly lower than those in group B (P<0.05). The Aldrete score and extubation time (ET) in group A were significantly higher than that in group B. Eye opening time (EOT), recovery orientation time (ROT) and post-anesthesia care unit (PACU) time in group A were significantly lower than those in group B (P<0.05). The VAS scores in group A during resting, exercise and cough were lower than those in group B (P<0.05). Compared with before anesthesia, the levels of COR and CRP increased significantly in both groups at 24 h after anesthesia (P<0.05), while the level of COR and CRP in group A was significantly lower than that in group B, at 24 h after surgery (P<0.05). Epidural anesthesia combined with epidural analgesia has better analgesic effect, higher safety, lower incidence of adverse reactions, and is beneficial to the recovery of patients with ovarian cancer after radical operation when compared with general anesthesia combined with intravenous analgesia.
