Understory planting of medicinal plants is a new planting mode that connects Chinese herbal medicine(CHM) with forest resources.The complex and variable understory environmental factors will inevitably affect the yield and quality of understory CHM.This research summarized the research progress on understory planting of medicinal plants based on forest types and environmental factors within the forest from the perspectives of understory light, air temperature and humidity, soil characteristics, and the interaction between crops within the forest.The results showed that the complex and variable light, temperature and humidity, and soil factors(such as fertility, acidity and alkalinity, and microorganisms) under the forest could affect the yield and quality of medicinal plants to varying degrees through physiological activities such as photosynthesis and respiration, resulting in a significant increase or decrease in yield and quality compared to open field cultivation.In addition, the competition or mutual benefit between different crops within the forest could lead to differences in the yield and quality of understory medicinal plants compared to open field cultivation.A reasonable combination of planting could achieve resource sharing and complementary advantages.Therefore, conducting systematic research on the effects of understory environmental factors on the yield and content of medicinal plants with different growth and development characteristics can provide theoretical guidance and technical references for formulating comprehensive strategies for understory planting of medicinal plants, such as selecting suitable medicinal plant varieties, optimizing planting density, and conducting reasonable forest management, thus contributing to the sustainable development and ecological protection of CHM.
Plants, Medicinal/growth & development* ; Forests ; Soil/chemistry* ; Environment ; Ecosystem ; Temperature

The natural forest and artificial shed are the main cropping modes of Coptis chinensis. This study is aimed to reveal the rhizosphere soil bacterial community structure difference between under tow C. chinensis cropping modes-natural forest and artificial shed, and to assist us to completely understand soil quality condition,and provide theoretical guidance for soil improvement and C. chinensis planting. The rhizosphere soil samples of 1-5-year-old C. chinensis under tow cropping modes-natural forest and artificial shed were collected. Illumina high-throughput sequencing technology was used to analyze the alpha diversity, community composition, community structure of soil bacteria under the tow cropping modes,and the effects of soil nutriment indices on soil bacterial community structure. Through the analysis of species number, Shannon, Chao1 index and ACE index of bacterial community, it was found that the bacterial diversity of 1-year-old C. chinensis soil under natural forest cropping mode was significantly lower than that under artificial shed cropping mode, and the diversity of bacterial communities in soil of 2-5-years old C. chinensis were not significant different between two cropping modes. A total of 53 phyla,60 classes,140 orders and 266 families were detected in the rhizosphere soil of C. chinensis under the cropping modes of natural forest, respectively. The rhizosphere soil of C. chinensis under the cropping modes of artificial shed included 54 phyla,65 classes,140 orders and 264 families, respectively. Under the two cropping modes, the top 10 dominant species of bacterial community abundance are the same, they are Proteobacteria, Acidobacteria, Actinobacteria,Bacteroidetes, Planctomycetes, Chloroflexi, Verrucomicrobia, Gemmatimonadetes, Firmicutes and Cyanobacteria, but there are differences in the abundance sequence. The top 10 dominant species of bacterial community abundance accounted for 74.36% to 74.30% of the total bacteria, and 3.15% to 3.92% of the bacteria are unclassified. The results of Metastat analysis showed that the abundance of Gemmatimonadetes in the rhizosphere soil of C. chinensis under the cropping modes the artificial shed was significantly higher than that under the natural forest cropping mode(P<0.05). MRPP analysis of community structure differences showed that under tow cropping modes, there were significant differences in the bacterial community structure of 1-4-year-old soil bacteria, among which the difference between 1-year-old soil samples was the largest. With the increase of cropping years, the difference gradually decreases, and there is no significant difference in the bacterial community structure between 5-year-old soil samples. RDA analysis and correlation analysis of bacterial community structure and soil physical and chemical properties showed that the order of environmental factors on the rhizosphere soil bacteria of Coptis chinensis was: pH>available P> total P> total K>bulk density>total N>available N>organic matter. The results are helpful to understand the soil health of C. chinensis and provide scientific basis and theoretical guidance for soil improvement and C. chinensis planting.
Child, Preschool ; Coptis ; Forests ; Humans ; Infant ; Rhizosphere ; Soil ; Soil Microbiology

Dendrobium huoshanense is a precious medicinal plant belonging to Dendrobium of Orchidaceae. It is a special medicinal material and extremely scarce in Huoshan county, Anhui province. At present, D. huoshanense has been greatly protected, which also makes it possible to industrialize relying on tissue culture and artificial cultivation technology. Three main planting methods were utilized for cultivating D. huoshanense including facility cultivation, under forest cultivation and simulative habitat cultivation. Firstly, the three cultivation modes and technical characteristics of D. huoshanense were compared and analyzed, and it was found that the ecological environment of D. huoshanense cultivated in the simulated environment was closer to that of wild D. huoshanense. Secondly, based on comparing the characters and quality of three cultivation modes, the results showed that the shape of D. huoshanense cultivated in simulated environment was more similar to that of "grasshopper thigh" recorded in Bencao Jing Jizhu, and its quality was better than that of facilities and under forest cultivation. The comprehensive benefit comparison of three modes showed that the simulated cultivation had high income, the lowest input-output ratio and significant economic benefit. The quality of cultivated D. huoshanense was further evaluated from four aspects of "excellent environment" "excellent shape" "high quality" "excellent effect", which summarized the comprehensive advantages of simulative habitat cultivation of D. huoshanense as follows: the original habitat and site environment of simulated wild D. huoshanense, the closer shape to the wild, the more content of main medicinal components, and higher economic benefit and better efficacy. The quality of D. huoshanense was improved by the use of simulative habitat cultivation, which has practical significance to guide its large-scale cultivation.
Dendrobium ; Ecosystem ; Forests ; Plants, Medicinal

The ecological environment is closely related to the growth and quality of authentic medicinal materials. Ginseng is very strict with its natural environment and grows mostly in the damp valleys of forests, and the appearance and chemical composition of ginseng under different growth environments are very different. This article reviews the effects of different ecological factors(including light, temperature, altitude, moisture, soil factors, etc.)on the appearance and chemical composition(mainly ginsenosides) of ginseng. Through systematic review, it is found that soil physical factors are the most important ecological factors that affect the appea-rance of ginseng, and soil bulk density plays a key role; temperature affects ginsenosides in ginseng medicinal materials The dominant ecological factors for the accumulation of chemical ingredents; strong light, high altitude, high soil moisture, low soil nutrient and strong acid soil can influence the accumulation of secondary metabolites in ginseng. Environmental stress can also stimulate the formation and accumulation of secondary metabolites in medicinal plants. Appropriate low temperature stress, high or low water stress, acid or alkali stress can also promote the accumulation of ginsenosides. This article systematically reviews the ecological factors that affect the appearance and chemical composition of ginseng, and clarifies the dominant ecological factors and limiting factors for the formation of ginseng's appearance and quality, as well as beneficial environmental stress factors, in order to provide a theoretical basis for ginseng ecological planting and ginseng quality improvement.
Forests ; Ginsenosides ; Panax ; Plants, Medicinal ; Soil

The aim in this literature review was (1) to explore the physiologically and psychologically therapeutic benefits of forest bathing on adults suffering from pre-hypertension or hypertension, and (2) to identify the type, duration, and frequency of an effective forest bathing intervention in the management of pre-hypertension and hypertension, so as to provide directions for future interventions or research. The electronic databases PubMed, Cochrane Library, CINAHL, PsyINFO, and the China Academic Journals (CAJ) offered through the Full-text Database (CNKI) were searched for relevant studies published from the inception of the databases to April 2019. Of the 364 articles that were identified, 14 met the criteria for inclusion in this review. The synthesis of the findings in the included studies revealed that forest bathing interventions were effective at reducing blood pressure, lowering pulse rate, increasing the power of heart rate variability (HRV), improving cardiac-pulmonary parameters, and metabolic function, inducing a positive mood, reducing anxiety levels, and improving the quality of life of pre-hypertensive or hypertensive participants. Forest walking and forest therapy programs were the two most effective forest bathing interventions. Studies reported that practicing a single forest walking or forest therapy program can produce short-term physiological and psychological benefits. It is concluded that forest bathing, particularly forest walking and therapy, has physiologically and psychologically relaxing effects on middle-aged and elderly people with pre-hypertension and hypertension.
Adult ; Aged ; Aged, 80 and over ; Female ; Forests ; Humans ; Hypertension ; prevention & control ; Male ; Middle Aged ; Prehypertension ; prevention & control ; Relaxation Therapy ; statistics & numerical data ; Young Adult

To clarify the difference of soil moisture characteristics between mixed broad leaf-conifer forest soil and artificial cultivation of Notopterygium incisum, the HYPROP system and the dew point potential meter were used to determine soil water retention curves(SWRC) for samples of two horizons(i.e. 2-7 cm, 10-15 cm). The basic physical and chemical properties of soil and its water characteristic parameters were also determined. The result showed as fllows:①The bulk density of mixed coniferous-broad leaf forest soil was between 0.33 and 0.52 g·cm~(-3), significantly lower than the corresponding value of field soil(1.01-1.18 g·cm~(-3))(P<0.05), While the organic matter content was significantly higher than the corresponding value of field soil(P<0.05). ②The saturated water content(θ_s), field water holding capacity(θ_(FC)) and Water that can be effectively utilized by plants(θ_(PAC)) of mixed coniferous-broadleaved forest soil were significantly higher than the corresponding value of field soil(P<0.05), while the retained water content(θ_r) value that cannot be effectively utilized by plants was significantly lower than that of field soil(P<0.05). ③The values of structural porosity(0.13-0.24 cm~3·cm~(-3)) and Matrix porosity(0.34-0.44 cm~3·cm~(-3)) of mixed coniferous-broadleaved forest soil were higher than the corresponding values of field soil. Therefore, with low bulk density and high content of organic matter, mixed coniferous-broadleaved forest soil can store more water in soil in the form of effective water to meet the needs of plants for water, thus possibly forming high quality medicinal materials of Notopterygii Rhizoma et Radix. In conclusion, the results of this study can provide theoretical basis guidance for soil structure improvement and water management to form high quality medicinal materials in the artificial cultivation of N. incisum.
Apiaceae ; China ; Forests ; Soil ; Tracheophyta ; Water ; analysis

This study is aimed to reveal the rhizosphere soil fungal community structure difference of Coptis chinensis cropping between natural forest and artificial shed modes, and provide theoretical guidance for soil improvement and C. chinensis planting. The rhizosphere soil samples of 1-5-year-old C. chinensis under natural forest and artificial shed modes were collected. Illumina high-throughput sequencing technology was used to analyze the community structure and diversity of soil fungi under the tow cropping modes,and the effects of soil nutriment indices on soil fungal community structure. The results suggested that the abundance and diversity of fungal communities in soil of 2-5-year-old C. chinensis were not significant different in both two cropping modes, but it was significantly higher than that in the 1-year-old C. chinensis. Comparing soil samples from the same year-old C. chinensis under the two cropping modes, it was found that there was no significant difference in the abundance and diversity of fungal communities. The fungal community of the rhizosphere soil was different in composition and abundance between tow cropping modes, and between different planting years. The 17 phyla,59 classes and 155 orders,and 17 phyla,59 classes and 157 orders were detected in the rhizosphere soil of C. chinensis under the cropping modes of natural forest and the artificial shed, respectively. Ascomycota, Basidiomycota and Mortierellomycota were dominant phyla in rhizosphere soil, and the average abundance of the 3 phyla accounted for 74.36% and 74.30% of the total fungi. The results of analysis of similarities showed that there were significant differences in the fungal community structure of 1-year-old and 2-year-old C. chinensis soil fungi, and there was no significant difference in the community structure of 3-5-year-old samples. Under the natural forest cropping mode, there were significant differences among the samples of different years. Under the artificial shed cropping, there were significant differences in fungal community structure between 1-year-old and 3-5-year-old C. chinensis soil, and between 2-year-old and 3-5-year-old C. chinensis soil. The results of canonical correlation analysis showed that soil pH and soil organic matter content were the main factors affecting the soil fungal community structure. Soil organic matter content was positively correlated with Basidiomycota and Cryptomycota, pH was negatively correlated with Basidiomycota and C. ryptomycota. The planting of C. chinensis has promoted the diversity and abundance of rhizosphere fungal community significantly. For the same year-old C. chinensis soil, abundance of fungal community was no significant difference between two cropping modes. There are significant differences in the rhizosphere soil fungal community structure between tow cropping modes in the first two years of planting. Through the interaction between the rhizosphere and the soil and the continuous selection of the rhizosphere to the fungal community, the fungal community structure tended to be the same between the two cropping modes in rhizosphere soil of 3-5-year old C. chinensis. The soil pH and orga-nic matter content were the main factors affecting the change of fungal community structure.
Coptis ; Forests ; Fungi ; Mycobiome ; Plant Roots ; Rhizosphere ; Soil ; Soil Microbiology

OBJECTIVES: The aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for APRI (aspartate aminotransferase-to-platelet ratio) and FIB-4 (fibrosis score) for the prediction and staging of fibrosis in children with chronic hepatitis C (CHC). METHODS: Random forest (RF) was utilized in this study for data cleansing; then, prediction and staging of fibrosis, APRI and FIB-4 scores and their areas under the ROC curve (AUC) have been obtained on the cleaned dataset. A cohort of 166 Egyptian children with CHC was studied. RESULTS: RF, APRI, and FIB-4 achieved high AUCs; where APRI had AUCs of 0.78, 0.816, and 0.77; FIB-4 had AUCs of 0.74, 0.828, and 0.78; and RF had AUCs of 0.903, 0.894, and 0.822, for the prediction of any type of fibrosis, advanced fibrosis, and differentiating between mild and advanced fibrosis, respectively. CONCLUSIONS: Machine learning is a valuable addition to non-invasive methods of liver fibrosis prediction and staging in pediatrics. Furthermore, the obtained cutoff values for APRI and FIB-4 showed good performance and are consistent with some previously obtained cutoff values. There was some agreement between the predictions of RF, APRI and FIB-4 for the prediction and staging of fibrosis.
Area Under Curve ; Child ; Cohort Studies ; Dataset ; Fibrosis ; Forests ; Hepacivirus ; Hepatitis C ; Hepatitis C, Chronic ; Hepatitis ; Humans ; Liver Cirrhosis ; Machine Learning ; Medical Informatics ; Pediatrics ; ROC Curve

OBJECTIVES: Triage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels. METHODS: This was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level. RESULTS: The models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917–0.925 and AUROC = 0.922, 95% confidence interval 0.918–0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models. CONCLUSIONS: Machine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.
Cross-Sectional Studies ; Dataset ; Emergencies ; Emergency Service, Hospital ; Forests ; Humans ; Logistic Models ; Machine Learning ; Natural Language Processing ; ROC Curve ; Triage

OBJECTIVES: Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used. METHODS: We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble. RESULTS: Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data. CONCLUSIONS: We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.
Breast Neoplasms ; Breast ; Classification ; Female ; Forests ; Humans ; Linear Models ; Machine Learning ; Medical Informatics ; Statistics as Topic