Journal articles on the topic 'Diseases of Tea'
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Dilek, Nursel, Aziz Ramazan Dilek, Yunus Saral, and Ahmet Metin. "Skin diseases in tea collectors." Open Journal of Preventive Medicine 04, no. 01 (2014): 13–16. http://dx.doi.org/10.4236/ojpm.2014.41003.
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Shen, Liang. "Tea intake and liver diseases." Hepatology 53, no. 1 (July 29, 2010): 373–74. http://dx.doi.org/10.1002/hep.23872.
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Lin, Ji, Di Bai, Renjie Xu, and Haifeng Lin. "TSBA-YOLO: An Improved Tea Diseases Detection Model Based on Attention Mechanisms and Feature Fusion." Forests 14, no. 3 (March 20, 2023): 619. http://dx.doi.org/10.3390/f14030619.
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Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea diseases also easily disturbed by the complex background of the growing region. In addition, some tea diseases are concentrated in the entire area of the leaves, needing to be inferred from global information. Common target detection models are difficult to solve these problems. Therefore, we proposed an improved tea disease detection model called TSBA-YOLO. We use the dataset of tea diseases collected at the Maoshan Tea Factory in China. The self-attention mechanism was used to enhance the ability of the model to obtain global information on tea diseases. The BiFPN feature fusion network and adaptively spatial feature fusion (ASFF) technology were used to improve the multiscale feature fusion of tea diseases and enhance the ability of the model to resist complex background interference. We integrated the Shuffle Attention mechanism to solve the problem of difficult identifications of small-target tea diseases. In addition, we used data-enhancement methods and transfer learning to expand the dataset and relocate the parameters learned from other plant disease datasets to enhance tea diseases detection. Finally, SIoU was used to further improve the accuracy of the regression. The experimental results show that the proposed model is good at solving a series of problems encountered in the intelligent recognition of tea diseases. The detection accuracy is ahead of the mainstream target detection models, and the detection speed reaches the real-time level.
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Ihsan, Candra Nur, Nova Agustina, Muchammad Naseer, Harya Gusdevi, Jack Febrian Rusdi, Ari Hadhiwibowo, and Fahmi Abdullah. "Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 1 (February 18, 2024): 135–41. http://dx.doi.org/10.29207/resti.v8i1.5587.
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Tea is one of the top ten export products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, although the value of the export impacts the country’s economic structure. In addition to market competition, Indonesia must maintain tea leaf production so that the increase in export decline is not significant or even increases tea leaf export production. To improve production quality and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early. This study compares machine learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying tea leaf diseases are random forest (RF), support vector classifier (SVC), extra tree classifier (ETC), decision tree (DT), XGBoost classifier (XGB), and convolutional neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC has been proven to be the most superior machine learning algorithm for detecting tea leaf diseases in this study.
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YAMADA, H., and H. WATANABE. "Tea polyphenols in preventing cardiovascular diseases." Cardiovascular Research 73, no. 2 (January 15, 2007): 439–40. http://dx.doi.org/10.1016/j.cardiores.2006.11.008.
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Wu, Wei. "Identification of tea leaf diseases based on deep transfer learning." Frontiers in Computing and Intelligent Systems 2, no. 3 (February 13, 2023): 75–77. http://dx.doi.org/10.54097/fcis.v2i3.5218.
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Tea trees are often vulnerable to diseases and insect pests in the process of growth, resulting in the decline of tea production and quality. It is of great significance to identify and prevent tea leaf diseases in time to ensure the steady increase of tea. This study proposes a tea leaf disease identification method based on deep transfer learning, which improves the recognition accuracy of the model through knowledge transfer. Besides, for the unbalanced distribution of the number of samples, the cross-entropy loss function is replaced with the focal loss function, which further improves the identification effect of the model. The experiment shows that the identification model of tea leaf disease proposed in this study can achieve the accuracy of more than 90.42%, which verifies the effectiveness of this research and has important theoretical and practical significance in promoting the development of intelligent agriculture.
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Wan, Yuhe, Yuxin Han, Xinyi Deng, and Yingjuan Chen. "Metabolomics Analysis Reveals the Effect of Two Alpine Foliar Diseases on the Non-Volatile and Volatile Metabolites of Tea." Foods 12, no. 8 (April 7, 2023): 1568. http://dx.doi.org/10.3390/foods12081568.
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Blister blight and small leaf spots are important alpine diseases that mainly attack tender tea leaves, affecting tea quality. However, there is limited information on the effect of these diseases on tea’s non-volatile and volatile metabolites. Metabolomic analysis based on UHPLC-Q-TOF/MS, HPLC and GC/MS was used to reveal the characteristic chemical profiles of tea leaves infected with blister blight (BB) and small leaf spots (SS). Flavonoids and monolignols were non-volatile metabolites that were enriched and significantly changed. Six main monolignols involved in phenylpropanoid biosynthesis were significantly induced in infected tea leaves. The accumulation of catechins, (−)-epigallocatechin gallate, (−)-epicatechin gallate, caffeine, amino acids and theanine were significantly decreased in both diseased tea leaves, while soluble sugar, (−)-epigallocatechin and phenol-ammonia were obviously increased. Among them, the amounts of sweet and umami-related soluble sugar, sucrose, amino acids and theanine were much higher in BB, while bitter and astringent taste-related catechins and derivatives were much higher in SS. Volatiles analysis showed that volatiles content in SS and BB was significantly decreased, and styrene was significantly induced in blister blight-infected tea leaves. The results indicate that the type and amount of volatiles were highly and differentially influenced by infection with the two alpine diseases.
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Yen Nhi, Tran Thi, Vo Tan Thanh, Nguyen Phu Thuong Nhan, Nguyen Ngoc Quy, Nguyen Thi Mong Tho, Nguyen Thi Bay, Tran Quoc Toan, et al. "Studies on Polyphenol Content, Flavonoid Content and Antioxidant Activity of Graviola Tea pulp (Annona muricata L.) from Tien Giang Province, Vietnam." Asian Journal of Chemistry 32, no. 12 (2020): 3075–78. http://dx.doi.org/10.14233/ajchem.2020.22468.
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Tea is the most commonly consumed beverage worldwide due to promote health and prevention of some human diseases. Soursop tea pulp is one of a new product focused on the values of total ascorbic acid (TAA), total polyphenol content (TPC) and total flavonoid content (TFC). A study using DPPH and ABTS method to detect the maximum of active ingredients in tea simultaneously. Different pulp tea samples with varying dates of production checked for TAA, TPC, TFC, antioxidation and sensory evaluation. There is a difference in TAA between sample (1) and (4), achieved 7.43 ± 0.04 mg/g DM and 12.21 ± 0.04 mg/g DM, respectively. TFC content ranged between 0.13 and 0.18. The phenolics group in tea is high and there are differences between the 3 recent production samples with (1): 3.81 mg QC/g DM fluctuation. In sample 1, the antioxidant activity of the ethanol extract of Soursop was correlated with total phenolic and flavonoid content with values IC50 of 0.26 mgAA/g DM, 2.61 mgAA/g DM for DPPH and ABTS scavenging activity, respectively. Moreover, the Lab* colour space of the soursop tea is measured in the dark area, the L* difference achieved at 30.82 ± 1.24 (2) lowest and the highest 37.42 ± 4.77 (1). Consumer sensory evaluation is performed with results based on a 5-point scale describing: flavour, aroma, colour and favourite.
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Reygaert, Wanda C. "Green Tea Catechins: Their Use in Treating and Preventing Infectious Diseases." BioMed Research International 2018 (July 17, 2018): 1–9. http://dx.doi.org/10.1155/2018/9105261.
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Green tea is one of the most popular drinks consumed worldwide. Produced mainly in Asian countries from the leaves of the Camellia sinensis plant, the potential health benefits have been widely studied. Recently, researchers have studied the ability of green tea to eradicate infectious agents and the ability to actually prevent infections. The important components in green tea that show antimicrobial properties are the catechins. The four main catechins that occur in green tea are (-)-epicatechin (EC), (-)-epicatechin-3-gallate (ECG), (-)-epigallocatechin (EGC), and (-)-epigallocatechin-3-gallate (EGCG). Of these catechins, EGCG and EGC are found in the highest amounts in green tea and have been the subject of most of the studies. These catechins have been shown to demonstrate a variety of antimicrobial properties, both to organisms affected and in mechanisms used. Consumption of green tea has been shown to distribute these compounds and/or their metabolites throughout the body, which allows for not only the possibility of treatment of infections but also the prevention of infections.
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Fitrasyah, Siti Ika, Ariani Ariani, Nurdin Rahman, Nurulfuadi Nurulfuadi, Ummu Aiman, Devi Nadila, Fendi Pradana, Aulia Rakhman, and Diah Ayu Hartini. "Analysis of Chemical Properties and Antioxidant Activity of Sambiloto (Andrographis paniculata Nees.) Leaf Tea Formula as a Functional Drink in Preventing Coronavirus Diseases and Degenerative Diseases." Open Access Macedonian Journal of Medical Sciences 9, A (April 29, 2021): 196–201. http://dx.doi.org/10.3889/oamjms.2021.5872.
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AIM: The purpose of this study was to develop a tea formula of sambiloto leaves, analyzed the chemical properties (polyphenols, tannins, water, extracts in water, total ash, water soluble ash, water-insoluble ash, insoluble acid ash, and water-soluble ash alkalinity) as well as the antioxidant capacity of the formula. METHODS: This research was divided into two stages. The first stage was the formulation of sambiloto leaves tea and the second stage was the analysis of chemical properties (levels of polyphenols, tannins, water, extracts water, total ash, water soluble ash, water-insoluble ash, insoluble acid ash, and water soluble ash alkalinity) and antioxidant capacity analysis. The analysis was carried out on all of the sambiloto leaves tea formulas to determine the effect of the formulations on chemical properties and antioxidant capacity. This study used Microsoft Excel 2013 in data processing management. RESULTS: The highest extract water was at F4, and the lowest was at F1. Based on the analysis, the formula of sambiloto leaves tea was in the range of 1.026–1.734%. Of all the formulas that have been prepared, sambiloto leaves tea formula F1 was obtained the best chemical and antioxidant properties among all formulas, which were composed of 100% sambiloto leaves, 5% honey, and 45% dates. CONCLUSION: Sambiloto leaves have good antioxidant activity and can be used as a drink in the form of an infusion, such as tea. The result of brewing the sambiloto tea formula has faded green color. The polyphenol content of the sambiloto leaves tea formula still did not meet the Indonesia National Standard (≥5.2%), was 1.5–3.1%.
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BORA, POPY, and L. C. BORA. "Revisiting non-chemical modes of diseases and pests management in tea (Camellia sinensis): A review." Indian Journal of Agricultural Sciences 92, no. 1 (January 31, 2022): 3–9. http://dx.doi.org/10.56093/ijas.v92i1.120819.
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Tea [Camellia sinensis (L) O. Kuntze] like any other perennial plantation crop is subjected to loss in biomass yield and functional quality, once exposed to beyond economic thresholds of diseases and pests load. A battery of diseases and pests are reported to badly affect the tea industry of India. Microbial antagonists and botanicals emerged as two most vibrant via-media of addressing tea diseases and pests amongst non-chemical modes, in a manner, closest to residue free production system. Authors revisited the use of microbial antagonists and botanicals through comprehensive updated analysis of breakthroughs recorded in the success of microbial antagonists-and botanicals mediated diseases and pests management in tea. Our efforts also portrays the futuristic viewpoints in terms of developments of commercial formulations of botanicals using their bioactive compounds, consortium of bioagents and fortification of botanicals with bioagents through area wide field response studies in the back drop of growing demand of organic tea, the most pressing developmental issue of tea industry.
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Khan, Naghma, and Hasan Mukhtar. "Tea Polyphenols in Promotion of Human Health." Nutrients 11, no. 1 (December 25, 2018): 39. http://dx.doi.org/10.3390/nu11010039.
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Tea is the most widely used beverage worldwide. Japanese and Chinese people have been drinking tea for centuries and in Asia, it is the most consumed beverage besides water. It is a rich source of pharmacologically active molecules which have been implicated to provide diverse health benefits. The three major forms of tea are green, black and oolong tea based on the degree of fermentation. The composition of tea differs with the species, season, leaves, climate, and horticultural practices. Polyphenols are the major active compounds present in teas. The catechins are the major polyphenolic compounds in green tea, which include epigallocatechin-3-gallate (EGCG), epigallocatechin, epicatechin-3-gallate and epicatechin, gallocatechins and gallocatechin gallate. EGCG is the predominant and most studied catechin in green tea. There are numerous evidences from cell culture and animal studies that tea polyphenols have beneficial effects against several pathological diseases including cancer, diabetes and cardiovascular diseases. The polyphenolic compounds present in black tea include theaflavins and thearubigins. In this review article, we will summarize recent studies documenting the role of tea polyphenols in the prevention of cancer, diabetes, cardiovascular and neurological diseases.
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Lubis, Arif Ridho, Santi Prayudani, Muharman Lubis, and Al Khowarizmi. "Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 1273. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1273-1281.
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The tea plants (Camellia Sinensis) are small tree species that use leaves and leaf buds to produce tea harvested through a monoculture system. It is an agriculture practice to cultivate one types of crop or livestock, variety or breed on a farm annually. Moreover, the emergence of pests, pathogens and diseases cause serious damages to tea plants significantly to its productivity and quality to optimum worst. All parts of the tea plant such as leaves, stems, roots, flowers and fruits are exposed to these harm lead to loss of yield 7 until 10% per year. The intensity of these attacks vary greatly on particular climate, the degree slope and the plant material used. Therefore, this study analyzes tea leaves as a common part used in recipes to create unique taste and flavor in tea production, especially in agro-industry. The decision making method used is Fuzzy Mamdani Inference as one of model with functional hierarchy with initial input based on established criteria. Fuzzy logic will provide tolerance to the set of value, so that small changes will not result in significant category differences, only affect the membership level on the variable value. Previous method using probabilities have shown 78% tea leaves have been attacked by category C (Gray Blight) while using Mamdani indicated 86% of tea leaves have been infected. In this case, this result pointed out that Fuzzy Mamdani Inferences have more optimal result compare to the previous method.
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Batta, Dr Anil. "Green Tea Treats and Prevents Infectious Diseases." Scholars International Journal of Biochemistry 3, no. 10 (October 18, 2020): 211–14. http://dx.doi.org/10.36348/sijb.2020.v03i10.002.
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Batta, Dr Anil. "Green Tea Treats and Prevents Infectious Diseases." Scholars International Journal of Biochemistry 3, no. 10 (October 18, 2020): 211–14. http://dx.doi.org/10.36348/sijb.2020.v03i10.002.
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Oz, Helieh. "Chronic Inflammatory Diseases and Green Tea Polyphenols." Nutrients 9, no. 6 (June 1, 2017): 561. http://dx.doi.org/10.3390/nu9060561.
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Oz, Helieh. "Chronic Inflammatory Diseases and Green Tea Polyphenols." Nutrients 9, no. 6 (June 1, 2017): 660. http://dx.doi.org/10.3390/nu9060660.
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Mao, Xiangbing, Changsong Gu, Daiwen Chen, Bing Yu, and Jun He. "Oxidative stress-induced diseases and tea polyphenols." Oncotarget 8, no. 46 (September 14, 2017): 81649–61. http://dx.doi.org/10.18632/oncotarget.20887.
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Tijburg, L. B. M., T. Mattern, J. D. Folts, U. M. Weisgerber, and M. B. Katan. "Tea flavonoids and cardiovascular diseases: A review." Critical Reviews in Food Science and Nutrition 37, no. 8 (December 1997): 771–85. http://dx.doi.org/10.1080/10408399709527802.
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Rezayee, Rifat Binth, Md Showkat Kabir, Mohammed Moinul Hoque, Nirupam Chowdhury, Mohammad Shish Rahman, Nawreen Binte Anwar, and Shah-Noor Hassan. "Pattern of Eye Diseases among Tea Workers." Open Journal of Ophthalmology 14, no. 02 (2024): 117–27. http://dx.doi.org/10.4236/ojoph.2024.142011.
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Janiak, Michał Adam, Adriana Slavova-Kazakova, Magdalena Karamać, Vessela Kancheva, Anastasiya Terzieva, Milena Ivanova, Tsvetelin Tsrunchev, and Ryszard Amarowicz. "Effects of Gamma-Irradiation on the Antioxidant Potential of Traditional Bulgarian Teas." Natural Product Communications 12, no. 2 (February 2017): 1934578X1701200. http://dx.doi.org/10.1177/1934578x1701200210.
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Food irradiation technologies are used to reduce the risk of food borne diseases by eliminating pathogenic microorganisms, prolonging shelf life and reducing storage losses by delaying ripening, germination or sprouting. However, application of irradiation in food technology can negatively influence the biologically active compounds in foods. In this research, the effect of gamma-irradiation on the antioxidant activity of Bulgarian teas was investigated. The aim of the study was to evaluate the total phenolic and tannin content and antioxidant activity of ethanolic extracts of Bulgarian herbal teas before and after gamma-irradiation. Mursalski tea ( Sideritis scardica), Mashterka tea ( Thymus serpyllum), Good Night tea (tea mix), Staroplaninski tea (Balkan tea mix), Trakia tea (tea mix), and Mountain tea (Planinski tea mix) were selected for this study. Gamma-irradiation was applied at the absorbed dose of 5 kGy. Antioxidant activity of non-irradiated and irradiated teas was determined by measuring antiradical activity against DPPH• and ABTS•+ and the ability to reduce ferrous ions. The highest total phenolic content was found in Mursalski tea (268 mg/g), and the highest tannin content in Good Night tea (168 mg/g). FRAP, TEAC and DPPH assays revealed that the most active samples were Staroplaninski (2.78 mmol Fe (II)/g), Planinski (0.87 mmol Trolox/g) and Planinski (0.032 mg/mL), respectively. The radical scavenging activity of irradiated tea samples was maintained after gamma-irradiation. The most interesting extract from irradiated tea studied was Staroplaninski, which demonstrated a higher antioxidant potential in the irradiated sample compared with the non-irradiated sample.
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ZHANG, Xiaoyang, Haozhi LONG, Da HUO, Masood I. AWAN, Jinhua SHAO, Athar MAHMOOD, Shuang LIU, et al. "Insights into the functional role of tea microbes on tea growth, quality and resistance against pests and diseases." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 50, no. 4 (December 5, 2022): 12915. http://dx.doi.org/10.15835/nbha50312915.
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Tea is an economical and most widely used beverage across the globe owing to its unique fragrance and flavor. Plant microbe interaction has emerged as an important topic which got the attention of scientists to improve plant performance. Tea microbes remained a prominent research topic for scientists over the years as tea microbes helps in nutrient cycling and stress management which in turn improve the tea growth, yield and quality. The roots of tea plants are colonized by various microbes including arbuscular mycorrhizal fungi (AMF), bacterial communities, and endophytes increase root growth, development and nutrient uptake which in turn improve tea growth, yield and quality. These microbes also increase the concentration of nutrients, amino acids, soluble proteins, flavonoids, catechuic acid, glucose, fructose, sucrose contents caffeine, and polyphenols concentration in tea plants. Besides this, these microbes also protect the tea plants from harmful pest and diseases which in turn leads to an appreciable improvement in plant growth and development. The most important goal of any farming system is to establish a system with production of maximum food while minimizing impacts on the environment. The present review article highlights the role of various microbes in improving the growth, yield and quality of tea plants. In addition, we also discussed the research gaps to improve our understanding about the role of tea microbes in improving tea growth, yield, pest and diseases resistance. We believe that this review will provide a better insight into the existing knowledge of tea microbes in improving tea growth and yield.
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Wang, Yinkai, Renjie Xu, Di Bai, and Haifeng Lin. "Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves." Forests 14, no. 5 (May 14, 2023): 1012. http://dx.doi.org/10.3390/f14051012.
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Currently, the detection of tea pests and diseases remains a challenging task due to the complex background and the diverse spot patterns of tea leaves. Traditional methods of tea pest detection mainly rely on the experience of tea farmers and experts in specific fields, which is complex and inefficient and can easily lead to misclassification and omission of diseases. Currently, a single detection model is often used for tea pest and disease identification; however, its learning and perception capabilities are insufficient to complete target detection of pests and diseases in complex tea garden environments. To address the problem that existing target detection algorithms are difficult to identify in the complex environment of tea plantations, an integrated learning-based pest detection method is proposed to detect one disease (Leaf blight) and one pest (Apolygus lucorμm), and to perform adaptive learning and extraction of tea pests and diseases. In this paper, the YOLOv5 weakly supervised model is selected, and it is found through experiments that the GAM attention mechanism’s introduction on the basis of YOLOv5’s network can better identify the Apolygus lucorμm; the introduction of CBAM attention mechanism significantly enhances the effect of identifying Leaf blight. After integrating the two modified YOLOv5 models, the prediction results were processed using the weighted box fusion (WBF) algorithm. The integrated model made full use of the complementary advantages among the models, improved the feature extraction ability of the model and enhanced the detection capability of the model. The experimental findings demonstrate that the tea pest detection algorithm effectively enhances the detection ability of tea pests and diseases with an average accuracy of 79.3%. Compared with the individual models, the average accuracy improvement was 8.7% and 9.6%, respectively. The integrated algorithm, which may serve as a guide for tea disease diagnosis in field environments, has improved feature extraction capabilities, can extract more disease feature information, and better balances the model’s recognition accuracy and model complexity.
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Kawooya, Ronald. "Accomplishments of Tea Research in Uganda in the Year 2019." European Journal of Agriculture and Food Sciences 3, no. 1 (January 23, 2021): 75–78. http://dx.doi.org/10.24018/ejfood.2021.3.1.214.
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Tea is Uganda’s third most important export earner and oscillates between coffee and fish. The current production in the country is 59,000 tonnes of made tea per year, earning 104 million United States Dollars to the Uganda’s economy. Despite its importance to Uganda, the tea sector is faced with a number of constraints that include rising production costs, age of tea bushes, high overhead costs, poor agronomic agricultural practices, pests and diseases, low genetic potential, poor seed system, low labour productivity,and climate change. Since the collapse of Tea Research Institute of East Africa, tea research in Uganda has been dormant for three decades, due to limitations in tea experts, finance, and infrastructure. This review paper highlights the current tea research accomplishments made with the help of government of Uganda Agricultural Technology and Agribusiness Advisory Service (ATAAS) funding. These endeavors include: a) Genetic structure of tea diversity in Uganda has been determined, b) Characterization of tea clones with desirable attributes initiated, c) Soil nutrient up take of tea clones has been determined, d) Screening tea clones for resistance to Xylaria and Armillaria diseases and e) Tea dissemination material accomplished. This paper further spells out the future perspective and strategic intervention of tea research in Uganda.
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Krishnan, Jayapal Santhana. "An integrated fuzzy-rough set model for identification of tea leaf diseases." Pakistan Journal of Agricultural Sciences 59, no. 06 (November 1, 2022): 947–52. http://dx.doi.org/10.21162/pakjas/22.1403.
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Tea is one of the major economic crops of India. People use tiny tea leaves to make beverages. The diseases in tea leaves affect the quality and yield of this cultivation. This paper proposes a disease classification model to prevent the major loss in crop yield.Tea leaf images are captured using a camera, and various image processing techniques are applied to the images to identify which disease is affected. The proposed model works for three major tea leaf diseases: blister blight, scab, and spot. The model extracts the Haralick features using Gray Level Co-occurrence Matrix (GLCM), and the most relevant features are selected with the help of the metaheuristic optimization technique. Fuzzy Rough Nearest Neighbor (FRNN) is used for classification techniques, and the model gave better accuracy than other existing techniques
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Wang, Shuzhen, Zhiliang Li, Yuting Ma, Yan Liu, Chi-Chen Lin, Shiming Li, Jianfeng Zhan, and Chi-Tang Ho. "Immunomodulatory Effects of Green Tea Polyphenols." Molecules 26, no. 12 (June 20, 2021): 3755. http://dx.doi.org/10.3390/molecules26123755.
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Green tea and its bioactive components, especially polyphenols, possess many health-promoting and disease-preventing benefits, especially anti-inflammatory, antioxidant, anticancer, and metabolic modulation effects with multi-target modes of action. However, the effect of tea polyphenols on immune function has not been well studied. Moreover, the underlying cellular and molecular mechanisms mediating immunoregulation are not well understood. This review summarizes the recent studies on the immune-potentiating effects and corresponding mechanisms of tea polyphenols, especially the main components of (–)-epigallocatechin-3-gallate (EGCG) and (–)-epicatechin-3-gallate (ECG). In addition, the benefits towards immune-related diseases, such as autoimmune diseases, cutaneous-related immune diseases, and obesity-related immune diseases, have been discussed.
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Ye, Rong, Guoqi Shao, Yun He, Quan Gao, and Tong Li. "YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea." Sensors 24, no. 9 (May 1, 2024): 2896. http://dx.doi.org/10.3390/s24092896.
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In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.
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Soboleva, O. A., V. I. Minina, E. E. Vorobyeva, I. S. Milentyeva, and E. A. Astafeva. "The physiological role of green tea catechins." Proceedings of the Voronezh State University of Engineering Technologies 85, no. 1 (February 1, 2023): 54–63. http://dx.doi.org/10.20914/2310-1202-2023-1-54-63.
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Green tea is one of the most popular drinks consumed in the world. Important components contained in green tea and having antioxidant and anticarcinogenic properties are catechins - organic substances belonging to the class of polyphenolic compounds. Green tea contains 4 main catechins: (-)-epicatechin, (-)-epicatechin-3-gallate, (-)-epigallocatechin and (-)-epigallocatechin-3-gallate. This review summarizes the results of key studies, predominantly performed over the last 5 years, which provide evidence that catechins in green tea protect against ultraviolet radiation, can improve the quality of life of patients suffering from cancer, cardiovascular, neurodegenerative, viral, infectious diseases, obesity and a number of other pathologies, as well as serve as a measure for the prevention of these diseases in healthy individuals. The most potent catechin found in green tea has been shown to be (-)-epigallocatechin-3-gallate. The anticarcinogenic effects of (-)-epigallocatechin-3-gallate in the case of prostate cancer, breast cancer, colorectal cancer are presented. The powerful antioxidant activity of catechins can form the basis for the prevention of the development of infectious and viral diseases, as well as improve the quality of life of patients with metabolic syndrome and obesity. Thus, green tea is not only a tonic drink, but also an important tool for the prevention and treatment of human diseases, in the pathogenesis of which free radical oxidation and oxidative stress play an important role. The areas of practical application of green tea catechins can be the treatment of malignant tumors (during a course of chemotherapy or radiation therapy), the treatment of viral, infectious and cardiovascular diseases, skin protection from ultraviolet rays, etc.
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Zhang, Jiahong, Honglie Guo, Jin Guo, and Jing Zhang. "An Information Entropy Masked Vision Transformer (IEM-ViT) Model for Recognition of Tea Diseases." Agronomy 13, no. 4 (April 19, 2023): 1156. http://dx.doi.org/10.3390/agronomy13041156.
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Tea is one of the most popular drinks in the world. The rapid and accurate recognition of tea diseases is of great significance for taking targeted preventive measures. In this paper, an information entropy masked vision transformation (IEM-ViT) model was proposed for the rapid and accurate recognition of tea diseases. The information entropy weighting (IEW) method was used to calculate the IE of each segment of the image, so that the model could learn the maximum amount of knowledge and information more quickly and accurately. An asymmetric encoder–decoder architecture was used in the masked autoencoder (MAE), where the encoder operated on only a subset of visible patches and the decoder recovered the labeled masked patches, reconstructing the missing pixels for parameter sharing and data augmentation. The experimental results showed that the proposed IEM-ViT had an accuracy of 93.78% for recognizing the seven types of tea diseases. In comparison to the currently common image recognition algorithms including the ResNet18, VGG16, and VGG19, the recognition accuracy was improved by nearly 20%. Additionally, in comparison to the other six published tea disease recognition methods, the proposed IEM-ViT model could recognize more types of tea diseases and the accuracy was improved simultaneously.
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Żuchnik, Olga, Justyna Szydłowska, Konrad Gładysz, Piotr Kwiatkowski, Marcin Szydłowski, Aleksander Kłos, Beata Kuczyńska, Krzysztof Gieroba, Magda Czelej, and Olgierd Król. "Consumption of green tea - potential health benefits." Journal of Education, Health and Sport 13, no. 1 (December 3, 2022): 229–34. http://dx.doi.org/10.12775/jehs.2023.13.01.034.
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Introduction: The health-promoting effects of green tea have been known since ancient times. Nowadays it is oneof the most consumed beverages in the world. The main bioactive components of green tea are the catechins:epicatechin, epigallocatechin, epicatechin gallate and epigallocatechin gallate. They have a number ofhealth-promoting properties.Aim of the study: The aim of this study was to compile the health benefits of drinking green tea.Methods and materials: A review of the literature available in the PubMed database and Google Scholar, thefollowing keywords were used: "green tea", “catechins", "health benefits".Results: Consumption of green tea carries a number of health benefits, such as anti-inflammatory, antioxidant andneuroprotective effects. Due to its potential for preventing many diseases and supporting cognitive function,regular consumption of green tea may have a positive effect on both physical and mental health.Conclusion: Studies on the effects of green tea on the human body have shown that it can be an important dietaryfactor in preventing and supporting the treatment of various diseases, such as inflammation, diabetes, obesity,neurological diseases and neurodegenerative disorders. By affecting crucial cellular processes, it prevents theformation, growth and progression of cancer. In addition, the catechins in green tea also play an important role inpreventing and treating infections. Daily consumption of adequate amounts of green tea inhibits brain aging byactivating nerve cells and reducing stress. When consumed regularly, green tea can support the body's efforts tomaintain health and prevent disease.
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Chen, Jing, Qi Liu, and Lingwang Gao. "Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model." Symmetry 11, no. 3 (March 7, 2019): 343. http://dx.doi.org/10.3390/sym11030343.
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The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.
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Karta, I. wayan. "UJI FITOKIMIA DAN KAPASITAS ANTIOKSIDAN TEH RING CANG SEBAGAI TEH HERBAL KESEHATAN." Meditory : The Journal of Medical Laboratory 8, no. 1 (June 30, 2020): 27–39. http://dx.doi.org/10.33992/m.v8i1.1107.
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Background: Diets that contain lots of antioxidants will reduce the risk caused by degenerative diseases. Based on this, many people develop food or health products that contain antioxidants. Jempiring leaves (Gardenia jasminoides Ellis) and Secang wood (Caesalpinia sappan L.) contain bioactive and antioxidant compositions that are beneficial to health, so they are developed into herbal products.Purpose: To analyze phytochemical reserves, antioxidant capacity and conduct organoleptic tests for Ring Cang tea, describing the benefits of ring rings based on laboratory test results and literature studies on jempiring leaves and secang wood.Method: This research is a descriptive study by conducting phytochemical testing laboratory tests and DPPH antioxidant methods and conducting organoleptic tests with 15 panelists.Results: Ring tea can contain flavonoids, tannins, terpenoids, alkaloids and polyphenols. Ring Cang tea has an antioxidant capacity of 291.43 mg / L and IC50 1.53 μg / mL which shows the antioxidant activity of ring cang tea in the very strong category. Ring tea can be tested organoleptically accepted by panelists in terms of color, taste, and aroma, so that it can be developed into herbal teas for the community.Conclusion: Cang tea is an antioxidant tea that can be used in handling generative or degenerative diseases, such as diabetes, antiviral, anti-inflammatory, and other diseases.
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Al-Karawi, Saja Bilal Hafedh, and Hakan Koyuncu. "Hybrid Neural Network Approach for Tea Leaf Disease Detection Using Pelican and Mayfly Optimization Algorithms." Jurnal Riset Informatika 6, no. 2 (March 11, 2024): 119–30. http://dx.doi.org/10.34288/jri.v6i2.274.
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This study addresses the problem of plant diseases and the difficulty of detecting them, and it presents a unique technique for the automatic detection of tea leaf diseases by combining neural networks and optimization techniques. Our research uses a curated database of tea plant leaf photographs that includes healthy and diseased specimens. The neural network (CNN) is trained and fine-tuned using optimization algorithms. To increase disease identification accuracy, we used a hybrid novel optimization algorithm called (POA-MA) which is Pelican Optimization Algorithm (POA), and Mayfly Optimization Algorithm (MA) for feature selection, followed by classification with Support Vector Machine (SVM). The suggested mechanism performance is evaluated using accuracy, MSE, F-score, recall, and sensitivity measures. The suggested CNN-POAMA hybrid model yielded 94.5%, 0.035, 0.91, 0.93, and 0.92, respectively. This study advances precision agriculture by establishing a strong framework for automated detection, allowing for early intervention, and eventually enhancing tea crop health.
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Purwanto, Djoko Agus, Asri Darmawati, and Purwaningsih Purwaningsih. "THE IMPACT OF GREEN TEA ON BLOOD FLUIDITY IMPROVEMENT AND WEIGHT LOSS." Jurnal NERS 9, no. 1 (January 27, 2017): 1. http://dx.doi.org/10.20473/jn.v9i1.2951.
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Introduction: Various studies have shown the benefi cial effects of green tea, not only on cardiovascular diseases butalso on type 2 diabetes. Method: In this study, the preparation of green tea water extract has been standardized to (-)-epigalocatechin gallate (EGCG), the major component of green tea. The role of green tea water extract on blood fl uidityand diabetes diseases has been studied in 13 Fructose-Fed Rat (FFR). The rats were given high fructose diet ad libitumfor one week and then combination with green tea water extract every day for 6 days. Results: The results show, greentea water extract can reduces 100 μL blood passage times of wistar rat signifi cantly (p<0.01) by Micro-Channel ArrayFlow Analyzer (MC-FAN) instrument. Green tea water extract also had strong effect in reducing abdominal fat (p<0.05),blood glucose level (p<0.01) and body weight (p<0.01). Discussion: These results suggest that green tea water extractmay has benefi cial effects for the treatment of diabetes and reduce blood viscosity.Keywords: green tea, (-)-epigallocatechin gallate, HPLC, blood fl uidity, fructose-fed rat
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YÜCEL, Nadide, and Muhammed YILDIRIM. "Classification of tea leaves diseases by developed CNN, feature fusion, and classifier based model." International Journal of Applied Mathematics Electronics and Computers 11, no. 1 (March 17, 2023): 30–36. http://dx.doi.org/10.18100/ijamec.1235611.
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Due to the increase in the world population day by day, the amount of food needed is also increasing day by day. Diseases that occur in plants reduce the amount and quality of the product obtained. In this study, a computer-aided model was developed to detect diseases in tea leaves. Because plant diseases can be difficult and misleading to detect with the naked eye by farmers or experts. It is very important to detect diseases in tea leaves using artificial intelligence methods. Three Convolutional Neural Network (CNN) architectures accepted in the literature were used as the basis for the classification of diseases in tea leaves. With these three CNN architectures, feature maps of the images in the data set were obtained. After combining the feature maps obtained in each architecture, they were classified in the Linear Discriminant classifier. In addition, the performance of the proposed model was compared with seven CNN architectures accepted in the literature. The performance of the models used in the study was evaluated using different performance measurement metrics. The obtained results showed that the proposed model can be used to classify diseases in tea leaves.
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ChandraKarmokar, Bikash, Mohammad Samawat Ullah, Md Kibria Siddiquee, and Kazi Md. Rokibul Alam. "Tea Leaf Diseases Recognition using Neural Network Ensemble." International Journal of Computer Applications 114, no. 17 (March 18, 2015): 27–30. http://dx.doi.org/10.5120/20071-1993.
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Kitajima, Naoharu, and Akemi Sugita-Kitajima. "Effect of herbal tea treatment on Ménière's diseases." Equilibrium Research 70, no. 4 (2011): 230–37. http://dx.doi.org/10.3757/jser.70.230.
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Imai, Kazue, Kenji Suga, and Kei Nakachi. "Drinking green tea prevents cardiovascular and liver diseases." Journal of the Institute of Health Education 34, no. 3 (January 1996): 71–74. http://dx.doi.org/10.1080/03073289.1996.10805913.
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Mansoori, Ruksar, Deepti Jain, Varsha Pandey, and Surendra Kumar Jain. "A Comprehensive Review on Biological Activity of Green Tea (Camellia sinensis)." Journal of Drug Delivery and Therapeutics 12, no. 5 (September 20, 2022): 250–65. http://dx.doi.org/10.22270/jddt.v12i5.5613.
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Green tea, the commonly consumed beverage, is gaining increased attention in promoting overall health. In specific, green tea is considered a healthful beverage due to the biological activity of its polyphenols namely catechins. The main green tea polyphenols are catechins: (±)-catechin C, (-)-epicatechin EC, (+)-gallocatechin GC, (-)-epigallocatechin EGC, (-)-epicatechin gallate ECG, and (-)-epigallocatechin gallate EGCG. Among the polyphenols Epigallocatechin 3 gallate and Epicatechin 3 Gallate are the most predominant catechins. There are also human studies on using green tea catechins to treat chronic diseases such as obesity, type II diabetes, and cardiovascular risk factors. Long-term consumption of tea catechins could be beneficial against high-fat diet-induced obesity and type II diabetes and could reduce the risk of coronary disease. Several epidemiological studies have proved that green tea also has some general health benefitting properties like anti-obesitive agent, anti-oxidant, anti-aging, anti-diabetic, anti-hyperlipidemic, anti-hypertension, anti-cancer, anti-bacterial, anti-viral, anti-inflammatory, anti-parkinson's effects, anti-alzheimer's effects, anti-arthritic, anticollagenase, and antimutagenic etc. The properties of these catechins proved to be helpful in the treatment of chronic diseases like obesity, aging, diabetes, cardiovascular disease, hyperlipidemic, hypertension, cancer, microbial, parkinson's, alzheimer's, arthritis, and periodontal disease. There are numerous studies in humans, animal models, and cell lines which provide the concepts for underlying functional mechanisms of green tea catechins and their biological actions and to suggest potential health benefits from the consumption of tea, including prevention of cancer and heart diseases. The review highlights also the potentials of green tea, its health benefits in terms of their properties. The present review concentrates on the effects of green tea on different chronic disease and general health.
Keywords: Green tea, Polyphenols, Epidemiological, Catechins, Animal models
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Li, Qingsheng, Junyan Zhu, Ning Ren, Da Li, Ya Jin, Wenyuan Lu, and Qinhua Lu. "Characteristics and Pathogenicity of Discula theae-sinensis Isolated from Tea Plant (Camellia sinensis) and Interaction with Colletotrichum spp." Plants 12, no. 19 (September 28, 2023): 3427. http://dx.doi.org/10.3390/plants12193427.
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Anthracnose is one of the primary diseases in tea plants that affect tea yield and quality. The geographical distribution, occurrence regularity, and agronomic measures of tea plants with anthracnose have been researched for decades. However, the pathogenic cause of anthracnose in tea plants is diverse in different regions of the world. Identifying the specific pathogenic fungi causing tea anthracnose is an essential control measure to mitigate this disease. In this study, 66 Discula theae-sinensis and 45 Colletotrichum isolates were obtained from three different types of diseased tea leaves. Based on multilocus phylogenetic and morphological analysis, eight known species of Colletotrichum, Colletotrichum fructicola, C. camelliae, C. aenigma, C. siamense, C. henanense, C. karstii, C. tropicicola, and C. gigasporum were identified. This study is the first to report C. tropicicola and C. gigasporum in tea plants in China. Discula theae-sinensis was the most common species in this study and caused disease lesions around wounded areas of tea leaves. The dual trials in vitro indicated Discula theae-sinensis and Colletotrichum were slightly inhibited. Co-inoculating Discula theae-sinensis and C. fructicola was superior to single inoculation at low concentrations. The main cause of anthracnose might be the concerted action of a variety of fungi.
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Sheng, Qi, Xinzhuan Yao, Hufang Chen, Hu Tang, and Litang Lu. "Identification of Nutritional Ingredients and Medicinal Components of Hawk Tea and Insect Tea Using Widely Targeted Secondary Metabolomics." Horticulturae 9, no. 4 (April 2, 2023): 457. http://dx.doi.org/10.3390/horticulturae9040457.
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In this study, the metabolites in insect tea and hawk tea were analyzed by ultra-high performance liquid chromatography–triple quadrupole mass spectrometry. We found 49 metabolites in insect tea and hawk tea that can be used as key active components in traditional Chinese medicine, as well as 98 metabolites that can be used as active components of pharmaceutical preparations for the treatment of cancers, hypertension, cardiovascular diseases, etc. Comparative analysis found that insect tea and hawk tea had significant differences in their metabolic profiles. Insect tea contains more metabolites beneficial to human health than hawk tea; insect tea also has higher antioxidant activity in vitro than hawk tea. The results of this study will contribute to the development of new health foods using insect tea.
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Yuniarto, Ari, Kusnandar Anggadiredja, and Riana Annisa Nur Aqidah. "ANTIFUNGAL ACTIVITY OF KOMBUCHA TEA AGAINST HUMAN PATHOGENIC FUNGI." Asian Journal of Pharmaceutical and Clinical Research 9, no. 5 (September 1, 2016): 253. http://dx.doi.org/10.22159/ajpcr.2016.v9i5.13432.
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ABSTRACTObjective: In general, infectious diseases still known as a major contributor to the number of morbidity and mortality in the worldwide besideother diseases. Infections are ordinarily caused by viruses, bacteria, and fungi. Several human pathogenic fungi involving Aspergillus flavus, Candidaalbicans, and Microsporum gypseum have responsible against infectious diseases. The objective of the research was to evaluate the antifungal activityof kombucha tea against human pathogenic fungi such as A. flavus, C. albicans, and M. gypseum.Methods: The antifungal activity of the kombucha tea against A. flavus, C. albicans, and M. gypseum were tested by disc diffusion method with durationof fermentation in 6, 12, and 18 days.Results: Based on the present study, inhibitory diameter of A. flavus, C. albicans, and M. gypseum are 16.83, 15.36, and 25.06 mm, respectively. Theinhibitory diameter was obtained from kombucha tea activity with duration of fermentation in 6 days.Conclusion: The results provide support for the use of kombucha tea as a potential antifungal source against human pathogenic fungi involvingA. flavus, C. albicans, and M. gypseum.Keywords: Antifungal, Kombucha tea, Pathogenic, Fungi.
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Xue, Zhenyang, Renjie Xu, Di Bai, and Haifeng Lin. "YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5." Forests 14, no. 2 (February 17, 2023): 415. http://dx.doi.org/10.3390/f14020415.
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Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves suffering from insect and disease infestation. However, photographs of tea tree leaves taken in a natural environment have problems such as leaf shading, illumination, and small-sized objects. Affected by these problems, traditional CNNs cannot have a satisfactory recognition performance. To address this challenge, we propose YOLO-Tea, an improved model based on You Only Look Once version 5 (YOLOv5). Firstly, we integrated self-attention and convolution (ACmix), and convolutional block attention module (CBAM) to YOLOv5 to allow our proposed model to better focus on tea tree leaf diseases and insect pests. Secondly, to enhance the feature extraction capability of our model, we replaced the spatial pyramid pooling fast (SPPF) module in the original YOLOv5 with the receptive field block (RFB) module. Finally, we reduced the resource consumption of our model by incorporating a global context network (GCNet). This is essential especially when the model operates on resource-constrained edge devices. When compared to YOLOv5s, our proposed YOLO-Tea improved by 0.3%–15.0% over all test data. YOLO-Tea’s AP0.5, APTLB, and APGMB outperformed Faster R-CNN and SSD by 5.5%, 1.8%, 7.0% and 7.7%, 7.8%, 5.2%. YOLO-Tea has shown its promising potential to be applied in real-world tree disease detection systems.
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Afzal, Obaid, Mahmood Hassan Dalhat, Abdulmalik S. A. Altamimi, Rabia Rasool, Sami I. Alzarea, Waleed Hassan Almalki, Bibi Nazia Murtaza, et al. "Green Tea Catechins Attenuate Neurodegenerative Diseases and Cognitive Deficits." Molecules 27, no. 21 (November 6, 2022): 7604. http://dx.doi.org/10.3390/molecules27217604.
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Neurodegenerative diseases exert an overwhelming socioeconomic burden all around the globe. They are mainly characterized by modified protein accumulation that might trigger various biological responses, including oxidative stress, inflammation, regulation of signaling pathways, and excitotoxicity. These disorders have been widely studied during the last decade in the hopes of developing symptom-oriented therapeutics. However, no definitive cure has yet been discovered. Tea is one of the world’s most popular beverages. The same plant, Camellia Sinensis (L.).O. Kuntze, is used to make green, black, and oolong teas. Green tea has been most thoroughly studied because of its anti-cancer, anti-obesity, antidiabetic, anti-inflammatory, and neuroprotective properties. The beneficial effect of consumption of tea on neurodegenerative disorders has been reported in several human interventional and observational studies. The polyphenolic compounds found in green tea, known as catechins, have been demonstrated to have many therapeutic effects. They can help in preventing and, somehow, treating neurodegenerative diseases. Catechins show anti-inflammatory as well as antioxidant effects via blocking cytokines’ excessive production and inflammatory pathways, as well as chelating metal ions and free radical scavenging. They may inhibit tau protein phosphorylation, amyloid beta aggregation, and release of apoptotic proteins. They can also lower alpha-synuclein levels and boost dopamine levels. All these factors have the potential to affect neurodegenerative disorders. This review will examine catechins’ neuroprotective effects by highlighting their biological, pharmacological, antioxidant, and metal chelation abilities, with a focus on their ability to activate diverse cellular pathways in the brain. This review also points out the mechanisms of catechins in various neurodegenerative and cognitive diseases, including Alzheimer’s, Parkinson’s, multiple sclerosis, and cognitive deficit.
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Li, Pengfei, Shuaishuai Huang, Shuangyan Xiao, Youhou Xu, Xinxian Wei, Jun Xiao, Zhongbao Guo, Qing Yu, and Mingzhu Liu. "Antiviral Activities of Green Tea Components against Grouper Iridovirus Infection In Vitro and In Vivo." Viruses 14, no. 6 (June 5, 2022): 1227. http://dx.doi.org/10.3390/v14061227.
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(1) Background: Singapore grouper iridovirus (SGIV) can cause extensive fish deaths. Therefore, developing treatments to combat virulent SGIV is of great economic importance to address this challenge to the grouper aquaculture industry. Green tea is an important medicinal and edible plant throughout the world. In this study, we evaluated the use of green tea components against SGIV infection. (2) Methods: The safe working concentrations of green tea components were identified by cell viability detection and light microscopy. Additionally, the antiviral activity of each green tea component against SGIV infection was determined with light microscopy, an aptamer (Q5c)-based fluorescent molecular probe, and reverse transcription quantitative PCR. (3) Results: The safe working concentrations of green tea components were green tea aqueous extract (GTAE) ≤ 100 μg/mL, green tea polyphenols (TP) ≤ 10 μg/mL, epigallocatechin-3-gallate (EGCG) ≤ 12 μg/mL, (-)-epigallocatechin (EGC) ≤ 10 μg/mL, (-)-epicatechin gallate (EGC) ≤ 5 μg/mL, and (-)-epicatechin (EC) ≤ 50 μg/mL. The relative antiviral activities of the green tea components determined in terms of MCP gene expression were TP > EGCG > GTAE > ECG > EGC > EC, with inhibition rates of 99.34%, 98.31%, 98.23%, 88.62%, 73.80%, and 44.31%, respectively. The antiviral effect of aptamer-Q5c was consistent with the results of qPCR. Also, TP had an excellent antiviral effect in vitro, wherein the mortality of fish in only the SGIV-injection group and TP + SGIV-injection group were 100% and 11.67%, respectively. (4) Conclusions: In conclusion, our results suggest that green tea components have effective antiviral properties against SGIV and may be candidate agents for the effective treatment and control of SGIV infections in grouper aquaculture.
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Hassan, MR, I. Hossain, MR Islam, and MAR Khokon. "Comparative Efficacy of Compost, Compost Tea, Poultry Litter and Bavistin in Controlling Diseases of Chili." Progressive Agriculture 24, no. 1-2 (June 9, 2014): 39–44. http://dx.doi.org/10.3329/pa.v24i1-2.19096.
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Compost, compost tea and poultry litter in comparison with Bavistin were evaluated for controlling diseases of chili. The experiments were conducted at Field Lab. and Eco-friendly Plant Disease Management Lab. of the Department of Plant Pathology, Bangladesh Agricultural University, Mymensingh, Bangladesh in 2011- 2012 using eight different treatments following Randomized Complete Block Design with three replications. The treatments were (1) Compost tea (1 : 5 w/v) as foliar spray, (2) Soil drenching with Compost tea (1 : 5 w/v), (3) Soil drenching with poultry litter extract (1 : 5 w/v), (4) Bavistin (0.1%) as foliar spray, (5) Soil drenching with Bavistin (0.1%), (6) Compost (10 t/ha) in soil application, (7) Poultry litter (10 t/ha) in soil application and (8) control. Minimum leaf curl incidence (80.89%) and severity grade (2) were recorded in plots under Compost tea @ 1:5 w/v as foliar spray, but severity and severity grade were maximum by 95.00% and 3, respectively under control plots. Minimum anthracnose infected plants (16.67%) and infected fruit (9.35%) were recorded in plots under Compost @ 10 t/ha in soil application and soil drenching with poultry litter extract (1.5 w/v). The highest Cercospora leaf spot having severity rating grade 3.15 was recorded in field where compost tea was applied as soil drenching and lowest grade 1.07 was determined where Bavistin was applied as foliar spray. The fruit yield (4.67 t/ha) was found significantly higher in plots under compost tea as foliar spray and Bavistin as foliar spray which was 79.62% higher yield over control. Physical and chemical compositions of compost tea and poultry litter were determined.DOI: http://dx.doi.org/10.3329/pa.v24i1-2.19096 Progress. Agric. 24(1&2): 39 - 44, 2013
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Zhang, Qiqi, Nini Guo, Yongheng Zhang, Youben Yu, and Shuyuan Liu. "Genome-Wide Characterization and Expression Analysis of Pathogenesis-Related 1 (PR-1) Gene Family in Tea Plant (Camellia sinensis (L.) O. Kuntze) in Response to Blister-Blight Disease Stress." International Journal of Molecular Sciences 23, no. 3 (January 24, 2022): 1292. http://dx.doi.org/10.3390/ijms23031292.
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Pathogenesis-related 1 (PR-1) proteins, which are defense proteins in plant–pathogen interactions, play an important role in the resistance and defense of plants against diseases. Blister blight disease is caused by Exobasidium vexans Massee and a major leaf disease of tea plants (Camellia sinensis (L.) O. Kuntze). However, the systematic characterization and analysis of the PR-1 gene family in tea plants is still lacking, and the defense mechanism of this family remains unknown. In this study, 17 CsPR-1 genes were identified from the tea plant genome and classified into five groups based on their signal peptide, isoelectric point, and C-terminus extension. Most of the CsPR-1 proteins contained an N-terminal signal peptide and a conserved PR-1 like domain. CsPR-1 genes comprised multiple cis-acting elements and were closely related to the signal-transduction pathways involving TCA, NPR1, EDS16, BGL2, PR4, and HCHIB. These characteristics imply an important role of the genes in the defense of the tea plant. In addition, the RNA-seq data and real-time PCR analysis demonstrated that the CsPR-1-2, -4, -6, -7, -8, -9, -10, -14, -15, and -17 genes were significantly upregulated under tea blister-blight stress. This study could help to increase understanding of CsPR-1 genes and their defense mechanism in response to tea blister blight.
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Diana, Arista Fitri, Widowati Widowati, R. H. Tjahjana, and Eka Triyana. "STABILITY ANALYSIS OF DYNAMICAL MODEL INTERACTIONS TEA PLANTS, PESTS, AND DISEASES WITH FUNGICIDES AND INSECTICIDES CONTROLS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (January 30, 2023): 3209–19. http://dx.doi.org/10.47191/ijmcr/v11i1.14.
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Tea plants are one of the exports commodities in Indonesia. In their development, the plantation ecosystem is heavily influenced by several factors, both internal and external factors. In the field of applied mathematics, mathematical modelling can be used to analyze the development of tea plant growth and their interactions each other in their ecosystem. The mathematical model in this research is combining three main models, there are logistic model, epidemiological model, and predator prey model by adding fungicide and insecticide controls. Furthermore, local and global stability analysis is carried out and the optimal control problem is solved by Pontryagin maximum principle. The results of the analysis obtained five equilibrium points. Local stability analysis was carried out using the Routh Hurwitz criteria which showed the fifth equilibrium point is locally asymptotically stable. The basic reproduction number in the model is 0.99. Because 16R0<1" style="width: 28pt; height: 11pt;">, it can be concluded that there is no spread of disease in the tea plantation ecosystem after a period of 5 years. The control provided can reduce pest and disease attacks. After being given control, the population of infected tea plants decreased by 93.21%, Empoasca pests decreased by 99.47%, and leaf roller caterpillars decreased by 99.31% compared to the model that was not given control.
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Markovic, D., B. Mirkovic, T. Jovanovic, A. Knezevic, and T. Nastovski. "The application of tea tree essential oil in dentistry." Serbian Dental Journal 54, no. 2 (2007): 106–14. http://dx.doi.org/10.2298/sgs0702106m.
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Essential oils are widely used in medicine, dentistry and cosmetology as flavour and odour corrigents in various substances for oral hygiene. The aim of this study was to present comprehensively the possibilities for application of Melaleuca Alternifolia essential oil in dentistry based on the analysis of contemporary scientific and professional publications. The application of Tea tree essential oil in the treatment of periodontal, fungal and viral diseases is very efficient. The study of antimicrobial potential of ten different essential oils confirmed the efficiency of Tea tree oil against numerous Gram+ and Gram- bacteria. In vitro studies of bacteriostatic, bactericidal and fungicidal effect of Tea tree oil solution against ten different microorganisms confirmed sensitivity of the following microorganisms: Actinobacillus actinomycetemcomitans, Fusobacterium nucleatum and Porphyromonas gingivalis, and slightly weaker effect against Streptococcus Mutans and Prevotella intermedia. Tea tree is very effective in the treatment of various diseases and is an introduction and momentum for the application of plant substances in the treatment of numerous diseases in dentistry. .
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Das, Chandrima, Arnab Banerjee, Moumita Saha, and Sirshendu Chatterjee. "A Review of the Health Benefits of Tea: Implications of the Biochemical Properties of the Bioactive Constituents." Current Research in Nutrition and Food Science Journal 10, no. 2 (September 2, 2022): 458–75. http://dx.doi.org/10.12944/crnfsj.10.2.5.
Full textAbstract:
Tea is the second-most drank and refreshing beverage after water since the time immemorial. Tea harbours more than 700 bioactive compounds viz, different classes of polyphenols, unique amino acid L-Theanine, alkaloids (Caffeine, Theobromine), and Volatile Flavor Compounds (VFC). Tea's polyphenols make its inherent therapeutic potential unlimited. Tea's significance in managing cancer, diabetes, stomach ulcer, influenza, neurological diseases, etc. is well-documented. However, advantageous biochemical capabilities of tea have yet to be fully utilised. Hence, this review aims at to examine tea's variety, drinking habits, biochemistry, and therapeutic qualities. A number of significant online resources, including Google Scholar, PubMed, Science Direct, and others, were searched for various research works on tea and its health-promoting qualities by using keywords like tea, health benefits, bioactive components against diseases, etc. Current review highlighted that drinking a cup or more green tea is recommended for improving antioxidant status and to manage diabetes and obesity related problem. However after detailed review work on tea it become clear that not only green tea but also other varies of tea like black, white tea are also harbour lots of bioactive molecules since they are processed from same plant. Tea improves antioxidant status and manages diabetes and obesity. It also helps prevent and cure, heart disease, malignancy, digestive dysfunction, and metabolic disorders including obesity and diabetes. Epigallocatechin Gallate (EGCG), found in tea, has been shown to reduce complications from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV 2) infection. When taken in its traditional form to manage ailments, tea is sometimes controversial due to a lack of confirming evidence of its benefits. The paper covers the numerous health advantages of tea, focusing on the specific components contributing to such benefits, and stresses the value of diverse brewing processes.