Efficacy of Natural Products in Preventing Oral Mucositis Resulting from Cancer Therapies: A Network Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Oral mucositis significantly compromises the quality of life for patients undergoing cancer therapies. This study aimed to evaluate the effectiveness of natural products in either preventing or alleviating oral mucositis resulting from cancer treatments. METHODS: A systematic review and network meta-analysis were conducted, sourcing data from the Cochrane Library, PubMed, Embase, Airiti Library, and Wan Fang Data Knowledge Service Platform until August 2023. The study was registered in PROSPERO (CRD42021285433). Confidence in Network Meta-Analysis (CINeMA) and R software 4.1.3 were used for analysis. RESULTS: From 1,556 identified articles, 36 randomized controlled trials (RCTs) were analyzed, involving 2,083 patients. Honey, notably, was found to significantly reduce the overall incidence of oral mucositis compared to standard care, with a relative risk (RR) of 0.80 (95% CI: 0.67-0.96). It was particularly effective against moderate-to-severe oral mucositis (grade ≥ 2), reducing incidence with RR of 0.48 (95% CI: 0.30-0.75) versus placebo and 0.56 (95% CI: 0.34-0.93) against standard care. Other natural products, including propolis, chamomile, and P. major L., also demonstrated significant efficacy in reducing the incidence of oral mucositis. Regarding pain relief, honey, and P. major L. emerged as effective, significantly reducing pain severity with a mean difference (MD) of -2.96 (95% CI: -3.80 to -1.94) compared to placebo. CONCUSSION: This network meta-analysis supports the use of honey, propolis, chamomile, and P. major L. as effective natural products in the prevention and treatment of oral mucositis among cancer patients. Specifically, honey is highlighted for its significant impact on reducing both the overall incidence and the severity of moderate-to-severe oral mucositis. By leveraging their anti-inflammatory and antioxidant properties, integrating these natural products into the standard care regimen could markedly improve the well-being of individuals undergoing cancer therapy.

The Cognitive Function and Taekwondo-Specific Kick Performance of Taekwondo Athletes at Different Hydration Statuses.

PURPOSE: Successful participation in taekwondo (TKD) requires athletes to possess quick decision-making abilities and demonstrate technical proficiency during competition. Dehydration, occurring during both training and competition, is widely recognized to have various negative effects. METHODS: This study investigated the impact of different levels of dehydration on cognitive function, as measured by the Vienna Test System, and the specific performance of kicking techniques among TKD athletes. Using a randomized crossover design, 12 participants were involved in the study. Before and after 1 hour of training at 80% of maximal heart rate, participants were weighed and provided urine samples. All participants were randomly assigned to 3 different hydration conditions: the euhydrated (EUH) group had unrestricted access to fluid consumption, while the hypohydrated (HYP) and severely HYP (S-HYP) groups experienced reductions of 2.0% and 4.0% of their initial body weight, respectively. RESULTS: The EUH group exhibited better reaction speed in reaction-time test-form S1 than the HYP and S-HYP groups. Notably, the EUH group demonstrated a significantly higher success rate in the front-side kick (EUH 98%, HYP 90%, S-HYP 88%; P < .05). However, the success rates of back roundhouse kick and free head kick were similar among the 3 statuses. Furthermore, postexercise heart rates were found to be significantly higher in the HYP and S-HYP groups compared with the EUH group. CONCLUSIONS: This study provides insight into the negative effects of dehydration on cognitive function and TKD-specific performance. It is recommended that TKD athletes maintain optimal hydration levels during training and competition to ensure optimal performance.

Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Causal roles of gut microbiota in cholangiocarcinoma etiology suggested by genetic study.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant biliary tract cancer with poor prognosis. Previous studies have implicated the gut microbiota in CCA, but evidence for causal mechanisms is lacking. AIM: To investigate the causal relationship between gut microbiota and CCA risk. METHODS: We performed a two-sample mendelian randomization study to evaluate potential causal associations between gut microbiota and CCA risk using genome-wide association study summary statistics for 196 gut microbial taxa and CCA. Genetic variants were used as instrumental variables. Multiple sensitivity analyses assessed result robustness. RESULTS: Fifteen gut microbial taxa showed significant causal associations with CCA risk. Higher genetically predicted abundance of genus Eubacteriumnodatum group, genus Ruminococcustorques group, genus Coprococcus, genus Dorea, and phylum Actinobacteria were associated with reduced risk of gallbladder cancer and extrahepatic CCA. Increased intrahepatic CCA risk was associated with higher abundance of family Veillonellaceae, genus Alistipes, order Enterobacteriales, and phylum Firmicutes. Protective effects against CCA were suggested for genus Collinsella, genus Eisenbergiella, genus Anaerostipes, genus Paraprevotella, genus Parasutterella, and phylum Verrucomicrobia. Sensitivity analyses indicated these findings were reliable without pleiotropy. CONCLUSION: This pioneering study provides novel evidence that specific gut microbiota may play causal roles in CCA risk. Further experimental validation of these candidate microbes is warranted to consolidate causality and mechanisms.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Recent progress on topological semimetal IrO2: electronic structures, synthesis, and transport properties.

5dtransition metal oxides, such as iridates, have attracted significant interest in condensed matter physics throughout the past decade owing to their fascinating physical properties that arise from intrinsically strong spin-orbit coupling (SOC) and its interplay with other interactions of comparable energy scales. Among the rich family of iridates, iridium dioxide (IrO2), a simple binary compound long known as a promising catalyst for water splitting, has recently been demonstrated to possess novel topological states and exotic transport properties. The strong SOC and the nonsymmorphic symmetry that IrO2possesses introduce symmetry-protected Dirac nodal lines (DNLs) within its band structure as well as a large spin Hall effect in the transport. Here, we review recent advances pertaining to the study of this unique SOC oxide, with an emphasis on the understanding of the topological electronic structures, syntheses of high crystalline quality nanostructures, and experimental measurements of its fundamental transport properties. In particular, the theoretical origin of the presence of the fourfold degenerate DNLs in band structure and its implications in the angle-resolved photoemission spectroscopy measurement and in the spin Hall effect are discussed. We further introduce a variety of synthesis techniques to achieve IrO2nanostructures, such as epitaxial thin films and single crystalline nanowires, with the goal of understanding the roles that each key parameter plays in the growth process. Finally, we review the electrical, spin, and thermal transport studies. The transport properties under variable temperatures and magnetic fields reveal themselves to be uniquely sensitive and modifiable by strain, dimensionality (bulk, thin film, nanowire), quantum confinement, film texture, and disorder. The sensitivity, stemming from the competing energy scales of SOC, disorder, and other interactions, enables the creation of a variety of intriguing quantum states of matter.

Concurrent Ingestion of Alkaline Water and L-Glutamine Enhanced Salivary α-Amylase Activity and Testosterone Concentration in Boxing Athletes.

Athletes often take sport supplements to reduce fatigue and immune disturbances during or after training. This study evaluated the acute effects of concurrent ingestion of alkaline water and L-glutamine on the salivary immunity and hormone responses of boxers after training. Twelve male boxing athletes were recruited in this study. During regular training, the participants were randomly divided into three groups and asked to consume 400 mL of alkaline water (Group A), 0.15 g/kg body weight of L-glutamine with 400 mL of water (Group G), and 0.15 g/kg of L-glutamine with 400 mL of alkaline water (Group A+G) at the same time each day for three consecutive weeks. Before and immediately after the training, saliva, heart rates, and the rate of perceived exertion were investigated. The activity of α-amylase and concentrations of lactoferrin, immunoglobulin A (IgA), testosterone, and cortisol in saliva were measured. The results showed that the ratio of α-amylase activity/total protein (TP) significantly increased after training in Group A+G but not in Group A or G, whereas the ratios of lactoferrin/TP and IgA/TP were unaffected in all three groups. The concentrations of salivary testosterone after training increased significantly in Group A+G but not in Group A or G, whereas the salivary cortisol concentrations were unaltered in all groups. In conclusion, concurrent ingestion of 400 mL of alkaline water and 0.15 g/kg of L-glutamine before training enhanced the salivary α-amylase activity and testosterone concentration of boxers, which would be beneficial for post-exercise recovery.

Supplementation of L-glutamine enhanced mucosal immunity and improved hormonal status of combat-sport athletes.

BACKGROUND: Maintaining proper immune function and hormone status is important for athletes to avoid upper respiratory tract infection (URTI) and insufficient recovery, which is detrimental to sport performance and health. The aim of this study was to evaluate whether three-week supplementation of L-glutamine could benefit the mucosal immunity and hormonal status of combat-sport athletes as well as their rates of upper respiratory tract infection (URTI) and subjective feelings of well-being after intensive training. METHODS: Twenty-one combat-sport athletes from the National Taiwan University of Sport were recruited in this study. After intensive training, two groups of the participants were asked to consume powder form of 0.3 g/kg body weight of L-glutamine (GLU group) or maltodextrin (PLA group) with drinking water in a randomized design at the same time every day during 3 weeks. Saliva samples were collected to measure immunoglobulin A (IgA), nitric oxide (NO), testosterone (T) and cortisol (C) before and after three-week supplementation; moreover, Hooper's index questionnaires were completed for wellness assessment. The incidence and duration of URTI were recorded by using a health checklist throughout the entire study period. RESULTS: Supplementation of L-glutamine significantly enhanced the concentrations of IgA and NO in saliva; additionally, the incidence of URTI was significantly reduced. Regarding hormones, T concentration was significantly decreased in the PLA group, whereas C concentration was significantly increased, resulting in a significant decrease of T/C ratio. In contrast, the GLU group showed a significant increase of T/C ratio, while the mood scores of the Hooper's index questionnaire were higher in the PLA group. CONCLUSIONS: Three-week supplementation of L-glutamine after intensive training enhanced the mucosal immunity, improved hormonal status and reduced the rate of URTI of combat-sport athletes while feelings of well-being were also enhanced. Therefore, L-glutamine would be beneficial for the sports performance and recovery of athletes.

Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.

PURPOSE: The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. METHODS: Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. RESULTS: The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. CONCLUSIONS: MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.

Aptamer-based flares hybridized with single-stranded DNA-conjugated MoS2 nanosheets for ratiometric fluorescence sensing and imaging of potassium ions and adenosine triphosphate in human fluids and living cells.

Addressing the limitations observed in previous studies, where the quantitative range of nanoprobes for detecting K+ and adenosine triphosphate (ATP) did not cover concentrations found within living cells, the present study aimed to develop ratiometric nanoprobes that can accurately sense changes in K+ and ATP levels in living cells and quantify them in human fluids. The proposed nanoprobes consisted of recognition flares modified with 6-carboxyfluorescein (FAM) and 5-carboxytetramethylrhodamine (TAMRA), along with thiolate single-stranded DNA (ssDNA) and molybdenum disulfide nanosheets (MoS2 NSs). The thiolate ssDNA acts as a linker between the flares and the MoS2 NSs, directly forming a functional nanostructure at room temperature. The direct conjugation of labeled flares to the MoS2 NSs simplifies the fabrication process. In the absence of K+ and ATP, the hybridization of flares and thiolate ssDNA caused FAM to move away from TAMRA, suppressing the fluorescence resonance energy transfer (FRET) process. However, upon the introduction of K+ and ATP, the flares undergo a structural transformation via the formation of G-quadruplex formation and the generation of hairpin-shaped structures, respectively. This structural change leads to the release of the flares from the ssDNA-conjugated nanosheet surface. The release of the flares brings FAM and TAMRA into close proximity, allowing FRET to occur, leading to FRET and static quenching. By monitoring the ratio between the fluorescence intensities of FAM and TAMRA, the concentration of K+ (5-100 mM) and ATP (0.3-5 mM) can be accurately determined by the proposed nanoprobes. The advantages of these nanoprobes lie in their ability to provide ratiometric measurements, which enhance the accuracy and reliability of the quantification process. The proposed nanoprobes offer potential applications as ratiometric imaging probes for monitoring K+ and ATP-related reactions in living cells, providing valuable insights into cellular processes. Additionally, they can be employed for determining the levels of K+ and ATP in human fluids, offering potential diagnostic applications in various clinical settings.

EGFR-mediated hyperacetylation of tubulin induced docetaxel resistance by downregulation of HDAC6 and upregulation of MCAK and PLK1 in prostate cancer cells.

Docetaxel-based chemotherapy has generally been considered as one of the effective treatments for castration-resistant prostate cancer (PCa). However, clinical treatment with docetaxel often encounters a number of undesirable effects, including drug resistance. Tubulin isoforms have been previously examined for their resistance to docetaxel in many cancers, but their real mechanisms remained unclear. In this study, a series of docetaxel-resistant PC/DX cell sublines were established by chronically exposing PC3 to progressively increased concentrations of docetaxel. Western blotting results showed significantly higher expression of acetyl-tubulin, α-tubulin, ß-tubulin, γ-tubulin, and ßIII-tubulin in PC/DX25 than in parental PC3 cells. PC/DX25 with greater resistance to docetaxel had higher levels of acetyl-tubulin and mitotic centromere-associated kinesin (MCAK) than PC3 cells. This study found that docetaxel induced the expression of acetyl-tubulin and MCAK in PC3 cells at a dose- and time-dependent manner. Both mRNA and protein levels of histone deacetylase 6 (HDAC6) were significantly decreased in PC/DX25 compared with PC3 cells. PC3 increased the resistance to docetaxel by HDAC6 knockdown and Tubastatin A (HDAC6 inhibitor). Conversely, PC/DX25 reversed the sensitivity to docetaxel by MCAK knockdown. Notably, flow cytometry analysis revealed that MCAK knockdown induced significantly sub G1 fraction in PC/DX cells. Overexpression of polo-like kinase-1 increased the cell survival rate and resistance to docetaxel in PC3 cells. Moreover, epidermal growth factor receptor (EGFR) activation induced the upregulation of acetyl-tubulin in docetaxel-resistant PCa cells. These findings demonstrated that the EGFR-mediated upregulated expression of acetyl-tubulin played an important role in docetaxel-resistant PCa.

Consumption of a Taiwanese cafeteria diet induces metabolic disorders and fecal flora changes in obese rats.

OBJECTIVES: Among diet-induced obesity animal models, the cafeteria diet, which contains human junk food and processed foods, is a popular experimental animal diets in Western countries. Consumption of a cafeteria diet can lead to the development of obesity and non-alcoholic liver disease in as soon as 2 mo, which more accurately reflects human eating patterns. The aim of this study was to establish a Taiwanese cafeteria diet and compare it with a traditional lard-based, 60% high-fat diet in a 12-wk animal model. METHODS: Six-wk-old male Wistar rats were assigned to the following three groups: control diet (C; LabDiet 5001); high-fat diet (HFD; 60% HFD); and the Taiwanese cafeteria diet (CAF). RESULTS: At the end of the study, weight gain and steatosis were observed in the HF and CAF groups. Compared with the HFD group, rats in the CAF group showed significantly higher plasma triacylglycerol concentrations and insulin resistance, which may have been correlated with increased inflammatory responses. Significantly lower hepatic sterol regulatory element-binding protein-1c and insulin receptor substrate-1 protein expressions were observed in the CAF group compared with the HFD group. Additionally, disruption of the microbiotic composition followed by increased obesity-related bacteria was observed in the CAF group. CONCLUSIONS: The present study confirmed that the Taiwanese cafeteria diet-induced rat model provided a potential platform for investigating obesity-related diseases.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.

Purpose: Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods: We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results: The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion: The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

Clinical significance of substrate characteristics and ablation outcomes in patients with atrial fibrillation and significant functional mitral regurgitation.

Background: Atrial fibrillation (AF) and mitral regurgitation (MR) have a complex interplay. Catheter ablation (CA) of AF may be a potential method to improve the severity of MR in AF patients. Methods: Patients with symptomatic AF and moderate to severe MR who underwent catheter ablation from 2011 to 2021 were retrospectively included in the study. Patients' baseline characteristics and electrophysiological features were examined. These patients were classified as group 1 with improved MR and group 2 with refractory MR after CA. Results: Fifty patients (age 60.2 ± 11.6 years, 29 males) were included in the study (32 in group 1 and 18 in group 2). Group 1 patients had a lower CHA2DS2-VASc score (1.7 ± 1.5 vs. 2.7 ± 1.5, P = 0.005) and had a lower incidence of hypertension (28.1% vs. 66.7%, P = 0.007) and diabetes mellitus (3.1% vs. 22.2%, P = 0.031) as compared to group 2 patients. Electroanatomic three-dimensional (3D) mapping showed that group 1 patients demonstrated less scars on the posterior bottom of the left atrium compared to group 2 patients (12.5% vs. 66.7%, P < 0.001). AF recurrence was not different between the two groups. After multivariate logistic regression analysis, a posterior bottom scar in the left atrium independently predicted refractory MR despite successful AF ablation. Conclusion: Most patients with AF and MR showed improvement of MR after AF ablation. A scar involving the posterior bottom of the left atrium is associated with poor recovery of MR.

Application of response surface methodology and quantitative NMR for the optimum extraction, characterization, and quantitation of Antrodia cinnamomea triterpenoids.

Antrodia cinnamomea (AC) is a treasured Asian medicinal mushroom, which has attracted attention due to recent research on its effectiveness in targeting a variety of serious ailments such as cancer and liver diseases. Among different A. cinnamomea constituents, triterpenoids are regarded as the most therapeutically attractive components because of their anti-inflammatory and cytotoxic activities. In the present study, we proposed a mathematical and statistical extraction protocol to evaluate the concentrations of total ergostane and lanostane triterpenoid derivatives from the ethanolic extract of the wild fruiting bodies of A. cinnamomea (EEAC) by utilizing response surface methodology (RSM) and quantitative NMR (qNMR) approaches. The optimum response surface model showed that the variations of the investigated response variables reached more than 90%, suggesting that the developed model is accurate in explaining response variability. Furthermore, the EEAC major characteristic triterpenoids were quantified through the comparison of the HPLC-tandem MS results with those of the qNMR results. The precision of the used techniques was also evaluated. The experimental design of the EEAC optimum extraction procedure obtained by using RSM and qNMR enabled accurate characterization and quantitation of A. cinnamomea triterpenoids.