Biocontrol of Diplodia bulgarica, the causal agent of apple canker, using Trichoderma zelobreve.

Apple (Malus domestica Borkh) is one of the most consumed and nutritious fruits. Iran is one of the main producers of the apple in the world. Diplodia bulgarica is the major causal agent of apple tree decline in Iran. Biological control is a nature-friendly approach to plant disease management. Trichoderma zelobreve was isolated from apple trees infected with Diplodia bulgarica in West Azarbaijan province of Iran. The results showed that T. zelobreve strongly inhibited the colony growth of D. bulgarica. In vivo assay on detached branches of apple tree cv. Golden Delicious using T. zelobreve mycelial plug showed that canker length/stem length (CL/SL) and canker perimeter/stem perimeter (CP/SP) indices decreased by 76 and 69%, respectively, 21 days after inoculation. Additionally, wettable powder formulation (WPF) containing the antagonistic fungus "T. zelobreve" decreased CL and CP/SP by 75 and 67%, respectively, 6 months after inoculation. Moreover, canker progress curves and the area under the disease progress curve (AUDPC) supported these findings. The growth temperatures of the antagonist and pathogen were similar, indicating the adaptation of T. zelobreve for biocontrol of apple canker caused by D. bulgarica. The results also showed that T. zelobreve-based WPF stored at 25 °C assure excellent shelf life at least 4 months, allowing the bioproduct to be stored at room temperature, which is a great advantage and cost-effective option.

New synergistic co-culture of Corylus avellana cells and Epicoccum nigrum for paclitaxel production.

Paclitaxel is a main impressive chemotherapeutic agent with unique mode of action and broad-spectrum activity against cancers. Hazel (Corylus avellana) is a paclitaxel-producing species through bioprospection. Endophytic fungi have significant roles in plant paclitaxel production. This study evaluated the effect of co-culture of C. avellana cells and paclitaxel-producing endophytic fungus, Epicoccum nigrum strain YEF2 and also the effect of elicitors derived from this fungal strain on paclitaxel production. The results clearly revealed that co-culture of C. avellana cells and E. nigrum was more effective than elicitation of C. avellana cells by only cell extract or culture filtrate of this fungal strain. Co-culture of C. avellana cells and E. nigrum surpassed monocultures in terms of paclitaxel production designating their synergistic interaction potential. Fungal inoculum amount, co-culture establishment time and co-culture period were important factors for achieving the maximum production of paclitaxel in this co-culture system. The highest total yield of paclitaxel (404.5 µg L-1) was produced in co-culture established on 13th day using 3.2% (v/v) of E. nigrum mycelium suspension, which was about 5.5 and 136.6 times that in control cultures of C. avellana cells and E. nigrum, respectively. This is the first report on positive effect of co-culture of paclitaxel-producing endophytic fungus and non-host plant cells for enhancing paclitaxel production.

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint.

Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d-1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.

Culture-based diversity of endophytic fungi of three species of Ferula grown in Iran.

A total of 1,348 endophytic fungal strains were isolated from Ferula ovina, F. galbaniflua, and F. persica. They included Eurotiales (16 species), Pleosporales (11 species), Botryosphaeriales (1 species), Cladosporiales (2 species), Helotiales (6 species), Hypocreales (31 species), Sordariales (7 species), Glomerellales (2 species), and Polyporales (1 species). F. ovina had the richest species composition of endophytic fungi, and the endophytic fungi were most abundant in their roots compared to shoots. Chao, Margalef, Shannon, Simpson, Berger-Parker, Menhinick, and Camargo indices showed that F. ovina roots had the most endophytic fungal species. The frequency distribution of fungal species isolated from Ferula spp. fell into the log-series model, and F. ovina roots had the highest Fisher alpha. The dominance indices showed that there are no dominant species in the endophytic fungal community isolated from Ferula spp., indicating community stability. Evenness values were 0.69, 0.90, 0.94, and 0.57 for endophytic fungi isolated from F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots, respectively, indicating a species distribution that tends toward evenness. The fungal species community isolated from each of F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots was a diverse species group originating from a homogeneous habitat. Their distribution followed a log-normal distribution, suggesting that the interactions of numerous independent environmental factors multiplicatively control species abundances. Principal component analysis showed that the highest species diversity and dominance were observed in the endophytic fungal community isolated from F. ovina and F. persica roots, respectively.

3-acetylpyridine induced behavioral dysfunction and neuronal loss in the striatum and hippocampus of adult male rats.

3-acetylpyridine (3-AP) is a neurotoxin that is known to mainly affect the inferior olivary nucleus (ION) in the brain stem. Although several studies have explored the effect of this neurotoxin, still further investigation is required to understand the impact of this toxin on different parts of the brain. In this research, two groups of rats were studied, the 3-AP-treated and the control groups. Behavioral, stereological, and immunohistochemical analyses were performed. The locomotor activity of the 3-AP-treated rats decreased whereas their anxiety levels were higher than in normal controls. Also, memory performance was impaired in animals in the 3-AP group. Microscopic observations showed a decline in the numerical density of neurons in the hippocampus and striatum along with gliosis. Although this toxin is used to affect the ION, it exerts a neurotoxic effect on different brain regions.

Multilayer perceptron-genetic algorithm as a promising tool for modeling cultivation substrate of Auricularia cornea Native to Iran.

Auricularia cornea Ehrenb (syn. A. polytricha) is a wood-decaying fungi known as black ear mushroom. Earlike gelatinous fruiting body distinguishes them from other fungi. Industrial wastes have the potential to be used as the basic substrate to produce mushrooms. Therefore, 16 substrate formulations were prepared from different ratios of beech (BS) and hornbeam sawdust (HS) supplemented with wheat (WB) and rice brans (RB). The pH and initial moisture content of substrate mixtures were adjusted to 6.5 and 70%, respectively. The comparison of in vitro growth characteristics of the fungal mycelia under the different temperatures (25, 28, and 30°C), and culture media [yeast extract agar (YEA), potato extract agar (PEA), malt extract agar (MEA), and also HS and BS extract agar media supplemented with maltose, dextrose, and fructose revealed that the highest mycelial growth rate (MGR; 7.5 mm/day) belonged to HS and BS extract agar media supplemented with three mentioned sugar at 28°C. In A. cornea spawn study, the substrate combination of BS (70%) + WB (30%) at 28°C and moisture contents of 75% displayed the highest mean MGR (9.3 mm/day) and lowest spawn run period (9.0 days). In the bag test, "BS (70%) + WB (30%)" was the best substrate displaying the shortest spawn run period (19.7 days), and the highest fresh sporophore yield (131.7 g/bag), biological efficiency (53.1%) and number of basidiocarp (9.0/bag) of A. cornea. Also, A. cornea cultivation was processed to model yield, biological efficiency (BE), spawn run period (SRP), days for pinhead formation (DPHF), days for the first harvest (DFFH), and total cultivation period (TCP) by multilayer perceptron-genetic algorithm (MLP-GA). MLP-GA (0.81-0.99) exhibited a higher predictive ability than stepwise regression (0.06-0.58). The forecasted values of the output variables were in good accordance with their observed ones corroborating the good competency of established MLP-GA models. MLP-GA modeling exhibited a powerful tool for forecasting and thus selecting the optimal substrate for maximum A. cornea production.

Guided blood transfusion of trauma patients with rotational thromboelastometry: a single-center cohort study.

BACKGROUND: Rotational thromboelastometry (ROTEM) is a blood test used to measure in vitro clot strength as a surrogate for a patient's ability to form clots in vivo. This provides information about induction, formation, and clot lysis, allowing goal-directed transfusion therapy for specific hemostatic needs. We sought to evaluate the effect of ROTEM-guided transfusion on blood product usage and in-hospital mortality among patients with a traumatic injury. METHODS: This was a single-center observational cohort analysis of emergency department patients in a Level 1 trauma center. We compared blood usage in trauma patients in whom ratio-based massive hemorrhage protocols were activated in the twelve months before the introduction of ROTEM (pre-ROTEM group) to the twelve months following the introduction of ROTEM (ROTEM-period group). ROTEM was implemented in this center in November 2016. The ROTEM device allowed clinicians to make real-time decisions about blood product therapy in resuscitation for trauma. RESULTS: The pre-ROTEM group contained 21 patients. Forty-three patients were included from the ROTEM-period, of whom 35 patients received ROTEM-guided resuscitation (81% compliance). The use of fibrinogen concentrate was significantly higher in the ROTEM-period group (pre-ROTEM mean 0.2 vs. ROTEM-period mean 0.8; p = 0.006). There was no significant difference in the number of units of red blood cells, platelets, cryoprecipitate, or fresh frozen plasma transfused between these groups. There was no significant difference in the mortality rate between the pre-ROTEM and ROTEM-period groups (33% vs. 19%; p = 0.22). CONCLUSIONS: The introduction of ROTEM-guided transfusion at this institution was associated with increased fibrinogen usage, but this did not impact mortality rates. There was no difference in the administration of red blood cell, fresh frozen plasma, platelet, and cryoprecipitate. Future research should focus on increased ROTEM compliance and optimizing ROTEM-guided transfusion to prevent blood product overuse among trauma patients.

Quality of Life and Use of Complementary and Alternative Medicines among Narcotics Anonymous Patients: A Cross-Sectional Study in Southeast Iran.

Background: Addiction, a chronic and recurrent disorder, is associated with lasting changes in the brain and can significantly affect the quality of life of people. Complementary and alternative medicine (CAM) along with modern medical treatments can improve the quality of life of individuals. This study aimed to investigate the relationship between the use of complementary and alternative medicines (CAMs) and quality of life in narcotics anonymous patients. Methods: This cross-sectional study was performed on 189 narcotics anonymous patients in southeastern Iran. Using questionnaires such as the demographic information, the World Health Organization Quality of Life-BREF (WHOQOL-BREF), CAM, and satisfaction with the use of CAM, the researcher was able to compile a comprehensive picture of the population. Results: The mean score of overall quality of life and general health was 64.02 ± 23.32. Overall, 66.1 percent (n = 125) of the participants reported using at least one type of CAM in the previous year. Last year, 25.9% of participants used at least one kind of CAM, 22.8% used two types of CAM, 7.9% used three types of CAM, and 4.8% used four to five types of CAM. Thirty-nine point seven percent of them reported using prayer, 36.5% reported using medicinal herbs, 15.3% of participants reported using massage, 14.3% of participants reported using dietary supplements, 12.2% reported using wet cupping, and 8.5% reported using meditation. There were no significant differences in physical, psychological, environmental, and overall quality of life between CAM users and non-CAM users. The prayer users had significantly higher scores in terms of social relationships, environment, and overall quality of life than nonprayer users. Employed participants and opium users had significantly higher overall quality of life than others. Conclusion: Although there was no difference in quality of life between CAM and non-CAM users, the present study showed that prayer and medicinal herbs were the most commonly used methods among narcotics anonymous patients. However, prayer and religious beliefs were successful in improving the quality of life of these individuals. Physicians and other healthcare providers must advise patients with addiction to use different CAMs in order to improve their quality of life and quit narcotics. Future in-depth studies could help these patients use CAMs and improve their quality of life.

Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.

Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms.

Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) Regression in the Quantitative Analysis of Respirable Crystalline Silica by Fourier-Transform Infrared Spectroscopy (FTIR).

Respirable crystalline silica (RCS) overexposure can lead to the development of silicosis which is a chronic, irreversible, potentially fatal respiratory disease. The most significant prerequisite for any silica exposure control plan is an accurate occupational exposure assessment. The results of crystalline silica analysis are often affected by other mineral interferences and are influenced by an analyst's knowledge of mineralogy to accurately interpret infrared spectra and correct matrix interferences. Partial least squares (PLS) and artificial neural networks (ANNs) are two multivariate calibration methods to overcome the problem of spectral interferences without the need for an analyst intervention. The performance of these two methods in quantitative analysis of quartz in the presence of mineral interferences was evaluated and compared in this study. Fifty mixtures with different crystalline silica content ratios were prepared by mixing quartz with four common mineral interferences including kaolinite, albite, muscovite, and amorphous silica. Fourier-transform infrared spectra of the mixtures were split into training and test datasets. The optimal architecture of the ANN model was achieved using a two-level full factorial design experiment and data were modeled using ANN and PLS regression analysis. Root mean squared error of prediction values of 1.69 and 6.12 µg quartz for ANN and PLS models, respectively, revealed the fact that the both models performed very well in quantitative analysis of quartz in the presence of mineral interferences, with a better relative performance of the ANN model which can be related to the inherent nonlinear predictive ability of ANNs. Given the excellent predictive ability of the ANN model which can deal with a completely overlapped peak without any need of user's intervention, it is recommended that the ANN model be optimized in future studies and utilized for reliable and rapid on-field assessment of RCS exposure.

Fungal canker agents in apple production hubs of Iran.

To identify apple canker casual agents and evaluate their pathogenicity and virulence in apple production hubs including West Azarbaijan, Isfahan and Tehran provinces; samples were collected from symptomatic apple trees. Pathogenic isolates on the detached branches were identified as Cytospora cincta, Diplodia bulgarica, Neoscytalidium dimidiatum and Eutypa cf. lata. E. cf. lata was reported as a potential apple canker causal agent in Iran for the first time based on the pathogenicity test on the detached branches, whereas it caused no canker symptoms in apple trees until 6 months after inoculation. Currently, E. cf. lata seems to be adapted to a single city. C. cincta, D. bulgarica and N. dimidiatum caused canker symptoms in apple trees. "C. cincta" and also "C. cincta and N. dimidiatum" were the most widespread and aggressive apple canker species, respectively, associated with apple canker in Iran. Therefore, they are considered to be the main threat to apple production in Iran and should be carefully monitored. Disease progress curve, area under the disease progress curve and optimum temperatures were determined for mentioned species. It is concluded that the establishment of each species occurs in appropriate areas and times in terms of the optimum temperature for their growth.

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture.

BACKGROUND: Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. RESULTS: GRNN-FOA models (0.88-0.97) showed the superior prediction performances as compared to regression models (0.57-0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l-1). CONCLUSIONS: Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

The Effects of Curcumin Supplementation on Muscle Damage, Oxidative Stress, and Inflammatory Markers in Healthy Females with Moderate Physical Activity: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial.

BACKGROUND: Exercise-induced oxidative stress, muscle damage, and inflammation represent major contributors to why athletes use ergogenic aids. Turmeric is used as a spice because of its polyphenol ingredient named curcumin. We assessed the effects of curcumin supplementation on inflammatory, oxidative stress markers, muscle damage, and anthropometric indices in women with moderate physical activity. METHODS: This double-blind, placebo-controlled clinical trial was conducted on 80 women with moderate physical activity levels (defined as walking or swimming for at least 1 h per day) for 8 weeks. Mean ± SD of age (years) all participants was 21 ± 2. Participants were randomly assigned into two groups: curcumin (500 mg/day) and placebo (500 mg/day cornstarch). Serum C-reactive protein (CRP), total antioxidant capacity (TAC), malondialdehyde (MDA), lactate dehydrogenase (LDH) levels, body composition, and maximum oxygen uptake (VO2 max) were evaluated before and after an intervention. RESULTS: Sixty-five subjects completed the 8-week intervention. Within analysis indicated a significant decrease in CRP, LDH, MDA levels, and a significant increase in VO2 max in the curcumin group after an intervention (P < 0.05). There were significant decreases in CRP (P = 0.002), LDH (P = 0.041), and MDA (P = 0.005), no significant increase in TAC, and significant increase in VO2 max (P = 0.0001) levels in the curcumin group compared with placebo group. There were no significant changes in weight, body mass index, body fat, and lean body mass between two groups. CONCLUSIONS: Our findings indicated that 8-week curcumin administration could significantly improve CRP, LDH, MDA, and VO2 max. Curcumin supplementation did not elicit significant changes in anthropometric indices in this study.

Optimization of the ANFIS using a genetic algorithm for physical work rate classification.

Purpose. Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Methods. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. Results. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. Conclusion. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.

Impact of text message-based intervention for weight control and health-promoting lifestyle behaviors of overweight and obese children.

BACKGROUND AND AIM: Little information is available on the use of text messages through mobile phones to address overweight/obesity in children. This study aims to evaluate the impact of a text message-based intervention for weight control and health-promoting lifestyle behaviors of overweight/obese children. MATERIALS AND METHODS: This quasi-experimental study was conducted among overweight/obese school students. Data on sociodemographic, dietary intake, sleep, sedentary behavior, physical activity (PA), and anthropometry were collected before and after the intervention. Weight and height were examined according to the standard protocols. The intervention was consisted of tailored messages for weight control and healthy lifestyle, including diet, PA, sedentary behavior, and sleep. Child attitude and his practice were asked before and after the intervention. The paired t-test was performed to compare means of continuous variables before and after the intervention for normal distribution data. The Wilcoxon test was also used for nonnormal data. RESULTS: A total of 71 boy students were included in the study (62% obese). The mean age was 10.07 years. The means of attitude score for PA, nutrition, and sleep after intervention were greater than before it, but it was significant only for PA. The mean of nighttime sleep duration of students after the intervention was significantly less. Furthermore, unhealthy score decreases after the intervention. CONCLUSION: Three-month lifestyle intervention as text messages had positive effects on the nutritional intake of obese children and their attitudes toward PA, but no effect on child body mass index.

Whole fungal elicitors boost paclitaxel biosynthesis induction in Corylus avellana cell culture.

Paclitaxel is an effective natural-source chemotherapeutic agent commonly applied to treat a vast range of cancers. In vitro Corylus avellana culture has been reported as a promising and inexpensive system for paclitaxel production. Fungal elicitors have been made known as the most efficient strategy for the biosynthesis induction of secondary metabolites in plant in vitro culture. In this research, C. avellana cell suspension culture (CSC) was exposed to cell extract (CE) and culture filtrate (CF) derived from Camarosporomyces flavigenus, either individually or combined treatment, in mid and late log phase. There is no report on the use of whole fungal elicitors (the combined treatment of CE and CF) for the elicitation of secondary metabolite biosynthesis in plant in vitro culture. The combined treatment of CE and CF significantly led to more paclitaxel biosynthesis and secretion than the individual use of them. Also, multivariate statistical approaches including stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR) were used to model and predict paclitaxel biosynthesis and secretion. Based on value account for (VAF), root mean square error (RMSE), coefficient of determination (R2), mean absolute percentage error (MAPE) and relative percent difference (RPD) can be concluded that mentioned regression models effectively worked only for modeling and predicting extracellular paclitaxel portion in C. avellana cell culture.

Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm.

Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89-0.95) than regression models (0.56-0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.

Fungal Cell Wall and Methyl-ß-Cyclodextrin Synergistically Enhance Paclitaxel Biosynthesis and Secretion in Corylus avellana Cell Suspension Culture.

Paclitaxel is the top-selling chemotherapeutic drug used for the treatment of lung, ovarian and breast cancer as well as Kaposi's sarcoma. Cell suspension culture (CSC) of Corylus avellana has been addressed as a promising alternative for producing paclitaxel. In this study, endophytic fungus strain YEF33 was isolated from Taxus baccata and identified as Coniothyrium palmarum. The effects of the elicitors derived from this fungus including cell extract, culture filtrate and cell wall (CW) and also chitin, alone or in combination with Methyl-ß-Cyclodextrin (MBCD), on paclitaxel biosynthesis in C. avellana CSC were assayed for the first time. CW of C. palmarum was the most efficient fungal elicitor for paclitaxel biosynthesis in C. avellana CSC. The results revealed that MBCD affected paclitaxel biosynthesis differently depending on fungal elicitor type and vice versa. MBCD, either alone or in combination with fungal elicitors, induced a high secretion of paclitaxel, suggesting the decrement of toxicity and retro-inhibition processes of paclitaxel for cells. The joint effects of C. palmarum CW (2.5% (v/v) on 17th day) and 50 mM MBCD synergistically enhanced paclitaxel biosynthesis (402.4 µg l-1; 5.8-fold), 78.6% of which (316.5 µg l-1) were secreted into culture medium, a level 146% higher than that in control.

Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods.

Paclitaxel as a microtubule-stabilizing agent is widely used for the treatment of a vast range of cancers. Corylus avellana cell suspension culture (CSC) is a promising strategy for paclitaxel production. Elicitation of paclitaxel biosynthesis pathway is a key approach for improving its production in cell culture. However, optimization of this process is time-consuming and costly. Modeling of paclitaxel elicitation process can be helpful to predict the optimal condition for its high production in cell culture. The objective of this study was modeling and forecasting paclitaxel biosynthesis in C. avellana cell culture responding cell extract (CE), culture filtrate (CF) and cell wall (CW) derived from endophytic fungus, either individually or combined treatment with methyl-ß-cyclodextrin (MBCD), based on four input variables including concentration levels of fungal elicitors and MBCD, elicitor adding day and CSC harvesting time, using adaptive neuro-fuzzy inference system (ANFIS) and multiple regression methods. The results displayed a higher accuracy of ANFIS models (0.94-0.97) as compared to regression models (0.16-0.54). The great accordance between the predicted and observed values of paclitaxel biosynthesis for both training and testing subsets support excellent performance of developed ANFIS models. Optimization process of developed ANFIS models with genetic algorithm (GA) showed that optimal MBCD (47.65 mM) and CW (2.77% (v/v)) concentration levels, elicitor adding day (16) and CSC harvesting time (139 h and 41 min after elicitation) can lead to highest paclitaxel biosynthesis (427.92 µg l-1). The validation experiment showed that ANFIS-GA method can be a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis, as a case study.