Comparing RNA extraction methods to face the variations in RNA quality using two human biological matrices.

BACKGROUND: Nucleic acids, RNA among them, are widely used in biomedicine and Biotechnology. Because of their susceptibility to degradation by RNases, the handling and extraction process of RNA from cells and tissues require specialized personnel and standardized methods to guarantee high purity and integrity. Due to the diversity of techniques found in the market, a comparative study between different RNA extraction methods is useful to facilitate the best choice for the researcher or in research service platforms such as biobanks to see the traceability of the samples. METHODS AND RESULTS: In this study, we have compared seven different RNA extraction methods: manual (TRIzol™), semiautomated (QIAGEN™, Bio-Rad, Monarch®, and Canvax™), and fully automated (QIAcube™ and Maxwell®) processes, from two biological matrices: human Jurkat T cells and peripheral blood mononuclear cells (PBMC). Results showed marked differences in the RNA quality and functionality according to the method employed for RNA extraction and the matrix used. DISCUSSION: QIAcube™ and semi-automated extraction methods were perceived as the best options because of their lower variability, good functionality, and lower cost (P < 0.001). These data contribute to facilitating researchers or research service platforms (Biobanks) in decision-making practices and emphasize the relevance of the selection of the RNA extraction method in each experimental procedure or traceability study to guarantee both quality standards and its reproducibility.

History, Phylogeny, Biodiversity, and New Computer-Based Tools for Efficient Micropropagation and Conservation of Pistachio (Pistacia spp.) Germplasm.

The word "pstk" [pistag], used in the ancient Persian language, is the linguistic root from which the current name "pistachio", used worldwide, derives. The word pistachio is generally used to designate the plants and fruits of a single species: Pistacia vera L. Both the plant and its fruits have been used by mankind for thousands of years, specifically the consumption of its fruits by Neanderthals has been dated to about 300,000 years ago. Native to southern Central Asia (including northern Afghanistan and northeastern Iran), its domestication and cultivation occurred about 3000 years ago in this region, spreading to the rest of the Mediterranean basin during the Middle Ages and finally being exported to America and Australia at the end of the 19th century. The edible pistachio is an excellent source of unsaturated fatty acids, carbohydrates, proteins, dietary fiber, vitamins, minerals and bioactive phenolic compounds that help promote human health through their antioxidant capacity and biological activities. The distribution and genetic diversity of wild and domesticated pistachios have been declining due to increasing population pressure and climatic changes, which have destroyed natural pistachio habitats, and the monoculture of selected cultivars. As a result, the current world pistachio industry relies mainly on a very small number of commercial cultivars and rootstocks. In this review we discuss and summarize the current status of: etymology, origin, domestication, taxonomy and phylogeny by molecular analysis (RAPID, RFLP, AFLP, SSR, ISSR, IRAP, eSSR), main characteristics and world production, germplasm biodiversity, main cultivars and rootstocks, current conservation strategies of both conventional propagation (seeds, cutting, and grafting), and non-conventional propagation methods (cryopreservation, slow growth storage, synthetic seed techniques and micropropagation) and the application of computational tools (Design of Experiments (DoE) and Machine Learning: Artificial Neural Networks, Fuzzy logic and Genetic Algorithms) to design efficient micropropagation protocols for the genus Pistacia.

Neurofuzzy logic predicts a fine-tuning metabolic reprogramming on elicited Bryophyllum PCSCs guided by salicylic acid.

Novel approaches to the characterization of medicinal plants as biofactories have lately increased in the field of biotechnology. In this work, a multifaceted approach based on plant tissue culture, metabolomics, and machine learning was applied to decipher and further characterize the biosynthesis of phenolic compounds by eliciting cell suspension cultures from medicinal plants belonging to the Bryophyllum subgenus. The application of untargeted metabolomics provided a total of 460 phenolic compounds. The biosynthesis of 164 of them was significantly modulated by elicitation. The application of neurofuzzy logic as a machine learning tool allowed for deciphering the critical factors involved in the response to elicitation, predicting their influence and interactions on plant cell growth and the biosynthesis of several polyphenols subfamilies. The results indicate that salicylic acid plays a definitive genotype-dependent role in the elicitation of Bryophyllum cell cultures, while methyl jasmonate was revealed as a secondary factor. The knowledge provided by this approach opens a wide perspective on the research of medicinal plants and facilitates their biotechnological exploitation as biofactories in the food, cosmetic and pharmaceutical fields.

Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots.

Hairy roots are made after the integration of a small set of genes from Agrobacterium rhizogenes in the plant genome. Little is known about how this small set is linked to their hormone profile, which determines development, morphology, and levels of secondary metabolite production. We used C. asiatica hairy root line cultures to determine the putative links between the rol and aux gene expressions with morphological traits, a hormone profile, and centelloside production. The results obtained after 14 and 28 days of culture were processed via multivariate analysis and machine-learning processes such as random forest, supported vector machines, linear discriminant analysis, and neural networks. This allowed us to obtain models capable of discriminating highly productive root lines from their levels of genetic expression (rol and aux genes) or from their hormone profile. In total, 12 hormones were evaluated, resulting in 10 being satisfactorily detected. Within this set of hormones, abscisic acid (ABA) and cytokinin isopentenyl adenosine (IPA) were found to be critical in defining the morphological traits and centelloside content. The results showed that IPA brings more benefits to the biotechnological platform. Additionally, we determined the degree of influence of each of the evaluated genes on the individual hormone profile, finding that aux1 has a significant influence on the IPA profile, while the rol genes are closely linked to the ABA profile. Finally, we effectively verified the gene influence on these two specific hormones through feeding experiments that aimed to reverse the effect on root morphology and centelloside content.

Artificial Neural Networks Elucidated the Essential Role of Mineral Nutrients versus Vitamins and Plant Growth Regulators in Achieving Healthy Micropropagated Plants.

The design of an adequate culture medium is an essential step in the micropropagation process of plant species. Adjustment and balance of medium components involve the interaction of several factors, such as mineral nutrients, vitamins, and plant growth regulators (PGRs). This work aimed to shed light on the role of these three components on the plant growth and quality of micropropagated woody plants, using Actinidia arguta as a plant model. Two experiments using a five-dimensional experimental design space were defined using the Design of Experiments (DoE) method, to study the effect of five mineral factors (NH4NO3, KNO3, Mesos, Micros, and Iron) and five vitamins (Myo-inositol, thiamine, nicotinic acid, pyridoxine, and vitamin E). A third experiment, using 20 combinations of two PGRs: BAP (6-benzylaminopurine) and GA3 (gibberellic acid) was performed. Artificial Neural Networks (ANNs) algorithms were used to build models with the whole database to determine the effect of those components on several growth and quality parameters. Neurofuzzy logic allowed us to decipher and generate new knowledge on the hierarchy of some minerals as essential components of the culture media over vitamins and PRGs, suggesting rules about how MS basal media formulation could be modified to assess the quality of micropropagated woody plants.

The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe).

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three Bryophyllum species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the Bryophyllum genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.

Computational Prediction and Experimental Values of Mechanical Properties of Carbon Nanotube Reinforced Cement.

The main objective of this study is to create a rigorous computer model of carbon nanotube composites to predict their mechanical properties before they are manufactured and to reduce the number of physical tests. A detailed comparison between experimental and computational results of a cement-based composite is made to match data and find the most significant parameters. It is also shown how the properties of the nanotubes (Young's modulus, aspect ratio, quantity, directionality, clustering) and the cement (Young's modulus) affect the composite properties. This paper tries to focus on the problem of modeling carbon nanotube composites computationally, and further study proposals are given.

Computer-Based Tools Unmask Critical Mineral Nutrient Interactions in Hoagland Solution for Healthy Kiwiberry Plant Acclimatization.

The aim of this study was to better understand the response of ex vitro acclimatized plants grown to a set of mineral nutrient combinations based on Hoagland solution. To reach that, two computer-based tools were used: the design of experiments (DOE) and a hybrid artificial intelligence technology that combines artificial neural networks with fuzzy logic. DOE was employed to create a five-dimensional IV-design space by categorizing all macroelements and one microelement (copper) of Hoagland mineral solution, reducing the experimental design space from 243 (35) to 19 treatments. Typical growth parameters included hardening efficiency (Hard), newly formed shoot length (SL), total leaf number (TLN), leaf chlorophyll content (LCC), and leaf area (LA). Moreover, three physiological disorders, namely, leaf necrosis (LN), leaf spot (LS), and curled leaf (CL), were evaluated for each treatment (mineral formulation). All the growth parameters plus LN were successfully modeled using neuro-fuzzy logic with a high train set R 2 between experimental and predicted values (72.67 < R 2 < 98.79). The model deciphered new insights using different sets of "IF-THEN" rules, pinpointing the positive role of Mg2+ and Ca2+ to improve Hard, SL, TLN, and LA and alleviate LN but with opposite influences on LCC. On the contrary, TLN and LCC were negatively affected by the addition of NO3 - into the media, while NH4 + in complex interaction with Cu2+ or Mg2+ positively enhanced SL, TLN, LCC, and LA. In our opinion, the approach and results achieved in this work are extremely fruitful to understand the effect of Hoagland mineral nutrients on the healthy growth of ex vitro acclimatized plants, through identifying key factors, which favor growth and limit physiological abnormalities.

Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy.

To adopt a transfer learning approach and establish a convolutional neural network (CNN) model for the prediction of rectum and bladder dose-volume histograms (DVH) in prostate patients treated with a VMAT technique. One hundred forty-four VMAT patients with intermediate or high-risk prostate cancer were included in this study. Data were split into two sets: 120 and 24 patients, respectively. The second set was used for final validation. To ensure the accuracy of the training data, we developed a ground-truth analysis for detecting and correcting for all potential outliers. We used transfer learning in combination with a pre-trained VGG-16 network. We dropped the fully connected layers from the VGG-16 and added a new fully connected neural network. The inputs for the CNN were a 2D image of the volumes contoured in the CT, but we only retained the geometrical information of every CT-slice. The outputs were the corresponding rectum and bladder DVH for every slice. We used a confusion matrix to analyze the performance of our model. Our model achieved 100% and 81% of true positive and true negative predictions, respectively. We have an overall accuracy of 87.5%, a misclassification rate of 12.5%, and a precision of 100%. We have successfully developed a model for reliable prediction of rectum and bladder DVH in prostate patients by applying a previously pre-trained CNN. To our knowledge, this is the first attempt to apply transfer learning to the prediction of DVHs that accounts for the ground truth problem.

Ectomycorrhizal fungal community structure in a young orchard of grafted and ungrafted hybrid chestnut saplings.

Ectomycorrhizal (ECM) fungal community of the European chestnut has been poorly investigated, and mostly by sporocarp sampling. We proposed the study of the ECM fungal community of 2-year-old chestnut hybrids Castanea × coudercii (Castanea sativa × Castanea crenata) using molecular approaches. By using the chestnut hybrid clones 111 and 125, we assessed the impact of grafting on ECM colonization rate, species diversity, and fungal community composition. The clone type did not have an impact on the studied variables; however, grafting significantly influenced ECM colonization rate in clone 111. Species diversity and richness did not vary between the experimental groups. Grafted and ungrafted plants of clone 111 had a different ECM fungal species composition. Sequence data from ITS regions of rDNA revealed the presence of 9 orders, 15 families, 19 genera, and 27 species of ECM fungi, most of them generalist, early-stage species. Thirteen new taxa were described in association with chestnuts. The basidiomycetes Agaricales (13 taxa) and Boletales (11 taxa) represented 36% and 31%, of the total sampled ECM fungal taxa, respectively. Scleroderma citrinum, S. areolatum, and S. polyrhizum (Boletales) were found in 86% of the trees and represented 39% of total ECM root tips. The ascomycete Cenococcum geophilum (Mytilinidiales) was found in 80% of the trees but accounted only for 6% of the colonized root tips. These results could help to unveil the impact of grafting on fungal symbionts, improving management of chestnut agro-ecosystems and production of edible fungal species.

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable "IF-THEN" rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp.

The subgenus Bryophyllum includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from Bryophyllum have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides. These compounds are found in limited amounts in plants because they are biosynthesized in response to different biotic and abiotic stresses. Therefore, novel approaches should be undertaken with the aim of achieving the phytochemical valorization of Bryophyllum sp., allowing a sustainable production that prevents from a massive exploitation of wild plant resources. This review focuses on the study of phytoconstituents reported on Bryophyllum sp.; the application of plant tissue culture methodology as a reliable tool for the valorization of bioactive compounds; and the application of machine learning technology to model and optimize the full phytochemical potential of Bryophyllum sp. As a result, Bryophyllum species can be considered as a promising source of plant bioactive compounds, with enormous antioxidant and anticancer potential, which could be used for their large-scale biotechnological exploitation in cosmetic, food, and pharmaceutical industries.

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies.

Radiotherapy treatment planning studies contribute significantly  to advances and improvements in radiation treatment of cancer patients. They are a pivotal step to support and facilitate the introduction of novel techniques into clinical practice, or as a first step before clinical trials can be carried out. There have been numerous examples published in the literature that demonstrated the feasibility of such techniques as IMRT, VMAT, IMPT, or that compared different treatment methods (e.g. non-coplanar vs coplanar treatment), or investigated planning approaches (e.g. automated planning). However, for a planning study to generate trustworthy new knowledge and give confidence in applying its findings, then its design, execution and reporting all need to meet high scientific standards. This paper provides a 'quality framework' of recommendations and guidelines that can contribute to the quality of planning studies and resulting publications. Throughout the text, questions are posed and, if applicable to a specific study and if met, they can be answered positively in the provided 'RATING' score sheet. A normalised weighted-sum score can then be calculated from the answers as a quality indicator. The score sheet can also be used to suggest how the quality might be improved, e.g. by focussing on questions with high weight, or by encouraging consideration of aspects given insufficient attention. Whilst the overall aim of this framework and scoring system is to improve the scientific quality of treatment planning studies and papers, it might also be used by reviewers and journal editors to help to evaluate scientific manuscripts reporting planning studies.

Shoot tip necrosis of in vitro plant cultures: a reappraisal of possible causes and solutions.

MAIN CONCLUSION: Shoot tip necrosis is a physiological condition that negatively impacts the growth and development of in vitro plant shoot cultures across a wide range of species. Shoot tip necrosis is a physiological condition and disorder that can arise in plantlets or shoots in vitro that results in death of the shoot tip. This condition, which can spread basipetally and affect the emergence of axillary shoots from buds lower down the stem, is due to the cessation of apical dominance. STN can occur at both shoot multiplication and rooting stages. One of the most common factors that cause STN is nutrient deficiency or imbalance. Moreover, the presence or absence of plant growth regulators (auxins or cytokinins) at specific developmental stages may impact STN. The cytokinin to auxin ratio within an in vitro plant can be modified by varying the concentration of cytokinins used in the culture medium. The supply of nutrients to in vitro shoots or plantlets might also affect their hormonal balance, thus modifying the occurrence of STN. High relative humidity within culture vessels and hyperhydricity are associated with STN. An adequate supply of calcium as the divalent cation (Ca2+) can hinder STN by inhibiting the accumulation of phenolic compounds and thus programmed cell death. Moreover, the level of Ca2+ affects auxin transport and ethylene production, and higher ethylene production, which can occur as a result of high relative humidity in or poor ventilation of the in vitro culture vessel, induces STN. High relative humidity can decrease the mobility of Ca2+ within a plant, resulting in Ca2+ deficiency and STN. STN of in vitro shoots or plantlets can be halted or reversed by altering the basal medium, mainly the concentration of Ca2+, adjusting the levels of auxins or cytokinins, or modifying culture conditions. This review examines the literature related to STN, seeks to discover the associated factors and relations between them, proposes practical solutions, and attempts to better understand the mechanism(s) underlying this condition in vitro.

Exploring the Use of Bryophyllum as Natural Source of Bioactive Compounds with Antioxidant Activity to Prevent Lipid Oxidation of Fish Oil-In-Water Emulsions.

The current industrial requirements for food naturalness are forcing the development of new strategies to achieve the production of healthier foods by replacing the use of synthetic additives with bioactive compounds from natural sources. Here, we investigate the use of plant tissue culture as a biotechnological solution to produce plant-derived bioactive compounds with antioxidant activity and their application to protect fish oil-in-water emulsions against lipid peroxidation. The total phenolic content of Bryophyllum plant extracts ranges from 3.4 to 5.9 mM, expressed as gallic acid equivalents (GAE). The addition of Bryophyllum extracts to 4:6 fish oil-in-water emulsions results in a sharp (eight-fold) increase in the antioxidant efficiency due to the incorporation of polyphenols to the interfacial region. In the emulsions, the antioxidant efficiency of extracts increased linearly with concentration and levelled off at 500 µM GAE, reaching a plateau region. The antioxidant efficiency increases modestly (12%) upon increasing the pH from 3.0 to 5.0, while an increase in temperature from 10 to 30 °C causes a six-fold decrease in the antioxidant efficiency. Overall, results show that Bryophyllum plant-derived extracts are promising sources of bioactive compounds with antioxidant activity that can be eventually be used to control lipid oxidation in food emulsions containing (poly)unsaturated fatty acids.

Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis.

Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the Bryophyllum subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected. The rules obtained along with the model allowed to decipher that BAP had a pleiotropic effect on the Bryophyllum spp., as it caused different organogenetic responses depending on its concentration and the genotype, including direct and indirect shoot organogenesis and callus formation. On the contrary, IAA showed an inhibiting role, restricted to indirect shoot regeneration. In this work, neurofuzzy logic emerged as a cutting-edge method to characterize the mechanism of action of two phytohormones, leading to the optimization of plant tissue culture protocols with high large-scale biotechnological applicability.

Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds.

We combined machine learning and plant in vitro culture methodologies as a novel approach for unraveling the phytochemical potential of unexploited medicinal plants. In order to induce phenolic compound biosynthesis, the in vitro culture of three different species of Bryophyllum under nutritional stress was established. To optimize phenolic extraction, four solvents with different MeOH proportions were used, and total phenolic content (TPC), flavonoid content (FC) and radical-scavenging activity (RSA) were determined. All results were subjected to data modeling with the application of artificial neural networks to provide insight into the significant factors that influence such multifactorial processes. Our findings suggest that aerial parts accumulate a higher proportion of phenolic compounds and flavonoids in comparison to roots. TPC was increased under ammonium concentrations below 15 mM, and their extraction was maximum when using solvents with intermediate methanol proportions (55-85%). The same behavior was reported for RSA, and, conversely, FC was independent of culture media composition, and their extraction was enhanced using solvents with high methanol proportions (>85%). These findings confer a wide perspective about the relationship between abiotic stress and secondary metabolism and could serve as the starting point for the optimization of bioactive compound production at a biotechnological scale.

Modeling and Optimizing Culture Medium Mineral Composition for in vitro Propagation of Actinidia arguta.

The design of plant tissue culture media remains a complicated task due to the interactions of many factors. The use of computer-based tools is still very scarce, although they have demonstrated great advantages when used in large dataset analysis. In this study, design of experiments (DOE) and three machine learning (ML) algorithms, artificial neural networks (ANNs), fuzzy logic, and genetic algorithms (GA), were combined to decipher the key minerals and predict the optimal combination of salts for hardy kiwi (Actinidia arguta) in vitro micropropagation. A five-factor experimental design of 33 salt treatments was defined using DOE. Later, the effect of the ionic variations generated by these five factors on three morpho-physiological growth responses - shoot number (SN), shoot length (SL), and leaves area (LA) - and on three quality responses - shoots quality (SQ), basal callus (BC), and hyperhydricity (H) - were modeled and analyzed simultaneously. Neurofuzzy logic models demonstrated that just 11 ions (five macronutrients (N, K, P, Mg, and S) and six micronutrients (Cl, Fe, B, Mo, Na, and I)) out of the 18 tested explained the results obtained. The rules "IF - THEN" allow for easy deduction of the concentration range of each ion that causes a positive effect on growth responses and guarantees healthy shoots. Secondly, using a combination of ANNs-GA, a new optimized medium was designed and the desired values for each response parameter were accurately predicted. Finally, the experimental validation of the model showed that the optimized medium significantly promotes SQ and reduces BC and H compared to standard media generally used in plant tissue culture. This study demonstrated the suitability of computer-based tools for improving plant in vitro micropropagation: (i) DOE to design more efficient experiments, saving time and cost; (ii) ANNs combined with fuzzy logic to understand the cause-effect of several factors on the response parameters; and (iii) ANNs-GA to predict new mineral media formulation, which improve growth response, avoiding morpho-physiological abnormalities. The lack of predictability on some response parameters can be due to other key media components, such as vitamins, PGRs, or organic compounds, particularly glycine, which could modulate the effect of the ions and needs further research for confirmation.

Use of phage ϕ6 to inactivate Pseudomonas syringae pv. actinidiae in kiwifruit plants: in vitro and ex vivo experiments.

Over the last years, the global production and trade of kiwifruit has been severely impacted by Pseudomonas syringae pv. actinidiae (Psa), a phytopathogen that causes a disease in kiwifruit plants known as bacterial canker. The available treatments for this disease are still scarce, with the most common involving frequently spraying the orchards with disinfectants, copper-based bactericides and/or antibiotics. Moreover, these treatments should be avoided due to their high toxicity to the environment and promotion of bacterial resistance. Phage therapy may be an alternative approach to inactivate Psa. The present study investigated the potential application of the already commercially available bacteriophage (or phage) ϕ6 to control Psa infections. The inactivation of Psa was assessed in vitro, using liquid culture medium, and ex vivo, using artificially contaminated kiwifruit leaves with two biovar 3 (a highly aggressive pathogen) strains (Psa CRA-FRU 12.54 and Psa CRA-FRU 14.10). In the in vitro experiments, the phage ϕ6 was effective against both strains (maximum reduction of 2.2 and 1.9 CFU/mL for Psa CRA-FRU 12.54 and Psa CRA-FRU 14.10, respectively). In the ex vivo tests, the decrease was lower (maximum reduction 1.1 log and 1.8 CFU/mL for Psa CRA-FRU 12.54 and Psa CRA-FRU 14.10, respectively). The results of this study suggest that the commercially available phage ϕ6 can be an effective alternative to control Psa infections in kiwifruit orchards.