Content Marketing

The size of the Internet interaction allowed the customer to be part of the marketing of any product, product, services, or other organization. To this end, digital internal marketing promotes a customer-cantered perspective where organizations should focus on customer support and engage them in the value proposition process. An important part of digital marketing is creating value and consumer engagement in content marketing. The purpose of this paper was to provide a ‘content marketing’ test and to gain an understanding of the critical dimensions of this digital marketing strategy, with its basic strategies that companies can use online. The ideas expressed in this study have an impact on the content marketing strategy, as does the effective strategy for other disruptive marketing strategies. In the digital age, the value of digital marketing has increased from one year to the next as part of a marketing strategy developed by organizations of any kind and size. Considering that digital marketing requires the presence of content marketing, the success or failure of a company’s online communication depends largely on the quality of its content marketing. In this context, in addition to advertising high quality content marketing, digital advertisers must perform targeted analysis to tailor their content and select the appropriate method to promote it.

Could Causal Discovery in Proteogenomics Assist in Understanding Gene–Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause–effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants.

Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Vitamin E Acetate Determination in Vaping Liquids and Non-targeted Analysis of Vaping Emissions of Diluents of Concern, Vitamin E Acetate and Medium-Chain Triglycerides Oil

During the summer of 2019, cases of lung injury associated with vaping emerged in North America, including among individuals who reported exclusive use of nicotine vaping liquids. Once vitamin E acetate was identified as a potential causative agent a quantitative method based on a simple sample dilution, separation by gas chromatography and analysis by triple quadrupole mass spectrometry (GC MSMS) was developed. Method detection limit (MDL) and limit of quantification (LOQ) were determined at 0.159 µg/mL and 0.505 µg/mL, respectively. The analysis was performed on a subset of 203 commercially sourced nicotine containing vaping liquids of various flavour profile and nicotine range (nicotine free-59 mg/mL) from an internal inventory. The target analyte, Vitamin E Acetate, was not detected in any samples analyzed, as expected, given the reported detection in literature and high association of the chemical with cannabis and not nicotine containing vaping products.

Metabolic and Transcriptional Changes across Osteogenic Differentiation of Mesenchymal Stromal Cells

Mesenchymal stromal cells (MSCs) are multipotent post-natal stem cells with applications in tissue engineering and regenerative medicine. MSCs can differentiate into osteoblasts, chondrocytes, or adipocytes, with functional differences in cells during osteogenesis accompanied by metabolic changes. The temporal dynamics of these metabolic shifts have not yet been fully characterized and are suspected to be important for therapeutic applications such as osteogenesis optimization. Here, our goal was to characterize the metabolic shifts that occur during osteogenesis. We profiled five key extracellular metabolites longitudinally (glucose, lactate, glutamine, glutamate, and ammonia) from MSCs from four donors to classify osteogenic differentiation into three metabolic stages, defined by changes in the uptake and secretion rates of the metabolites in cell culture media. We used a combination of untargeted metabolomic analysis, targeted analysis of 13C-glucose labelled intracellular data, and RNA-sequencing data to reconstruct a gene regulatory network and further characterize cellular metabolism. The metabolic stages identified in this proof-of-concept study provide a framework for more detailed investigations aimed at identifying biomarkers of osteogenic differentiation and small molecule interventions to optimize MSC differentiation for clinical applications.