Francisco de Paula Santander University

Medicines, considered emerging pollutants, have been frequently detected in soils and water bodies, where they can affect key biological processes in ecosystems; among them, paracetamol, diclofenac, and carbamazepine stand out for their constant environmental presence. This review comparatively analyzes studies on the cytotoxic and genotoxic effects of these drugs on plants used as biomodels, such as Allium cepa, Vicia faba, Pisum sativum, Lens culinaris, and Lactuca sativa. The studies consistently report reductions in the mitotic index and the formation of micronuclei and other cellular alterations, particularly in Allium cepa, reflecting the sensitivity of this bioindicator. However, the analysis has limitations due to the use of the mitotic index as the sole biomarker, as well as methodological differences and the limited integration of oxidative stress parameters. Overall, the results confirm the potential of plant biomodels as ecotoxicological tools, but also highlight the need to standardize protocols, incorporate multiple biomarkers, and evaluate prolonged exposures and drug mixtures at relevant environmental concentrations.

This study evaluates the cytotoxic effects of aluminum hydroxide (Al(OH)2) on root growth and cell division of Elaeis guineensis roots. Using Al(OH)₃ concentrations ranging from 5 to 250 mg/L, both radicle growth and nuclear and chromosomal abnormalities, including mitotic indices and the presence of micronuclei, anaphase bridges, and other alterations, were analyzed. The results showed that elevated concentrations of Al(OH)₃ (particularly 150 to 250 mg/L) significantly inhibit root growth, reduce the mitotic index, and increase the frequency of nuclear abnormalities. The presence of aluminum-induced reactive oxygen species (ROS) appears to cause DNA damage, which contributes to the formation of micronuclei and other genetic abnormalities. These findings underscore the cytotoxic and genotoxic potential of aluminum in acidic soils, which may negatively impact oil palm productivity in cropping contexts. It is concluded that exposure to aluminum hydroxide represents a risk to the genetic stability of E. guineensis and suggests the need to adequately manage aluminum levels in soils used for this crop.

PurposeThe authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).Design/methodology/approachThe authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.FindingsIn this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.Research limitations/implicationsIt is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.Practical implicationsACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.Originality/valueThe main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.

This research assesses a triphasic extraction technique for the sequential retrieval of C-phycocyanin (C-PC) and polyhydroxybutyrate (PHB) from a thermotolerant Potamosiphon sp. strain. A two-stage design-of-experiments methodology was employed (Minimum Run Resolution V factorial design involving six variables, followed by a central composite design (CCD)) to optimize the chosen region. In the factorial stage, PHB ranged from 109.396 to 168.995 mg/g, and the model was significant (F = 22.63, p < 0.0001). Freeze-milling and vortexing were identified as critical elements, underscoring the importance of the t-butanol × (NH4)2SO4 interaction for phase selectivity. The CCD concentrating on freeze-milling and vortex cycles yielded a robust quadratic model (F = 78.18, p < 0.0001), forecasting a peak PHB yield of 191.82 mg/g at six freeze-milling cycles and three vortex cycles (desirability 0.921), while maintaining t-butanol at 19.9 mL, t-butanol concentration at 94.7% (v/v), (NH4)2SO4 at 49.9% (w/v), and vortex duration at 1.2 min. Ten separate trials validated the model’s accuracy, yielding an observed PHB of 191.5 mg/g, which closely matched the model’s prediction. The platform facilitates an integrated downstream process in which C-PC is recovered under moderate conditions before triphasic partitioning. This enables the simultaneous valorization of pigment, lipophilic fraction, and biopolymer inside a unified cyanobacterial biorefinery process.

Phytophthora palmivora causes significant economic losses in Colombian cacao crops, yet its population structure and pathogenicity remain poorly characterized. This study tested the hypothesis that the Colombian Andes mountain complex acts as a geographic barrier limiting gene flow among P. palmivora populations. We analyzed 73 isolates from western, central, and eastern cacao-growing regions using DArTseq genotyping and standardized pathogenicity assays. High genotypic diversity was observed (Simpson's index = 0.99), with corrected association index analysis indicating predominantly clonal reproduction, consistent with all isolates being the A2 mating type. Discriminant analysis of principal components identified seven genetic clusters, but these showed no correlation with geographic distribution, contradicting our geographic barrier hypothesis. Mantel tests confirmed no isolation-by-distance ( r = 0.004, P = 0.650). Pathogenicity tests on detached CCN51 leaves revealed extensive virulence variation that was independent of genetic clustering or geography. These findings suggest that observed genetic diversity results from multiple pathogen introductions and anthropogenic dispersal rather than geographic barrier. The lack of correlation between genotype and virulence indicates that pathogenicity determinants are independent of neutral molecular markers used for population structure analysis. Management recommendations include stringent phytosanitary measures to prevent further introductions and consideration of pathogen diversity in resistance breeding programs.