An Efficient Approach for Lipase-Catalyzed Synthesis of Retinyl Laurate Nutraceutical by Combining Ultrasound Assistance and Artificial Neural Network Optimization

Ultrasonication-assisted lipase-catalyzed esterification of chlorogenic acid: A comparative study using fatty alcohol and acids in solvent and solvent-free conditions

Chlorogenic acid, a well-known antioxidant, has potential applications in health care, food, and cosmetic sectors. However, its low solubility hinders its application at the industrial scale. The primary goal of the present study was to increase the lipophilic property of chlorogenic acid through esterification using an ultrasonication approach and Novozym® 435 as the catalyst. The esterification was executed in two ways. In the first method, chlorogenic acid was converted to chlorogenic acid ester using octanol in a solvent-free reaction. Catalytic factors such as reaction time (12 h ∼ 36 h), enzyme dosage (10 ∼ 50 mg), and ultrasonication power (90 ∼ 150 W) were optimized using Box-Behnken design (BBD) while temperature (60 ℃) and molar ration (chlorogenic acid/octanol, 1:500) were kept constant. A maximum conversion rate of 95.3 % was achieved when the esterification was performed for 12 h at 120 W ultrasonication power and 50 mg enzyme dosage. Contrary to the first method, when esterification was done using caprylic acid in the presence of 2-methyl-2-butanol as a solvent, the conversion rate was relatively low. Despite optimization of factors including molar ratio, enzyme dosage, and reaction time, the highest conversion rate achieved was of only 36.8 %. Moreover, molecular docking results revealed that the lowest binding energy was between lipase and octanol. The finding of the study clearly stated that the esterification of chlorogenic acid was more effective in a solvent-free condition as compared to in the presence of solvent.

Recent insight into the advances and prospects of microbial lipases and their potential applications in industry

Enzymes play a crucial role in various industrial sectors. These biocatalysts not only ensure sustainability and safety but also enhance process efficiency through their unique specificity. Lipases possess versatility as biocatalysts and find utilization in diverse bioconversion reactions. Presently, microbial lipases are gaining significant focus owing to the rapid progress in enzyme technology and their widespread implementation in multiple industrial procedures. This updated review presents new knowledge about various origins of microbial lipases, such as fungi, bacteria, and yeast. It highlights both the traditional and modern purification methods, including precipitation and chromatographic separation, the immunopurification technique, the reversed micellar system, the aqueous two-phase system (ATPS), and aqueous two-phase flotation (ATPF), moreover, delves into the diverse applications of microbial lipases across several industries, such as food, vitamin esters, textile, detergent, biodiesel, and bioremediation. Furthermore, the present research unveils the obstacles encountered in employing lipase, the patterns observed in lipase engineering, and the application of CRISPR/Cas genome editing technology for altering the genes responsible for lipase production. Additionally, the immobilization of microorganisms' lipases onto various carriers also contributes to enhancing the effectiveness and efficiencies of lipases in terms of their catalytic activities. This is achieved by boosting their resilience to heat and ionic conditions (such as inorganic solvents, high-level pH, and temperature). The process also facilitates the ease of recycling them and enables a more concentrated deposition of the enzyme onto the supporting material. Consequently, these characteristics have demonstrated their suitability for application as biocatalysts in diverse industries.

Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors

Children, adolescents, and young adult cancer survivors (CAYAs) constitute a growing population requiring a customized approach to mitigate the incidence of severe complications throughout their lifetimes. During cancer treatment, CAYAs cancer survivors undergo significant disruptions in their nutritional status, elevating the risks of mortality, morbidity, and cardiovascular events. The assessment of nutritional status during cancer treatment involves anthropometric and dietary evaluations, emphasizing the necessity for regular assessments and the timely identification of risk factors. Proactive nutritional interventions, addressing both undernutrition and overnutrition, should be tailored to specific age groups and incorporate a family-centered approach. Despite encouraging interventions, a notable evidence gap persists. The goal of this review is to comprehensively examine the existing evidence on potential nutritional interventions for CAYAs cancer survivors. We explore the evidence so far collected on the nutritional intervention strategies elaborated for CAYAs cancer survivors that should target both undernutrition and overnutrition, being age-specific and involving a family-based approach. Furthermore, we suggest harnessing artificial intelligence (AI) to anticipate and prevent malnutrition in CAYAs cancer survivors, contributing to the identification of novel risk factors and promoting proactive, personalized healthcare.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Ultrasound Plus Vacuum-System-Assisted Biocatalytic Synthesis of Octyl Cinnamate and Response Surface Methodology Optimization

Cinnamic acid is one of the phenolic compounds that is isolated from cinnamon, or other natural plants, and has a wide range of physiological activities. However, the application of cinnamic acid is limited due to its poor solubility and low oral bioavailability. In this study, the feasibility of producing octyl cinnamate by ultrasonic assistance, combined with a rotary evaporation under vacuum, was studied using methyl cinnamate and octanol as the starting materials. A Box–Behnken design (BBD) was employed to evaluate the effects of the operation parameters, including reaction temperature (55–75 °C), reaction time (4–12 h), and ultrasonic power (90–150 W) on the production of octyl cinnamate. Meanwhile, the synthesis process was further optimized by the modeling response surface methodology (RSM). The data indicated that octyl cinnamate was efficiently synthesized from methyl cinnamate and octanol using the ultrasound plus vacuum system; further, this system was superior to the conventional method. According to the RSM model for the actual experiments, a reaction temperature of 74.6 °C, a reaction time of 11.1 h, and an ultrasound power of 150 W were determined to be the best conditions for the maximum molar conversion of octyl cinnamate (93.8%). In conclusion, the highly efficient synthesis of octyl cinnamate by a rotary evaporator with an ultrasound plus vacuum system was achieved via RSM optimization.

Recent Advances in Lipases and Their Applications in the Food and Nutraceutical Industry

Lipases are efficient enzymes with promising applications in the nutraceutical and food industry, as they can offer high yields, pure products under achievable reaction conditions, and are an environmentally friendly option. This review addresses the production of high-value-added compounds such as fatty acid esters, with the potential to be used as flavoring agents or antioxidant and antimicrobial agents, as well as structured lipids that offer specific functional properties that do not exist in nature, with important applications in different food products, and pharmaceuticals. In addition, the most recent successful cases of reactions with lipases to produce modified compounds for food and nutraceuticals are reported.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

Synthesis of DHA/EPA Ethyl Esters via Lipase-Catalyzed Acidolysis Using Novozym® 435: A Kinetic Study

DHA/EPA ethyl ester is mainly used in the treatment of arteriosclerosis and hyperlipidemia. In this study, DHA+EPA ethyl ester was synthesized via lipase-catalyzed acidolysis of ethyl acetate (EA) with DHA+EPA concentrate in n-hexane using Novozym® 435. The DHA+EPA concentrate (in free fatty acid form), contained 54.4% DHA and 16.8% EPA, was used as raw material. A central composite design combined with response surface methodology (RSM) was used to evaluate the relationship between substrate concentrations and initial rate of DHA+EPA ethyl ester production. The results indicated that the reaction followed the ordered mechanism and as such, the ordered mechanism model was used to estimate the maximum reaction rate (Vmax) and kinetic constants. The ordered mechanism model was also combined with the batch reaction equation to simulate and predict the conversion of DHA+EPA ethyl ester in lipase-catalyzed acidolysis. The integral equation showed a good predictive relationship between the simulated and experimental results. 88–94% conversion yields were obtained from 100–400 mM DHA+EPA concentrate at a constant enzyme activity of 200 U, substrate ratio of 1:1 (DHA+EPA: EA), and reaction time of 300 min.