ZnO-mesoporous glass scaffolds loaded with osteostatin and mesenchymal cells improve bone healing in a rabbit bone defect

The use of 3D scaffolds based on mesoporous bioactive glasses (MBG) enhanced with therapeutic ions, biomolecules and cells is emerging as a strategy to improve bone healing. In this paper, the osteogenic capability of ZnO-enriched MBG scaffolds loaded or not with osteostatin (OST) and human mesenchymal stem cells (MSC) was evaluated after implantation in New Zealand rabbits. Cylindrical meso-macroporous scaffolds with composition (mol %) 82.2SiO2–10.3CaO–3.3P2O5–4.2ZnO (4ZN) were obtained by rapid prototyping and then, coated with gelatin for easy handling and potentiating the release of inorganic ions and OST. Bone defects (7.5 mm diameter, 12 mm depth) were drilled in the distal femoral epiphysis and filled with 4ZN, 4ZN + MSC, 4ZN + OST or 4ZN + MSC + OST materials to evaluate and compare their osteogenic features. Rabbits were sacrificed at 3 months extracting the distal third of bone specimens for necropsy, histological, and microtomography (µCT) evaluations. Systems investigated exhibited bone regeneration capability. Thus, trabecular bone volume density (BV/TV) values obtained from µCT showed that the good bone healing capability of 4ZN was significantly improved by the scaffolds coated with OST and MSC. Our findings in vivo suggest the interest of these MBG complete systems to improve bone repair in the clinical practice

ZnO-mesoporous glass scaffolds loaded with osteostatin and mesenchymal cells improve bone healing in a rabbit bone defect.

The use of 3D scaffolds based on mesoporous bioactive glasses (MBG) enhanced with therapeutic ions, biomolecules and cells is emerging as a strategy to improve bone healing. In this paper, the osteogenic capability of ZnO-enriched MBG scaffolds loaded or not with osteostatin (OST) and human mesenchymal stem cells (MSC) was evaluated after implantation in New Zealand rabbits. Cylindrical meso-macroporous scaffolds with composition (mol %) 82.2SiO2–10.3CaO–3.3P2O5–4.2ZnO (4ZN) were obtained by rapid prototyping and then, coated with gelatin for easy handling and potentiating the release of inorganic ions and OST. Bone defects (7.5 mm diameter, 12 mm depth) were drilled in the distal femoral epiphysis and filled with 4ZN, 4ZN+MSC, 4ZN+OST or 4ZN+MSC+OST materials to evaluate and compare their osteogenic features. Rabbits were sacrificed at 3 months extracting the distal third of bone specimens for necropsy, histological and microtomography (µCT) evaluations. Systems investigated exhibited bone regeneration capability. Thus, trabecular bone volume density (BV/TV) values obtained from µCT showed that the good bone healing capability of 4ZN was significantly improved by the scaffolds coated with OST and MSC. Our findings in vivo suggest the interest of these MBG complete systems to improve bone repair in the clinical practice

Solid pseudopapillary tumor of the pancreas (SPPT). Still an unsolved enigma

Solid pseudo-papillary tumor (SPPT) is a rare cystic tumor of the pancreas (1-3% of exocrine tumors of the pancreas) which shows an “enigmatic” behavior on the clinical and molecular pattern. A retrospective analysis of the citological studies and resected specimens of pancreatic cystic tumors from May 1996 to February 2010 was carried out. Three cases of SPPT were found, which are the objective of this study. The diagnosis was established upon occasional finding in the abdominal CT, in spite of sizing between 3 and 6 cm of diameter. In the three cases the preoperative diagnosis was confirmed by citology and specific immunohistochemical staining. Cases 2 and 3 showed strong immunoreactivity for Beta-Catenina and E-Cadherina staining. Radical resection (R0) was carried out in the three cases. A young male –21 years of age (case 1)- who had duodenal infiltration and two lymph nodes metastases died of hepatic and peritoneal recurrence 20 months following surgery. The other two cases are free of disease. The current review of the literature reports roughly 800 cases since the first report in 1959, and shows the enigmatic character of this tumor regarding the cellular origin, molecular pathways, prognostic factors and clinical behavior

Growing old with the immune system: a study of immunosenescence in the zebra finch (Taeniopygia guttata)

Immunosenescence has not received much attention in birds and the few existing studies indicate that the occurrence of immunosenescence and/or its extent may differ between species. In addition, not much information is available on the immunosenescence patterns of different immune parameters assessed simultaneously in both sexes within a single species. The present study reports the results on immunosenescence in innate immunity and both cellular and humoral acquired immunity of both sexes in a captive population of zebra finch (Taeniopygia guttata) using three age groups (approximately 0.2, 2.5 and 5.1 years). Both male and female finches showed an inverse U-shaped pattern in cellular immune function with age, quantified by a PHA response. Males showed stronger responses than females at all ages. In contrast, an increase with age in humoral immunity, quantified through total plasma immunoglobulin Y levels, was found in both sexes. However, our measurements of innate immunity measured through the bacteria-killing ability against Escherichia coli gave inconclusive results. Still, we conclude that both cellular and humoral acquired immunity are susceptible to immunosenescence, and that the sexes differ in cellular immunity

Spread of a SARS-CoV-2 variant through Europe in the summer of 2020.

Following its emergence in late 2019, the spread of SARS-CoV-21,2 has been tracked by phylogenetic analysis of viral genome sequences in unprecedented detail3–5. Although the virus spread globally in early 2020 before borders closed, intercontinental travel has since been greatly reduced. However, travel within Europe resumed in the summer of 2020. Here we report on a SARS-CoV-2 variant, 20E (EU1), that was identified in Spain in early summer 2020 and subsequently spread across Europe. We find no evidence that this variant has increased transmissibility, but instead demonstrate how rising incidence in Spain, resumption of travel, and lack of effective screening and containment may explain the variant’s success. Despite travel restrictions, we estimate that 20E (EU1) was introduced hundreds of times to European countries by summertime travellers, which is likely to have undermined local efforts to minimize infection with SARS-CoV-2. Our results illustrate how a variant can rapidly become dominant even in the absence of a substantial transmission advantage in favourable epidemiological settings. Genomic surveillance is critical for understanding how travel can affect transmission of SARS-CoV-2, and thus for informing future containment strategies as travel resumes. © 2021, The Author(s), under exclusive licence to Springer Nature Limited

Edible bio-based nanostructures: delivery, absorption and potential toxicity

The development of bio-based nanostructures as nanocarriers of bioactive compounds to specific body sites has been presented as a hot topic in food, pharmaceutical and nanotechnology fields. Food and pharmaceutical industries seek to explore the huge potential of these nanostructures, once they can be entirely composed of biocompatible and non-toxic materials. At the same time, they allow the incorporation of lipophilic and hydrophilic bioactive compounds protecting them against degradation, maintaining its active and functional performance. Nevertheless, the physicochemical properties of such structures (e.g., size and charge) could change significantly their behavior in the gastrointestinal (GI) tract. The main challenges in the development of these nanostructures are the proper characterization and understanding of the processes occurring at their surface, when in contact with living systems. This is crucial to understand their delivery and absorption behavior as well as to recognize potential toxicological effects. This review will provide an insight into the recent innovations and challenges in the field of delivery via GI tract using bio-based nanostructures. Also, an overview of the approaches followed to ensure an effective deliver (e.g., avoiding physiological barriers) and to enhance stability and absorptive intestinal uptake of bioactive compounds will be provided. Information about nanostructures potential toxicity and a concise description of the in vitro and in vivo toxicity studies will also be given.Joana T. Martins, Oscar L. Ramos, Ana C. Pinheiro, Ana I. Bourbon, Helder D. Silva and Miguel A. Cerqueira (SFRH/BPD/89992/2012, SFRH/BPD/80766/2011, SFRH/BPD/101181/2014, SFRH/BD/73178/2010, SFRH/BD/81288/2011, and SFRH/BPD/72753/2010, respectively) are the recipients of a fellowship from the Fundacao para a Ciencia e Tecnologia (FCT, POPH-QREN and FSE, Portugal). The authors thank the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the project "BioInd-Biotechnology and Bioengineering for improved Industrial and Agro-Food processes," REF.NORTE-07-0124-FEDER-000028, co-funded by the Programa Operacional Regional do Norte (ON.2-O Novo Norte), QREN, FEDER. We also thank to the European Commission: BIOCAPS (316265, FP7/REGPOT-2012-2013.1) and Xunta de Galicia: Agrupamento INBIOMED (2012/273) and Grupo con potencial de crecimiento. The support of EU Cost Action FA1001 is gratefully acknowledged

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. Report, ERASynBio. 2014. https://www.erasynbio.eu/lw_resource/datapool/_items/item_58/erasynbiostrategicvision.pdf .Way J, Collins J, Keasling J, Silver P. Integrating biological redesign: Where synthetic biology came from and where it needs to go. Cell. 2014; 157(1):151–61.Canton B, Labno A, Endy D. Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. 2008; 26(7):787–93.De Lorenzo V, Danchin A. Synthetic biology: discovering new worlds and new words. EMBO Rep. 2008; 9(9):822–7.Church GM, Elowitz MB, Smolke CD, Voigt CA, Weiss R. Realizing the potential of synthetic biology. Nat Rev Mol Cell Biol. 2014; 15(4):289–94.Takahashi CN, Miller AW, Ekness F, Dunham MJ, Klavins E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth Biol. 2015; 4(1):32–8. [doi: 10.1021/sb500165g ].Cooling MT, Rouilly V, Misirli G, Lawson J, Yu T, Hallinan J, Wipat A. Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics. 2010; 26(7):925–31.Medema MH, van Raaphorst R, Takano E, Breitling R. Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol. 2012; 10(3):191–202.Marchisio MA, Stelling J. Automatic design of digital synthetic gene circuits. PLoS Comput Biol. 2011; 7(2):e1001083. [doi: 10.1371/journal.pcbi.1001083 ].Rodrigo G, Carrera J, Landrain TE, Jaramillo A. Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Lett. 2012; 586(15):2037–42.Crook N, Alper HS. Model-based design of synthetic, biological systems. Chem Eng Sci. 2013; 103:2–11.Jayanthi S, Nilgiriwala K, Del Vecchio D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth Biol. 2013; 2(8):431–41.Mélykúti B, Hespanha JP, Khammash M. Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks. J R Soc Interface. 2014; 11(97):20140054.Oyarzún DA, Lugagne JB, Stan GB. Noise propagation in synthetic gene circuits for metabolic control. ACS Synth Biol. 2015; 4(2):116–25. [doi: 10.1021/sb400126a ].Picó J, Vignoni A, Picó-Marco E, Boada Y. Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido. Revista Iberoamericana de Automática e Informática Industrial RIAI. 2015; 12(3):241–52.Feng X-j-J, Hooshangi S, Chen D, Li G, Weiss R, Rabitz H. Optimizing genetic circuits by global sensitivity analysis. Biophys J. 2004; 87(4):2195–202.Dasika MS, Maranas CD. Optcircuit: An optimization based method for computational design of genetic circuits. BMC Syst Biol. 2008; 2:24.Rodrigo G, Carrera J, Jaramillo A. Genetdes. Bioinformatics. 2007; 23(14):1857–8.Otero-Muras I, Banga JR. Multicriteria global optimization for biocircuit design. 2014. arXiv preprint arXiv:1402.7323.Banga JR. Optimization in computational systems biology. BMC Syst Biol. 2008; 2:47.Sendin J, Exler O, Banga JR. Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol. 2010; 4(3):236–48.Miller M, Hafner M, Sontag E, Davidsohn N, Subramanian S, Purnick PE, Lauffenburger D, Weiss R. Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity. PLoS Comput Biol. 2012; 8(7):1002579.Ellis T, Wang X, Collins JJ. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol. 2009; 27(5):465–71.Koeppl H, Hafner M, Lu J. Mapping behavioral specifications to model parameters in synthetic biology. BMC Bioinforma. 2013; 14(Suppl 10):9.Chiang AWT, Hwang M-JJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinforma. 2013; 14 Suppl 16:5.Ma W, Trusina A, El-Samad H, Lim WA, Tang C. Defining network topologies that can achieve biochemical adaptation. Cell. 2009; 138(4):760–73.Chiang AWT, Liu W-CC, Charusanti P, Hwang M-JJ. Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters. BMC Syst Biol. 2014; 8:4.Reynoso-Meza G, Blasco X, Sanchis J, Martínez M. Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract. 2014; 28:58–73.Alon U. An Introduction To Systems Biology. Design Principles of Biological Circuits. London: Chapman & Hall/ CRC Mathematical and computational Biology Series; 2006.Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403(6767):335–8.Hsiao V, de los Santos ELC, Whitaker WR, Dueber JE, Murray RM. Design and implementation of a biomolecular concentration tracker. ACS Synth Biol. 2015; 4(2):150–61. [doi: 10.1021/sb500024b ].Franco E, Giordano G, Forsberg P-O, Murray RM. Negative autoregulation matches production and demand in synthetic transcriptional networks. ACS Synth Biol. 2014; 3(8):589–99. [doi: 10.1021/sb400157z ].Strelkowa N, Barahona M. Switchable genetic oscillator operating in quasi-stable mode. J R Soc Interface. 2010; 7(48):1071–82.Basu S, Mehreja R, Thiberge S, Chen MT, Weiss R. Spatiotemporal control of gene expression with pulse-generating networks. Proc Natl Acad Sci U S A. 2004; 101(17):6355–60.Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol Syst Biol. 2011; 7(519):1–12. [doi: 10.1038/msb.2011.49 ].Hart Y, Antebi YE, Mayo AE, Friedman N, Alon U. Design principles of cell circuits with paradoxical components. Proc Natl Acad Sci. 2012; 109(21):8346–51.Zhang Q, Bhattacharya S, Andersen ME. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks. Open Biol. 2013; 3(4):130031.Weber M, Buceta J, Others. Dynamics of the quorum sensing switch: stochastic and non-stationary effects. BMC Syst Biol. 2013; 7(1):6.Womelsdorf T, Valiante TA, Sahin NT, Miller KJ, Tiesinga P. Dynamic circuit motifs underlying rhythmic gain control, gating and integration. Nat Neurosci. 2014; 17(8):1031–9.Arpino JAJ, Hancock EJ, Anderson J, Barahona M, Stan G-BVB, Papachristodoulou A, Polizzi K. Tuning the dials of synthetic biology. Microbiology. 2013; 159(Pt 7):1236–53.Zagaris A, Kaper HGG, Kaper TJJ. Analysis of the computational singular perturbation reduction method for chemical kinetics. J Nonlinear Sci. 2004; 14(1):59–91.Anderson J, Chang Y-C-C, Papachristodoulou A. Model decomposition and reduction tools for large-scale networks in systems biology. Automatica. 2011; 47(6):1165–74.Prescott TP, Papachristodoulou A. Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. J Theor Biol. 2014; 356:113–22.Hancock EJ, Stan GB, Arpino JAJ, Papachristodoulou A. Simplified mechanistic models of gene regulation for analysis and design. J R Soc Interface. 2015; 12(108).Miettinen K, Vol. 12. Nonlinear Multiobjective Optimization. Boston: Kluwer Academic Publishers; 1999.Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multiobjective optimization: interactive approaches. In: Multiobjective Optimization. Berlin: Springer: 2008. p. 27–57.Deb K, Bandaru S, Greiner D, Gaspar-Cunha A, Tutum CC. An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Appl Soft Comput. 2014; 15(0):42–56.Ang J, Ingalls B, McMillen D. Probing the input-output behavior of biochemical and genetic systems: System identification methods from control theory In: Johnson ML, Brand L, editors. Methods in Enzymology. Academic Press: 2011. p. 279–317, doi: 10.1016/B978-0-12-381270-4.00010-X .Mattson CA, Messac A. Pareto frontier based concept selection under uncertainty, with visualization. Optim Eng. 2005; 6(1):85–115.Reynoso-Meza G, Sanchis J, Blasco X, Martínez M. Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. Appl Evol Comput. 2010;532–541.Reynoso-Meza G, García-Nieto S, Sanchis J, Blasco X. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Trans Control Syst Technol. 2013; 21(2):445–58.Reynoso-Meza G, Sanchis J, Blasco X, Herrero JM. Multiobjective evolutionary algortihms for multivariable PI controller tuning. Expert Syst Appl. 2012; 39:7895–907.Anderson C. Anderson promoter collection [online]. 2006. http://parts.igem.org/Promoters/Catalog/Anderson . Accesed 20 Feb 2015.Salis HM, Mirsky EA, Voigt CA. Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol. 2009; 27(10):946–50.Egbert RG, Klavins E. Fine-tuning gene networks using simple sequence repeats. PNAS. 2012; 109(42):16817–22. [doi: 10.1073/pnas.1205693109 ].Hair JF, Suárez MG. Análisis Multivariante vol. 491. Madrid: Prentice Hall; 1999.Blasco X, Herrero JM, Sanchis J, Martínez M. A new graphical visualization of n-dimensional pareto front for decision-making in multiobjective optimization. Inf Sci. 2008; 178(20):3908–24. [doi: 10.1016/j.ins.2008.06.010 ].Reynoso-Meza G, Blasco X, Sanchis J, Herrero JM. Comparison of design concepts in multi-criteria decision-making using level diagrams. Inform Sci. 2013; 221:124–41.Goentoro L, Shoval O, Kirschner MW, Alon U. The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell. 2009; 36(5):894–9.Rodrigo G, Elena SF. Structural discrimination of robustness in transcriptional feedforward loops for pattern formation. PloS ONE. 2011; 6(2):16904.Kim J, Khetarpal I, Sen S, Murray RM. Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res. 2014; 42(2):6078–89. [doi: 10.1093/nar/gku233 ].Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, et al. Biomodels: ten-year anniversary. Nucleic Acids Res. 2015; 43(D1):542–8.Ang J, Bagh S, Ingalls BP, McMillen DR. Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network. J Theor Biol. 2010; 266(4):723–38.Biobrick Foundation. 2006. Part Registry [online]. http://partsregistry.org/ . Accessed 20 Feb 2015.BIOSS. 2006. BIOSS Toolbox [online]. http://www.bioss.uni-freiburg.de/cms/toolbox-home.html . Accessed 20 Feb 2015.BioFab. 2006. International Open Facility Advancing Biotechnology [online]. http://www.biofab.org/ . Accessed 20 Feb 2015.Vallerio M, Hufkens J, Van Impe J, Logist F. An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty. Expert Syst Appl. 2015; 42(21):7710–31.Frangopol DM, Maute K. Life-cycle reliability-based optimization of civil and aerospace structures. Comput Struct. 2003; 81(7):397–410.Lozano M, Molina D, Herrera F. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 2011; 15(11):2085–7.Santana-Quintero LV, Montano AA, Coello CAC. A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Computational Intelligence in Expensive Optimization Problems. Berlin: Springer: 2010. p. 29–59

EFSA NDA Panel (EFSA Panel on Dietetic Products, Nutrition and Allergies), 2013. Scientific Opinion on nutrient requirements and dietary intakes of infants and young children in the European Union.

Following a request from the European Commission, the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA) was asked to deliver a Scientific Opinion on the nutrient requirements and dietary intakes of infants and young children in the European Union. This Opinion describes the dietary requirements of infants and young children, compares dietary intakes and requirements in infants and young children in Europe and, based on these findings, concludes on the potential role of young-child formulae in the diets of infants and young children, including whether they have any nutritional benefits when compared with other foods that may be included in the normal diet of infants and young children. The Panel concluded on the levels of nutrient and energy intakes that are considered adequate for the majority of infants and young children, and evaluated the risk of inadequate nutrient intakes in infants and young children in living Europe. Dietary intakes of alpha-linolenic acid (ALA), docosahexaenoic acid (DHA), iron, vitamin D and iodine (in some European countries) are low in infants and young children living in Europe, and particular attention should be paid to ensuring an appropriate supply of ALA, DHA, iron, vitamin D and iodine in infants and young children with inadequate or at risk of inadequate status of these nutrients. No unique role of young -child formulae with respect to the provision of critical nutrients in the diet of infants and young children living in Europe can be identified, so that they cannot be considered as a necessity to satisfy the nutritional requirements of young children when compared with other foods that may be included in the normal diet of young children (such as breast milk, infant formulae, follow-on formulae and cow\u2018s milk)