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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗
DOE OSTI2015

Monte Carlo based investigation of Berry phase for depth resolved characterization of biomedical scattering samples

The propagation of light in turbid media is an active area of research with relevance to numerous investigational fields, e.g., biomedical diagnostics and therapeutics. The statistical random-walk nature of photon propagation through turbid media is ideal for computational based modeling and simulation. Ready access to super computing resources provide a means for attaining brute force solutions to stochastic light-matter interactions entailing scattering by facilitating timely propagation of sufficient (>10million) photons while tracking characteristic parameters based on the incorporated physics of the problem. One such model that works well for isotropic but fails for anisotropic scatter, which is the case for many biomedical sample scattering problems, is the diffusion approximation. In this report, we address this by utilizing Berry phase (BP) evolution as a means for capturing anisotropic scattering characteristics of samples in the preceding depth where the diffusion approximation fails. We extend the polarization sensitive Monte Carlo method of Ramella-Roman, et al.,1 to include the computationally intensive tracking of photon trajectory in addition to polarization state at every scattering event. To speed-up the computations, which entail the appropriate rotations of reference frames, the code was parallelized using OpenMP. The results presented reveal that BP is strongly correlated to the photon penetration depth, thus potentiating the possibility of polarimetric depth resolved characterization of highly scattering samples, e.g., biological tissues.

back scattered Mueller matrix↗
DOE OSTI2021

Rationally Programming Nanomaterials with DNA for Biomedical Applications

Abstract DNA is not only a carrier of genetic information, but also a versatile structural tool for the engineering and self‐assembling of nanostructures. In this regard, the DNA template has dramatically enhanced the scalability, programmability, and functionality of the self‐assembled DNA nanostructures. These capabilities provide opportunities for a wide range of biomedical applications in biosensing, bioimaging, drug delivery, and disease therapy. In this review, the importance and advantages of DNA for programming and fabricating of DNA nanostructures are first highlighted. The recent progress in design and construction of DNA nanostructures are then summarized, including DNA conjugated nanoparticle systems, DNA‐based clusters and extended organizations, and DNA origami‐templated assemblies. An overview on biomedical applications of the self‐assembled DNA nanostructures is provided. Finally, the conclusion and perspectives on the self‐assembled DNA nanostructures are presented.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Polyvinyl Alcohol (PVA)–Azadirachta indica (Neem) Nanofibrous Mat for Biomedical Application: Formation and Characterization

The present study provides credence to the formation of polyvinyl alcohol (PVA)–Azadirachta indica (neem) nanofibrous mat (PNNM) under optimum processing conditions of electrospinning technique from a mixer of PVA and neem extract to utilize the inherent medicinal properties of this herb for biomedical application. The bonding behavior, orientation of fibers, thermal behavior, and moisture management properties were evaluated by Fourier transforms infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermo gravimetric analysis (TGA) and moisture management tester (MMT) reports respectively. The antibacterial activity of the developed sample at the maximum mixing ratio of neem extract (80%) was tested against Gram-positive (S. aureus) bacteria using agar disc diffusion method. The results reveal that the prepared nanofibrous mat exhibited better thermal and moisture management properties in comparison with PVA nanofiber alone. The formation of smooth fibers was confirmed by SEM images having average diameter of 185 nm under 5k, 10k and 15k magnifications. The characteristic peaks of PVA and neem constituents in FTIR spectra of the developed mat confirmed the presence of both components. Bacterial resistance was reached up to 20 mm due to the antibacterial constituents of neem extract. Thus the developed mat could be used as a biocompatible and bio based in biomedical applications. Graphic Abstract: .

36 MATERIALS SCIENCE↗
DOE OSTI2018

Synthesis and characterization of intrinsically radiolabeled lanthanide phosphate nanoparticles toward biomedical and environmental applications

Lanthanide phosphate nanomaterials are appealing as multifunctional platforms for biomedical applications because of their low toxicity, radiation resistance, and unique luminescence and magnetic properties. Lanthanide phosphate nanoparticles (NPs) radiolabeled with 156 Eu were synthesized by an aqueous route using sodium tripolyphosphate as a phosphate source and complexing agent. GdPO 4 was used as a host matrix, while synthetic concentrations of Ce 3+ /Tb 3+ and Eu 3+ were varied between 5–40% and 10–30%, respectively, and yielded spherical NPs with hexagonal crystal system and particle size < 5 nm. Luminescence emission and magnetic susceptibility were influenced by lanthanide concentrations where characteristic 5 D 0 - 7 F J and 5 D 4 - 7 F J transitions were observed for Eu 3+ - and Tb 3+ -doped NPs, respectively. Chemical yield of > 90% was achieved for GdPO 4 and Gd 0.6 Ce 0.3 Tb 0.1 PO 4 NPs intrinsically radiolabeled with 156 Eu. Lastly, in vitro retention of 156 Eu within LnPO 4 NPs was > 97% over 3 weeks. Intrinsically radiolabeled LnPO 4 NPs have the potential to combine the luminescence and magnetic properties of Ln 3+ ions with lanthanide radioisotopes for biomedical and environmental applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effect of Molybdenum Content on Structure and Properties of a Co-Cr Biomedical Alloy

The present study aimed to develop several Co-Cr biomedical alloys with different amounts of molybdenum. The weight percentages of molybdenum were in the range of 0-4 wt.% in the base alloy (Co-30Cr). The microstructures, phases, density, mechanical strength, wear behavior and corrosion characteristics of these alloys were evaluated. The results showed that density, hardness and compressive strength increased by increase in Mo content in the alloy. In contrast, corrosion rate decreased when the Mo weight percentage was increased. Furthermore, addition of Mo up to 3 wt.% caused the volumetric wear loss to increase. However, 4 wt.% Mo in the alloy led to a reduction in the volumetric wear loss, even lower than that of Co-30Cr alloy without molybdenum. The variations of friction coefficient, however, were different under different operating conditions. Since no single alloy could merely satisfy all the desired characteristics, the entropy–analytic hierarchy process (AHP)–gray relational analysis (GRA) technique was utilized to select the best alloy among the examined materials. From the ranking order gained, it was indicated that M-4 (having 4 wt.% Mo) biomedical alloy possesses the best combination of all the properties.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Fungus mediated synthesis of biomedically important cerium oxide nanoparticles

Graphical abstract: - Highlights: • First time biological synthesis of cerium oxide oxide nanoparticles using fungus Humicola sp. • Complete characterization of cerium oxide nanoparticles. • Biosynthesis of naturally protein capped, luminescent and water dispersible CeO{sub 2} nanoparticles. • Biosynthesized CeO{sub 2} nanoparticles can be used for many biomedical applications. - Abstract: Nanomaterials can be synthesized by chemical, physical and the more recently discovered biological routes. The biological routes are advantageous over the chemical and physical ones as unlike these, the biological synthesis protocols occur at ambient conditions, are cheap, non-toxic and eco-friendly. Although purely biological and bioinspired methods for the synthesis of nanomaterials are environmentally benign and energy conserving processes, their true potential has not been explored yet and attempts are being made to extend the formation of technologically important nanoparticles using microorganisms like fungi. Though there have been reports on the biosynthesis of oxide nanoparticles by our group in the past, no attempts have been made to employ fungi for the synthesis of nanoparticles of rare earth metals or lanthanides. Here we report for the first time, the bio-inspired synthesis of biomedically important cerium oxide (CeO{sub 2}) nanoparticles using the thermophilic fungus Humicola sp. The fungus Humicola sp. when exposed to aqueous solutions of oxide precursor cerium (III) nitrate hexahydrate (CeN{sub 3}O{sub 9}·6H{sub 2}O) results in the extracellular formation of CeO{sub 2} nanoparticles containing Ce (III) and Ce (IV) mixed oxidation states, confirmed by X-ray Photoemission Spectroscopy (XPS). The formed nanoparticles are naturally capped by proteins secreted by the fungus and thus do not agglomerate, are highly stable, water dispersible and are highly fluorescent as well. The biosynthesized nanoparticles were characterized by UV–vis spectroscopy, Photoluminescence spectroscopy (PL), Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD) and X-ray Photoemission Spectroscopy (XPS)

36 MATERIALS SCIENCE↗
DOE OSTI2020

Biomedical Integration of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) have recently been demonstrated to be excellent platforms for biomedical applications. On account of their porous nature and modular structure, MOFs have served as bioactive-compound carriers, cell-imaging materials, and therapeutic agents. MOF cavities allow the encapsulation of a variety of guest molecules, facilitating controllable drug release and optical imaging. The metal components of MOFs make them suitable for magnetic resonance imaging (MRI) and computed tomography (CT). Further, MOF-embedded therapeutic agents have demonstrated promise in photodynamic and photothermal therapies. In this review, we describe several advanced examples that utilize MOFs in these cutting-edge applications. Investigation of the relationship between their structure and designated application will enable future, informed exploration of MOFs for biomedical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and -related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Imaging from Macro to Nanoscale: Multimodal Advances in Chemical and Biomedical Imaging

Imaging increasingly serves as a multiscale framework for linking molecular mechanisms to cellular behavior, tissue architecture, and organ phenotypes in biology and unraveling fundamental processes in chemistry, physics and materials science. This Perspective highlights recent advances in chemical and biomedical imaging across macro-, micro-, and nanoscales, using representative examples published in Chemical and Biomedical Imaging (CBMI). At the macroscale, we discuss chemically selective MRI, including endogenous and exogenous CEST strategies, together with photoacoustic imaging as a hybrid modality with functional and chemical contrast. At the microscale, we consider fluorescence, label-free optical and vibrational imaging, and selected X-ray approaches that expand sensitivity, specificity, and temporal resolution in biological and materials systems. At the nanoscale, we highlight super-resolution fluorescence microscopy, single-molecule methods, tip-enhanced Raman spectroscopy, and correlative imaging strategies that resolve local heterogeneity and molecular organization. Across scales, a common theme emerges that advances in probes, contrast mechanisms, instrumentation, and sample handling are enabling chemically informed imaging that connects molecular specificity with biological context.

multiscale imaging↗
DOE OSTI2023

Porosity modeling in a TiNbTaZrMo high-entropy alloy for biomedical applications

High-entropy alloys (HEAs) have attracted great attention for many biomedical applications. However, the nature of interatomic interactions in this class of complex multicomponent alloys is not fully understood. We report, for the first time, the results of theoretical modeling for porosity in a large biocompatible HEA TiNbTaZrMo using an atomistic supercell of 1024 atoms that provides new insights and understanding. Our results demonstrated the deficiency of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with available experimental data to reduce Young's modulus. We utilized the novel concepts of the total bond order density (TBOD) and partial bond order density (PBOD) via ab initio quantum mechanical calculations as an effective theoretical means to chart a road map for the rational design of complex multicomponent HEAs for biomedical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Nanomaterials driven energy, environmental and biomedical research

We have developed state-of-the-art nanomaterials such as nanofibers, nanotubes, nanoparticles, nanocatalysts and nanostructures for clean energy, environmental and biomedical research. Energy can neither be created nor be destroyed, but it can be converted from one form to another. Based on this principle, chemical energy such as hydrogen has been produced from water electrolysis at a much lower voltage using RuO{sub 2} nanoparticles on the Si wafer substrate. Once the hydrogen is produced from the clean sources such as solar energy and water, it has to be stored by physisorption or chemisorption processes on to the solid state systems. For the successful physical adsorption of hydrogen molecule, we have developed novel polyaniline nanostructures via chemical templating and electrospinning routes. Chemical or complex hydrides involving nano MgH{sub 2} and transition metal nanocatalysts have been synthesized to tailor both the thermodynamics and kinetics of hydrogen (chemi) sorption respectively. Utilization of solar energy (UV-Vis) and a coupling of novel semiconductor oxide nanoparticles have been recently demonstrated with enhancement in photo-oxidation and/or photo-reduction processes for the water/air detoxification and sustainable liquid fuel production respectively. Magnetic nanoparticles such as ZnFe{sub 2}O{sub 4} have been synthesized and optimized for biomedical applications such as targeted drug delivery and tumor diagnostic sensing (MRI)

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning

Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BioPortal: an open community resource for sharing, searching, and utilizing biomedical ontologies

Abstract BioPortal (https://bioportal.bioontology.org) is the world’s most comprehensive repository of biomedical ontologies. It provides infrastructure for finding, sharing, searching, and utilizing biomedical ontologies. Launched in 2005, BioPortal now includes 1549 ontologies (1182 of them public). Its open, freely accessible website enables anyone (i) to browse the ontology library, (ii) to search for terms across ontologies, (iii) to browse mappings between terms, (iv) to see popularity ratings and recommendations on which ontologies are most relevant to their use cases, (v) to annotate text with ontology terms, (vi) to submit an ontology, and (vii) to request ontology changes. The library of ontologies can be accessed programmatically via a REST application programming interface (API). Recent enhancements include a BioPortal knowledge graph that integrates knowledge from multiple ontologies; a unified data model for interoperability with other knowledge sources; ontology popularity ratings and recommendations for relevant ontologies; and the ability to request ontology changes via a simple user interface that automatically converts user change requests to GitHub Pull Requests that specify the edits that will be made to the ontology upon approval.

Vendetti, Jennifer↗
DOE OSTI2014

Disease Ontology 2015 update: An expanded and updated database of human diseases for linking biomedical knowledge through disease data

The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. In conclusion, this will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗
DOE OSTI2020

The surface stress of biomedical silicones is a stimulant of cellular response

Silicones are commonly used for lubrication of syringes, encapsulation of medical devices, and fabrication of surgical implants. While silicones are generally viewed as relatively inert to the cellular milieu, they can mediate a variety of inflammatory responses and other deleterious effects, but the mechanisms underlying the bioactivity of silicones remain unresolved. Here, we report that silicone liquids and gels have high surface stresses that can strongly resist deformation at cellular length scales. Biomedical silicones, including syringe lubricants and fillings from FDA-approved breast implants, readily adsorb matrix proteins and activate canonical rigidity sensing pathways through their surface stresses. In 3D culture models, liquid silicone droplets support robust cellular adhesion and the formation of multinucleated monocyte-derived cell masses that recapitulate phenotypic aspects of granuloma formation in the foreign body response. Together, our findings implicate surface stress as a cellular stimulant that should be considered in application of silicones for biomedical purposes.

60 APPLIED LIFE SCIENCES↗