Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making.
La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations réelles en ARN de SARS-CoV-2 dans les eaux usées à partir de ce type de données, nous proposons un nouveau modèle linéaire dynamique avec censure à gauche. Une validation croisée de ces lisseurs, ainsi que d'un simple lissage par moyenne glissante, sur des données provenant de 286 stations d'épuration couvrant l'Angleterre, valide de façon complète l'approche proposée. Le modèle présenté est plus parcimonieux, offre un cadre de modélisation plus flexible et nécessite un temps de calcul réduit par rapport au Lisseur de Kalman équivalent. Les données issues des eaux usées ainsi lissées sont en outre plus fortement corrélées avec le taux d'incidence régional produit par le bureau des statistiques nationales (ONS) Coronavirus Infection Survey. Elles se montrent plus robustes que les données brutes, ou lissées par simple moyenne glissante, et donc plus à même de compléter la surveillance traditionnelle, renforçant ainsi la confiance en l'épidémiologie fondée sur les eaux usées et son utilité pour la prise de décisions de santé publique.
Citation: Luke Lewis-Borrell, Jessica Irving, Chris J. Lilley, Marie Courbariaux, Gregory Nuel, Leon Danon, Kathleen M. O'Reilly, Jasmine M. S. Grimsley, Matthew J. Wade, Stefan Siegert. Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater[J]. AIMS Mathematics, 2023, 8(7): 16790-16824. doi: 10.3934/math.2023859
[1] | Stephen W. Sawyer, Kairui Zhang, Jason A. Horton, Pranav Soman . Perfusion-based co-culture model system for bone tissue engineering. AIMS Bioengineering, 2020, 7(2): 91-105. doi: 10.3934/bioeng.2020009 |
[2] | Tanishka Taori, Anjali Borle, Shefali Maheshwari, Amit Reche . An insight into the biomaterials used in craniofacial tissue engineering inclusive of regenerative dentistry. AIMS Bioengineering, 2023, 10(2): 153-174. doi: 10.3934/bioeng.2023011 |
[3] | Maria Júlia Bento Martins Parreira, Bruna Trazzi Pagani, Matheus Bento Medeiros Moscatel, Daniela Vieira Buchaim, Carlos Henrique Bertoni Reis, Beatriz Flávia de Moraes Trazzi, Acácio Fuziy, Rogerio Leone Buchaim . Effects of systemic administration of the retinoid Isotretinoin on bone tissue: A narrative literature review. AIMS Bioengineering, 2024, 11(2): 212-240. doi: 10.3934/bioeng.2024012 |
[4] | P. Mora-Raimundo, M. Manzano, M. Vallet-Regí . Nanoparticles for the treatment of osteoporosis. AIMS Bioengineering, 2017, 4(2): 259-274. doi: 10.3934/bioeng.2017.2.259 |
[5] | Chao Hu, Qing-Hua Qin . Bone remodeling and biological effects of mechanical stimulus. AIMS Bioengineering, 2020, 7(1): 12-28. doi: 10.3934/bioeng.2020002 |
[6] | Fabrizio Belleggia . Hard and soft tissue augmentation of vertical ridge defects with the “hard top double membrane technique”: introduction of a new technique and a case report. AIMS Bioengineering, 2022, 9(1): 26-43. doi: 10.3934/bioeng.2022003 |
[7] | José Luis Calvo-Guirado, Marta Belén Cabo-Pastor, Francisco Martínez-Martínez, Miguel Ángel Garcés-Villalá, Félix de Carlos-Villafranca, Nuria García-Carrillo, Manuel Fernández-Domínguez . Histologic and histomorphometric evaluation of minicono abutment on implant surrounding tissue healing and bone resorption on implants placed in healed bone. An experimental study in dogs. AIMS Bioengineering, 2023, 10(3): 183-201. doi: 10.3934/bioeng.2023013 |
[8] | Izgen Karakaya, Nuran Ulusoy . Basics of dentin-pulp tissue engineering. AIMS Bioengineering, 2018, 5(3): 162-178. doi: 10.3934/bioeng.2018.3.162 |
[9] | Jéssica de Oliveira Rossi, Gabriel Tognon Rossi, Maria Eduarda Côrtes Camargo, Rogerio Leone Buchaim, Daniela Vieira Buchaim . Effects of the association between hydroxyapatite and photobiomodulation on bone regeneration. AIMS Bioengineering, 2023, 10(4): 466-490. doi: 10.3934/bioeng.2023027 |
[10] | Yihan Zhang . Manufacture of complex heart tissues: technological advancements and future directions. AIMS Bioengineering, 2021, 8(1): 73-92. doi: 10.3934/bioeng.2021008 |
Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making.
La détection et la surveillance du SARS-CoV-2 dans les eaux usées ont été développées et réalisées à un rythme sans précédent, mais l'interprétation des mesures de concentrations en ARN viral, et de leurs variations spatio-temporelles, pose question. En particulier, l'importante proportion de mesures en deçà du seuil de quantification, généralement pendant les périodes non endémiques, constitue un défi pour l'analyse de ces séries temporelles. Inspirés par un travail de recherche ayant produit un lisseur de Kalman adapté pour estimer les concentrations réelles en ARN de SARS-CoV-2 dans les eaux usées à partir de ce type de données, nous proposons un nouveau modèle linéaire dynamique avec censure à gauche. Une validation croisée de ces lisseurs, ainsi que d'un simple lissage par moyenne glissante, sur des données provenant de 286 stations d'épuration couvrant l'Angleterre, valide de façon complète l'approche proposée. Le modèle présenté est plus parcimonieux, offre un cadre de modélisation plus flexible et nécessite un temps de calcul réduit par rapport au Lisseur de Kalman équivalent. Les données issues des eaux usées ainsi lissées sont en outre plus fortement corrélées avec le taux d'incidence régional produit par le bureau des statistiques nationales (ONS) Coronavirus Infection Survey. Elles se montrent plus robustes que les données brutes, ou lissées par simple moyenne glissante, et donc plus à même de compléter la surveillance traditionnelle, renforçant ainsi la confiance en l'épidémiologie fondée sur les eaux usées et son utilité pour la prise de décisions de santé publique.
The term and concept of “tissue engineering” was officially coined over 30 years ago, in 1988, at a National Science Foundation workshop as “the application of engineering principles and methods from life sciences to a fundamental understanding of the structure-function relationships of normal and pathological mammalian tissues and the development of biological substitutes to restore, maintain or improve tissue function”. While the field of tissue engineering is relatively new, the idea of replacing one tissue by another dates back several centuries [1]. Thus, and for many years now, the replacement by implantation and transplantation of a tissue from a given site to another site in the same patient (an autograft) or in a different patient (a transplant or an allograft) has been innovative and beneficial, but both techniques present major problems. On the one hand, harvesting tissue for an autograft is expensive, painful, limited by anatomical limitations, and associated with donor site morbidity due to infection and injury. On the other hand, allografts and tissue grafts have serious constraints: limit of access to enough tissues or organs for all patients who need it, rejection by the immune system of the recipient patient, potential risks of introduction of infection or disease... [2]. It is in this context that the development of biofabrication strategies arouses considerable interest in order to develop methods, tools and products having the objectives of mimicking and replicating the anatomical and functional characteristics of human tissues.
Bone tissue is the second largest transplant tissue in the world. Millions of bone grafts (autografts and allografts) are performed each year around the world, while there is currently no satisfactory alternative to bone grafting. Traditional surgical treatments for fractures and bone defects, mainly comprising bone grafts and implantation of metal prostheses, achieve good clinical results, but these treatments also have serious drawbacks, such as infection, pain, cost. high, and the need for additional surgery. In some cases, such as bone defects, osteoporotic fractures, or bone defects/fractures in oncologic patients after radiation therapy, regeneration of bone tissue is hampered, and then requires modern strategies such as bone tissue engineering [3].
Tissue-engineering technologies involve the intimate interaction between three different components: (1) the scaffolding material that holds and cohesions cells together and forms the physical structure of tissue, i.e. acts as a model for the formation of new tissues, (2) the cells synthesizing the final tissue and (3) the signaling mechanisms (mechanical and/or chemical) that direct cells to express the desired tissue phenotype. Particular attention must be paid to the specific properties of each of these three components, in order to ensure the quality of the modified and neo-synthetized tissue and therefore the potential for clinical success when the engineered tissue is subsequently implanted into an injured site in vivo [4].
Biological and clinical evaluation of medical devices, including biomaterials, has also been implemented by ISO's ad hoc technical committee since 1988. Since then, sets of standards have been produced and are kept up to date. technological innovation in this highly biomedical field. If it is absorbable, its degradation products must also be biocompatible and not harmful. For application in a living organism, such as humans, all scaffolding materials must have very specific characteristics. First of all, to be defined as a biomaterial, the material must be biocompatible so that it can exist in harmony with the biological fluids, tissues, and cells of the host, without causing harmful effects locally or systemically. In the case of bone tissue, the scaffolding material must exhibit three fundamental bioactivities and properties: osteoinduction, osteoconduction, and osteointegration. Osteoinduction belongs to the ability to induce osteogenic differentiation of a cell that is not yet engaged [5]. In that context, an osteoinductive biomaterial can directly induce osteogenesis through the recruitment, proliferation, and differentiation of bone related stem cells such as mesenchymal stem cells. Osteoconduction refers to the ability to provide the microenvironment allowing the occurrence of in-place bone formation (osteogenesis) and bone growth [6]. Osseointegration refers to the ability of a biomaterial to elaborate direct contacts and anchoring between the exogenic material it-self and the host bone tissue, without growth of fibrous tissue at the bone-implant interface [7]. These 3 fundamental bioactivities and properties can be supported by different physical characteristics related to this specific mineralized hard material, such as stiffness and mechanical strength, viscosity, shear stress, geometry, hydrophilicity, surface charge, wettability, surface roughness and topography, porosity and pore size, permeability, mechanical stability, as well as controlled degradation rate [8]. All these parameters are crucial to encourage cell migration, adhesion, proliferation, and differentiation, but also for nutrients and oxygen transport throughout the scaffolds [9]. Another key consideration for designing a scaffold is its delivery capability through the releasing biological active agents that can induce important properties such as faster healing, antimicrobial, and antitumoral activity.
Biomaterials can be classified depending on their composition in metals (such as Fe, Mg, Zn and their alloys), ceramics (calcium phosphate ceramics such as hydroxyapatite, biphasic calcium phosphate [BCP] and tricalcium phosphate [TCP], bioactive glasses, zirconia), natural polymers (collagen, silk, chitosan, alginate, elastin, hyaluronic acid and cellulose) or synthetic polymers (such as polylactid acid [PLA], polyglycolic acid [PGA], polylactic co-glycolic acid [PLGA], poly e-caprolactone [PCL], polyethylene glycol [PEG], polybutylene terephthalate [PBT], polyethylene terephthalate [PET], polypropylene fumarate [PPF] or polyacrylic acid [PAA]), composites (defined as made of two or more substrates belonging to the same or different class of materials) and recently nanomaterials including nanoparticles [10]. All these materials can also be functionalized to improve their properties (including the three fundamental properties described above, but also to induce angiogenesis for example) [11].
The second component for bone tissue engineering belongs to the cell component where several cell types can be used to develop a bone construct, including osteoblast, embryonic stem cells (ESC), mesenchymal stem cells (MSC) and induced pluripotent stem cells (iPSC). All these cell types have largely demonstrated their ability to induce and promote and bone formation, remodeling, and healing [12].
A finally, the third part of bone tissue engineering is dedicated to signaling mechanisms that can be conveyed by the material itself, depending on its intrinsic properties and characteristics, or provided secondarily exogenously by enriching the final product with growth factors or cytokines (including TGFbeta and BMP, among others), as well as drugs [13].
At last, the recent advent and democratization of 3D printing processes, allowing the development of complex 3D structures using various biomaterials, opens up new opportunities and great flexibility in the design and production of scaffolds of different structural complexity combining adapted mechanical properties, vascularization and multicellular component [14]. In parallel, computational approaches can be carried out in order to optimize and predict the expected performances of the bone substitute produced by integrated bone tissue engineering. Finally, advances in artificial intelligence (AI), and deep learning in particular, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine [15]. With an improved perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool for improving patient outcomes in bone tissue engineering.
[1] | F. Balloux, Mass COVID testing and sequencing is unsustainable – here's how future surveillance can be done, The Conversation, 2022. Available from: https://theconversation.com/mass-covid-testing-and-sequencing-is-unsustainable-heres-how-future-surveillance-can-be-done-177404. |
[2] | M. J. Wade, D. Jones, A. Singer, A. Hart, A. Corbishley, C. Spence, et al., Wastewater COVID-19 monitoring in the UK: summary for SAGE, 2020. Available from: https://www.gov.uk/government/publications/defrajbc-wastewater-covid-19-monitoring-in-the-uk-summary-19-november-2020. |
[3] |
A. Bivins, D. North, A. Ahmad, W. Ahmed, E. Alm, F. Been, et al., Wastewater-Based Epidemiology: Global Collaborative to Maximize Contributions in the Fight Against COVID-19, Environ. Sci. Technol., 54 (2020), 7754–7757. https://doi.org/10.1021/acs.est.0c02388 doi: 10.1021/acs.est.0c02388
![]() |
[4] | UC Merced Researchers, COVIDPoops19: Summary of Global SARS-CoV-2 Wastewater Monitoring Efforts, 2022. Available from: https://www.arcgis.com/apps/dashboards/c778145ea5bb4daeb58d31afee389082. |
[5] |
H. R. Safford, K. Shapiro, H. N. Bischel, Wastewater analysis can be a powerful public health tool - if it's done sensibly, Proc. Natl. Acad. Sci., 119 (2022), e2119600119. https://doi.org/10.1073/pnas.2119600119 doi: 10.1073/pnas.2119600119
![]() |
[6] |
D. A. Larsen, H. Green, M. B. Collins, B. L. Kmush, Wastewater monitoring, surveillance and epidemiology: a review of terminology for a common understanding, FEMS Microbes, 2 (2021), xtab011. https://doi.org/10.1093/femsmc/xtab011 doi: 10.1093/femsmc/xtab011
![]() |
[7] |
N. Sims, B. Kasprzyk-Hordern, Future perspectives of wastewater-based epidemiology: Monitoring infectious disease spread and resistance to the community level, Environ. Int., 139 (2020), 105689. https://doi.org/10.1016/j.envint.2020.105689 doi: 10.1016/j.envint.2020.105689
![]() |
[8] |
M. Huizere, T. L. ter Lak, P. de Voogt, A. P. van Wezel, Wastewater-based epidemiology for illicit drugs: A critical review on global data, Water Res., 207 (2021), 117789. https://doi.org/10.1016/j.watres.2021.117789 doi: 10.1016/j.watres.2021.117789
![]() |
[9] |
M. J. Wade, A. Lo Jacomo, E. Armenise, M. R. Brown, J. T. Bunce, G. J. Cameron, et al., Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes, J. Hazard. Mater., 424 (2022), 127456. https://doi.org/10.1002/essoar.10507606.1 doi: 10.1002/essoar.10507606.1
![]() |
[10] |
M. A. Cohen, P. B. Ryan, Observations Less than the Analytical Limit of Detection: A New Approach, JAPCA, 39 (1989), 328–329. https://doi.org/10.1080/08940630.1989.10466534 doi: 10.1080/08940630.1989.10466534
![]() |
[11] |
D. R. Helsel, Fabricating data: How substituting values for nondetects can ruin results, and what can be done about it, Chemosphere, 65 (2006), 2434–2439. https://doi.org/10.1016/j.chemosphere.2006.04.051 doi: 10.1016/j.chemosphere.2006.04.051
![]() |
[12] |
M. Courbariaux, N. Cluzel, S. Wang, V. Maréchal, L. Moulin, S. Wurtzer, et al., A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater, Front. Appl. Math. Stat., 8 (2022), 836349. https://doi.org/10.3389/fams.2022.836349 doi: 10.3389/fams.2022.836349
![]() |
[13] |
S. Wurtzer, P. Waldman, M. Levert, N. Cluzel, J. L. Almayrac, C. Charpentier, et al., SARS-CoV-2 genome quantification in wastewaters at regional and city scale allows precise monitoring of the whole outbreaks dynamics and variants spreading in the population, Sci. Tot. Environ., 810 (2022), 152213. https://doi.org/10.1016/j.scitotenv.2021.152213 doi: 10.1016/j.scitotenv.2021.152213
![]() |
[14] | Stan Development Team, Stan Modeling Language User's Guide and Reference Manual, Version 2.30, 2022. |
[15] | C. Sweetapple, M. J. Wade, J. M. S. Grimsley, J. T. Bunce, P. Melville-Shreeve, A. S. Chen, Dynamic population normalisation in wastewater-based epidemiology for improved understanding of the SARS-CoV-2 prevalence: a multi-site study, J. Water Health, (2023), in press. https://doi.org/10.2166/wh.2023.318 |
[16] |
A. L. Rainey, S. Liang, J. H. Bisesi Jr., T. Sabo-Attwood, A. T. Maurelli, A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19, PLOS ONE, 18 (2023), e0284370. https://doi.org/10.1371/journal.pone.0284370 doi: 10.1371/journal.pone.0284370
![]() |
[17] | A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, D. B. Rubin Bayesian Data Analysis, 3rd Ed. CRC Press, 2013. https://doi.org/10.1201/b16018 |
[18] |
E. Pebesma, Simple Features for R: Standardized Support for Spatial Vector Data, The R Journal, 10 (2018), 439–446. https://doi.org/10.32614/RJ-2018-009 doi: 10.32614/RJ-2018-009
![]() |
[19] |
M. Morvan, A. Lo Jacomo, C. Souque, M. J. Wade, T. Hoffmann, K. Pouwels, et al., An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence, Nat. Commun., 13 (2022), 4313. https://doi.org/10.1038/s41467-022-31753-y doi: 10.1038/s41467-022-31753-y
![]() |
[20] |
C. S. McMahan, S. Self, L. Rennert, C. Kalbaugh, D. Kriebel, D. Graves, et al., COVID-19 wastewater epidemiology: a model to estimate infected populations, Lancet Planet. Health, 5 (2021), e874–881. https://doi.org/10.1016/S2542-5196(21)00230-8 doi: 10.1016/S2542-5196(21)00230-8
![]() |
[21] |
X. Li, J. Kulandaivelu, S. Zhang, J. Shi, M. Sivakumar, J. Mueller, et al., Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology, Sci. Total Environ., 789 (2021), 147947. https://doi.org/10.1016/j.scitotenv.2021.147947 doi: 10.1016/j.scitotenv.2021.147947
![]() |
[22] | Office for National Statistics, Coronavirus (COVID-19) Infection Survey: methods and further information, 2022. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/methodologies/covid19infectionsurveypilotmethodsandfurtherinformation#coronavirus-covid-19-infection-survey. |
[23] | UK Health Security Agency, EMHP wastewater monitoring of SARS-CoV-2 in England: 15 July 2020 to 30 March 2022, 2022. Available from: https://www.gov.uk/government/publications/monitoring-of-sars-cov-2-rna-in-england-wastewater-monthly-statistics-15-july-2020-to-30-march-2022/emhp-wastewater-monitoring-of-sars-cov-2-in-england-15-july-2020-to-30-march-2022. |
[24] |
G. Vogel, Signals from the sewer, Science, 375 (2022), 1100–1104. https://doi.org/10.1126/science.adb1874 doi: 10.1126/science.adb1874
![]() |
[25] |
A. Xiao, F. Wu, M. Bushman, J. Zhang, M. Imakaev, P. R. Chai, et al., Metrics to relate COVID-19 wastewater data to clinical testing dynamics, Water Res., 212 (2022), 118070. https://doi.org/10.1016/j.watres.2022.118070 doi: 10.1016/j.watres.2022.118070
![]() |
[26] |
P. M. D'Aoust, X. Tian, S. Tasneem Towhid, A. Xiao, E. Mercier, N. Hegazy, et al., Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities, Sci. Total Environ., 853 (2022), 158547. https://doi.org/10.1016/j.scitotenv.2022.158547 doi: 10.1016/j.scitotenv.2022.158547
![]() |
1. | Vincent Deplaigne, Gael Y. Rochefort, Cell–Biomaterial Interactions, 2023, 10, 2306-5354, 241, 10.3390/bioengineering10020241 | |
2. | Katarzyna Klimek, Krzysztof Palka, Wieslaw Truszkiewicz, Timothy E. L. Douglas, Aleksandra Nurzynska, Grazyna Ginalska, Could Curdlan/Whey Protein Isolate/Hydroxyapatite Biomaterials Be Considered as Promising Bone Scaffolds?—Fabrication, Characterization, and Evaluation of Cytocompatibility towards Osteoblast Cells In Vitro, 2022, 11, 2073-4409, 3251, 10.3390/cells11203251 | |
3. | Alexis Delpierre, Guillaume Savard, Matthieu Renaud, Gael Y. Rochefort, Tissue Engineering Strategies Applied in Bone Regeneration and Bone Repair, 2023, 10, 2306-5354, 644, 10.3390/bioengineering10060644 | |
4. | Marco Ruggeri, Barbara Vigani, Silvia Rossi, Giuseppina Sandri, 2025, 9780443220173, 413, 10.1016/B978-0-443-22017-3.00009-3 | |
5. | Weijie Liu, Nalini Cheong, Zhuling He, Tonghan Zhang, Application of Hydroxyapatite Composites in Bone Tissue Engineering: A Review, 2025, 16, 2079-4983, 127, 10.3390/jfb16040127 |