Summary
Osteoporosis, a widespread disease that causes bone fragility, currently afflicts over 32 million Europeans, with rapidly increasing
prevalence expected due to population aging. The costs of therapies for osteoporotic fragility fractures total more than €50 billion per
year in the EU. Postmenopausal females are at especially increased risk for osteoporosis, and the regenerative capacity of healthy
bone also differs between the biological sexes.
Although biomaterials have been developed to aid bone regeneration after injury, the influence of biological sex and osteoporotic
disease have not been taken into account for such biomaterials design. Excitingly, the advent of machine learning (ML) offers the
potential to unravel the effects of sex and osteoporotic disease on biomaterials’ bone regenerative capacity, and to apply these
insights to design biomaterials optimized to these characteristics.
Therefore, the SeDiBone project will – for the first time – exploit ML to elucidate the influence of sex and osteoporotic disease on
bone regeneration, and to translate these insights towards design of an entirely new class of biomaterials with sex and osteoporotic
specificity. By enabling automated localization of tissue defect sites and detailed tissue, cell, and interface identification, SeDiBone will deliver an Analysis Tool that efficiently and reproducibly quantifies bone regeneration outcomes in male or female and healthy or osteoporotic preclinical animal models (Aim 1). These in-depth insights will then serve as inputs for a Design Tool to determine the biomaterials designs correlating to optimal regenerative success based on sex and osteoporosis, with validation through ex vivo demonstration (Aim 2). SeDiBone will thus crucially synergize and advance efforts to design a new generation of patient-specific biomaterials, and accordingly inform therapeutic solutions for regeneration of bone tissue in the unexplored areas of sex- and disease-based specificity.
prevalence expected due to population aging. The costs of therapies for osteoporotic fragility fractures total more than €50 billion per
year in the EU. Postmenopausal females are at especially increased risk for osteoporosis, and the regenerative capacity of healthy
bone also differs between the biological sexes.
Although biomaterials have been developed to aid bone regeneration after injury, the influence of biological sex and osteoporotic
disease have not been taken into account for such biomaterials design. Excitingly, the advent of machine learning (ML) offers the
potential to unravel the effects of sex and osteoporotic disease on biomaterials’ bone regenerative capacity, and to apply these
insights to design biomaterials optimized to these characteristics.
Therefore, the SeDiBone project will – for the first time – exploit ML to elucidate the influence of sex and osteoporotic disease on
bone regeneration, and to translate these insights towards design of an entirely new class of biomaterials with sex and osteoporotic
specificity. By enabling automated localization of tissue defect sites and detailed tissue, cell, and interface identification, SeDiBone will deliver an Analysis Tool that efficiently and reproducibly quantifies bone regeneration outcomes in male or female and healthy or osteoporotic preclinical animal models (Aim 1). These in-depth insights will then serve as inputs for a Design Tool to determine the biomaterials designs correlating to optimal regenerative success based on sex and osteoporosis, with validation through ex vivo demonstration (Aim 2). SeDiBone will thus crucially synergize and advance efforts to design a new generation of patient-specific biomaterials, and accordingly inform therapeutic solutions for regeneration of bone tissue in the unexplored areas of sex- and disease-based specificity.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101154946 |
| Start date: | 01-06-2025 |
| End date: | 31-05-2027 |
| Total budget - Public funding: | - 203 464,00 Euro |
Cordis data
Original description
Osteoporosis, a widespread disease that causes bone fragility, currently afflicts over 32 million Europeans, with rapidly increasingprevalence expected due to population aging. The costs of therapies for osteoporotic fragility fractures total more than €50 billion per
year in the EU. Postmenopausal females are at especially increased risk for osteoporosis, and the regenerative capacity of healthy
bone also differs between the biological sexes.
Although biomaterials have been developed to aid bone regeneration after injury, the influence of biological sex and osteoporotic
disease have not been taken into account for such biomaterials design. Excitingly, the advent of machine learning (ML) offers the
potential to unravel the effects of sex and osteoporotic disease on biomaterials’ bone regenerative capacity, and to apply these
insights to design biomaterials optimized to these characteristics.
Therefore, the SeDiBone project will – for the first time – exploit ML to elucidate the influence of sex and osteoporotic disease on
bone regeneration, and to translate these insights towards design of an entirely new class of biomaterials with sex and osteoporotic
specificity. By enabling automated localization of tissue defect sites and detailed tissue, cell, and interface identification, SeDiBone will deliver an Analysis Tool that efficiently and reproducibly quantifies bone regeneration outcomes in male or female and healthy or osteoporotic preclinical animal models (Aim 1). These in-depth insights will then serve as inputs for a Design Tool to determine the biomaterials designs correlating to optimal regenerative success based on sex and osteoporosis, with validation through ex vivo demonstration (Aim 2). SeDiBone will thus crucially synergize and advance efforts to design a new generation of patient-specific biomaterials, and accordingly inform therapeutic solutions for regeneration of bone tissue in the unexplored areas of sex- and disease-based specificity.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
25-11-2024
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