2026
|
Wu, Zixiong; Chen, Shuwen; Fan, Shicheng; Qiao, Zheng; Qi, Jiaming; Zhang, Yusheng; Lim, Chwee Teck Strain-localized luminescent e-skin for high-resolution pressure mapping
and visual force feedback NATURE COMMUNICATIONS, 17 (1), 2026, DOI: 10.1038/s41467-026-73073-5. Abstract | BibTeX | Endnote @article{WOS:001827700500010,
title = {Strain-localized luminescent e-skin for high-resolution pressure mapping
and visual force feedback},
author = {Zixiong Wu and Shuwen Chen and Shicheng Fan and Zheng Qiao and Jiaming Qi and Yusheng Zhang and Chwee Teck Lim},
doi = {10.1038/s41467-026-73073-5},
times_cited = {1},
year = {2026},
date = {2026-05-01},
journal = {NATURE COMMUNICATIONS},
volume = {17},
number = {1},
publisher = {NATURE PORTFOLIO},
address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
abstract = {Electronic skins (e-skins) that emulate human intuitive tactile
perception with visual feedback capabilities are vital for
next-generation wearables, robotics, and biomedical applications.
However, existing visualized pressure mapping systems either require
dense pixelated arrays that add bulk, complexity and signal crosstalk,
or rely on rigid or semi-rigid architecture, and are not able to conform
to dynamically changing curved, soft surfaces. Here, we report a soft
mechano-electroluminescent e-skin for high-resolution visualized
pressure mapping. Quantitative pressure sensing is achieved on curved
and compliant substrates. Built with an ultrathin, entirely soft
microstructured architecture, it harnesses strain-localized deformation
and intrinsic, in-situ force-electric-optical coupling within a
continuous device stack to achieve high-fidelity pressure mapping
without discrete sensing pixels. It achieves a spatial resolution of 30
mu m (847 dpi) and high luminescent sensitivity (1.12 cd &
centerdot;m(-)& sup2;& centerdot;kPa(-)& sup1;). This e-skin allows
real-time mapping of pressure distributions and recognition of fine
tactile features like fingerprints. Integrated with plantar sensors and
also laparoscopic tools, it also enables visual force feedback,
enhancing gait analysis and surgical training, respectively. This
approach offers a soft, compact, multimodal platform for intelligent
tactile interfaces.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Electronic skins (e-skins) that emulate human intuitive tactile
perception with visual feedback capabilities are vital for
next-generation wearables, robotics, and biomedical applications.
However, existing visualized pressure mapping systems either require
dense pixelated arrays that add bulk, complexity and signal crosstalk,
or rely on rigid or semi-rigid architecture, and are not able to conform
to dynamically changing curved, soft surfaces. Here, we report a soft
mechano-electroluminescent e-skin for high-resolution visualized
pressure mapping. Quantitative pressure sensing is achieved on curved
and compliant substrates. Built with an ultrathin, entirely soft
microstructured architecture, it harnesses strain-localized deformation
and intrinsic, in-situ force-electric-optical coupling within a
continuous device stack to achieve high-fidelity pressure mapping
without discrete sensing pixels. It achieves a spatial resolution of 30
mu m (847 dpi) and high luminescent sensitivity (1.12 cd &
centerdot;m(-)& sup2;& centerdot;kPa(-)& sup1;). This e-skin allows
real-time mapping of pressure distributions and recognition of fine
tactile features like fingerprints. Integrated with plantar sensors and
also laparoscopic tools, it also enables visual force feedback,
enhancing gait analysis and surgical training, respectively. This
approach offers a soft, compact, multimodal platform for intelligent
tactile interfaces. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFZixiong Wu
Shuwen Chen
Shicheng Fan
Zheng Qiao
Jiaming Qi
Yusheng Zhang
Chwee Teck Lim
- TIStrain-localized luminescent e-skin for high-resolution pressure mapping
and visual force feedback - SONATURE COMMUNICATIONS
- DTArticle
- ABElectronic skins (e-skins) that emulate human intuitive tactile
perception with visual feedback capabilities are vital for
next-generation wearables, robotics, and biomedical applications.
However, existing visualized pressure mapping systems either require
dense pixelated arrays that add bulk, complexity and signal crosstalk,
or rely on rigid or semi-rigid architecture, and are not able to conform
to dynamically changing curved, soft surfaces. Here, we report a soft
mechano-electroluminescent e-skin for high-resolution visualized
pressure mapping. Quantitative pressure sensing is achieved on curved
and compliant substrates. Built with an ultrathin, entirely soft
microstructured architecture, it harnesses strain-localized deformation
and intrinsic, in-situ force-electric-optical coupling within a
continuous device stack to achieve high-fidelity pressure mapping
without discrete sensing pixels. It achieves a spatial resolution of 30
mu m (847 dpi) and high luminescent sensitivity (1.12 cd &
centerdot;m(-)& sup2;& centerdot;kPa(-)& sup1;). This e-skin allows
real-time mapping of pressure distributions and recognition of fine
tactile features like fingerprints. Integrated with plantar sensors and
also laparoscopic tools, it also enables visual force feedback,
enhancing gait analysis and surgical training, respectively. This
approach offers a soft, compact, multimodal platform for intelligent
tactile interfaces. - Z91
- PUNATURE PORTFOLIO
- PAHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
- VL17
- DI10.1038/s41467-026-73073-5
- UTWOS:001827700500010
- ER
- EF
|
2025
|
Le, Truong-Son Dinh; Tran, Y-Van; Gao, Yuji; Valerio, Von Luigi; Ge, Zhixing; Lim, Chwee Teck Laser-induced graphene for biomedical applications: innovations in
health monitoring and diagnostics 14 NANOSCALE HORIZONS, 10 (11), pp. 2688-2721, 2025, DOI: 10.1039/d5nh00377f. Abstract | BibTeX | Endnote @article{WOS:001557002400001,
title = {Laser-induced graphene for biomedical applications: innovations in
health monitoring and diagnostics},
author = {Truong-Son Dinh Le and Y-Van Tran and Yuji Gao and Von Luigi Valerio and Zhixing Ge and Chwee Teck Lim},
doi = {10.1039/d5nh00377f},
times_cited = {14},
issn = {2055-6756},
year = {2025},
date = {2025-10-01},
journal = {NANOSCALE HORIZONS},
volume = {10},
number = {11},
pages = {2688-2721},
publisher = {ROYAL SOC CHEMISTRY},
address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND},
abstract = {Laser-induced graphene (LIG) has emerged as a versatile and sustainable
nanomaterial for biomedical applications, offering a unique combination
of tunable surface chemistry, high electrical conductivity, mechanical
flexibility, and biocompatibility. These superior properties, coupled
with its facile and mask-free fabrication process, have positioned LIG
as a promising platform for next-generation wearable and point-of-care
sensors. This review presents a comprehensive overview of LIG synthesis,
microstructures, properties, and functionalization strategies, with a
particular focus on its applications in health monitoring and
diagnostics. We highlight recent advances in LIG-based sensors for
detecting physical, electrophysiological, chemical, and biochemical
signals. Key challenges including material variability, miniaturization,
scalability, stability, and biocompatibility are critically discussed.
Finally, we explore future directions for integrating LIG biomedical
sensors with emerging technologies such as artificial intelligence, big
data, and eco-friendly materials to enable intelligent, personalized,
and sustainable healthcare solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Laser-induced graphene (LIG) has emerged as a versatile and sustainable
nanomaterial for biomedical applications, offering a unique combination
of tunable surface chemistry, high electrical conductivity, mechanical
flexibility, and biocompatibility. These superior properties, coupled
with its facile and mask-free fabrication process, have positioned LIG
as a promising platform for next-generation wearable and point-of-care
sensors. This review presents a comprehensive overview of LIG synthesis,
microstructures, properties, and functionalization strategies, with a
particular focus on its applications in health monitoring and
diagnostics. We highlight recent advances in LIG-based sensors for
detecting physical, electrophysiological, chemical, and biochemical
signals. Key challenges including material variability, miniaturization,
scalability, stability, and biocompatibility are critically discussed.
Finally, we explore future directions for integrating LIG biomedical
sensors with emerging technologies such as artificial intelligence, big
data, and eco-friendly materials to enable intelligent, personalized,
and sustainable healthcare solutions. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFTruong-Son Dinh Le
Y-Van Tran
Yuji Gao
Von Luigi Valerio
Zhixing Ge
Chwee Teck Lim
- TILaser-induced graphene for biomedical applications: innovations in
health monitoring and diagnostics - SONANOSCALE HORIZONS
- DTArticle
- ABLaser-induced graphene (LIG) has emerged as a versatile and sustainable
nanomaterial for biomedical applications, offering a unique combination
of tunable surface chemistry, high electrical conductivity, mechanical
flexibility, and biocompatibility. These superior properties, coupled
with its facile and mask-free fabrication process, have positioned LIG
as a promising platform for next-generation wearable and point-of-care
sensors. This review presents a comprehensive overview of LIG synthesis,
microstructures, properties, and functionalization strategies, with a
particular focus on its applications in health monitoring and
diagnostics. We highlight recent advances in LIG-based sensors for
detecting physical, electrophysiological, chemical, and biochemical
signals. Key challenges including material variability, miniaturization,
scalability, stability, and biocompatibility are critically discussed.
Finally, we explore future directions for integrating LIG biomedical
sensors with emerging technologies such as artificial intelligence, big
data, and eco-friendly materials to enable intelligent, personalized,
and sustainable healthcare solutions. - Z914
- PUROYAL SOC CHEMISTRY
- PATHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND - SN2055-6756
- VL10
- BP2688
- EP2721
- DI10.1039/d5nh00377f
- UTWOS:001557002400001
- ER
- EF
|
Chen, Shuwen; Fan, Shicheng; Qiao, Zheng; Wu, Zixiong; Lin, Baobao; Li, Zhijie; Riegler, Michael A; Wong, Matthew Yu Heng; Opheim, Arve; Korostynska, Olga; Nielsen, Kaare Magne; Glott, Thomas; Martinsen, Anne Catrine T; Telle-Hansen, Vibeke H; Lim, Chwee Teck Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart
Materials and Artificial Intelligence 180 ADVANCED MATERIALS, 37 (21), 2025, DOI: 10.1002/adma.202500412. Abstract | BibTeX | Endnote @article{WOS:001457625300001,
title = {Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart
Materials and Artificial Intelligence},
author = {Shuwen Chen and Shicheng Fan and Zheng Qiao and Zixiong Wu and Baobao Lin and Zhijie Li and Michael A Riegler and Matthew Yu Heng Wong and Arve Opheim and Olga Korostynska and Kaare Magne Nielsen and Thomas Glott and Anne Catrine T Martinsen and Vibeke H Telle-Hansen and Chwee Teck Lim},
doi = {10.1002/adma.202500412},
times_cited = {180},
issn = {0935-9648},
year = {2025},
date = {2025-05-01},
journal = {ADVANCED MATERIALS},
volume = {37},
number = {21},
publisher = {WILEY-V C H VERLAG GMBH},
address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY},
abstract = {Intelligent wearable sensors, empowered by machine learning and
innovative smart materials, enable rapid, accurate disease diagnosis,
personalized therapy, and continuous health monitoring without
disrupting daily life. This integration facilitates a shift from
traditional, hospital-centered healthcare to a more decentralized,
patient-centric model, where wearable sensors can collect real-time
physiological data, provide deep analysis of these data streams, and
generate actionable insights for point-of-care precise diagnostics and
personalized therapy. Despite rapid advancements in smart materials,
machine learning, and wearable sensing technologies, there is a lack of
comprehensive reviews that systematically examine the intersection of
these fields. This review addresses this gap, providing a critical
analysis of wearable sensing technologies empowered by smart advanced
materials and artificial Intelligence. The state-of-the-art smart
materials-including self-healing, metamaterials, and responsive
materials-that enhance sensor functionality are first examined. Advanced
machine learning methodologies integrated into wearable devices are
discussed, and their role in biomedical applications is highlighted. The
combined impact of wearable sensors, empowered by smart materials and
machine learning, and their applications in intelligent diagnostics and
therapeutics are also examined. Finally, existing challenges, including
technical and compliance issues, information security concerns, and
regulatory considerations are addressed, and future directions for
advancing intelligent healthcare are proposed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Intelligent wearable sensors, empowered by machine learning and
innovative smart materials, enable rapid, accurate disease diagnosis,
personalized therapy, and continuous health monitoring without
disrupting daily life. This integration facilitates a shift from
traditional, hospital-centered healthcare to a more decentralized,
patient-centric model, where wearable sensors can collect real-time
physiological data, provide deep analysis of these data streams, and
generate actionable insights for point-of-care precise diagnostics and
personalized therapy. Despite rapid advancements in smart materials,
machine learning, and wearable sensing technologies, there is a lack of
comprehensive reviews that systematically examine the intersection of
these fields. This review addresses this gap, providing a critical
analysis of wearable sensing technologies empowered by smart advanced
materials and artificial Intelligence. The state-of-the-art smart
materials-including self-healing, metamaterials, and responsive
materials-that enhance sensor functionality are first examined. Advanced
machine learning methodologies integrated into wearable devices are
discussed, and their role in biomedical applications is highlighted. The
combined impact of wearable sensors, empowered by smart materials and
machine learning, and their applications in intelligent diagnostics and
therapeutics are also examined. Finally, existing challenges, including
technical and compliance issues, information security concerns, and
regulatory considerations are addressed, and future directions for
advancing intelligent healthcare are proposed. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFShuwen Chen
Shicheng Fan
Zheng Qiao
Zixiong Wu
Baobao Lin
Zhijie Li
Michael A Riegler
Matthew Yu Heng Wong
Arve Opheim
Olga Korostynska
Kaare Magne Nielsen
Thomas Glott
Anne Catrine T Martinsen
Vibeke H Telle-Hansen
Chwee Teck Lim
- TITransforming Healthcare: Intelligent Wearable Sensors Empowered by Smart
Materials and Artificial Intelligence - SOADVANCED MATERIALS
- DTArticle
- ABIntelligent wearable sensors, empowered by machine learning and
innovative smart materials, enable rapid, accurate disease diagnosis,
personalized therapy, and continuous health monitoring without
disrupting daily life. This integration facilitates a shift from
traditional, hospital-centered healthcare to a more decentralized,
patient-centric model, where wearable sensors can collect real-time
physiological data, provide deep analysis of these data streams, and
generate actionable insights for point-of-care precise diagnostics and
personalized therapy. Despite rapid advancements in smart materials,
machine learning, and wearable sensing technologies, there is a lack of
comprehensive reviews that systematically examine the intersection of
these fields. This review addresses this gap, providing a critical
analysis of wearable sensing technologies empowered by smart advanced
materials and artificial Intelligence. The state-of-the-art smart
materials-including self-healing, metamaterials, and responsive
materials-that enhance sensor functionality are first examined. Advanced
machine learning methodologies integrated into wearable devices are
discussed, and their role in biomedical applications is highlighted. The
combined impact of wearable sensors, empowered by smart materials and
machine learning, and their applications in intelligent diagnostics and
therapeutics are also examined. Finally, existing challenges, including
technical and compliance issues, information security concerns, and
regulatory considerations are addressed, and future directions for
advancing intelligent healthcare are proposed. - Z9180
- PUWILEY-V C H VERLAG GMBH
- PAPOSTFACH 101161, 69451 WEINHEIM, GERMANY
- SN0935-9648
- VL37
- DI10.1002/adma.202500412
- UTWOS:001457625300001
- ER
- EF
|
2024
|
Qi, Jiaming; Yu, Longteng; Khoo, Eng Tat; Ng, Kian Wei; Gao, Yujia; Kow, Alfred Wei Chieh; Yeo, Joo Chuan; Lim, Chwee Teck Bridging the digital-physical divide using haptic and wearable
technologies 34 NATURE ELECTRONICS, 7 (12), pp. 1098-1110, 2024, DOI: 10.1038/s41928-024-01325-7. Abstract | BibTeX | Endnote @article{WOS:001381415500004,
title = {Bridging the digital-physical divide using haptic and wearable
technologies},
author = {Jiaming Qi and Longteng Yu and Eng Tat Khoo and Kian Wei Ng and Yujia Gao and Alfred Wei Chieh Kow and Joo Chuan Yeo and Chwee Teck Lim},
doi = {10.1038/s41928-024-01325-7},
times_cited = {34},
issn = {2520-1131},
year = {2024},
date = {2024-12-01},
journal = {NATURE ELECTRONICS},
volume = {7},
number = {12},
pages = {1098-1110},
publisher = {NATURE PORTFOLIO},
address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
abstract = {The metaverse could provide an immersive environment that integrates
digital and physical realities. However, this will require appropriate
haptic feedback and wearable technologies. Here we explore the
development of haptic and wearable technologies that can be used to
bridge the digital-physical divide and build a more realistic and
immersive metaverse. We examine the mechanisms of haptic technology and
the haptic devices that can replicate the sense of touch, and examine
the development of wearable technology that can provide motion tracking
through the integration of artificial intelligence. We highlight the
potential applications of such technology in the areas of entertainment,
commerce, education, training and healthcare. Finally, we consider the
ethical and technological challenges that the field faces.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The metaverse could provide an immersive environment that integrates
digital and physical realities. However, this will require appropriate
haptic feedback and wearable technologies. Here we explore the
development of haptic and wearable technologies that can be used to
bridge the digital-physical divide and build a more realistic and
immersive metaverse. We examine the mechanisms of haptic technology and
the haptic devices that can replicate the sense of touch, and examine
the development of wearable technology that can provide motion tracking
through the integration of artificial intelligence. We highlight the
potential applications of such technology in the areas of entertainment,
commerce, education, training and healthcare. Finally, we consider the
ethical and technological challenges that the field faces. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFJiaming Qi
Longteng Yu
Eng Tat Khoo
Kian Wei Ng
Yujia Gao
Alfred Wei Chieh Kow
Joo Chuan Yeo
Chwee Teck Lim
- TIBridging the digital-physical divide using haptic and wearable
technologies - SONATURE ELECTRONICS
- DTArticle
- ABThe metaverse could provide an immersive environment that integrates
digital and physical realities. However, this will require appropriate
haptic feedback and wearable technologies. Here we explore the
development of haptic and wearable technologies that can be used to
bridge the digital-physical divide and build a more realistic and
immersive metaverse. We examine the mechanisms of haptic technology and
the haptic devices that can replicate the sense of touch, and examine
the development of wearable technology that can provide motion tracking
through the integration of artificial intelligence. We highlight the
potential applications of such technology in the areas of entertainment,
commerce, education, training and healthcare. Finally, we consider the
ethical and technological challenges that the field faces. - Z934
- PUNATURE PORTFOLIO
- PAHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
- SN2520-1131
- VL7
- BP1098
- EP1110
- DI10.1038/s41928-024-01325-7
- UTWOS:001381415500004
- ER
- EF
|
She, David T; Nai, Mui Hoon; Lim, Chwee Teck Atomic force microscopy in the characterization and clinical evaluation
of neurological disorders: current and emerging technologies 12 MED-X, 2 (1), 2024, DOI: 10.1007/s44258-024-00022-6. Abstract | BibTeX | Endnote @article{WOS:001816799700001,
title = {Atomic force microscopy in the characterization and clinical evaluation
of neurological disorders: current and emerging technologies},
author = {David T She and Mui Hoon Nai and Chwee Teck Lim},
doi = {10.1007/s44258-024-00022-6},
times_cited = {12},
issn = {2097-440X},
year = {2024},
date = {2024-06-01},
journal = {MED-X},
volume = {2},
number = {1},
publisher = {SPRINGERNATURE},
address = {THE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
abstract = {This review examines the significant role of Atomic Force Microscopy
(AFM) in neurobiological research and its emerging clinical applications
in diagnosing neurological disorders and central nervous system (CNS)
tumours. AFM, known for its nanometre-scale resolution and
piconewton-scale force sensitivity, offers ground breaking insights into
the biomechanical properties of brain cells and tissues and their
interactions within their microenvironment. This review delves into the
application of AFM in non-clinical settings, where it characterizes
molecular, cellular, and tissue-level aspects of neurological disorders
in experimental models. This includes studying ion channel distribution,
neuron excitability in genetic disorders, and axonal resistance to
mechanical injury. In the clinical context, this article emphasizes
AFM's potential in early detection and monitoring of neurodegenerative
diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD) and
amyotrophic lateral sclerosis (ALS), through biomarker characterization
in biofluids such as cerebrospinal fluid and blood. It also examines the
use of AFM in enhancing the grading and treatment of CNS tumours by
assessing their stiffness, providing a more detailed analysis than
traditional histopathological methods. Despite its promise, this review
acknowledges challenges in integrating AFM into clinical practice, such
as sample heterogeneity and data analysis complexity, and discusses
emerging solutions such as machine learning and neural networks to
overcome these hurdles. These advancements, combined with commercial
nanotechnology platforms, herald a new era in personalized treatment
strategies for management, treatment and diagnosis of neurological
disorders.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This review examines the significant role of Atomic Force Microscopy
(AFM) in neurobiological research and its emerging clinical applications
in diagnosing neurological disorders and central nervous system (CNS)
tumours. AFM, known for its nanometre-scale resolution and
piconewton-scale force sensitivity, offers ground breaking insights into
the biomechanical properties of brain cells and tissues and their
interactions within their microenvironment. This review delves into the
application of AFM in non-clinical settings, where it characterizes
molecular, cellular, and tissue-level aspects of neurological disorders
in experimental models. This includes studying ion channel distribution,
neuron excitability in genetic disorders, and axonal resistance to
mechanical injury. In the clinical context, this article emphasizes
AFM's potential in early detection and monitoring of neurodegenerative
diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD) and
amyotrophic lateral sclerosis (ALS), through biomarker characterization
in biofluids such as cerebrospinal fluid and blood. It also examines the
use of AFM in enhancing the grading and treatment of CNS tumours by
assessing their stiffness, providing a more detailed analysis than
traditional histopathological methods. Despite its promise, this review
acknowledges challenges in integrating AFM into clinical practice, such
as sample heterogeneity and data analysis complexity, and discusses
emerging solutions such as machine learning and neural networks to
overcome these hurdles. These advancements, combined with commercial
nanotechnology platforms, herald a new era in personalized treatment
strategies for management, treatment and diagnosis of neurological
disorders. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFDavid T She
Mui Hoon Nai
Chwee Teck Lim
- TIAtomic force microscopy in the characterization and clinical evaluation
of neurological disorders: current and emerging technologies - SOMED-X
- DTArticle
- ABThis review examines the significant role of Atomic Force Microscopy
(AFM) in neurobiological research and its emerging clinical applications
in diagnosing neurological disorders and central nervous system (CNS)
tumours. AFM, known for its nanometre-scale resolution and
piconewton-scale force sensitivity, offers ground breaking insights into
the biomechanical properties of brain cells and tissues and their
interactions within their microenvironment. This review delves into the
application of AFM in non-clinical settings, where it characterizes
molecular, cellular, and tissue-level aspects of neurological disorders
in experimental models. This includes studying ion channel distribution,
neuron excitability in genetic disorders, and axonal resistance to
mechanical injury. In the clinical context, this article emphasizes
AFM's potential in early detection and monitoring of neurodegenerative
diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD) and
amyotrophic lateral sclerosis (ALS), through biomarker characterization
in biofluids such as cerebrospinal fluid and blood. It also examines the
use of AFM in enhancing the grading and treatment of CNS tumours by
assessing their stiffness, providing a more detailed analysis than
traditional histopathological methods. Despite its promise, this review
acknowledges challenges in integrating AFM into clinical practice, such
as sample heterogeneity and data analysis complexity, and discusses
emerging solutions such as machine learning and neural networks to
overcome these hurdles. These advancements, combined with commercial
nanotechnology platforms, herald a new era in personalized treatment
strategies for management, treatment and diagnosis of neurological
disorders. - Z912
- PUSPRINGERNATURE
- PATHE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
- SN2097-440X
- VL2
- DI10.1007/s44258-024-00022-6
- UTWOS:001816799700001
- ER
- EF
|