title: Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

authors: Chen, Yifan

subjects: Science & Technology

abstract: We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted “activator” nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

text: Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted “activator” nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

year: 2019

title: Solar wind fluctuations: waves and turbulence

authors: Oughton, Sean

subjects: solar wind

abstract: We present a brief review of observations and theory regarding the nature and radial evolution of MHD‐scale solar wind fluctuations. Emphasis is placed on the fact that the fluctuations consist of both waves and turbulence, and on their dual dynamical roles.

text: Solar wind fluctuations: waves and turbulence We present a brief review of observations and theory regarding the nature and radial evolution of MHD‐scale solar wind fluctuations. Emphasis is placed on the fact that the fluctuations consist of both waves and turbulence, and on their dual dynamical roles.

year: 2003

title: Ki te kapu o taku ringa - In the palm of my hand

authors: Hill, Rodrigo

subjects: Mātauranga Māori

abstract: This publication accompanies the launching of our exhibition 'Ki te Kapu o Taku Ringa - In the Palm of My Hand' at the Ōtorohanga Museum in June 2024.
The title is taken from a line of King Tāwhiao's song-poem 'Maioha mō Waikato'. Ejected from their homelands he and his Waikato brethren took refuge with their Maniapoto relations in Te Nehenehenui; parts of which are mentioned in the song-poem. Teh 'maioha' was composed expressing King Tāwhiao's love for his homelands, and his solastagic holding fast to those lands '...in the palm of his hand.'

text: Ki te kapu o taku ringa - In the palm of my hand This publication accompanies the launching of our exhibition 'Ki te Kapu o Taku Ringa - In the Palm of My Hand' at the Ōtorohanga Museum in June 2024.
The title is taken from a line of King Tāwhiao's song-poem 'Maioha mō Waikato'. Ejected from their homelands he and his Waikato brethren took refuge with their Maniapoto relations in Te Nehenehenui; parts of which are mentioned in the song-poem. Teh 'maioha' was composed expressing King Tāwhiao's love for his homelands, and his solastagic holding fast to those lands '...in the palm of his hand.'

year: 2024