研究目的
Investigating the use of glioblastoma tumor spheroids as three-dimensional deep computational reservoirs for optical neural networks to extract information about cancer morphodynamics and chemotherapy effects.
研究成果
The study demonstrates that a random and hybrid photonic/living system can serve as a novel artificial machine for computing and real-time investigation of tumour dynamics. The optical neural network based on disordered tumor spheroids is capable of detecting subcellular cancer morphodynamics and quantifying the effects of chemotherapy with higher sensitivity than conventional imaging methods.
研究不足
The study is limited by the complexity of controlling the internal weights of the living reservoir through external stimuli and the need for further validation of the network's sensitivity and specificity in detecting cancer morphodynamics and chemotherapy effects.
1:Experimental Design and Method Selection:
The study employs a hybrid bio/photonic scheme where tumor model cellular layers act as diffractive deep layers of an optical neural network. Structured light propagation in the disordered assembly is exploited to demonstrate the network's functionality.
2:Sample Selection and Data Sources:
Glioblastoma tumor spheroids are used as samples, serving as computational reservoirs with thousands of cells acting as wave-mixing nodes.
3:List of Experimental Equipment and Materials:
An SLM (Spatial Light Modulator) with feedback by the output layer tailors the input signal. An infrared pump laser is used for inducing hyperthermia.
4:Experimental Procedures and Operational Workflow:
The network's response to external stimuli (thermal or chemical) is monitored to control the internal weights of the living reservoir. The network's output is analyzed to detect subcellular cancer morphodynamics and the effects of chemotherapy.
5:Data Analysis Methods:
The response of the optical neural network is compared with conventional imaging methods to assess sensitivity and effectiveness.
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