Electron transfer and energy exchange between a covalent organic framework and CuFeS nanoparticles.

CuFeS is a prominent chalcogenide that possesses similar optical properties and a significantly lower cost, compared to gold. Additionally, covalent organic frameworks are a class of materials at the forefront of current research, mainly used as photoactive components and porous absorbers. Hence, in this work, hydrophilic CuFeS particles are coupled with multi-functional covalent organic frameworks through ionic bonding to produce a hybrid material with unique and optimized properties.

Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications.

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis.

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins.

Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy.

Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics.

Novel Meta-Learning Techniques for the Multiclass Image Classification Problem.

Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler problems, allowing the application of numerous well-established learning algorithms that may not apply directly to the original task.