Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

GLASSR-Net: Glass Substrate Spectral Restoration Neural Network for Fourier Transform Infrared Microspectroscopy in the Fingerprint Region

Fourier transform infrared (FTIR) microspectroscopy has emerged as a pivotal pathological tool, offering informative spectral biomarkers for numerous diseases. However, the dependency on specialized infrared (IR) substrates limits effective and widespread clinical translation. IR transparent bases like calcium/barium fluoride (CaF2/BaF2) are costly and fragile, while IR reflective bases cannot be used for microscopic screening due to their opacity to visible light. In comparison, 1 mm thick pathological glass substrates are cost-effective, reliable, and widely utilized in clinical pathology. Therefore, establishing a methodology for collecting high-quality FTIR spectra on glass substrates is highly desired and beneficial. Here, we develop a glass substrate spectral restoration neural network (GLASSR-Net) to restore the fingerprint absorbance spectra from glass-based spectra spanning the wavenumbers from 1800 to 1000 cm-1. The model is trained and validated by acquiring input glass-based spectra and ground truth spectra, respectively, through FTIR raster scanning on contiguous tissue sections of papillary thyroid carcinoma (PTC) mounted on glass and CaF2 substrates. The GLASSR-Net successfully restores the sample absorbance and accurately reconstructs the biochemical distribution in both the spatial and spectral domains. Furthermore, the biochemical signatures of PTC are effectively extracted and analyzed from the restored spectra with traditional spectral histology, indicating a decrease in amide I/II absorption and an accumulation of lipids and nucleic acids in cancerous regions. The proposed GLASSR-Net presents a novel framework for data collection, spectral restoration, and integration of traditional methodology in glass-based IR microspectroscopy, which facilitates the incorporation of FTIR microspectroscopy into clinical histological scenarios.

Green plastics: Direct production from grocery wastes to bioplastics and structural characterization by using synchrotron FTIR

Lignocellulosic bioplastics were produced using four different green wastes: hemp, parsley stem, pineapple leaves and walnut shell. Two different solutions were used to dissolve the green wastes: trifluoroacetic acid (TFA) and pure water. The changes in their natural structures and the solvent effect during the regeneration in biofilm formation were investigated by using Synchrotron FTIR Microspectroscopy (SR-µFTIR). The presence of cellulose, hemicellulose and lignin components in the water-based biofilms was confirmed. After dissolving in TFA, the spectra demonstrated some additional bands especially in the hemicellulose region. This is due to the hydrolysis of ester bonds and conversion to carboxylic acids. Principal component analysis showed grouping due to different solvents and polymer addition. Hemp-PVA (Polyvinyl Alcohol) composite biofilms were obtained by adding polyvinyl alcohol to the hemp solution to give extra strength to the hemp biofilms. It has been shown that water-based hemp-PVA biofilms do not cause any significant spectral changes, comparing with pure hemp and PVA spectra. However, after dissolving in TFA, unlike water-based biofilms, it appears that TFA molecules are retained by PVA through hydrogen bonds of TFA's carboxylic acid and hydroxyl groups and distinct spectral regions belong to TFA bands are clearly identified.

Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy

Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm-1 (low wavenumber range, LWR) and 2700-3700 cm-1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.

Vibrational microspectroscopy as a tool to unveil new chemotherapeutic strategies against osteosarcoma

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd2SpmCl4) and spermidine (Pd3Spd2Cl6) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested.

Optical Sensors Technology and Applications

Optical methods are non-invasive tools, and their use in various fields, including sensing applications, is continuously increasing, which is thanks to the continuous development of innovative low-cost sources and detectors [...].

Monitoring Neurochemistry in Traumatic Brain Injury Patients Using Microdialysis Integrated with Biosensors: A Review

In a traumatically injured brain, the cerebral microdialysis technique allows continuous sampling of fluid from the brain’s extracellular space. The retrieved brain fluid contains useful metabolites that indicate the brain’s energy state. Assessment of these metabolites along with other parameters, such as intracranial pressure, brain tissue oxygenation, and cerebral perfusion pressure, may help inform clinical decision making, guide medical treatments, and aid in the prognostication of patient outcomes. Currently, brain metabolites are assayed on bedside analysers and results can only be achieved hourly. This is a major drawback because critical information within each hour is lost. To address this, recent advances have focussed on developing biosensing techniques for integration with microdialysis to achieve continuous online monitoring. In this review, we discuss progress in this field, focusing on various types of sensing devices and their ability to quantify specific cerebral metabolites at clinically relevant concentrations. Important points that require further investigation are highlighted, and comments on future perspectives are provided.