Using Fourier transform IR spectroscopy to analyze biological materials

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Supplementing probiotics during intermittent fasting proves more effective in restoring ileum and colon tissues in aged rats

This study aimed to explore the impact of SCD Probiotics supplementation on biomolecule profiles and histopathology of ileum and colon tissues during a 30-day intermittent fasting (IF) program. Male Sprague-Dawley rats, aged 24 months, underwent 18-h daily fasting and received 3 mL (1 × 108 CFU) of SCD Probiotics. The differences in biomolecule profiles were determined using FTIR Spectroscopy and two machine learning techniques, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), which showed significant differences with high accuracy rates. Spectrochemical bands indicating alterations in lipid, protein and nucleic acid profiles in both tissues. The most notable changes were observed in the group subjected to both IF and SCD Probiotics, particularly in the colon. Both interventions, individually and in combination, decreased protein carbonylation levels. SCD Probiotics exerted a more substantial impact on membrane dynamics than IF alone. Additionally, both IF and SCD Probiotics were found to have protective effects on intestinal structure and stability by reducing mast cell density and levels of TNF-α and NF-κB expression in ileum and colon tissues, thus potentially mitigating age-related intestinal damage and inflammation. Furthermore, our results illustrated that while IF and SCD Probiotics individually instigate unique changes in ileum and colon tissues, their combined application yielded more substantial benefits. This study provides evidence for the synergistic potential of IF and SCD Probiotics in combating age-related intestinal alterations.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 μg/mL and >0.5 μg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of highresolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This approach enhances imaging speed without compromising image quality and ensures robust tissue segmentation. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology. It represents a significant leap forward from traditional histopathological methods, offering profound implications for cancer diagnostics and treatment decision-making.

Wide-field mid-infrared hyperspectral imaging beyond video rate

Mid-infrared hyperspectral imaging has become an indispensable tool to spatially resolve chemical information in a wide variety of samples. However, acquiring three-dimensional data cubes is typically time-consuming due to the limited speed of raster scanning or wavelength tuning, which impedes real-time visualization with high spatial definition across broad spectral bands. Here, we devise and implement a high-speed, wide-field mid-infrared hyperspectral imaging system relying on broadband parametric upconversion of high-brightness supercontinuum illumination at the Fourier plane. The upconverted replica is spectrally decomposed by a rapid acousto-optic tunable filter, which records high-definition monochromatic images at a frame rate of 10 kHz based on a megapixel silicon camera. Consequently, the hyperspectral imager allows us to acquire 100 spectral bands over 2600-4085 cm-1 in 10 ms, corresponding to a refreshing rate of 100 Hz. Moreover, the angular dependence of phase matching in the image upconversion is leveraged to realize snapshot operation with spatial multiplexing for multiple spectral channels, which may further boost the spectral imaging rate. The high acquisition rate, wide-field operation, and broadband spectral coverage could open new possibilities for high-throughput characterization of transient processes in material and life sciences.

Aberrant ATM signaling and homology-directed DNA repair as a vulnerability of p53-mutant GBM to AZD1390-mediated radiosensitization

ATM is a key mediator of radiation response, and pharmacological inhibition of ATM is a rational strategy to radiosensitize tumors. AZD1390 is a brain-penetrant ATM inhibitor and a potent radiosensitizer. This study evaluated the spectrum of radiosensitizing effects and the impact of TP53 mutation status in a panel of IDH1 wild-type (WT) glioblastoma (GBM) patient-derived xenografts (PDXs). AZD1390 suppressed radiation-induced ATM signaling, abrogated G0-G1 arrest, and promoted a proapoptotic response specifically in p53-mutant GBM in vitro. In a preclinical trial using 10 orthotopic GBM models, AZD1390/RT afforded benefit in a cohort of TP53-mutant tumors but not in TP53-WT PDXs. In mechanistic studies, increased endogenous DNA damage and constitutive ATM signaling were observed in TP53-mutant, but not in TP53-WT, PDXs. In plasmid-based reporter assays, GBM43 (TP53-mutant) showed elevated DNA repair capacity compared with that in GBM14 (p53-WT), whereas treatment with AZD1390 specifically suppressed homologous recombination (HR) efficiency, in part, by stalling RAD51 unloading. Furthermore, overexpression of a dominant-negative TP53 (p53DD) construct resulted in enhanced basal ATM signaling, HR activity, and AZD1390-mediated radiosensitization in GBM14. Analyzing RNA-seq data from TCGA showed up-regulation of HR pathway genes in TP53-mutant human GBM. Together, our results imply that increased basal ATM signaling and enhanced dependence on HR represent a unique susceptibility of TP53-mutant cells to ATM inhibitor-mediated radiosensitization.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA)

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

High-performance plasmonic mid-infrared bandpass filters by inverse design

Plasmonic spectral filters composed of periodic nanostructured metal films offer novel opportunities for the development of multispectral imaging technologies in the mid-infrared region. However, traditional plasmonic filters, which typically feature simplistic structures such as nanoholes or nanorings, are constrained by a narrow bandpass and significant crosstalk, leading to limited practical performance. Filters designed using inverse techniques allow a substantial degree of freedom in creating intricate structures that align with desired spectral characteristics, including a quasi-square spectral profile, high transmission, wide full width at half maximum, and reduced crosstalk. In this study, we have utilized an inverse design algorithm to engineer high-performance bandpass filters for the mid-infrared range, achieving an average transmittance exceeding 80% within the bandpass window and below 10% in the stop band, which is comparable to that of commercial multilayer Bragg filters. Nanofabrication processes were employed to transfer the designed pattern into the gold film on ZnS substrate that is transparent in the mid-infrared range. The resulting filters exhibit spectral performance analogous to that of the inversely designed models, making them suitable for direct integration with mid-infrared photodetector arrays in multispectral imaging systems.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

In Vitro Models for Investigating Intestinal Host-Pathogen Interactions

Infectious diseases are increasingly recognized as a major threat worldwide due to the rise of antimicrobial resistance and the emergence of novel pathogens. In vitro models that can adequately mimic in vivo gastrointestinal physiology are in high demand to elucidate mechanisms behind pathogen infectivity, and to aid the design of effective preventive and therapeutic interventions. There exists a trade-off between simple and high throughput models and those that are more complex and physiologically relevant. The complexity of the model used shall be guided by the biological question to be addressed. This review provides an overview of the structure and function of the intestine and the models that are developed to emulate this. Conventional models are discussed in addition to emerging models which employ engineering principles to equip them with necessary advanced monitoring capabilities for intestinal host-pathogen interrogation. Limitations of current models and future perspectives on the field are presented.

Staphylococcus aureus surface attachment selectively influences tolerance against charged antibiotics

The threat of infection during implant placement surgery remains a considerable burden for millions of patients worldwide. To combat this threat, clinicians employ a range of anti-infective strategies and practices. One of the most common interventions is the use of prophylactic antibiotic treatment during implant placement surgery. However, these practices can be detrimental by promoting the resilience of biofilm-forming bacteria and enabling them to persist throughout treatment and re-emerge later, causing a life-threatening infection. Thus, it is of the utmost importance to elucidate the events occurring during the initial stages of bacterial surface attachment and determine whether any biological processes may be targeted to improve surgical outcomes. Using gene expression analysis, we identified a cellular mechanism of S. aureus which modifies its cell surface charge following attachment to a medical grade titanium surface. We determined the upregulation of two systems involved in the d-alanylation of teichoic acids and the lysylation of phosphatidylglycerol. We supported these molecular findings by utilizing synchrotron-sourced attenuated total reflection Fourier-transform infrared microspectroscopy to analyze the biomolecular properties of the S. aureus cell surface following attachment. As a direct consequence, S. aureus quickly becomes substantially more tolerant to the positively charged vancomycin, but not the negatively charged cefazolin. The present study can assist clinicians in rationally selecting the most potent antibiotic in prophylaxis treatments. Furthermore, it highlights a cellular process that could potentially be targeted by novel technologies and strategies to improve the outcome of antibiotic prophylaxis during implant placement surgery. STATEMENT OF SIGNIFICANCE: The antibiotic tolerance of bacteria in biofilm is a well-established phenomenon. However, the physiological adaptations employed by Staphylococcus aureus to increase its antibiotic tolerance during the early stages of surface attachment are poorly understood. Using multiple techniques, including gene expression analysis and synchrotron-sourced Fourier-transform infrared microspectroscopy, we generated insights into the physiological response of S. aureus following attachment to a medical grade titanium surface. We showed that this phenotypic transition enables S. aureus to better tolerate the positively charged vancomycin, but not the negatively charged cefazolin. These findings shed light on the antibiotic tolerance mechanisms employed by S. aureus to survive prophylactically administered antibiotics and can help clinicians to protect patients from infections.

Protein Nanotubes as Advanced Material Platforms and Delivery Systems

Protein nanotubes (PNTs) as state-of-the-art nanocarriers are promising for various potential applications both in the food and pharmaceutical industries. Derived from edible starting sources like α-lactalbumin, lysozyme, and ovalbumin, PNTs bear properties of biocompatibility and biodegradability. Their large specific surface area and hydrophobic core facilitate chemical modification and loading of bioactive substances, respectively. Moreover, their enhanced permeability and penetration ability across biological barriers such as intestinal mucus, extracellular matrix, and thrombus clot, make it promising platforms for health-related applications. Most importantly, their simple preparation processes enable large-scale production, supporting applications in the biomedical and nanotechnological fields. Understanding the self-assembly principles is crucial for controlling their morphology, size, and shape, and thus provides the ground to a multitude of applications. Here, the current state-of-the-art of PNTs including their building materials, physicochemical properties, and self-assembly mechanisms are comprehensively reviewed. The advantages and limitations, as well as challenges and prospects for their successful applications in biomaterial and pharmaceutical sectors are then discussed and highlighted. Potential cytotoxicity of PNTs and the need of regulations as critical factors for enabling in vivo applications are also highlighted. In the end, a brief summary and future prospects for PNTs as advanced platforms and delivery systems are included.

Effects of Benzo (a) Pyrene and 2,2',4,4'-Tetrabromodiphenyl Ether Exposure on the Thyroid Gland in Rats by Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy

Benzo[a]pyrene (B[a]P) and 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) are widespread environmental pollutants and can destroy thyroid function. We assessed the biochemical changes in the thyroid tissue of rats exposed to B[a]P and BDE-47 using attenuated total reflection Fourier-transform infrared spectroscopy combined with support vector machine(SVM). After B[a]P and BDE-47 treatment in rats, the structure of thyroid follicles was destroyed and epithelial cells were necrotic, indicating that B[a]P and BDE-47 may lead to changes of the thyroid morphology of the rats. These damages are mainly related to C=O stretch vibrations of lipids (1743 cm-1), as well as the secondary structure of proteins [amide I (1645 cm-1) and amide II (1550 cm-1)], and carbohydrates [C-OH (1138 cm-1), C-O (1106 cm-1, 1049 cm-1, 991 cm-1), C-C (1106 cm-1) stretching] and collagen (phosphodiester stretching at 922 cm-1) vibration modes. When SVM was used for classification, there was a substantial separation between the control and the exposure groups (accuracy = 96%; sensitivity = 98%; specificity = 87%), and there was also a major separation between the exposed groups (accuracy = 93%; sensitivity = 94%; and specificity = 92%).

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Reactive oxygen species may be involved in the distinctive biological effects of different doses of 12C6+ ion beams on Arabidopsis

Introduction

Heavy ion beam is a novel approach for crop mutagenesis with the advantage of high energy transfer line density and low repair effect after injury, however, little investigation on the biological effect on plant was performed. 50 Gy irradiation significantly stimulated the growth of Arabidopsis seedlings, as indicated by an increase in root and biomass, while 200 Gy irradiation significantly inhibited the growth of seedlings, causing a visible decrease in plant growth.

Methods

The Arabidopsis seeds were irradiated by 12C6+. Monte Carlo simulations were used to calculate the damage to seeds and particle trajectories by ion implantation. The seed epidermis received SEM detection and changes in its organic composition were detected using FTIR. Evidence of ROS and antioxidant systems were analyzed. RNA-seq and qPCR were used to detect changes in seedling transcript levels.

Results and discussion

Monte Carlo simulations revealed that high-dose irradiation causes various damage. Evidence of ROS and antioxidant systems implies that the emergence of phenotypes in plant cells may be associated with oxidative stress. Transcriptomic analysis of the seedlings demonstrated that 170 DEGs were present in the 50 Gy and 200 Gy groups and GO enrichment indicated that they were mainly associated with stress resistance and cell wall homeostasis. Further GO enrichment of DEGs unique to 50 Gy and 200 Gy revealed 58 50Gy-exclusive DEGs were enriched in response to oxidative stress and jasmonic acid entries, while 435 200 Gy-exclusive DEGs were enriched in relation to oxidative stress, organic cyclic compounds, and salicylic acid. This investigation advances our insight into the biological effects of heavy ion irradiation and the underlying mechanisms.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Application of Fourier Transform Infrared Spectroscopy to Discriminate Two Closely Related Bacterial Species: Bacillus anthracis and Bacillus cereus Sensu Stricto

Fourier transform infrared spectroscopy (FTIRS) is a diagnostic technique historically used in the microbiological field for the characterization of bacterial strains in relation to the specific composition of their lipid, protein, and polysaccharide components. For each bacterial strain, it is possible to obtain a unique absorption spectrum that represents the fingerprint obtained based on the components of the outer cell membrane. In this study, FTIRS was applied for the first time as an experimental diagnostic tool for the discrimination of two pathogenic species belonging to the Bacillus cereus group, Bacillus anthracis and Bacillus cereus sensu stricto; these are two closely related species that are not so easy to differentiate using classical microbiological methods, representing an innovative technology in the field of animal health.

Antimicrobial susceptibility testing using infrared attenuated total reflection (IR-ATR) spectroscopy to monitor metabolic activity

Fast and accurate detection of antimicrobial resistance in pathogens remains a challenge, and with the increase in antimicrobial resistance due to mis- and overuse of antibiotics, it has become an urgent public health problem. We demonstrate how infrared attenuated total reflection (IR-ATR) can be used as a simple method for assessment of bacterial susceptibility to antibiotics. This is achieved by monitoring the metabolic activities of bacterial cells via nutrient consumption and using this as an indicator of bacterial viability. Principal component analysis of the obtained spectra provides a tool for fast and simple discrimination of antimicrobial resistance in the acquired data. We demonstrate this concept using four bacterial strains and four different antibiotics, showing that the change in glucose concentration in the growth medium after 2 h, as monitored by IR-ATR, can be used as a spectroscopic diagnostic technique, to reduce detection time and to improve quality in the assessment of antimicrobial resistance in pathogens.

Estimation of time since deposition of semen stain on different fabric types using ATR-FTIR spectroscopy and chemometrics

Various body fluids such as blood, semen, vaginal secretions, and saliva are frequently encountered at crime scene. In cases of sexual assault, semen stains are one of the most reliable evidence of biological origin. In this study, our objective was to develop a method for estimating the time since deposition of semen stains on five different fabric types using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy, with a focus on a time frame of up to 8 weeks. Semen samples from six different volunteers were dripped onto five distinct fabric materials, and ATR-FTIR measurements were obtained at 17 different time points. Principal component analysis (PCA) and partial least squares (PLS) methods were employed to differentiate semen stains on various fabric samples and estimate the age of semen stains. Models constructed using PCA and PLSR achieved high R2 values and low root-mean-square error (RMSE). While the performance varies depending on fabric types, it was observed that age estimation of semen stains can be made within following intervals: 0.39-0.76 days for 0-7 day range, 2.59-3.38 days for the 1-8 week range, and 3.98-8.1 days for the 0-56 day range. This study demonstrates the effectiveness of using ATR-FTIR spectroscopy in combination with chemometrics to estimate the age of human semen stains on various fabric types based on time-dependent spectral changes.

Vibrational Spectra of Highly Anharmonic Water Clusters: Molecular Dynamics and Harmonic Analysis Revisited with Constrained Nuclear-Electronic Orbital Methods

Vibrational spectroscopy is widely used to gain insights into structural and dynamic properties of chemical, biological, and materials systems. Thus, an efficient and accurate method to simulate vibrational spectra is desired. In this paper, we justify and employ a microcanonical molecular simulation scheme to calculate the vibrational spectra of three challenging water clusters: the neutral water dimer (H4O2), the protonated water trimer (H7O3+), and the protonated water tetramer (H9O4+). We find that with the accurate description of quantum nuclear delocalization effects through the constrained nuclear-electronic orbital framework, including vibrational mode coupling effects through molecular dynamics simulations can additionally improve the vibrational spectrum calculations. In contrast, without the quantum nuclear delocalization picture, conventional ab initio molecular dynamics may even lead to less accurate results than harmonic analysis.

Synergistic/antagonistic toxicity characterization and source-apportionment of heavy metals and organophosphorus pesticides by the biospectroscopy-bioreporter-coupling approach

Many anthropogenic chemicals are manufactured and eventually enter the surrounding environment, threatening food security and human health. Considering the additive or synergistic effects of pollutant mixtures, there is an expanding need for rapid, cost-effective and field-portable screening methods in environmental monitoring. This study used a recently developed biospectroscopy-bioreporter-coupling (BBC) approach to investigate the binary toxicity of Ag(I), Cr(VI) and four organophosphorus pesticides (dichlorvos, parathion, omethoate and monocrotophos). Ag(I) and Cr(VI) altered the toxicity mechanisms of pesticides, explained by the synergistic or antagonistic effect of Ag/Cr-induced cytotoxicity and pesticide-induced genotoxicity. The discriminating Raman spectral peaks associated with organophosphorus pesticides were 1585 and 1682 cm-1, but 750, 1004, 1306 and 1131 cm-1 were found in heavy metal and pesticide mixtures. More spectral alterations were related to pesticides rather than Ag(I) or Cr(VI), hinting at the dominant toxicity mechanisms of pesticides in mixtures. Ag(I) supplement significantly increased the levels of reactive oxygen species induced by organophosphorus pesticides, attributing to the increased permeability of cell membrane and entrance of toxic substances into the cells by the oligodynamic actions. This study lends deeper insights into the interactions between microbes and pollutant mixtures, offering clues to assess the cocktail effects of multiple pollutants comprehensively.

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms

We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fourier transform-infrared (FT-IR) spectroscopy coupled with machine learning (ML) algorithms. This prospective, observational, multicenter study enrolled 62 UC patients and 30 healthy controls. The patients were treated with adalimumab for 56 weeks, and clinical remission was evaluated using the Mayo score. Baseline fecal samples were collected and analyzed using FT-IR spectroscopy. Various data preprocessing methods were applied, and prediction models were established by 10-fold cross-validation using various ML methods. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation of healthy controls and UC patients, applying area normalization and Pareto scaling. OPLS-DA models predicting short- and long-term remission (8 and 56 weeks) yielded area-under-the-curve values of 0.76 and 0.75, respectively. Logistic regression and a nonlinear support vector machine were selected as the best prediction models for short- and long-term remission, respectively (accuracy of 0.99). In external validation, prediction models for short-term (logistic regression) and long-term (decision tree) remission performed well, with accuracy values of 0.73 and 0.82, respectively. This was the first study to develop prediction models for clinical remission associated with adalimumab treatment in UC patients by fecal analysis using FT-IR spectroscopy coupled with ML algorithms. Logistic regression, nonlinear support vector machines, and decision tree were suggested as the optimal prediction models for remission, and these were noninvasive, simple, inexpensive, and fast analyses that could be applied to personalized treatments.

Vibrational spectroscopy methods for investigation of the animal models of glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and devastating primary brain tumor among adults. It is highly lethal disease, as only 25% of patients survive longer than 1 year and only 5% more than 5 years from the diagnosis. To search for the new, more effective methods of treatment, the understanding of mechanisms underlying the process of tumorigenesis is needed. The new light on this problem may be shed by the analysis of biochemical anomalies of tissues affected by tumor growth. Therefore, in the present work, we applied the Fourier transform infrared (FTIR) and Raman microspectroscopy to evaluate changes in the distribution and structure of biomolecules appearing in the rat brain as a result of glioblastoma development. In turn, synchrotron X-ray fluorescence microscopy was utilized to determine the elemental anomalies appearing in the nervous tissue. To achieve the assumed goals of the study animal models of GBM were used. The rats were subjected to the intracranial implantation of glioma cells with different degree of invasiveness. For spectroscopic investigation brain slices taken from the area of cancer cells administration were used. The obtained results revealed, among others, the decrease content of lipids and compounds containing carbonyl groups, compositional and structural changes of proteins as well as abnormalities in the distribution of low atomic number elements within the region of tumor.

Bond-selective full-field optical coherence tomography

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Conventional OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bond-selective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 µm PMMA beads embedded in agarose gel. Next, we show 3D hyperspectral imaging of up to 75 µm of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FF-OCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on imaging a highly scattering myelinated axons region in a mouse brain tissue slice.

Biomolecular composition of porcine ovarian follicles following in vitro treatment of vitamin D3 and insulin alone or in combination

The study aimed to analyze changes in biomolecular composition of granulosa and theca interna cells of porcine ovarian follicles following in vitro treatment of vitamin D3 and insulin alone or in combination. Medium antral follicles (n = 4/each group) were cultured alone (C; control) or in the presence of 1α,25(OH)2D3 (VD; 100 ng/mL) and insulin (I; 10 ng/mL) separately or in combination, VD and I (VD+I). Then paraplast-embedded ovarian follicles were used for Fourier Transform Infrared (FTIR) spectroscopy and respective histological stainings. FTIR analysis revealed changes in the content of fibrous proteins (mainly collagens) within theca interna following vitamin D3 and insulin co-administration that was verified by Masson's trichrome staining. Treatment-dependent differences were also observed in the secondary structure of proteins, indicating enhanced conversion to α-helices in response to vitamin D3 and insulin action/interaction in both follicular compartments. In the granulosa and theca interna layers, tendency to lower DNA content in the VD+I group was noted and confirmed by Fulgen's staining. Finally, altered monosaccharides production in both follicular layers was found. Based on FTIR results, it is possible to attribute the observed alterations to biological processes that could be regulated by vitamin D3 and insulin in the porcine ovarian follicles.

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue

To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a "multimodal spectroscopic scan" (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49-52) for IHD, 47 (IQR 45-50) for DCM and 36 (IQR 33-52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm-1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts. Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined "stack" (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model  Logistic regression 0.980 0.944 0.933 0.967  SGD 0.550 0.281 0.400 0.700  SVM 0.840 0.806 0.800 0.900  Stack 0.933 0.794 0.800 0.900 (b) Raman model  Logistic regression 0.985 0.940 0.929 0.960  SGD 0.892 0.869 0.857 0.932  SVM 0.992 0.940 0.929 0.960  Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients  Logistic regression 0.975 0.841 0.828 0.917  SGD 0.847 0.803 0.793 0.899  SVM 0.971 0.853 0.828 0.917  Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients  Logistic regression 0.961 0.969 0.966 0.984  SGD 0.944 0.967 0.966 0.923  SVM 1.000 1.000 1.000 1.000  Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses.

Fully Integrated Ultrathin Solid Immersion Grating Microspectrometer for Handheld Visible and Near-Infrared Spectroscopic Applications

Despite advances in microfabrication, compact spectrometers still face challenges in shrinking their size without sacrificing optical performance. Here,  the solid immersion grating microspectrometer (SIG-µSPEC) for high spectral resolution in a broad operational wavelength range is reported. The spectroscopic module incorporates a silicon microslit, index-matched lens, plane mirrors, solid immersion grating (SIG), and a CMOS line sensor within a small form factor. The SIG facilitates high angular dispersion of light on a planar focal plane, resulting in an average spectral resolution of 5.8 nm, with over 76% maximum sensitivity from 400 to 800 nm. SIG-µSPEC measures the spectral reflectance of fruits at different ripening stages, clearly revealing changes in the chlorophyll absorption band. The measured spectrum is further utilized for the precise prediction of the soluble solid content (SSC) levels, achieving a high correlation (R2 = 0.91) and a ratio of prediction-to-deviation of 2.36. This compact microspectrometer holds the potential for precise and non-invasive spectral analysis across point-of-care fields.

Cryopreservation affects platelet macromolecular composition over time after thawing and differently impacts on cancer cells behavior in vitro

Cryopreservation affects platelets' function, questioning their use for cancer patients. We aimed to investigate the biochemical events that occur over time after thawing to optimize transfusion timing and evaluate the effect of platelet supernatants on tumor cell behavior in vitro. We compared fresh (Fresh-PLT) with Cryopreserved platelets (Cryo-PLT) at 1 h, 3 h and 6 h after thawing. MCF-7 and HL-60 cells were cultured with Fresh- or 1 h Cryo-PLT supernatants to investigate cell proliferation, migration, and PLT-cell adhesion. We noticed a significant impairment of hemostatic activity accompanied by a post-thaw decrease of CD42b+ , which identifies the CD62P--population. FTIR spectroscopy revealed a decrease in the total protein content together with changes in their conformational structure, which identified two sub-groups: 1) Fresh and 1 h Cryo-PLT; 2) 3 h and 6 h cryo-PLT. Extracellular vesicle shedding and phosphatidylserine externalization (PS) increased after thawing. Cryo-PLT supernatants inhibited cell proliferation, impaired MCF-7 cell migration, and reduced ability to adhere to tumor cells. Within the first 3 hours after thawing, irreversible alterations of biomolecular structure occur in Cryo-PLT. Nevertheless, Cryo-PLT should be considered safe for the transfusion of cancer patients because of their insufficient capability to promote cancer cell proliferation, adhesion, or migration.

Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells

The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC.

SCD Probiotics mitigate cafeteria diet-induced liver damage in Wistar rats during development

BACKGROUND AND AIM

The liver plays a critical role in metabolic homeostasis, and its health is often compromised by poor dietary habits. This study aimed to investigate the therapeutic potential of SCD Probiotics in mitigating adverse liver effects induced by a cafeteria diet in male Wistar rats during their developmental period.

METHODS

Four groups of seven male Wistar rats each were subjected to different dietary regimens from day 21 (weaning) to day 56. The groups were as follows: a control group on normal feed; a probiotic-supplemented group on normal feed; a group on a cafeteria diet mixed with normal feed; and a group on a cafeteria diet mixed with normal feed, supplemented with SCD Probiotics. Liver health was assessed using Fourier transform infrared spectroscopy and histopathological evaluations.

RESULTS

Rats on the cafeteria diet exhibited significant disruptions in lipid, protein, cholesterol, triglyceride levels, and glycogen/phosphate content. Histopathological abnormalities such as lymphocytic infiltration, steatosis, and necrosis were also observed. However, SCD Probiotics supplementation led to notable improvements in the liver's biomolecular composition and mitigated histopathological abnormalities. Serum liver enzyme levels (AST, ALT, ALP, and LDH) also showed beneficial effects, while serum albumin levels remained stable.

CONCLUSIONS

SCD Probiotics demonstrated a promising potential to counteract the adverse liver effects induced by a cafeteria diet in male Wistar rats. The study revealed significant improvements in biomolecular composition, histopathology, and serum enzyme levels. However, these findings are preliminary and necessitate further in vivo studies and clinical trials for validation.

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Applications of Fourier Transform-Infrared spectroscopy in microbial cell biology and environmental microbiology: advances, challenges, and future perspectives

Microorganisms play pivotal roles in shaping ecosystems and biogeochemical cycles. Their intricate interactions involve complex biochemical processes. Fourier Transform-Infrared (FT-IR) spectroscopy is a powerful tool for monitoring these interactions, revealing microorganism composition and responses to the environment. This review explores the diversity of applications of FT-IR spectroscopy within the field of microbiology, highlighting its specific utility in microbial cell biology and environmental microbiology. It emphasizes key applications such as microbial identification, process monitoring, cell wall analysis, biofilm examination, stress response assessment, and environmental interaction investigation, showcasing the crucial role of FT-IR in advancing our understanding of microbial systems. Furthermore, we address challenges including sample complexity, data interpretation nuances, and the need for integration with complementary techniques. Future prospects for FT-IR in environmental microbiology include a wide range of transformative applications and advancements. These include the development of comprehensive and standardized FT-IR libraries for precise microbial identification, the integration of advanced analytical techniques, the adoption of high-throughput and single-cell analysis, real-time environmental monitoring using portable FT-IR systems and the incorporation of FT-IR data into ecological modeling for predictive insights into microbial responses to environmental changes. These innovative avenues promise to significantly advance our understanding of microorganisms and their complex interactions within various ecosystems.

Probing Aberrantly Glycosylated Mucin 1 in Breast Cancer Extracellular Vesicles

Aberrantly glycosylated mucin 1 is a critical prognostic biomarker in breast epithelial cancers. Hypoglycosylated mucin 1 coats the surface of the cancer cells, where O-glycans are predominantly linked via an N-acetylgalactosamine moiety (GalNAc). Cancer cell-derived extracellular vesicles (EVs) carry biomarkers from parent cancer cells to the recipient cells and, therefore, could potentially be leveraged for diagnostics and noninvasive disease monitoring. We devised a label-free approach for identifying glycoprotein mucin 1 overexpression on breast cancer EVs. While exploring a plethora of biochemical (enzyme-linked immunosorbent assay, flow cytometry, and SDS-PAGE) and label-free biophysical techniques (circular dichroism and infrared spectroscopy (IR)) along with multivariate analysis, we discovered that mucin 1 is significantly overexpressed in breast cancer EVs and aberrant glycosylation in mucin 1 could be critically addressed using IR and multivariate analysis targeting the GalNAc sugar. This approach emerges as a convenient and comprehensive method of distinguishing cancer EVs from normal samples and holds potential for nonintrusive breast cancer liquid biopsy screening.

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.

Optical photothermal infrared spectroscopy and discrete wavenumber imaging for high content screening of single cells

Major drawbacks of direct mid-infrared spectroscopic imaging of single cells in an aqueous buffer are strong water absorption, low resolution typically above 10 μm, and Mie scattering effects. This study demonstrates how an indirect detection principle can overcome these drawbacks using the optical photothermal infrared (O-PTIR) technique for high-resolution discrete wavenumber imaging and fingerprint spectroscopy of cultivated cells as a model system in a simple liquid sample chamber. The O-PTIR spectra of six leukemia- and cancer-derived cell lines showed main IR bands near 1648, 1547, 1447, 1400, 1220, and 1088 cm-1. Five spectra of approximately 260 single cells per cell type were averaged, the O-PTIR data set was divided into leukemia-derived cells (THP-1, HL 60, Jurkat, and Raji) and cancer cells (HeLa and HepaRG), and partial least squares linear discriminant analysis (PLS-LDA) was applied in the spectral range 800-1800 cm-1 to train three classification models. A leukemia versus cancer cell model showed an accuracy of 90.0%, the HeLa versus HepaRG cell model had an accuracy of 95.4%, and the model for the distinction of leukemia cells had an accuracy of 75.4%. IR bands in linear discriminants (LDs) of the models were correlated with second derivative spectra that resolved more than 25 subbands. The IR and second derivative spectra of proteins, DNA, RNA and lipids were collected as references to confirm band assignments. O-PTIR images of single cells at a 200 nm step size were acquired at 1086, 1548, and 1746 cm-1 to visualize the nucleic acid, protein, and lipid distribution, respectively. Variations in subcellular features and in the lipid-to-protein and nucleic acid-to-protein ratios were identified that were consistent with biomolecular information in LDs. In conclusion, O-PTIR can provide high-quality spectra and images with submicron resolution of single cells in aqueous buffers that offer prospects in high-content screening applications.

Label-Free High-Resolution Photothermal Optical Infrared Spectroscopy for Spatiotemporal Chemical Analysis in Fresh, Hydrated Living Tissues and Embryos

Label-free chemical imaging of living and functioning systems is the holy grail of biochemical research. However, existing techniques often require extensive sample preparation to remove interfering molecules such as water, rendering many molecular imaging techniques unsuitable for in situ structural studies. Here, we examined freshly extracted tissue biopsies and living small vertebrates at submicrometer resolution using optical photothermal infrared (O-PTIR) microspectroscopy and demonstrated the following major advances: (1) O-PTIR can be used for submicrometer structural analysis of unprocessed, fully hydrated tissue biopsies extracted from diverse organs, including living brain and lung tissues. (2) O-PTIR imaging can be performed on living organisms, such as salamander embryos, without compromising their further development. (3) Using O-PTIR, we tracked the structural changes of amyloids in functioning brain tissues over time, observing the appearance of newly formed amyloids for the first time. (4) Amyloid structures appeared altered following standard fixation and dehydration procedures. Thus, we demonstrate that O-PTIR enables time-resolved submicrometer in situ investigation of chemical and structural changes in diverse biomolecules in their native conditions, representing a technological breakthrough for in situ molecular imaging of biological samples.

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.

Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning

Introduction

Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples.

Methods

We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.

Results

Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature (R 2 = 0.842) and mature (R 2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively).

Conclusion

This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

A spectroscopic liquid biopsy for the earlier detection of multiple cancer types

BACKGROUND

A rapid, low-cost blood test that can be applied to reliably detect multiple different cancer types would be transformational.

METHODS

In this large-scale discovery study (n = 2092 patients) we applied the Dxcover® Cancer Liquid Biopsy to examine eight different cancers. The test uses Fourier transform infrared (FTIR) spectroscopy and machine-learning algorithms to detect cancer.

RESULTS

Area under the receiver operating characteristic curve (ROC) values were calculated for eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We assessed the test performance when all eight cancer types were pooled to classify 'any cancer' against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of Stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%).

CONCLUSIONS

This spectroscopic blood test can effectively detect early-stage disease and can be fine-tuned to maximise either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. This low-cost strategy could facilitate the requisite earlier diagnosis, when cancer treatment can be more effective, or less toxic.

STATEMENT OF TRANSLATIONAL RELEVANCE

The earlier diagnosis of cancer is of paramount importance to improve patient survival. Current liquid biopsies are mainly focused on single tumour-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed enough genetic material. This pan-omic liquid biopsy analyses the full complement of tumour and immune-derived markers present within blood derivatives and could facilitate the earlier detection of multiple cancer types. There is a low barrier to integrating this blood test into existing diagnostic pathways since the technology is rapid, simple to use, only minute sample volumes are required, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy described in this study has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

Single-detector double-beam modulation for high-sensitivity infrared spectroscopy

Balanced detection based on double beams is widely used to reduce common-mode noises, such as laser intensity fluctuation and irregular wavelength scanning, in absorption spectroscopy. However, employing an additional detector can increase the total system noise due to added non-negligible thermal noise of the detector, particularly with mid-infrared (IR) detectors. Herein, we demonstrate a new optical method based on double-beam modulation (DBM) that uses a single-element detector but keeps the advantage of double-beam balanced detection. The sample and reference path beams were modulated out-of-phase with each other at a high frequency, and their average and difference signals were measured by two lock-in amplifiers and converted into absorbance. DBM was coupled with our previously reported solvent absorption compensation (SAC) method to eliminate the IR absorption contribution of water in aqueous solutions. The DBM-SAC method enabled us to acquire IR absorption spectra of bovine serum albumin solutions down to 0.02 mg/mL. We investigated the noise characteristics of DBM measurements when the wavelength was either fixed or scanned. The results demonstrate that DBM can lower the limit of detection by ten times compared to the non-modulation method.

Cladophora glomerata extracts produced by Ultrasound-Assisted Extraction support early growth and development of lupin (Lupinus angustifolius L.)

The effect of the extract obtained by Ultrasound-Assisted Extraction (UAE) from green macroalga Cladophora glomerata on the germination and early growth of three narrow-leaved lupin varieties (cv. Homer, Jowisz, and Tytan) was examined. The seeds of these varieties came from five growing seasons (2015-2019) and this was their successive propagation stage. In total, 45 groups were tested. Narrow-leaved lupin like other legumes have a beneficial effect on the physical properties and fertility of the soil. Its high nutritive value makes it suitable for the production of valuable fodder. The algal extract, which was screened for the content of active compounds responsible for their biostimulant effect was applied in two concentrations: 10 and 20%. The germination percentage, root, hypocotyl, epicotyl length and chlorophyll content in cotyledons were evaluated at the end of the experiment. The 20% extract stimulated the growth of seedlings of all lupin cultivars better than the 10% application. The Jowisz variety deserves special attention, as it has the longest root system of seedlings.

AFM-IR for Nanoscale Chemical Characterization in Life Sciences: Recent Developments and Future Directions

Despite the ubiquitous absorption of mid-infrared (IR) radiation by virtually all molecules that belong to the major biomolecules groups (proteins, lipids, carbohydrates, nucleic acids), the application of conventional IR microscopy to the life sciences remained somewhat limited, due to the restrictions on spatial resolution imposed by the diffraction limit (in the order of several micrometers). This issue is addressed by AFM-IR, a scanning probe-based technique that allows for chemical analysis at the nanoscale with resolutions down to 10 nm and thus has the potential to contribute to the investigation of nano and microscale biological processes. In this perspective, in addition to a concise description of the working principles and operating modes of AFM-IR, we present and evaluate the latest key applications of AFM-IR to the life sciences, summarizing what the technique has to offer to this field. Furthermore, we discuss the most relevant current limitations and point out potential future developments and areas for further application for fruitful interdisciplinary collaboration.

Intermediate Antiparallel β Structure in Amyloid β Plaques Revealed by Infrared Spectroscopic Imaging

Aggregation of amyloid β (Aβ) peptides into extracellular plaques is a hallmark of the molecular pathology of Alzheimer's disease (AD). Amyloid aggregates have been extensively studied in vitro, and it is well-known that mature amyloid fibrils contain an ordered parallel β structure. The structural evolution from unaggregated peptide to fibrils can be mediated through intermediate structures that deviate significantly from mature fibrils, such as antiparallel β-sheets. However, it is currently unknown if these intermediate structures exist in plaques, which limits the translation of findings from in vitro structural characterizations of amyloid aggregates to AD. This arises from the inability to extend common structural biology techniques to ex vivo tissue measurements. Here we report the use of infrared (IR) imaging, wherein we can spatially localize plaques and probe their protein structural distributions with the molecular sensitivity of IR spectroscopy. Analyzing individual plaques in AD tissues, we demonstrate that fibrillar amyloid plaques exhibit antiparallel β-sheet signatures, thus providing a direct connection between in vitro structures and amyloid aggregates in the AD brain. We further validate results with IR imaging of in vitro aggregates and show that the antiparallel β-sheet structure is a distinct structural facet of amyloid fibrils.

Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review

The incidence of thyroid nodules (TNs) is estimated at 36.5% and 23% in females and males, respectively. A single thyroid nodule is usually detected during ultrasound assessment in patients with symptoms of thyroid dysfunction or neck mass. TNs are classified as benign tumours (non-malignant hyperplasia), benign neoplasms (e.g., adenoma, a non-invasive follicular tumour with papillary nuclear features) or malignant carcinomas (follicular cell-derived or C-cell derived). The differential diagnosis is based on fine-needle aspiration biopsies and cytological assessment (which is burdened with the bias of subjectivity). Raman spectroscopy (RS) is a laser-based, semiquantitative technique which shows for oscillations of many chemical groups in one label-free measurement. RS, through the assessment of chemical content, gives insight into tissue state which, in turn, allows for the differentiation of disease on the basis of spectral characteristics. The purpose of this study was to report if RS could be useful in the differential diagnosis of TN. The Web of Science, PubMed, and Scopus were searched from the beginning of the databases up to the end of June 2023. Two investigators independently screened key data using the terms "Raman spectroscopy" and "thyroid". From the 4046 records found initially, we identified 19 studies addressing the differential diagnosis of TNs applying the RS technique. The lasers used included 532, 633, 785, 830, and 1064 nm lines. The thyroid RS investigations were performed at the cellular and/or tissue level, as well as in serum samples. The accuracy of papillary thyroid carcinoma detection is approx. 90%. Furthermore, medullary, and follicular thyroid carcinoma can be detected with up to 100% accuracy. These results might be biased with low numbers of cases in some research and overfitting of models as well as the reference method. The main biochemical changes one can observe in malignancies are as follows: increase of protein, amino acids (like phenylalanine, tyrosine, and tryptophan), and nucleic acid content in comparison with non-malignant TNs. Herein, we present a review of the literature on the application of RS in the differential diagnosis of TNs. This technique seems to have powerful application potential in thyroid tumour diagnosis.

Current Advances in Nanotheranostics for Molecular Imaging and Therapy of Cardiovascular Disorders

Cardiovascular diseases (CVDs) refer to a collection of conditions characterized by abnormalities in the cardiovascular system. They are a global problem and one of the leading causes of mortality and disability. Nanotheranostics implies to the combination of diagnostic and therapeutic capabilities inside a single nanoscale platform that has allowed for significant advancement in cardiovascular diagnosis and therapy. These advancements are being developed to improve imaging capabilities, introduce personalized therapies, and boost cardiovascular disease patient treatment outcomes. Significant progress has been achieved in the integration of imaging and therapeutic capabilities within nanocarriers. In the case of cardiovascular disease, nanoparticles provide targeted delivery of therapeutics, genetic material, photothermal, and imaging agents. Directing and monitoring the movement of these therapeutic nanoparticles may be done with pinpoint accuracy by using imaging modalities such as cardiovascular magnetic resonance (CMR), computed tomography (CT), positron emission tomography (PET), photoacoustic/ultrasound, and fluorescence imaging. Recently, there has been an increasing demand of noninvasive for multimodal nanotheranostic platforms. In these platforms, various imaging technologies such as optical and magnetic resonance are integrated into a single nanoparticle. This platform helps in acquiring more accurate descriptions of cardiovascular diseases and provides clues for accurate diagnosis. Advances in surface functionalization methods have strengthened the potential application of nanotheranostics in cardiovascular diagnosis and therapy. In this Review, we have covered the potential impact of nanomedicine on CVDs. Additionally, we have discussed the recently developed various nanoparticles for CVDs imaging. Moreover, advancements in the CMR, CT, PET, ultrasound, and photoacoustic imaging for the CVDs have been discussed. We have limited our discussion to nanomaterials based clinical trials for CVDs and their patents.