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Molecules (Basel, Switzerland) Jul 2023Omega-3 fatty acids v(ω-3 FAs) such as EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) and omega-6 fatty acids (ω-6 FAs) such as linoleic acid and... (Review)
Review
Omega-3 fatty acids v(ω-3 FAs) such as EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) and omega-6 fatty acids (ω-6 FAs) such as linoleic acid and arachidonic acid are important fatty acids responsible for positive effects on human health. The main sources of ω-3 FAs and ω-6 FAs are marine-based products, especially fish oils. Some food, supplements, and pharmaceutical products would include fish oils as a source of ω-3 FAs and ω-6 FAs; therefore, the quality assurance of these products is highly required. Some analytical methods mainly based on spectroscopic and chromatographic techniques have been reported. Molecular spectroscopy such as Infrared and Raman parallel to chemometrics has been successfully applied for quantitative analysis of individual and total ω-3 FAs and ω-6 FAs. This spectroscopic technique is typically applied as the alternative method to official methods applying chromatographic methods. Due to the capability to provide the separation of ω-3 FAs and ω-6 FAs from other components in the products, gas and liquid chromatography along with sophisticated detectors such as mass spectrometers are ideal analytical methods offering sensitive and specific results that are suitable for routine quality control.
Topics: Humans; Fatty Acids; Fatty Acids, Omega-3; Fish Oils; Eicosapentaenoic Acid; Docosahexaenoic Acids; Dietary Supplements; Spectrum Analysis; Linoleic Acid
PubMed: 37513396
DOI: 10.3390/molecules28145524 -
Poultry Science Aug 2023This study determined the effect of water bath cooking (70°C and 90°C for 40 min) and the extreme heat treatment by an autoclave (121°C for 40 min) on the quality of...
This study determined the effect of water bath cooking (70°C and 90°C for 40 min) and the extreme heat treatment by an autoclave (121°C for 40 min) on the quality of breast meat of a fast-growing chicken, commercial broiler (CB), and slow-growing chickens, Korat chicken (KC), and Thai native chicken (NC) (Leung Hang Khao), by vibrational spectroscopic techniques, including synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy and Fourier transform Raman (FT-Raman) spectroscopy. Taste-enhancing compounds, including inosine-5'-monophosphate (IMP) and guanosine-5'-monophosphate (GMP), were better retained in cooked KC and NC meats than in cooked CB meat (P < 0.05). The high heat treatment at 121°C depleted the amount of insoluble collagen in all breeds (P < 0.05). Shear force values of slow-growing chicken meat were not affected by high heating temperatures (P > 0.05). In addition, the high heat treatment increased protein carbonyl (P < 0.05), while no effect on in vitro protein digestibility (P > 0.05). SR-FTIR microspectroscopy performed better in differentiating the meat quality of different chicken breeds, whereas FT-Raman spectroscopy clearly revealed differences in meat qualities induced by heating temperature. Based on principal component analysis (PCA), distinct characteristics of chicken meat cooked at 70°C were high water-holding capacity, lightness (L*), moisture content, and predominant α-helix structure, correlating with Raman spectra at 3,217 cm (O-H stretching of water) and 1,651 cm (amide I; α-helix). The high heating temperature at 90°C and 121°C exposed protein structure to a greater extent, as evidenced by an increase in β-sheets, which was well correlated with the Raman spectra at 2,968 and 2,893 cm (C-H stretching), tryptophan (880 cm), tyrosine (858 cm), and 1,042, 1,020, and 990 cm (C-C stretching; β-sheet). SR-FTIR and FT-Raman spectroscopy show potential for differentiation of chicken meat quality with respect to breeds and cooking temperatures. The marked differences in wavenumbers would be beneficial as markers for determining the quality of cooked meats from slow- and fast-growing chickens.
Topics: Animals; Temperature; Chickens; Heating; Cooking; Spectrum Analysis, Raman; Water; Collagen; Meat
PubMed: 37276701
DOI: 10.1016/j.psj.2023.102754 -
Scientific Reports Oct 2023Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This...
Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
Topics: Humans; Artificial Intelligence; Diagnosis, Differential; Thyroid Neoplasms; Algorithms; Spectrum Analysis, Raman
PubMed: 37789191
DOI: 10.1038/s41598-023-43856-7 -
Analytical Chemistry Sep 2023Multifunctional gold nanoparticles (AuNPs) are of great interest, owing to their vast potential for use in many areas including sensing, imaging, delivery, and medicine....
Multifunctional gold nanoparticles (AuNPs) are of great interest, owing to their vast potential for use in many areas including sensing, imaging, delivery, and medicine. A key factor in determining the biological activity of multifunctional AuNPs is the quantification of surface conjugated molecules. There has been a lack of accurate methods to determine this for multifunctionalized AuNPs. We address this limitation by using a new method based on the deconvolution and Levenberg-Marquardt algorithm fitting of UV-visible absorption spectrum to calculate the precise concentration and number of cytochrome (Cyt ) and zinc porphyrin (Zn Porph) bound to each multifunctional AuNP. Dynamic light scattering (DLS) and zeta potential measurements were used to confirm the functionalization of AuNPs with Cyt and Zn Porph. Transmission electron microscopy (TEM) was used in conjunction with UV-visible absorption spectroscopy and DLS to identify the AuNP size and confirm that no aggregation had taken place after functionalization. Despite the overlapping absorption bands of Cyt and Zn Porph, this method was able to reveal a precise concentration and number of Cyt and Zn Porph molecules attached per AuNP. Furthermore, using this method, we were able to identify unconjugated molecules, suggesting the need for further purification of the sample. This guide provides a simple and effective method to quickly quantify molecules bound to AuNPs, giving users valuable information, especially for applications in drug delivery and biosensors.
Topics: Gold; Metal Nanoparticles; Spectrum Analysis; Dynamic Light Scattering; Multifunctional Nanoparticles; Cytochromes c
PubMed: 37621249
DOI: 10.1021/acs.analchem.3c01649 -
Biosensors Nov 2023The interaction of light with biological tissues is an intriguing area of research that has led to the development of numerous techniques and technologies. The...
The interaction of light with biological tissues is an intriguing area of research that has led to the development of numerous techniques and technologies. The randomness inherent in biological tissues can trap light through multiple scattering events and provide optical feedback to generate random lasing emission. The emerging random lasing signals carry sensitive information about the scattering dynamics of the medium, which can help in identifying abnormalities in tissues, while simultaneously functioning as an illumination source for imaging. The early detection and imaging of tumor regions are crucial for the successful treatment of cancer, which is one of the major causes of mortality worldwide. In this paper, a bimodal spectroscopic and imaging system, capable of identifying and imaging tumor polyps as small as 1 mm, is proposed and illustrated using a phantom sample for the early diagnosis of tumor growth. The far-field imaging capabilities of the developed system can enable non-contact in vivo inspections. The integration of random lasing principles with sensing and imaging modalities has the potential to provide an efficient, minimally invasive, and cost-effective means of early detection and treatment of various diseases, including cancer.
Topics: Humans; Diagnostic Imaging; Lasers; Neoplasms; Spectrum Analysis
PubMed: 38131763
DOI: 10.3390/bios13121003 -
Aging Clinical and Experimental Research Mar 2024Osteoporosis in males is largely under-diagnosed and under-treated, with most of the diagnosis confirmed only after an osteoporotic fracture. Therefore, there is an...
BACKGROUND
Osteoporosis in males is largely under-diagnosed and under-treated, with most of the diagnosis confirmed only after an osteoporotic fracture. Therefore, there is an urgent need for highly accurate and precise technologies capable of identifying osteoporosis earlier, thereby avoiding complications from fragility fractures.
AIMS
This study aimed to evaluate the diagnostic accuracy and precision of the non-ionizing technology Radiofrequency Echographic Multi Spectrometry (REMS) for the diagnosis of osteoporosis in a male population in comparison with conventional Dual-energy X-ray Absorptiometry (DXA).
METHODS
A cohort of 603 Caucasian males aged between 30 and 90 years were involved in the study. All the enrolled patients underwent lumbar and femoral scans with both DXA and REMS. The diagnostic agreement between REMS and DXA-measured BMD was expressed by Pearson correlation coefficient and Bland-Altman method. The accuracy of the diagnostic classification was evaluated by the assessment of sensitivity and specificity considering DXA as reference.
RESULTS
A significant correlation between REMS- and DXA-measured T-score values (r = 0.91, p < 0.0001) for lumbar spine and for femoral neck (r = 0.90, p < 0.0001) documented the substantial equivalence of the two measurement techniques. Bland-Altman outcomes showed that the average difference in T-score measurement is very close to zero (-0.06 ± 0.60 g/cm for lumbar spine and - 0.07 ± 0.44 g/cm for femoral neck) confirming the agreement between the two techniques. Furthermore, REMS resulted an effective technique to discriminate osteoporotic patients from the non-osteoporotic ones on both lumbar spine (sensitivity = 90.1%, specificity = 93.6%) and femoral neck (sensitivity = 90.9%, specificity = 94.6%). Precision yielded RMS-CV = 0.40% for spine and RMS-CV = 0.34% for femur.
CONCLUSION
REMS, is a reliable technology for the diagnosis of osteoporosis also in men. This evidence corroborates its high diagnostic performance already observed in previous studies involving female populations.
Topics: Humans; Male; Female; Aged; Aged, 80 and over; Bone Density; Osteoporosis; Osteoporotic Fractures; Femur Neck; Spectrum Analysis
PubMed: 38494464
DOI: 10.1007/s40520-024-02728-4 -
Sensors (Basel, Switzerland) Aug 2023Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging...
Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great potential of deep learning (DL) methodology in solving this problem, the inherent lack of an efficient gold standard method for model training and validation remains a grand challenge. This work investigates whether DL can be leveraged to accurately and efficiently simulate photon propagation in biological tissue, enabling photoacoustic image synthesis. Our approach is based on estimating the initial pressure distribution of the photoacoustic waves from the underlying optical properties using a back-propagatable neural network trained on synthetic data. In proof-of-concept studies, we validated the performance of two complementary neural network architectures, namely a conventional U-Net-like model and a Fourier Neural Operator (FNO) network. Our in silico validation on multispectral human forearm images shows that DL methods can speed up image generation by a factor of 100 when compared to Monte Carlo simulations with 5×108 photons. While the FNO is slightly more accurate than the U-Net, when compared to Monte Carlo simulations performed with a reduced number of photons (5×106), both neural network architectures achieve equivalent accuracy. In contrast to Monte Carlo simulations, the proposed DL models can be used as inherently differentiable surrogate models in the photoacoustic image synthesis pipeline, allowing for back-propagation of the synthesis error and gradient-based optimization over the entire pipeline. Due to their efficiency, they have the potential to enable large-scale training data generation that can expedite the clinical application of photoacoustic imaging.
Topics: Humans; Deep Learning; Spectrum Analysis; Forearm; Monte Carlo Method; Neural Networks, Computer
PubMed: 37631628
DOI: 10.3390/s23167085 -
The Journal of Surgical Research Aug 2023Identifying colorectal liver metastases (CRLM) during liver resection could assist in achieving clear surgical margins, which is an important prognostic variable for...
INTRODUCTION
Identifying colorectal liver metastases (CRLM) during liver resection could assist in achieving clear surgical margins, which is an important prognostic variable for both disease-free and overall survival. The aim of this study was to investigate the effect of auto-fluorescence (AF) and Raman spectroscopy for ex vivo label-free discrimination of CRLMs from normal liver tissue. Secondary aims include exploring options for multimodal AF-Raman integration with respect to diagnosis accuracy and imaging speed on human liver tissue and CRLM.
METHODS
Liver samples were obtained from patients undergoing liver surgery for CRLM who provided informed consent (15 patients were recruited). AF and Raman spectroscopy was performed on CRLM and normal liver tissue samples and then compared to histology.
RESULTS
AF emission spectra demonstrated that the 671 nm and 775/785 nm excitation wavelengths provided the highest contrast, as normal liver tissue elicited on average around eight-fold higher AF intensity compared to CRLM. The use of the 785 nm wavelength had the advantage of enabling Raman spectroscopy measurements from CRLM regions, allowing discrimination of CRLM from regions of normal liver tissue eliciting unusual low AF intensity, preventing misclassification. Proof-of-concept experiments using small pieces of CRLM samples covered by large normal liver tissue demonstrated the feasibility of a dual-modality AF-Raman for detection of positive margins within few minutes.
CONCLUSIONS
AF imaging and Raman spectroscopy can discriminate CRLM from normal liver tissue in an ex vivo setting. These results suggest the potential for developing integrated multimodal AF-Raman imaging techniques for intraoperative assessment of surgical margins.
Topics: Humans; Spectrum Analysis, Raman; Margins of Excision; Colorectal Neoplasms; Liver Neoplasms; Hepatectomy
PubMed: 36940563
DOI: 10.1016/j.jss.2023.02.014 -
PloS One 2023Organismal transparency constitutes a significant concern in whole-body live imaging, yet its underlying structural, genetic, and physiological foundations remain...
Organismal transparency constitutes a significant concern in whole-body live imaging, yet its underlying structural, genetic, and physiological foundations remain inadequately comprehended. Diverse environmental and physiological factors (multimodal factors) are recognized for their influence on organismal transparency. However, a comprehensive and integrated quantitative evaluation system for biological transparency across a broad spectrum of wavelengths is presently lacking. In this study, we have devised an evaluation system to gauge alterations in organismal transparency induced by multimodal factors, encompassing a wide range of transmittance spanning from 380 to 1000 nm, utilizing hyperspectral microscopy. Through experimentation, we have scrutinized the impact of three environmental variables (temperature, salinity, and pH) and the effect of 11 drugs treatment containing inhibitors targeting physiological processes in the ascidian Ascidiella aspersa. This particular species, known for its exceptionally transparent eggs and embryos, serves as an ideal model. We calculated bio-transparency defined as the mean transmittance ratio of visible light within the range of 400-760 nm. Our findings reveal a positive correlation between bio-transparency and temperature, while an inverse relationship is observed with salinity levels. Notably, reduced pH levels and exposure to six drugs have led to significant decreasing in bio-transparency (ranging from 4.2% to 58.6%). Principal component analysis (PCA) on the measured transmittance data classified these factors into distinct groups. This suggest diverse pathways through which opacification occurs across different spectrum regions. The outcome of our quantitative analysis of bio-transparency holds potential applicability to diverse living organisms on multiple scales. This analytical framework also contributes to a holistic comprehension of the mechanisms underlying biological transparency, which is susceptible to many environmental and physiological modalities.
Topics: Hyperspectral Imaging; Light; Microscopy; Principal Component Analysis; Salinity
PubMed: 37819990
DOI: 10.1371/journal.pone.0292524 -
Nature Communications Sep 2023The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods....
The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm, at resolution equivalent to 400x digital histopathology. Quantitative validation suggests strong concordance with conventional histology in benign and malignant prostate and breast tissues. In diagnostic utility studies we demonstrate a mean sensitivity and specificity of 0.96 and 0.91 in breast specimens, and respectively 0.87 and 0.94 in prostate specimens. We also find virtual stain quality is preferred (P = 0.03) compared to frozen section analysis in a blinded survey of pathologists.
Topics: Male; Humans; Microscopy; Deep Learning; Remote Sensing Technology; Spectrum Analysis; Coloring Agents
PubMed: 37749108
DOI: 10.1038/s41467-023-41574-2