Monitoring and modifying brain oxygenation in patients at risk of hypoxic ischaemic brain injury after cardiac arrest.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2021. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2021 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin.

We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (~20 min, cf. ELISA ~4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.

ANOVA-Simultaneous Component analysis modelling of low-level-fused spectroscopic data: A food chemistry case-study.

Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

MCR-ALS analysis of IR spectroscopy and XRD for the investigation of ibuprofen - nicotinamide cocrystal formation.

To improve aqueous solubility, a poorly water-soluble active ingredient is classically combined with a conformer to form cocrystals. Hot melt extrusion is one preparation method for the formation of cocrystal solids. The aim of our study was to determine the optimal temperature conditions for the formation of ibuprofen and nicotinamide cocrystals using real-time infrared (IR) and X-ray diffraction (XRD) measurements. IR spectra and XRD patterns were subjected to multivariate curve resolution alternating least squares (MCR-ALS) analysis and decomposed into several components. Each component was descriptive of a specific step in the formation of the cocrystal. Cocrystal formation was followed by a separation phase between amorphous ibuprofen and crystalline nicotinamide. Our results suggest that, when using the hot melt exclusion method, careful consideration should be made towards optimizing processing temperatures in order to prevent amorphization and promote control over the process of cocrystal formation.

Chemical changes that occur in Jerusalem artichoke silage.

The effects of 10 and 20 days of fermentation followed by freeze-drying on the vitamin C and fatty acids contents, chemical conversions and overall chemical composition of Jerusalem artichoke were studied. Fermentation between the 10th and 20th days increased content of all saturated fatty acids and two of the four unsaturated fatty acids. The only fatty acid content that decreased was that of C18:1 cis 9 acid, which was suggested to be converted to other fatty acids. The experimental data, which were supported by energetical feasibility, suggested the reaction pathways of the mutual conversions of fatty acids and confirmed the decreased vitamin C content during fermentation. Discriminant modelling of the spectral data successfully distinguished the fresh, 10 days and 20 days fermented samples. The correlation of the spectral and reference data allowed to construct reference models for predicting the content of vitamin C and C18:1 cis 9 fatty acid.

Potentiality of front-face fluorescence and mid-infrared spectroscopies coupled with partial least square regression to predict lipid oxidation in pound cakes during storage.

The potentialities of front-face fluorescence (FFF) and mid-infrared (MIR) spectroscopies coupled with partial least square regression (PLSR) were compared to predict the lipid oxidation of pound cakes. The level of lipid oxidation in pound cakes determined using classical methods showed some changes. Similarly, the fluorescence emission (305-490 nm) and excitation (252-390 nm) spectra and MIR spectra scanned in the 4000-700 cm-1 region showed some changes in pound cakes as a function of both storage time and the type of oil used in the formulation. The application of PLSR to the MIR spectra, provided excellent predictive results for free fatty acid (R2 = 0.97) and peroxide values (R2 = 0.87). Similar results were obtained from both tryptophan and MIR spectra for the prediction of TOTOX (R2 > 0.86) demonstrating the efficiency of the MIR and FFF spectroscopies to qualify and quantify the level of lipid oxidation in pound cakes.

Avaliação da resistência adesiva e características da zircônia após diferentes tratamentos de superfície / Bond strength and surface characteristics evaluation of zirconia after different treatments

O objetivo desse estudo in vitro foi avaliar a resistência adesiva, rugosidade superficial, ângulo de contato e o espectro infravermelho da zircônia, após diferentes tratamentos de superfície. Para isso, blocos (n=9) de zircônia Y-TZP (e.max ZirCad - Ivoclar, Schann, Liechtenstein) foram seccionados em 39 fatias (12 x 11x 3 mm) divididas em grupos de acordo com cada teste. Para resistência adesiva, 20 fatias foram divididos em: 2 grupos controle; JAT, jateamento com óxido de alumínio e sílica + primer (Monobond Plus - Ivoclar, Schann, Liechtenstein); HFP, condicionamento com ácido fluorídrico 10% + primer, e 3 experimentais: PL, plasma (Plasma Não Térmico de Superfície - modelo SAP); PLP, plasma + primer; HFPLP, ácido fluorídrico + plasma + primer. Cada fatia de zircônia foi cimentada (Multilink N - Ivoclair, Schann, Liechtenstein) a um bloco pré-polimerizado de resina composta. Após a cimentação os espécimes foram seccionados, e 48 palitos com área adesiva de 1 mm2 foram obtidos em cada grupo para o ensaio de microtração (µTBS). Quatro outras fatias foram submetidas à análise de rugosidade superficial e 12 fatias submetidas à análise do ângulo de contato, após os seguintes tratamentos de superfície: sem tratamento (AV), aplicação de ácido fluorídrico a 10% (AHF), aplicação de plasma (AP) e jateamento (AJ), sendo que para o ângulo de contato foram feitas medições imediatamente após o tratamento, após 12 e 24 horas. Os resultados foram submetidos à análise estatística Anova e teste de Tukey a 5% (p< 0,05). Os espectros das ligações químicas da superfície da zircônia foram determinados em 3 outras amostras após as seguintes condições: sem tratamento (AV), jateamento (JAT) e aplicação de ácido fluorídrico 10% seguido da aplicação do plasma (AHFP). Em termos de adesão, não houve diferença estatística significante entre os grupos JAT e HFPLP, porém esses 2 foram estatisticamente superiores ao grupos HFP, PL e PLP. O jateamento demonstrou aumentar significantemente a rugosidade superficial em relação aos demais grupos. O plasma demonstrou diminuir o ângulo de contato da superfície da zircônia imediatamente e ao longo de 48 horas(AU)

The purpose of this in vito study was to evaluate zirconia bond strength, surface roughness, contact angle and to mesure infrared spectrum after different surface treatments. Y-TZP zirconium (e.max ZirCAD - Ivoclair, Schann, Liechtenstein) blocks (n=9) were sliced into 36 discs (12 x 11 x 3 mm) and divided (n=4) into surface treatment groups as following: two control groups: JAT, airborneparticle abrasion and HFP, 10% hydrofluoric acid etching + primer (Monobond Plus - Ivoclair, Schann, Liechtenstein)application ,and three experimental groups: PL, non-thermal plasma (Surface Non-Thermal Plasma SAP model) application; PLP non-thermal plasma + primer application, and HFPLP, 10% hydrofluoric acid etching + non-thermal plasma + primer application. Each zirconia disk was cemented (Multilink N - Ivoclair, Schann, Liechtenstein) to a pre-polymerized resin block (Filtek Z350 - 3M, St Paul, MN, USA). After cementation the specimens were sectioned and 48 sticks with adhesive area of 1 mm2 obtained from each group for micro-tensile testing (µTBS). Four discs were submitted to surface roughness analysis and 12 discs to contact angle analysis, after the following surface treatments: no treatment (AV), 10% hydrofluoric acid etching (AHF), non-thermal plasma application (AP); and airborne- particle abrasion (AJ). The results were submitted to ANOVA and Tukey's test (p < 0.05). Zirconia surface chemical bonds spectrum was determined in infrared spectroscopy for three specimens after the conditions: no treatment; airborne- particle abrasion and hydrofluoric acid etching + non-thermal plasma application. The results showed no statistically significant differences between the groups JAT and HFPLP, but these two groups were statistically superior to HFP, PL and PLP. Airborne- particle abrasion resulted in a significant increase of surface roughness compared to the other groups. Non-thermal plasma was shown to decrease the contact angle of the surface, within 48 hours(AU)

Applications of two-dimensional infrared spectroscopy.

Two-dimensional infrared (2D IR) spectroscopy has recently emerged as a powerful tool with applications in many areas of scientific research. The inherent high time resolution coupled with bond-specific spatial resolution of IR spectroscopy enable direct characterization of rapidly interconverting species and fast processes, even in complex systems found in chemistry and biology. In this minireview, we briefly outline the fundamental principles and experimental procedures of 2D IR spectroscopy. Using illustrative example studies, we explain the important features of 2D IR spectra and their capability to elucidate molecular structure and dynamics. Primarily, this minireview aims to convey the scope and potential of 2D IR spectroscopy by highlighting select examples of recent applications including the use of innate or introduced vibrational probes for the study of nucleic acids, peptides/proteins, and materials.

A modified infrared spectrometer with high time resolution and its application for investigating fast conformational changes of the GTPase Ras.

Time-resolved infrared spectroscopy is a valuable tool for the investigation of proteins and protein interactions. The investigation of many biological processes is possible by means of caged compounds, which set free biologically active substances upon light activation. Some caged compounds could provide sub-nanosecond time resolution, e.g., para-hydroxyphenacyl-guanosine 5'-triphosphate (GTP) forms GTP in picoseconds. However, the time resolution in single shot experiments with rapid-scan Fourier transform infrared (FT-IR) spectrometers is limited to about 10 ms. Here we use an infrared diode laser instead of the conventional globar and achieve a time resolution of 100 ns. This allows for the time-resolved measurement of the fast Ras(off) to Ras(on) conformational change at room temperature. We quantified the activation parameters for this reaction and found that the free energy of activation for this reaction is mainly enthalpic. Investigation of the same reaction in the presence of the Ras binding domain of the effector Raf (RafRBD) reveals a four orders of magnitude faster reaction, indicating that Ras·RafRBD complex formation directly induces the conformational change. Recent developments of broadly tunable quantum cascade lasers will further improve time resolution and usability of the setup. The reported 100 ns time resolution is the best achieved for a non-repetitive experiment so far.

Infrared spectroscopic studies of cells and tissues: triple helix proteins as a potential biomarker for tumors.

In this work, the infrared (IR) spectra of living neural cells in suspension, native brain tissue, and native brain tumor tissue were investigated. Methods were developed to overcome the strong IR signal of liquid water so that the signal from the cellular biochemicals could be seen. Measurements could be performed during surgeries, within minutes after resection. Comparison between normal tissue, different cell lineages in suspension, and tumors allowed preliminary assignments of IR bands to be made. The most dramatic difference between tissues and cells was found to be in weaker IR absorbances usually assigned to the triple helix of collagens. Triple helix domains are common in larger structural proteins, and are typically found in the extracellular matrix (ECM) of tissues. An algorithm to correct offsets and calculate the band heights and positions of these bands was developed, so the variance between identical measurements could be assessed. The initial results indicate the triple helix signal is surprisingly consistent between different individuals, and is altered in tumor tissues. Taken together, these preliminary investigations indicate this triple helix signal may be a reliable biomarker for a tumor-like microenvironment. Thus, this signal has potential to aid in the intra-operational delineation of brain tumor borders.

Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis.

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

Application of image spectrometer to in situ infrared broadband optical monitoring for thin film deposition.

A path-folded infrared image spectrometer with five sub-gratings and five linear-array detectors was applied to a broadband optical monitoring (BOM) system for thin film deposition. Through in situ BOM, we can simultaneously acquire the thickness and refractive index of each layer in real time by fitting the measured spectra, and modify the deposition parameters during deposition process according to the fitting results. An effective data processing method was proposed and applied in the BOM process, and it shortened the data processing time and improved the monitoring efficiency greatly. For demonstration, a narrow band-pass filter (NBF) at 1540 nm with ~10 nm full width at half-maximum (FWHM) had been manufactured using the developed BOM system, and the results showed that this BOM method was satisfying for monitoring deposition of thin film devices.

Can infrared spectroscopy provide information on protein-protein interactions?

For most biophysical techniques, characterization of protein-protein interactions is challenging; this is especially true with methods that rely on a physical phenomenon that is common to both of the interacting proteins. Thus, for example, in IR spectroscopy, the carbonyl vibration (1600-1700 cm(-1)) associated with the amide bonds from both of the interacting proteins will overlap extensively, making the interpretation of spectral changes very complicated. Isotope-edited infrared spectroscopy, where one of the interacting proteins is uniformly labelled with (13)C or (13)C,(15)N has been introduced as a solution to this problem, enabling the study of protein-protein interactions using IR spectroscopy. The large shift of the amide I band (approx. 45 cm(-1) towards lower frequency) upon (13)C labelling of one of the proteins reveals the amide I band of the unlabelled protein, enabling it to be used as a probe for monitoring conformational changes. With site-specific isotopic labelling, structural resolution at the level of individual amino acid residues can be achieved. Furthermore, the ability to record IR spectra of proteins in diverse environments means that isotope-edited IR spectroscopy can be used to structurally characterize difficult systems such as protein-protein complexes bound to membranes or large insoluble peptide/protein aggregates. In the present article, examples of application of isotope-edited IR spectroscopy for studying protein-protein interactions are provided.

Application of spectral derivative data in visible and near-infrared spectroscopy.

The use of the spectral derivative method in visible and near-infrared optical spectroscopy is presented, whereby instead of using discrete measurements around several wavelengths, the difference between nearest neighbouring spectral measurements is utilized. The proposed technique is shown to be insensitive to the unknown tissue and fibre contact coupling coefficients providing substantially increased accuracy as compared to more conventional techniques. The self-calibrating nature of the spectral derivative techniques increases its robustness for both clinical and industrial applications, as is demonstrated based on simulated results as well as experimental data.

Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance.

[Study of quantitative analysis of protein in barley using OSC-PLS algorithm].

Spectra of barley containing vast information were obtained with the dispersion spectrograph. The contents of protein in barley were determined by dispersive near infrared (NIR) spectroscopy. Pretreatment method of orthogonal signal correction (OSC) was used to reject uncorrelated variables in the original spectra before building the partial least squares NIR method (OSC-PLS). The results were compared with the regular PLS model. With the OSC-PLS method, the determination coefficient R2 was 0.901. The correlation coefficient of validation set was 0.971 7. The standard deviation (SD) and relative standard deviation (RSD)were 0.545 0 and 4.2% respectively. Applying OSC-PLS resulted in removal of non-correlated variation in spectra and reduced model's complexity with preserved ability and improved interpretative ability of variation in spectra. It means that the OSC-PLS is a fungible model to predict the contents of protein in barley veraciously to meet the demand of fast analysis of agricultural products.

[Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM].

Based on multispectral digital image texture feature, a new rapid and nondestructive method for discriminating tea categories was put forward. The new method combines the advantages of DCT (discrete cosine transform) and least squares-support vector machine (LS-SVM). In the present study, the images for each sample were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multispectral digital imager. The three wavebands of image can be combined into one image, which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. Three images for one sample can be obtained simultaneously. Eighty filters were designed based on DCT. One hundred twenty images (twenty for each category) were used for calibration set and one hundred twenty mages (twenty for each category) were used as the prediction. Finally, tea category was classified by LS-SVM. The classification rate using Sd of NIR image was only 73.33%, while it reached 100% using 8 filtered images. The overall results show that the technique combining DCT and SVM can be efficiently utilized for texture recognition of multispectral image, and it also is an effective and simple discrimination way for the tea categories. The whole process is simple and easy to operate, and can be transferred to the industrial world for on-line application.

[Classification of pharmaceutical tablet with canonical variates analysis method in spectra analysis].

A novel classifier was constructed in the present paper by combination of an improved canonical variates analysis (ICVA) with Fish linear discriminant analysis (LDA). The resulting discrimination model based on this proposed approach (ICVA-LDA) was divided into two parts: the inner part that estimated the robust weight vector of canonical variates by linear partial least square algorithm and the outer part that built the LDA discrimination model by making use of the extracted canonical variates. The method utilized partial least squares regression as an engine for solving an eigenvector problem involving singular covariance matrices and the canonical variates were more relevant for discriminative purposes. Thus, the weight vectors found in the modified CVA method not only possessed the same properties as weight vectors of the standard CVA method, but also forced the discriminative information into the first fewer of canonical variates. The improved discrimination model was more concise and efficient in dealing with the problem of the effect sensitivity and numerous predictor variables with serious multicollinearity in the spectra data. Furthermore, in ICVA-LDA the interpretation could be performed with respect to the original high-dimensional data space. Finally, application to a four-group problem with near-infrared transmittance spectroscopy data consisting of 310 samples and 404 variables of the proposed ICVA-LDA approach was presented with comparison to the LDA combined with principal component analysis (PCA-LDA) and standard CVA-LDA methods. All the three discrimination models were validated using fivefold segmented cross-validation. The result demonstrates that the limitations of LDA were overcome with PLS algorithm and then the classification performance of LDA was improved by ICVA. This proposed approach can also be widely used in other fields for classification and discrimination of small samples and collinear data.

[Nondestructive identification of different oil content maize kernels by near-infrared spectra].

Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.