Green Synthesis of the Triazine Derivatives and their Application for the Benign Electrocatalytic Reaction of Nitrate Reduction to Ammonia.

This work uses green sustainable reactions twice. Firstly, it is microwave synthesis: 2,4,6-tribiphenyl-4-yl-1,3,5-triazine and similar compounds were prepared in a closed microwave reactor in n-octane by the Friedel-Crafts reaction. Second, a hybrid electrocatalyst for the highly demanded electrochemical reaction of nitrate reduction to ammonia (NO3RR) was prepared based on this material. This reaction has great potential to replace the energy-intensive Haber-Bosch process, and in addition, has independent value for the elimination of nitrate contamination of water resources. As shown in the work, microwave synthesis is an eco-friendly method for the synthesis of complex organic compounds; fast, selective and with a high yield of the target product. The electrocatalyst deposited on the graphite electrode consisted of a layer of 2,4,6-tribiphenyl-4-yl-1,3,5-triazine and related compounds coated with cobalt oxide. The hybrid catalyst was firmly retained on the graphite electrode during NO3RR and the material showed impressive stability with almost no decrease in catalytic activity even after the fifth cycle. Both 2,4,6-tribiphenyl-4-yl-1,3,5-triazine and the catalyst based on this substance were characterized by SEM, XPS, XRD, UV-vis spectra, cyclic (and linear) voltammetry, and chronoamperometry. This work can serve as a starting point for the development of stable and durable electrocatalysts for NO3RR using triazine derivatives.

A Promising 1,3,5-Triazine-Based Anion Exchanger for Perrhenate Binding: Crystal Structures of Its Chloride, Nitrate and Perrhenate Salts.

The reaction of pyridine with cyanuric chloride was studied under microwave activation as well as in the presence of silver nitrate. The product of hydrolysis containing two pyridinium rings and chloride anion was isolated. The structures of these anion exchanger salts with chloride, nitrate and perrhenate anions are discussed.

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra.

In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification.

Polymer-Colloid Complexes Based on Cationic Imidazolium Amphiphile, Polyacrylic Acid and DNA Decamer.

The solution behavior and physicochemical characteristics of polymer-colloid complexes based on cationic imidazolium amphiphile with a dodecyl tail (IA-12) and polyacrylic acid (PAA) or DNA decamer (oligonucleotide) were evaluated using tensiometry, conductometry, dynamic and electrophoretic light scattering and fluorescent spectroscopy and microscopy. It has been established that PAA addition to the surfactant system resulted in a ca. 200-fold decrease in the aggregation threshold of IA-12, with the hydrodynamic diameter of complexes ranging within 100-150 nm. Electrostatic forces are assumed to be the main driving force in the formation of IA-12/PAA complexes. Factors influencing the efficacy of the complexation of IA-12 with oligonucleotide were determined. The nonconventional mode of binding with the involvement of hydrophobic interactions and the intercalation mechanism is probably responsible for the IA-12/oligonucleotide complexation, and a minor contribution of electrostatic forces occurred. The latter was supported by zeta potential measurements and the gel electrophoresis technique, which demonstrated the low degree of charge neutralization of the complexes. Importantly, cellular uptake of the IA-12/oligonucleotide complex was confirmed by fluorescence microscopy and flow cytometry data on the example of M-HeLa cells. While single IA-12 samples exhibit roughly similar cytotoxicity, IA-12-oligonucleotide complexes show a selective effect toward M-HeLa cells (IC50 1.1 µM) compared to Chang liver cells (IC50 23.1 µM).

Quantitative assessment of textural features in the early detection of diabetic retinopathy with optical coherence tomography angiography.

The diagnostic power of the textural features and fractal dimension (FD) for early detection of diabetic retinopathy (DR) were quantitatively evaluated with optical coherence tomography angiography (OCTA). 41 normal healthy OD eyes (age: 46.41 ± 13.69), and 10 diabetic OD eyes (age: 60.90 ± 13.46) in the early stages of DR (mild or moderate non-proliferative diabetic retinopathy (NPDR)) were employed. Four retinal vascular plexuses including nerve fiber layer vascular plexus (NFLVP), superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) were generated by using OCTA. The FD and the five textural features including contrast, correlation, entropy, energy, and homogeneity in four parafoveal sectors were extracted from OCTA images. The factor of aging on textural features and FD was evaluated based on the comparisons among five normal healthy subgroups. Our results showed that FD in superior sector of NFLVP and in nasal sector of ICP had the significant decreases when comparing the older healthy subgroup (age range: 60-69) with the younger healthy subgroup (age range: 20-29). Our results also indicated that the correlation did not show the significant differences in all sectors of the four retinal sublayers among the normal healthy subgroups except in the temporal sector in NFLVP. Furthermore, our results indicated that the correlation in nasal and inferior sectors in SVP can effectively differentiate diabetic patients in early stages of DR from normal healthy subjects with the highest AUROC values. In our study, the specific textural feature - correlation can effectively stage the early DR, which may contribute to the diagnosis of DR in clinic practice.

Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification.

In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.

Classification of skin cancer using convolutional neural networks analysis of Raman spectra.

BACKGROUND AND OBJECTIVE: Skin cancer is the most common malignancy in whites accounting for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups for skin cancer "optical biopsy" are utilized to detect tumors based on their spectral features caused by the comparative presence of different chemical components. However, low signal-to-noise ratio in such systems may prevent accurate tumors classification. Thus, there is a challenge to develop methods for efficient skin tumors classification. METHODS: We compare the performance of convolutional neural networks and the projection on latent structures with discriminant analysis for discriminating skin cancer using the analysis of Raman spectra with a high autofluorescence background stimulated by a 785 nm laser. We have registered the spectra of 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable Raman setup and created classification models both for convolutional neural networks and projection on latent structures approaches. To check the classification models stability, a 10-fold cross-validation was performed for all created models. To avoid models overfitting, the data was divided into a training set (80% of spectral dataset) and a test set (20% of spectral dataset). RESULTS: The results for different classification tasks demonstrate that the convolutional neural networks significantly (p<0.01) outperforms the projection on latent structures. For the convolutional neural networks implementation we obtained ROC AUCs of 0.96 (0.94 - 0.97; 95% CI), 0.90 (0.85-0.94; 95% CI), and 0.92 (0.87 - 0.97; 95% CI) for classifying a) malignant vs benign tumors, b) melanomas vs pigmented tumors and c) melanomas vs seborrheic keratosis respectively. CONCLUSIONS: The performance of the convolutional neural networks classification of skin tumors based on Raman spectra analysis is higher or comparable to the accuracy provided by trained dermatologists. The increased accuracy with the convolutional neural networks implementation is due to a more precise accounting of low intensity Raman bands in the intense autofluorescence background. The achieved high performance of skin tumors classifications with convolutional neural networks analysis opens a possibility for wide implementation of Raman setups in clinical setting.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning.

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm-1, 724 cm-1, 1094 cm-1 and 1393 cm-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

Raman spectroscopy of human skin for kidney failure detection.

The object of this paper is in vivo study of skin spectral-characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS-DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS-DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm-1 . In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost-effective and accurate detection of kidney failure and associated metabolic changes in the skin.

Multiple origins of foot-and-mouth disease virus serotype Asia 1 outbreaks, 2003-2007.

We investigated the molecular epidemiology of foot-and-mouth disease virus (FMDV) serotype Asia 1, which caused outbreaks of disease in Asia during 2003-2007. Since 2004, the region affected by outbreaks of this serotype has increased from disease-endemic countries in southern Asia (Afghanistan, India, Iran, Nepal, Pakistan) northward to encompass Kyrgyzstan, Tajikistan, Uzbekistan, several regions of the People's Republic of China, Mongolia, Eastern Russia, and North Korea. Phylogenetic analysis of complete virus capsid protein 1 (VP1) gene sequences demonstrated that the FMDV isolates responsible for these outbreaks belonged to 6 groups within the Asia 1 serotype. Some contemporary strains were genetically closely related to isolates collected historically from the region as far back as 25 years ago. Our analyses also indicated that some viruses have spread large distances between countries in Asia within a short time.

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis.

The present paper studies the applicability of a portable cost-effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near-infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals.

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions.

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification.

Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers.

Malignant skin tumors of different types were studied in vivo using optical coherence tomography (OCT), backscattering (BS), and Raman spectroscopy (RS). A multimodal method is proposed for early cancer detection based on complex analysis of OCT images by their relative alteration of scattered-radiation spectral intensities between malignant and healthy tissues. An increase in average accuracy of diagnosis was observed for a variety of cancer types (9% sensitivity, 8% specificity) by a multimodal RS-BS-OCT system in comparison with any of the three methods used separately. The proposed approach equalizes the processing rates for all methods and allows for simultaneous imaging and classification of tumors.

Three-dimensional computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas.

We present a three-dimensional (3-D) computational method to detect soft tissue sarcomas with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Three parameters are investigated and quantified from OCT images as the indicators for the tissue diagnosis including the signal attenuation (A-line slope), the standard deviation of the signal fluctuations (speckles), and the exponential decay coefficient of its spatial frequency spectrum. The detection of soft tissue sarcomas relies on the combination of these three parameters, which are related to the optical attenuation characteristics and the structural features of the tissue. Pilot experiments were performed on ex vivo human tissue samples with homogeneous pieces (both normal and abnormal) and tumor margins. Our results demonstrate the feasibility of this computational method in the differentiation of soft tissue sarcomas from normal tissues. The features of A-line-based detection and 3-D quantitative analysis yield promise for a computer-aided technique capable of accurately and automatically identifying resection margins of soft tissue sarcomas during surgical treatment.

Indirect foot-and-mouth disease vaccine potency testing based on a serological alternative.

Foot-and-mouth disease (FMD) vaccine potency testing has historically been performed by experimentally infecting vaccinated cattle. A few alternative approaches to the in vivo challenge test based on the correlation between serum titres of primo-vaccinated cattle and protection against infection have been proposed, but none have been accepted by the European Pharmacopoeia (Ph.Eur.) due to the lack of statistical power and the pooling of data over time. The present study addresses these issues and presents data of 150 cattle vaccinated according to Ph.Eur. standards. Four laboratories took part in the serological testing and different serological assays were used, including virus neutralisation assays and ELISA formats. Models correlating specific anti-FMD virus antibody titres to protection were built using logistic regression followed by Receiver Operating Characteristic (ROC) analysis. The best models accurately predicted the in vivo protection status in 80.0% of the cases. Although differences were observed between laboratories and assays used, the majority of antibody pass-levels, determined using ROC analysis, corresponded to at least 75.0% probability of protection. The indirect potency assessment procedure proposed is at least as precise (repeatability=65.8%, reproducibility=60.7%) as the in vivo test, can be standardised and results in a quantitative PD50 value. The validity of the procedure was also demonstrated.