Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

Carcinogenic and non-carcinogenic health hazards of potentially toxic elements in commonly consumed rice cultivars in Dhaka city, Bangladesh.

The study aimed to assess the level of potentially toxic elements (As, Cd, Pb, Zn, Cu, Cr, Mn, and Ni) and associated health implications through commonly consumed rice cultivars of Bangladesh available in Capital city, Dhaka. The range of As, Cd, Pb, Zn, Cu, Cr, Mn, and Ni in rice grains were 0.04-0.35, 0.01-0.15, 0.01-1.18, 10.74-34.35, 1.98-13.42, 0.18-1.43, 2.51-22.08, and 0.21-5.96 mg/kg fresh weight (FW), respectively. The principal component analysis (PCA) identified substantial anthropogenic activities to be responsible for these elements in rice grains. The estimated daily intake (EDI) of the elements was below the maximum tolerable daily intake (MTDI) level. The hazard index (HI) was above the threshold level, stating non-carcinogenic health hazards from consuming these rice cultivars. The mean target cancer risk (TCR) of As and Pb exceeded the USEPA acceptable level (10-6), revealing carcinogenic health risks from the rice grains.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer.

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm-1 (FTIR) and at 2944 cm-1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm-1, 2974 cm-1 (FTIR) and 1714 cm-1, 2817 cm-1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

Preoperative profiles of plasma amino acids and derivatives distinguish periampullary cancer and benign disease.

Periampullary cancers, including pancreatic ductal adenocarcinoma, ampullary-, cholangio-, and duodenal carcinoma, are frequently diagnosed in an advanced stage and are associated with poor overall survival. They are difficult to differentiate from each other and challenging to distinguish from benign periampullary disease preoperatively. To improve the preoperative diagnostics of periampullary neoplasms, clinical or biological markers are warranted.In this study, 28 blood plasma amino acids and derivatives from preoperative patients with benign (N = 45) and malignant (N = 72) periampullary disease were analyzed by LC-MS/MS.Principal component analysis and consensus clustering both separated the patients with cancer and the patients with benign disease. Glutamic acid had significantly higher plasma expression and 15 other metabolites significantly lower plasma expression in patients with malignant disease compared with patients having benign disease. Phenylalanine was the only metabolite associated with improved overall survival (HR = 0.50, CI 0.30-0.83, P < 0.01).Taken together, plasma metabolite profiles from patients with malignant and benign periampullary disease were significantly different and have the potential to distinguish malignant from benign disease preoperatively.

Risk assessment of trace elements in vegetables grown in river Yamuna floodplain in Delhi.

Urban agriculture is common in fertile river floodplains of many developing countries. However, there is a risk of contamination in highly polluted regions. This study quantifies health risks associated with the consumption of vegetables grown in the floodplain of the urban river 'Yamuna' in the highly polluted yet data-scarce megacity Delhi, India. Six trace elements are analyzed in five kinds of vegetable samples. Soil samples from the cultivation area are also analyzed for elemental contamination. Ni, Mn, and Co are observed to be higher in leafy vegetables than others. Fruit and inflorescence vegetables are found to have higher concentrations of Cr, Pb, and Zn as compared to root vegetables. Transfer Factor indicates that Cr and Co have the highest and least mobility, respectively. Vegetable Pollution Index indicates that contamination levels follow as Cr > Ni > Pb > Zn. Higher Metal Pollution Index of leafy and inflorescence vegetables than root and fruit vegetables indicate that atmospheric deposition is the predominant source. Principal Component Analysis indicates that Pb and Cr have similar sources and patterns in accumulation. Among the analyzed vegetables, radish may pose a non-carcinogenic risk to the age group of 1-5 year. Carcinogenic risk is found to be potentially high due to Ni and Cr accumulation. Consumption of leafy vegetables was found to have relatively less risk than other vegetables due to lower Cr accumulation. Remediation of Cr and Ni in floodplain soil and regular monitoring of elemental contamination is a priority.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Monitoring plastic pellet pollution in coastal environments through handheld Raman spectroscopy: Data from the Mediterranean coasts (Southern Italy).

This paper examines the distribution and chemical properties of beached plastic pellets along the Ionian and Tyrrhenian coasts of Southern Italy. Three locations have been sampled: Agnone Bagni (SR) and Paradiso (ME) on the Ionian coast of Sicily, Baia del Tono in Milazzo (ME) on the Sicilian Tyrrhenian coast, and Pizzo Calabro (VV) in Calabria on the Tyrrhenian coast. Variations in shape, size, compactness, color, and other physical features, correlated with residence times and transport, has been highlighted. Raman spectroscopy, used in a portable configuration, enabled rapid identification of polymer types, demonstrating its utility for on-site plastic pollutant monitoring. Polyethylene and polypropylene were the predominant polymers. Principal component analysis of the spectra determined the optimal chemometric classification of pellets by composition, avoiding interference or distortion. In conclusion, the study provided preliminary insights into pellet abundance, composition, weathering extent, and distribution across these shorelines, underscoring the importance of regular beach monitoring.

Egg vs. Oil in the Cookbook of Plasters: Differentiation of Lipid Binders in Wall Paintings Using Gas Chromatography-Mass Spectrometry and Principal Component Analysis.

Wall paintings are integral to cultural heritage and offer rich insights into historical and religious beliefs. There exist various wall painting techniques that pose challenges in binder and pigment identification, especially in the case of egg/oil-based binders. GC-MS identification of lipidic binders relies routinely on parameters like the ratios of fatty acids within the plaster. However, the reliability of these ratios for binder identification is severely limited, as demonstrated in this manuscript. Therefore, a more reliable tool for effective differentiation between egg and oil binders based on a combination of diagnostic values, specific markers (cholesterol oxidation products), and PCA is presented in this study. Reference samples of wall paintings with egg and linseed oil binders with six different pigments were subjected to modern artificial ageing methods and subsequently analysed using two GC-MS instruments. A statistically significant difference (at a 95% confidence level) between the egg and oil binders and between the results from two GC-MS instruments was observed. These discrepancies between the results from the two GC-MS instruments are likely attributed to the heterogeneity of the samples with egg and oil binders. This study highlights the complexities in identifying wall painting binders and the need for innovative and revised analytical methods in conservation efforts.

Biomarker profiling in plants to distinguish between exposure to chlorine gas and bleach using LC-HRMS/MS and chemometrics.

Since its first employment in World War I, chlorine gas has often been used as chemical warfare agent. Unfortunately, after suspected release, it is difficult to prove the use of chlorine as a chemical weapon and unambiguous verification is still challenging. Furthermore, similar evidence can be found for exposure to chlorine gas and other, less harmful chlorinating agents. Therefore, the current study aims to use untargeted high resolution mass spectrometric analysis of chlorinated biomarkers together with machine learning techniques to be able to differentiate between exposure of plants to various chlorinating agents. Green spire (Euonymus japonicus), stinging nettle (Urtica dioica), and feathergrass (Stipa tenuifolia) were exposed to 1000 and 7500 ppm chlorine gas and household bleach, pool bleach, and concentrated sodium hypochlorite. After sample preparation and digestion, the samples were analyzed by liquid chromatography high resolution tandem mass spectrometry (LC-HRMS/MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS). More than 150 chlorinated compounds including plant fatty acids, proteins, and DNA adducts were tentatively identified. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed clear discrimination between chlorine gas and bleach exposure and grouping of the samples according to chlorine concentration and type of bleach. The identity of a set of novel biomarkers was confirmed using commercially available or synthetic reference standards. Chlorodopamine, dichlorodopamine, and trichlorodopamine were identified as specific markers for chlorine gas exposure. Fenclonine (Cl-Phe), 3-chlorotyrosine (Cl-Tyr), 3,5-dichlorotyrosine (di-Cl-Tyr), and 5-chlorocytosine (Cl-Cyt) were more abundantly present in plants after chlorine contact. In contrast, the DNA adduct 2-amino-6-chloropurine (Cl-Ade) was identified in both types of samples at a similar level. None of these chlorinated biomarkers were observed in untreated samples. The DNA adducts Cl-Cyt and Cl-Ade could clearly be identified even three months after the actual exposure. This study demonstrates the feasibility of forensic biomarker profiling in plants to distinguish between exposure to chlorine gas and bleach.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy.

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm-1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm-1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

Radiomics-based discriminant analysis of principal components to stratify the treatment response of lung metastases following stereotactic body radiation therapy.

PURPOSE: Discriminant analysis of principal components (DAPC) was introduced to describe the clusters of genetically related individuals focusing on the variation between the groups of individuals. Borrowing this approach, we evaluated the potential of DAPC for the evaluation of clusters in terms of treatment response to SBRT of lung lesions using radiomics analysis on pre-treatment CT images. MATERIALS AND METHODS: 80 pulmonary metastases from 56 patients treated with SBRT were analyzed. Treatment response was stratified as complete, incomplete and null responses. For each lesion, 107 radiomics features were extracted using the PyRadiomics software. The concordance correlation coefficients (CCC) between the radiomics features obtained by two segmentations were calculated. DAPC analysis was performed to infer the structure of "radiomically" related lesions for treatment response assessment. The DAPC was performed using the "adegenet" package for the R software. RESULTS: The overall mean CCC was 0.97 ± 0.14. The analysis yields 14 dimensions in order to explain 95 % of the variance. DAPC was able to group the 80 lesions into the 3 different clusters based on treatment response depending on the radiomics features characteristics. The first Linear Discriminant achieved the best discrimination of individuals into the three pre-defined groups. The greater radiomics loadings who contributed the most to the treatment response differentiation were associated with the "sphericity", "correlation" and "maximal correlation coefficient" features. CONCLUSION: This study demonstrates that a DAPC analysis based on radiomics features obtained from pretreatment CT is able to provide a reliable stratification of complete, incomplete or null response of lung metastases following SBRT.

Characterizing Factors Associated with Excess Body Weight: A Descriptive Study Using Principal Component Analysis in a Population with Overweight and Obesity.

Obesity is a worldwide epidemic, making it crucial to understand how it can be effectively prevented/treated. Considering that obesity is a multifactorial condition, this article carried out a baseline cross-sectional study of the variables involved in the disorder. Eighty-four subjects with overweight/obesity were recruited. Dietary baseline information was obtained by analysing three 24 h recalls. Resting metabolic rate was measured using indirect calorimetry, physical activity was measured through accelerometry, cardiometabolic parameters were determined in blood samples and body composition via anthropometry and bioimpedance. A univariant and multivariate exploratory approach was carried out using principal component analysis (PCA). Large inter-individual variability was observed in dietetic, biochemical, and physical activity measurements (coefficient of variation ≥ 30%), but body composition was more uniform. Volunteers had an unbalanced diet and low levels of physical activity. PCA reduced the 26 analysed variables to 4 factors, accounting for 65.4% of the total data variance. The main factor was the "dietetic factor", responsible for 24.0% of the total variance and mainly related to energy intake, lipids, and saturated fatty acids. The second was the "cardiometabolic factor" (explaining 16.8% of the variability), the third was the "adiposity factor" (15.2%), and the last was the "serum cholesterol factor" (9.4%).

Distribution, levels, sources and risk assessment of polycyclic aromatic hydrocarbons in the bottom sediments of a Mediterranean river under multiple anthropopressures (Soummam River), Algeria.

The Soummam River, a vital watercourse in Algeria is threatened by anthropogenic activities despite its protected wetland status. This study is the first to assess sediment pollution in the Soummam River, examining levels, compositions, sources of 16 PAHs and their effects on the environment and human health. Analysis employing Principal Component Analysis (PCA) and molecular diagnostic ratios pointed to petrogenic sources, likely stemming from petroleum leaks originating from aging pipeline and vehicles, as well as pyrogenic sources arising from vehicle exhaust and biomass combustion. Environmental and health risks were assessed through risk quotients (RQ), Sediments Quality Guidelines (SQG) and Total Lifetime Cancer Risk (TLCR). Ecological risk was found to range from moderate to high, with anticipated biological impacts, while cancer risk was deemed low. Toxicity assessment, measured by TEQ, revealed that the majority of monitoring stations exceeded safe levels. Consequently, urgent action by local authorities is warranted to implement ecosystem rehabilitation measures.

Volatile N-nitrosamines in processed meat products: An approach for monitoring dietary exposure, assessing human risk, and evaluating variable correlations by principal component analysis and heat map.

Several epidemiological studies have reported a positive association between the consumption of processed meats containing N-nitrosamines (NAs) and the incidence of hepatocellular and colon cancer. The health risk assessment in this investigation was based on the concentration of six volatile N-nitrosamines (VNAs) (N-nitrosodimethylamine, N-nitrosodiethylamine, N-nitrosomethylethylamine, N-nitrosopiperidine, N-nitrosodibutylamine, and N-nitrosodi-n-propylamine) found in processed meat products (sausage and kielbasa) in the Iranian market. Direct supported liquid membrane two-phase hollow fiber electromembrane extraction coupled to gas chromatography/mass spectrometry was used to analyse six VNAs. The mean concentration of the six VNAs in sausages and kielbasa was 38.677 ± 27.56 and 48.383 ± 35.76 µg/kg, respectively. The 95th percentile for the chronic daily intake of total VNAs for children (3-14 years) and adults (15-70 years) were calculated to be 5.06 × 10-4 and 1.09 × 10-4 mg/kg bw/day, respectively. The cancer risk assessment showed that the risk associated with NDEA was the highest among the other VNAs studied in Iranian processed meat, with a 95th percentile for the child and adult groups. Based on an incremental lifetime cancer risk (ILCR) value of ≤10-4 for the carcinogenic effects of exposure to a total of six VNAs, it indicates low concern for all age groups.

Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma.

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

Multivariate Analysis of Light-Activated SMOX Gas Sensors.

Chemoresistive gas sensors made from SnO2, ZnO, WO3, and In2O3 have been prepared by flame spray pyrolysis. The sensors' response to CO and NO2 in darkness and under illumination at different wavelengths, using commercially available LEDs, was investigated. Operation at room temperature turned out to be impractical due to the condensation of water inside the porous sensing layers and the irreversible changes it caused. Accordingly, for sensors operated at 70 °C, a characterization procedure was developed and proven to deliver consistent data. The resulting data set was so complex that usual univariate data analysis was intricate and, consequently, was investigated by correlation and principal component analysis. The results show that light of different wavelengths affects not only the resistance of each material, both under exposure to the target gases in humidity and in its absence, but also the sensor response to humidity and the target gases. It was found that each of the materials behaves differently under light exposure, and it was possible to identify conditions that need further investigations.

Specificity of carbon nanotube accumulation and distribution in cancer cells revealed by K-means clustering and principal component analysis of Raman spectra.

Single-walled carbon nanotubes (SWCNTs) show great potential for their application as cancer therapeutic nanodrugs, but the efficiency and mechanism of their accumulation in the cell, the modulation of cell activity, and the strong dependence of the results on the type of capping molecule still hinder the transfer of SWCNTs to the clinic. In the present study, we determined the mechanism and sequence of accumulation, distribution and type discrimination of SWCNTs in glioma cells by applying K-means clustering and principal component analysis (PCA) of Raman spectra of cells exposed to SWCNTs capped with either DNA or oligonucleotides (ON). Based on the specific biochemical information uncovered by PCA and further applied to K-means, we show that the accumulation of SWCNT-DNA occurs in two phases. The first phase involves the transport of SWCNT-DNA through vesicles and its redistribution in the cytoplasm, which is reflected in two SWCNT-related clusters. The second phase begins after 18 hours of interaction between cells and SWCNT-DNA. PCA shows the appearance of two SWCNT-associated PC loadings, reflected by the addition of a new cluster of SWCNTs with a narrowed and shifted G-peak in the spectra. It is caused by the loss of DNA capping and clumping of SWCNTs and triggered by the acidic conditions in autolysosomes resulting from the fusion of transport vesicles with lysosomes. SWCNTs penetrate all cellular compartments after 42-66 hours and lead to cell death. The clumped SWCNTs are released to the outside. In contrast, SWCNT-ON is hardly accumulated in glioma cells and after 72 hours of exposure to SWCNT-ON, the accumulation of SWCNTs corresponds to the first stage without reaching the second. PCA made it possible to separate the characteristics of cellular components against the high-intensity Raman signal from nanotubes and, thus, to propose the mechanism of accumulation and metabolism of nanomaterials in living cells without the use of additional research approaches. Our results elucidate the time dependence of the accumulation of SWCNTs on the capping molecule. We expect that our results can make an important contribution to the use of these nanomaterials in the clinic.

Multivariate analysis of carcinogenic equivalence (CEQ) to characterize carcinogenic VOC emissions in a typical petrochemical industrial park in Taiwan.

Large industrial emissions of volatile organic compounds (VOCs) from the petrochemical industry are a critical concern due to their potential carcinogenicity. VOC emissions vary in composition depending on the source and occur in mixtures containing compounds with varying degrees of toxicity. We proposed the use of carcinogenic equivalence (CEQ) and multivariate analysis to identify the major contributors to the carcinogenicity of VOC emissions. This method weights the carcinogenicity of each VOC by using a ratio of its cancer slope factor to that of benzene, providing a carcinogenic equivalence factor (CEF) for each VOC. We strategically selected a petrochemical industrial park in southern Taiwan that embodies the industry's comprehensive nature and serves as a representative example. The CEQs of different emission sources in three years were analyzed and assessed using principal component analysis (PCA) to characterize the major contributing sectors, vendors, sources, and species for the carcinogenicity of VOC emissions. Results showed that while the study site exhibited a 20.7 % (259.8 t) decrease in total VOC emissions in three years, the total CEQ emission only decreased by 4.5 % (15.9 t), highlighting a potential shift in the emitted VOC composition towards more carcinogenic compounds. By calculating CEQ followed by PCA, the important carcinogenic VOC emission sources and key compounds were identified. More importantly, the study compared three approaches: CEQ followed by PCA, PCA followed by CEQ, and PCA only. While the latter two methods prioritized sources based on emission quantities, potentially overlooking less abundant but highly carcinogenic compounds, the CEQ-first approach effectively identified vendors and sources with the most concerning cancer risks. This distinction underscores the importance of selecting the appropriate analysis method based on the desired focus. Our study highlighted how prioritizing CEQ within the analysis framework empowered the development of precise control measures that address the most carcinogenic VOC sources.