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Near-Infrared Spectroscopy for Growth Estimation of Spirulina platensis Cultures

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of Spirulina platensis cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000–2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.6 nm, connected with reduced water absorption and scattering effects by the microalga, was identified from PCA as the positive peak in the first loading plot, correlating diffuse reflectance with dilution levels. The calibration curve of diffuse reflectance at 1062.6 nm in response to dilution presented strong linearity, supported by a coefficient of determination (R2) of 0.995. Cross-validation of NIR spectra with a S. platensis culture confirmed the method’s reliability, showing that the growth follows an exponential pattern. The study shows that diffuse reflectance NIR spectroscopy can be used for the rapid monitoring of Spirulina platensis growth.

B-332 Use of urinary volatile organic compounds in monitoring prostate cancer after radical prostatectomy

Abstract Background Prostate cancer (PCa) is the most commonly diagnosed non-cutaneous cancer among males and the second cause of cancer-related deaths. PCa treatment involves different methods, and most common is radical prostatectomy (RP), which entails removal of prostate-gland from men who have been diagnosed with PCa. Prostatic-specific-antigen (PSA) has been used for detection of recurrent disease for years. After its introduction as biochemical recurrence (BCR) marker, men with distinct diseases were treated similarly. Overtime, it has been observed that PSA relapse presents several meanings according to clinicopathological features, like Gleason score, PSA-doubling-time, clinical stage, surgical margin status. Despite this, PSA remains main tool to assess disease progression after RP. RP remains primary treatment for localized PCa and has been performed for years with excellent oncologic control. However, approximately 20-40% of patients with clinically localized PCa will present biochemical-recurrence (BCR) after RP, while some with recurrent-metastasis. The study aims to investigate potential volatile organic compounds (VOCs) associated with men who have undergone RP but still abhor the disease. Methods Urine samples were collected from 110 male adults with biopsy-proven PCa-positive results before and after RP. VOCs in urine were extracted by stir-bar-sorptive-extraction and analyzed using thermal-desorption with gas chromatography-mass spectrometry. Metabolomics and machine-learning tools used to develop and evaluate models for PCa diagnosis before and after RP. Results PLS-DA and VIP loading plots identified potential VOC panels that could be used to differentiate PCa patients before and after RP and those with BCR and recurrent metastasis. Furthermore, logistic-regression results with validation provide a promising diagnostic model for PCa diagnosis after RP. Conclusions This novel method using urinary VOCs was developed to fill gaps in PCa monitoring after RP. These research findings could aid and fast-track the provision of rapid point of care to the treated PCa patients who still abhor the disease.

The principal component analysis of antioxidant activities of Songgak, a traditional herbal drink in West Nusa Tenggara, Indonesia

Songgak is a traditional herbal tea of the Sasak Tribe in Lombok Island, Indonesia, formulated to strengthen people's stamina. Songgak is composed of seven kinds of roasted spices: Piper nigrum L., Coriandrum sativum L., Piper retrofractum Vahl, Myristica fragrans Houtt, Syzygium aromaticum, Elaeocarpus grandiflorus, and Helicteres isora. This study aimed to assess the benefits of Songgak and its constituent spices by evaluating their antioxidant activities, total phenolic content (TPC) and flavonoid content (TPC). Antioxidant activity tests were performed on the water extract of Songgak, ethanolic extract of Songgak, and ethanolic extract of seven spices of Songgak including 2,2′- diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging, Trolox equivalent antioxidant capacity (TEAC), and ferric reducing antioxidant power (FRAP) assay. The antioxidant activity using DPPH method showed that the water extract of Songgak has a strong categorisation of antioxidant activity with DPPH IC50 value of 63.78±4.29 µg/mL. The constituent species of Songgak with the strongest antioxidant activity was S. aromaticum. Total phenolic content (TPC) and total flavonoid content (TFC) of Songgak tea were 26.85±4.29 mg GAE/g and 6.99±0.29 mg RE/gram, respectively. The correlation between TPC, TFC, and antioxidant activities was determined using principal component analysis (PCA). The loading plot showed that TPC and TFC were strongly correlated with the antioxidant activity (FRAP method) of Songgak tea and its constituent spices. These results revealed that the presence of flavonoid and phenolic compounds in the seven herbal medicinal plants were responsible for the antioxidant properties of Songgak as a healthy herbal drink. Therefore, consuming Songgak every day can be beneficial for preventing various diseases involving free radicals.

Determination of antioxidant capacity and phenolic content of haskap berries (Lonicera caerulea L.) by attenuated total reflectance-Fourier transformed-infrared spectroscopy

The total phenolic content (TPC) and antioxidant capacity (TAC) of haskap berries cultivated in various locations across Alberta were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The Folin–Ciocalteu assay was used to determine TPC, while TAC was quantified by 2,2-diphenyl-1-picrylhydrazl radicals (DPPH) assay and oxygen radical absorbance capacity (ORAC) assay. Three tenfold cross-validated partial least-squares regression (PLSR) models and three fivefold cross-validated deep learning models were developed separately based on FT-IR spectra collected from 22 haskap berry samples and their corresponding reference values determined through Folin-Ciocalteu, DPPH, and ORAC assays. The deep learning models (R2 = 0.95, 0.93, and 0.90 for Folin-Ciocalteu, DPPH, and ORAC assays, respectively) demonstrated better prediction capability compared to the PLSR models (R2 = 0.74, 0.72, and 0.66 for Folin-Ciocalteu, DPPH, and ORAC assays, respectively). In addition, PLS loading plots indicated that phenolic contents and polysaccharides in haskap berries could contribute to their antioxidant capacity. Using ATR-FTIR to estimate the TPC and TAC of fruits offers a rapid alternative to the conventional chemical assays.

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.

Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outcomes. This paper proposed a label-free and highly sensitive classification method for serum of patients with different degrees of gastric lesions by combining surface-enhanced Raman scattering (SERS) and machine learning analysis. Specifically, we prepared Au lotus-shaped (AuLS) nanoarrays substrates using seed-mediated and liquid-liquid interface self-assembly method for measuring SERS spectra of serum, and then the collected spectra were processed by principal component analysis (PCA) - multi-local means based nearest neighbor (MLMNN) model to achieve differentiation. By employing this pattern analysis, AuLS nanoarray substrates can achieve fast, sensitive, and label-free serum spectral detection. The classification accuracy can reach 97.5%, the sensitivity is higher than 96.7%, and the specificity is higher than 95.0%. Moreover, by analyzing the PCs loading plots, the most critical spectral features distinguishing different degrees of gastric lesions were successfully captured. This discovery lays the foundation for combining SERS with machine learning for real-time diagnosis and recognition of gastric lesions.

Efficient Taste Analysis via Single-Channel Hybrid Electronic Tongue

The prospective applications of electronic tongues (e-tongues) in the food, water, beverage, and pharmaceutical industries have long been hampered by the inconvenient nature of series measurements using expensive and specialised sensor arrays. A cost-efficeint and easier solution to this problem arises in the shape of a single-channel hybrid e-tongue, presenting a novel approach to overcoming restrictions. This progress uses data fusion from potentiometry and impedimetry to combine these measures into a single channel made up of unfunctionalized metal electrodes supported by a pH electrode.The hybrid e-tongue combines potentiometric response, which includes pH and open circuit potential, with impedimetric response, which takes into account comparable electric circuit parameters such as charge transfer resistance and double-layer capacitance. The taste discriminability of this low-cost single-channel hybrid e-tongue is validated using two metal electrodes, Pt and Ni, and two separate sample preparations of basic taste analytes, each at concentrations around the human tongue threshold.Principal component analysis (PCA) plots are used to test qualitative discrimination by reducing dimensionality and clustering analytes. Silhouette coefficients (SC) and hierarchical cluster analysis are used to assess clustering quality. Even without particular sample preparation, the Pt and Ni electrodes exhibit substantial clustering with SC values of 0.83 and 0.74, respectively. This significant outcome highlights the efficacy of the hybrid e-tongue in improving taste discriminability.Comparisons with potentiometric and impedimetric measurements performed independently show that the hybrid e-tongue, assisted by data fusion at a single channel, improves accuracy and clustering significantly. This finding not only speeds up the analysis process, but it also improves the overall effectiveness of taste discrimination.Correlations between taste analytes and physicochemical factors are formed using heatmap plots and PCA loading plots to provide a fuller knowledge of the hybrid e-tongue's mechanics. These visualisations provide vital insights into the complex interplay of several physicochemical processes, offering insight into the mechanism underlying the hybrid e-tongue's excellent performance.discrimination.Despite its remarkable capabilities, the study acknowledges that taste discriminability diminishes due to the buffer activity of the standard reference solution used for sample preparation. This recognition prompts further exploration and optimization of the solution to ensure the broad applicability of the hybrid e-tongue across various scenarios.In conclusion, the reported single-channel hybrid e-tongue represents a significant advancement in taste analysis technology. By addressing the challenges associated with series measurements on complex sensor arrays, this innovation opens new avenues for commercial e-tongues in diverse industries. The amalgamation of potentiometry and impedimetry in a single channel not only enhances accuracy and clustering but also provides valuable mechanistic insights. As the research community continues to refine and optimize this technology, the future holds great promise for the widespread adoption of single-channel hybrid e-tongues in analytical applications, advancing how we perceive and understand taste.

Thiolated nanoclays as a potential mucoadhesive material: Optimization by design of experiments and multivariate regression

An integrated strategy based on design of experiments (DoE) and multivariate regression was carried out to produce an optimized surface functionalized nanoclay (Mt–SH) with potential mucoadhesive properties. A mercaptosilane was grafted in montmorillonite in order to immobilize thiol groups (–SH) on the clay. The functionalization was confirmed by IR, TGA, XRF and XRD analyses. Five variables involved in the synthesis were optimized in order to maximize two responses: mass of functionalized nanoclay and amount of thiol groups attached in the clay surface. This optimization was performed by a two–step DoE strategy using Plackett–Burman and Box–Behnken designs. The five variables and two responses were simultaneously calibrated in a single ANOVA–validated PLS regression model. Analyzing scores, loading and surface response plots, the main processing variables, interactions and quadratic terms were found achieving the optimal processing conditions for the synthesis. The quadratic model (R2 > 0.8589) predicted an optimized functionalized nanoclay of mass 3.29 ± 0.13 g of Mt–SH with 19.65 ± 0.78 μmol –SH per g Mt–SH (P < 0.05). The optimized Mt–SH was evaluated by in vitro and in situ mucoadhesion assays with porcine mucin and fresh intestinal mucus by using rheometry. The complex viscosity showed an increase of ∼54-fold higher compared to the unmodified Mt, and ∼12.8-fold increase in the elastic modulus, which indicates a more resistance to elastic deformation probably due to an increase in cross-linking points between Mt–SH and mucus. The formation of disulfide bonds between thiol groups from Mt–SH and glycoproteins in the mucin could be a reasonably explanation which shows a correlation respect other thiolated materials which show the same mucoadhesion properties. These results could project this functionalized silicate material as a potential mucoadhesive prospect for pharmaceutical applications.

Non-destructive monitoring of qualitative properties of salted cabbage using hyperspectral image analysis

The feasibility of using VIS-NIR and SWIR hyperspectral imaging (HSI) to determine the salinity, soluble solids, and water content in salted kimchi cabbages during the salting process was assessed. Hyperspectral images of kimchi cabbages acquired at different salt concentrations and periods of the salting process were developed as prediction and discriminant models for the qualitative properties. Principal component analysis showed that the distribution according to the spectral characteristics of kimchi cabbages could be grouped, and data reduction for optimal model developing was attempted based on the key wavelengths selected in the loading plot. The optimal partial least squares regression models for the prediction of salinity and soluble solids had best performance by achieving R2p values of over 0.86 and 0.90 in the VIS-NIR and SWIR regions, respectively. The accuracy and specificity of the optimal PLS-DA model for salinity level have improved efficiency to over 0.93. Visualization of the spectrum information in each pixel of the hyperspectral image using the PCA model displayed the salinity level in kimchi cabbages under different salting conditions. The prediction models were sufficiently accurate to consider HSI as a useful tool for controlling and optimizing the salting process.

Changes in Metabolite Profiles of Chinese Soy Sauce at Different Time Durations of Fermentation Studied by 1H-NMR-Based Metabolomics.

Fermentation parameters, especially the duration, are important in imparting a peculiar taste and flavor to soy sauce. The main purpose of this research was to monitor metabolic changes occurring during the various time intervals of the fermentation process. NMR-based metabolomics was used to monitor the compositional changes in soy sauce during fermentation. The 1H-NMR spectra of the soy sauce samples taken from the fermentation tanks at 0 to 8 months were analyzed using 1H-NMR spectroscopy, and the obtained spectra were analyzed by multivariate statistical analysis. The Principal Component Analysis (PCA) and Partial Least Square Discriminate analysis (PLSDA) revealed the separation of samples fermented for various time durations under identical conditions. Key metabolites shown by corresponding loading plots exhibited variations in amino acids (lysine, threonine, isoleucine, etc.), acetate, glucose, fructose, sucrose, ethanol, glycerol, and others. The levels of ethanol in soy sauce increased with longer fermentation durations, which can be influenced by both natural fermentation and the intentional addition of ethanol as a preservative. The study shows that the variation in metabolite can be very efficiently monitored using 1H-NMR-based metabolomics, thus suggestion to optimize the time duration to get the soy sauce product with the desired taste and flavor.

Laser-induced breakdown spectroscopy analysis of tourmaline: protocols, procedures, and predicaments

Abstract. Laser-induced breakdown spectroscopy (LIBS) is an appropriate choice of analytical tool for analysis of complex minerals because it is rapid, requires little sample preparation, and acquires major and trace element compositional information on all naturally occurring elements at concentrations above their intrinsic levels of detection for the specific analyte material. Tourmaline, a complex borosilicate mineral supergroup, was chosen as a test mineral due to the complexity of its major and minor element composition. Four analytical issues were investigated during project development: (1) the spacing between analytical laser shots to avoid analysis of the recast from previous laser ablations, (2) the efficacy of using a cleaning shot prior to data acquisition, (3) the number of ablations required to collect an average spectrum that is representative of the tourmaline sample, and (4) the effect of spectrometer drift on principal component analysis (PCA) when using the entire LIBS spectra to model the compositional variations within the sample suite. The minimum spacing between locations of analysis was determined to be 800 µm for the analytical conditions used in this study by examining spectra acquired in a 2×2 grid across a quartz–tourmaline boundary. At a spacing of 100 µm, twice the diameter of the laser beam, the intensity of the boron I peaks at 249.68 and 249.77 nm was very low in the first location (quartz) but quite high in the fourth location (quartz) due to deposition of tourmaline-composition recast by laser shots in the second and third locations (both on tourmaline). Increasing the distance between locations to 800 µm ensured that the area analyzed largely avoided the recast layer from previous ablations. Given that the distribution of recast was taken into account, no cleaning shots were collected. PCA score plots calculated using successively larger numbers of spectra from the same sample show that a total of 64 spots, or 16 2×2 grids, are needed to acquire a representative average analysis of tourmaline. Spectrometer drift was recognized in PCA loading plots by a characteristic splitting of element peaks; half the peak indicates positive loading and the other half of the peak indicates negative loading. Drift correction was aligned by placing the Ca II peak at 393.34 nm in the 393.398 bin; this correction eliminated split peaks in loading plots. The resolution of these issues yielded LIBS spectra suitable for multivariate statistical analysis that can be applied to understanding geologic processes. These results contribute to the application of rapid LIBS analysis of complex geomaterials.

Essential and toxic elements analysis of wild boar tissues from north-eastern Romania and health risk implications

IntroductionThe level of essential minerals in meat is an important factor in human nutrition and health. Meat from responsibly managed wildlife is an alternative raw material with considerable nutritional benefits. Meat from hunted animals has essential and non-essential elements for the human body. It is important to carefully monitor the levels of heavy metals accumulated in the tissues of hunted animals in polluted areas to ensure food safety and environmental contamination. High levels of heavy metals in food and the environment can pose a danger to human health.MethodsThe study aims to investigate the levels of essential mineral elements and heavy metals in the muscle tissue and organs of wild boar harvested through the herd density control plan over the last decade in north-eastern Romania.Results and discussionThe statistical analysis indicates that the age of the animals had a significant impact on Fe, Cu, and Zn levels in Longissimus lumborum. In the kidney a highly significant difference in Fe content by sex, with males showing higher values than females. The sex was shown to significantly influence the Mg levels. However, there are concerns about the accumulation of heavy metals such as Lead (Pb) and Cadmium (Cd), which may hurt the health of game meat consumers in the study area. Cd level it shows significant differences according to both age and sex, with higher concentrations in adults and males. Statistical analysis shows a negative correlation between Fe and Zn concentrations in muscle samples, while a positive correlation was found between Fe and Mn in kidney samples. There was also a positive association between Zn and Cu in muscle samples, but a negative association in kidney samples. Principal component analysis shows significant variation in essential element and heavy metals data between muscle and kidney samples. The loading plot shows a direct correlation between Pb and Cu and between Pb and Cd. However, an opposite correlation also is observed between Cu and Mg, Cd and Mg, and Pb and Mg. HQ (Hazard Quotient) for children compared to adults indicates a potentially higher risk associated with meat consumption among children because children are more vulnerable than adults. We report for the first time, to the best authors’ knowledge, various levels of essential minerals and exceeded maximum admitted level of heavy metals in the muscle tissues and kidneys of Sus scrofa ferus from Romania intended for human consumption, moreover, our findings highlight the need for strict monitoring and implementation of appropriate corrective measures, given the significant percentages of muscle and kidney samples exceeding the allowable limits for two of the most common toxic metals in the environment.

Experimental and Analytical Study on Recycled Aggregate RC Columns: Short and Slender Loaded Axially and Eccentrically

To protect the environment and preserve natural resources, it is crucial to use recycled aggregate (RA) in construction. The recycled coarse aggregate reinforced concrete columns with the addition of steel fiber evaluated under concentric and eccentric loadings for short and slender columns were examined experimentally and analytically in this research. Twenty-four column specimens were built for this study to examine the impact of steel fiber, recycled aggregate, slenderness, and eccentricity on the behavior of reinforced concrete columns. This research examined the failure mode, maximum load-carrying capacity, strain in the concrete, strain in the reinforcement, and ductility. Based on the results, it can be concluded that employing recycled concrete aggregate is a potential approach to meet design codes. The addition of 1% steel fiber effectively prevents concrete from crushing and spalling. Steel fiber, however, improved the columns’ ductility and strength. The results showed the maximum load-carrying capacity of the specimens and the results of using ACI-318 code equations agreed very well. Furthermore, a model is proposed for columns with both natural and recycled aggregate and which accounts for the eccentricity and slenderness to forecast the load-carrying capacity. The outcomes demonstrated that the design principles were met well. Plots of load–moment interaction diagrams for short and slender columns made with the ACI-318 method are compared to the findings of the experiments.

A forensic spectroscopic identification analysis on skin evanescent trauma by chemometrics

In contemporary biomedicine, rapid diagnosis and accurate treatment of trauma is a top priority. The skin, as the organ with the most extensive contact with the outside world, is always inevitably and easily scarred when subjected to varying degrees of violence. However, when evanescent trauma occurs, the judgment of trauma becomes quite difficult, especially for closed trauma. Evanescent trauma mainly refers to the postmortem autolysis and corruption of body tissues, which lead to the failure to identify the traumatic state occurring before death using traditional detection techniques. Rapid and accurate identification of trauma and even trauma in the evanescent state plays an important role in the actual forensic examination. There have been few records on the development of quick and accurate models for the recognition and prediction of evanescent trauma on the skin. In this study, a predictive model for rapid identification of evanescent trauma in skin tissue was constructed by combining forensic spectroscopy and chemometrics analysis. Based on the mean spectra, principal component analysis (PCA) and corresponding loading plots, suggested that certain biomolecules, such as proteins and lipid molecules, might be the source of the difference between the control group and trauma group, and furthermore, there was a certain pattern of change of each molecule in the continuation of postmortem time. Partial least squares discriminant analysis (PLS-DA) was then applied to estimate the identification power of the training dataset and the testing dataset. The AUC values were 89.55 % and 94.67 %. In addition, the AUC values of the fresh-phase trauma recognition model were 100 % and 100 %, respectively, and the AUC values of the evanescent-phase trauma recognition model were respectively 92.52 % and 98.77 %. In summary, the combination of forensic spectroscopy and chemometrics completely applies their advantages of rapidity, accuracy, objectivity, high resolution and discriminative power to the study of evanescent trauma identification. Moreover, in judicial reality, the combination of spectroscopy and stoichiometry is also expected to make a huge difference in medical and criminal law applications.

Infrared microspectroscopy to elucidate the underlying biomolecular mechanisms of FLASH radiotherapy

Background:FLASH-radiotherapy (FLASH-RT) is an emerging modality that uses ultra-high dose rates of radiation to enable curative doses to the tumor while preserving normal tissue. The biological studies showed the potential of FLASH-RT to revolutionize radiotherapy cancer treatments. However, the complex biological basis of FLASH-RT is not fully known yet. Aim:Within this context, our aim is to get deeper insights into the biomolecular mechanisms underlying FLASH-RT through Fourier Transform Infrared Microspectroscopy (FTIRM). Methods:C57Bl/6J female mice were whole brain irradiated at 10 Gy with the eRT6-Oriatron system. 10 Gy FLASH-RT was delivered in 1 pulse of 1.8μs and conventional irradiations at 0.1 Gy/s. Brains were sampled and prepared for analysis 24 h post-RT. FTIRM was performed at the MIRAS beamline of ALBA Synchrotron. Infrared raster scanning maps of the whole mice brain sections were collected for each sample condition. Hyperspectral imaging and Principal Component Analysis (PCA) were performed in several regions of the brain. Results:PCA results evidenced a clear separation between conventional and FLASH irradiations in the 1800–950 cm−1 region, with a significant overlap between FLASH and Control groups. An analysis of the loading plots revealed that most of the variance accounting for the separation between groups was associated to modifications in the protein backbone (Amide I). This protein degradation and/or conformational rearrangement was concomitant with nucleic acid fragmentation/condensation. Cluster separation between FLASH and conventional groups was also present in the 3000–2800 cm−1 region, being correlated with changes in the methylene and methyl group concentrations and in the lipid chain length. Specific vibrational features were detected as a function of the brain region. Conclusion:This work provided new insights into the biomolecular effects involved in FLASH-RT through FTIRM. Our results showed that beyond nucleic acid investigations, one should take into account other dose-rate responsive molecules such as proteins, as they might be key to understand FLASH effect.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined With Deep Learning for Classification of Idiopathic Recurrent Spontaneous Miscarriage (IRSM)

Idiopathic recurrent spontaneous miscarriage (IRSM) is characterized by the repeated losses of two or more clinically detected pregnancies before 24 week of gestation with the cause remaining unknown. We aim to ascertain whether attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with deep learning (DL) can accurately distinguish IRSM from controls. Endometrial tissue (internal lining of the uterine cavity) was collected from 20 women with IRSM and 20 controls during window of implantation. ATR-FTIR spectroscopy was used to acquire spectra (400–4000 cm−1) from three random locations across each specimen and averaged. Multivariate analysis was used to assess group differentiation. Furthermore, DL classifiers were implemented to predict IRSM. Hierarchical cluster analysis (HCA) demonstrated distinct clustering of the two groups. The principal component analysis (PCA) loading plot indicated contributions from amide I, II and III bands, symmetric methyl (CH3) bending of proteins, lipids, nucleic acids, asymmetric stretching of CH2 due to lipids, collagen, and glucose in discriminating between IRSM and controls. Supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) exhibited optimized discrimination between the groups. Convolutional neural network (CNN) and variational autoencoder-artificial neural network (VAE-ANN) classifiers exhibited good performance and showed classification accuracy of 94% and 94% and F1 scores (mean of recall and precision) of 95% and 93%, respectively. The optimal area under the receiver operating characteristic curve (AUC) value of both CNN and VAE-ANN was 0.94. ATR-FTIR spectroscopy combined with DL approach is promising for understanding complex disease pathophysiology and enabling early prediction.

Abstract 7097: Integrated proteomics and metabolomics reveals new insight into the synergistic interaction of valproic acid plus simvastatin in prostate cancer xenograft model associated with down-modulation of YAP and TAZ signaling

Abstract Background: Despite advancements in therapeutic approaches, including taxane-based chemotherapy and androgen receptor targeting agents, metastatic castration-resistant prostate cancer (mCRPC) remains an incurable disease, underlying the need of novel strategies that can target the complexities of this disease and bypass the development of drug-resistance mechanisms. We previously demonstrated the synergistic antitumor interaction of the antiepileptic with histone deacetylase inhibitory activity valproic acid (VPA), and the lipid-lowering drug simvastatin (SIM). This combination sensitizes mCRPC cells to docetaxel treatment and revert docetaxel-resistance, both in vitro and in vivo models, by targeting cancer stem cells compartment via mevalonate pathway/YAP axis modulation. Methods: Proteomic and metabolomic/lipidomic analysis on tumor samples were performed by LC-MS/MS-based shotgun proteomics and by 1H-NMR approach, respectively. Progenesis QI for proteomics v. 4.2 was used as label-free quantification platform. Functional annotation analysis were performed by using the DAVID v6.8 (Database for Annotation, Visualization and Integrated Discovery); g:Profiler WikiPathways databases; Reactome version 66; Ingenuity Pathway Analysis software. Partial least squares-discriminant analysis (PLS-DA) and Loading plot were performed by Metabo Analyst v5.0 tool. Results: Tumor samples derived from 22Rv1 mCRPC cells xenografted mice, treated or not with VPA/SIM combination were characterized by a combined proteomic and metabolomic/lipidomic approach. In deep bioinformatics analysis confirmed a specific impact of VPA/SIM on Hippo pathway, with a clear down regulation of YAP and its targets (CTGF, CycD1 ANKH1) in treated vs untreated tumors, functionally related with modulation of cancer-related extracellular matrix remodelling and metabolic reprogramming. Treatment also downregulated TEAD transcription factor mediating YAP-induced expression, along with the upregulation of 14-3-3, responsible for YAP cytoplasmic retention and degradation, paralleled by the up-regulation of YAP up-stream negative regulators such as LATS1 and AMPK. VPA/SIM also modulated intermediates of TCA, urea cycles and glycolysis and decreased cholesterol and fatty acids. Conclusion: In summary, this study provides further insights into the molecular mechanism of the VPA/SIM antitumor effect suggesting the potential of repurposing these two generic and safe drugs in combination with standard antitumor therapies also in other tumors besides mCRCP. Citation Format: Federica Iannelli, Rita Lombardi, Susan Costantini, Maria Serena Roca, Laura Addi, Francesca Bruzzese, Elena Di Gennaro, Alfredo Budillon, Biagio Pucci. Integrated proteomics and metabolomics reveals new insight into the synergistic interaction of valproic acid plus simvastatin in prostate cancer xenograft model associated with down-modulation of YAP and TAZ signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7097.

Metabolite Profiles of Sukabumi Arabica Green Coffee Beans Evaluated by 1H NMR-Based Metabolomics

Although Sukabumi arabica coffee is one of the famous coffees in Indonesia, however its chemical information is still limited in the literature. In this work, the metabolite profiles of arabica green coffee beans obtained from various plantations in Sukabumi, including Ciayunan, Pondok Halimun, and Selabintana were evaluated by 1H NMR-based metabolomics. In total, 19 metabolites were successfully identified, including the major and minor metabolites of the coffee. The score plot of the OPLS-DA (Orthogonal Partial Least Square Discriminant Analysis) model was successful in classifying the metabolite profiles of the coffees based on their origins. The loading plot analysis showed that the signals belonging to fatty acids, sucrose, trigonelline, chlorogenic acid, lactic acid, and quinic acid, contributed to the classification. S-plot analysis revealed that Selabintana coffee was characterized with higher concentrations of trigonelline and sucrose, whereas Ciayunan sample had higher levels of fatty acids. Meanwhile, the metabolite profile of Pondok Halimun coffee demonstrated an intermediate characteristic between the Ciayunan and Selabintana samples. This work provided valuable scientific information for coffee development especially in West Java and generally in Indonesia.&#x0D; &#x0D; Keywords: metabolomics, multivariate data analysis, NMR, Sukabumi arabica green coffee beans.

Water-soluble ions and nitrogen and oxygen stable isotope ratios in nitrate in sake in Akita, Japan

Sake is a traditional alcoholic beverage in Japan, and demand for it has increased overseas in recent years. In this study, we analyzed oxygen and nitrogen of stable isotope ratios in nitrate (δ18O–NO3– and δ15N–NO3–) in beverages by using the denitrifying bacteria for the first time. We measured the concentrations of five water-soluble ions and the δ18O–NO3– and δ15N–NO3– values of 49 commercial sakes (11 sake breweries) made in Akita Prefecture, Japan. The similarity of the concentrations of NO3− in sake and groundwater suggested that these ions were derived from brewing water. The δ18O–NO3– value of the 49 sake samples ranged from 2.2 to 12.3‰ (mean, 4.7 ± 2.3‰). The δ15N–NO3– value in the sake ranged from 1.8 to 8.7‰ (mean, 6.1 ± 1.8‰). The δ15N–NO3– value of sake could be used to identify breweries because the δ15N–NO3– value was similar for different sake types in the same brewery. Principal component analysis revealed that the first four principal components could account for 85.2% of the variance of the experimental data. The factor loading plots was found to be similar for different sake types in the same brewery.

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis.

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6). Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM. The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

THE NEW APPROACHES TO IDENTIFICATION OF TINCTURES AND MEDICINAL PLANTS

Objective: The objective of this study is to develop methods for identifying herbal medicines and tinctures by processing spectral results across a wide range of wavelengths using principal component analysis (PCA). Methods: Medicinal plants and tinctures of valerian, motherwort, and hawthorn have been analyzed using UV spectrophotometry, spectrofluorimetry, ATR FTIR spectrometry, and X-ray fluorescence spectrometry. PCA was used to process the results of spectral analysis. Statistical processing of spectral results was carried out using the OriginPro program (OriginLab Corporation, USA, 2021). Results: For herbal medicines with sedative, hypotensive, and cardiotonic effects, spectral data libraries have been created in the following dimensions: UV spectrophotometry with 1800 absorption units (Ai), spectrofluorimetry with 4010 fluorescence intensity units (Ii), IR spectroscopy with a light transmittance of 50250 units (Ti), and X-ray fluorescence spectrometry with an intensity of 1568 (Ii). These libraries were used as the primary matrices for PCA. Visualization of the PCA results was done using a scores plot and a loadings plot, which illustrate the contribution of each principal component (PC) to the PCA model. After performing chemometric processing on the original spectral results, it was discovered that samples belonging to the same botanical genus occupy distinct and compact regions in two-dimensional or three-dimensional space. Unknown plant samples (blind samples) and samples of other botanical species were successfully tested using new method. Conclusion: For the first time, tinctures and medicinal plants were identified based on their botanical genus using spectral techniques coupled with principal component analysis, eliminating the need for a chemical reference substance.