Peptidomic profile of human milk as influenced by fortification with different protein sources: An in vitro dynamic digestion simulation.

Fortification of human milk (HM) is often necessary to meet the nutritional requirements of preterm infants. The present experiment aimed to establish whether the supplementation of HM with either an experimental donkey milk-derived fortifier containing whole donkey milk proteins, or with a commercial bovine milk-derived fortifier containing hydrolyzed bovine whey proteins, affects peptide release differently during digestion. The experiment was conducted using an in vitro dynamic system designed to simulate the preterm infant's digestion followed by digesta analysis by means of LC-MS-MS. The different fortifiers did not appear to influence the cumulative intensity of HM peptides. Fortification had a differential impact on the release of either donkey or bovine bioactive peptides. Donkey milk peptides showed antioxidant/ACE inhibitory activities, while bovine peptides showed opioid, dipeptil- and propyl endo- peptidase inhibitory and antimicrobial activity. A slight delay in peptide release from human lactoferrin and α-lactalbumin was observed when HM was supplemented with donkey milk-derived fortifier.

Multivariate analysis of structural and functional properties of soluble dietary fiber from corn bran using different modification methods.

This study comprehensively investigated the effects of high-temperature cooking (HT), complex enzyme hydrolysis (CE), and high-temperature cooking combined enzymatic hydrolysis (HE) on the chemical composition, microstructure, and functional attributes of soluble dietary fiber (SDF) extracted from corn bran. The results demonstrated that HE-SDF yielded the highest output at 13.80 ± 0.20 g/100 g, with enhancements in thermal stability, viscosity, hydration properties, adsorption capacity, and antioxidant activity. Cluster analysis revealed three distinct categories of SDF's physicochemical properties. Principal component analysis (PCA) confirmed the superior functional properties of HE-SDF. Correlation analysis showed positive relationships between the monosaccharide composition, purity, and viscosity of SDF and most of its functional attributes, whereas particle size and zeta potential were inversely correlated. Furthermore, a highly significant positive correlation was observed between crystallinity and thermal properties. These findings suggest that the HE method constitutes a viable strategy for enhancing the quality of SDF sourced from corn bran.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Analysis and discrimination of adhesive species using ATR-FTIR combined with Raman, and HS-GC-IMS together with multivariate statistical analysis.

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science.

FT4 is a novel indicator for risk assessment of severe hypocalcemia following parathyroidectomy.

OBJECTIVE: To analyze the risk factors associated with the development of severe hypocalcemia (SH) in patients who have undergone parathyroidectomy (PTX). METHODS: This research involved patients with chronic kidney disease-secondary hyperparathyroidism who underwent PTX between June 1, 2021, and May 31, 2023. SH was characterized by a serum total calcium (tCa) level below 1.8 mmol/L. This study aimed to analyze differences in preoperative laboratory findings and clinical manifestations between patients with and without SH. Logistic regression analysis was used to identify potential risk factors associated with the development of SH. RESULTS: The incidence of SH was 23% (n = 176). Significant differences were observed in free thyroxine (FT4), free triiodothyronine, alanine aminotransferase, osteocalcin, tCa, alkaline phosphatase (ALP), C-terminal cross-linked telopeptide of type I collagen, and parathyroid hormone between the SH and non-SH groups. The three independent risk factors for SH were tCa [odds ratio (OR) 0.063, 95% confidence interval (95% CI) 0.006-0.663], ALP (OR 1.003, 95% CI 1.001-1.005), and FT4 (OR 0.439, 95%CI 0.310-0.621). The area under the curve, sensitivity, specificity, and overall accuracy of this model were 0.904 (95% CI 0.856-0.952), 46.3%(95% CI 32.0%-61.3%), 94.8% (95% CI 89.7%-97.5%), and 83.5% (95% CI 77.3%-88.3%), respectively. CONCLUSION: The preoperative level of FT4 plays a crucial role in predicting the risk of SH after PTX. The combined FT4-ALP-tCa model demonstrates the ability to predict SH risk, providing valuable insights for customizing calcium supplementation strategies and improving clinical decision-making.

Is visual information use during facial emotion recognition related to eating disorder symptoms in college-aged men and women? An experimental study.

BACKGROUND: Previous studies of emotion recognition abilities of people with eating disorders used accuracy to identify performance deficits for these individuals. The current study examined eating disorder symptom severity as a function of emotion categorization abilities, using a visual cognition paradigm that offers insights into how emotional faces may be categorized, as opposed to just how well these faces are categorized. METHODS: Undergraduate students (N = 87, 50 women, 34 men, 3 non-binary) completed the Bubbles task and a standard emotion categorization task, as well as a set of questionnaires assessing their eating disorder symptomology and comorbid disorders. We examined the relationship between visual information use (assessed via Bubbles) and eating disorder symptomology (EDDS) while controlling for anxiety (STAI), depression (BDI-II), alexithymia (TAS), and emotion regulation difficulties (DERS-sf). RESULTS: Overall visual information use (i.e. how well participants used facial features important for accurate emotion categorization) was not significantly related to eating disorder symptoms, despite producing interpretable patterns for each emotion category. Emotion categorization accuracy was also not related to eating disorder symptoms. CONCLUSIONS: Results from this study must be interpreted with caution, given the non-clinical sample. Future research may benefit from comparing visual information use in patients with an eating disorder and healthy controls, as well as employing designs focused on specific emotion categories, such as anger.

Men and women with severe eating disorder symptoms may find it harder to identify and describe emotions than people with less severe eating disorder symptoms. However, previous work makes it difficult to determine why emotion recognition deficits exist, and what underlying abilities or strategies are actually different due to a deficit. In addition to a typical emotion recognition task (emotion categorization), this study used the Bubbles task, which allowed us to determine which parts of an image are important for emotion recognition, and whether participants used these parts during the task. In 87 undergraduate students (47 female; 49 with clinically-significant eating disorder symptoms), there was no significant relationship between task performance and eating disorder symptom severity, before and after controlling for the relationship with other comorbid disorders. Our results imply that emotion recognition deficits are unlikely to be an important mechanism underlying eating disorder pathology in participants with a range of eating disorders symptoms.

Study of archaeological ceramics from the north coast of Peru by EDXRF and exploratory multivariate analysis.

This article presents the first results for elemental composition of 193 fragments ceramic from four sites located in Ventarrón-Collud archaeological complex, Peru. The results from EDXRF and multivariate analysis indicated statistical similarity among four groups of ceramic fragments, selected by remarkable stylistic variation, collected from different archaeological space/time contexts. The correspondence in the elemental composition of the ceramic paste of the artifacts corroborates the hypothesis that similar raw materials were used consistently throughout the entire occupation of this archaeological complex.

Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ9-tetrahydrocannabinol content.

In this work, we investigated the ability of an electrochemical sensor to recognize Cannabis sativa L. samples with different total content of Δ9-tetrahydrocannabinol (Δ9-THC), determined by the levels of the psychoactive cannabinoid and of its biosynthetic precursor Δ9-tetrahydrocannabinolic acid (Δ9-THCA), using a multivariate approach. The voltammetric responses recorded with screen-printed electrodes modified with carbon black reflected the compositional differences from the different samples, in terms of cannabinoids of the vegetal material. PLS-DA models allowed for the correct classification of most C. sativa samples into the classes of legal and illegal samples according to total Δ9-THC content, based on threshold limits defined by the EU/US (0.3 % w/w) and Italian (0.6 % w/w) regulations. Satisfactory results were achieved in both cases, obtaining classification efficiency values in prediction of the external test set equal to 85 % and 100 % for the EU/US and Italian thresholds, respectively. The obtained results suggest the possibility to consider the proposed method as a starting point for the implementation of an automated device for rapid prescreening of total Δ9-THC content directly on site.

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy.

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Ecotoxicity of bromate and human health risks resulting from wastewater treatment units' effluents associated with some key physicochemical parameters in two hotspots connected to the Egyptian Mediterranean Sea.

This study is the first of its kind in terms of focusing on the seasonal monitoring of bromine species (bromide- and bromate) and some of the main physicochemical parameters in the surface water of stations inside and in front of the El Noubareya and El-Umum drains that flow directly or indirectly to the Egyptian Mediterranean coast at A (El Noubareya Drain) and B (El-Mex Bay) sites. Among the bromine species, bromate (BrO3-) is a disinfection byproduct considered by many international agencies to have a potential carcinogenic effect in humans and is also known to be ecologically toxic to aquatic organisms. Drain water samples collected from the studied sites A and B had a bromide/chlorinity ratio (3.85E-03 - 6.25E-03 and 3.27E-03 - 6.97E-03, respectively) significantly higher than the typical value for open seawater (3.50E-03), showing significant dilution with wastewater at drain stations in the investigated sites. The source and origin of bromine species and the major ions studied associated with the wastewater units were identified and tracked by calculating the ion/chlorinity ratio and multivariate analysis. The total hazard quotient (THQ) for bromate intake and dermal exposure in children, females, and males demonstrates negligible harm to human health. The toxic unit (TU) and the sum of toxic units (STU) values of the three trophic levels in the surface water for the two sites under investigation yielded approximately comparable values for risk quotient (RQ) and mixture risk characterization ratios (RCRmix(MEC/PNEC)), indicating that invertebrates are more sensitive to bromate dangers than fish and algae. The study highlights the importance of conducting large-scale laboratory tests on the effluents resulting from wastewater treatment units, including bromide levels, to prevent the formation of dangerous side compounds such as bromate, which may have negative effects on populations and may lead to the toxicity of trophic levels in ecosystems.

A systematic review and guide for using multi-response statistical models in co-infection research.

The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections, ecologists and epidemiologists rely on models capable of accommodating multiple response variables. However, given the diversity of available approaches, choosing a model that is suitable for drawing meaningful conclusions from observational data is not a straightforward task. To provide clearer guidance for statistical model use in co-infection research, we conducted a systematic review to (i) understand the breadth of study goals and host-pathogen systems being pursued with multi-response models and (ii) determine the degree of crossover of knowledge among disciplines. In total, we identified 69 peer-reviewed primary studies that jointly measured infection patterns with two or more pathogens of humans or animals in natural environments. We found stark divisions in research objectives and methods among different disciplines, suggesting that cross-disciplinary insights into co-infection patterns and processes for different human and animal contexts are currently limited. Citation network analysis also revealed limited knowledge exchange between ecology and epidemiology. These findings collectively highlight the need for greater interdisciplinary collaboration for improving disease management.

Molecular profiling of blood plasma-derived extracellular vesicles derived from Duchenne muscular dystrophy patients through integration of FTIR spectroscopy and machine learning reveals disease signatures.

PURPOSE: To identify and monitor the FTIR spectral signatures of plasma extracellular vesicles (EVs) from Duchenne Muscular Dystrophy (DMD) patients at different stages with Healthy controls using machine learning models. MATERIALS AND METHODS: Whole blood samples were collected from the DMD (n = 30) and Healthy controls (n = 12). EVs were extracted by the Total Exosome Isolation (TEI) Method and resuspended in 1XPBS. We characterize the morphology, size, particle count, and surface markers (CD9, Alix, and Flotillin) by HR-TEM, NTA, and Western Blot analysis. The mid-IR spectra were recorded from (4000-400 cm-1) by Bruker ALPHA II FTIR spectrometer model, which was equipped with an attenuated total reflection (ATR) module. Machine learning algorithms like Principal Component Analysis (PCA) and Random Forest (RF) for dimensionality reduction and classifying the two study groups based on the FTIR spectra. The model performance was evaluated by a confusion matrix and the sensitivity, specificity, and Receiver Operating Characteristic Curve (ROC) was calculated respectively. RESULTS: Alterations in Amide I & II (1700-1470 cm-1) and lipid (3000-2800 cm-1) regions in FTIR spectra of DMD compared with healthy controls. The PCA-RF model classified correctly the two study groups in the range of 4000-400 cm-1 with a sensitivity of 20 %, specificity of 87.50 %, accuracy of 71.43 %, precision of 33.33 %, and 5-fold cross-validation accuracy of 82 %. We analyzed the ten different spectral regions which showed statistically significant at P < 0.01 except the Ester Acyl Chain region. CONCLUSION: Our proof-of-concept study demonstrated distinct infrared (IR) spectral signatures in plasma EVs derived from DMD. Consistent alterations in protein and lipid configurations were identified using a PCA-RF model, even with a small clinical dataset. This minimally invasive liquid biopsy method, combined with automated analysis, warrants further investigation for its potential in early diagnosis and monitoring of disease progression in DMD patients within clinical settings.

Determination of pesticide residues in beans using QuEChERS technique coupled to gas chromatography-mass spectrometry: Multivariate optimization of CEN and AOAC methods.

The use of pesticides has led to environmental pollution and posed a global health risk, since they remain as residues on foods. Beans one of the most widely cultivated crop in Africa, and susceptible to attack by insects both on field and during storage, leading to the application of pesticides to control pests' infestation. However, misuse of these chemicals by farmers on beans has resulted in the rejection of beans exported to European countries, due to the presence of pesticide residues at concentrations higher than the maximum residues levels (MRLs). In this study, the effectiveness of the Association Official Analytical Chemists (AOAC) Official Method and the European Committee of Standardization (CEN) Standard Method, were determined using multivariate approach for the analysis of organochlorine pesticide residues in 6 varieties of beans samples. The significance of factors (mass of sample, volume of acetonitrile, mass of magnesium sulphate, sample pH, centrifugation time and speed) affecting the efficiency of extraction was estimated using Plackett-Burman design, while central composite design was used to optimize the significant factors. The following optimum factors were subsequently used for method validation, recovery tests, and real sample analysis: 4 g of sample sludge (1:1 v/v), 10 mL of acetonitrile, 4.45 g of MgSO4, and 5 min of centrifugation at 5000 rpm. The figure of merit of analytical methodology estimated using matrix-matched internal standard calibration method gave linearity ranging from 0.25 to 500 µg/kg, with correlation coefficient (R2) greater than 0.99, the recovery ranged from 75.55 to 110.41 (RSD = 0.70-16.65), with LOD and LOQ of 0.23-1.77 µg/kg and 0.76-5.88 µg/kg, respectively.

Association between cardiometabolic Index (CMI) and endometriosis: a cross-sectional study on NHANES.

BACKGROUND: Endometriosis is intricately linked to metabolic health. The Cardiometabolic Index (CMI), a novel and readily accessible indicator, is utilized to evaluate metabolic status. This study seeks to investigate the potential correlation between CMI and endometriosis. METHODS: Data from four consecutive survey cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2006 were utilized. This included adult females with self-reported diagnoses of endometriosis and complete information required for calculating the CMI. The calculation formula for CMI is Triglycerides(TG) / High-density lipoprotein cholesterol (HDL-C) × WHtR (WHtR = waist circumference / height). A multivariable logistic regression model was employed to investigate the linear association between CMI and endometriosis. Subgroup analyses were performed to explore potential influencing factors. Additionally, the linear relationship was validated using restricted cubic spline (RCS) curve plotting and threshold effect analysis. RESULTS: This study, based on the National Health and Nutrition Examination Survey (NHANES), included a cohort of 2,224 adult women. The multivariable logistic regression analysis demonstrated that in the fully adjusted model, individuals with the highest CMI exhibited a 78% elevated likelihood of endometriosis compared to those with the lowest CMI (OR = 1.78; 95% CI, 1.02-3.11, P < 0.05). The subgroup analysis indicated that there were no significant interactions between CMI and specific subgroups (all interaction P > 0.05), except for the subgroup stratified by stroke status (P < 0.05). Additionally, the association between CMI and endometriosis was linear, with a 20% increase in the association for each unit increase in CMI when CMI > 0.67 (OR = 1.20; 95% CI, 1.05-1.37, P < 0.01). CONCLUSION: The study found that CMI levels are closely correlated with endometriosis, with this correlation increasing when the CMI exceeds 0.67. This finding implies that by regularly monitoring CMI levels, physicians may be able to screen women at risk for endometriosis at an earlier stage, thereby enabling the implementation of early interventions to slow the progression of the disease. To further validate these findings, larger-scale cohort studies are required to support the results of this research.

Geochemical exploration for tantalum in coltan-rich pegmatites at Bewadze-Mankoadze area of the Kibi-Winneba Belt, southern Ghana: Constraints from exploratory data analysis.

The Bewadze-Mankoadze pegmatites in the Kibi-Winneba Belt of Ghana host several columbite group minerals (WGM) and wodginite group minerals (WGM) as well as other rare and radioactive elements such as uraninite and cesium. In this study, petrographic studies of rock samples from pegmatite outcrops and statistical analysis of the major and minor elements were conducted to identify the pathfinder elements of a new tantalum deposit in the area. Ten samples were obtained from each town for whole-rock geochemistry and thin sections were prepared from some of the samples taken for petrographic analysis. The petrographic analyses showed the presence of quartz, K-feldspars, plagioclase, muscovite, spodumene, albite, tourmaline, columbite group minerals, and montebrasite, which indicate that the studied samples are granitic pegmatites. The geochemical data of the 10 samples obtained from each town showed high concentrations of Cr, Cs, Rb, Sm, and Ta. The Ta concentrations ranged from 10.5 to 773 ppm with an average value of 260 ppm. Q-Q plots showed outliers and variations from the dataset's normal distribution, which were fixed by centred log-ratio transformation and demonstrated to be normal by the Kolmogorov-Smirnov test and Shapiro-Wilk test for normality. Spearman correlation revealed that Sc, Ga, Nb, and Cs showed a moderate to strong correlation with Ta. Factor analysis indicated elemental association of Ta with Cs, Zn, Nb, MgO, Sc, Ga, and V. Three (3) multi-element relationships were discovered by hierarchical cluster analysis: (1) As, La, Hf, CaO, U, Co, Pb, Ce, Ba and Na2O, (2) V, Nb, Ta, Ga, Sc, Cr, Cu, Nb and MgO, and (3) Ni, SiO2, Cs, Zn, Sm, Rb, K2O, Y, Th, Al2O3 and Fe2O3. Hence, a comparison of the results of the multivariate statistics established Sc, Ga, Nb, and Cs as the pathfinders of tantalum in the Bewadze-Mankoadze area. Geochemical anomalies involving these elements can be observed in the south-western portion of the study area, according to single and multi-element halo mapping. It is therefore recommended that exploration activities for tantalum mineralization should focus on the south-western part of the study area, where the anomalies of the pathfinder elements are located.

Multivariate Flexible Metal-Organic Frameworks and Covalent Organic Frameworks.

Precise control of the void environment, achieved through multiple functional groups and enhanced by structural adaptations to guest molecules, stands at the forefront of scientific inquiry. Flexible multivariate open framework materials (OFMs), including covalent organic frameworks and metal-organic frameworks, meet these criteria and are expected to play a crucial role in gas storage and separation, pollutant removal, and catalysis. Nevertheless, there is a notable lack of critical evaluation of achievements in their chemistry and future prospects for their development or implementation. To provide a comprehensive historical context, the initial discussion explores into the realm of "classical" flexible OFMs, where their origin, various modes of flexibility, similarities to proteins, advanced tuning methods, and recent applications are explored. Subsequently, multivariate flexible materials, the methodologies involved in their synthesis, and horizons of their application are focussed. Furthermore, the reader to the concept of spatial distribution is introduced, providing a brief overview of the latest reports that have contributed to its elucidation. In summary, the critical review not only explores the landscape of multivariate flexible materials but also sheds light on the obstacles that the scientific community must overcome to fully unlock the potential of this fascinating field.

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000-1,287,500 cell/mL), cell viability (29.76-100.00%), glucose (8.700-10.500 g/), lactate (0.019-0.400 g/L), glutamine (0.925-1.520 g/L), glutamate (0.552-1.610 g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074-0.0478 g/L) were, respectively, 41,533 ± 45,273 cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065 g/L (PLS), 0.007 ± 0.007 g/L (ANN), 0.007 ± 0.006 g/L (ANN), 0.006 ± 0.006 g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026 g/L (PLS) or 0.0027 ± 0.0034 g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.