Multivariate Data Analysis Research Articles

Analysis of causes and prognostic impact of tube occlusion during hyperthermic intraperitoneal chemotherapy for appendiceal pseudomyxoma peritonei

BackgroundAppendiceal pseudomyxoma peritonei (PMP), a rare tumor from mucinous appendiceal origins, is treated with Cytoreductive Surgery (CRS) and Hyperthermic Intraperitoneal Chemotherapy (HIPEC). However, tubing blockages during HIPEC treatment pose a common challenge, impeding the smooth progression of therapy. Few studies to date have explored the incidence and risk factors of tube occlusion during HIPEC in patients with appendiceal PMP, as well as its adverse impact on postoperative complications.MethodsFrom October 2017 to June 2023, a total of 80 patients with appendiceal PMP undergoing combined CRS and HIPEC were included in this study. Tubing blockage events were strictly defined, with patients experiencing blockages during HIPEC treatment allocated to the study group, while those with unobstructed perfusion were assigned to the control group. A comparative analysis was conducted between the two groups regarding post-HIPEC health assessments and occurrence of complications. Risk factors for luminal occlusion during closed HIPEC procedures were identified through univariate and multivariate analysis of data from 303 HIPEC treatments.ResultsTubing blockages occurred in 41 patients (51.3%). The study group experienced prolonged gastrointestinal decompression time (4.1 ± 3.0 vs. 2.5 ± 1.7 days, P = 0.003) and prolonged time to bowel movement (6.1 ± 2.3 vs. 5.1 ± 1.8 days, P = 0.022) compared to the control group. There was no significant difference in the incidence of complications between the two groups. The 1-year survival rate postoperatively was 97%, and the 3-year survival rate was 81%, with no association found between tubing blockage and poorer survival. Additionally, In 303 instances of HIPEC treatment among these 80 patients, tube occlusion occurred in 89 cases (89/303, 29.4%). Multivariable logistic regression analysis revealed age, diabetes, hypertension, and pathology as independent risk factors for tube occlusion.ConclusionTubing blockages are a common occurrence during HIPEC treatment, leading to prolonged postoperative gastrointestinal functional recovery time. When patients are elderly and have concomitant hypertension and diabetes, along with a histological type of low-grade mucinous tumor, the risk of tube occlusion increases. However, this study did not find a significant correlation between tubing blockage and the incidence of postoperative complications or overall patient survival.

Metabolome classification of olive by-products from different oil presses providing insights into its potential health benefits and valorization as analyzed via multiplex MS-based techniques coupled to chemometrics.

The Olive (Olea europaea L.) is one of the most popular edible oil-producing fruits, consumed worldwide for its myriad nutritional and health benefits. Olive oil production generates huge quantities of by-products from the fruit, which are considered environmental hazards. Recently, more and more efforts have been made to valorize olive by-products as a source of low-cost, value-added food applications. The main objective of this study was to globally assess the metabolome of olive fruit by-products, including olive mill wastewater, olive pomace, and olive seeds from fruits from two areas, Siwa and Anshas, Egypt. Gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography with mass spectrometry (UPLC-MS) were used for profiling primary and secondary metabolites in olive by-products. Also, multivariate data analyses were used to assess variations between olive by-product samples. A total of 103 primary metabolites and 105 secondary metabolites were identified by GC-MS and UPLC-MS, respectively. Fatty acids amounted to a major class in the olive by-products at 53-91%, with oleic acid dominating, especially in the pomace of Siwa. Mill wastewater was discriminated from other by-products by the presence of phenolics mainly tyrosol, hydroxyl tyrosol, and α-tocopherol as analyzed by UPLC-MS indicating their potential antioxidant activity. Pomace and seeds were rich in fatty acids/esters and hydroxy fatty acids and not readily distinguishable from each other. The current work discusses the metabolome profile of olive waste products for valorization purposes. Pomace and seeds were enriched in fatty acids/esters, though not readily distinguishable from each other.

Hubungan Antara Faktor Risiko Hendaya Kognitif Ringan Pada Pasien Lanjut Usia Di Jakarta Timur

The research objectives in detail are to determine the risk factors for mild cognitive impairment in the elderly and understanding the factors that affect the majority of mild cognitive impairment in the elderly. This research is an observational case-control study and the research method used is multi-variant data analysis and epidemiological information. The results between methods describe the effect of hypertension and physical activity have big influence on mild cognitive. Conclusion The factors that most influence mild cognitive impairment are hypertension and physical activity

Deciphering complexity in Pd–catalyzed cross-couplings

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.

Multivariate sensor data analysis for fault detection toward feedback loop-based continuous learning in FFF 3D printer

Multivariate sensor data analysis for fault detection toward feedback loop-based continuous learning in FFF 3D printer

A review of the developed methods for the analysis of lard and pork in food and pharmaceutical products for halal authentication

The development of an analytical method for halal authentication analysis is urgent to assure the halalness of food and pharmaceutical products. Halal authentication analysis is widely applied for the analysis of non-halal components present in food and pharmaceutical products using some analytical methods. Non-halal components typically found in products are pig derivatives, any components derived from a pig (Sus scrofa), and alcohol. Pig derivatives such as lard and pork are widely applied in food and pharmaceutical products. Some methods based on chromatographic and molecular spectroscopy are widely applied for the analysis of pork and lard. The analytical responses obtained during the analysis of lard and pork are complex and involve a large dataset, therefore, the employment of multivariate data analysis is a must. This review described comprehensively the application of chemical analytical methods, mainly based on chromatographic and spectroscopic techniques, for the analysis of pork and pork fat (lard) in food and pharmaceutical products. The successful application of analytical methods for halal authentication can assist the regulatory bodies in confirming the absence of non-halal components in the products which support the implementation of halal certification.

Influence of seasonality and habitat on chemical composition, cytotoxicity and antimicrobial properties of the Libidibia ferrea

Libidibia ferrea Mart, belonging to the Fabacee family, is a medicinal plant known for its biological properties and production of phenolic compounds. Previous studies reveal the biological activity of its phenolic constituents, making it very promising for the development of new medicines. Seasonality and geographic distribution of species can modify the production of secondary metabolites in Fabaceae species in terms of the preferentially activated metabolic pathways and, consequently, interfere with the medicinal properties of these species. Studying the influence of seasonality on the production of phenolic constituents is essential to establish conditions for “cultivation,” species collection, standardization, production, and safety in traditional medicine. This unprecedented study proposed to evaluate the influence of seasonal variations and habitat on the production of phenolic compounds and biological properties of the ethanolic extracts of the stem bark from L. ferrea, whose specimens were collected from the Caatinga and the Atlantic Forest, biomes of Brazil. Antimicrobial activity was determined by broth microdilution. Cytotoxicity was evaluated through a colorimetric assay using MTT. ABTS and DPPH radical reduction methods estimated antioxidant capacities. Folin-Ciocalteu and AlCl3 spectrophotometric methods quantified total phenolics and flavonoids, respectively. In turn, radial diffusion quantified tannin content. PCA score plot and HCA dendogram were obtained by multivariate analysis of 1H NMR data. The cytotoxicity against C6 glioma cells was observed only for Atlantic Forest extracts (EC50 = 0.13–0.5 mg mL−1). These extracts also showed selectivity against Gram-positive bacteria Bacillus subtilis (ATCC 6633) [MICs 500–2000 μg mL−1], B. cereus CCT 0096) [MIC = 250 μg mL−1], Staphylococcus aureus (ATCC 6538) [MICs = 250–500 μg mL−1], S. epidermidis (ATCC 12228) [62.5–1000 μg mL−1], mainly to Staphylococcus sp. Caatinga extracts showed higher production of flavonoids and antioxidants in the summer [7.36 ± 0.19 μg QE mg−1 extract; IC50ABTS = 4.86 ± 0.05 μg mL−1], spring [5.96 ± 0.10 μg QE mg−1 extract; IC50ABTS = 5.96 ± 0.08 μg mL−1 ], winter [4.89 ± 0.25 μg QE mg−1 extract; IC50ABTS = 6.72 ± 0.08 μg mL−1 ]. Regarding habitat, two discriminating compound patterns in the studied biomes were revealed by NMR. The results indicated that the Caatinga biome offers better conditions for activating the production of phenolics [336.34 ± 18.1 μgGAE mg−1 extract], tannins [328.38 ± 30.19 μgTAE mg−1 extract] in the summer and flavonoids in winter, spring, and summer. The extracts that showed the best antioxidant activities were also those from the Caatinga. In turn, extracts from the Atlantic Forest are more promising for discovering antibacterial compounds against Staphylococcus sp and cytotoxic for C6 glioma cells. These findings corroborated the traditional use of L. ferrea bark powder for treating skin wounds and suggest the cytotoxic potential of these extracts for glioblastoma cell lines.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology.

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science. The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes. This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques. Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process. The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

Amino acids as stabilizers for lysozyme during the spray-drying process and storage

Amino acids (AAs) have been used as excipients in protein formulations both in solid and liquid state products due to their stabilizing effect. However, the mechanisms by which they can stabilize a protein have not been fully elucidated yet. The purpose of this study was to investigate the effect of AAs with distinct physicochemical properties on the stability of a model protein (lysozyme, LZM) during the spray-drying and subsequent storage. Molecular descriptor based multivariate data analysis was used to select distinct AAs from the group of 20 natural AAs. Then, LZM and the five selected AAs (1:1 wt ratio) were spray-dried (SD). The solid form, residual moisture content (RMC), hygroscopicity, morphology, secondary/tertiary structure and enzymatic activity of LZM were evaluated before and after storage under 40 °C/75 % RH for 30 days. Arginine (Arg), leucine (Leu), glycine (Gly), tryptophan (Trp), aspartic acid (Asp) were selected because of their distinct properties by using principal component analysis (PCA). The SD LZM powders containing Arg, Trp, or Asp were amorphous, while SD LZM powders containing Leu or Gly were crystalline. Recrystallization of Arg, Trp, Asp and polymorph transition of Gly were observed after the storage under accelerated conditions. The morphologies of the SD particles vary upon the different AAs formulated with LZM, implying different drying kinetics of the five model systems. A tertiary structural change of LZM was observed in the SD powders containing Arg, while a decrease in the enzymatic activity of LZM was observed in the powders containing Arg or Asp after the storage. This can be attributed to the extremely basic and acidic conditions that Arg and Asp can create, respectively. This study suggests that when AAs are used as stabilizers instead of traditional disaccharides, in addition to the classic vitrification theory and water replacement theory, the microenvironmental pH condition that the basic or acidic AAs may create in the starting solution or during the storage of solid matter is crucial for the stability of SD protein products.

1H NMR-metabolomics studies on acute toxicity effect of lead in adult zebrafish (Danio rerio) model

Zebrafish (Danio rerio) is ideal for studying the effects of toxins like lead or plumbum (Pb) which persist in the environment and harm body systems when absorbed. Increasing Pb concentration could result in a higher mortality rate and alteration of behavior and metabolism. The present study evaluates the acute toxicity effect of Pb on metabolome and behavior in adult zebrafish. The zebrafish were exposed to various Pb concentrations ranging from 0 to 30 mg/L for different periods (24, 48, and 72 h) before the fish samples were subjected to Nuclear Magnetic Resonance (NMR)-multivariate data analysis (MVDA) with additional support from behavioral assessment. The behavior of zebrafish was significantly altered after Pb inducement and the differential metabolites increased in low (5 mg/L) while decreased in high (10 mg/L) Pb concentrations. An ideal Pb induction could be achieved by 5 mg/L concentration in 24 h, which induced significant metabolite changes without irreversible damage. Continuing research on the effects of lead toxicity is crucial to develop effective prevention and treatment strategies.

Species-Specific quantification of bioactive boswellic acids in Boswellia resin using NIR spectroscopy, HPLC and Multivariate data analysis

The bioactive compounds Acetyl-11-keto-β-boswellic acid (AKBA) and 11-keto-β-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R2val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques.

Diversity of fungal communities on Cabernet and Aglianico grapes from vineyards located in Southern Italy.

Grape-associated microbial community is influenced by a combination of viticultural, climatic, pedological and anthropological factors, collectively known as terroir. Therefore, grapes of the same cultivar grown in different areas can be appreciated for their distinctive biogeographic characteristics. In our previous study, we showed that the phenotypic response of Aglianico and Cabernet grapevines from Molise and Sicily regions is significantly influenced by the prevailing pedoclimatic conditions, particularly soil physical properties. However, the scale at which microbial communities differ could be important in clarifying the concept of terroir, including whether it is linked to the grape variety present in a particular vineyard. To explore this further, in the research presented here, a comparative study on the fungal communities inhabiting the berry surfaces of Cabernet and Aglianico cultivars was conducted on different vineyards located in Southern Italy (Molise, Sicily and Campania regions, the first two of which had been involved in our previous study) by using high-throughput sequencing (HTS) and multivariate data analysis. The descriptive approach through relative abundance analysis showed the most abundant phyla (Ascomycota, Basidiomycota, and Chytridiomycota), families (Cladosporiaceae, Saccotheciaceae, Pleosporaceae, Saccharomycodaceae, Sporidiobolaceae, Didymellaceae, Filobasidiaceae, Bulleribasidiaceae, and Saccharomycetaceae) and genera (Cladosporium, Aureobasidium, Alternaria, Stemphylium and Filobasidium) detected on grape berries. The multivariate data analysis performed by using different packages (phyloseq, Vegan, mixOmics, microbiomeMarker and ggplot2) highlighted that the variable "vineyard location" significantly affect the fungal community, while the variable "grape variety" has no significant effect. Thus, some taxa are found to be part of specific vineyard ecosystems rather than specific grape varieties, giving additional information on the microbial contribution to wine quality, thanks to the presence of fermentative yeasts or, conversely, to the involvement in negative or detrimental roles, due to the presence of grape-deriving fungi implied in the spoilage of wine or in grapevine pathogenesis. In this connection, the main functions of core taxa fungi, whose role in the vineyard environment is still poorly understood, are also described.

Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development.

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics.

Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

Synthesis, Characterization and DNA Binding Study of Silver N-Heterocyclic Carbene Complex [Bis (1,3-Dipentyl-1,3-Dihydro-2-Imidazole-2-Ylidene) Silver (I) hexafluorophosphate (V)] by Surface-Enhanced Raman Spectroscopy (SERS)

The silver organometallic compounds favorably target DNA in cancerous cells and stop their growth by inhibiting DNA replication. Specific properties of these compounds help in their action as drugs and different changes occur in the structure of DNA. In-depth study of the binding mechanism of drugs is of potential use for novel drug synthesis. Surface-enhanced Raman spectroscopy (SERS) coupled with multivariate data analysis was employed to probe the interaction of silver N-heterocyclic carbene complex (drug) with DNA. In this study, silver N-heterocyclic carbene complex was synthesized and various concentrations were exposed to known concentrations of DNA. Silver nanoparticles were used for enhancement of Raman signals, which enabled sensitive detection and characterization of molecular changes occurring during binding process. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to extract meaningful information from the spectral data. Changes in intensity and position of significant SERS signals were noted, which gave an indication of the bonding of the drug with DNA. The results reveal the strong intercalative binding of drug with nitrogenous bases of DNA. PCA and PLS-DA provide evidence for successful DNA-drug interaction.

Clinical feasibility of a label-free SERS assay for therapeutic drug monitoring of methotrexate

We present a therapeutic drug monitoring (TDM) feasibility study using a label-free surface-enhanced Raman spectroscopy (SERS)-based assay for quantifying methotrexate (MTX) from samples collected from children with acute lymphoblastic leukemia undergoing high-dose MTX therapy. We show that, when combined with an appropriate sample preparation (solid phase extraction) and multivariate data analysis (partial least squares regression), the SERS assay demonstrated a good correlation with a reference chromatographic method (r = 0.889, p < 0.005). We also found that the SERS-based approach underestimated MTX concentration, with a 10% bias calculated, but both methods showed similar variability (RSD of 8.4% for HPLC and 11.6% for SERS). The presented results are strong evidence that SERS can be used for TDM of MTX, and this work brings us one step closer to implementing a SERS-based assay in clinics.

Evidence on the inhibitory effect of Brassica plants against Acinetobacter baumannii lipases: phytochemical analysis, in vitro, and molecular docking studies

BackgroundInfections caused by Acinetobacter baumannii are becoming a rising public health problem due to its high degree of acquired and intrinsic resistance mechanisms. Bacterial lipases penetrate and damage host tissues, resulting in multiple infections. Because there are very few effective inhibitors of bacterial lipases, new alternatives for treating A. baumannii infections are urgently needed. In recent years, Brassica vegetables have received a lot of attention since their phytochemical compounds have been directly linked to diverse antimicrobial actions by inhibiting the growth of various Gram-positive and Gram-negative bacteria, yeast, and fungi. Despite their longstanding antibacterial history, there is currently a lack of scientific evidence to support their role in the management of infections caused by the nosocomial bacterium, A. baumannii. This study aimed to address this gap in knowledge by examining the antibacterial and lipase inhibitory effects of six commonly consumed Brassica greens, Chinese cabbage (CC), curly and Tuscan kale (CK and TK), red and green Pak choi (RP and GP), and Brussels sprouts (BR), against A. baumannii in relation to their chemical profiles.MethodsThe secondary metabolites of the six extracts were identified using LC-QTOF-MS/MS analysis, and they were subsequently correlated with the lipase inhibitory activity using multivariate data analysis and molecular docking.ResultsIn total, 99 metabolites from various chemical classes were identified in the extracts. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed the chemical similarities and variabilities among the specimens, with glucosinolates and phenolic compounds being the major metabolites. RP and GP showed the highest antibacterial activity against A. baumannii, followed by CK. Additionally, four species showed a significant effect on the bacterial growth curves and demonstrated relevant inhibition of A. baumannii lipolytic activity. CK showed the greatest inhibition (26%), followed by RP (21%), GP (21%), and TK (15%). Orthogonal partial least squares-discriminant analysis (OPLS-DA) pinpointed 9 metabolites positively correlated with the observed bioactivities. Further, the biomarkers displayed good binding affinities towards lipase active sites ranging from −70.61 to −30.91 kcal/mol, compared to orlistat.ConclusionThis study emphasizes the significance of Brassica vegetables as a novel natural source of potential inhibitors of lipase from A. baumannii.

Critical Review of Selected Analytical Platforms for GC-MS Metabolomics Profiling-Case Study: HS-SPME/GC-MS Analysis of Blackberry's Aroma.

Data processing and data extraction are the first, and most often crucial, steps in metabolomics and multivariate data analysis in general. There are several software solutions for these purposes in GC-MS metabolomics. It becomes unclear which platform offers what kind of data and how that information influences the analysis's conclusions. In this study, selected analytical platforms for GC-MS metabolomics profiling, SpectConnect and XCMS as well as MestReNova software, were used to process the results of the HS-SPME/GC-MS aroma analyses of several blackberry varieties. In addition, a detailed analysis of the identification of the individual components of the blackberry aroma club varieties was performed. In total, 72 components were detected in the XCMS platform, 119 in SpectConnect, and 87 and 167 in MestReNova, with automatic integral and manual correction, respectively, as well as 219 aroma components after manual analysis of GC-MS chromatograms. The obtained datasets were fed, for multivariate data analysis, to SIMCA software, and underwent the creation of PCA, OPLS, and OPLS-DA models. The results of the validation tests and VIP-pred. scores were analyzed in detail.

Assessment of carcinogenic and non-carcinogenic risk of exposure to potentially toxic elements in tea infusions: Determination by ICP-OES and multivariate statistical data analysis

BackgroundThe perennial evergreen tea (Camellia sinensis) plant is one of the most popular nonalcoholic drinks in the world. Fertilizers and industrial, agricultural, and municipal activities are the usual drivers of soil contamination, contaminating tea plants with potentially toxic elements (PTEs). These elements might potentially accumulate to larger amounts in the leaves of plants after being taken up from the soil. Thus, frequent monitoring of these elements is critically important. MethodsThe present study intended to determine PTEs (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in both tea leaves and infusions using ICP-OES. Various multivariate data analysis methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to elucidate the potential sources of PTEs contamination, whether from anthropogenic activities or natural origins. Additionally, Pearson's correlation coefficient (PCC) was calculated to assess the relationships between the variables under study. ResultsThe mean contents (mg/L) of all studied elements in tea infusions decreased in order Mn (150.59 ± 1.66) > Fe (11.39 ± 0.99) > Zn (6.62 ± 0.89) > Cu (5.86 ± 0.62) > Co (3.25 ± 0.64) > Ni (1.69 ± 0.23) > Pb (1.08 ± 0.16) > Cr (0.57 ± 0.09) > Cd (0.46 ± 0.09) > Al (0.05 ± 0.008), indicating that Mn exhibits the highest abundance. The mean concentration trend in tea leaf samples mirrored that of infusions, albeit with higher concentrations of PTEs in the former. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd surpassed the standards set by the WHO and EFSA. Calculated hazard index (HI < 1) and cumulative cancer risk (CCR) values suggest negligible exposure risk. ConclusionElevated levels of PTEs in commonly consumed tea products concern the public and regulatory agencies.

Accuracy of two-dimensional digital planning in uncemented primary hip arthroplasty: monocentric analysis of eight hundred implants.

In the last decades, there has been a refinement in total hip arthroplasty, which allowed surgeons to achieve the highest performance and better patient outcomes. Preoperative planning in primary hip arthroplasty is an essential step that guides the surgeon in restoring the anatomy and biomechanics of the joint. This study aims to evaluate the accuracy of the 2D digital planning, considering cup sizing, stem sizing, and limb length discrepancy. Additionally, we conducted a multivariable analysis of demographic data and comorbidities to find factors influencing preoperative planning. This retrospective study analyzed the planning accuracy in 800 consecutive uncemented primary total hip arthroplasty. We compared the preoperatively planned total hip arthroplasty with postoperative results regarding the planned component size, the implanted size, and the lower limb length restoration. Therefore, we investigated factors influencing planning accuracy: overweight and obesity, sex, age, past medical history, comorbidities, and implant design. All the surgeries were performed in the posterolateral approach by one expert surgeon who did the preoperative planning. The preoperative planning was determined to be (a) exact if the planned and the implanted components were the same size and (b) accurate if exact ± one size. The restoration of postoperative limb length discrepancy was classified into three groups: ± 3mm, ± 5mm, and ± 10mm. This assessment was performed through a digital method 2D based on a standard hip X-ray. This court of 800 implants showed that planning was exact in 60% of the cups and 44% of the stems and was accurate in 94% of the cups and 80% of the stems. The postoperative limb length discrepancy was ± 3mm in 91% and ± 5mm in 97%. This study showed preoperative 2D digital planning great precision and reliability, and we demonstrated that it was accurate in 94% of the cups and 80% of the stems. Therefore, the preoperative limb length discrepancy analysis was essential to guarantee the recovery of the operated limb's correct length.