Search Results (32)

The integration of multi-omics data offers transformative potential for elucidating complex molecular mechanisms underlying biological processes and diseases. In this study, we developed a lipid–metabolite–protein network that combines a protein–protein interaction network and enzymatic and genetic interactions of proteins with metabolites and lipids to provide a unified framework for multi-omics integration. Using hyperbolic embedding, the network visualizes connections across omics layers, accessible through a user-friendly Shiny R (version 1.10.0) software package. This framework ranks molecules across omics layers based on functional proximity, enabling intuitive exploration. Application in a cardiovascular disease (CVD) case study identified lipids and metabolites associated with CVD-related proteins. The analysis confirmed known associations, like cholesterol esters and sphingomyelin, and highlighted potential novel biomarkers, such as 4-imidazoleacetate and indoleacetaldehyde. Furthermore, we used the network to analyze empagliflozin’s temporal effects on lipid metabolism. Functional enrichment analysis of proteins associated with lipid signatures revealed dynamic shifts in biological processes, with early effects impacting phospholipid metabolism and long-term effects affecting sphingolipid biosynthesis. Our framework offers a versatile tool for hypothesis generation, functional analysis, and biomarker discovery. By bridging molecular layers, this approach advances our understanding of disease mechanisms and therapeutic effects, with broad applications in computational biology and precision medicine. Full article

The use of edible films has garnered significant interest in the food and environmental sectors due to their potential to prevent food deterioration and their biodegradability. This study aimed to develop and characterize edible films based on camu-camu residue, gelatin, and glycerol, evaluating their solubility, thermal, degradability, antioxidant, and water vapor permeability properties of the gelatin matrix. This is the first study incorporating camu-camu into a gelatin and glycerol matrix. The films produced with camu-camu residue were manageable and soluble, with some non-soluble residues, providing a shiny and well-presented appearance. In the biodegradation results, samples 3 and 4 appeared to degrade the most, being two of the three most affected samples in the triplicate. The films showed degradation modifications from the third day of the experiment. In the germination and plant growth analysis, sample 4 exhibited satisfactory development compared to the other samples, emerging as the sample with the best overall result in the analyses, attributed to a 13.84 cm increase in the growth of the upper part of the seedling. These results indicate that the produced materials have potential for food packaging applications. Full article

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research. Full article

In the early 2000s, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification, allowing researchers to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering its wide use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database or to the insufficient quality of query MS spectra (i.e., lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating a control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simple navigation using the link of the package on Github v.3.10.0. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR. Full article

Zolpidem is a widely prescribed hypnotic Z-drug used to treat short-term insomnia. However, a growing number of individuals intentionally overdose on these drugs. This study aimed to develop a predictive tool for physicians to assess patients with zolpidem overdose. A population pharmacokinetic (PK) model was established using digitized data obtained from twenty-three healthy volunteers after a single oral administration of zolpidem. Based on the final PK model, a web application was developed using open-source R packages such as rxode2, nonmem2rx, and shiny. The final model was a one-compartment model with first-order absorption and elimination with PK parameters, including clearance (CL, 16.9 L/h), absorption rate constant (K_a, 5.41 h⁻¹), volume of distribution (V_d, 61.7 L), and lag time (ALAG, 0.394 h). Using the established population PK model in the current study, we developed a web application that enables users to simulate plasma zolpidem concentrations and visualize their profiles. This user-friendly web application may provide essential clinical information to physicians, ultimately helping in the management of patients with zolpidem intoxication. Full article

Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able to be employed in different contexts, all of which offer their own advantages. Therefore, this study focuses on monitoring the variability in univariate processes through variance using the Binomial version of the ATTRIVAR Same Sample S² (B-ATTRIVAR SS S²) control chart, given that it allows coupling attribute and variable inspections (ATTRIVAR means attribute + variable), i.e., taking advantage of the cost-effectiveness of the former and the wealth of information and greater performance of the latter. Its Binomial version was used for such a purpose, since inspections are made using two attributes, and the Same Sample was used due to being submitted to both the attribute and variable stages of inspection. A computational application was developed in the R language using the Shiny package so as to create an interface to facilitate its application and use in the quality control of the production processes. Its application enables users to input process parameters and generate the B-ATTRIVAR SS control chart for monitoring the process variability with variance. By comparing the data obtained from its application with a simpler code, its performance was validated, given that its results exhibited striking similarity. Full article

L-4-[(¹⁰B)]Boronophenylalanine (BPA) is an amino acid analogue with a boron-10 moiety. It is most widely used as a boron carrier in boron neutron capture therapy. In this study, a Bayesian predictive platform of blood boron concentration based on a BPA pharmacokinetic (PK) model was developed. This platform is user-friendly and can predict the individual boron PK and optimal time window for boron neutron capture therapy in a simple way. The present study aimed to establish a PK model of L-4-boronophenylalanine and develop a Bayesian predictive platform for blood boron PKs for user-friendly estimation of boron concentration during neutron irradiation of neutron capture therapy. Whole blood boron concentrations from seven previous reports were graphically extracted and analyzed using the nonlinear mixed-effects modeling (NONMEM) approach. Model robustness was assessed using nonparametric bootstrap and visual predictive check approaches. The visual predictive check indicated that the final PK model is able to adequately predict observed concentrations. The Shiny package was used to input real-time blood boron concentration data, and during the following irradiation session blood boron was estimated with an acceptably short calculation time for the determination of irradiation time. Finally, a user-friendly Bayesian estimation platform for BPA PKs was developed to optimize individualized therapy for patients undergoing BNCT. Full article

Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App. Full article

Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R²: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering. Full article

CardioRVAR is a new R package designed for the complete evaluation of closed-loop cardiovascular interactions and baroreflex sensitivity estimated from continuous non-invasive heart rate and blood pressure recordings. In this work, we highlight the importance of this software tool in the context of human cardiovascular and autonomic neurophysiology. A summary of the main algorithms that CardioRVAR uses are reviewed, and the workflow of this package is also discussed. We present the results obtained from this tool after its application in three clinical settings. These results support the potential clinical and scientific applications of this tool. The open-source tool can be downloaded from a public GitHub repository, as well as its specific Shiny application, CardioRVARapp. The open-source nature of the tool may benefit the future continuation of this work. Full article

Handling uncertainty is important in decision making, especially for SDGs problems. Robust Optimization (RO) is an applied optimization method that can be employed to handle optimization under uncertain data. With SDGs problems, many uncertain data have been considered in decision making. With RO, the data uncertainties are assumed to lay within a compact, convex continuous set. There are three special sets that can be used to represent the data, i.e., box, ellipsoidal, or polyhedral uncertainty sets. These special sets lead the SDGs problems to a computationally tractable optimization model, such that the global optimal solution is attained. However, literature reviews on the application of RO in SDGs decision-making is sparse, especially during the COVID-19 pandemic period. This paper examines the following topics: (1) the purposes of studies of RO and SDGs during the COVID-19 pandemic, (2) the state-of-the-art in RO-SDGs to determine the research objectives, and (3) the SDGs type of problems that have been modeled using RO. A systematic literature review is conducted in this paper, wherein discussion is based on a PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flowchart. To this end, the database reference searching conducted on the Scopus, Science Direct, and SAGE databases, is completed using the help RStudio software. The analysis was carried out on two datasets, assisted by the output visualization using RStudio software with the “bibliometrix” package, and using the ‘biblioshiny()’ command to create a link to the “shiny web interface”. In this paper, the research gap on application of RO to SDGs problems is analyzed in order to identify the research objectives, methods, and specific RO-SDGs problems. As a result, the application of RO to SDGs problems is rare; this finding provides a motivation to conduct a further study of RO and SDGs during the COVID-19 pandemic. An expansion is presented using the key phrase “Operations Research and Optimization Modeling”, or “OROM”. SDGs in Indonesia may be referenced as an example of the capacity building available through RO/OROM. Full article

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations’ behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean −34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time. Full article

To date, most metabolomics biomarker research has focused on identifying disease biomarkers. However, there is a need for biomarkers of early metabolic dysfunction to identify individuals who would benefit from lifestyle interventions. Concomitantly, there is a need to develop strategies to analyse metabolomics data at an individual level. We propose “MetaboVariation”, a method that models repeated measurements on individuals to explore fluctuations in metabolite levels at an individual level. MetaboVariation employs a Bayesian generalised linear model to flag individuals with intra-individual variations in their metabolite levels across multiple measurements. MetaboVariation models repeated metabolite levels as a function of explanatory variables while accounting for intra-individual variation. The posterior predictive distribution of metabolite levels at the individual level is available, and is used to flag individuals with observed metabolite levels outside the 95% highest posterior density prediction interval at a given time point. MetaboVariation was applied to a dataset containing metabolite levels for 20 metabolites, measured once every four months, in 164 individuals. A total of 28% of individuals with intra-individual variations in three or more metabolites were flagged. An R package for MetaboVariation was developed with an embedded R Shiny web application. To summarize, MetaboVariation has made considerable progress in developing strategies for analysing metabolomics data at the individual level, thus paving the way toward personalised healthcare. Full article

Starch and gelatin are natural biopolymers that offer a variety of benefits and are available at relatively low costs. In addition to this, they are an appealing substitute for synthetic polymers for the manufacturing of packaging films. Such packaging films are not only biodegradable but are also edible. Moreover, they are environmentally friendly and remain extremely cost-effective. In lieu of this, films made from fish gelatin and cornstarch have been the subject of several experiments. The pristine gelatin films have poor performance against water diffusion but exhibit excellent flexibility. The goal of this study was to assess the performance of pristine gelatin films along with the addition of food plasticizers. For this purpose, solutions of gelatin/cornstarch were prepared and specified quantities of food colors/plasticizers were added to develop different shades. The films were produced by using a blade coating method and were characterized by means of their shaded colors, water vapor transmission rate (WVTR), compositional changes via Fourier transform infrared spectroscopy (FTIR), hardness, bendability, transparency, wettability, surface roughness, and thermal stability. It was observed that the addition of several food colors enhanced the moisture blocking effect, as a 10% reduction in WVTR was observed in the shaded films as compared to pristine films. The yellow-shaded films exhibited the lowest WVTR, i.e., around 73 g/m²·day when tested at 23 °C/65%RH. It was also observed that the films’ WVTR, moisture content, and thickness were altered when different colors were added into them, although the chemical structure remained unchanged. The mechanical properties of the shaded films were improved by a factor of two after the addition of colored plasticizers. Optical examination and AFM demonstrated that the generated films had no fractures and were homogeneous, clear, and shiny. Finally, a biscuit was packaged in the developed films and was monitored via shore hardness. It was observed that the edible packed sample’s hardness remained constant even after 5 days. This clearly suggested that the developed films have the potential to be used for packaging in various industries. Full article

The Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual’s variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening. Full article