Hepatitis C virus infection and Parkinson's disease: insights from a joint sex-stratified BioOptimatics meta-analysis.

Hepatitis C virus (HCV) infection poses a significant public health challenge and often leads to long-term health complications and even death. Parkinson's disease (PD) is a progressive neurodegenerative disorder with a proposed viral etiology. HCV infection and PD have been previously suggested to be related. This work aimed to identify potential biomarkers and pathways that may play a role in the joint development of PD and HCV infection. Using BioOptimatics-bioinformatics driven by mathematical global optimization-, 22 publicly available microarray and RNAseq datasets for both diseases were analyzed, focusing on sex-specific differences. Our results revealed that 19 genes, including MT1H, MYOM2, and RPL18, exhibited significant changes in expression in both diseases. Pathway and network analyses stratified by sex indicated that these gene expression changes were enriched in processes related to immune response regulation in females and immune cell activation in males. These findings suggest a potential link between HCV infection and PD, highlighting the importance of further investigation into the underlying mechanisms and potential therapeutic targets involved.

A Cost Model for Neurological Diseases in Puerto Rico: Parkinson's Disease, Alzheimer's Disease and Huntington's Disease.

OBJECTIVE: This article proposes an engineering-economics model to determine the total cost of a neurological disease along its temporal progression. The objective was to develop a planning tool faithful to the reality of this type of ailment as well as to that of Puerto Rico (PR). METHODS: The proposed model organizes a given neurological disease into 3 progressive phases of deterioration; in each, the model collects the typical associated costs and adjusts them based on their value over time. This way, the total cost of the ailment is calculated and its present day dollar value expressed. Model verification was carried out using data from Puerto Rico related to Parkinson's, Alzheimer's, and Huntington's diseases. RESULTS: The method demonstrated here considered Parkinson's disease in PR. Our model calculated a total annual cost of $64,915 for a patient at the medium stage. This figure is larger than estimates from other authors, which fall between $41,689 and $51,600 for the USA. This difference is partially due to the proposed model considering the individual's opportunity cost of the loss of productive years, an original contribution of our work. CONCLUSION: A neurological disease is one in which an individual goes through progressive phases of deterioration that will require significant economic resources. The model proposed here is designed across the commonalities between Alzheimer's, Parkinson's, and Huntington's diseases and illustrated using costs from PR. As an additional contribution, it allows the consideration of the opportunity cost of lost productivity, a characteristic that makes it more realistic.

Individual emergency response and recovery: What people worried about during and after Hurricane Maria.

Hurricane Maria was a disastrous weather event that devastated Puerto Rico (PR) in September 2017. Yet, little is known about people's perceptions of this event. In this investigation, we offer insight about Hurricane Maria's impact on PR's inhabitants. More specifically, we study a sample (n = 542 responses) of individuals' worry levels through four time points during the aftermath of Hurricane Maria: their variation through time, their relationship to decision making, and if and how certain demographic variables may influence them. For these purposes, we designed and implemented the Individual Emergency Response and Recovery Questionnaire, a web-based survey that measured several aspects of the objective and subjective experiences of individuals who underwent Hurricane Maria in PR. Results of a statistical analysis using nonparametric tests show that some of the demographic variables selected as factors of interest influence the worry levels reported by respondents. Most significant results coincide with conclusions drawn by literature: that time, age group, and the level of information influence worry levels. Another key finding is that the worry level may influence individual decision-making frequency. Understanding principal factors in people's behavior and perceptions during hurricanes is crucial to help us learn how to better prepare for and respond to natural disasters in the future.

Potential colon cancer biomarker search using more than two performance measures in a multiple criteria optimization approach.

OBJECTIVE: A new method using Multiple Criteria Optimization (MCO) proposed by our research group has shown evidence of being able to identify gene-based biomarkers for the detection of cancer using microarray data. Herein, we explore this method, considering more than two conflicting criteria for the MCO problem. Using this method would result in stronger outcomes when using different results from microarray analyses. It would also demonstrate that the method is suitable for carrying out meta-analysis. METHODS: Statistical comparisons between normal and cancer tissues were performed using a colon cancer microarray database. The different comparisons were carried out with a Mann-Whitney non-parametric test using partial permutations of the data. An MCO problem was built using the different p-values obtained. The associated solution was the set of genes reaching the best compromises between the p-values under consideration that were located in the so-called efficient frontier. Data Envelopment Analysis (DEA) was used to find the efficient frontier of the MCO problem. The capacity of DEA was explored using different numbers of p-values (criteria) in the model. RESULTS: The set of identified genes was consistent across the instances using different numbers of p-values in the DEA model, thereby providing evidence of the outcome stability of the proposed strategy. It was also observed that convergence to a larger number of potential biomarkers is faster with additional criteria, i.e., more p-values. CONCLUSION: The MCO problem proposed for the cancer biomarker search using microarray data can be solved efficiently with DEA using more than two conflicting criteria. This approach can result in robust results when using different analyses of microarray data and, indeed, in a faster convergence to highly potential biomarkers.

Individual response and recovery: A learning experience from Hurricane María.

Situations faced in the advent of powerful hurricanes can be stressful for individuals due to the uncertainty they bring along. The consequences of these phenomena can leave individuals' recovery in their own hands until order is re-established, and support can reach out to them. This work aims to develop a tool to guide individuals through their decision-making during a hurricane disaster and recovery, using the experience of Hurricane María. The tool is a classic inventory model adapted to monitor individual's wellness through 48 hours after a hurricane arrival. This article presents the three stages followed in the development in this work: the assessment of individuals' sentiments toward Hurricane María via an online questionnaire, the development of the mathematical model, and the creation of a prototype in the form of a mobile application. Each phase presents an important contribution: a summary of first-hand knowledge obtained from the reactions of individuals who survived Hurricane María, a novel modeling approach to the problem, and a convenient framework that synthesizes both previous components.

Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

Two novel nonparametric methods for cancer diagnosis through microarray analysis.

Diagnosing cancer using microarray analysis to study differential gene expression has been a recent focus of intense research Although several very sophisticated analysis tools have been developed with this aim in mind, it still remains a challenge to keep these methods free of parametric adjustments as well as maintain their transparency for the final user. Nonparametric methods in general have been associated with these last two characteristics, thus becoming attractive tools for microarray analysis in cancer research. In particular, diagnosing cancer via microarray analysis is an exercise whereby tissue is characterized according to its differential gene expression levels. In this manuscript, two novel nonparametric methods for cancer diagnosis using microarray data are described and their performance assessed against a baseline approach that utilizes the Mann-Whitney test for median differences. Both methods show promising results in terms of their potential use in making diagnoses.

Biological signaling pathways and potential mathematical network representations: biological discovery through optimization.

Establishing the role that different genes play in the development of cancer is a daunting task. A step toward this end is the detection of genes that are important in the illness from high-throughput biological experiments. Furthermore, it is safe to say that it is highly unlikely that these show expression changes independently, even with a list of potentially important genes. A biological signaling pathway is a more plausible underlying mechanism as favored in the literature. This work attempts to build a mathematical network problem through the analysis of microarray experiments. A preselection of genes is carried out with a multiple criteria optimization framework previously published by our research group . Afterward, application of the Traveling Salesperson Problem and Minimum Spanning Tree network optimization models are proposed to identify potential signaling pathways via the most correlated path among the genes of interest. Biological evidencing is provided to assess the effectiveness of the proposed methods. The capability of our analysis strategy is also demonstrated through the undertaking of meta-analysis studies. Three important aspects are novel in this work: (1) our joint analyses of different groups of lung cancer states reveal new correlations, biologically evidenced, and previously undocumented; (2) computation of the correlation coefficients from expression differences leads to an effective use of network optimization methods; and (3) the methods yield mathematically optimal correlation structures: no other configuration is better correlated using the available information.

A Selection of Important Genes and Their Correlated Behavior in Alzheimer's Disease.

In 2017, approximately 5 million Americans were living with Alzheimer's disease (AD), and it is estimated that by 2050 this number could increase to 16 million. In this study, we apply mathematical optimization to approach microarray analysis to detect differentially expressed genes and determine the most correlated structure among their expression changes. The analysis of GSE4757 microarray dataset, which compares expression between AD neurons without neurofibrillary tangles (controls) and with neurofibrillary tangles (cases), was casted as a multiple criteria optimization (MCO) problem. Through the analysis it was possible to determine a series of Pareto efficient frontiers to find the most differentially expressed genes, which are here proposed as potential AD biomarkers. The Traveling Sales Problem (TSP) model was used to find the cyclical path of maximal correlation between the expression changes among the genes deemed important from the previous stage. This leads to a structure capable of guiding biological exploration with enhanced precision and repeatability. Ten genes were selected (FTL, GFAP, HNRNPA3, COX1, ND2, ND3, ND4, NUCKS1, RPL41, and RPS10) and their most correlated cyclic structure was found in our analyses. The biological functions of their products were found to be linked to inflammation and neurodegenerative diseases and some of them had not been reported for AD before. The TSP path connects genes coding for mitochondrial electron transfer proteins. Some of these proteins are closely related to other electron transport proteins already reported as important for AD.

Hyper-phosphorylation of Rb S249 together with CDK5R2/p39 overexpression are associated with impaired cell adhesion and epithelial-to-mesenchymal transition: Implications as a potential lung cancer grading and staging biomarker.

Prediction of lung cancer metastasis relies on post-resection assessment of tumor histology, which is a severe limitation since only a minority of lung cancer patients are diagnosed with resectable disease. Therefore, characterization of metastasis-predicting biomarkers in pre-resection small biopsy specimens is urgently needed. Here we report a biomarker consisting of the phosphorylation of the retinoblastoma protein (Rb) on serine 249 combined with elevated p39 expression. This biomarker correlates with epithelial-to-mesenchymal transition traits in non-small cell lung carcinoma (NSCLC) cells. Immunohistochemistry staining of NSCLC tumor microarrays showed that strong phospho-Rb S249 staining positively correlated with tumor grade specifically in the squamous cell carcinoma (SCC) subtype. Strong immunoreactivity for p39 positively correlated with tumor stage, lymph node invasion, and distant metastases, also in SCC. Linear regression analyses showed that the combined scoring for phospho-Rb S249, p39 and E-cadherin in SCC is even more accurate at predicting tumor staging, relative to each score individually. We propose that combined immunohistochemistry staining of NSCLC samples for Rb phosphorylation on S249, p39, and E-cadherin protein expression could aid in the assessment of tumor staging and metastatic potential when tested in small primary tumor biopsies. The intense staining for phospho-Rb S249 that we observed in high grade SCC could also aid in the precise sub-classification of poorly differentiated SCCs.

Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge.

Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization-based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

Simulation-based process windows simultaneously considering two and three conflicting criteria in injection molding.

Process windows in injection molding are habitually built with only one performance measure in mind. In reality, a more realistic picture can be obtained when considering multiple performance measures at a time, especially in the presence of conflict. In this work, the construction of process windows for injection molding (IM) is undertaken considering two and three performance measures in conflict simultaneously. The best compromises between the criteria involved are identified through the direct application of the concept of Pareto-dominance in multiple criteria optimization. The aim is to provide a formal and realistic strategy to set processing conditions in IM operations. The resulting optimization approach is easily implementable in MS Excel. The solutions are presented graphically to facilitate their use in manufacturing plants.

Identification of potential biomarkers from microarray experiments using multiple criteria optimization.

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.