Internal Psychometric Validation of an International Burden of Illness Survey for Idiopathic Multicentric Castleman Disease.

INTRODUCTION: Idiopathic multicentric Castleman disease (iMCD) is a rare, chronic, debilitating lymphoproliferative disorder where the mainstay of treatment is symptom management. Our recent international patient survey showed that patients with iMCD have a high symptom burden that has a significant negative patient-reported impact on several aspects of daily life. As part of our ongoing work towards the development of an iMCD symptom burden scale, assessing the survey's psychometric properties is a critical step in understanding its adequacy, relevance, and usefulness. As iMCD is a rare disease, there are challenges to conducting such psychometric analyses which we describe. METHODS: As part of the exploratory psychometric analysis, three a priori hypothesis sets (HS) were generated by interviewing an iMCD-experienced clinician, a patient, and a caregiver to explore the iMCD patient survey's internal construct validity, given no gold standard iMCD measure exists for external construct validation. HS-1 hypothesized that a convergent or discriminant relationship exists with the patients' self-assessment of symptom effect on daily life between two potentially related or unrelated symptoms, respectively. HS-2 hypothesized that having a greater number of symptoms has a positive convergent relationship with the patients' assessment of symptoms' effect on daily life. Finally, HS-3 hypothesized that patients receiving treatment versus no treatment was associated with patients reporting less effect of symptom burden on their daily life. Spearman's rank absolute correlation strength (ACS) was used for HS-1 and HS-2 (convergent relationship, ACS ≥ 0.3 and p value < 0.05; divergent relationship, ACS < 0.3), and Cohen's d to quantify standardized absolute effect sizes (AES) for HS-3 (AES ≥ 0.5 and p value < 0.05). RESULTS: Our analyses partially supported HS-1. None of the three positive convergent relationships were supported. Of the six discriminant relationships, only dizziness with impaired cognitive function and tiredness with dizziness were supported. HS-2 analyses showed there was convergent validity between the number of symptoms and their effect on aspects of daily life. HS-3 analyses did not provide evidence to support the hypothesis. CONCLUSION: These internal psychometric construct analyses provide initial support for the bespoke iMCD patient survey and will guide additional work towards the development of the first iMCD-specific symptom burden scale.

First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes: an exploratory analysis of immune biomarkers.

BACKGROUND: IMCY-0098, a synthetic peptide developed to halt disease progression via elimination of key immune cells in the autoimmune cascade, has shown a promising safety profile for the treatment of type 1 diabetes (T1D) in a recent phase 1b trial. This exploratory analysis of data from that trial aimed to identify the patient biomarkers at baseline associated with a positive response to treatment and examined the associations between immune response parameters and clinical efficacy endpoints (as surrogates for mechanism of action endpoints) using an artificial intelligence-based approach of unsupervised explainable machine learning. METHODS: We conducted an exploratory analysis of data from a phase 1b, dose-escalation, randomized, placebo-controlled study of IMCY-0098 in patients with recent-onset T1D. Here, a panel of markers of T cell activation, memory T cells, and effector T cell response were analyzed via descriptive statistics. Artificial intelligence-based analyses of associations between all variables, including immune responses and clinical responses, were performed using the Knowledge Extraction and Management (KEM®) v 3.6.2 analytical platform. RESULTS: The relationship between all available patient data was investigated using unsupervised machine learning implemented in the KEM® environment. Of 15 associations found for the dose C group (450 µg subcutaneously followed by 3 × 225 µg subcutaneously), seven involved human leukocyte antigen (HLA) type, all of which identified improvement/absence of worsening of disease parameters in DR4+ patients and worsening/absence of improvement in DR4- patients. This association with DR4+ and non-DR3 was confirmed using the endpoints normalized area under the curve C-peptide from mixed meal tolerance tests where presence of DR4 HLA haplotype was associated with an improvement in both endpoints. Exploratory immune analysis showed that IMCY-0098 dose B (150 µg subcutaneously followed by 3 × 75 µg subcutaneously) and dose C led to an increase in presumed/potentially protective antigen-specific cytolytic CD4+ T cells and a decrease in pathogenic CD8+ T cells, consistent with the expected mechanism of action of IMCY-0098. The analysis identified significant associations between immune and clinical responses to IMCY-0098. CONCLUSIONS: Promising preliminary efficacy results support the design of a phase 2 study of IMCY-0098 in patients with recent-onset T1D. TRIAL REGISTRATION: ClinicalTrials.gov NCT03272269; EudraCT: 2016-003514-27.

A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA).

Spearman (Am J Psychol 15(1):201-293, 1904. https://doi.org/10.2307/1412107 ) marks the birth of factor analysis. Many articles and books have extended his landmark paper in permitting multiple factors and determining the number of factors, developing ideas about simple structure and factor rotation, and distinguishing between confirmatory and exploratory factor analysis (CFA and EFA). We propose a new model implied instrumental variable (MIIV) approach to EFA that allows intercepts for the measurement equations, correlated common factors, correlated errors, standard errors of factor loadings and measurement intercepts, overidentification tests of equations, and a procedure for determining the number of factors. We also permit simpler structures by removing nonsignificant loadings. Simulations of factor analysis models with and without cross-loadings demonstrate the impressive performance of the MIIV-EFA procedure in recovering the correct number of factors and in recovering the primary and secondary loadings. For example, in nearly all replications MIIV-EFA finds the correct number of factors when N is 100 or more. Even the primary and secondary loadings of the most complex models were recovered when the sample sizes were at least 500. We discuss limitations and future research areas. Two appendices describe alternative MIIV-EFA algorithms and the sensitivity of the algorithm to cross-loadings.

Factors affecting overall care experience for people living with rare conditions in the UK: exploratory analysis of a quantitative patient experience survey.

BACKGROUND: Although individually rare, collectively, rare conditions are common and affect a large number of people and are often chronic, life threatening and affect multiple body systems; the majority of them have no effective treatment. The literature has identified many specific challenges for those living with rare conditions, however, we do not know which of these in combination are most likely to impact how someone rates their overall experience of care. The aim of this study is to do further exploratory analysis of the Genetic Alliance UK 2020 Rare Experience survey data to identify which variables are most strongly associated with respondents' overall care experience. RESULTS: There were strong associations between most of the selected survey variables and the overall rated experience of care variable. In the multiple linear regression only nine variables remained in the best fit model: 'Trust and confidence in hospital staff involved in ongoing care'; 'Satisfaction with information provided by healthcare professionals-following diagnosis'; 'The professionals providing care work as a team'; 'Feel care is coordinated effectively'; 'The timing and frequency of appointments are convenient for the patient/carer/family'; 'Whether or not there is a specific healthcare professional to ask questions of about the rare/undiagnosed condition'; 'Experience of searching for a diagnosis'; 'Knowledge of whether there is a specialist centre for the condition'; and 'Number of different clinics attend for the condition'. CONCLUSIONS: Our findings indicate the challenges that play the largest part in explaining the varied experiences with rare disease healthcare in the UK for our survey respondents. These challenges should be further investigated with a broader sample of people affected by rare conditions, ideally through the implementation of a comprehensive national rare condition patient registry. Our findings highlight an important potential gap in the Framework, 'trust and confidence in healthcare professionals'; further research is required to fully understand the foundations of trust and confidence.

Predicting Trifluridine/Tipiracil Treatment Outcomes in Refractory Metastatic Colorectal Cancer Patients: A Multicenter Exploratory Analysis.

INTRODUCTION: There are no recommended biomarkers to identify patients with refractory metastatic colorectal cancer (mCRC) who would benefit the most from trifluridine/tipiracil (TTP). The exploratory analysis of the RECOURSE trial revealed that patients with low tumor burden and indolent disease derive greater benefit in terms of both progression-free survival (PFS) and overall survival (OS). Nevertheless, the final answer on the TTP real impact on the well-being of patients with late-stage mCRC will come from real-world data. METHODS: The aim of this retrospective exploratory study was to investigate the effectiveness of TTP in mCRC with regard to the duration of standard treatment and other influencing variables. The study included 260 patients from the three largest Croatian oncology centers who began treatment with TTP in the third or fourth line between 2018 and 2020. RESULTS: The median OS and PFS for the entire cohort were 6.53 and 2.50 months, respectively. Patients with more aggressive disease, defined as those whose time to progression on the first two lines of standard therapy was less than 18 months, had significantly shorter PFS (2.40 vs. 2.57 months, hazard ratio [HR] 1.34, 95% confidence interval [CI]: 1.03-1.84). There was also a tendency toward shorter OS (6.10 vs. 6.30 months, HR 1.32, 95% CI: 0.99-1.78) but without statistical significance. Patients with ECOG PS 0, without liver metastases, and with RAS mutation had both longer OS and PFS. No influence was detected from other variables including age, sex, primary tumor location, and tumor burden. CONCLUSION: With regard to the results of the previously conducted trials, the study concludes that indolent disease, good general condition, and absence of liver metastases are positive predictive factors for TTP treatment.

MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination.

Studying a large number of variables measured on the same observations and organized in blocks - denoted multiblock data - is becoming standard in several domains especially in biology. To explore the relationships between all these variables - at the block- and the variable-level - several exploratory multiblock methods were proposed. However, most of them are only designed for numeric variables. In reality, some data sets contain variables of different measurement levels (i.e., numeric, nominal, ordinal). In this article, we focus on exploratory multiblock methods that handle variables at their appropriate measurement level. Multi-Block Principal Component Analysis with Optimal Scaling (MBPCA-OS) is proposed and applied to multiblock data from the CURIE-O-SA French cohort. In this study, variables are of different measurement levels and organized in four blocks. The objective is to study the immune responses according to the SARS-CoV-2 infection and vaccination statuses, the symptoms and the participant's characteristics.

Step-by-Step Approach to Build Multiple Reaction Monitoring (MRM) Profiling Instrument Acquisition Methods for Class-based Lipid Exploratory Analysis by Mass Spectrometry.

Multiple reaction monitoring (MRM) profiling is a strategy for the exploratory analysis of small molecules and lipids by direct sample injection, ie, without the use of chromatographic separation. It is based on instrument methods that comprise a list of ion transitions (MRMs), in which the precursor ion is the expected ionized m/z of the lipid at its species level, ie, the description of lipid class and number of carbon and double bonds in the fatty acid chain(s), and the product ion is a fragment expected for the lipid class or for the fatty acid neutral loss. The Lipid Maps database is expanding constantly, and therefore the MRM-profiling methods associated with this database need to be continuously updated. Here, we provide a comprehensive overview and the key references for the MRM-profiling methodology and workflow, followed by a step-by-step approach to build MRM-profiling instrument acquisition methods for class-based lipid exploratory analysis based on the Lipid Maps database. The detailed workflow includes (1) importing the list of lipids from the database; (2) for a given class, combining isomeric lipids described at full structural level into 1 entry to obtain the neutral mass at species level; (3) attributing the standard Lipid Maps abbreviated nomenclature for the lipid at its species level; (4) predicting the ionized precursor ions; and (5) adding the expected product ion. We also describe how to simulate the precursor ion for the suspect screening of modified lipids using lipid oxidation and their expected product ions as an example. After determining the MRMs, information about collision energy, dwell time, and other instrument parameters are added to finalize the acquisition method. As an example of final method output, we describe the format for Agilent MassHunter v.B.06 and provide the parameters in which optimization can be performed by lipid class using one or more lipid standards.

Multivariate analysis of heavy metals content of beef from Soroti, Uganda.

Information about food hygiene and quality in the sub-Saharan African countries remains scarce at a time when many of their citizenry are beginning to acquire the much coveted middle income status. Confounding this are challenges linked to monitoring on a continuous basis the safety of food produced by such lucrative industries as the beef industry. The objective of the current study was to initiate a process of encouraging changes in the status quo, by showing how a first step in that direction might look like. Using heavy metal contents of representative beef samples from butcheries in Soroti, Uganda, typical of a sub-Saharan country, we demonstrate how relationships and common sources of metals in food could be identified in a multivariate space. Beef samples from 40 sites were analyzed by atomic absorption spectrometry for iron (Fe), zinc (Zn), nickel (Ni), chromium (Cr), lead (Pb), copper (Cu), cobalt (Co) and cadmium (Cd). The study showed that all beef samples contained these metals, the extent of which were in the order: Fe > Zn >> Ni, Cr > Pb > Cu, Co > Cd. By correlation analysis, the pairs Ni and Cr, Cd and Co, Ni and Fe or Cr and Fe were found to be most likely coming from similar sources. At least three distinct characteristics of beef consumed in Soroti were also found, a distinction perhaps arising from three major categories of feedlots used to raise donor cattle. The incremental risk of children or adults developing cancer over a lifetime was estimated and found to fall into three categories, two of which are separately explained by the presence of Cr or Ni. The sources of these metals remain a matter of speculation on our part. More studies are needed to determine these sources and to understand the nature of cancer risk in the three categories of beef identified here.

Using theory to guide exploratory network analyses.

The use of exploratory network analysis has increased in psychopathology research over the past decade. A benefit of exploratory network analysis is the wealth of information it can provide; however, a single analysis may generate more inferences than what can be discussed in one manuscript (e.g., centrality indices of each node). This necessitates that authors choose which results to discuss in further detail and which to omit. Without a guide for this process, the likelihood of a biased interpretation is high. We propose that the integration of theory throughout the research process makes the interpretation of exploratory networks more manageable for the researcher and more likely to result in an interpretation that advances science. The goals of this paper are to differentiate between exploratory and confirmatory network analyses, discuss the utility of exploratory work, and provide a practical framework that uses theory as a guide to interpret exploratory network analyses.

Types of Analysis: Planned (prespecified) vs Post Hoc, Primary vs Secondary, Hypothesis-driven vs Exploratory, Subgroup and Sensitivity, and Others.

In research, there are different, overlapping ways in which the plan of analysis may be described. This article explains planned (prespecified) vs post hoc, primary vs secondary, hypothesis-driven vs exploratory, and subgroup and sensitivity analyses; intent-to-treat vs per-protocol vs completer analysis was explained in an earlier article in this column. A prespecified analysis is one that is outlined before the study starts; it is usually separated into primary and secondary analyses for the single primary outcome and the many secondary outcomes, respectively. Exploratory analyses examine relationships between variables in the absence of well-defined expectations; these tend to be statistical fishing expeditions. Subgroup analyses examine findings in categories of subjects in the sample. Sensitivity analyses examine whether the findings remain similar if the data are handled in a way that differs from the original plan of analysis. Post hoc analyses examine hypotheses that are conceptualized after the data are seen. All these terms are explained with the help of examples, and strengths and limitations are briefly discussed.

Spatial Video and EpiExplorer: A Field Strategy to Contextualize Enteric Disease Risk in Slum Environments.

Disease risk associated with contaminated water, poor sanitation, and hygiene in informal settlement environments is conceptually well understood. From an analytical perspective, collecting data at a suitably fine scale spatial and temporal granularity is challenging. Novel mobile methodologies, such as spatial video (SV), can complement more traditional epidemiological field work to address this gap. However, this work then poses additional challenges in terms of analytical visualizations that can be used to both understand sub-neighborhood patterns of risk, and even provide an early warning system. In this paper, we use bespoke spatial programming to create a framework for flexible, fine-scale exploratory investigations of simultaneously-collected water quality and environmental surveys in three different informal settlements of Port-au-Prince, Haiti. We dynamically mine these spatio-temporal epidemiological and environmental data to provide insights not easily achievable using more traditional spatial software, such as Geographic Information System (GIS). The results include sub-neighborhood maps of localized risk that vary monthly. Most interestingly, some of these epidemiological variations might have previously been erroneously explained because of proximate environmental factors and/or meteorological conditions.

Exploratory analysis of multi-trait coadaptations in light of population history.

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation. To understand the whole history of populations, it is ideal to capture the history of their range expansion with reference to the series of surrounding environments and to infer the multitrait coadaptation. To this end, we propose a complementary approach; it is an exploratory analysis using up-to-date methods that integrate population genetic features and features of selection on multiple traits. First, we conduct correspondence analysis of site frequency spectra, traits, and environments with auxiliary information of population-specific fixation index (F ST). This visualizes the structure and the ages of populations and helps infer the history of range expansion, encountered environmental changes, and selection on multiple traits. Next, we further investigate the inferred history using an admixture graph that describes the population split and admixture. Finally, principal component analysis of the selection on edge-by-trait (SET) matrix identifies multitrait coadaptation and the associated edges of the admixture graph. We introduce a newly defined factor loadings of environmental variables in order to identify the environmental factors that caused the coadaptation. A numerical simulation of one-dimensional stepping-stone population expansion showed that the exploratory analysis reconstructed the pattern of the environmental selection that was missed by analysis of individual traits. Analysis of a public dataset of natural populations of black cottonwood in northwestern America identified the first principal component (PC) coadaptation of photosynthesis- vs growth-related traits responding to the geographical clines of temperature and day length. The second PC coadaptation of volume-related traits suggested that soil condition was a limiting factor for aboveground environmental selection.

Prevalence of and factors associated with lactational mastitis in eastern and southern Africa: an exploratory analysis of community-based household surveys.

BACKGROUND: Lactational mastitis is an extremely painful and distressing inflammation of the breast, which can seriously disrupt breastfeeding. Most of the evidence on the frequency of this condition and its risk factors is from high-income countries. Thus, there is a crucial need for more information on lactational mastitis and its associated factors in Sub-Saharan Africa (SSA). METHODS: We used data from representative, community-based cross-sectional household surveys conducted in 2020 with 3,315 women from four countries (Ethiopia, Kenya, Malawi, and Tanzania) who reported ever-breastfeeding their last child born in the two years before the survey. Our measure of lactational mastitis was self-reported and defined using a combination of breast symptoms (breast redness and swelling) and flu-like symptoms (fever and chills) experienced during the breastfeeding period. We first estimated country-specific and pooled prevalence of self-reported lactational mastitis and examined mastitis-related breastfeeding discontinuation. Additionally, we examined factors associated with reporting mastitis in the pooled sample using bivariate and multivariable logistic regression accounting for clustering at the country level and post-stratification weights. RESULTS: The prevalence of self-reported lactational mastitis ranged from 3.1% in Ethiopia to 12.0% in Kenya. Close to 17.0% of women who experienced mastitis stopped breastfeeding because of mastitis. The adjusted odds of self-reported lactational mastitis were approximately two-fold higher among women who completed at least some primary school compared to women who had no formal education. Study participants who delivered by caesarean section had 1.46 times higher odds of reporting lactational mastitis than women with a vaginal birth. Despite wide confidence intervals, our models also indicate that young women (15 - 24 years) and women who practiced prelacteal feeding had higher odds of experiencing lactational mastitis than older women (25 + years) and women who did not give prelacteal feed to their newborns. CONCLUSIONS: The prevalence of lactational mastitis in four countries of SSA might be somewhat lower than estimates reported from other settings. Further studies should explore the risk and protective factors for lactational mastitis in SSA contexts and address its negative consequences on breastfeeding.

ERP Analysis Using a Multi-Channel Matching Pursuit Algorithm.

In this study, we propose a new algorithm for analysing event-related components observed in EEG signals in psychological experiments. We investigate its capabilities and limitations. The algorithm is based on multivariate matching pursuit and clustering. It is aimed to find patterns in EEG signals which are similar across different experimental conditions, but it allows for variations in amplitude and slight variability in topography. The method proved to yield expected results in numerical simulations. For the real data coming from an emotional categorisation task experiment, we obtained two indications. First, the method can be used as a specific filter that reduces the variability of components, as defined classically, within each experimental condition. Second, equivalent dipoles fitted to items of the activity clusters identified by the algorithm localise in compact brain areas related to the task performed by the subjects across experimental conditions. Thus this activity may be studied as candidates for hypothetical latent components. The proposed algorithm is a promising new tool in ERP studies, which deserves further experimental evaluations.

SOCRAT: a Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization.

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.

Effects of Social Capital and Leisure Participation on Self-Rated Health of Urban Residents in Southwest China.

Background: Leisure provides opportunities for urban and rural residents to relax, recover their vitality, and improve their personal growth, development, and well-being. However, the impact of the leisure participation process, types, obstacles, participation motivation, and satisfaction on health is not very clear, especially the impact of leisure behavior on health, and is worthy of in-depth discussion. Objective: The objective of this study was to explore the impact of social capital and leisure participation on the self-rated health of urban residents in China so as to provide an important reference for national health promotion activities. Methods: the questionnaire on the relationship between social capital, leisure behavior, and self-rated health was compiled by ourselves. The residents participating in leisure and fitness in 25 residential fitness centers in Chengdu were investigated in the morning and evening, and the obtained data were processed by exploratory and confirmatory factor analysis. Results: (1) Social capital had no direct influence on leisure hindrance; leisure motivation and leisure participation had no direct influence on self-rated health. (2) Leisure satisfaction has a direct positive impact on self-rated health, while leisure hindrance has a significant negative impact on self-rated health. (3) Social capital has a direct positive impact on leisure satisfaction, and social capital has a direct positive impact on self-rated health. Leisure satisfaction plays an intermediary role in the path of social capital affecting self-rated health, and the intermediary force exceeds the direct impact of social capital on self-rated health. Conclusion: The effect of leisure satisfaction on self-rated health is higher than that of social capital, and it plays an intermediary role in the impact path of social capital on self-rated health. Therefore, how to make urban community residents with different backgrounds obtain leisure satisfaction through leisure activities is an important topic of national health promotion.

pyResearchInsights-An open-source Python package for scientific text analysis.

With an increasing number of scientific articles published each year, there is a need to synthesize and obtain insights across ever-growing volumes of literature. Here, we present pyResearchInsights, a novel open-source automated content analysis package that can be used to analyze scientific abstracts within a natural language processing framework.The package collects abstracts from scientific repositories, identifies topics of research discussed in these abstracts, and presents interactive concept maps to visualize these research topics. To showcase the utilities of this package, we present two examples, specific to the field of ecology and conservation biology.First, we demonstrate the end-to-end functionality of the package by presenting topics of research discussed in 1,131 abstracts pertaining to birds of the Tropical Andes. Our results suggest that a large proportion of avian research in this biodiversity hotspot pertains to species distributions, climate change, and plant ecology.Second, we retrieved and analyzed 22,561 abstracts across eight journals in the field of conservation biology to identify twelve global topics of conservation research. Our analysis shows that conservation policy and landscape ecology are focal topics of research. We further examined how these conservation-associated research topics varied across five biodiversity hotspots.Lastly, we compared the utilities of this package with existing tools that carry out automated content analysis, and we show that our open-source package has wider functionality and provides end-to-end utilities that seldom exist across other tools.

Exploratory analysis of factors affecting levels of home deliveries before, during, and post- COVID-19.

The COVID-19 pandemic has significantly affected shopping behavior and has accelerated the adoption of online shopping and home deliveries. We administered an online survey among the population in the Portland-Vancouver-Hillsboro Metropolitan area on household and demographic characteristics, e-commerce preferences and factors, number of deliveries made before and during the COVID-19 lockdown, and number of deliveries expected to make post-pandemic. In this research, we conduct an exploratory analysis of the factors that affect home delivery levels before, during, and post-COVID-19. There was a significant increase in home deliveries during the COVID-19 lockdown relative to the before COVID-19 period. A high proportion of the households that made less than three deliveries per month before the pandemic stated they would order more online post-pandemic. A majority of the households that ordered more than three deliveries per month before COVID-19 are expected to revert to their original levels post-pandemic. The two variables most positively affecting the likelihood of online shopping were access to delivery subscriptions and income. Tech-savvy individuals are expected to make more home delivery orders post-pandemic compared to before and during COVID-19. Health concerns positively increase the likelihood of ordering online during the pandemic and post-pandemic. Older and retired individuals are less likely to use online deliveries. However, the likelihood of older and retired individuals ordering more home deliveries increased during the pandemic lockdown. Households with disabled members, single workers, and respondents concerned about online experience and health are more likely to be first-time online shoppers during the pandemic.

Analysis of Metabolite and Lipid Association Networks Reveals Molecular Mechanisms Associated with 3-Month Mortality and Poor Functional Outcomes in Patients with Acute Ischemic Stroke after Thrombolytic Treatment with Recombinant Tissue Plasminogen Activator.

Here, we present an integrated multivariate, univariate, network reconstruction and differential analysis of metabolite-metabolite and metabolite-lipid association networks built from an array of 18 serum metabolites and 110 lipids identified and quantified through nuclear magnetic resonance spectroscopy in a cohort of 248 patients, of which 22 died and 82 developed a poor functional outcome within 3 months from acute ischemic stroke (AIS) treated with intravenous recombinant tissue plasminogen activator. We explored differences in metabolite and lipid connectivity of patients who did not develop a poor outcome and who survived the ischemic stroke from the related opposite conditions. We report statistically significant differences in the connectivity patterns of both low- and high-molecular-weight metabolites, implying underlying variations in the metabolic pathway involving leucine, glycine, glutamine, tyrosine, phenylalanine, citric, lactic, and acetic acids, ketone bodies, and different lipids, thus characterizing patients' outcomes. Our results evidence the promising and powerful role of the metabolite-metabolite and metabolite-lipid association networks in investigating molecular mechanisms underlying AIS patient's outcome.

On Redefining the Body Image Satisfaction Questionnaire: A Preliminary Test of Multidimensionality.

The aim of the present study was to examine the Body Image Satisfaction Questionnaire (BISQ) as a multidimensional instrument, designed to measure individuals' body image satisfaction. A sample of 790 Portuguese healthy adults (female = 399; male = 391) aged 18 and 49 years old (M = 28.61, SD = 7.97) completed the BISQ. Exploratory factor analysis of the BISQ provided initial psychometric validity for a five-factor model assessing five dimensions of body image, namely, face, upper torso, lower torso, lower body, and overall body appearance. Confirmatory factor analysis supported this five-correlated model, in which a bifactor model provided the best fit to the data, defining a body image satisfaction factor and five specific factors. The BISQ clearly distinguished between various dimensions of body image satisfaction and showed satisfactory psychometric quality through factor analyses. This measure may have a broad application for research and practice, as a tool for capturing individual body image satisfaction.