The DR-TB is a multifaceted illness with expensive treatment regimens, toxic medications and most often the long duration of treatment constitutes a substantial financial burden on both infected patients and the health system. Despite significant research advances in the diagnosis and treatment, there is a paucity of synthesized evidence on how socio-economic factors are associated with DR-TB. This review aims to address this gap by synthesizing available evidence and data on the common socio-economic drivers of DR-TB infection in Africa.Drug-resistant TB A systematic search was conducted on PUBMED and Google Scholar databases from January 2011 to January 2020 using Joanna Briggs Institute’s scoping review approach. An updated search was conducted on 21 September 2020. The eligibility criteria only included systematic reviews and studies with quantitative research methods . Studies conducted in Africa and focusing on socio-economic factors influencing DR-TB burden in African countries were also considered. Data was extracted from all the studies that met the eligibility criteria based on the study’s objectives.Out of the 154 articles that were retrieved for review, 20 abstracts of these articles met all the eligibility criteria. Of the 20 articles, 17 quantitative and 3 reviews. Two additional articles were found eligible, following the updated search. The following themes were identified as major findings: Social and economic drivers associated with DR-TB. Substance abuse of which, stigma and discrimination were the prominent social drivers. Economic drivers included poverty, financial constraints because of job loss, loss of productive time during hospital admission and treatment costs.This review has highlighted which socio-economic factors contribute to DR- TB This is relevant to assist DR-TB management program and TB stakeholders in different settings to address identified socio-economic gaps and to reduce its negative impact on the programmatic management of DR TB. Therefore, redirecting strategies with more focus on socio-economic empowerment of DR-TB patients could be one of the innovative solutions to reduce the spread and eliminate DR-TB in Africa.The online version contains supplementary material available at 10.1186/s12889-021-10267-0. Tuberculosis (TB) is a global public health concern and one of the leading causes of morbidity and mortality worldwide –3. Drug-The precise burden of DR-TB in the African countries is poorly described; with only 51% of countries having done official reviews . Thus, DWhat are the social factors that contribute to the burden of DR- TB and treatment outcomes in Africa?What are the economic factors influencing the burden of DR-TB and treatment outcomes in Africa?Drug resistant TB is a huge public health problem as discussed, hence this review set out to answer these questions:The objective of this review was to synthesize published articles to identify socio-economic drivers contributing to the burden of DR- TB in Africa.https://osf.io/m23pk).This scoping review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for scoping review  and framPubMed and Google Scholar databases were searched to retrieve articles published between the year 2011 and 2020. Accordingly, only studies meeting the eligibility criteria discussed below was included in the review. The initial search was done on 7 January 2020 and an updated search on 21 September 2020. The following search terms and keywords were used; socio-economic factors, drug-resistance TB, multidrug-resistant tuberculosis, extremely/extensively drug-resistant TB, Africa, social factors, economic factors, contributing factors, and risk factors. During the data sources search process, sorting by year of publication was first applied and next, we only considered the first 10 pages of the search results due to multiple similarities (duplicates) and unrelated articles (not about TB/ not SES).Population (P) – Drug Resistant TB patientsConcept (C) – quantitative studies investigating the influence of socio- economic factors/ drivers/ status on treatment outcomes such as adherence/ cure and treatment completed rates/ Death rates/ Reinfections.Context (C) – we carried out our search process between 1 January 2011 to 7 January 2020. Only cohort, case-control, cross-section studies and systematic reviews evaluating socio-economic factors influencing adult DR-TB patients from African countries were included for further screening and synthesis. An updated search was carried out on 21 September 2020.Since this is a scoping review, we used the PCC (Population / Concept / Context) framework recommended by JBI  to identPublication not peer-reviewedStudies on drug sensitive TB and general TB were excludedOther reviews , book chapters, editorials, letters, and conference abstracts.Publication of any other language than English.Qualitative studiesStudies including children under 12 years and participants in prison.Exclusion criteriaWe adopted a selection process described by the Joanna Briggs Institute online manual. Two reviewers independently searched for articles using the eligibility criteria. Articles retrieved were exported into MS Excel sheet by both reviewers and assessed for duplication and eligibility. There were no discrepancies in articles selected and screened for inclusion in this review.Two review authors (LLC/ KEO) independently screened titles and abstracts of the articles for eligibility using the earlier discussed criteria. Full text screening was then further carried out on identified eligible studies from title and abstract screening. Both the reviewers selected the studies based on full text review. Data was thereafter extracted based on the aim of this study and thematically analyzed. The data extracted is presented in Table The search process yielded 388 articles on PubMed and 57,560 on Google Scholar on the 7th January 2020 . After sAmong the 20 eligible articles selected for review, eight studies (7) were conducted in Ethiopia, five (5) in South Africa and then respectively one (1) in each of the following countries: Cameroon, Nigeria, Ghana, and Angola. The three systematic reviews and one (1) cross- sectional study featured more than one nation all including Sub Sahara African countries. The 2 eligible articles in the updated search were done in Ethiopia  and Sub Sahara African countries (systematic review). This is illustrated in Fig. Table Social issues influencing MDR-TB1.1 Substance use and abuse: Five articles on treatment outcomes among the eligible studies found that alcohol consumption influenced MDR-TB. Alcohol consumption was found to be a predictor of MDR-TB poor treatment outcomes [outcomes , while soutcomes . Hence, outcomes .1.2 Poverty: The highest rates of DS-TB and DR-TB were discovered in the disadvantaged settings of the community such as the rural areas, overcrowded households and congested areas [ed areas , 23, 38.ed areas  . Low-ined areas , 40. Pov1.3 Stigma and Discrimination: Thomas et al. (2016) highlight stigma and discrimination as the drivers for the infection and transmission of MDR-TB [f MDR-TB . Stigma f MDR-TB .2.Economic Factors influencing MDR-TBThirteen studies identified in the initial search and both the studies in the updated search looked at the social issues influencing MDR-TB. This included apprehensions on stigma and prejudice, the impact of ethanol on the management of MDR-TB, low educational level, overcrowding, lack of treatment support and problems related to MDR-TB/HIV coinfection.MDR-TB has a vast financial effect on patients due to the complex nature and long duration and of treatment regimens. Socio-economic barriers affecting patient care included treatment distance, inaccessibility, transport costs and costs experienced during hospitalization . Oga-OmeThe Mixed Method Quality Appraisal Tool (MMAT) Version 2018  was utilThis review specifically focused on the African region as this area has a high rate of DR- TB with limited resources to combat the disease burden. Thus, it is of utmost importance to identify these factors to stimulate the development of interventions to improve DR-TB treatment outcomes and decrease the incidence rate of DR- TB.killer disease”, TB clients could suffer detrimental effects of being isolated, negatively affecting their treatment adherence status, thus, creating a risk for increased morbidity and mortality. Substance use is frequently observed among patients with poor adherence and has an impact on MDR-TB management. Substance abuse is also associated with high poverty and unemployment levels. In the population with an increased drug and alcohol utilization, it is empirical to study the impact of this increased substance abuse on MDR-TB and to identify intervention strategies to curb the problem.The social factors identified in the reviewed articles include apprehensions around stigma and prejudice, the impact of alcohol on the management of DR-TB, low educational level, overcrowding, and lack of treatment support. The social causes of TB are attributed to the mode of spread of TB, as well as the risk factors for acquiring the disease. This review highlights the social factors influencing DR-TB specifically emphasizing overcrowding as a major contributing factor. Eradicating extreme poverty and providing social protection could significantly reduce DR-TB. Patients with DR-TB would willingly isolate themselves for fear of infecting other members of the family, due to discrimination and stigma. This can often lead to separation, cancellation of engagements, failure of relationships and separation within the family . In placThe review also identified economic factors influencing DR-TB. Numerous factors were identified to cause financial constraints- job loss, loss of productive time, absenteeism, and regular reviews all due to the long duration of care and admission time. Breadwinners who are admitted cause significant emotional distress in families- these are also reasons for defaulting and absconding, to avoid admission- to be able to provide for the family. There are also treatment-related costs- getting to review date has associated transport cost. Serious effort needs to be made to address and develop strategies to decrease the financial burden.There is, however, limited evidence to notify policymakers and TB control officers on how socioeconomic interferences should be used to improve MDR-TB control and reduce MDR-TB-related health disparities. There needs to be an immense scale-up of collaborative efforts toward the implementation of integrated model care, considering the health systems, socio-economic factors as well as medical factors.Although a thorough literature search was conducted, only a small number of eligible articles were available. This limited number of articles could be because only two databases were used to retrieve information. The authors used these databases because they were robust open-access databases that required no institutional payments before they could be searched for eligible articles. Lastly, this scoping review focused on socio-economic factors that influence DR-TB in African regions. However, these factors could differ in other continents.In conclusion, this review has highlighted socio-economic factors that contribute to DR-TB. Furthermore, it is crucial to understand these socio-economic factors driving DR-TB in a context-specific setting, to come up with evidence-based intervention strategies that would curb the burden of DR-TB. To achieve the End TB strategy targets, it is imperative to harness both medical and socio-economic efforts to fight against DR-TB disease. DR-TB elimination would require a holistic, comprehensive approach, utilization of available strategies, and associated medical, socio and economic challenges. The management of DR-TB require concerted efforts that address the socio-economic factors as well. To strengthen the findings from this review and the body of evidence, larger-scale randomized control trials to test socio-economic interventions should be undertaken.Additional file 1:. Addendum 1: Search Strategy.Additional file 2:. Addendum 2: Excluded studies with reasons.Additional file 3:. Addendum 3: Summary of the main socio-economic and other factors associated with DR-TB in Africa.Additional file 4:. Addendum 4.1: MMAT Quality Assessment Checklist.Additional file 5:. Addendum 4.2: CASP Quality Assessment Checklists.


In “ColistinDose, a Mobile App for Determining Intravenous Dosage Regimens of Colistimethate in Critically Ill Adult Patients: Clinician-Centered Design and Development Study” :e20525) the authors noted one error due to an editing issue.In the Abbreviations section, two abbreviations were listed incorrectly as follows:CVVD: continuous veno-venous hemofiltrationCVVH: continuous veno-venous hemofiltrationThese abbreviations have been corrected to:CVVH: continuous veno-venous hemofiltrationCVVHD: continuous veno-venous hemodialysisThe correction will appear in the online version of the paper on the JMIR Publications website on December 18, 2020, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


Recent studies have demonstrated that connector hubs, regions considered critical for the flow of information across neural systems, are mostly involved in neurodegenerative dementia. Considering that aging can significantly affect the brain’s intrinsic connectivity, identifying aging’s impact on these regions’ overall connection strength is important to differentiate changes associated with healthy aging from neurodegenerative disorders. Using resting state functional magnetic resonance imaging data from a carefully selected cohort of 175 healthy volunteers aging from 21 to 86 years old, we computed an intrinsic connectivity contrast (ICC) metric, which quantifies a region’s overall connectivity strength, for whole brain, short-range, and long-range connections and examined age-related changes of this metric over the adult lifespan. We have identified a limited number of hub regions with ICC values that showed significant negative relationship with age. These include the medial precentral/midcingulate gyri and insula with both their short-range and long-range (and thus whole-brain) ICC values negatively associated with age, and the angular, middle frontal, and posterior cingulate gyri with their long-range ICC values mainly involved. Seed-based connectivity analyses further confirmed that these regions are connector hubs with connectivity profile that strongly overlapped with multiple large-scale brain networks. General cognitive performance was not associated with these hubs’ ICC values. These findings suggest that even healthy aging could negatively impact the efficiency of regions critical for facilitating information transfer among different functional brain networks. The extent of the regions involved, however, was limited. Studies have shown that the human brain is functionally organized into several large-scale networks, which can be identified using the spontaneous low frequency fluctuations of resting state functional magnetic resonance imaging (fMRI) data . Some ofA number of brain regions called cortical hubs, characterized by numerous strong interconnections with several other regions, are considered critical for the flow of information within and between brain networks . PromineSince the human brain processes information not only within adjacent areas but also from distant projections, recent studies have also investigated hub profiles in a distance-dependent manner . Some huConsidering that aging can significantly affect the brain’s intrinsic connectivity, characterizing age-related changes in the overall connectivity strength of hub regions is important in order to differentiate changes associated with healthy aging from that due to neurodegenerative disorders. In this study, we examined aging’s impact on the overall connection strength of hub regions. For this, we used resting state fMRI data to construct an intrinsic connectivity contrast (ICC) map , which rResting state fMRI data from 175 healthy volunteers  ranging in age from 21 to 86 years were included in the analysis. The included participants were carefully selected from those enrolled in our ongoing Brain and Mind Research Center Aging Cohort Study , 2019 us2, and total scan time of 5 min and 49 s. For the resting state fMRI data, an ascending gradient echo (GE) echo planar imaging (EPI) pulse sequence was used with the following imaging parameters: TR = 2.5 s, TE = 30 ms, FOV = 192 mm, 64 × 64 matrix dimension, FA = 80 degrees, 39 transverse slices with a 0.5-mm inter-slice interval and 3-mm slice thickness, and total scan time of 8 min and 15 s. During resting state fMRI scan, participants were instructed to close their eyes but not to fall asleep.All participants underwent MRI scanning at the Brain and Mind Research Center using a Siemens Magnetom Verio  3.0T scanner with a 32-channel head coil. For each participant, a high resolution T1-weighted (T1w) image and resting state fMRI data were acquired. The T1w image was acquired using a 3D MPRAGE  pulse sequence  with the3 voxel resolution, and smoothed using an isotropic 8-mm full-width-at-half-maximum 3-dimensional Gaussian filter. The preprocessed images were then corrected for head motion and contribution from other nuisance signals. In particular, we regressed out 24 motion-related signals given by , where Rt =  represents the estimated motion parameters  at time t. Mean signals from spherical regions of interest (radius = 4 mm) within the CSF and WM, the global signal, as well as derivatives of these signals were also removed. Finally, a bandpass filter within the frequency range from 0.01 to 0.1 Hz was also applied. These additional image preprocessing steps were performed using functions available in Matlab.All images were preprocessed using Statistical Parametric Mapping  running on Matlab . Using SPM12’s segmentation approach , the T1wth) with the threshold set to 0 and computed using the following formula:From the preprocessed functional images, we computed the voxel-wise ICC  using Mar is the connectivity value between voxels i and j and n is the number of voxels. To exclude unnecessary voxels, we limit our analysis to only include GM-relevant voxels. For this, we generated a GM mask by applying a threshold value of 0.2 to SPM12’s GM tissue probability map. Voxels with values exceeding this threshold were included in the GM mask. The ICC values of all voxels within the mask were then computed resulting in a map of ICC values. Finally, the ICC values were standardized by subtracting the mean ICC computed from all voxels in the image, and dividing the difference by the standard deviation. This conversion to z-score does not affect the topography of the individual maps but enables the maps to be averaged and compared across participants (where icipants .dthr (Euclidean) from the reference voxel were included in the computation. On the other hand, for long-range ICC values, only voxels with distance greater than dthr were included. In this study, we used dthr = 75 mm to be consistent with the value used in previous studies . We performed one-sample t-tests of the ICC maps for the whole brain, long-range, and short-range ICC values using SPM12. To identify voxels with the most significant ICC values, we applied a more stringent p-value threshold equal to 0.05 corrected for multiple comparisons using a family-wise error (FWE) rate and cluster size equal to or greater than 25 voxels to the resulting statistical maps. We note that this analysis is primarily aimed to establish the spatial profile of the ICC metric across the whole brain, rather than to make comparisons across different age groups. To examine age-related changes in ICC values, which is our primary goal, we used regression analysis as described next.Although hub regions identified using the network metric called degree are known , hub regp-value equal to 0.05 corrected for multiple comparisons using FWE at the cluster level (FWEc) with a cluster defining threshold (CDT) set at p = 0.001.We examined the relationship between ICC values and age and cognitive performance using data from all participants. Since all participants were cognitively normal, the variability of several ACE-R sub-scores were very limited , preventt-test was then performed. The resulting statistical map was corrected for multiple comparisons using FWEc p < 0.05 with a CDT set at p = 0.001 to obtain the group-level functional connectivity map of the given ROI. To quantify the seed ROI’s connectivity with several RSNs, we estimated the spatial overlap of the thresholded statistical map with well-known RSNs. For this, we used the 14 RSN templates from R  with centers located at the MNI coordinates of the significant clusters’ peak locations and radius equal to 4 mm were used. For each seed ROI, its mean time series was extracted and correlated to that of all voxels within the brain (whole-brain connections) to generate its corresponding functional connectivity map. Using the generated connectivity maps from participants in the young adult subgroup, a one-sample s from R  using thNoverlap is the number of voxels in the RSN template that overlapped with the FWEc-corrected statistical map of the ROI, and NRSN is the total number of voxels in the RSN template. An overlap ratio of 1 means that the RSN template is fully within the corrected connectivity map of the given seed ROI (Noverlap = NRSN), whereas a value of 0 means no overlap at all (Noverlap = 0). We used the RSN overlap ratio to identify the different RSNs where the seed ROI has significant functional connection.where We first characterized the spatial distribution of the ICC maps in a subgroup of young adult participants. Whole-brain ICC maps identified hub regions showing significant ICC values in the bilateral middle frontal gyrus, angular gyrus, middle temporal gyrus, precuneus, medial superior frontal gyrus, lateral orbital gyrus, lingual gyrus, and central operculum, among others . For theRegions with whole-brain ICC values that showed significant negative linear relationship with age included the bilateral insula, bilateral medial segment of the precentral gyrus/midcingulate, left middle temporal gyrus, left anterior cingulate gyrus, and right angular gyrus . Age-relRegions in the bilateral caudate and right cerebellum have whole-brain, short-range, and long-range ICC values that showed significant positive relationship with age. Other regions with ICC values exhibiting positive association with age included the right thalamus and left hippocampus for whole-brain ICC values, the right lingual gyrus for both whole-brain and long-range ICC values, and the right planum polare/temporal pole for both whole-brain and short-range ICC values. Peak locations in MNI coordinates and sizes of the significant clusters are given in To quantify the connectivity between regions with ICC values showing significant negative relationship with age and large-scale functional networks, we computed the overlap ratio between the estimated connectivity map of each region and 14 well-known RSN templates. Spider plots of the overlap ratio for the different seed ROIs showing age-related changes in whole-brain, short-range, and long-range ICC values are shown in From Using resting state fMRI, we estimated ICC values at the voxel-level. We first characterized whole-brain as well as distance-dependent ICC maps in a subgroup of young adult participants, and then examined how the ICC values change with age over the adult lifespan. Our results showed that regions with significantly higher ICC values were generally consistent with previously identified hubs , thus vaSeveral studies have investigated the effect of normal aging in the connectivity of large-scale brain networks and the organization of functional brain networks across the lifespan . Age-relIn this study, we focused on aging’s effect on a specialized set of regions called hubs, which still remains largely unexplored. For this, we used ICC, a voxel-level metric, which enabled us to account for both the presence of connections among voxels as well as the strength of these connections . Since IA related work by Characterizing changes in hub regions during normal aging is important since these regions are pivotal in the flow of information within and between networks  and are in situ information while simultaneously being able to associate distributed information with distant connections (By differentiating the contribution of the different connections in terms of (Euclidean) distance, we were also able to identify differences in hub profiles using distance-dependent ICC values . Similarnections .Given the above, it is not surprising that hubs with predominant long-range connections  have long-range ICC values that exhibited negative association with age, whereas those with predominant short-range connections  have short-range ICC values that were negatively associated with age. Since whole-brain ICC values simply represent the combination of short-range and long-range values, changes in whole-brain ICC values could be a reflection of the changes in connections in either short-range or long-range or both. For example, age-related changes in the anterior insula were observed in both whole-brain and long-range ICC values , 4. FromAdditional seed-based connectivity analyses on regions with ICC values showing significant negative association with age showed that most of the identified regions have connectivity maps that strongly overlapped with not just one but multiple large-scale RSNs. The values of the estimated overlap ratio, computed using whole-brain connections, confirmed that the identified regions are mainly connector hubs, considered important in facilitating information transfer among different brain networks. For instance, the connectivity map of the right medial precentral gyrus overlapped with auditory, sensorimotor, ventral default mode, precuneus, as well as salience networks. Regions in the posterior/anterior insula and posterior/central operculum also overlapped with primary processing networks  as well as salience network. These regions are referred to as “control-processing” connector hubs since they linked sensory and motor systems to control networks. Among other functions, control-processing hubs may enable goal-directed control of motor function . In contIn terms of the hubs’ association with general cognitive performance, we have identified regions in the visual and sensorimotor networks with whole-brain and distance-dependent ICC values that showed positive relationship with ACE-R total score. Intriguingly, none of the identified regions overlapped with those having ICC values that negatively associated with age except for a region in the left lingual gyrus, which showed positive relationship with ACE-R total score and negative relationship with age. This finding is consistent with our previous results, which indicated that the integrity of the visual and sensorimotor networks is associated with the participants’ general cognitive performance during healthy aging . In a prHealthy aging is associated with whole-brain intrinsic functional connectivity changes even in the absence of neurodegenerative diseases. Using ICC, we have identified a limited number of regions with overall connectivity strength that showed significant negative relationship with age. More importantly, these regions have functional connectivity profiles that significantly overlapped with not just one but multiple large-scale RSNs indicating that these regions are connector hubs. Connector hubs associated with primary processing  and control networks tended to have whole-brain ICC values that exhibited significant negative relationship with age, whereas control-default connector hubs have predominantly long-range ICC values that exhibited significant negative relationship with age. These findings suggest that even healthy aging could negatively impact, albeit in a limited way, the overall connectivity strength of regions critical in facilitating information transfer among different networks.sobueg@med.nagoya-u.ac.jp.The datasets presented in this article are not readily available because of privacy and ethical restrictions. Requests to access the datasets should be directed to GS, The studies involving human participants were reviewed and approved by the Ethics Committee of Nagoya University Graduate School of Medicine. The patients/participants provided their written informed consent to participate in this study.HW, SM, TT, HT, MKa, TW, MKu, MH, HI, SN, NO, and GS contributed to conception and design of the study. SM, DM, KH, KK, NY, RO, KI, MM, TY, AO, SK, MH, and HI were involved in data acquisition, data organization, and data curation. EB, HW, KK, TT, and HT contributed to the methodology, analysis, and interpretation of the data. EB, HW, SM, and GS wrote the draft of the manuscript. All authors reviewed and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology. The acquisition of multi-omics data sets has become an integral component of modern molecular biology and biotechnology. This is due to technological advancements, such as the next-generation sequencing technology  and mass spectrometry coupled with gas- and liquid chromatography, which offer high-throughput data generation . The corMOI approach has been extensively studied and reviewed in studies on human , animalsThe “conceptual” integration refers to multiple omics data sets being analyzed separately and are matched without further statistical analysis. Even though this approach can produce valuable insights, it may miss reproducible associations when multiple omics data sets are analyzed together . We therThus, we re-define the MOI workflow into three main integration levels (levels 1 to 3) with increasing complexity Figure 1The first level of MOI is an element-based integration approach specifically using correlation analysis Figure 1Persicaria minor Huds. herbal plants, with poor overall proteome-transcriptome correlation (r=0.341) (Solanum lycopersicum)  level. However, this is often not the case, most studies reported weak correlations (different patterns) between transcript-protein levels. For instance, salt treatment on salt-tolerant Earlistaple 7 and salt-sensitive Nan Dan Ba Di Da Hua cotton revealed scarce correlation (r=0.03) between transcript and corresponding protein patterns, regardless of genotypic background . Anotherr=0.341) . Similarersicum) . This suersicum)  as well ersicum) .P. minor  such that clear, distinctive groupings emerge to differentiate expression patterns. Meanwhile, random forest classifies a group of genes/proteins/metabolites based on prior training data sets (from omics experiments) to associate them to a particular characteristic/trait of interest  or non-hierarchical methods. However, the latter approach  is more applicable in the integration of multiple omics especially using machine learning algorithms, such as the m forest . k-meansinterest . These tk-means clustering approach  gene, potentially important for wood development. Evidently, several biomarkers related to the wood formation and secondary cell wall components have been successfully documented using this approach. Other forms of multivariate analyses, such as MCIA  and GFLASSO (graph-guided fused least absolute shrinkage and selection operator) have also been applied in multi-omics plant studies. For instance, MCIA was used to integrate metabolome and proteome of a near-isogenic maize line (control) and its transgenic counterpart   . The stu, NK603) . Another, NK603) .Furthermore, the integration of proteomics and metabolomics data of early and middle season Hass avocado using multivariate dimension reduction discriminant analysis method, DIABLO  led to the identification of correlated discriminatory variables that linked the effect of heat treatment to ripening homogeneity . Both ofWhile this level 1 MOI proves to be useful in plant omics data integration, these omics data sets are often analyzed numerically, without emphasis on interacting partners or co-expressed molecules as well as underlying biological pathways . This may impede further understanding of interrelated molecular regulation between different omics and their biological significance in plants upon certain treatments or conditions. Hence, level 2 MOI, which is a pathway-based integration, will be required for the next degree of omics data integration.https://www.genome.jp/kegg/), but other more organism-specific databases such as AraCyc for Arabidopsis (https://www.arabidopsis.org/biocyc/), CitrusCyc for citrus (https://www.citrusgenomedb.org/node/1136703), and SolCyc for Solanaceae species (https://solgenomics.net/tools/solcyc/index.pl)   do existndex.pl) Table 2.Quercus ilex) metabolic pathways to be visualized and reconstructed  , Paintomata sets . Other tpothesis . In the Phytophthora sojae fungus  was also successfully reconstructed using multi-omics annotation to available KEGG database  and accepting the best hit from the BLAST results. Inexperience researchers tend to accept this annotation without considering if the conserved domains or functions are significantly recognized. The lack of confidence in cross-species annotation may restrict the reliability of generated biochemical pathway relationships  and otheCo-expression analysis heavily relies on statistical correlations between different omics data sets, as discussed earlier in the first level of MOI, to assess the strength of relationships between expressed molecules . Such reVitis vinifera L.) during prolonged drought  upon certain pre-set conditions or treatments in specific species of interest, which will be detailed in level 3 MOI, mathematical-based integration.The mathematical-based MOI poses as the most complex integration of all, and this approach requires extensive omics data coverage and well-characterized plants. One of the most basic aim in the mathematical-based approach is to develop a well-defined differential equation and modeling for a systems-level understanding. Such analysis consists of four main steps: identification of systems components, determination of systems regulation and topology, the development of appropriate mathematical equations, and lastly, parameter selection and optimization .tk) and degradation (dk). The result suggests that the equation reliably predicts the expression of nearly 50% of 2,400 transcript-protein pairs from the study and that the protein level was regulated strongly by the translation rate rather than degradation  was successfully modelled using the ODE approach , and growth experiment  and Metabolic Adjustment by Differential Expression (https://omictools.com/made-tool) which have been reviewed comprehensively by These studies show that differential analysis can be useful for MOI in plants either with or without specific target pathways . IntegraPreviously in differential analysis, the stoichiometric equation is only developed for a specific purpose, such as measuring translation rate or metabolic flux in one isolated system or pathway. Furthermore, this top-down approach relies upon experimental results to construct a functional mathematical model . In contGSMs for plants and other eukaryotes are significantly more complicated than those for prokaryotes due to their extensive compartmentalization, size, polyploidy as well as numerous and variegated secondary metabolic pathways . For pla3 plants such as Arabidopsis, but not C4 model plants such as maize and foxtail millet, Setaria italic  was developed for such purpose before transcriptomics, proteomics, and metabolomics data sets were mapped to the reconstructed network for functional analysis  using tools such as Plant/Eukaryotic and Microbial Metabolomics Systems Resource  and MetNetDB (https://omictools.com/metnetdb-tool) (Brassica napus) has also been metabolically reconstructed through its Bna572+ database annotation update and a model was developed using Flux Variability Analysis (FVA), one of the methods in FBA  . This sts in FBA . Transcrgenotype . Similargenotype .Nonetheless, out of all MOI strategies, only mathematical-based integration can accurately predict changes or perturbation in specific organisms due to the application of extensive database annotation as well as validated models using experimental evidence. This includes metabolic flux analysis using isotopic labelling techniques to quantitatively measure cellular flux state . HoweverThe integrative multi-omics approach can often be hindered by differences in data output, variability in data structure, and even noise between technological platforms . As diffLevel 1 element-based MOI, such as correlation, clustering, and multivariate, are relatively direct and intuitive analyses, and hence will continue to be among the initial choices for integrating transcriptomics, proteomics, and metabolomics. However, each of these approaches may have individual weaknesses that must be considered by potential users. For example, correlation analysis can be limited in scope and insights into biological knowledge , and cerThe next level of MOI (level 2), pathway-based integration which comprises pathway mapping and co-expression analyses also come with their own perks and benefits. For instance, this type of integration is highly intuitive to biologists with many annotation tools and databases available online . Potentiin silico  ranging from simple element-based integration  and pathway-based approaches (pathway mapping and co-expression) to the more complex integration using mathematical approach . These approaches were explained in view of current literature to highlight their applications and practicality in plant MOI. However, the limitation of each approach needs to be considered before embarking on any MOI studies particularly for less characterized non-model plants. Future work in improving MOI strategies can be dedicated to complete gene and metabolite annotation for specific plant species, as well as developing user-friendly tools utilizing machine learning algorithms to allow accurate metabolic model reconstruction.WA, MA, and IJ designed the framework of the manuscript. IJ wrote the first draft of the manuscript. JR wrote sections of the manuscript. MA, KA, NN, H-HG, and WA revised the manuscript critically. All authors contributed to manuscript revision, read, and approved the submitted version.This research was funded by the UKM Research University grant (DIP-2018-001 and GUP-2018-122) to WA. The work is also partly supported by NBDC Database Integration Program (MA), and NIG-JOINT grant 2019 (2A2019) (IJ and H-HG), Japan.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Circular RNAs (circRNAs) may be potential biomarkers or therapeutic targets of hemophagocytic syndrome (HPS) due to their high stability, covalently closed structure and implicated roles in gene regulation. The aim of the present study was to determine and characterize the circRNAs from natural killer (NK)/T-cell lymphoma-associated hemophagocytic syndrome (NK/T-LAHS). CircRNA in NK/T-LAHS and healthy control patient serum were assessed using next-generation sequencing (NGS). One hundred and forty-three differentially expressed circRNAs of which 114 were up-regulated and 29 were down-regulated in NK/T-LAHS patients were identified. Next, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to explore the roles of these circRNAs were utilized, and a microRNA (miRNA) target gene prediction software to predict the interaction of circRNAs and miRNAs was used. Moreover, five circRNAs were then selected as NK/T-LAHS candidate circRNAs which were related to tumors and contained NK/T-LAHS-related miRNA-binding sites. Using real-time PCR, the significant up-regulation of these five circRNAs in NK/T-LAHS patient serum were verified. Together these results show that circRNAs may serve as valuable diagnostic biomarkers of early NK/T-LAHS, with potential therapeutic targets in disease progression. Hemophagocytic lymphohistiocytosis (HLH), also called hemophagocytic syndrome (HPS), is a potentially life-threatening disease characterized by impaired natural killer (NK) and cytotoxic T-cell function, cytokine storm and overwhelming inflammation ,2. RecenCircular RNA (circRNA) is a novel molecule that is formed by a covalently closed loop with different sizes and sources and represents a class of abundant, stable and widely existing RNA molecules in animals ,8. It plWith the continuous development of sequencing technology, RNA-Seq (RNA sequencing) based on a high-throughput platform has become an effective tool for mining and identifying new transcripts. Due to the application of high-throughput RNA sequencing and bioinformatics methods, a large amount of circRNA has been found in human cells. Emerging research evidence shows that many circRNAs are specifically expressed in different cells and are related to the normal physiological development of cells and various diseases . CircRNAIn the study, we identified a group of circRNAs with differential expression using next-generation sequencing (NGS) technology and explored the roles of these circRNAs by an miRNA target gene prediction software. With real-time PCR, five selected NK/T-LAHS candidate circRNAs were verified for the significant up-regulation.From 2018 to 2019, in the Department of Geriatric Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province, the serum of 4 NK/T-LAHS patients who did not receive cancer treatment and 4 healthy control serums were collected, each with 200μl. The diagnosis of HLH is established according to the 2004 HLH diagnostic criteria . The diaTotal RNA was extracted from the eight serum samples with TRIzol . The NanoDrop ND-1000 instrument  was used to evaluate the concentration of each RNA sample. All RNA samples involved in this study met the qualification ratio (1.8-2.1) of OD 260 to OD 280, and met the quality control standards. RNA integrity and gDNA contamination were measured by modified agarose gel electrophoresis. Agilent 2100 Bioanalyzer was used to determine the quality of the test library.in situ, and sequenced in 150 cycles with PE mode on an Illumina HiSeq  sequencer.RNA high-throughput sequencing was performed by Cloud-Seq Biotech . Briefly, total RNA was used to remove the rRNAs with the NEBNext, rRNA Depletion Kit  following the manufacturer’s instructions. RNA libraries were constructed by using the NEBNext® Ultra™ II Directional RNA Library Prep Kit  according to the manufacturer’s instructions. Libraries were quality controlled and quantified using the BioAnalyzer 2100 system . Library sequencing was performed on an Illumina HiSeq instrument with 150-bp paired end reads. The 10 pM library was transformed into single-stranded DNA molecules, which was captured on an Illumina Flowcell , amplified into clusters P-values ≤0.05 were designated as being significantly differentially expressed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the differentially expressed circRNA-associated genes. The interaction network between circRNAs and their downstream miRNAs was constructed by Cytoscape software (v2.8.0) based on data of circRNAs with specific miRNA binding sites and their predicted miRNA partners.Paired-end reads were obtained from the Illumina HiSeq 4000 sequencer data and were quality controlled by Q30. Then, 3′ adaptor trimming and removal of low-quality reads were performed by cutadapt software (v1.9.3) . The higThe qPCR verification of specific circRNA was performed to confirm that the data generated by the sequencing work is reliable. Total RNA obtained from serum with TRIzol  was used for synthesizing cDNA with the ReverTraAc real-time qPCR kit . First-strand cDNA was used for PCR, which was performed in triplicate with SYBR Green Super Mix . The specific primers sequences for the 5 upregulated circRNAs used for RT-qPCR are summarized in All reactions were conducted according to the following procedures, 40 PCR cycles ). The fusion curve of PCR products was established as follows: 95°C, 10 s; 60°C, 60 s; and 95°C, 15 s after the amplification reaction, followed by slowly heating from 60 to 99°C (the ramp up rate of the instrument was 0.05°C/s). Each sample was assayed three times.http://www.geneongolo-ty.org/) and KOBAS (KEGG Orthology Based Annotation System) are used to assess the function of genes associated with detected differentially expressed circRNAs. Genes associated with differentially expressed circRNA detected. The first 300 circRNA-miRNA interaction networks were drawn using Cytoscape software in order to understand the interactions between circRNAs and miRNAs. Based on the two databases previously described, miRNA response element (MRE) is predicted and explained [The interaction between circRNA and miRNAs was analyzed and predicted using TargetScan and miRanda database, where they might act as sponges. Based on its related mRNA, Gene Ontology  to correct the P value of high-throughput sequencing analysis. In qRT-PCR analysis, the 2(-ΔΔCt) method is used to express the level of each circRNA as a fold change.Statistical analysis was performed using SPSS 19.0  statistical software package. Student's (here to remove).We utilized Illumina HiSeq instrument to profile circRNA expression in four NK/T-LAHS serum samples and four healthy control serum samples . In the http://www.circbase.org) and 42 were documented in the literature. The remaining 8741 are novel circRNAs first identified in this document  statistical criteria. Finally, of the identified circRNAs, 143 were specifically and significantly differentially expressed in NK/T-LAHS samples. In contrast with those in the normal control group, a total of 114 circRNAs were markedly up-regulated and 29 were significantly down-regulated in the NK/T-LAHS group, as shown by a cluster heatmap  and hsa_circ_0123217 , respectively.The differential circRNAs between the NK/T-LAHS and normal control groups were determined through fold change and  heatmap A. The mo2 scaled) up- or down-regulation, and the horizontal green line indicates P-values of 0.05 . Red squares represent circRNAs with statistically significant differences in expression.Differentially expressed circRNAs between the NK/T-LAHS group and normal controls are displayed by scatter plots B and volhttp://www.geneontology.org) project provides a controlled vocabulary for describing the attributes of genes and gene products in any organism. The ontology covers three domains: BP, cellular component (CC), and molecular function (MF). The top ten GO terms were identified. ‘Positive regulation of pseudopodium assembly,’ ‘regulation of pseudopodium assembly,’ and ‘pseudopodium assembly’ were the top three BPs , chr1:77620134-77635080− (hsa_circ_0114150), chr12:62715245-62749256+ (hsa_circ_0027353), chr20:34304662-34320057− (hsa_circ_0115056), chr6:56989532-57025950+ (hsa_circ_0131996). These five circRNAs had increased differential expression in NK/T-LAHS compared with normal samples. In addition, these 5 circRNAs all contain miRNA targets related to NK/T-LAHS.Therefore, we conclude that these 5 circRNAs may be closely related to NK/T-LAHS. Since circRNA can have many miRNA targets, we summarize the top 5 sites of each circRNA in . The figThis study first evaluated circRNA in the serum of NK/T-LAHS and healthy control patients through next-generation high-throughput sequencing. Then qRT-PCR was used to detect the circRNA expression levels of the serum samples of ten NK/T-LAHS patients and ten healthy volunteers to verify the accuracy of the sequencing data.In addition, GO, Pathway analysis and circRNA-miRNA network analysis were also carried out to preliminarily explore the biological functions of these circRNAs in the occurrence and development of HPS. We selected five circRNAs as NK/T-LAHS candidate circRNAs associated with tumors and containing NK/T-LAHS-related miRNA binding sites to verify NGS data. They were found to be consistent between RNA-seq and qRT-PCR, and circRNA in the serum of NK/T-LAHS patients was up-regulated, suggesting its potentially important clinical significance .Hematological malignancies are the most common risk factors for HLH. Most of them have a poor prognosis, and LAHS patients have the worst prognosis. Early diagnosis of the underlying conditions, especially NK/T-LAHS, may lead to better outcomes ,34. At pRecent studies have revealed that circRNAs are involved in gene regulation during the transcriptional and posttranscriptional stages . CircRNAMany studies have proved that circRNAs are involved in the pathogenesis of various diseases and are potential biomarkers ,37–39. STherefore, it is significant to profile the circRNA expression and find new biomarkers, which may help to provide new directions and strategies for the diagnosis and treatment of diseases, and is reasonable to hypothesize that there might be certain circRNAs’ expression styles which were characteristic of NK/T-LAHS. In this study, we compared circRNA expression patterns between NK/T-LAHS and healthy control serum samples using second-generation circRNA sequencing.Through our sequencing work, we were able to identify a large number of specific circRNAs, including many that are not yet available in the circRNA databasess. In addition, in order to explore the full content of circRNA, we described the length and chromosome distribution model of circRNAs in serum of NK/T-LAHS patients.(here to remove). GO function analysis of the host genes of differentially expressed circRNAs was performed to annotate and predict the function of these circRNAs. KEGG pathway analysis is the process of mapping molecular datasets in genomics, transcriptomics, proteomics, and metabolomics to KEGG pathway maps to explain the biological functions of these molecules. By analyzing the pathways of differentially expressed genes derived from circRNAs, we can infer the pathways in which they participate and their biological functions (here to remove). Through KEGG pathway analysis, we determined that these circRNAs were enriched in pathways including insulin signaling, endometrial cancer, Hippo signaling, basal cell carcinoma, colorectal cancer, PI3K-Akt signaling, prolactin signaling, focal adhesion, ECM–receptor interaction, and regulation of actin cytoskeleton. These pathway analysis methods together emphasize the broad regulatory role that circRNAs can play in the context of some diseases, and circRNAs may affect a variety of cellular functions by changing their regulation. Most Akt substrates are involved in the regulation of cell survival and cell cycle progression. This is the core role of the PI3K/AKT pathway in promoting the progression of human malignant tumors. There have been some reports on the influence of AKT signaling in tumorigenesis and cancer progression [Progressively, we identified circRNA expression in 143 NK/T-LAHS and control serum samples, most of which were up-regulated in NK/T-LAHS patients, indicating that they may be related to the occurrence and development of NK/T-LAHS. Others have similar views to us. For example, Huang et al. detected bladder urothelial carcinoma and renal clear cell carcinoma and found that circRNA was significantly upregulated  and Wanggression ,48.The rgression . HoweverIn addition, substantial evidence has suggested that circRNAs can play many significant roles, such as acting as competitive endogenous RNAs to sponge miRNAs efficiently, attenuating or compromising the inhibitory effect of miRNAs on target genes, and modulating protein synthesis accordingly ,35,50. CThe present study had several limitations. The human NK/T-LAHS samples of the present study were only from four patients, and the sample size was too small. Furthermore, identification of differentially expressed circRNAs in subjects with NK/T-LAHS based on RNA-sequencing (RNA-seq) and verification of the circRNA profile by qPCR needs to be conducted in another independent large-size sample cohort. Additionally, because functional validation assays were not included in the present study, the specific mechanisms by which circRNAs may be involved in NK/T-LAHS development were not speculated on. Finally, the role of circRNA in the pathogenesis of the disease has not been explored in this experiment. In the future, we will collect patients' peripheral blood mononuclear cells to detect the expression of circRNA in the cells. The target gene of circRNA is predicted by bioinformatics technology, and the signal pathway of the predicted target gene is verified by dual luciferase report.In summary, we identified 11481 cirRNAs in human NK/T-LAHS samples. Based on comparisons with current databases, 76% of circRNAs were identified as novel. Further analysis showed that 114 up- and 29 down-regulated circRNAs were distinctly associated with NK/T-LAHS disease. The results suggested that the 143 identified circRNAs might serve as valuable diagnostic biomarkers of early NK/T-LAHS.


Even though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS.https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files.MEPHAS is a web-based GUI (MEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results. There is a demand from the medical and pharmaceutical practitioners and researchers for free and accessible graphical user interfaces (GUIs) as well as step-by-step guidance to run data analysis.Statistical analysis is crucial to research endeavors, but it can be intimidating for medical and pharmaceutical practitioners and students. Few pharmaceutical researchers and practitioners can afford the time and costs to learn how to use commercial statistical tools, such as SAS  for public access. The underlying R functions were created as the R package named mephas (https://mephas.github.io). Unlike R-Commander and EZR, the webserver MEPHAS is highly accessible via various computing devices such as laptops, tablets, and smartphones.Considering the powerful functions in R, we created a ny-based  web applshiny framework, MEPAHS provides a web-based interactive GUI that can dynamically visualize real-time analytical results across multiple panels simultaneously. Notably, this analytical mode avoids repetitive and redundant operations when finding optimal parameters, and therefore, the analytical process is facilitated.MEPHAS was designed to support researchers in medical and pharmaceutical fields and can enable medical and pharmaceutical practitioners and researchers to conduct confirmatory and exploratory data analyses. Through a Fundamentals of Biostatistics [Applied Regression Analysis [and Other Multivariable Methods and Modeling Survival Data [, we added regression models for continuous, binary, or time-to-event outcomes. To support the high throughput omics data analysis and quantitative structure-activity relationship (QSAR) models, we included partial least square regression (PLS-R) [shiny application to support the AFT model, PLS-R, and SPLS-R with an interactive GUI with downloadable result tables and figures. Additionally, based on those statistical models, MEPHAS can generate prediction results of the new data.MEPHAS provides separate interfaces to access numerous medical and pharmaceutical statistics, and it offers easy-to-follow instructions when necessary so that users can analyze their data without the need to acquire a profound knowledge of statistical analysis beforehand. The methods available in MEPHAS refer to several popular textbooks and critical requests on medical and pharmaceutical statistical analysis. We created probability and hypothesis testing interfaces based on the concepts from the atistics . With reAnalysis  and Otheval Data , we adde (PLS-R)  and spar (PLS-R) . To fill (PLS-R)  and exte (PLS-R) , 10. NotThe objectives of MEPHAS are (1) to guide researchers in using basic and advanced statistical methods in medical and pharmaceutical research; (2) to create an integrated platform for medical and pharmaceutical statistics and fill the void as with the extended Cox regression, AFT model, PLS-R, and SPLS-R; and (3) to facilitate the data analytical process by allowing various interactive operations.shiny for the web-based GUI. The MEPHAS web server was built on CentOS 7. The R package mephas was developed with R (version 3.6.1) and RStudio (version Version 1.2.5001).MEPHAS was implemented with various R packages , Android, or Linux. Users can use the webserver by referring to the help and tutorials.The MEPAHS webserver can be accessed at mephas and mephas.tools for users to install locally. The R package mephas.tools contains the subsidiary functions for MEPHAS, while the R package mephas includes the services to activate the interfaces. Both packages are published on GitHub (https://mephas.github.io). The installation command is based on the R package remotes [remotes, users can download and install the latest version from GitHub by typing the following command in R console (Code 1).The underlying functions of MEPHAS have been wrapped in R package  remotes . After i> > install.packages(“remotes”)> > remotes::install_github,upgrade = “never”)mephas.tools and mephas after installation and use function mephasOpen to activate the web-based interfaces (Code 2).Users need to load the functions in the package > > library(“mephas”).> > mephasOpen (method = “condist”).mephas can be found in the documentation (https://mephas.github.io/reference/index.html).The list of methods and contents of the functions in R package The GUI is activated on the users’ default browsers when they use the R console. If users run the codes in RStudio’s console, the GUI is shown in RStudio’s window. Users can minimize the console and use only the web-based GUI to input data, choose parameters, and generate results.probability, hypothesis testing, regression model, and dimensional analysis) that were developed in 12 independent web-based interfaces .To help users find the proper statistical methods from the interfaces, MEPHAS provided a flowchart  step is to choose the independent and dependent variables from the prepared dataset. The second step is to remove or keep the addictive terms. For example, whether to add interactive terms and a constant, whether to add the random effect term, such as cluster, strata, and frailty terms, in the Cox regression and AFT model. After the formula of the statistical model is constructed correctly, the third step is to check the formula and click the button to generate results. In the outputs, MEPHAS presented abundant results in parameter estimation and model evaluation. Furthermore, MEPHAS enables users to upload new datasets and derive prediction and evaluation results based on the existed model.The interfaces for the regression model include The dimensional analysis includes two interfaces. One interface contains the principal components analysis and explanatory factor analysis; the other contains Principal Component Regression (PCR), PLS-R, and SPLS-R. These methods are often used to analyze high-dimensional data, such as gene expression and chemical data. PLS-R and the related method are widely used in QSAR analysis . SPLS-R https://www.wessa.net/stat.wasp), and Radiant - Business analytics using R and Shiny (https://radiant-rstats.github.io/docs/). All of the three software are well-structured and cover the four categories of statistics . EZR is R console-based GUI and was designed for the application of statistical functions that are frequently used in clinical studies. FSFS is published on web-server, and the results are generated from R. Radiant is based on the shiny framework and was designed for business data analysis. In the general comparison on the utilities, MEPHAS added prediction functions in the statistical models and facilitate users to upload new data to predict and evaluate the prediction results. MEPHAS provided more reactive tables and plots to illustrate the analytical results. Notably, MEPHAS is the only one that included 3D visualization.We compared MEPHAS with another three open-source software: EZR, Free Statistics and Forecasting Software  includes the time-to-event records of 93 fictional patients who received Allo-SCT for acute leukemia. Time-to-event data include information of both time and censor. Therefore, survival analysis is the optimal choice for modeling.Data downloaded from the EZR website  non-parametric model such as Kaplan-Meier method to estimate the survival probability and log-rank test to compare the differences between survival probability from different groups; (2) semi-parameter model such as the Cox regression; and (3) parametric model such as AFT model. We used MEPHAS to generate the same results from EZR in the log-rank test and basic cox regression  to 0.794 (frailty). The frailty model indicated that the hazard of “Relapse = 1” is approximately 4.5 times of “Relapse = 0” . The predicted results could be download without affecting any other parameters or results in the model .Project home page: https://mephas.github.io/Operating system(s): Platform independent.Programming language: R.Other requirements: Internet browser.License: MIT.Any restrictions to use by non-academics: None.Additional file 1: AF_table1.docx Graphic user interfaces and statistical methods in MEPHAS; AF_table2.docx Comparison of methods in MEPHAS with EZR, FSFS, and Radiant; AF_list.docx R packages used in MEPHAS; AF_result.docx The results in Example 1 and Example 2.


Introduction: Regular physical activity is associated with multiple health benefits for children. Evidence from cross-sectional studies suggests that physical activity is positively associated with health-related quality of life (HRQoL). The promotion of physical activity, and hence HRQoL, through a school-based intervention is therefore an important endeavor, particularly in disadvantaged areas of low- and middle-income countries, including South Africa.Methods: We designed a multicomponent physical activity intervention that was implemented over a 20-week period in 2015 in eight disadvantaged primary schools of Port Elizabeth, South Africa. Overall, 758 children aged 8–13 years participated. HRQoL was measured with the 27-item KIDSCREEN questionnaire. Self-reported physical activity was assessed with a single item of the Health-Behavior of School-Aged Children test, and cardiorespiratory fitness with the 20-m shuttle run test. Post-intervention scores were predicted with mixed linear regression models, taking into consideration the clustered nature of the data.Results: Higher baseline levels as well as increasing levels of self-reported physical activity predicted all dimensions of children's HRQoL. Baseline levels and increases in cardiorespiratory fitness predicted children's self-perceived physical well-being . Participation in the multicomponent physical activity intervention did not affect children's HRQoL.Conclusion: Higher and increasing self-reported physical activity predict all assessed HRQoL dimensions, which underlines that the promotion of regular physical activity among children living in disadvantaged settings is an important public health measure. Policy makers should encourage schools to create physical activity friendly environments, while schools should implement regular physical education as proposed by the school curriculum. Physical activity is a cornerstone for people's health and well-being . With reSeveral pathways have been proposed on how regular physical activity might impact HRQoL . These pAgainst this background, the World Health Organization (WHO) recommends at least 60 min of moderate-to-vigorous intensity physical activity (MVPA) for children and young people (aged 5–17 years) . Yet, thSchools are considered an appropriate platform for the promotion of physical activity, since a large number of children are reached and a considerable amount of children's daily physical activity can be acquired during school hours . As showThe empirical evidence with regard to the prospective association between physical activity and HRQoL is growing. Nevertheless, to date, research on children from African countries is completely missing. Additionally, little is known about the potential of school-based interventions , 44. ThiIn light of these findings and in view of the absence of physical education lessons in most South African schools in disadvantaged areas , the curData presented in this paper are based on a cluster-randomized controlled trial that served to evaluate the potential impact of several school-based health promotion measures to improve the health and well-being of primary schoolchildren in disadvantaged settings in the Nelson Mandela Bay district, Port Elizabeth region, South Africa. In brief, trial schools were randomized based on a computer-generated number list either to one of four interventions or to a control condition. As described previously , 30, 48,The physical activity intervention component was thus carried out in three of the eight schools. It lasted for 20 (school) weeks and contained four elements: (i) two 40 min physical education lessons per week; (ii) one weekly 40 min moving-to-music lesson; (iii) regular in-class physical activity breaks; and (iv) improving school environments to promote physical activity, including the implementation of physical play structures  and colorful floor-painted games. Prior to the intervention, workshops were held in which lessons and games were demonstrated and class management techniques were shared. The class teachers, supported by an experienced physical education coach, held the two physical education lessons. The lessons were pre-made and structured progressively, ~40 min in duration and contained four parts: (i) warm-up (5–10 min); (ii) fitness component (10–15 min); (iii) modified invasion games (10–15 min); and (iv) cool-down and stretching activities (5–10 min). The aerobic dancing-to-music lessons lasted 40 min and were conducted by student-dancers from the Nelson Mandela University after school to the entire grade (ranging from 80 to 160 learners at one time). The lessons were structured to contain a fast-paced dance to form the warm-up component, followed by an aerobic dancing-to-music sequence as the main component, and concluded with a slow-paced routine to music for the cool-down component. Baseline assessment took place between January and March 2015, whereas the post-intervention assessment was carried out between May and June 2016.The study was approved by the ethics committee of Northwestern and Central Switzerland , the Nelson Mandela University (NMU) Ethics Committee  and the ethics review boards of the Eastern Cape Department of Education , and the Eastern Cape Department of Health . The study is registered at ISRCTN registry under controlled-trials.com . Prior to beginning the data assessment, written informed consent was obtained from the parents/legal guardians of children, while children assented orally. All procedures were in line with the ethical principles described in the Declaration of Helsinki.Eight primary schools participated in the Disease, Activity and Schoolchildren's Health (DASH) study. Schools were selected according to geographic location, representation of the target communities, and commitment shown by school principals to support the project activities. The detailed inclusion criteria can be found in the study protocol . Partici2-tests , in order to compare those 758 children who were included in the present analyses with those who dropped out or were excluded due to missing data. No significant differences (p > 0.05) existed between these two groups in any of the covariates, the predictor or outcome variables. Moreover, included/excluded students were similarly represented in the intervention and control condition.The initial sample at baseline consisted of 1,009 children . For the present data analyses, 190 children were excluded because they left the study between the baseline assessment and post-intervention. Moreover, 61 children were excluded due to missing baseline data in at least one of the covariates . Thus, the final sample for the current analyses consisted of 758 children . Hereof, 448 belonged to the control group , whereas 310 received the physical activity intervention component . We performed a series of univariate analyses of variance (ANOVAs) (for metric study variables) and χOver the past 7 days (1 week), on how many days were you physically active for a total of at least 60 min (1 h) a day?” Answering options ranged from 0 to 7 days. Previous studies showed that this question has acceptable validity when compared with physical activity assessed by accelerometers , and household-level living standards. Scores of the SES index ranged from one to nine, with higher scores being indicative of higher family SES. Evidence for the validity of similar SES scales has been reported in previous studies . To asserometers , 55; it rometers . A 20 m All children underwent a clinical examination by a registered medical nurse to identify any health problems. Children who did not pass the health examination were excluded from the maximal exercise test. The 20-m shuttle run test has a moderate-to-high mean criterion-related validity for estimating cardiorespiratory fitness .2 tests were employed for examining differences in categorical variables. To examine whether intervention allocation , baseline levels of physical activity and cardiorespiratory fitness, and changes in physical activity and cardiorespiratory fitness from baseline to post-intervention predicted HRQoL, a series of mixed linear regression analyses were performed, with random intercepts for school classes, in order to adjust for cluster effects. These analyses were carried out with the multilevel mixed effects linear regression procedure (covariance structure = independent) in STATA. Separate analyses were carried out for the five KIDSCREEN subscales and the two overall HRQoL indices. Before testing the effect of the condition , all regression analyses were controlled for children's age, gender, BMI, SES, and baseline KIDSCREEN scores. Change scores were calculated by subtracting baseline scores from post-intervention scores. For all regression analyses, we display the unstandardized Beta coefficients and the 95% confidence intervals (CIs). Statistical significance was set at p < 0.05 across all analyses. Based on a detailed missing data inspection, we found no evidence of systematic missing data pattern. Hence, we decided not to impute missing data, and to perform the regression analyses with data of children who had complete data records.Data were double entered and validated using EpiData version 3.1 . Statistical analyses were performed with IBM SPSS statistics version 25 for Windows  and STATA version 13.0 . For metric study variables, univariate ANOVAs were utilized to test differences between the intervention and control group at baseline and at post-intervention, whereas χWith regard to the covariates, higher age was associated with lower overall HRQoL (mean across dimensions and 10-item index) and lower scores for psychological well-being at post-intervention. Girls reported better physical well-being at post-intervention, rated the school environment more positively, and reported better overall HRQoL (mean across dimensions and 10-item index). Students with higher SES reported higher scores for physical well-being, autonomy and parent relations, had a more positive perception of the school environment, and scored higher in the overall HRQoL (mean across dimensions). Except for psychological well-being, baseline scores in HRQoL significantly predicted the respective KIDSCREEN scales at post-intervention.Condition  was not associated with the outcomes. Baseline levels and change of physical activity predicted all KIDSCREEN subscales  as well as the overall HRQoL (mean across dimensions). The positive association between the predictor and outcomes indicates that higher baseline physical activity levels predicted better HRQoL scores at post-intervention. The findings also show that increases in self-reported physical activity were associated with better HRQoL at post-intervention. Whereas, this pattern was found consistently across all KIDSCREEN subscales, no significant association was observed for the overall 10-item HRQoL index. Moreover, baseline levels of, and improvements in, cardiorespiratory fitness predicted one domain, namely better physical well-being at post-intervention.The key findings of the present study highlight that assignment to the intervention or control condition did not affect the HRQoL among schoolchildren from the Port Elizabeth area in South Africa. Importantly, however, our analyses revealed that baseline levels and levels of self-reported physical activity positively  predicted children's HRQoL perceptions. Additionally, baseline levels and increases in cardiorespiratory fitness made a significant contribution to the prediction of children's self-perceived physical well-being.The observation that higher self-reported physical activity levels result in higher HRQoL in our study population is important and in line with previous research. This finding might be attributed to several factors. Gopinath et al.  found thContrary to our working hypothesis, baseline levels of, and improvements in, cardiorespiratory fitness predicted only one HRQoL domain, namely physical well-being. This observation might be explained as follows. First, similar to our study, Morales et al.  observedWith regard to the investigated covariates, our results suggest that higher age was associated with lower overall HRQoL  and lower psychological well-being at post-intervention. This is in line with prior research  and can Our observation that a multi-dimensional physical activity intervention had no effect on children's HRQoL is in line with the equivocal findings reported in recent reviews. In their systematic review of school-based physical activity interventions, Rafferty et al.  evaluateThe lack of impact of our intervention on children's HRQoL may be due to limited exposure and intensity of our program. Our intervention lasted for 20 weeks, including two physical education and one moving-to-music lesson per week. Moreover, we observed that overall physical activity levels increased more in the control group, compared to the self-reported levels of physical activity which increased only slightly in the intervention group. Hence, it is conceivable that unrelated changes, or a participant bias in the physical activity behavior in control schools, have superimposed the effects of our intervention.r = 0.94, 95% CI 0.87–1.00). Yet, the protocol performed quite well in children .Although our study provides new insights with regard to the effect of regular physical activity on HRQoL in South African schoolchildren, our findings must be considered in light of several limitations. First, physical activity was assessed with a relatively simple single-item instrument. Of note, this item was successfully employed in large-scale studies with children and adolescents in a European context (HBSC study), in which meaningful relationships were found between physical activity and health-related outcomes . Second,Higher physical activity and positive change in physical activity was prospectively associated with better HRQoL in a sample of South African children attending primary schools from disadvantaged neighborhoods. In view of our findings, we suggest that South African policy makers might reflect on how children's overall physical activity can be increased and how schools can be supported to provide physical activity-friendly environments that promote intramural physical activity. Concerted efforts are required to implement high quality physical education in schools in disadvantaged settings in order to sustainably increase children's physical literacy and to promote physically active lifestyles.The datasets generated for this study are available on request to the corresponding author.controlled-trials.com (unique identifier: ISRCTN68411960). Prior to beginning the data assessment, written informed consent was obtained from the parents/legal guardians of children, while children assented orally.This study was approved by the ethical review board of Northwestern and Central Switzerland, the Nelson Mandela University (NMU) Human Ethics Committee, the Eastern Cape Department of Education, and the Eastern Cape Department of Health in Port Elizabeth, South Africa. The study is registered at ISRCTN registry under CW, IM, UP, JU, and MG: conceptualization. SG, HS, and MG: data curation. SG and MG: formal analysis and writing-original draft. CW, RdR, IM, UP, JU, and MG: funding acquisition. CW, IM, HS, PS, and MG: methodology. IM: project administration. IM, UP, HS, and MG: supervision. CW, RdR, LA, NJ, IM, SN, UP, HS, DS, PS, JU, and MG: writing-review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Among the PIGD subtype, higher daily physical activity was negatively associated with more severe ON bradykinesia , motor symptoms -III scores), gait difficulties , motor complications , and balance . In contrast, among the TD subtype, disease-related characteristics were not related to daily-living physical activity. Intriguingly, physical activity was not related to self-report of ADL difficulties (scores of the MDS-UPDRS Parts I or II) in both motor subtypes. These findings highlight the importance of objective daily-living physical activity monitoring and suggest that self-report does not necessarily reflect objective physical activity levels. Furthermore, the results point to important differences in factors related to physical activity in PD motor subtypes, setting the stage for personalized treatment programs.The benefits of daily-living physical activity are clear. Nonetheless, the relationship between physical activity levels and motor subtypes of Parkinson’s disease (PD), i.e., tremor dominant (TD) and postural instability gait difficulty (PIGD), have not been well-studied. It is also unclear if patient perspectives and motor symptom severity are related to objective, sensor-based assessment of daily-living activity in those subtypes. To address these questions, total daily-living physical activity was quantified in 73 patients with PD and 29 healthy controls using a 3D-accelerometer worn on the lower back for at least three days. We found that individuals with the PIGD subtype were significantly less active than healthy older adults ( The positive benefits of a daily-living physical activity on health in general, and motor function, in particular, and the negative consequences of reduced physical activity have been well-described ,2. EmergPrevious studies among patients with Parkinson’s disease (PD) have shown, not surprisingly, that objectively measured, sensor-based estimates of daily-living physical activity are reduced, compared to that seen in age-matched controls . MoreoveIn PD, especially in the PIGD subtype, walking is affected by the burden of motor symptoms, yet partially improved by levodopa medication intake. Therefore, one could speculate that motor severity during the ON medication state should be more closely associated with daily-living physical activity than motor severity measured during the OFF medication state. Nonetheless, one study reported no differences in the amount of walking captured by a smartwatch accelerometer during self-reported ON medication state and during wearing off in mild to moderately affected people with PD ,10. In cPrevious studies suggest that there is a mild-to-moderate association between motor symptom severity -III) in the ON state and daily-living physical activity ,11,12. ATo gain additional insight into daily-living physical activity among patients with the common motor subtypes of PD, we tested the following hypotheses: (1) Sensor-based assessment of total daily-living physical activity is lower in subjects with the PIGD motor subtype, compared to those with the TD subtype and compared to healthy controls (HC); (2) disease severity in the ON-medication state is associated with daily-living physical activity, and more than the association in the OFF-medication state, and (3) higher self-reported difficulty of non-motor and motor experiences of daily living  are related to lower levels of daily physical activity. To our surprise, the results below are only consistent with a subset of these hypotheses.This cross-sectional study was undertaken in the Center for the study of Movement, Cognition, and Mobility (CMCM) at the Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.The study was approved by the Tel Aviv Medical Center Ethics Committee . All subjects provided informed written consent for the study according to the Declaration of Helsinki.This work is based on the secondary analysis of data that was originally collected to examine the association between white matter hyperintensities and PD motor subtypes . A full Demographics and subject characteristics were collected from the entire cohort. Parkinsonian symptoms and disease severity were evaluated using the Unified Parkinson’s Disease Rating Scale-new version, the MDS-UPDRS . The MDSThe levodopa equivalent daily dose (LEDD) scores were calculated . The cliAfter testing in the lab, participants were asked to wear a small, lightweight body-fixed sensor  attached with a belt to their lower back for three consecutive days (except during activities like showering). The hybrid contains a triaxial accelerometer and a tri-axial gyroscope . The three acceleration axes were: Vertical (V), medio-lateral (ML), and anterior posterior (AP). To measure daily-living physical activity among the healthy controls, participants wore a similar tri-axial accelerometer  for one week. The device was held in place with skin tape . In ordeThe raw acceleration signals were sampled at 100 Hz, saved locally on the sensors, and later transferred to a personal computer for further analysis using MATLAB  software. The data was extracted from 100 Hz raw tri-axial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear/non-wear episodes. Then, the signal vector magnitude (SVM) of the measured acceleration signal from three axes, calculated for each 15 s window during the daytime (between 06:00 AM and 22:00 PM each day):Per each day, the sum of all windows was calculated and normalized by the wear time in hours. Finally, the median value of all days was extracted for the sum values for each subject. This parameter represents total daily-living physical activity , as previously described  and has p-values are below 0.017 (0.05/3 groups). Finally, we used Spearman’s partial correlations to examine the relationship between disease-related characteristics and daily-living physical activity in the PD subtypes. Due to the association between age and BMI with physical activity, and the association of sex with disease severity, Spearman’s correlation analyses adjusted to these factors. According to the Bonferroni correction, significance levels were set at p = 0.0033 (0.05/15 outcomes).Statistical analyses were carried out using SPSS v25 . Descriptive statistics (median/means and range/SD) were calculated for subject characteristics. To compare the PIGD and the TD subtypes, Mann–Whitney U and Student t-tests were used. To compare subject characteristics, Kruskal–Wallis tests were used. Univariate General Linear Model (GLM), controlling for age, sex, and body-mass index (BMI) with 95% confidence intervals examined the relationship between the three groups in total daily-living activity counts. Since only one subject from the PIGD subgroup remained in stage 4 (ON medication) in an exploratory analysis, we removed this subject from the PIGD subgroup. The decision to control for age, sex, and BMI was due to the previously reported association between these subject characteristics and total daily physical activity  (see p = 0.011), 95% CI , and non-significant for HC and TD (p = 0.064) after adjusting to multiple comparison, 95% CI . In addition, an exploratory analysis of interactions between age and other variables  only slightly changed the p-value .Daily-living physical activity was significantly lower , and higher BMI  were mildly correlated with lower levels of daily-living physical activity. Gender was not associated with daily-living physical activity (p > 0.05). Cognitive function, as represented by the MoCA scores, was related to total daily physical activity in the HC group  but not among all the participants with PD (p > 0.05) nor in each subtype separately (p > 0.05). Also, mood disorders (GDS-15 scores) were not associated with the physical activity levels in PD subjects .Among the entire cohort (PD and HC), higher age  and in OFF  medication states, and with motor complications (UPDRS Part IV score) . Conversely, these associations were not observed in the TD subtype.S = −0.499, p = 0.002). However, the lower extremity bradykinesia score in the OFF medication state had only a slightly weaker association  with physical activity  associated with physical activity only in the PIGD group. Conversely, in the TD subtype, no significant association was found between disease-related characteristics and daily physical activity. MDS-UPDRS total score in ON was moderately and significantly correlated with physical activity as well  in the PIGD group, but not in the TD group . No associations were found between daily physical activity and the MDS-UPDRS scores of Parts I  or II  among the PIGD and the TD subtypes, respectively  and in the TD subtype . Moreover, daily-living physical activity was negatively associated with disease stage  scale, as measured in the OFF  and in the ON state .Daily-living physical activity was associated with better balance as represented by higher Berg Balance Test scores in the PIGD subtype  and the TD . Contrary to expectations, overall FOG severity was not associated with physical activity in the PIGD subtype  or the TD subtype . Fear of falling  tended to be associated with physical activity in the PIGD subtype significance , but this trend was not observed in the TD subtype .In addition, no significant correlation was found between daily-living physical activity and the number of falls, both in the PIGD (rDifferences in subjective activity of daily living (ADL), health-related quality of life, performance-based tests, and the quality of daily-living activity between the PD motor subtypes have been established ,7. The cConsistent with previous reports ,10,11, wIn contrast to the manifestations of the PIGD subtype, overall disease severity in both ON and OFF medication states and PD symptoms fluctuations appear to have mild, non-significant association with daily-living physical activity in the TD subtype subjects. There may be several possible explanations regarding the differences in physical activity between those motor subtypes. For example, previous reports indicate that people with the TD subtype have a lower annual rate of progression in the MDS-UPDRS scores . MoreoveHowever, there are some surprising findings related to the PIGD subtype too. For instance, FOG, another hallmark of disease severity of the PIGD subtype, was not directly related to the daily-living physical activity levels. This finding may indicate that symptoms like freezing of gait, fear of falling, and fall history could be mediators, secondarily related to the physical activity causing gait and balance disturbances. Poor balance performance  was negatively associated with the total daily-living physical activity in both subtypes recall , yet morParts I and II of MDS-UPDRS (representing self-report of ADL difficulties and non-motor symptoms) are often used to evaluate therapeutic effects in clinical trials, to track longitudinal changes in PD cohorts over time, and to guide clinical decision-making. Previous studies have demonstrated varying results on the agreement of MDS-UPDRS Part II with objective measures of daily-living activity ,31,32. IIt appears that ADL difficulties do not accurately reflect the objective measurement of physical activity. This finding is consistent with other studies that examined the validity and interpretation of the MDS-UPDRS Part II due to psychometric limitations ,35. NoneThis study has several limitations: First, PD subjects and controls were recruited as part of different studies. To address this, all data preparation, processing, and analysis were performed in the same manner. Second, and perhaps more importantly, the control group was about 5 years older than the two PD subtypes. We addressed this limitation by controlling for age in all the statistical analysis. Still, even with this adjustment, the PIGD group was less active than the control subjects who were, on average, 5 years older, suggesting that the age difference did not explain the reduced physical activity that was observed in the PIGD patients. Third, the cross-sectional study design limits our ability to interpret cause and effect. Finally, a larger sample size would allow us to further explore the daily-living physical activity of PD motor subtypes compared to HC .Our results suggest that the amelioration of the severity of motor response fluctuations, motor complications, and lower extremity bradykinesia may lead to a reduction in daily-living physical activity in subjects with the PIGD subtype. It will be interesting to examine the impact of physical therapy/physical training programs and other types of interventions on daily-living physical activity, and whether this reduces bradykinesia and improves gait index, especially in subjects with the PIGD subtype. Furthermore, since balance impairment was associated with the amount of daily-activity in both subtypes, it may be beneficial to explore whether balance-targeted interventions enhance total daily physical activity levels among patients with PD.The present results point to important differences between PD motor subtypes that set the stage for future research and for personalized treatment programs for people with PD, especially considering the positive health benefits associated with higher levels of daily-living physical activity in PD. These findings also highlight the importance of sensor-based assessment of daily-living physical activity, along with patients’ perspectives based on self-report.