Oryza sativa. To this end, we have constructed a multiple abiotic stress responsive TF-miRNA-gene regulatory network for O. sativa using a transcriptome and degradome sequencing data meta-analysis approach. The theoretical network approach has shown the networks to be dense, scale-free, and small-world, which makes the network stable. They are also invariant to scale change where an efficient, quick transmission of biological signals occurs within the network on extrinsic hindrance. The analysis also deciphered the existence of communities  working together to help plants in acclimatizing to multiple stresses. It highlighted that genes, TFs, and miRNAs shared by multiple stress conditions that work as hubs or bottlenecks for signal propagation, for example, during the interaction between stress-responsive genes (TFs/miRNAs/other genes) and genes involved in floral development pathways under multiple environmental stresses. This study further highlights how the fine-tuning feedback mechanism works for balancing stress tolerance and how timely flowering enable crops to survive in adverse conditions. This study developed the abiotic stress-responsive regulatory network, APRegNet database (http://lms.snu.edu.in/APRegNet), which may help researchers studying the roles of miRNAs and TFs. Furthermore, it advances current understanding of multiple abiotic stress tolerance mechanisms.Climate changes and environmental stresses have a consequential association with crop plant growth and yield, meaning it is necessary to cultivate crops that have tolerance toward the changing climate and environmental disturbances such as water stress, temperature fluctuation, and salt toxicity. Recent studies have shown that trans-acting regulatory elements, including microRNAs (miRNAs) and transcription factors (TFs), are emerging as promising tools for engineering naive improved crop varieties with tolerance for multiple environmental stresses and enhanced quality as well as yield. However, the interwoven complex regulatory function of TFs and miRNAs at transcriptional and post-transcriptional levels is unexplored in  Oryza sativa) is the most imperative crop across the globe, grown in over a hundred countries (including India), with a production rate greater than 700 million tons per annum  and microRNAs play an important role in regulating the activity of the genes at transcriptional and post-transcriptional levels, respectively, involving a complex series of events .NFYA5 gene expression leads to susceptibility toward drought stress in plants  are a major class of small endogenous RNAs of length 20–24 nucleotides. They assemble with ARGONAUTE (AGO) proteins forming an RNA-induced silencing complex (RISC) in the cytoplasm. AGO protein has PAZ and PIWI domains. PIWI domain creates an RNaseH-like fold that helps in cleaving RNA targets complementary to the miRNA strand assembled with the AGO in RIS-complex. It is often found that miRNAs regulate the target genes at the protein level without causing major change at the mRNA level. These findings suggest the capability of plant miRNAs to control gene expression at mRNA and protein levels . Recent thaliana  and incrthaliana . Over-exthaliana . TFs andthaliana . This inA. thaliana integrated from experimental methods like- EMSA, ChIP-seq, yeast one-hybrid analysis, etc.; and, (c) AtmiRNet contains information about the transcriptional regulation of miRNAs based on experimental methods like—EMSA, yeast one-hybrid analysis, transgenic plant expressing an inducible TF-GR (glucocorticoid receptor) fusion protein experiments, etc. PASmiR and miRNEST are comprehensive literature curated databases for stress-responsive miRNA and its targets  and ArrayExpress Archive.3We retrieved the small RNA and mRNA (GPL2025) expression high-throughput datasets of cold, drought, and salt stress conditions  from theO. sativa AGO1-associated sRNA HTS (high-throughput sequencing) datasets GSM455962, GSM455963, and GSM455964 were downloaded from the GEO database (The database .O. sativa degradome sequencing datasets – GSE17398 and GSE19050 (The GSE19050  were dow4) (O. sativa genes and the gene annotations from the O. sativa Genome Annotation Project (RGAP), available at ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_7.0/all.dir/ (The miRNA sequences were downloaded from miRBase (release 214) . We retrall.dir/ .The GCRMA R package  was used5 and Rfam (12.2) database.6 The cleaned reads were then aligned to reference genome (MSU7) RGAP, available at ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_7.0/all.dir/. The sequence coordinates were then compared to O. sativa miRNA GFF file and different measures of the expression level were generated, such as, the read count , adjusted read count  and normalized RPM (reads per million) which was done by miRanalyzer  the miRNA should be detectable in at least one of the AGO1-associated sRNA HTS datasets and (2) its normalized accumulation levels should be three RPM or higher.O. sativa  for . sativa . The miR. sativa . The tar. sativa  and miRN. sativa  using de. sativa .We then examined certain local and global properties, namely; the scale-free behavior , small-wA. thaliana, O. sativa, and Z. mays. We considered TFs, miRNAs, and target_mimics as regulators. We captured both transcriptional and post-transcriptional regulation of genes under abiotic stress in O. sativa discussed in this manuscript, for A. thaliana (Z. mays (unpublished)]. Thereafter we fetched the transcriptional regulation of genes, miRNAs, and other transcription factors and post-transcriptional regulation of miRNA by target_mimics from reliable databases restricted to experimentally validated interactions only ].We performed transcriptome meta-analysis for DEGs and miRNAs identification [for thaliana  and Z. mons only . We valithaliana  and Z. m7 PlantTFDB,8 INTERPRO,9 and DAVID10 sources were also integrated for KEGG pathway,A. thaliana, O. sativa, and Z. mays). The query result page shows results in three sections:We developed an apprehensible and user-friendly web interface, APRegNet (see text footnote 1) for users to query and download the regulatory relationships and networks. APRegNet focuses on providing better navigation through individual sections to increase data discoverability. There are five tabs provided at the top of the interface  through which users can navigate and explore the required information. It runs on a XAMPP web server with an MYSQL database in the backend for data storage and management. Text query box is provided at the top of each page to search by various types of components , by stress , and by species ((1)Network properties: The network properties  of the queried component (gene/TF/miRNA), confined to selected species and stress-specific regulatory network;(2)Functional annotation: This section includes Gene Model, Primary gene Symbol, GO term: Biological process, GO term: Molecular Function, GO term: Cellular Component, Transcription factor family, and KEGG pathway.(3)Interaction: Displays first interacting patterns of the query component and type of interaction between source and target.The user can download the complete regulatory network in CSV file format for each species stress-wise from the download page. Query processing scripts are written in PHP and SQL. The database is tested and works well with commonly available web browsers, such as Mozilla Firefox, Google Chrome, Safari, and Microsoft Internet Explorer. The schema of the database is given in O. sativa deciphered through transcriptome (both coding and non-coding) meta-analysis. We further looked into the structural perspective of the proposed abiotic responsive networks which elucidated the significant role of some genes/TF/miRNA in the abiotic stress response mechanism.In this study, miRNAs and transcription factors were woven into a complex inter-regulatory network within the cell liable for reprogramming gene expression in response to abiotic stresses in O. sativa [PFP  <0.01; We performed a meta-analysis of 15 studies of gene expression in response to abiotic stresses   for eachO. sativa; we identified drought, cold, and salt stress-responsive miRNAs. In the present study, with the aid of publically available abiotic stress-specific sRNA HTS data for O. sativa  selectively binds with miRNAs and short interfering RNAs in plants and catalyzes the cleavage of target mRNAs . The pre. sativa .O. sativa degradome sequencing datasets, which is a high-throughput technique for large-scale validation of the miRNA-target duplex interactions  miRNA-target interaction was validated by degradome sequencing data analysis from which 63 were classified as Category 0, 2 as Category 11, 82 as Category 2, 9 as Category 3, and 373 as Category 4. Category 0: >1 raw tags at the position, abundance at the position was equal to the maximum on the transcript. There was only one maximum on the transcript; Category 1: >1 raw tag at the position, abundance at the position was equal to the maximum on the transcript. There was over one maximum position on the transcript; Category 2: >1 raw tag at the position, abundance at the position was less than maximum but higher than the median for the transcript; Category 3: >1 raw tag at the position, abundance at the position was equal to or less than the median for transcript; Category 4: only 1 raw tag at the position. The validated interactions involved 445 genes and 80 miRNAs highly abundant under the influence of abiotic stresses .O. sativa in response to drought, cold, and salt stress. The networks contain five types of regulatory information: miRNA regulating genes, TF regulating genes, TF regulating TF, TF regulating miRNA genes, and target-mimics regulate miRNAs. The transcription factor regulation of abiotic responsive genes and miRNAs were derived from EGRINs, target-mimics regulation of abiotic stress-responsive miRNAs was derived from the publically available database: PeTMbase. The abiotic stress-responsive miRNAs validated targeted genes through degradome sequence data analysis. After assembly of all these relations, we built large directed graphs comprising , , and  nodes-edges pairs showing the relationship between genes, miRNA, and transcription factors in response to cold, drought, and salt stress, respectively. To study the nature of the constructed networks (a random or complex or regular type of network), the degree distribution was studied in the context of the power law using the method described by We constructed the abiotic stress-responsive miRNA-TF gene regulatory networks for G) and average shortest path length (ASLG) for three TF-miRNA induced stress-specific networks and compared their values with transitivity TER and average shortest path length ASLER of the Erdos–Renyi random network of the same order and sizes. We then calculated small-world-ness (S) for cold, drought, and salt stress-specific regulatory networks. The results showed that all the networks had a value >1, which suggested the small-world nature of all three networks  of the networks. The details of the top 10 nodes of each stress-specific network of O. In the directed graph like- regulatory networks of the present study, the two nodes “x” and “y” belong to the same strongly connected component, if there are directed paths both from “x” and “y” and from “y” and “x” . We founA. thaliana, O. sativa, and Z. mays in response to abiotic stresses . It contains regulatory relationships at both transcriptional and post-transcriptional levels as well as interaction among TF/miRNAs and their targets with easily downloadable options. It also provides the data source information for the regulatory interactions. A. thaliana, O. sativa, and Z. mays, respectively.The APRegNet database developed by integrating the experimentally validated regulatory interactions between TFs, miRNAs, genes, and target_mimics from various sources; as a comprehensive repository for genome-wide regulatory networks operating in The query result page displays few network centrality measures  of the query component (gene/miRNA/TF) related to specific species stress network, which quantifies the importance of the component in the network for the quick and efficient flow of information.O. sativa abiotic stress-responsive miRNA and transcriptional factor gene regulatory networks in the present study were constructed and quantitatively described using the network theoretical approach. The observed topologies of the regulatory networks provide clues about the influence of the organization on the function and dynamic responses of the plant toward multiple abiotic stresses.Cells use signal transduction pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The biological system of components that interact with or regulate each other can be represented by a mathematical object called a graph , which cA study of the structural measures of the constructed regulatory networks revealed the scale-free and small-world nature of the abiotic stress-responsive miRNA-TF-gene regulatory networks. This implies that the networks are highly tolerant to random failures within the system, a quick and efficient flow of environmental stress signal from the source to sink takes place, which helps the plant adjust to a stressful environment . The netO. sativa  responsive regulatory networks in . sativa . This TF. sativa . Accordi. sativa . Accordi. sativa . Recent hypodium . When thhypodium . This shContemporary investigation of literature about transcription factors and miRNAs mediated regulation associated with plant abiotic stresses indicate that transcription factors and miRNAs act as key regulators in plant physiological adaption mechanisms during the response to various stress conditions. Information regarding the transcriptional and post-transcriptional regulation by TFs and miRNAs during plant responses to abiotic stress is distributed over numerous recent studies. The APRegNet database (see text footnote 1) construction provides a comprehensive repository of abiotic stress-responsive transcriptional and post-transcriptional regulatory networks.A. thaliana, O. sativa, and Z. mays, and was developed by incorporating various data sources. It is a comprehensive collection of the interactions among TFs, miRNAs, and genes, occurring in response to abiotic stress, reconstructed for public access. The established regulatory networks from APRegNet provide genome-wide regulatory interactions that lay an initial foundation and establish a prior background network to identify or verify molecular and functional regulations in pathways.This database contains knowledge-based abiotic stress-specific regulatory networks in Within a plant species under multiple abiotic stresses, certain miRNAs or transcription factors show common expression. The study of such miRNAs and TFs using a systematic, comprehensive database approach like APRegNet (see text footnote 1), will expedite the enhancement of understanding of the stress-specific transcriptional and post-transcriptional regulatory role of transcription factors and miRNAs and their functional evolutionarily relationship in various plant species. The regulatory information stored in this database has promising uses for experimental biologists who intend to improve plant crop performance under multiple Abiotic stress environments.We also include additional information such as links to other databases . We are planning to extend the APRegNet to include other plant species and capture information about more abiotic stress responses.O. sativa were reconstructed and analyzed to reveal information about how environmental stress induces these regulatory networks. Network structural measures were studied using the network theoretical approach, and deciphered several important features of the networks such as- scale-free, and small-world behavior that established the stable nature and quick, efficient transmission of signals . This process also highlighted the genes, TFs, and miRNAs shared by multiple stresses, working as a hub or bottleneck for signal propagation.A complex interwoven network of multiple molecular species at various steps such as- transcription, post-transcription, translation, and post-translation control the expression of a gene. The alteration in expression of the gene in response to environmental cues enables the plant to acclimatize and survive in adverse environments, but this complex interwoven regulatory network is mostly unrevealed. In the present study, abiotic stress-responsive miRNA-TF-gene regulatory networks for In crop plants, the right flowering time is pivotal for acclimatizing under the altered environmental condition and directly linked to grain yield . Under sIn summary, this study postulated some structural features of the miRNA and transcription factor regulated networks operating in a cell under the influence of multiple environmental stresses and prioritize the mainstream function of miRNAs and transcription factors in gene expression control.The original contributions presented in the study are included in the article/RS, SU, SB, and AS conceived and designed the study. RS and SU performed the analysis. RS wrote the manuscript. All authors read and approved the manuscript. 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.


Caregivers (n = 78) were assessed by the PD caregiver burden inventory (PDCB). PDQ-8 and PDCB scores were regressed on MDS-UPDRS subscales and MoCA subscores. PDQ-8 correlated with attention  and executive  MoCA subscores and all parts of the MDS-UPDRS. PDCB correlated most strongly with MDS-UPDRS part III motor symptoms  and the MoCA attention subscore . While all facets of PD symptoms assessed by the MDS-UPDRS relate to PD patients' quality of life, motor symptoms are the most relevant factor for the prediction of caregiver burden. In addition, patients' attentional symptoms seem to affect not only them, but also their caregivers. These findings show the potential of a detailed analysis of MDS-UPDRS and MoCA performance in PD patients.Parkinson's disease (PD) is a chronic progressive movement disorder with severe reduction in patients' health-related quality of life (HR-QoL). Motor and cognitive symptoms are especially linked with decreased PD patients' HR-QoL. However, the relationship of these symptoms to caregiver burden is relatively unclear. Influence of the Montreal Cognitive Assessment scale (MoCA) as a cognitive screening tool and Movement Disorders Society Unified Parkinson's disease Rating Scale MDS-UPDRS symptoms in relation to patients' HR-QoL and caregivers` burden was analyzed. PD patients ( Parkinson's disease (PD) is a common progressive neurodegenerative disease with a prevalence estimated from German health insurance data of up to 4,00,000 people in Germany . RigiditAfter its revision in 2008, the MDS-UPDRS is the most important clinical score for rating of motor symptoms, nonmotor symptoms, and treatment complications in PD . MDS-UPDAnother important factor known to correlate with poor HR-QoL is cognitive impairment, which is also suspected to correlate with increased caregiver burden , 10, 11.This study aims to investigate the contribution of distinct MDS-UPDRS parts and MoCA subdomains to patients' HR-QoL and caregiver burden in a German monocentric cross-sectional sample of PD patients and their caregivers. Based on prior research, we expect to find positive correlations of MDS-UPDRS and MoCA scores with HR-QoL and caregiver burden.We obtained approval from the local Ethics Committee of Hannover Medical School . Our sample included 124 PD patients and 78 caregivers of PD patients, all of whom gave their written informed consent. Inclusion criteria for PD patients were defined as neurologically determined Parkinson's disease with a duration of at least one year. All included patients met the MDS-diagnosis criteria for PD  and nonePD patients were examined by using MDS-UPDRS   , 16. In Caregiver and patient depression was assessed by Beck´s depression inventory  .α = 0.05/n . To test whether any predictors explained unique variance in our outcomes, we included significant predictors in a multiple regression model for patients' HR-QoL and caregiver burden. Analyses were carried out using SPSS 25.0 . The biometric institute of Hannover Medical School, Hannover, Germany, counseled these statistical analyses.Linear regression analyses were used to examine potential predictors of PDQ-8 and PDCB scores. To correct for multiple testing, the level of significance was set to n = 46) of the patients, and 41.9% (n = 53) of the patients scored a MoCA result suggestive of dementia (<21)  . Regresss whether variance in patients' HR-QoL can be explained by the severity of their PD symptoms or cognitive impairment, PDQ-8 scores were regressed on MDS-UPDRS and MoCA scores. After correction for multiple comparisons, all parts of the MDS-UPDRS were found to correlate significantly with PDQ-8 scores. Similarly, PDSI scores were significantly related to the attention index score and executive index score of the MoCA. Better performance in these domains and lower MDS-UPDRS scores predicted better HR-QoL. Further analyses revealed that patients' HR-QoL also decreased with increasing BDI scores and disease duration. All data from these linear regression analyses are displayed in To assesR2 0.2007; beta 0.389 (95%CI 0.170–0.727); p 0.002) explains 15.1% of variance in the analysis, and BDI ; p 0.040) explains 6% of variance in the paradigm.Furthermore, we tested against cocorrelation and added all significant variables in a multiple regression analysis. After 4 rounds of backward-selection, we found MDS-UPDRS II and BDI significantly correlating with HR-QoL of the PD patients. MDS-UPDRS II (∗∗). Furthermore, we tested against cocorrelation and added all significant variables correlating with caregiver burden in a multiple regression analysis. After 6 rounds of backward-selection, we found only PDQ-8 ; p 0.064) and MDS-UPDRS part III ; p 0.086) exposing a tendency to correlate with caregiver burden.Factors we evaluated in relation to caregiver burden are displayed in Table 4In this study, we evaluated the contribution of MDS-UPDRS and MoCA subscores to HR-QoL of PD patients and caregivers' burden measured by the disease-specific, validated PDCB , 24. We Patients with PD suffer from a variety of clinical symptoms, which include motor symptoms like rigidity, tremor, bradykinesia, and postural instability . FurtherCognitive impairments are frequent in PD and may already occur in early stages of the disease . HoweverZarrit et al. defined caregiver burden as a multifactorial construct summarizing the adverse impacts that giving care for someone else have on the caregiver . RecentlWe were interested in assessing the impact of specific MDS-UPDRS parts and patient cognition on caregivers' burden measured by the disease-specific and validated PDCB , 17, 45.In our study, we also found a significant correlation of the PDCB score with the attention index of the MoCA. These data are supported by a study from the UK finding attentional deficits to be most strongly associated with caregiver burden . Hence, Auxiliary analyses further revealed caregiver burden to be significantly related to patients' quality of life in our study. This has also been demonstrated in the German validation study of the PDCB for the PDQ-39 . Moreoven = 124) for a monocentric study, we were not able to reach patient numbers of other MDS-coordinated analyses. Since we were aware of the pronounced cognitive deficits in the study sample, we usually made a cognitive pretest and integrated the caregiver in the assessment of the patient if necessary. However, we could not exclude some anosognosia in the patient sample and, therefore, a possible underestimation of the patient impairment. We used the MoCA as cognitive screening tool for cognitive impairments and analyzed the subdistribution of these deficits in PD patients. Nevertheless, a cognitive screening tool cannot replace an extensive cognitive testing and is not sufficient to raise the diagnosis of PD-related dementia. The patient's educational level was only assessed for the MoCA screening, and in an ordinal way, we did not correct the analysis for years of education of the individual patient. Another restriction is that we only report cross-sectional data from one time-point, while longitudinal data would be desirable to ensure that motor and cognitive symptoms precede the decreases in HR-QoL and evaluation of caregiver burden described in this study. Since patients were free to decide to participate in our study, we cannot exclude a selection bias including more motivated patients and their caregivers. While we did not evaluate patients' medication and comorbidities in our study, we only included patients without severe comorbidities [We report data from a monocentric cross-sectional observational study. Despite the good sample size (bidities .MDS-UPDRS is the gold standard measure for the assessment of PD symptoms and shows a robust correlation with patient HR-QoL. It provides assessment of nonmotor symptoms, motor symptoms, and motor complications. However, while having an extraordinary importance for patients' HR-QoL and caregiver burden, cognitive symptoms are underrepresented in the MDS-UPDRS, which indicates that the use of an additional cognitive screening test would be reasonable. The MoCA is excellently validated for PD, and recent subscores provide a clinically relevant possibility to further differentiate cognitive impairments. We show that executive and attentional impairments in particular contribute to PD patients' HR-QoL. Caregiver burden measured by the PDCB showed the most robust correlation with the MDS-UPDRS part III in the best medical on, confirming the disease specificity of the assessment tool. In addition, caregiver burden seems to relate patients' attentional deficits. Further studies are required to explore possible means to mitigate the effect of PD symptoms on HR-QoL and caregiver burden.


HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures.Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization.To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles.These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development. HLA receptors bind peptides produced by the cleavage of intracellular proteins, which is a continuous process present in almost every cell. The resulting peptide-HLA (pHLA) complexes are then exposed at the surface of cells. Also present in cancer cells, this mechanism allows circulating T-cell lymphocytes to recognize tumor-associated peptides, thus triggering T-cell activation, tumor elimination, and immunologic memory against the tumor.2Immunotherapy treatments are now at the forefront of methods used for cancer therapy. These treatments aim at harvesting a patient’s own immunologic defenses to identify and eliminate cancer cells.Key ObjectiveEnabling large-scale structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy applications.Knowledge GeneratedWe created a customizable environment, called HLA-Arena, with user-friendly computational workflows that allow for varied structure-based analyses of peptide-HLA complexes. To illustrate this, we show how researchers can use HLA-Arena to perform geometry prediction of peptide binding modes, peptide binding energy prediction, and structure-based virtual screening of tumor-derived peptides, for any classic class I HLA of interest.RelevanceHLA-Arena can be integrated in computational pipelines to support basic cancer research or to help inform physicians in preclinical settings. It can be used to perform structure-based selection of peptides for T cell–based immunotherapy, neoantigen discovery, and vaccine development.4 Therefore, the structural modeling and analysis of these complexes are becoming essential to ensure the efficacy and safety of immunotherapy treatments.2 However, pHLA structural features are affected by the genetic variability of both patients and tumors.5 First, the set of peptides available for presentation reflects the patient’s genetic background and cancer-specific alterations.5 Second, each individual has up to 6 class I HLA alleles,6 among the nearly 19,000 alleles in the human population.7 Each allele encodes for a receptor with specific characteristics, which will display a different pool of peptides. Therefore, the structural modeling and analysis of pHLA complexes for cancer immunotherapy require fast and customizable methods that can handle patient-specific data.It has been shown that immunologic outcomes are partially driven by structural features of pHLA complexes.5 As an alternative, in previous work, we have developed several computational tools for the accurate and efficient modeling of pHLA complexes. For example, we have described a fast method, called APE-Gen, to generate ensembles of peptide conformations bound to a given HLA receptor.8 We have also developed a meta-docking approach, called DINC, which allows prediction of binding modes of pHLA complexes.10Unfortunately, the cost and time requirements of gold-standard experimental techniques in structural biology prevent their use in personalized medicine. In addition, few structures of pHLA complexes have been determined experimentally. Therefore, researchers have turned toward computational methods for the structural modeling of pHLA complexes. However, the length and flexibility of displayed peptides represent major challenges for traditional methods.11 for homology modeling, MHCflurry12 for binding affinity prediction, and NGL Viewer13 for structure visualization, among others.In this report, we present a higher-level platform, called HLA-Arena, that allows the carrying out of sophisticated structural modeling and analysis of pHLA complexes. Instead of having to deal with several computational tools, HLA-Arena provides researchers with a single customizable environment that fully integrates the tools we have developed, as well as other commonly used software. HLA-Arena simplifies the interactions with these tools by leveraging the capabilities of Jupyter Notebook and Docker. It allows users to perform various workflows, each involving a specific combination of tools and steps within a coherent scenario. In addition to APE-Gen and DINC, HLA-Arena currently integrates MODELLERHere, we present 3 example workflows illustrating the capabilities of HLA-Arena. The first relies on DINC to predict the binding modes of 2 known peptides with their corresponding HLA receptors . The second relies on APE-Gen to assess differences in binding between peptides restricted to a given HLA receptor, based on generated binding mode ensembles . The third aims at performing structure-based virtual screening, which requires speed and scalability. Using real immunopeptidomic data and a fictitious diplotype , we show how MHCflurry and APE-Gen can complement each other to select target peptides for a hypothetic immunotherapy treatment.16 Such conserved folding makes HLA modeling an easy task with tools leveraging homology modeling.18 In contrast, predicting the binding modes of peptides to HLA receptors is much harder because of the size and flexibility of these peptides. As recently reviewed, strategies used to overcome this challenge include constrained backbone prediction, constrained termini prediction, and incremental prediction.5Despite their huge sequence diversity, HLA receptors feature conserved secondary and tertiary structures, as illustrated by available data.8 The second, called DINC, can incrementally dock a peptide in the binding site and does not require any template , can quickly produce an ensemble of binding modes for a pHLA complex, using termini templates to position the peptide in the HLA binding cleft ; HLA-Arena will then fetch the proper sequence from IMGT/HLA,7 and a reasonable template (based on the HLA supertype28 classification) from the PDB. In addition, binding affinity of peptides can be estimated with MHCflurry12 to select the most relevant ones. Minimal example: HLA_allele = arena.model_hla(‘HLA-A*24:02’)Available structures of HLA receptors are obtained from the Protein Data Bank (PDB)24 Minimal example: structure = arena.dockStructures of pHLA complexes are modeled with APE-Gen and/or DINC, which only require the sequence of the target peptide(s) and the HLA structure(s) obtained previously. Modeled structures can also be minimized with a force field using OpenMM.13 among others. Minimal example: arena.visualize(structure)A variety of postprocessing options for data analysis can be incorporated into a workflow. These include binding mode rescoring or peptide ranking with DINC and structure visualization with NGL Viewer,31For a smooth user experience, all computational tools involved in HLA-Arena are packaged within a Docker image , thus eliminating the burden of managing software dependencies. Another advantage of Docker containerization is that it makes HLA-Arena platform agnostic. As a result, it can be deployed on a desktop computer or a high-performance computing cluster, across different operating systems. Users can customize available workflows by adding modeling or analysis steps. We plan to continuously expand the capabilities of HLA-Arena by providing support for additional tools.We now present the results we obtained when carrying out 3 different workflows that exemplify the diversity of applications offered by HLA-Arena. Each workflow leverages the functionalities of several tools in a coherent scenario.10 To illustrate this, using the geometry prediction workflow based on DINC , running 5 replicates in each case. Default values were used for other DINC parameters.23 Results  involving HLA-B*51:01 and a 9-mer peptide derived from HIV-1. It has been suggested that the fifth residue acts as a secondary anchor for this peptide, leading to structural rearrangement of its central and amino-terminal residues.Appendix  show tha5 The first relates to sampling . The second relates to scoring . HLA-Arena relies on the incremental process of DINC to overcome the sampling issue. It also includes a filtering step to remove peptide conformations with reverse orientation in the binding cleft. To address the scoring issue, HLA-Arena makes use of multiple scoring functions. For instance, in this self-docking experiment, conformations were ranked with the scoring functions of AutoDock4,33 Vina,20 and Vinardo.34 All 3 scoring functions were able to identify near-native conformations. However, in the case of AutoDock4  involving HLA-A*02:01 and a 9-mer peptide derived from the MART-1/melan-A protein.Appendix  and A2. Appendix . Note thAppendix  and 4D.40 and crystal structures in complex with HLA-A*02:01 available in the PDB. For each peptide, we generated an ensemble of bound conformations with APE-Gen. The binding energy of each peptide was then estimated as the median score within the conformation ensemble for each scoring function . Correlations between these predicted binding energies and experimentally determined binding affinities were then determined .24 Despite increasing binding energies, the OpenMM minimization had a positive impact on overall correlations.In addition to the default local optimization performed by APE-Gen, HLA-Arena provides the option of minimizing the resulting complexes with OpenMM.unctions . This mi42 Note that contrary to the geometry prediction workflow, in which a scoring function was only used to rank different conformations of a given peptide, here the scoring function also had to rank different peptides. Although the same function can be used for both purposes,5 it is possible that better results are obtained when using functions optimized for each task.Interestingly, the best correlation with experimental binding affinities was obtained when using Vina. This result is in agreement with previous studies evaluating the performance of Vina in virtual screening of drug-like ligands.For the HLA-A*02:01 binders in our data set, we can compute RMSDs between their associated crystal structures and conformations generated by APE-Gen. This allows verification that APE-Gen ensembles include near-native conformations Appendix  and evalHLA-Arena allows researchers to perform, for the first time to our knowledge, a large-scale structure-based virtual screening of HLA-binding peptides. In addition, by combining sequence- and structure-based methods, HLA-Arena represents a fresh alternative for the identification of tumor-derived peptide targets considering patient-specific HLAs. To demonstrate this application, we used the virtual screening workflow  to predi43 where they were derived from immunopeptidomics studies. Sequences of decoys were obtained from the training set of NetMHCpan.44We considered 6 alleles: HLA-A*24:02, HLA-A*26:01, HLA-B*15:01, HLA-B*35:01, HLA-C*04:01, and HLA-C*05:01. We built a peptide data set by selecting 500 known binders and 1,000 decoys for each allele, for a total of 9,000 peptides. Sequences of known binders were obtained from the SysteMHC Atlas,12 using an affinity threshold specified by the user. This allows the user to quickly select the most likely binders for each HLA receptor, before moving on to the more computationally expensive steps. In this example, a threshold of 500 nM selected 2,604 peptides. Then, we proceeded with the structural modeling of the full pHLA complex for all selected peptides. Finally, peptides were ranked based on binding energies derived from the modeled structures. The entire pipeline took 86 hours on a desktop computer or 5 hours on a high-performance cluster (Appendix).First, the whole data set of peptides was screened for HLA binding with MHCflurry,45 with great variation in accuracy between HLA alleles.46 On our data set, a 500-nM threshold produced several false-positive  can yield a sensitivity as low as 40%,Because our workflow allowed variation of the MHCflurry threshold, we repeated the aforementioned virtual screening experiment with a 50,000-nM value. This led to all 9,000 peptides being selected for modeling and ranking. The observed enrichment of true binders among the top-ranking peptides .50 Thanks to high-performance computing and efficient sampling, molecular dynamics could play a bigger role in providing accurate estimates of pHLA binding affinity and complex stability.52These workflows can be modified to allow for additional analysis of the modeled pHLA complexes (eg, to perform molecular dynamics with OpenMM8 In addition to HLA binding prediction, immunotherapy applications require identification of peptides that are uniquely displayed by cancer cells. This important task will be addressed in future updates of HLA-Arena.HLA-Arena can be integrated into computational pipelines for basic cancer research or to help inform physicians in preclinical settings. It can be used to perform the large-scale modeling and selection of tumor-associated peptides, computer-aided design of altered peptide ligands, and study of T-cell cross-reactivity.23 and facilitates the integration of these solutions with other tools for structural analysis. However, the accuracy of structure-based peptide ranking is limited by existing scoring functions. As they improve, new scoring functions will be incorporated into HLA-Arena to replace current ones or be combined with consensus methods.54 In time, we expect that structure-based analyses will become essential to peptide target prediction for neoantigen discovery, vaccine development, and cancer immunotherapy, especially for patients with less prevalent HLA alleles.It is important to note that HLA-Arena provides efficient solutions to sampling challenges associated with pHLA modeling


The onset of myasthenia (MG) gravis with anti-muscle-specific tyrosine kinase (MuSK) antibodies most commonly peaks in the fourth decade of life, and MG with MuSK antibodies (MuSK-MG) rarely coexists with a malignant tumor. To date, MuSK-MG has not been reported in multiple myeloma (MM).A 60-year-old male with MM who was receiving treatment with bortezomib and thalidomide presented diplopia, ptosis, and limb weakness. A diagnosis of MM with Bence-Jones proteinuria was established when he was 56 years old, and he received chemotherapy with four courses of bortezomib and dexamethasone. Although he received thalidomide as maintenance therapy, it was discontinued a year before hospital admission because of sensory neuropathy as a side effect. Six months before hospital admission, he developed mild diplopia. One month before admission, his chemotherapy was interrupted because of viral infection and fatigability. Then he developed neck weakness and bilateral ptosis. A diagnosis of MuSK-MG was made based on neurological and serological examinations. According to the previous relevant literature, this is the first report of MuSK-MG in a patient with MM.In patients with MM, the possibility of co-existing of autoimmune disease, including MuSK-MG, should be considered. This case emphasizes the need to still consider testing for anti-MuSK antibodies in older MM patients where there is clinical suspicion for possible MG despite negative anti-acetylcholine receptor antibodies and lacking classic MuSK MG phenotype at onset. The onset of myasthenia gravis with anti-muscle-specific tyrosine kinase antibodies (MuSK-MG) most commonly peaks in the late 30s, and an onset at a later age is unusual , 2. PatiA 60-year-old man came to our hospital with diplopia, ptosis, and fatigue. A diagnosis of MM with Bence-Jones proteinuria was established when he was 56. His bone marrow biopsy revealed hypercellular tissue with > 70% of CD138 positive cells. The biopsy was negative for CD20 and CD3, and was consistent with plasma cell myeloma. Blot clonality was not observed on immunoelectrophoresis. He had received chemotherapy with bortezomib and dexamethasone, followed by other drugs and agents (Fig. To the best of our knowledge, this is the first report of an MM patient who developed serologically-proven MuSK-MG. This case highlights two important issues. First, MuSK-MG occurred in this case during MM treatment, demonstrating that the symptoms of MuSK-MG might be masked by chemotherapy for MM. Second, onset of ocular complaints in the 60s and limb weakness without bulbar symptoms are atypical presentations of MuSK-MG.Several cases of MG accompanied by MM or plasmacytoma have been reported in the literature –6. AlthoMore than 40% of patients with MuSK-MG present with bulbar weakness as their first symptom. Further, limb weakness is uncommon and ocular muscles are often unaffected , 14. MM In conclusion, this case emphasizes the need to still consider testing for anti-MuSK antibodies in older MM patients where there is clinical suspicion for possible MG despite negative AChR antibodies and lacking classic MuSK MG phenotype.


Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieve this goal is to be able to observe the dynamics of large populations of neurons over a large area of the brain. Light-field microscopy (LFM), a type of scanless microscope, is a particularly attractive candidate for high-speed three-dimensional (3D) imaging. It captures volumetric information in a single snapshot, allowing volumetric imaging at video frame-rates. Specific features of imaging neuronal activity using LFM call for the development of novel machine learning approaches that fully exploit priors embedded in physics and optics models. Signal processing theory and wave-optics theory could play a key role in filling this gap, and contribute to novel computational methods with enhanced interpretability and generalization by integrating model-driven and data-driven approaches. This paper is devoted to a comprehensive survey to state-of-the-art of computational methods for LFM, with a focus on model-based and data-driven approaches. Achieving this goal requires the ability to capture the dynamics of large populations of neurons at high speed and resolution over a large area of the brain. Whilst there are many viable techniques for observing brain activity, optical imaging with fluorescent indicators is a popular strategy to record the activity of neurons owing to the high spatial resolution and potential scalability.A fluorescent indicator transduces biophysical changes into changes in fluorescence. The most commonly used indicators in functional neuroimaging are those that respond to changes in membrane potential or calcium concentration. Although calcium indicators monitor neuronal membrane potential indirectly, they are most commonly used because of their slow transients and high signal amplitude which make them easier to detect than membrane potential indicators. In particular, due to their slow decay, calcium indicators can be used with diffraction-limited point scanning techniques and single-pixel detectors that collect the emitted fluorescence photons irrespective of the path which they take to reach the detectors, as shown in the leftmost sub-figure in Neurons communicate through electrical impulses lasting around 1 millisecond and repeated at rates up to hundreds of Hertz. Therefore, the study of neural information coding requires high temporal resolution imaging methods. In recent years, we have witnessed substantial progress in fluorescence microscopy imaging in particular due to improved fluorescent indicators of neuronal activity and to the use of alternative scanning strategies for faster acquisition . For exaThe original LFM was designed by Levoy et al. , where aIn what follows, we review fundamental aspects of LFM, describe in 2Capturing the 4D light-field information with a single camera sensor introduces inherent trade-offs between spatial resolution and angular resolution because the camera pixels are now encoding 4 dimensions rather than 2. Under the application background of imaging neuronal activity, the reduced spatial resolution makes faithful reconstruction of the monitored volume from light-field data problematic, and this issue may become more severe due to light scattering in deep tissues which cannot be mitigated as in the case of raster-scanning, single-detector microscopes. This makes it more difficult to distinguish and localize neurons deep in the tissue. Moreover, some detailed neural structures, such as axons, dendrites, synapses, may not be clearly visualized from individual views. Accordingly, neural dynamics recorded from these fine neural structures may suffer from low signal-to-noise ratio ,10. FurtFacing these challenges, two parallel viable solutions can be exploited independently or jointly to address the above issues: (1) to improve the acquisition by designing alternative optical systems, or (2) to improve the reconstruction by designing advanced computational algorithms.With advanced optical systems design, LFM is endowed with desirable properties. For example, it is able to produce diverse sampling patterns by recoding the 4D information at the pupil plane instead of the native imaging plane, which improves information acquisition performance. This can be combined with refined selective illumination modalities, such as light-sheet excitation, to provide optical sectioning. These new features contribute to eliminating interference from background caused by out-of-focus light and scattering, thus increasing the signal-to-noise ratio, spatial resolution and volume coverage, which further fulfills the potential of LFM in imaging whole-brain neuronal activity on freely behaving organisms.Aside from improving LFM optical systems, the other alternative is to improve post-processing performance by developing advanced computational methods capable of better exploiting angular information. Furthermore, proper learned or model-based priors can be incorporated in these methods. At present, we are witnessing an emerging trend that an increasing amount of effort is devoted to developing more efficient and effective computational algorithms for fast volumetric reconstruction, neuron localization and neuronal activity demixing using light-field data. These methods can be generally categorized into two classes: model-based approaches and data-driven approaches. The model-based category attempts to enhance the reconstruction by incorporating additional priors, such as smoothness, spatial and temporal sparsity, low-rank, depth-related shearing property and shift-invariance property in phase-space. In contrast, the data-driven a.k.a. learning-based category capitalizes on machine learning to pave the way to advanced computational LFM. In particular, more researchers are using deep learning approaches to achieve unprecedented performance. Also, the flexibility of the microscope system opens up the possibility for the creative combination of model-based methods with data-driven methods to improve the interpretability and credibility of learned models.3diverse sampling patterns to improve acquisition performance [refined illumination strategies to provide optical sectioning [Since the first design of LFM proposed in , there hformance , 11, or ctioning ,13, or actioning .Representative designs that exploit the insight of diverse sampling include eXtended field of view light-field microscopy (XLFM)  and FourOther works that leverage the insight of refined illumination include hybrid light-sheet and light-field microscopy (LSLFM)    proposes4Advanced optical hardware systems could introduce extra system complexity. Furthermore, changes to the optical system do not in themselves address the computational issues related to reconstructing volumes from light-fields. Therefore, to mitigate or overcome aforementioned issues, an increasing amount of research is devoted to advanced computational post-processing strategies. At present, we are witnessing an emerging trend of more efficient and effective computational algorithms being developed for fast volumetric reconstruction, neuron localization and signal demixing.In particular, some of these methods fall into the model-based category which advances the light-field model by incorporating additional priors, such as smoothness, non-negativity, spatial and temporal sparsity, low-rank, phasespace priors, etc. ,9,15–21.4.14.1.1Based on the pioneering light-field microscopy design , BroxtonHowever, as 3D deconvolution with light-field images is an ill-posed tomographic inverse problem , whereinStefanoiu et al.  propose Furthermore, Verinaz et al.  incorpor4.1.2In certain scenarios, volumetric reconstruction is not the ultimate goal, as it may be of more interest to localize target neurons and extract functional activity. To identify target neurons and their activity, Nobauer et al.  proposedConsidering that sparsity priors can improve reconstruction performance, spatial resolution and SNR, Yoon et al.  propose 4.1.3Phase-space, a.k.a. spatial-angle space or epi-polar plane image (EPI) space , 27, is Based on these principles, Liu et al.  demonstrAlong this line, Song et al.  propose 4.2In contrast to model-based methods, data-driven approaches attempt to automatically learn models from training data and incorporate discovered information into models, without requiring prior knowledge. This provides an appealing algorithmic alternative to overcome the shortcomings of model-based methods. Among data-driven approaches, deep neural networks, a.k.a. deep learning has attracted considerable attention as they provide unprecedented performance and efficiency in a variety of real-world signal and image processing tasks, including image de-noising, de-blurring, super-resolution, etc. To date, an explosive amount of effort is being invested into applying deep learning to the neuroimaging domain –26. Mult4.2.1Wang et al.  adapted A similar concept was adopted in  to reconAlthough deep learning based-methods empirically demonstrate excellent image reconstruction performance and good generalization ability, no theoretical guarantees on generalization can be given. Out of this concern, Wagner et al.  suggest 4.2.2Despite the good performance of modern deep networks in a variety of tasks, such purely data-driven approaches are also subject to some limitations . Apart fSong et al.  proposedX denotes the EPI constructed from an observed LFM image, M for the number of depths to be covered, ‖ · ‖2 is the l2 norm and ‖ · ‖1 is the l1 norm. The network architecture is designed systematically by unrolling the Convolutional Iterative Shrinkage and Thresholding Algorithm (CISTA) so that each iteration step of CISTA gives one layer of the network. The complete network is formed by concatenating multiple such layers. Therefore, forward passing through the network is equivalent to executing the CISTA algorithm a finite number of times. Note that the network contains domain specific layers which explicitly exploit physical priors, while the parameters are learned from a training dataset. Such design enables the network to leverage both domain knowledge implied in the model, as well as new knowledge learned from the data, thereby combining advantages of model-based and learning-based methods.Here, Verinaz et al.  proposed5When applying data-driven approaches to map light-field images to high-resolution 3D volumes, the ground truth can be given by other high-resolution imaging modalities including but not limited to confocal, light-sheet and multi-photon microscopy images , 29. HowDue to video-rate imaging speed, LFM can generate a large amount of data, which brings a severe challenge for real-time processing and analysing of neural dynamics. Fortunately, relying on much higher inference efficiency than iterative approaches, deep learning models are a promising avenue for real-time post-processing and analysis, such as volumetric reconstruction, 3D localization, neural activity demixing –26, 29.To train a compact model efficiently, knowledge distillation –36 conce6In this article, we provided an extensive review to the latest progress on LFM for imaging neuronal activity. Advanced optical systems focus on improving the information acquisition performance via enhanced sampling diversity and refined illumination strategies, while advanced computational algorithms attempt to improve the post-processing performance via establishing more powerful models equipped with predefined or learned priors. Both strategies and their combinations promise to provide an unprecedented capability for imaging neuronal activity at a high resolution and speed across large volumes. We envision that more creative applications of deep learning and relevant data-driven approaches are being conceived and implemented for light-field microscopy. Along with computational algorithm becoming increasingly essential component of the post-processing, we will witness more uncovered potential and widespread usage of LFM.


However, studies have yet to explore whether the Great Recession was also associated with obesity-related health th percentile, using the Oken reference. We use the annual 1-year lagged value for county-level unemployment (2007–2010) and limit our analyses to singleton, term births. Linear probability models, with county and year fixed-effects were used to examine the unemployment-LGA association. All models control for county-level foreclosure rates, child gender, and maternal age, parity, education, and race/ethnicity.We investigated whether increasing county-level unemployment was associated with large-for-gestational-age (LGA) births, using repeated cross-sectional data from California birth records between 2008 and 2011 . LGA was defined as >90An increase in county-level unemployment was not statistically significantly associated with the prevalence of LGA . But, over the period of observation, for every one standard deviation increase in unemployment, LGA prevalence increased by 5% and p = 0.08.in utero.These results cautiously suggest some deleterious effects of the Great Recession on obesity-related health  For There are multiple pathways through which recessions may affect birth outcomes . The Grein utero are critical given the long-term implications of LGA on child obesity [A better understanding of the systemic risk factors for obesity-related health  obesity ,35 and tMaternal demographic and health characteristics, child sex, birthweight, and gestational age were obtained from the California birth records between 2008 and 2011. County-level annual unemployment estimates (2007–2010) were obtained from the Bureau of Labor Statistics. County-level annual foreclosure rates (2008–2011) were obtained from RealtyTrac LLC.th percentile compared with the 2000 U.S. birthweight reference [The primary dependent variable was LGA births, defined as weight-for-gestational-age >90eference . Oken aneference . We limith percentile (termed “very-LGA”), high birthweight (> 4000 grams) and preterm births . We also investigated whether county-level unemployment was associated with maternal pre-pregnancy BMI (kg/m2) and the probability of excessive GWG. We calculated pre-pregnancy BMI and GWG using self-reported maternal pre-pregnancy height and weight and measured delivery weight from the birth certificate. To calculate excessive GWG, we first subtracted maternal pre-pregnancy weight from maternal delivery weight. We then classified weight gain based on the Institute of Medicine guidelines for weight gain during pregnancy  [To address the secondary aims of this manuscript, we examined five other related dependent variables: LGA >97an 9 kg) .Our main independent variable was one-year-lagged values of annual county-level unemployment (2007–2010). We used the value of county-level unemployment from the year preceding each newborn’s birth , since we would expect any impact from unemployment on birthweight to occur during or before pregnancy.We explored the association between county-level unemployment (2007–2010) and LGA births (2008–2011) over a four-year period that overlaps with the Great Recession because the recession had longer-term economic impacts beyond its official end date in 2009 . We examTime-invariant confounders (e.g. “baseline” county urbanization) were controlled for by using county fixed-effects in the regression models, which are explained below. We also included a year fixed-effect to control for secular decreases in LGA births in California over time.Prior studies suggest that the composition of women having children changes during a recession . TherefoWe hypothesized that the association between increasing unemployment and LGA births might be heterogenous by race/ethnicity . TherefoIn our primary specification, we used linear probability models, with county and year fixed-effects, to test the association between changes in unemployment rates (2007–2010) and the probability of LGA births (2008–2011) in California. Standard errors were clustered at the county-level. By using county fixed-effects, we are able to compare each county to itself over time, and control for all baseline time-invariant measured and unmeasured confounding factors. Coefficients were multiplied by 100 so that they can be interpreted as the percentage point (PP) change in relation to a 1-pp increase in unemployment.To address the secondary aims of this paper, we tested heterogeneity in the unemployment-LGA association by race/ethnicity with the inclusion of an interaction term (unemployment X race/ethnicity) and assessed statistical significance with a post-hoc Wald test of the interaction terms. The test of the interaction suggested that there was heterogeneity in the association by race/ethnicity ; therefore, we also presented race/ethnicity-stratified results.Similar to our primary analyses, we used linear probability models, with county and year fixed-effects, to assess the relationship between unemployment and secondary outcomes: very-LGA, high birthweight, excessive GWG, and preterm births. Linear regression models, with county and year fixed-effects, were employed to assess the relationship between unemployment and pre-pregnancy BMI.We assessed whether our primary results would have changed given the following alternative specifications: 1) when including preterm births; 2) when only including mothers that did not smoke during pregnancy; 3) excluding Los Angeles (LA) county, since 25% of Californians reside in LA; and 4) when excluding foreclosure rates as a covariate. We also assessed our primary results when excluding Pacific Islanders and people of an “Other” race, because post-hoc race/ethnicity-stratified results suggested that the magnitude of effect for these races were 30 times higher than the effect for people of other race/ethnicities. For comparison, we modeled the association between unemployment and LGA births during the years prior to the Great Recession (2003–2007). Finally, we employed logit regression models, and estimate average marginal effects, to test the association between unemployment and our primary and secondary outcomes, given that several outcomes (e.g. very LGA births) were rare.Alpha was set to 0.05 and analyses were performed using Stata 15.1 . The University of Washington School of Public Health deemed that this analysis of de-identified secondary data was not human subjects research. The California Health and Human Services Agency Committee for the Protection of Human Subjects approved the use of California birth records for these analyses.Over the observation period there were 138,970 LGA births in California  . StatewiIncreasing county-level unemployment was not statistically significantly associated with prevalence of LGA, but the direction of the effect was positive and p = 0.08  . ResultsIn race/ethnicity-stratified results, increasing unemployment was not associated with the prevalence of LGA for most race/ethnicities; however, among Filipinos, increasing county-level unemployment was associated with a significantly higher prevalence of LGA births  . AlthougEvery 1-pp increase in unemployment was associated with a statistically significantly higher prevalence of very-LGA births  . But, inThis study leveraged repeat cross-sectional data from birth certificates in California to identify whether increasing county-level unemployment, during the Great Recession, was associated with LGA births. Country-level unemployment was not statistically significantly associated with LGA births, although the direction of the association was positive (0.12-pp) and the p-value was < 0.10. Moreover, over the period of observation, for every one standard deviation increase in unemployment, LGA prevalence increased by 0.43-pp or 5%. Although the estimate is not statistically significant at the 0.05 level, this is a meaningful effect. In addition, an increase in county-level unemployment was associated with a statistically significantly higher prevalence of very-LGA births. Our analysis of subgroups revealed some differences in the statistical significance and magnitude of effect for the unemployment-LGA association by race/ethnicity. Increasing county-level unemployment was associated with a higher prevalence of LGA births among Filipinos. An increase in county-level unemployment was also significantly associated with a lower prevalence of preterm births during the recession.decreased overweight among adults [The direction of the unemployment-LGA association, and the fact that an increase in county-level unemployment was associated with a higher risk of very-LGA births is generally consistent with prior literature, which suggests adverse effects of the Great Recession on diet –8 and weg adults ,43.Relatedly, our findings suggest the unemployment-LGA association is heterogenous by race/ethnicity, which is generally consistent with prior studies . But, thUnexpectedly, increasing annual county-level unemployment was associated with a significantly lower prevalence of preterm births. Although these results were contrary to our hypothesis, Margerison-Zilko and Luo also report unexpected results. These authors find that during the recession (2007–2008), state-level unemployment in the first trimester of pregnancy was associated with higher odds of preterm births, whereas state-level unemployment during the second trimester of pregnancy was associated with lower odds of preterm birth . Thus, tOur data analysis and approach have several strengths including using a large sample of births and using county fixed-effects, which controls for time-fixed county-level unobservable factors. However, limitations should be noted. We are not able to control for potential unmeasured time-varying confounding; but biased coefficients would only result if a factor covaried with county-level unemployment and affected LGA. Relatedly, we do not have repeated birth records from the same women, which could allow us to better estimate causal effects and control for individual-level time-fixed unobservables (e.g. genetics). Gestational age is reliant on women’s self-report of last menstrual period (LMP); although LMP is very commonly used in research to calculate gestational age, some studies suggest that up to 20% of women underreport the length of time since their LMP . Findingin utero. Future studies could consider investigating the effects of the Great Recession on birth outcomes using longitudinal data, with repeated birth records.A better understanding of the systemic risk factors for LGA are critical given the long-term implications for health ,35. IncrS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


COVID-19 is typically a primary respiratory illness with multisystem involvement. The prevalence and clinical significance of cardiovascular and multisystem involvement in COVID-19 remain unclear.This is a prospective, observational, multicentre, longitudinal, cohort study with minimal selection criteria and a near-consecutive approach to screening. Patients who have received hospital care for COVID-19 will be enrolled within 28 days of discharge. Myocardial injury will be diagnosed according to the peak troponin I in relation to the upper reference limit  . Multisystem, multimodality imaging will be undertaken during the convalescent phase at 28 days post-discharge (Visit 2). Imaging of the heart, lung, and kidneys will include multiparametric, stress perfusion, cardiovascular magnetic resonance imaging, and computed tomography coronary angiography. Health and well-being will be assessed in the longer term. The primary outcome is the proportion of patients with a diagnosis of myocardial inflammation.CISCO-19 will provide detailed insights into cardiovascular and multisystem involvement of COVID-19. Our study will inform the rationale and design of novel therapeutic and management strategies for affected patients.ClinicalTrials.gov identifier NCT04403607. Figure 1).,COVID-19 presents the most significant threat to human health in modern times.,,Coronavirus (Coronaviridae family) is named after the crown morphology of its outer membrane. SARS-CoV-1, which caused an epidemic in 2002–2003, and SARS-CoV-2, infect human cells when the surface spike (S) protein, a type 1 membrane glycoprotein,,Like SARS-CoV-2, SARS-CoV-1 also uses ACE2 and transmembrane protease serine 2 (TMPRSS2) as mechanisms of cell invasion. During SARS-CoV-1 infection of transgenic mice expressing human ACE2, ACE2 becomes dysregulated and depleted.et al.et al. studied the histopathology of heart, lung, kidney, and liver tissue samples from three patients who died from COVID-19. They found evidence of viral elements within endothelial cells and endotheliitis.Chan ,,,,,High levels of ACE2 are expressed by pericytes in the heartEndothelial damage and thrombotic microvascular angiopathy may underpin systemic vascular dysfunction.,The prevalence of renal involvement in patients with SARS-CoV-2 infection is low,Acute myocardial injury is defined as a rise in the circulating concentration of troponin above the 99th percentile of the upper reference limit and then a fall. Acute MI is myocardial injury in the context of myocardial ischaemia. Acute MI is categorized into one of five types.Clinical scenarios of Type 2 MI include severe hypertension, hypoxia, and tachyarrhythmia. Type 3 MI is the classification used for patients who suffer cardiac death due to MI, and Types 4 and 5 MI are iatrogenic, consequent on MI arising from percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG), respectively.Myocardial injury is diagnosed based on elevated cardiac biomarkers without myocardial ischaemia. Acute myocardial injury is associated with a rise and fall of troponin, whereas chronic myocardial injury is associated with a stable troponin concentration. Myocardial injury may be primarily due to a cardiac or non-cardiac cause.The plasma concentration of troponin I will be measured using the Abbott Architect assay. The sex-specific upper reference limits (99th centile) are >16 ng/L and >34 ng/L for females and males, respectively.,,,Cardiovascular magnetic resonance imaging (CMR) is a non-invasive diagnostic test for myocardial pathology.In COVID-19, myocardial inflammation due to viral myocarditis, ischaemia, or stress cardiomyopathy, as well as MINOCA (Type 1 or Type 2 MI) and other cardiac complications are potentially more common than previously thought based on current crude biomarker studies. Establishing their real frequency using powerful imaging techniques is essential for optimizing risk stratification and therapy. In our study, MRI will be used to assess for and classify clinical endotypes. The endotypes of myocardial involvement are (1) myocardial inflammation, e.g. (1.1) myocarditis, (1.2) ischaemia, or (1.3) stress (Takotsubo) cardiomyopathy; (2) myocardial infarction; (3) indeterminate; or (4) none. A diagnosis of viral myocarditis typically requires endomyocardial biopsy (EMB). We do not anticipate that EMB will be undertaken in our study population; therefore, a presumptive diagnosis may be made based on the available clinical information.The wide field of view of the chest and abdomen also permits imaging of the lungs and kidneys.A computed tomography (CT) coronary angiogram/chest protocol will image for coronary artery disease (CAD), delayed enhancement, lung perfusion, pulmonary thrombo-embolism, and parenchymal pathology.Taken together, integration of multimodality imaging of the heart, lungs, and kidneys within a single visit represents a highly novel approach to investigate the cardiovascular complications of COVID-19. The imaging scans will also support advanced computational modelling to better understand this multisystem disease.We hypothesize that myocarditis  is common after SARS-CoV-2 infection.To assess the incidence, nature, time-course and clinical significance of cardiovascular involvement in patients with COVID-19.To quantify myocardial perfusion as a measure of coronary microvascular function.To determine whether patients with COVID-19 and pre-existing cardiovascular disease are at increased risk of cardiac involvement.To assess the incidence of pulmonary thrombo-embolism and right ventricular dysfunction.To assess for renal involvement in patients with COVID-19 and suspected cardiac involvementTo assess mechanisms using circulating biomarkers of cardiac injury , inflammation [C-reactive protein (CRP)], vascular injury , and thrombotic microangiopathy, and their changes over time.To establish substudies using computational cardiovascular modelling, electrocardiography, and pathology.The CISCO-19 study has a prospective, observational, multicentre, longitudinal, cohort design, minimal selection criteria, and a near-consecutive approach to screening.The study population will be focused on COVID-19 patients who have survived the initial, acute illness. Accordingly, the findings from our study are most relevant to patients in the convalescent phase looking forward to the longer term. We will characterize cardiac involvement evidenced by an increase in circulating high sensitivity troponin I > upper reference limit at any time during the hospital episode of care. Control data may be drawn from local cohorts, as appropriate. Patients who received hospital care with COVID-19 will be enrolled within 28 days of discharge. This enrolment strategy is intended to maximize enrolment of all-comers affected by COVID-19. Patients who die or are ineligible for other reasons will be recorded in a screening log.Figure 2).Multiparametric, stress perfusion, cardiovascular MRI, CT coronary angiography (CTCA), and a 12-lead ECG will be acquired at ∼28 days post-discharge. The study is designed to assess for imaging evidence of multiorgan injury .The study will involve multiple centres in the West of Scotland (population 2.2 million) including the Queen Elizabeth University Hospital, the Royal Infirmary in Glasgow, and the Royal Alexandra Hospital in Paisley. These hospitals provide secondary care services for NHS Greater Glasgow and Clyde Health Board  Emergency Medicine and in-patient wards; and (ii) laboratory records.The inclusion criteria are: (i) age >18 years old; (ii) history of hospital attendance or hospitalization for COVID-19, confirmed clinical diagnosis e.g. case definition according to criteria of the World Health Organisation and Health Protection Scotland), laboratory test [e.g. polymerase chain reaction (PCR)], and/or a radiological test (e.g. CT chest or chest X-ray); (iii) able to comply with study procedures; and (iv) able to provide written informed consent. The radiology results will be reported by accredited radiologists according to contemporary, national guidelines.The exclusion criteria are: (i) contra-indication to CMR, e.g. severe claustrophobia or metallic foreign body; (ii) contra-indication to intravenous adenosine, i.e. severe asthma, long QT syndrome, second- or third-degree atrioventricular (AV) block, and sick sinus syndrome; and (iii) lack of informed consent.We aim to screen a near-consecutive cohort of patients diagnosed with COVID-19. A screening log will be prospectively completed. The reasons for being ineligible, including lack of inclusion criteria and/or presence of exclusion criteria, will be prospectively recorded. This information will characterize selection bias, if any.One hundred and eighty patients will be enrolled following written informed consent. If a consented patient is subsequently found to be ineligible, unless consent is withdrawn, they will remain included in the study population, including consent for long-term follow-up using linkage of electronic government and patient records (EPRs).A diagnosis of COVID-19 will be based either laboratory evidence of SARS-CoV-2 infection using a PCR test on a biospecimen or a radiological diagnosis consistent with COVID-19 but biospecimen negative.The current protocol involves two visits. The first visit involves informed consent and baseline assessments. The second visit occurs at 28 days after discharge. A third visit is intended at 1 year, contingent on additional funding and a protocol amendment being secured. A proposed vascular biology substudy involves a gluteal skin biopsy after 28 days. The participants will be invited to consent for life-long follow-up using electronic record linkage without direct contact.The study assessments involve gathering information from standard of care procedures and research tests. The standard of care information includes demographics, medical history (including multimorbidity), limited examination, laboratory and radiological tests, cardiology tests , and treatment. The research assessments at both visits include blood and urine samples, a 12-lead digital ECG , health status questionnaires, and assessments of adverse events. Visit 2 involves cardiovascular imaging, including stress perfusion MRI and CTCA, and an ECG. By designating imaging during the convalescent phase, at 28 days post-discharge, the participants are not anticipated to be infectious. This approach aligns with other contemporary studies, such as the International Severe Acute Respiratory and Emerging Infection Coronavirus Clinical Characterisation Consortium-4 (ISARIC-4C) study.Prior cardiovascular disease includes diabetes, hypertension, myocardial infarction, heart failure, stroke, and transient ischaemic attack.Cardiovascular risk factors include age, cigarette smoking, obesity, hypertension, hyperlipidaemia, and glycated haemoglobin. Cardiovascular risk will be described using the established JBS3 and Heart Age Scores.The primary outcome for our study is a diagnosis of myocarditis . The relevant endotypes are (1) myocardial inflammation due to (1.1) myocarditis, (1.2) ischaemia, or (1.3) stress (Takotsubo) cardiomyopathy, (1.4) combination; (2) myocardial infarction; (3) indeterminate; or (4) none.,Myocardial inflammation is caused by the immune response to virus infection, autoimmune disease, ischaemic injury, or toxic agents.A panel of three or more cardiologists will assess the clinical information to make a diagnosis (endotype) and related certainty  before and after disclosure of the MRI and CTCA findings. The diagnosis will be based on consensus. This approach reflects the uncertainty in determining a diagnosis in patients with myocardial injury. The diagnosis draws upon clinical information and test results, rather than any single test modality in isolation.,A prioritized secondary outcome is the endotype for myocardial injury, including myocardial infarction type according to the 4th Universal Definition of MI,,We aim to assess the impact of cardiovascular complications on health status, well-being, and physical function. We will prospectively collect patient-reported outcome measures (PROMS) in order to assess for associations with the cardiovascular complications of COVID-19, reflected by the primary and secondary outcomes. Self-reported health status will be assessed using the generic EuroQOL EQ-5D-5L questionnaire and the Brief Illness Perception Questionnaire (Brief-IPQ).In order to research the mechanisms of cardio-pulmonary and renal involvement of SARS-CoV-2 infection, we will measure circulating biomarkers of inflammation , cardiac injury , renin–angiotensin system , and haemostasis , and renal function  and their changes over time.The measurements will be undertaken in a central laboratory, blind to the other clinical data. The associations between the circulating concentrations of these mechanistic biomarkers, including their changes over time, and the primary and secondary outcomes will be assessed.,2] will be considered for contrast MRI according to local radiology protocols.Cardiovascular MRI is the reference diagnostic method for myocardial injury, including myocarditis and acute cardiomyopathy. Stress perfusion MRI using intravenous adenosine enables dynamic imaging of myocardial blood flow during stress and rest conditions. In-line pixel mapping enables fully quantitative read-out of myocardial blood flow (mL/min/g tissue), classified at a subsegmental level  with the percentage extent of myocardium with impaired perfusion during stress (% ischaemic burden).SARS-CoV-2 causes vascular dysfunction and microthrombotic angiopathy.MRI will be undertaken at 3.0 Tesla (Siemens PRISMA) ∼28 days after discharge. This timepoint may be considered as the subacute, convalescent phase. The rationale for undertaking the multiparametric, stress perfusion cardiovascular MRI at this timepoint is to determine whether there is persisting evidence of myocardial injury and/or MI, LV function, and incidental pathology  in the convalescent phase. The patients will be advised to withhold cardiovascular medications and to avoid caffeine-containing drinks for 24 h before the scan.MRI will also provide information for incidental findings in the chest  and abdomen. Imaging of renal anatomy, size, and tissue characterization will be exploratory.The CT scanner has 320 detectors enabling full heart coverage within a single heartbeat . Intravenous metoprolol will be used where required to control the heart rate (target 60 b.p.m.) and sublingual glyceryl trinitrate will be given to all patients immediately before the scan acquisition. An initial low radiation dose helical scan of the thorax will be acquired for comprehensive assessment of the lungs. A contrast bolus timing scan will be acquired which will provide information on cardiopulmonary transit times. Non-contrast and contrast-enhanced angiographic breath-hold ECG-gated volumes will be acquired timed for optimum pulmonary and systemic arterial (coronary) opacification.CTCA will provide information on the presence and extent of coronary calcification , CAD, and whether CAD is obstructive (flow-limiting) including the CAD-RADS score. Intracardiac thrombus will be assessed. Late enhancement ECG-gated CT will be acquired to assess for delayed enhancement (scar)The CT and MRI findings will be correlated with the other clinical data. Cardiac and extracardiac findings will be reported and managed according to local standards of care. Patients with severe renal dysfunction thought to be at risk of acute kidney injury as determined by local radiology clinical protocols will undergo non-contrast CT.In order to assess the natural history, longer term follow-up for health outcomes will be undertaken using electronic record linkage, removing the need for participants to undergo further research visits after the 12-month visit.The scans will be pseudoanonymized, i.e. identifiers removed and assigned a unique study number to enable linkage. The scans will be shared with research collaborators for cardiovascular modelling to better understand the relationships between tissue pathology, blood flow, and function.COVID-19 infection and treatment are associated with changes on the ECG. The changes include alterations in heart rate, conduction, and ventricular repolarization. Drug treatment for COVID-19 may prolong the QTc interval. Whether these changes persist after the acute phase and their relationships to myocardial pathology are unknown. Paper and/or digital ECGs will be acquired, de-identified, and provided to the University of Glasgow Electrocardiography Core Laboratory for automated analysis. The ECG measures will be linked with the clinical and imaging data.We will undertake exploratory research into the vascular biology of COVID-19 infection. This work will be undertaken in collaboration with cardiovascular scientists and virologists in the University of Glasgow. We will study the vascular biology of cells and molecules (e.g. RNA and cytokines) implicated in SARS-CoV-2. Informed consent will be obtained for post-mortem examination in the event of death during the study period. Histology samples of the heart will be examined for features of myocarditis, MI, and microvascular disease. The histopathological findings will be linked to the CMR findings.This study has been developed with input from members of a multidisciplinary research team and the Scientific Strategy Group of the University of Glasgow. The study has been peer reviewed by panel members of the Chief Scientist Office of the Scottish Government. The study has been reviewed by the Patient and Public Involvement group of NHS Greater Glasgow and Clyde Health Board.The statistical analyses will be pre-defined according to a Statistical Analysis Plan.The primary outcome is myocarditis . To detect an association between a history of pre-existing cardiovascular disease and the incidence of myocardial inflammation (myocarditis) at 2–4 weeks, we have assumed 25% of patients with prior cardiovascular disease and the incidence of myocardial inflammation in those with/without prior cardiac problems to be 33% and 10%, respectively. To have 80% power to detect this difference will require 140 participants  to be scanned. We aim for 160 patients to attend the imaging visit, anticipating that 10–15% of the participants may have incomplete imaging data due to technical reasons such as imaging artefact or claustrophobia.Pre-specified subgroup analyses are intended for patients without cardiovascular disease, as defined by the absence of (i) prior cardiovascular disease and (ii) obstructive CAD on CTCA. Given the public health significance of COVID-19, interim reports may be undertaken.Outcome assessments (endpoint adjudication) will be undertaken in blinded fashion. The primary outcome evaluation  will be adjudicated by a panel of cardiologists blind to the clinical status of the patient and performed according to a pre-specified charter.Guidelines for Good Clinical Practice in Clinical Trials and STROBE guidelines.The trial will be conducted in line with the current The CISCO-19 study is approved by the UK National Research Ethics Service (Reference 20/NS/0066).CISCO-19 is an investigator-initiated clinical study that is funded by the Chief Scientist Office of the Scottish Government (COV/GLA/Portfolio project number 311300). The funder has no role in the study design, conduct (non-voting TSC member), data analysis and interpretation, manuscript writing, or dissemination of the results. C.B, C.D., N.S., R.M.T. are supported by the British Heart Foundation (BHF RE/18/6134217).The MRI study involves imaging and analyses technologies provided by Siemens Healthcare and the National Institutes of HealthThe trial is co-sponsored by NHS Greater Glasgow & Clyde and the University of Glasgow.The ClinicalTrials.gov identifier is NCT04403607.Our observational, multimodality, imaging cohort study will prospectively gather information on the cardiovascular complications and their clinical significance in COVID-19. A relatively unselected approach to patient enrolment will minimize selection bias beyond those who do not survive or who are unable to comply with the protocol. The findings will be generalizable to patients in the convalescent phase of the illness and informative for the natural history. On the other hand, the findings will not necessarily be generalizable to all patients with COVID-19, since our enrolment strategy focuses on survivors following the acute phase of the illness.,Cardiovascular MRI will be used to clarify clinical endotypes according to contemporary guidelines.High-sensitivity troponin is a cardiac protein that is ubiquitously released from injured cardiomyocytes. However, troponin is not cause specific, and circulating concentrations may increase due to hypoxia, hypotension, and renal failure, as well from direct cardiac toxicity. It is unclear whether cardiovascular involvement in COVID-19 is mainly secondary to severe pneumonia, or whether there is direct viral infection of the heart and blood vessels. A recent expert consensus guideline highlighted the pivotal value of CMR in the diagnosis of myocardial inflammation due to viruses, autoimmune disease, ischaemic injury, and toxic agents. MRI is diagnostically useful to identify endotypes for stratified therapy. We will assess whether this could be the case in COVID-19.To our knowledge, CISCO-19 is the first to apply stress perfusion CMR to assess and quantify abnormalities in myocardial perfusion that may be secondary to microvascular dysfunction. The information on myocardial perfusion will help classify patients with myocardial injury into ischaemic or non-ischaemic groups. Stress perfusion MRI is not usually undertaken in patients with myocarditis, and quantitative measurements of myocardial blood flow are a key attribute of our study design. We aim to advance new knowledge into the pathogenesis of myocardial inflammation (ischaemic vs. non-ischaemic) and into the associations between the aetiology of disease and abnormalities in myocardial perfusion. Recent advances in fully quantitative, in-line pixel mapping of myocardial perfusion are uniquely enabling the quantification of myocardial blood flow in near real-time without the need for time-intensive, off-line post-processing. In addition, multimodality imaging involves multiparametric cardiovascular MRI and CTCA during the same visit. As such, our study will provide methodologically robust estimates of persisting myocardial injury and the impact on the physical and mental well-being of the participants. The results should be helpful to inform clinical management strategies for the diagnosis and management of patients recovering from COVID-19. Our study will provide complementary information to add to the growing body of knowledge on multisystem involvement in COVID-19.We will collect information on participants’ characteristics at baseline including demographics, anthropometry, cardiovascular and medical history, and health status. The participants will be invited to give informed consent for life-long follow-up by linkage of EPRs. Using multivariable analyses, we will link the patients’ characteristics at baseline to observations during longer term follow-up in order to characterize the natural history of this condition.Our imaging research will clarify the prevalence and clinical significance of cardiopulmonary injury  in patients with COVID-19, which is a major knowledge gap in the NHS. By adopting an all-comers approach, we will identify patients with myocardial inflammation that is subclinical (i.e. not diagnosed) or clinically overt. By correlating the MRI findings with troponin and other measures of cardiovascular injury, such as BNP, our results will potentially inform NHS care pathways to use these blood tests in a more directed manner for the clinical management of patients with COVID-19. Currently, there are no disease-modifying therapies for myocarditis, including due to SARS-CoV-2.Overall, our study will add new knowledge on the natural history of COVID-19 in a comparatively unselected population. Our study will create a unique biorepository of clinical samples and images, which in turn may be exploited by scientists undertaking mechanistic research into the vascular biology of SARS-CoV-2 infection, and cardiovascular modelling.