The emergence of new pathogenic viral strains is a constant threat to global health, with the new coronavirus strain COVID-19 as the latest example. COVID-19, caused by the SARS-CoV-2 virus has quickly spread around the globe. This pandemic demands rapid development of drugs and vaccines. Plant-based vaccines are a technology with proven viability, which have led to promising results for candidates evaluated at the clinical level, meaning this technology could contribute towards the fight against COVID-19. Herein, a perspective in how plant-based vaccines can be developed against COVID-19 is presented. Injectable vaccines could be generated by using transient expression systems, which offer the highest protein yields and are already adopted at the industrial level to produce VLPs-vaccines and other biopharmaceuticals under GMPC-processes. Stably-transformed plants are another option, but this approach requires more time for the development of antigen-producing lines. Nonetheless, this approach offers the possibility of developing oral vaccines in which the plant cell could act as the antigen delivery agent. Therefore, this is the most attractive approach in terms of cost, easy delivery, and mucosal immunity induction. The development of multiepitope, rationally-designed vaccines is also discussed regarding the experience gained in expression of chimeric immunogenic proteins in plant systems. The coronaviruses (CoVs) are enveloped viruses having a positive-sense, single-stranded genomic RNA  and are Since the end of 2019, COVID-19 has disseminated over the world. Every continent has reported presence of the virus and the World Health Organization has declared a Public Health Emergency of International Concern on 30 January, 2020 . COVID-1Health control measures mainly include drugs to relieve symptoms and maintain vital functions. The most frequent form of human–human transmission is by spread through aerial drops that are discharged upon sneezing, coughing, or exhaling . AdditioPresently, there is no specific treatment for COVID-19. As an emerging pathogen, several knowledge gaps exist about the SARS-CoV-2 virus and the coming months will be critical to refine clinical management and advancement in the validation of possible treatments. A myriad of efforts is ongoing to perform clinical trials and determine the efficacy of preexisting drugs, with hydroxychloroquine and Remdesivir as the most promising candidates . ReceptoAlthough inactivated vaccines will perhaps be the main avenue for generating the first experimental vaccines, alternative approaches should be explored in parallel, namely the development of subunit vaccines. Since the coronavirus spike (S) glycoproteins initiate entry into cells; they are considered the primary target for neutralizing antibodies . CytotoxAlthough the conventional developmental paths for vaccines take several years, the vaccine against hand, foot, and mouth disease (HFMD) that was approved in China; demonstrates how joint efforts can lead to new vaccines that are accepted in relatively short periods after a pathogen emerges . Similarities with the SARS-CoV-1 virus constitute a valuable reference for vaccine development, however it should be considered that the genetic variability of SARS-CoV-1 seems higher as more than two variants have been described. Despite this, the cross protection of SARS-CoV-1 and MERS experimental vaccines remains to be explored. Thus far, there is evidence that some SARS-CoV-1 neutralizing antibodies cross react with SARS-CoV-2, however extensive research in this regard is needed. The epitopes conserved among SARS-CoV-1 and SARS-CoV-2 have been identified and proposed for vaccine development . The race for generating fundamental knowledge on COVID-19 is leading to rapid advances, which will be useful to refine vaccine design. For instance, Walls et al. , reporteIn parallel with these advances in vaccine design, defining vaccine platforms amenable for rapid and large scale production is required. These must keep in mind that low cost, safe, and easy distribution and delivery are the required attributes to implement broad coverage of vaccination campaigns; especially in the developing countries.Genetically engineered plants constitute a consolidated platform for the manufacturing of biopharmaceuticals. Plants have been used over the last three decades for this purpose. Thus far, a diverse group of biopharmaceuticals has been functionally produced in plant systems that include antibodies, vaccines, growth factors, and cytokines . RemarkaAgrobacterium-mediated transformation is the most popular method to achieve this modification since this bacterium has the ability to transfer large segments of DNA with minimal rearrangement at high efficiency with low number of insertions. Nonetheless, the transgene is randomly inserted into the genome, which often leads to positional effects that make expression levels unpredictable and interruption of endogenous genes a possibility. Another limitation is the induction of silencing mechanisms that hamper productivity. Nevertheless, it should be considered that new technologies are emerging to cope with these limitations by providing ways to achieve site-directed insertion through a number of mechanisms [The current expression approaches for recombinant proteins using the plant cell as host comprise stably-transformed plants at the nuclear or chloroplast levels and transiently-transformed plants. These expression modalities are summarized in chanisms . In the case of transplastomic technologies, they typically comprise the site-directed insertion of the foreign DNA into the chloroplast genome in an event induced by homologous recombination. The main advantage of this technology is the high protein yield, which is a consequence of the high copy number of the transgene and the fact that silencing events have not been reported thus far, and position effects are avoided due to the site-directed insertion. In addition, transgene confinement is achieved given the maternal inheritance shown by most plant species and several proteins can be produced from a single transformation event through the use of operon-like arrangements ,38. For Agrobacterium-mediated delivery of viral vectors has consolidated as a highly efficient strategy to achieve rapid production of biopharmaceuticals with yield up to 5 g of protein per kg of fresh plant biomass. The tobamoviruses, potexviruses, tobraviruses, geminiviruses, and comoviruses have served as a basis for the development of transient, efficient expression systems in plants [Nicotiana benthamiana), as well as bacterial residues, especially endotoxins. Therefore, at present, this technology is applied for the formulation of injectable or nasal vaccines.Another approach to express heterologous protein in plants relies on the use of viral-based vectors, which exploit the efficient promoters, UTRs, and DNA/RNA replication mechanisms found in plant viruses. Interestingly, the n plants . This teN. benthamiana, which has led to injectable vaccine candidates [Thus far, some plant-vaccine candidates have entered clinical trials, including candidates against swine influenza, rabies, and hepatitis B . The mosndidates ,43. Overndidates . Nonetheless, the ultimate goal for low-cost vaccine development could be achieved by generating oral formulations not requiring purification and being composed of freeze-dried biomass encapsulated in gelatin pills or tablets. Under this focus, the goal is to trigger specific immune responses via the gut-associated lymphoid tissues (GALT). For this purpose, edible plants lacking toxic metabolites should be used. Perhaps the main disadvantage of this technology is the long time required to generate transformed lines of edible crops efficiently expressing the antigen of interest  . These oTherefore plant-based vaccines are becoming a reality, although it should be recognized that the progress has been slower than initially expected. This is particularly true for the development of oral vaccines, with the main drawbacks being poor characterization of antigen stability, bioavailability, and reproducibility ,55. The currently available expression strategies for foreign proteins in plants allow projecting the generation of several vaccine candidates against COVID-19 in the short term. The expression approach and targeted organelle will depend on the nature of the selected target antigen. In the following section, possible avenues for the development of plant-based vaccines are envisaged .One prominent approach for vaccine design is based on the use of virus-like particles (VLPs), which are macromolecular complexes resembling viruses, but lacking their genome. In this way, VLPs mimic the native structure of viruses, but are not infective. This avoids the disadvantages of vaccines formulated with attenuated or inactivated viruses that include reactogenicity and reversion to pathogenic forms ,62. A mySf9 cells), which was based on the following structural proteins: S (spike), E (envelope), M (membrane), and N (nucleocapsid) of SARS-CoV, either individually or simultaneously. Simultaneous expression at high levels was achieved for S, E, and M proteins, leading to an efficient VLPs assembly and release, evidenced by electron microscopy and immunofluorescence. The authors demonstrated that the formed VLPs were similar in morphology to the SARS-CoV-1 virions [The precedents of SARS-CoV-1 and MERS antigens expressed in recombinant systems leading to the formation of VLPs constitute important guides for the topic of COVID-19 vaccine development. Mortola and Roy (2004) produced coronavirus VLPs applying baculovirus/insect cells expression  doses of VLPs emulsified with Freund’s adjuvant showed high antibody titers against SARS CoV. Furthermore, VLPs elicited cellular immunity following increased production of IFN-γ and IL-4 . SubsequGiven that immunization by mucosal routes is the most pertinent for vaccination, SARS-CoV-1 VLPs were analyzed in a mouse model based on intraperitoneal or nasal immunization. Both routes led to SARS-CoV-1-specific IgG responses, IgG levels in the groups immunized intraperitoneally were higher. Nasal immunization usually results in the induction of secretory IgA responses at the genital tract, saliva, and lungs; a type of response that is not efficiently induced by systemic administration. In the mentioned study, IgA production at the intestinal tract was higher for intraperitoneal administration when compared to intranasal administration, however only genital tract secretions from the i.n.-immunized group showed neutralization activity .Another approach for the production of VLPs consisted in co-expressing the SARS-CoV-1 S protein and the M1 influenza protein in the baculovirus/insect cell expression system. Interestingly, the chimeric VLPs showed similar size and morphology compared to the wild type SARS-CoV-1. Immunogenicity and protective efficacy of these chimeric VLPs were tested in a mouse lethal challenge model. Complete protection from death was observed in mice vaccinated intramuscularly or intranasally with the chimeric VLPs, which contained 0.8 µg of the SARS-CoV-1 protein S .In the case of the Middle East respiratory syndrome (MERS), VLPs with proteins S, E, and M were generated using the baculovirus/insect cell expression system by Wang et al. (2017) . ElectroIn a more recent study, the structural proteins of MERS-CoV were expressed in silkworm larvae and Bm5 cells for the development of vaccine candidates. However, protein S was not detected in the form of VLPs despite the fact that E and M proteins were secreted to the culture supernatant. Surfactant treatment and mechanical extrusion using protein S or Bm5 cells expressing structural proteins allowed producing nanovesicles exhibiting protein S with a diameter ranging from 100 to 200 nm as revealed by immuno-TEM .N-acetylglucosaminidase (soluble or membrane-bound form), which trims an intermediate product common to mammalian and insect pathways resulting in the insect-specific paucimannose as a final product that avoids the galactosylation process occurring in mammals [Although insect cells possess a protein processing capacity similar to that of higher eukaryotic cells, the protein processing pathways are not totally equivalent . In term mammals .Considering that VLPs of enveloped viruses have been successfully expressed in plants by expressing the HA protein, it is anticipated that SARS-CoV-2 VLPs could be assembled by expressing the S protein. In fact, although not published in the literature yet, Medicago has described the generation of VLPs in plants . For thiBesides VLPs resembling the SARS-CoV-2 virus, another possible approach is to adopt SARS-CoV-2 epitopes and express them in chimeric VLPs. In this way, a VLP from an unrelated virus can serve as the scaffold to present the target SARS-CoV-2 epitopes. For this purpose, the hepatitis B core protein has been applied as scaffold to display unrelated antigens of some pathogens. In particular, this has been applied at the immunodominant c/e1 loop region, which allows displaying the target on the spike structures of the VLPs . A recenN. benthamiana plants for beta1,2-xylosyltransferase (XylT) and alpha1,3-fucosyltransferase (FucT) genes, which showed significant reduction in the production of xylosylated and/or core alpha1,3-fucosylated proteins with no phenotypic alterations [The glycosylation patterns of proteins that form VLPs can impact their immunogenicity and protective capacity. Interestingly, glycoengineering strategies have been successfully implemented for plants, which allows diversifying their application as hosts for the production of biopharmaceuticals. This is a relevant aspect considering that plants lack the ability to perform glycosylation, which is a characteristic of mammalian systems. For instance, plants perform beta1,2-xylosylation, core alpha1,3-fucosylation, and the addition of a second N-acetylglucosamine (GlcNAc) to the mannose core. Moreover, plant glycans lack β -galactose and sialic acid, as well as bi-antennary N-glycans . In someerations . These lAnother approach that deserves attention is the development of multiepitopic vaccines, which offers the opportunity of achieving a fine rational vaccine design by selecting epitopes that potentially induce robust and protective immune responses. At the same time, this approach will discard those related to non-protective responses or even those inducing antibody-dependent enhancement of the disease. In this way, a highly efficacious and safe vaccine can be obtained. Of special interest are the reports proving that specific S protein epitopes enhance the disease upon a pathogen challenge, which highlights the relevance of recurring to the rational design of vaccines to ensure not only immunoprotection, but safety . For the case of multiepitope vaccines against infectious agents, several reports have focused on selecting the most promising Th, B cell, and T cell epitopes that might allow the rational design of a vaccine inducing robust protective responses, while at the same time avoiding deleterious or non-relevant responses. One key factor that adds importance to the development of multiepitopic vaccines against viral disease is genetic variability. In fact, it has been suggested that the SARS-CoV-2 virus evolved into two major types, L and S. The L type (∼70%) predominates versus the S type (∼30%), with the latter being proposed as the ancestral version. The L type is more aggressive than the S type and the analysis performed by Tang et al.  suggestsEscherichia coli have been extensively used to carry unrelated antigens. The main advantage of these molecules is their adjuvant effects at the mucosal levels, as well as their ability to efficiently deliver the antigens to the submucosa thanks to a mechanism of translocation mediated by binding to the GM1 receptor at the epithelial cells in the mucosal surfaces [The selected epitopes are the targets to which adaptive immune responses should be induced, but they lack the required complexity to trigger robust immune responses. Therefore, a carrier is required to increase antigen complexity and, through adjuvant effects, enhance potency of the induced immune response, making it properly polarized. Among these carriers, the B subunit of the cholera toxin and the heat-labile enterotoxin from surfaces . Once atsurfaces . The accsurfaces . Given that plant systems have been successfully adopted for the production of multiepitope proteins, the generation of such vaccines against COVID-19 is considered highly viable ,89,90. BMycobacterium tuberculosis and the GP1 antigen from the Ebola virus [The production of immune complexes (ICs) in plants is another approach that renders highly immunogenic agents. ICs consist of antigens complexed to antibodies recognizing them, constituting macromolecular entities that are efficiently captured and processed by antigen presenting cells . This rela virus ,97. Perhla virus .M. tuberculosis antigens Ag85B and ESAT-6 were fused to ELP and expressed in transgenic tobacco [The purification of antigens is a laborious and expensive activity as part of the production of injectable vaccines . One alt tobacco , and the tobacco . TherefoThe common steps to the above-mentioned antigen design will comprise determining antigen yields and antigenic activity, assessing the immunogenic activity in test animals under distinct administration routes, and validating the safety and stability of the vaccine. The latter will be critical to justify the application and get approval to conduct clinical trials. Agrobacterium-mediated method. Mice orally immunized with this transgenic tomato revealed significantly increased levels of SARS-CoV-1-specific IgA. Sera of mice parenterally primed with the transgenic tobacco showed the presence of anti-SARS-CoV-1-IgG [A key precedent for this field is the vaccine candidates already reported for SARS-CoV-1 and MERS, which are both closely related to SARS-CoV-2. Following nuclear expression approaches, the N-terminal fragment of the SARS-CoV-1 S protein (S1) was expressed in tomato and low-nicotine tobacco plants using an oV-1-IgG .Agrobacterium-mediated transformation. The expression of the chimeric antigen was also achieved in tobacco chloroplasts. However, no immunization assays were performed [Another study focused on expressing a chimeric protein of GFP and amino acids 1-658 of the SARS-CoV-1 spike protein (S1:GFP) by using tobacco leaves subjected to transient expression. After microscopy localization, the fusion protein was observed in the cytosol and the periphery of the nucleus. Stable transgenic tobacco and lettuce plants were generated by erformed ,100.N. benthamiana. Yields reached up to 79 µg/g of leaf fresh weight  at 3 dpi under silencing suppression conditions (p19). Mice immunization revealed that three doses led to effective B-cell maturation and differentiation, achieving high levels of IgG1 and IgG2a. IFN-γ and IL-10 were up-regulated in splenocytes, while the expression of IL-2 and IL-4 was not [N. benthamiana. Yields reached up to 3–4 µg/g fresh leaf weight (0.2% TSP) for the N protein, while the M protein yields were 0.1%–0.15% TSP. The purified N protein reacted with human sera having N-specific antibodies. No immunization assays were performed [The SARS-CoV-1 nucleocapsid (rN) protein of 423-aa was transiently expressed in  was not . The nucerformed . These sAlthough most of the vaccines against respiratory diseases are administered by parenteral routes, it should be recognized that immune profiles deserve improvements, especially in terms of immunogenicity and efficacy in the elderly . MucosalAlthough some companies have already started the production of plant-based VLPs vaccines ,104 usinSimilarly, pure antigens produced in plants have been assessed with promising results in schemes where a conventional vaccine is used as a priming agent and the plant-made antigen is administered by the oral route as boosting . ExploriN. benthamiana as host will allow for immediate exploitation of plants as efficient biofactories of injectable vaccine candidates, which are expected to be implemented and entered into clinical trials in a year. The development of vaccines based on transplastomic lines or edible plant species transformed at the nuclear level and intended to result in oral vaccines  are considered long term goals. However, they have special importance given their low cost and potential to serve as effective boosting agents, which could lead to attractive immune profiles characterized by proper humoral response in the mucosal compartments and long-lasting protection, especially for the elderly. In parallel to these developments, the production of monoclonal antibodies in plants will provide another strategy to generate alternatives to the convalescent plasma transfusion, in which plant-made antibodies will constitute a low cost and safer intravenous treatment for critically ill patients.The emergence of COVID-19 has led to a global emergency that demands the development of new biologics, especially vaccines, to counteract against this threat. In this scenario, a plant-made vaccine is a viable approach to rapidly respond to this need. The current expression technologies offer relevant paths for developing anti-COVID-19 vaccines. VLPs constitute an attractive approach for the development of efficient and safe vaccines, which is associated with high immunogenicity, preservation of the antigenic determinants, and lack of replicative capacity. Thus, VLPs based on the main SARS-CoV-2 structural proteins is an attractive approach for vaccine development against coronavirus infections. The transient expression systems based on deconstructed viral vectors and Perhaps the main challenge envisioned for the development of COVID-19 plant-based vaccines will be, as is typical for all vaccines, testing their efficacy in large clinical trials to validate their safety while fulfilling the requirements of regulatory agencies. The fact that there are precedents of a plant-made biopharmaceutical approved for human use and plant-made vaccines against influenza under clinical trials (with promising safety and efficacy) is encouraging. Therefore, plant-based vaccines have a realistic potential to contribute to the fight against COVID-19.As the COVID-19 epidemic advances, the exploitation of plant-made vaccines is a promise to generate low cost, easy to administer, and safe/effective vaccines to fight against this pandemic. The next few months will be critical to envision the real potential of this technology.


The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations. The main idea of COMMODAR is the feature augmentation by the cooperation of multimodal representations for relation extraction. We leveraged biomedical domain knowledge as well as canonical linguistic information for more comprehensive representations of textual sources. The models based on multiple modalities outperformed those solely based on the linguistic modality. We applied COMMODAR to the 14 million PubMed abstracts and extracted 9214 context-specific molecular relations. All corpora, extracted data, evaluation results, and the implementation code are downloadable at • Computing methodologies~Information extraction • Computing methodologies~Neural networks • Applied computing~Biological networks. Complex biological systems are known to comprise the coordination of molecular interactions and the relationship between molecules will consequently determine the behavior of the entire system. Molecular network models are often considered to be valuable for elucidating the organizing principles of biological systems and promoting public health. For example, biological networks are of pharmacological interest as an aid to the prediction of the side effects or multi-targeting drug efficacy.a protein A INCREASES the activity of a protein B, whereas another one supports that the protein A DECREASES the activity of the protein B. Yoon et al. partially attributed these discrepancies to the lack of the contextual information, which specified the biological circumstance of the molecular relations. As a solution, this study enhanced the resolution of context-free data and resolved the rate of the information conflict. That is to say, if the protein A has a positive influence on the protein B in HEALTHY cases while negative in MELANOMA patients, two augmented relations no longer are contradictory. The context types considered in this study include cell type, organ, disease, and drug.In the pursuit to develop network models, biomedical researchers have increasingly depended on informatics resources which serve various patterns of molecular relations . Yoon etdSysMap  and PinSAn enormous wealth of biomedical information resides in unstructured written languages such as journal articles, which has been unprecedentedly growing. To be more specific, nearly 30 million references are available in PubMed and has annually published more than one million papers. As the number of biomedical publications continues to grow, such an exponentially growing volume of literature has become infeasible to be structured. Thereby, the gap between published knowledge and well-tailored information in databases has been widening.To assist this situation, several text mining efforts , 8–10 ha<subject, predicate, object>. The biomedical domain information from these knowledge resources can play a complementary role in the holistic representation of the unstructured text.Conventional machine learning frameworks for biomedical relation extraction often rely on linguistic information, such as word n-gram or syntactic dependency, directly derived from literature text. Quan et al.  employedIn the present paper, we propose a machine learning approach COMMODAR, which extracts context-specific molecular relations from biomedical literature. COMMODAR cooperatively uses three discriminative features from multiple modalities, which includes linguistics (word n-gram and syntactic dependency) and biomedical knowledge (knowledge triplet). The overall process of COMMODAR is illustrated in Fig. The specification of the information to be extracted from the literature is summarized below.(i)Molecular type: Gene, Gene product(ii)Context type: Disease(iii)Relation type: Increase, Decrease, Regulate (sign unknown), Binding (sign and direction unknown)We considered the extraction of the context-specific molecular relations as a sentence classification problem. To solve the classification problem, we used a supervised-learning paradigm based on the ground-truth data.. Special characters other than the aforementioned punctuation marks were eliminated.We downloaded PubMed raw files in an XML format and retrieved abstract texts published until 2016 resulting in 14,891,354 abstracts. Collected abstracts were segmented sentence-by-sentence by GENIA Sentence Splitter . All chaTo recognize molecular entities in the sentence, BANNER , the mosN-gram-based word embedding is the distributed representation of word to capture the semantic information in the unsupervised manner. After sentence segmentation and tokenization of collected abstracts described in the previous section, the skip-gram model  was emplsaw has a dependency on its direct objective cat in a sentence I saw a cat and it can be represented as saw-cat/dobj. Dependency-based word embedding has been known to have different properties from n-gram-based word embedding and to capture the functional properties of words [In contrast to n-gram-based word embedding defining a target word by the neighbor words of the target word in sentences, dependency-based word embedding defines a word by its syntactic dependency . Syntactof words . Intuitiof words . The embm × 3 matrix, then feeds it into a CNN with the 1 × 3 filters, where m is the dimension of the embedding vector. Thus, ConvKB is a generalized version of the transitional knowledge graph embedding framework TransE [The UMLS Semantic Network stratifies biomedical concepts in the UMLS Metathesaurus and presents useful relations between the sets of these concepts. The nodes and edges in the network are the semantic types and the relations between semantic types, respectively and 2018AB version offers 133 semantic types, 54 relation types, and totally 6105 relations. To capture global relations and transitional characteristics between semantic types, we employed ConvKB, a knowledge graph embedding model based on convolutional neural networks (CNN) . ConvKB k TransE . The dimUnfortunately, to our knowledge, any labeled sentence set of context-specific molecular relations has never been developed for the public purpose. Therefore, we considered a transfer learning scheme to manually generate a small volume of completely labeled sentences and additionally leverage the relatively sizable corpora for related tasks . PartialTo fully capture the relational information implicitly expressed in a sentence, we conducted automated feature learning with CNN obviating manual feature engineering. The convolutional layers and the pooling layers in CNN extract local features in a sentence and merge local feature patterns, respectively. In addition, the multi-group norm constraint CNN (MGNC-CNN) architecture independently extracts features from the multiple embedding sets and generates a final feature vector by concatenating the extracted high-level features at the penultimate layer . MGNC-CNAccording to the transfer learning scheme, the parameters in MGNC-CNN were initialized by pre-training with context-free source corpora established by both the manual review and computational inference and then, fine-tuned by the context-specific target corpus which had been manually generated for the present study. The filters with the length of 21, 22, and 23 were applied for the linguistic representation (both n-gram-based and dependency-based embeddings) and the width was dependent on the embedding vector size, therefore, 200. Because the dimension of the filter for the biomedical knowledge representation (knowledge triplet-based embedding) was 3 × 10 which was congruent with the knowledge matrix, the convolution operation over the knowledge matrix was applied once without any stride. The numbers of filters were 100, 100, and 100 for the linguistic representation and 50 for the biomedical knowledge representation, respectively. We set the learning rate of 0.001, the drop-out probability of 0.5, ADAM optimizer, and early stopping  for both pre-training and fine-tuning. The size of the mini-batches was set by 50 and 5 for pre-training and fine-tuning, respectively.After predicting class labels of putative context-specific relations described in the sentences, we analyzed the frequency of extracted relations to estimate the significance. Based on the assumption that more frequently reported the relation is at the research articles, the more significant the relation can be considered, the rare relations reported less than the empirical threshold were discarded. The empirical threshold was determined as six times, the frequency of the relation which was the top 1% (0.01) in the frequency distribution of the entire extracted relations. The relations reported more than 14 times were grouped by the sequence of three CUIs of one contextual and two molecular entities. The conflict of relation classes in a group was resolved by voting, in other words, the majority of relation classes. In case of the tie, the relation classes were abstracted, for example, Increase and Decrease with the same direction result in Regulation whereas relation classes with contradictory directions result in Binding.The performance of MGNC-CNN model was evaluated in the micro-average f-score because the class distribution in the context-specific corpus was highly skewed. The context-free and context-specific corpora were respectively separated into training and test datasets 2:1) for 3-fold cross-validation. Figure  for 3-foWe applied the context-specific model to the preprocessed unlabeled sentences and inspected 177 randomly sampled sentences with predicted labels to estimate the end-to-end reliability of the proposed framework. 73 sentences were correctly predicted, while 104 sentences were assigned wrong labels with various circumstances. 67 out of 73 correctly predicted sentences were False sentences and the other 6 implied the specific relations. 30 out of 67 true negative sentences were describing valid molecular relations while mentioned context words were invalid. The model based on the co-occurrence assumption is likely to classify these False sentences as valid while COMMODAR was enabled to exclude. Furthermore, these correctly classified False sentence support that COMMODAR is not fully biased on the pre-trained model which extracts context-free molecular relations and is expected to classify these False sentences as valid. 80 False sentences out of 104 failures incorrectly labeled as specific relations, the 7 relational sentences were predicted to the wrong relation types or the opposite directions, and the rest 17 sentences were false negative. 80 False sentences include 13 with NER errors, 48 without any relation between molecular entities, 19 describing experimental designs or objectives rather than unraveled facts or results. Because of the false discovering behavior of the classification model extracting relations from False sentences, the conservative threshold, 0.01 was applied in the post-processing step.https://github.com/jae-hyun-lee/commodar.After large-scale extraction and post-processing, we yielded 9214 context-specific molecular relations  previously reported in the literature. The most frequent contextual concept was Neoplasm (C0027651) and the 2030 relations were extracted to be specific to Neoplasm. The example sentences for the end-to-end inspection and all the extracted relations are downloadable in COMMODAR focuses on the context type of the disease in the proposed research. However, it can be technically extended to deal with alternative context types such as medication or anatomy if only relevant NER tools and a small volume of the completely labeled sentence set are available. Thus, it can be utilized to build and maintain databases containing various context types for molecular relations. Moreover, molecular relations extracted by COMMODAR can resolve the protein in the isoform level thanks to the entity normalization according to UMLS which distinguishes protein isoforms. This fine resolution provides the extracted context-specific molecular relations with versatility.COMMODAR, however, has some limitations to be noted. Firstly, it requires the entity normalization step which is mostly unnecessary for relation extraction. Two molecule entities and one context entity should be normalized with UMLS CUI to utilize background knowledge from the knowledge graph, i.e. UMLS as well as linguistic information. Secondly, COMMODAR showed the false discovering behavior by producing 80 false positive out of 177 sentences and we proposed the frequency-based post-processing procedure. Nevertheless, it is partially attributed to the nature of the given task, i.e. highly skewed true/false balance and the conventional co-occurrence-based models may well produce a larger number of false positives.We have proposed COMMODAR, a literature mining framework for context-specific molecular relations using multimodal representations. COMMODAR can utilize multiple representations from multiple modalities so that comprehensive information from various resources can cooperate to analyze the unstructured text. The superiority of multimodal information in the relation extraction task was substantiated by the outperformance of the MGNC-CNN model using both linguistic and biomedical knowledge representations. The expansibility of COMMODAR enables the biomedical database curators to adopt various state-of-the-art and off-the-shelf embedding models appropriate to the characteristics of the data resources.Additional file 1. Table A1 Semantic types for molecular and context entities. Table A2 Rule-based decomposition of nested events. Table A3 Rule-based conversion from event trigger types to relation classes. Table A4 The statistics of corpora. A.1 Conversion from events to relations. A.2 Entity tagging feature (ETF). A.3 The distribution of the token distance between entities in the context-specific corpus. Figure A1 The distribution of the token distance between entities.


Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization. Human speech is a natural communication method in human–computer interaction (HCI) and human–robot interaction (HRI). Speech emotion recognition (SER), which is based on natural human language, is a key method used to recognize individual emotions in everyday speech. SER uses the acoustic features of a speech segment, not the lexical features having the semantic information of the segment . Hence, Prior to deploying SER models in real applications, the lack of SER databases tagged with emotion labels must be addressed, because they are not sufficient for training deep-SER models. Another challenge is the limited generality of the SER model, owing to the high variability of the acoustic signals of the emotional speech samples.Emotions have characteristics of high subjectivity and diversity, depending on the individual or culture. Therefore, it is time-consuming and expensive to build a large-scale emotional database annotated with reliable gold-standard emotion labels via human observation. Most SER datasets having gold-standard labels contain thousands of speech samples collected from a limited number of speakers in a specific environment ,5,6,7. TMany studies have effectively utilized limited emotion databases to improve the SER performance. In addition to the typical augmentation methods of speech samples ,12, therWe propose a multi-path and group-loss-based network (MPGLN) for SER, which supports supervised domain adaptation in multi-domain datasets acquired from multiple environments. The proposed MPGLN for SER (MPGLN SER) is based on an ensemble learning structure for multi-level embedding vector learning for speech segments. It includes a temporal embedding feature generator, transferred feature extractor, and prediction function network that classifies the emotion labels based on the generated and extracted feature vectors. The bidirectional long short-term memory (BLSTM)-based temporal feature generator network learns an embedding vector as a 74-D input of handcrafted low-level descriptions (LLD) of a speech segment. The transferred feature extractor creates feature vectors from the pre-trained VGG-like audio classification model (VGGish) , and theThe proposed MPGLN SER is evaluated over five multi-domain SER datasets: the benchmark English Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset , which wIn our evaluation, we use an SER model comprising a BLSTM-based temporal feature generator and the MPGLN predicting network, excluding transferred features, as our baseline model. We then verify the reliability of the baseline SER model using the IEMOCAP dataset. Comparing it with the performance of the baseline SER model, it is confirmed that the proposed MPGLN SER is effective in supporting supervised multi-domain adaptations and reinforcing generalizations  of the SThis paper is organized as follows. In Recent SER models based on deep-learning architectures ,28,29,30The input features for deep-learning-based SER models are generally extracted from the time or spectrum axis in units of speech segments or frames. There are various LLDs and high-level statistical functions of the LLD single features ,31,32,33A typical deep-learning model requires large-scale samples for training. Unfortunately, SER datasets annotated with emotion labels are scarce. Furthermore, collecting SER speech samples and tagging them with emotion labels is time-consuming and expensive. Thus, to overcome the limitations of volume and diversity of labeled speech samples for deep-learning SER models, studies have been performed using data augmentation ,40,41,42Park et al.  presenteActive-learning methods have been used to present greedy selection methods of speech samples to construct an initial SER model suitable for a target speaker based on limited samples ,43. AbdeDomain adaptation techniques are actively being studied in the field of visual classification ,45. MetrThe domain adaptation for SER models based on multi-domain datasets has the purpose of building an SER model that is not overfitted to a specific dataset and is generalized for unknown target-domain speech data. However, the SER model based on multi-domain datasets has a different applicability from the case that applies data augmentation by oversampling a single domain dataset. It does not guarantee the SER performance improvement, even if several multi-domain speech samples are used to train the SER model, because there is high domain discrepancy in the speech signal, which depends on the collection environments ,10,13,14Liang et al.  proposedk-th domain dataset, and datasets in each speech sample have multiple Y labels. The discrete emotion label is We propose an ensemble learning model to improve the performance of SER generalization in multi-domain datasets. The operational flow of the supervised multi-domain adaptation of the proposed MPGLN SER is shown in The source-domain dataset used for model training is domain ectively . The goal of the SER model is to learn the classifier function, sh model . In the prediction function, In this study, the speech segments of an utterance unit are embedded in the feature space through The feature generator, The attention layer focuses on relevant parts of the output sequence of the BLSTM by giving different weight scores and generates the high-level features  shows how classifier fication ,49.(3)LThe proposed MPGLN SER is trained to simultaneously minimize multiple losses, which are induced by the association of multi-dimensional emotion labels. The discrete emotion labels are intuitive for expressing the emotion, but it has difficulty in expressing complex emotions. The dimensional emotion labels are capable of normalized expressions of complex emotions. However, doing so, it is difficult to intuitively distinguish emotions at similar positions  in the arousal-valence axis . This stAs shown in The primary loss, We evaluated the proposed model using five multi-domain datasets contained in three real SER databases. For the evaluation of the MPGLN SER based on multi-cultural datasets, two KESD databases  constructed for this study, and the IEMOCAP are used. KESDy18 and KESDy19 comprise two domain datasets based on heterogeneous microphone devices. In the IEMOCAP dataset, data were collected from the scenarios for inducing the five target emotions , and annotators selected one of the six basic emotions   along wiThe KESDy18 comprises speech samples in which 30 voice actors uttered 20 sentences while expressing the four given emotions of “angry”, “happy”, “neutral”, and “sad”. The six external taggers evaluated the speech segments while listening to the recorded utterances as shown in The KESDy19 includes the speech samples of 40 voice-actors who speak Korean as their native language using collection scenarios similar to those of the IEMOCAP. KESDy19 consists of 20 sessions collected from speech and electrocardiogram signals produced during the dyadic acting of two voice actors, the process of acting was recorded. Each session consists of 10 plays having lengths of 4–10 min. Six plays were based on scenarios written to induce specific emotions, and the other four were improvised during the dyadic interactions. Each speech segment per speaker was tagged using one of seven categorical emotion labels, and the average value of the five-point scale of arousal and valence-level was annotated by 10 external taggers using the same tagging application as shown in The IEMOCAP is a widely used SER performance evaluation model organized into five sessions of multi-modal audio, visual, and textual data taken from interactive dyadic interactions performed by 10 voice actors. In each session, two voice actors emotionally performed improvisations or scripted scenarios. The speech segments of their utterance-levels were tailored to discrete emotion labels of “happy,” “sad,” “neutral,” “angry,” “surprise,” “frustration,” “excited,” “disgust,” or “fear” based on the majority opinions of three external human annotators. The IEMOCAP data were also tagged with labels of arousal and valence based on a five-point dimensional emotion scale ,51. The In In this study, we mapped the valence-level labels of the five-point scale to a three-point scale using the induced association between discrete and dimensional emotion labels, as shown in As shown in Speech samples of each class in the multi-domain datasets were trained in the SER model by the units of the speech segment, which consisted of the voiced part of the vocal-cord vibrations and unvoiced parts such as a silence section between voiced parts . This stWe present four performance metrics in consideration of the sample imbalance of each emotion class: weighted accuracy (WA), unweighted accuracy (UA), precision (PR), and F1 score. WA is the overall accuracy, calculated as the ratio of the total number of test data and the number of samples accurately predicted by the actual label. UA is calculated as the average of the recall values of four classes and is an important performance indicator in the evaluation of the SER model based on imbalanced datasets ,20,26.z-normalization [p-subjects-out (LpSO) validation technique, where This study applied lization  of the mFor the evaluation of IEMOCAP, we used a leave-two-subjects-out evaluation that applied speech data from two speakers participating in one session as the test data, which was the leave-one-session-out (LOSO) validation. KESDy18 was evaluated as a leave-six-subjects-out sample from the set of 30 speakers. The evaluation of KESDy19 was conducted as a leave-eight-subjects-out sample for four sessions of the 20 sessions played in pairs by 40 speakers. The training and test data separated for speaker-independent evaluation in each dataset were equally applied to the evaluation of a single domain, multi-domain, or domain generalization, as shown in In the evaluation of this study, a model based on the temporal embedding features and the learning loss, The baseline SPSL SER model uses the 74-D LLD integration per-frame of speech segment, which comprises 13-D MFCC and 40-D Mel-spectrogram (Mel-spec), along with 21-D time- and spectral-domain  LLDs such as zero-crossing rate, energy, spectral centroid, and spectral roll-off. We evaluated the performance of each combination of LLDs with our baseline SER model based on multiple SER datasets. A previous study by Zheng et al.  demonstrIn As shown in When compared with the harmonic-mean F1 score based on the KESDy18_PM dataset shown in t-stochastic neighbor embedding (t-SEN). The 64-D embedding vectors were generated in the FC layer just prior to the MPSL and MPGL softmax activations of the evaluation in We determined that it is essential to improve the generalization of the SER model for deployment to real applications. This paper proposed the MPGLN for SER in support of supervised multi-domain adaptation and generalization based on multi-domain datasets. The proposed MPGLN SER includes a temporal feature generator for the BLSTM network using the input of handcrafted LLD features of a speech sample. Additionally, we leveraged the transferred feature extractor from the pre-trained VGGish model for the MPGLN. The proposed MPGLN SER learned simultaneous multiple losses induced by associations between discrete emotion and dimension labels.The proposed MPGLN SER was evaluated using five real SER datasets of various speaker domains, language cultures, collecting devices, and procedural environments. This included KESDy18 and KESDy19 databases. KESDy18 comprised speech samples delivered by voice actors who uttered Korean short sentences by expressing specific discrete emotions. The KESDy18 database consisted of KESDy18_PM and KESDy18_EM datasets from heterogeneous devices and environments with different device locations. The KESDy19 database comprised KESDy19_EM and KESDy19_PM, which contained the collected speech sample voices acted using a similar procedure as that of the IEMOCAP and that of the simulated dataset based on the cell-phone’s built-in microphone, respectively.This study assumed that the SER model was trained only with the BLSTM-based temporal embedding feature generator included with MPGLN without transferred feature as the baseline SER model. We verified the performance reliability of the baseline SER model using the IEMOCAP. The BLSTM-baseline SER model showed competitive UA results of 59% when classifying the four categorical emotion labels. The multi-domain adaptation and domain generalization evaluation of the proposed MPGLN SER was performed using the English-speaking IEMOCAP and the Korean KESDy18 and KESDy19 datasets by comparing the performances of the baseline model according to various evaluation environments.The proposed MPGLN SER model trained on multiple losses showed an F1 performance improvement of up to 3.7% over the baseline model when classifying four emotion labels in a single domain dataset. The performance evaluation of the MPGLN SER for supervised multi-domain adaptation, which trained and tested on the SER model using the aggregated speech samples of the multi-domain datasets, also showed an improvement of up to 3.7% over the baseline F1 score. From the evaluation of the multi-domain generalization of the proposed MPGLN SER, the F1 score enjoyed an improvement of 3.5% over the baseline SER when using samples from other language cultures not used for training. From these results, we found that our MPGLN SER, which supports supervised multi-domain adaptations, is also effective in reinforcing the generalization of the SER model based on multi-domain datasets.For future works, we plan to derive the differences in acoustic features of emotional expressions based on multi-cultural SER datasets and study the learning method for the deep-learning-based SER model considering the domain discrepancy. Furthermore, we will continue enhancing our model’s generalizability through evaluations of speech data in the wild by deploying the proposed MPGLN SER to real applications.


Sickle cell disease (SCD) disproportionately affects Black or African American persons in the United States and can cause multisystem organ damage and reduced lifespan. Among 178 persons with SCD in the United States who were reported to an SCD–coronavirus disease case registry, 122 (69%) were hospitalized and 13 (7%) died. Sickle cell disease (SCD), an inherited hemoglobinopathy that most commonly affects persons of African ancestry, is estimated to affect 1 in 365 Black persons in the United States  was distributed to healthcare providers caring for patients with SCD by medical professional and patient advocacy networks and was made available on the Centers for Disease Control and Prevention website. Providers were asked to report all confirmed COVID-19 cases among patients with SCD to this registry; they were specifically asked to report only confirmed COVID-19 cases and to report cases after resolution of acute illness or death. Persons who had the sickle cell trait were not included in this registry, nor were persons with SCD who had suspected but not confirmed COVID-19. Providers were asked to report if the patient died of COVID-19 or complications of COVID-19. All data were deidentified without protected health information.The Medical College of Wisconsin established the SECURE-SCD Registry to collect data on COVID-19 cases occurring globally in persons living with SCD. A link to the registry  patients died . Their mOur findings suggest that persons who have SCD and become infected with SARS-CoV-2 have a high risk for a severe disease course and a high case-fatality rate. Among confirmed COVID-19 case-patients reported to the registry, the 69% hospitalization rate, 11% ICU admission rate, and 7% mortality rate are alarming, given that the mean patient age was <40 years. Comparison to a previous report of COVID-19 in the general US population indicates that hospitalization, ICU admission, and case-fatality rates for persons with SCD could be much higher than persons of similar ages in the US population-at-large (These data should be considered in the context that these cases may not be representative of all COVID-19 cases among persons with SCD. There may be bias toward more severe cases in this registry; however, providers were asked to report all COVID-19 cases among their SCD patients. Also, whereas providers were specifically instructed to report only confirmed COVID-19 cases, further guidance was not provided on case confirmation, nor were laboratory testing results included in the registry; thus, we cannot rule out the possibility that a small number of suspected cases were erroneously reported.Our findings are consistent with expectations based on SCD pathophysiology. SCD can cause multisystem organ damage, life-long disability, and reduced lifespan. Nonetheless, in this case series, COVID-19 deaths occurred in persons who had severe and mild-to-moderate SCD genotypes. Also, deaths occurred in COVID-19 case-patients classified as having mild-to-moderate disease severity. We did not have data to assess whether this finding was caused in part by an impact of SARS-CoV-2 exacerbating preexisting cardiac or SCD concurrent conditions; further study is needed.,Persons with SCD face socioeconomic and healthcare access disparities that might compound their already high risk for severe COVID-19 because of their underlying disease and concurrent conditions. SCD complications might negatively impact educational achievement and employment (Our findings underscore the need to consider the unique circumstances faced by high-risk subgroups. SCD is one of many possible explanations for higher rates of illness and mortality from COVID-19 among Black populations in the United States. As with all high-risk groups, staying home, social distancing, and hand hygiene are necessary for persons with SCD (


The clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated. Despite the high cure rates for DLBCL, outcomes remain varied in part due to heterogeneity in the disease at the clinical, pathological, and molecular levels5. The clinical risk stratification of DLBCL patients is currently assessed by the International Prognostic Index (IPI) scoring system, which was developed more than two decades ago and utilizes five clinical factors: age, performance status, stage, number of extranodal sites, and serum lactate dehydrogenase (LDH)6. However, IPI is sub-optimal in its identification of high-risk DLBCL patients as it does not differentiate low-risk and high-risk stratification groups, especially when considering response to first-line therapy7. To develop individualized treatment strategies, increasing efforts have been directed toward identifying prognostic factors for accurate risk stratification of patients with DLBCL9. Strategies involving mutation analyses and gene-expression profiling have been employed to subtype the malignant cells in the tumor. In particular, activated B-cell-like DLBCL and germinal center B-cell-like DLBCL, and subgroup clusters defined by tumor sequencing have been shown to carry prognostic significance10.Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma in the United States, accounting for about one-third of adult cases of non-Hodgkin’s lymphoma13. In hematological cancers, malignant cells may participate as part of the dysregulated immune milieu through altered secretion of those cytokines that normally keep proliferation in check. Understanding the types and roles of immune cells in the TME is therefore key to develop strategies aimed at targeting the tumor and improving patient outcomes. The immune response to cancer is characterized by numerous tumor-infiltrating immune cells which interact with each other. Novel therapies like anti-CD19 chimeric antigen receptor (CAR) T-cells and NK  cells have taken advantage of this immune response to provide new treatment options for patients with relapsed DLBCL16. Characterization of the TME in previously untreated DLBCL could offer important insights into the complex relationship between certain immune cell types, paving the way for a more personalized approach to treatment planning in DLBCL.Emerging evidence highlights the important role of tumor microenvironment (TME) in cancer initiation, metastasis, progression, and response to therapeutic agents17. CIBERSORT is a deconvolution approach that has been shown to outperform other existing methods in resolving closely related cell subsets, unknown mixture content, and noise18. In this study, we propose a comprehensive statistical framework designed to identify the best-performing prognostic model for the personalized risk prediction of DLBCL patients using genetic and clinical features from a large RNA-seq dataset. CIBERSORT was applied to profile the diversity and landscape of tumor-infiltrating immune cells in DLBCL and evaluate the relationship between immune cell populations and prognostic outcomes.Recently developed computational methods to estimate the relative proportions of immune cell types using gene-expression data profiled from tissues such as bulk tumors can aid in this effort2 was used. RNA-seq data was collected from the fresh-frozen paraffin-embedded tumor block collected prior to initiation on a rituximab-containing standard regimen. Aligned read counts were subsequently summarized and quantified using featureCounts program19. The built-in human gene annotation of featureCounts was used as a reference genome assembly. Exons were grouped into genes and the read summarization was performed at the gene level. The quantification was not strand specific and paired ends were excluded from the quantification. The default method (union) was kept, which selects the gene with the strongest overlap if two genes are associated with a read. Gene IDs were annotated to gene symbols using MyGene20. Gene IDs without an associated symbol were removed from further analysis. Duplicate gene symbol entries were also filtered, favoring to keep the entry with the greatest read strength.The data from 775 preprocessed and aligned tumor RNA-seq transcriptomes published by Reddy et al.2 normalized21. Differential expression analysis was performed with edgeR. To determine deferentially expressed genes, genes were identified according to a p-value cut-off of 5% and then applied a fold-change cut-off of 50% and then were selected. To interpret the differential expression results in biological context, gene ontology (GO) enrichment analysis using the goana function in edgeR with focus on the ontology of biological process was conducted. To identify the functions underlying these genes, pathway enrichment analysis was performed using the GOenrichmentAnalysis (GEA) method of the Weighted Correlation Network Analysis package (WGCNA)22. The WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) tool for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the gene signatures23 was used. The pathways with p-values < 0.05 (after FDR correction) were regarded as significantly enriched.To reduce the potentially adverse effects of noise in statistical analyses, 35 samples with expression of fewer than 12,000 genes was omitted; 22 samples with unknown survival and censoring was also omitted. The remaining 718 patient cases were designated as the core set for the statistical analyses. Gene-expression measurements were normalized using the Trimmed Mean of M-values normalization method of edgeR package and the data was log24, quantile normalization (QN) on RNA-seq data25 was disabled. The LM22 reference matrix was adapted to exclude B-cells from further study to represent the TME instead of the total sample/biopsy. The tumor immune infiltrate with and without B-cells, which we refer to as tumor and TME, respectively, was measured.In order to determine immune cell types and origin from the tumor transcriptomes, CIBERSORT analysis using the LM22 default reference was applied. Based on prior publications>60 years), sex , IPI (0–1 vs. 2 or more), the cell of origin classification (ABC vs. GCB), response to treatment, and survival outcome at 2 years from diagnosis was compared. The changes in the proportion of immune cell subtypes between groups were assessed by log2 fold-change, where the poorer prognostic factor was selected as the reference. In addition to these clinical risk groups, the changes in the immune infiltration across risk groups with distinct survival outcomes  at 2 and 5 years from diagnosis was also evaluated, where the fold-change was calculated as log2 for each cell subtype.To assess the association between immune infiltration and prognosis, the relative proportions of the immune cell types within subgroups of DLBCL patients by various clinical traits and outcomes including age . To overcome the inflexibility of the first method for cluster detection, the Dynamic Tree Cut method was implemented, which is a top-down approach that detects clusters through an iterative process of cluster decomposition and combination on a dendrogram27. The clustering methods are described in detail in the Supplementary Material. When defining the final set of gene-expression signatures, the gene signatures that were significant predictors of survival only in the training set and not in the testing set were excluded. The signatures of poor and good prognosis genes are referred to as “unfavorable” and “favorable”.Hierarchical clustering procedures on the training test to detect gene signatures was implemented and evaluated the association between the gene signatures and survival in the training and testing sets. For hierarchical clustering, Pearson correlation to construct dissimilarity matrix and average linkage method to define the distance between clusters was used. Two different methods to detect gene clusters— (1) clusters are defined by cutting off branches using a constant cut-off value of dissimilarity  and (2) clusters are defined by Dynamic Tree Cut method28. The Lasso method shrinks the regression coefficients toward zero by penalizing the size of the coefficients with L1 penalty term29. If the log partial likelihoodTo determine the best set of gene signatures for prognostic prediction, the Lasso method was implemented to identify the important gene signatures on the training set and developed multivariable Cox models using these signaturesl(β), the penalized log partial likelihood becomes p is the number of predictors28 and λ is the tuning parameter determining the amount of shrinkage. The Lasso variable was the chosen selection method as it helps to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, and therefore, reduces over-fitting. To choose the tuning parameter λ, 10-fold cross-validation was performed on the training set. The optimal tuning parameter was defined as the value within one standard deviation of the minimum cross-validated partial likelihood deviance to obtain the most parsimonious model. The gene signatures with non-zero coefficients at the optimal cross-validated log-likelihood were referred as survival signatures. The set of survival signatures found using the predefined cut-off clustering method is referred to as signature set 1 and the set of survival signatures detected by the Dynamic Cut Tree method as signature set 2.is denoted as 30. Log-rank tests were used to determine whether there was a significant difference between the Kaplan-Meier survival curves for the resulting risk groups.Multivariable models of survival were developed using signature sets 1 and 2 on the training set and validated the prognostic ability of the multivariable models by calculating survival-predictor scores for the validation set cases using the coefficients of the Cox models estimated on the training set. Patients were stratified in the validation set based on their risk scores into high- and low-risk groups according to the optimal cut-off for the survival-predictor score determined by the survminer package31. Since the majority of adverse DLBCL events occur in the first 2 years after diagnosis32, the ability to accurately identify high-risk individuals could improve the selection of appropriate treatment for these patients. Therefore, the AUC of the time-dependent ROC was evaluated at time points of 2, 5, and 10 years from diagnosis. To assess the independence of the risk groups defined by the IPI and the outcome predicated on gene-expression profiles, multivariable Cox regression analysis was conducted.In addition to log-rank tests, the time-dependent area (AUC) under the receiver operator curve (ROC) was used to evaluate the prognostic accuracy of survival predictorsTo investigate whether certain tumor-infiltrating immune cell sub-populations significantly impact prognosis, the risk score was correlated derived from our gene-expression prediction model with the relative proportions of immune cell types enumerated by the CIBERSORT algorithm. The prognostic implications of this analysis were further confirmed by assessing the correlation between cell proportions and gene signatures.>30% of the entire tumor sample. Neutrophils, CD4 + naive T-cells, CD8 + T-cells, CD4 + memory resting cells, and M0 macrophages were notably visible in the TME.Figure 2 where signatures, in the stromal and immune response groups, such as the regulatory T-cells were associated with improved survival outcomes. Follicular T-cells were not included in the analysis performed by Reddy et al.Heatmaps in Fig. p < 0.1 using a Wald test. The genes associated with good prognosis (1139 genes) and poor prognosis (850 genes) in the training set were clustered separately via hierarchical clustering algorithms. The predefined cut-off clustering method identified six gene signatures among the genes predicting good prognosis and six signatures among the genes predicting poor prognosis . The AUC of the 2-, 5-, and 10-year ROC curve achieved 0.69, 0.68, and 0.65 for signature set 1, and 0.78, 0.78, and 0.80 for signature set 2, respectively. The AUC of the ROC curve in the entire set at time points of 2, 5, and 10 years were 0.68, 0.68, and 0.68 for signature set 1 and 0.75, 0.75, and 0.78 for signature set 2, respectively.Based on the Lasso feature selection method, four signatures comprised signature set 1  and signature set 2 . Multivariable Cox models were developed with signature sets 1 and 2 on the training set. The patients in the training set were recruited into the high- and low-risk groups according to the optimal cut-off for survival-predictor scores . As depicted in Fig. p < 0.02). The survival-predictor scores from these models were highly predictive of survival in validation sets (p < 0.001). Moreover, the survival-predictor scores from signature set 2 model resulted in a larger log-likelihood in the validation set, reflecting a higher degree of association with survival (p < 0.001). Therefore, signature set 2 was used for further predictive modeling analyses. Each unit increase in the gene expression-based predictor score was associated with an increase in the relative risk of death by a factor of 2.04 (95% CI: 1.49−2.78) in the validation set and by a factor of 2.45 (95% CI: 2.10−2.95) in the entire set.Kaplan-Meier plots of overall survival Fig.  showed dThe performance of the gene expression-based score was examined in the known clinical risk groups defined by the gene-expression markers . The gene expression-based method was able to distinguish patients with significantly distinct outcomes across subsets, demonstrating the survival-predictor score’s greater prognostic power as compared with that derived from the use of clinical subgroups of DLBCL Fig. .Fig. 4GeThere were 593 patients with IPI scores available in the core set: 414 patients in the training and 179 patients in the validation sets. In the multivariable Cox models that combined both the IPI scores and the gene expression-based scores from signature set 2, the gene expression-based score was an independent predictor for overall survival in the validation sets (Table p < 0.001). Based on these findings, the IPI was included together with the gene expression-based predictor score in our final model.To evaluate the impact of integrating the gene expression-based predictor score and the IPI score on the prognostic accuracy, time-dependent ROC analysis was conducted using the survival-predictor scores based on the multivariable model developed on the training set with these two predictors. The AUC of the ROC curve on the entire set at time points of 2, 5, and 10 years were 0.79, 0.78, and 0.83, respectively, indicating that the combination of the gene expression-based predictor score with the IPI score improved discrimination on the entire set over the gene expression-based predictor score alone. Moreover, the survival-predictor scores from the model with the gene expression-based predictor score combined with the IPI score resulted in a larger log-likelihood in the validation set, reflecting a higher degree of association with the survival .Table In this study, supervised and unsupervised machine learning methods were implemented to identify gene-expression signatures that were associated with survival and subsequently used to construct a multivariable regression model for predicting the overall survival of DLBCL patients. The gene expression-based predictor scores and the IPI scores were both independent prognostic indicators, and the combination of the two scores improved the identification of high-risk DLBCL patients.33. The negative correlation of this signature with CD4+ naive T-cells, follicular helper T-cells, and activated NK cells may imply the need for successful therapeutic interventions to activate these cell subtypes.In the risk prediction model, Favorable signature 2 and Unfavorable signature 1 were independent predictors for overall survival  and found that the stacking model was superior when non-IPI clinical factors were included in the algorithm. When considering real-life application of an algorithm, gaining insight on the TME and estimating survival outcomes is more beneficial, which our algorithm provides.Our algorithm incorporates IPI scores and is supported by CIBERSORT, something which has not been done by other published models. Ciavarella et al.In conclusion, we developed and validated a robust survival-prediction model which may facilitate the prognostic evaluation and risk stratification of patients with DLBCL. Our analysis of immune cell subsets in DLBCL has revealed important associations with the clinical outcomes. Coupling the changes noted in immune cell content of the TME with the reliable risk predictions can aid personalized decision making regarding individual disease course and treatment outcomes.Supplementary Material


We report a 35-year-old female nurse who possibly received the SARS-CoV-2 virus during the blood sampling of a 35-year-old male patient initially suspected as a dengue infection. The patient had mild thrombocytopenia and positive dengue IgG and IgM whereas the clinicians were not aware of the possibility of false-positive dengue serology revealed in the published case report from Singapore. The nurse put on a pair of gloves but did not wear a mask during the only encounter with this patient. This nosocomial transmission raised a safety concern among healthcare professionals in an area with a relatively low Covid-19 prevalence, especially when the clinical and laboratory characteristics could be confused with other viral infections. Healthcare professionals are at risk of getting an infection while they are providing care to the patients. More than two thousand Chinese healthcare workers had been infected with coronavirus disease 2019 (Covid-19) because of the lack of awareness and incorrect use of personal protective equipment (PPE) [On January 25, a 35-year-old salesman at a local shop approximately 15 kilometers from Suvarnabhumi airport developed fever, myalgia, mild productive cough, nausea, and vomiting. He usually wears a mask while selling shoes. He had no history of travel to China, but he had some encounters with Chinese tourists at the shop. On January 28, his symptoms were not improved so he decided to get medical care at a local private hospital covered by his Social Security Scheme. The physician prescribed some medications for his symptoms and asked him to come back for follow-up on the next day. On January 29, he went back for the follow-up visit but the symptoms were not improved.On January 30, his symptoms became worse, so he decided to go to another private hospital. His body temperature was 37.8 degrees Celsius, pulse rate 88 per minute, blood pressure 114/64 millimeters of mercury, oxygen saturation 99%. Nasopharyngeal swab tested negative for influenza A, influenza B, and respiratory syncytial virus. Dengue infection was suspected, and he was admitted. At the hospital ward, after being informed that dengue infection was suspected, a 35-year-old nurse put on only a pair of gloves but did not wear a mask. The initial blood tests revealed mild thrombocytopenia and positive dengue IgG and IgM. The chest radiograph revealed reticular infiltration. At that time, the clinicians were not aware of the possibility of false-positive dengue serology revealed in a case report from Singapore [The clinical conditions of the patient had been worse during the first three days of admission. On February 2, he reported shortness of breath; the chest radiograph revealed progressive infiltration. As Covid-19 was suspected, throat and nasopharyngeal swabs were collected and tested positive for SARS-CoV-2 on real-time reverse-transcription – polymerase-chain-reaction (RT–PCR).The nurse who took his blood sample developed a fever on February 4. She had no underlying conditions except the Hemoglobin E trait. She had lived alone in a dormitory and had not been outside for non-work-related business. None of her co-workers or other patients who visited the hospital during the time were reported to have Covid-19 infection at the time. Identified as a close contact of the index case during the blood sample collection, the nurse was isolated at the private hospital. Her throat and nasopharyngeal swabs collected on February 6 tested positive for SARS-CoV-2 on RT–PCR so she was admitted on February 8. She was considered the first Thai healthcare personnel who got Covid-19 infection. She was prescribed with oral lopinavir and ritonavir as well as intravenous ceftriaxone. She reported shortness of breath on February 13; the chest radiograph revealed pneumonia of left lower lung so the medications were changed to darunavir, ritonavir, meropenem, oral chloroquine, and zinc. As her pneumonia rapidly worsened, she was referred to Bamrasnaradura Infectious Diseases Institute (BIDI) on February 15. Oral favipiravir was added and her clinical conditions were improved. The nurse was discharged after her RT–PCR became negative and isolated at home for another 14 days before resuming her work at the private hospital.All individuals who were in close contact with the nurse and male patients were interviewed and underwent RT–PCR tests but none were positive for Covid-19. Twenty-five hospital staff and 21 patients and relatives who were in close contact with the nurse tested negative for SAR-CoV-2 on RT–PCR. Two of the 25 hospital staff had minor upper respiratory tract symptoms and were asked to rest at home whereas the other 23 were allowed to resume their duty during the two-day waiting period.While standard infection control measure is essential, the provisional diagnosis of dengue infection reduced the awareness of Covid-19 and, therefore, got the nurse infected. Had the patient been suspected only Covid-19, a more appropriate personal protective equipment (PPE) would have been used. The Ministry of Public Health mandated PPE availability in all hospitals and the PPE was readily available at the private hospital during the incident so compliance was the key problem. The blood drawing at the hospital ward usually takes 5–10 minutes and was the only encounter between the nurse and the patient. As the nurse did not wear a mask, the droplets were the potential route of transmission.This nosocomial transmission has raised a safety concern among healthcare professionals, especially when the clinical and laboratory characteristics of Covid-19 are non-specific and could be confused with other viral infections.


Physical fitness is a powerful indicator of health. Sleep condition plays an essential role in maintaining quality of life and is an important marker that predicts physical fitness. This study aimed to determine the relationship between sleep conditions  and multiple physical fitness indicators (body mass index (BMI), flexibility, abdominal muscle strength and endurance, cardiopulmonary endurance) in a well-characterized population of Taiwanese adults aged 23 to 65. The applied data were obtained from the National Physical Fitness Examination Survey 2014 conducted in Taiwan. We assessed the association of the sleep conditions with physical fitness performances in Taiwanese adults by using the multivariate adaptive regression spline (MARS) method with a total of 69,559 samples. The results show that sleep duration, sleep quality, and bedtime were statistically significant influence factors on physical fitness performances with different degrees. Gender was an important factor that affects the effects of daily sleep conditions on performances of physical fitness. Sleep duration was the most important factor as it was simultaneously correlated with BMI, sit-ups, and sit-and-reach indicators in both genders. Bedtime and sleep quality were only associated with sit-ups performance in both genders. Sleep condition is an important predictor of health status. Many studies have explored the relationship between sleep conditions and health responses. For example, research has determined that short and long sleep durations were significantly associated with poor self-rated health and lower quality of life . Sleep qPhysical fitness is a powerful indicator of health and should be a major goal in current and future public health promotion policies . It compMany studies have explored the relationship between lifestyle and physical fitness, as it provides useful information for designing effective health improvement programs. For example, Liao et al. (2016) showed that sedentary behavior and daily traveling by private motorized vehicle was associated with poor performance in abdominal muscular strength and endurance . CabanasThe accurate quantification of physical fitness is essential to evaluate health outcomes and the effectiveness of intervention programs . The SpoThe NPFES is a cross-sectional assessment conducted by MOE-SA to investigate physical fitness performance. Participants were recruited using convenience sampling at 35 test stations in 22 cities and counties in Taiwan. The physical fitness outcomes  were collected and a standardized face-to-face interview was conducted using a structured questionnaire which contained sleep condition variables. The questionnaire design and survey process was uniformly set and implemented by MOE-SA. The survey was approved by the MOE-SA and written informed consent was obtained from each participant. Both the investigators and the participants understood the investigation process prior to the survey. The computerized data set of the NPFES released by the Sports and Health Information Application Research Center of the MOE-SA for public research purposes contained information on the demographic characteristics, lifestyle behavior, and physical fitness measurements of each participant. These data comprised de-identified secondary data, which were released to the public for research purposes [All physical fitness tests were measured by trained examiners. A standardized training instruction manual, which included detailed procedures for measuring various physical fitness indicators, was provided to trained examiners who conducted the fitness tests. BMI was calculated as body weight divided by height squared. Body height and weight were measured using wall-mounted tape measures and electronic scales. The measurement unit of body height is cm and that of body weight is kg. The abdominal muscular strength and endurance was measured using a timed bent-leg sit-up test. The unit of sit-ups is frequency/min. Flexibility was measured using a modified sit-and-reach test, which was considered a useful field test to evaluate hamstring and low-back flexibility. The measurement unit of sit-and-reach test is cm. The cardiorespiratory endurance was measured using an adapted 3-min step test from the Harvard Step Test, which involved repeatedly stepping on and off a 35-cm step for 3 min. The cardiorespiratory endurance index (CEI) was used as an indicator of cardiorespiratory fitness performance and calculated using the post exercise recovery heartbeat counts in intervals of 1 to 1.5 min, 2 to 2.5 min, and 3 to 3.5min.In addition, after the physical fitness test, a standardized questionnaire of NPFES for demographic and behavioral items including questions about sleep conditions was provided to the participants by trained interviewers, and each participant answered the questionnaire voluntarily. The question about sleep duration included in the questionnaire was the self-assessed average daily sleep duration. Similarly, the question about the bedtime in the questionnaire was the average daily bedtime that the subjects were also asked to self-assess. The question about sleep quality used a 4-point Likert scale to query self-assessed sleep quality. The principle of the score was the number of days when the respondent found it difficult to fall asleep in a week: answer 1 means no or very few (less than 1 day); 2 means sometimes (1–2 days); 3 represents often (3–4 days); 4 represents always (5–7 days).A total of 69,559 Taiwanese adults aged 23 to 65 years were recruited to participate in the 2014 National Physical Fitness Survey (NPFES 2014). NPFES is conducted once a year, and the content of the questionnaire is slightly different each year. We selected the data of NPFES 2014 for analysis as it contained the most sleep condition variables  and physical fitness variables . After the data cleaning process, through deleting invalid questionnaires, missing values and outlier interpolation, we obtained 62,094 valid data samples.We used the ‘earth’ package of version 5.1.1 proposed by  in R sofp-value of the correlation coefficient estimate for significance was set to 0.05.For correlation analysis, the ‘psych’ package of version 1.9.12.31 proposed by  in R sofThe statistical description results of the used data are shown in p-value (<0.001). There was an approximately low negative correlation between sleep duration and bedtime with a correlation coefficient of −0.26, also accompanied by a significant p-value (<0.001). Almost negligible correlations existed between the variables, except for age and bedtime. Whether or not there were nonlinear correlations between these variables we would need to discuss further, using the MARS method.The results of spearman correlation analysis are shown in t-test results indicate that there were gender differences in the physical fitness indicators . Although the difference between gender groups was also significant for each sleep condition variable, the difference was minimal.The The MARS models were constructed by assuming the sleep conditions as independent variables and physical fitness indicators as dependent variables. As the gender variables had stronger influence on the four physical fitness indicators see , we straIn The sit-and-reach performances by gender are shown in In short, from p ≤ 0.01).The detailed statistical verification results of the MARS model for the four physical fitness indicators, including BMI, sit-ups, sit-and-reach and CEI, are shown in This study successfully explored the effects of daily sleep conditions on the performances of the four core physical fitness indicators among Taiwanese adults using an effective MARS regression method. Sleep duration, sleep quality and bedtime were statistically significant influence factors on physical fitness performances with different levels. Gender and age were the important predictor variables for all four physical fitness indicators. As existing studies have only explored the relationship between sitting time, lifestyle habits, sedentary behavior and physical fitness performance ,24,25, oAs gender may have different body mechanism-corresponding modes on behavioral habits and physical fitness ,38,39,40Within the three sleep conditions, we found that sleep duration is the most important factor, as it associated with three physical fitness indictors including BMI, sit-ups and sit-and-reach. Since the MARS model evaluated the effects of independent variables on dependent variables by using estimated important knot position and segment estimation coefficients of independent variables, we also found that 7-h sleep duration was an important estimated knot for influencing the BMI of both genders, sit-ups of both genders, and sit-and-reach of males based on the MARS regression models of this study. Sleep duration less than or more than 7 h presented different degrees of influence on the increase in the BMI indicator and the decrease in sit-ups and sit-and-reach performances. As many of existing studies pointed out that individuals who slept for ≤ 6−8 hours and ≥ 9 hours were associated with an increased risk of diseases and decreased health responses ,42,43,44This study focused on evaluating the relationships between the three sleep conditions on the four core physical fitness indicators since we aimed at exploring the association between lifestyle and physical fitness indictors. There are many studies that have concentrated on the effects of physical fitness indicators on clinical issues, such as obesity, diabetes, hypertension, heart disease and other chronic diseases ,46,47. FThe research design of this study is similar to that of some of the existing studies that used regression models to evaluate the influence of various lifestyle habits on physical fitness using cross-sectional data through regression models ,55. TherThis study focused on assessing the relationships between the three sleep conditions including sleep quality, sleep duration and bedtime on the four core physical fitness indictors involving BMI, sit-up, sit-and-reach based on NPFES data and the MARS method. By considering gender division, the results reveal that all four daily sleep conditions are correlated with all three physical fitness indicators to different degrees. This study also suggests that 7-h sleep duration was a potential reference knot point of sleep duration that may influence performances of the core physical fitness indicators among Taiwanese adults.