Computational and structural biotechnology journal最新文献

{"title":"Intraoperative Trajectory Realignment Algorithm for Minimizing Screw Misplacement in Cervical Spinal Fusion.","authors":"Pasit Komonsuwan, Jackrit Suthakorn, Shen Treratanakulchai, Sorayouth Chumnanvej","doi":"10.34133/csbj.0222","DOIUrl":"10.34133/csbj.0222","url":null,"abstract":"<p><p>Spinal fusion is a surgical procedure designed to stabilize the spine by connecting vertebrae with screws and rods, where placement accuracy is critical, as even small deviations can cause severe neurovascular complications. The accuracy of traditional preoperative image-based navigation is limited by intraoperative alignment shifts, particularly in the highly mobile cervical spine. This study presents an intraoperative trajectory realignment algorithm that reconciles preoperative computed tomography data with simulated and phantom-derived intraoperative 2-dimensional (2D) projections by integrating forward kinematics, digitally reconstructed radiographs, and optimization-based search strategies. The algorithm was evaluated through computational simulations and physical experiments using a modular 3D-printed cervical spine phantom, with accuracy evaluated through clinical grading and assessment metrics, including entry/end point displacement, angular deviation, and minimum inter-trajectory distance. Results demonstrated high accuracy, with all screws graded as perfectly placed and deviations consistently below clinically relevant thresholds. While these findings confirm the feasibility of this approach under controlled experimental conditions, future validation using cadaveric models and real intraoperative fluoroscopic imaging remains necessary prior to clinical integration.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 2","pages":"0222"},"PeriodicalIF":4.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Heterologous Production of Bagworm Silk-Inspired Proteins in <i>Aspergillus oryzae</i>.","authors":"Adiphol Dilokpimol, Ulrike Abendroth, Mari Nyyssönen, Anna S Borisova, Anni Seisto, Jenni Muli, Dominik Mojzita, Fiona Småros, Hans Mattila, Antti P Aalto, Mari Valkonen, Pezhman Mohammadi","doi":"10.34133/csbj.0233","DOIUrl":"https://doi.org/10.34133/csbj.0233","url":null,"abstract":"<p><p>Silk-inspired proteins are attractive biomacromolecules owing to their structural diversity, sustainability, and tunable physicochemical properties. Among natural silks, the bagworm silk produced by <i>Eumeta variegata</i> has attracted increasing attention because of its unique sequence architecture and remarkable reported mechanical performance. However, the recombinant production of bagworm silk-inspired proteins remains largely unexplored. In this study, we designed and heterologously expressed 2 bagworm silk-inspired protein variants in <i>Aspergillus oryzae</i> using a synthetic expression system. The variants comprised a representative bagworm silk with an alanine- and glycine-rich repeat motif flanked by carbohydrate-binding modules at the N- and C-termini. Expression screening in engineered <i>A. oryzae</i> strains demonstrated stable accumulation of C-tag-reactive species corresponding to both variants in a protease-deficient host, whereas comparable accumulation was not detected in a carbon-catabolite-repressor-deficient strain under the tested conditions. The selected variant was further produced in a 2-l bioreactor, demonstrating the feasibility of scale-up and establishing <i>A. oryzae</i> as a promising platform for bagworm silk-inspired protein production. The recovered H-eBGW-containing protein fractions were readily processed into thin films and exhibited temperature-dependent changes in mechanical properties and surface wettability, demonstrating the processability and curing-responsive behavior of the recovered material. Collectively, our results demonstrate the feasibility of producing structurally complex bagworm silk-inspired proteins in filamentous fungi and provide insights into the design, expression, and processing of recombinant silk-inspired proteins for future bio-based applications.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0233"},"PeriodicalIF":4.8,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Boolean Seven-Layer Platelet Decision Model Models Consistently the Logic and Semiquantitatively the Dynamics of Platelet Activation and Inhibition.","authors":"Özge Osmanoglu, Johannes Balkenhol, Juan Prada, Martin Kaltdorf, Maral Afshar, Sebastian Mammen, Katrin G Heinze, Stepan Gambaryan, Harald Schulze, Thomas Dandekar","doi":"10.34133/csbj.0228","DOIUrl":"https://doi.org/10.34133/csbj.0228","url":null,"abstract":"<p><p>A platelet decision model integrates available experimental data to consistently model the Boolean logic in line with all available pathophysiological experimental platelet data. Due to insufficient kinetic data on involved protein networks, we semiquantitatively describe the dynamics of platelet activation and inhibition, i.e., the order of events and the relative strength of receptor activation or protein phosphorylation. The Boolean network model (76 nodes, 115 interactions) incorporates 7 layers of platelet signaling and regulatory feedback loops: (a) platelet activation stimuli resulting from vascular injury, (b) the platelet receptor layer (GPVI, GPIb-IX-V, PAR1/4, P2Y₁/P2Y₁₂, P2X1, TP, 5-HT2A, and IP) that receives signals, (c) central kinases (Src, Fyn, Lyn, PKC, PKA, and PKG) that converge diverse signals, (d) the translation of signaling into changes in platelet ion homeostasis, (e) alterations in platelet shape/adhesion, (f) the activation of other platelets, and (g) the formation of a platelet plug. Dynamic simulations based on curated omics, genetics, and functional data from experiments demonstrate network robustness by multiple and redundant positive feedback mechanisms. These allow rapid and irreversible aggregation and make the platelets resilient to even genetic knockouts (e.g., Trpm7). Signaling in chronic inflammatory conditions is modeled, such as in atherosclerosis, inflammatory arthritis, and aging, as well as inherited platelet disorders. We describe how acute decision responses can become maladaptive leading to chronic inflammatory decisions: This implies a risk of thrombo-inflammatory and cardiovascular diseases.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0228"},"PeriodicalIF":4.8,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.","authors":"Anna Nicolaou, Yan Hong, Delong Zhou, Geovanni Geukgeuzian, Vasisht Yegneshwaran, Nehal Kamal Ali, Diego Fraidenraich, Xueqing Huang","doi":"10.34133/csbj.0212","DOIUrl":"https://doi.org/10.34133/csbj.0212","url":null,"abstract":"<p><p>Stem cell (SC) research plays a central role in disease modeling and regenerative medicine, yet its clinical translation remains challenged by biological heterogeneity, incomplete cellular maturation, stringent quality-control requirements, and increasingly complex biological and clinical data. Machine learning (ML) has emerged as a powerful computational approach for extracting biologically meaningful information from these data, enabling quantitative characterization of SC behavior and predictive modeling across diverse experimental and translational applications. This review presents a unified framework for ML in SC research by organizing the literature according to SC data sources and representations, ML methodologies and model families, and biological and clinical application domains. We review ML applications spanning SC reprogramming, differentiation, postdifferentiation maturation, quality control, disease modeling, therapeutic development, and clinical decision support, highlighting how diverse experimental data, including molecular and cellular profiling, biomedical imaging, biochemical profiling, functional measurements, and clinical data, enable biological interpretation, predictive modeling, and translational decision-making. We further discuss benchmark dataset selection, validation strategies, transformer-based foundation models, and emerging multimodal learning approaches and propose benchmark dataset selection criteria emphasizing biological diversity, experimental rigor, metadata completeness, reproducibility, and public accessibility. Collectively, this review provides a comprehensive framework for understanding current ML applications in SC research and outlines future directions toward robust, interpretable, and clinically translatable computational frameworks.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0212"},"PeriodicalIF":4.8,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TattleTail: A Rule-Based Tool for Identifying Canonical Pyocin-Encoding Gene Clusters in <i>Pseudomonas aeruginosa</i>.","authors":"Rayhaan Gerard Pais, Weilian Chen, Sebastian Leptihn, Xiaoting Hua, Belinda Loh","doi":"10.34133/csbj.0230","DOIUrl":"https://doi.org/10.34133/csbj.0230","url":null,"abstract":"<p><p>Tailocins are phage tail-like bacteriocins derived from prophages that have lost the ability to package viral genomes while retaining bactericidal activity against related bacterial strains. In <i>Pseudomonas aeruginosa</i>, these elements are referred to as pyocins and contribute to ecological fitness and antimicrobial applications. Despite lacking essential phage components, pyocin-encoding gene clusters share substantial sequence homology with phage tail genes, creating a major challenge for genome annotation. Current prophage prediction tools, which rely on phage gene homology, can frequently misclassify pyocins as prophages, potentially confounding genomic analyses and downstream experimental design. Here, we present TattleTail, a rule-based bioinformatic pipeline for identifying canonical pyocin-encoding gene clusters in <i>P. aeruginosa</i>. The current implementation targets the characteristic <i>trpE</i>-<i>trpG</i>-associated genomic architecture of these loci and integrates the detection of conserved pyocin-associated genes with the exclusion of canonical phage features, enabling discrimination between pyocin-encoding gene clusters and prophage regions. Evaluation across manually curated and independently selected <i>P. aeruginosa</i> genomes, together with <i>P. aeruginosa</i> genomes computationally depleted of their pyocin loci and non-<i>P. aeruginosa</i> controls, demonstrated accurate identification of canonical pyocin loci without false-positive predictions. Application to clinical isolates identified representative pyocin clusters, which were experimentally validated by mitomycin C induction, transmission electron microscopy, and bactericidal activity assays, supporting that predicted loci in these isolates correspond to functional pyocin particles. TattleTail complements existing prophage prediction tools and supports accurate annotation of phage-derived elements.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0230"},"PeriodicalIF":4.8,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probiogenomics as a Computational Biotechnology Framework: Safety-Gated Genome Analytics for Candidate Probiotic Prioritization and Validation.","authors":"Nattarika Chaichana, Komwit Surachat","doi":"10.34133/csbj.0229","DOIUrl":"10.34133/csbj.0229","url":null,"abstract":"<p><p>Whole-genome sequencing has transformed probiotic discovery into a strain-resolved computational problem, but genome data alone cannot establish probiotic efficacy, complete safety, or regulatory acceptability. This review frames probiogenomics as a safety-gated decision-support framework for candidate probiotic prioritization and validation. The framework integrates strain provenance, genome quality, strain authentication, taxonomic confidence, antimicrobial resistance screening, virulence and toxin assessment, plasmid, prophage and mobile-element analysis, undesirable metabolite screening, functional prediction, comparative genomics, intended-use context, and validation planning. We emphasize that genome-based safety screening should be interpreted as early-stage risk triage under specified tools, databases, thresholds, and genome quality. Candidates with acceptable safety evidence can then be prioritized through pathway-level functional trait mining, comparative and evolutionary interpretation, systems biology, multi-omics, artificial intelligence or machine learning-assisted prioritization, and targeted phenotypic validation. However, predicted genes and pathways should be treated as hypotheses until expression, biological activity, product accumulation, or matched phenotypes are demonstrated under relevant host, product-matrix, dose, exposure-route, and application conditions. The framework is modular rather than one-size-fits-all: Human probiotics, animal feed probiotics, aquaculture probiotics, plant-associated beneficial microbes, starter cultures, dietary supplements, postbiotic source strains, and live biotherapeutic products require different safety questions, validation endpoints, environmental-release considerations, manufacturing controls, and regulatory pathways. We conclude that probiogenomics is most useful when it preserves uncertainty, reports negative and ambiguous findings, uses versioned and reproducible workflows, and links genome-derived predictions to auditable decision rules, an application-dependent evidence continuum, and context-specific validation.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0229"},"PeriodicalIF":4.8,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13598324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Foundation Models for the Characterization of Small-RNA Properties.","authors":"Shivprasad Jamdade, Coyun Oh, Heba Sailem","doi":"10.34133/csbj.0224","DOIUrl":"10.34133/csbj.0224","url":null,"abstract":"<p><p>Small interfering RNAs (siRNAs) provide a promising therapeutic approach capable of selectively silencing disease-associated genes; however, achieving high efficacy and specificity while minimizing off-target effects remains a marked challenge. Endogenous small RNAs, such as microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs), exhibit structural features supporting their functions and interactions with other biomolecules. Recent advances in RNA foundation models, such as RNA-FM, enable large-scale learning of sequence and structural representations of RNA sequences, offering a powerful framework for studying small-RNA functions. Here, we leverage the RNA-FM alongside interpretable biological features to systematically compare endogenous small RNAs (miRNAs and piRNAs) with synthetic siRNAs. Biological features highlighted type-specific patterns: piRNAs showed significantly higher GC content and melting temperature than miRNAs and siRNAs, suggesting higher stability. Importantly, we mapped RNA-FM embeddings to interpretable features to better understand deep-learning outputs and facilitate effective extraction of functionally relevant information. To support predictive and comparative analyses of small RNAs, we implemented these functionalities in RNAExplorer (www.rnaexplorer.com), a web-based application that allows analyzing and visualizing small-RNA features interactively. Together, our integrative analysis provides a framework for understanding small-RNA biology and improving siRNA therapeutic design strategies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0224"},"PeriodicalIF":4.8,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149024004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.","authors":"Hyung-Eun An, Min-Ho Mun, Jung-Il Kim, Chang-Bae Kim","doi":"10.34133/csbj.0221","DOIUrl":"10.34133/csbj.0221","url":null,"abstract":"<p><p>DNA metabarcoding analysis has emerged as a powerful tool for biological monitoring, enabling the identification and assessment of biodiversity across ecosystems. Conventional analytical methods, including operational taxonomic unit (OTU) clustering and amplicon sequence variant (ASV) analysis, have demonstrated strengths and limitations, including bias in estimating biodiversity and challenges in detecting rare sequences. To address these challenges, we developed unsupervised machine learning (UML)-based methods utilizing hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and ordering points to identify the clustering structure. This study focused on 3 genetic markers: 12S rRNA, cytochrome c oxidase subunit I, and 18S rRNA. Mock community datasets comprising diverse taxa were analyzed using UML-based methods, OTU clustering, and ASV analysis. Nineteen biological feature descriptors were evaluated across all datasets. Nucleic acid composition and composition of k-spaced nucleic acid pairs consistently produced the best clustering performance. A comparative analysis of the clustering methods was performed. HDBSCAN produced relative abundance profiles broadly consistent with OTU clustering and ASV analysis. Additionally, comparisons of sensitivity and precision based on detected species demonstrated that UML-based methods, especially HDBSCAN, provided a favorable balance between false-positive and false-negative detections in several datasets, suggesting their promise as alternative clustering approaches for DNA metabarcoding. However, their applicability to real environmental systems remains to be validated.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0221"},"PeriodicalIF":4.8,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149024054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting the Predictive Power of Virus-Specific T-Cell Clinical Trials via Generative Models.","authors":"Pamela Chansky, Jeremy Rubinstein, Michael Grimley, Stella M Davies, Emily Blyth, David Gottlieb, Michael A Pulsipher, Catherine Bollard, Michael D Keller, Wei Li","doi":"10.34133/csbj.0208","DOIUrl":"10.34133/csbj.0208","url":null,"abstract":"<p><p>The modest number of participants enrolled in phase I/II clinical trials limits the development of accurate models to predict treatment response. This is evidenced by early-phase clinical trials evaluating partially human leukocyte antigen-matched virus-specific T cells (VSTs) to treat virus infections in immunocompromised persons. <i>In silico</i> models for VST response are lacking due to the small number of participants enrolled in these clinical trials. Here, we developed a generative artificial intelligence approach to overcoming the small <i>n</i> and then trained a binary classifier to predict the likelihood of response to third-party VST therapy. We generated synthetic patient-response training data using a variational autoencoder, whose presence improves the robustness of machine learning models. The binary classification model determines patient response with high accuracy, evidenced by cross-validation using data from 3 third-party VST trials. The combined variational autoencoder and binary classifier provide a potential solution when considering patient suitability for third-party VSTs and also offer a broadly applicable framework to enhance the performance of machine learning models using small-scale clinical datasets.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0208"},"PeriodicalIF":4.8,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13590963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149016034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vertex Assignment for Frequency Chaos Game Representation of Proteins Affects Classification Performance.","authors":"Bálint Biró, Tímea Subicz, Róbert Barta, Soma Sándor Hartai, László Hiripi, Orsolya Ivett Hoffmann","doi":"10.34133/csbj.0225","DOIUrl":"10.34133/csbj.0225","url":null,"abstract":"<p><p>Chaos game representation and its frequency matrix variant are popular sequence encodings commonly assumed to be invariant to alternative nodal orientations. In this paper, we systematically test this assumption on 3 protein benchmark datasets with 2 convolutional neural networks and show that different vertex assignments lead to significant differences in classification performance. Evaluating 1,000 random encodings revealed a wide performance range, proving that vertex assignment is a critical parameter rather than an arbitrary choice. Furthermore, in most of the cases, frequency chaos game representation-based data augmentation proved to be effective only under moderate settings, while aggressive augmentation degraded performance due to distributional shifts introduced by permuted encodings.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0225"},"PeriodicalIF":4.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13572797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}