1-(2-Fluoro­benzyl­ideneamino)pyridinium bis­(1,2-dicyano­ethene-1,2-dithiol­ato)nickelate(II)

In the title complex, (C12H10FN2)2[Ni(C4N2S2)2], the anion lies on an inversion center with the NiII ion coordinated by four S atoms in a slightly distorted square-planar environment. In the unique cation, the dihedral angle between the benzene and pyridine rings is 7.1 (2) Å

Yiguanjian cataplasm attenuates opioid dependence in a mouse model of naloxone-induced opioid withdrawal syndrome

AbstractObjectiveTo investigate the effect of Yiguanjian (YGJ) cataplasm on the development of opioid dependence in a mouse model of naloxone-induced opioid withdrawal syndrome.MethodsOne hundred Swiss albino mice, of equal male to female ratio, were randomly and equally divided into 10 groups. A portion (3 cm2) of the backside hair of the mice was removed 1 day prior to the experiment. Morphine (5 mg/kg) was intraperitoneally administered twice daily for 5 days. YGJ cataplasm was prepared and pasted on the bare region of the mice immediately before morphine administration on day 3 and subsequently removed at the end day 5. On day 6, naloxone (8 mg/kg) was intraperitoneally injected to precipitate opioid withdrawal syndrome. Behavioral observation was performed in two 30-min phases immediately after naloxone injection.ResultsThe YGJ cataplasm significantly and dose-dependently attenuated morphine-naloxone-induced experimental opioid withdrawal, in terms of withdrawal severity score and the frequencies of jumping, rearing, forepaw licking, and circling behaviors. However, YGJ cataplasm treatment did not alter the acute analgesic effect of morphine.ConclusionYGJ cataplasm could attenuate opioid dependence and its associated withdrawal symptoms. Therefore, YGJ cataplasm could serve as a potential therapy for opioid addiction in the future

Development and validation of a predictive nomogram for lower extremity deep vein thrombosis dislodgement in orthopedic patients

ObjectiveTo analyze the risk factors of lower extremity deep venous thrombosis (DVT) detachment in orthopedic patients, and to establish a risk nomogram prediction model.MethodsThe clinical data of 334 patients with orthopedic DVT admitted to the Third Hospital of Hebei Medical University from January 2020 to July 2021 were retrospectively analyzed. General statistics included gender, age, BMI, thrombus detachment, inferior vena cava filter window type, filter implantation time, medical history, trauma history, operation, use of tourniquet, thrombectomy, anesthesia mode, anesthesia grade, operative position, blood loss during operation, blood transfusion, immobilization, use of anticoagulants, thrombus side, thrombus range, D-dimer content before filter implantation and during removal of inferior vena cava filter. Logistic regression was used to perform univariate and multivariate analysis on the possible factors of thrombosis detachment, screen out independent risk factors, establish a risk nomogram prediction model by variables, and internally verify the predictability and accuracy of the model.ResultsBinary logistic regression analysis showed that Short time window filter (OR = 5.401, 95% CI = 2.338–12.478), lower extremity operation (OR = 3.565, 95% CI = 1.553–8.184), use of tourniquet (OR = 3.871, 95% CI = 1.733–8.651), non-strict immobilization (OR = 3.207, 95% CI = 1.387–7.413), non-standardized anticoagulation (OR = 4.406, 95% CI = 1.868–10.390), distal deep vein thrombosis (OR = 2.212, 95% CI = 1.047–4.671) were independent risk factors for lower extremity DVT detachment in orthopedic patients (P < 0.05). Based on these six factors, a prediction model for the risk of lower extremity DVT detachment in orthopedic patients was established, and the risk prediction ability of the model was verified. The C-index of the nomogram model was 0.870 (95% CI: 0.822–0.919). The results indicate that the risk nomogram model has good accuracy in predicting the loss of deep venous thrombosis in orthopedic patients.ConclusionThe nomogram risk prediction model based on six clinical factors, including filter window type, operation condition, tourniquet use, braking condition, anticoagulation condition, and thrombosis range, has good predictive performance

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications

Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/

Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations