Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling.
Ontology highlight
ABSTRACT:
SUBMITTER: Wu Y
PROVIDER: S-EPMC8314199 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-BSST379 | biostudies-other
Project description:The sudden loss of smell is among the earliest and most prevalent symptoms of COVID-19 when measured with a clinical psychophysical test. Research has shown the potential impact of frequent screening for olfactory dysfunction, but existing tests are expensive and time consuming. We developed a low-cost ($0.50/test) rapid psychophysical olfactory test (KOR) for frequent testing and a model-based COVID-19 screening framework using a Bayes Network symptoms model. We trained and validated the model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 33% prevalence, 309 RT-PCR confirmed) and healthy miners (n = 1,365; 1.1% prevalence, 15 RT-PCR confirmed). The model predicted COVID-19 status with 76% and 96% accuracy in the healthcare and miners samples, respectively (healthcare: AUC = 0.79 [0.75-0.82], sensitivity: 59%, specificity: 87%; miners: AUC = 0.71 [0.63-0.79], sensitivity: 40%, specificity: 97%, at 0.50 infection probability threshold). Our results highlight the potential for low-cost, frequent, accessible, routine COVID-19 testing to support society's reopening.
| S-EPMC8580551 | biostudies-literature
Project description:BackgroundWhen tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease "on-ramps" to be identified and targeted.MethodsThe system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo.ResultsWe have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child's BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents' BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different.ConclusionsChildhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence.
| S-EPMC10031893 | biostudies-literature
Project description:Diagnostic tests that detect antibodies (AB) against SARS-CoV-2 for evaluation of seroprevalence and guidance of health care measures are important tools for managing the COVID-19 pandemic. Current tests have certain limitations with regard to turnaround time, costs and availability, particularly in point-of-care (POC) settings. We established a hemagglutination-based AB test that is based on bi-specific proteins which contain a dromedary-derived antibody (nanobody) binding red blood cells (RBD) and a SARS-CoV-2-derived antigen, such as the receptor-binding domain of the Spike protein (Spike-RBD). While the nanobody mediates swift binding to RBC, the antigen moiety directs instantaneous, visually apparent hemagglutination in the presence of SARS-CoV-2-specific AB generated in COVID-19 patients or vaccinated individuals. Method comparison studies with assays cleared by emergency use authorization demonstrate high specificity and sensitivity. To further increase objectivity of test interpretation, we developed an image analysis tool based on digital image acquisition (via a cell phone) and a machine learning algorithm based on defined sample-training and -validation datasets. Preliminary data, including a small clinical study, provides proof of principle for test performance in a POC setting. Together, the data support the interpretation that this AB test format, which we refer to as 'NanoSpot.ai', is suitable for POC testing, can be manufactured at very low costs and, based on its generic mode of action, can likely be adapted to a variety of other pathogens.
| S-EPMC8718524 | biostudies-literature
Project description:Genus Aspergillus represents a widely spread genus of fungi that is highly popular for possessing potent medicinal potential comprising mainly antimicrobial, cytotoxic and antioxidant properties. They are highly attributed to its richness by alkaloids, terpenes, steroids and polyketons. This review aimed to comprehensively explore the diverse alkaloids isolated and identified from different species of genus Aspergillus that were found to be associated with different marine organisms regarding their chemistry and biology. Around 174 alkaloid metabolites were reported, 66 of which showed important biological activities with respect to the tested biological activities mainly comprising antiviral, antibacterial, antifungal, cytotoxic, antioxidant and antifouling activities. Besides, in silico studies on different microbial proteins comprising DNA-gyrase, topoisomerase IV, dihydrofolate reductase, transcriptional regulator TcaR (protein), and aminoglycoside nucleotidyl transferase were done for sixteen alkaloids that showed anti-infective potential for better mechanistic interpretation of their probable mode of action. The inhibitory potential of compounds vs. Angiotensin-Converting Enzyme 2 (ACE2) as an important therapeutic target combating COVID-19 infection and its complication was also examined using molecular docking. Fumigatoside E showed the best fitting within the active sites of all the examined proteins. Thus, Aspergillus species isolated from marine organisms could afford bioactive entities combating infectious diseases.
| S-EPMC7918500 | biostudies-literature
Project description:COVID-19 caused by SARS-CoV-2 have become a global pandemic with serious rate of fatalities. SARS-CoV and MERS-CoV have also caused serious outbreak previously but the intensity was much lower than the ongoing SARS-CoV-2. The main infectivity factor of all the three viruses is the spike glycoprotein. In this study we have examined the intrinsic dynamics of the prefusion, lying state of trimeric S protein of these viruses through Normal Mode Analysis using Anisotropic Network Model. The dynamic modes of the S proteins of the aforementioned viruses were compared by root mean square inner product (RMSIP), spectral overlap and cosine correlation matrix. S proteins show homogenous correlated or anticorrelated motions among their domains but direction of Cα atom among the spike proteins show less similarity. SARS-CoV-2 spike shows high vertically upward motion of the receptor binding motif implying its propensity for binding with the receptor even in the lying state. MERS-CoV spike shows unique dynamical motion compared to the other two S protein indicated by low RMSIP, spectral overlap and cosine correlation value. This study will guide in developing common potential inhibitor molecules against closed state of spike protein of these viruses to prevent conformational switching from lying to standing state.
| S-EPMC7567490 | biostudies-literature
Project description:Dysregulated immune responses contribute to the excessive and uncontrolled inflammation observed in severe COVID-19. However, how immunity to SARS-CoV-2 is induced and regulated remains unclear. Here we uncover a role of the complement system in the induction of innate and adaptive immunity to SARS-CoV-2. Complement rapidly opsonizes SARS-CoV-2 particles via the lectin pathway. Complement-opsonized SARS-CoV-2 efficiently induces type-I interferon and pro-inflammatory cytokine responses via activation of dendritic cells, which are inhibited by antibodies against the complement receptors (CR) 3 and 4. Serum from COVID-19 patients, or monoclonal antibodies against SARS-CoV-2, attenuate innate and adaptive immunity induced by complement-opsonized SARS-CoV-2. Blocking of CD32, the FcγRII antibody receptor of dendritic cells, restores complement-induced immunity. These results suggest that opsonization of SARS-CoV-2 by complement is involved in the induction of innate and adaptive immunity to SARS-CoV-2 in the acute phase of infection. Subsequent antibody responses limit inflammation and restore immune homeostasis. These findings suggest that dysregulation of the complement system and FcγRII signaling may contribute to severe COVID-19.
| S-SCDT-10_1038-S44318-024-00061-0 | biostudies-other
Project description:Despite the fact that preconditions of political participation were thoroughly examined before, there is still not enough understanding of which factors directly affect political participation and which factors correlate with participation due to common background variables. This article scrutinises the causal relations between the variables associated with participation in online activism and introduces a three-step approach in learning a reliable structure of the participation preconditions' network to predict political participation. Using Bayesian network analysis and structural equation modeling to stabilise the structure of the causal relations, the analysis showed that only age, political interest, internal political efficacy and no other factors, highlighted by the previous political participation research, have direct effects on participation in online activism. Moreover, the direct effect of political interest is mediated by the indirect effects of internal political efficacy and age via political interest. After fitting the parameters of the Bayesian network dependent on the received structure, it became evident that given prior knowledge of the explanatory factors that proved to be most important in terms of direct effects, the predictive performance of the model increases significantly. Despite this fact, there is still uncertainty when it comes to predicting online participation. This result suggests that there remains a lot to be done in participation research when it comes to identifying and distinguishing factors that stimulate new types of political activities.
| S-EPMC8699968 | biostudies-literature
Project description:Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements' dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.
| S-EPMC8319423 | biostudies-literature
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of individuals worldwide, causing a severe global pandemic. Mice models are wildly used to investigate viral infection pathology, antiviral drugs, and vaccine development. However, since wild-type mice do not express human angiotensin-converting enzyme 2 (hACE2), which mediates SARS-CoV-2 entry into human cells, they are not susceptible to infection with SARS-CoV-2 and are not suitable to simulate symptomatic COVID-19 disease. HACE2 transgenic mice could provide an efficient model, but they are expensive, not always readily available and practically restricted to specific strain(s). Since additional models are needed to study the disease at varying genetic and immune backgrounds, there is a dearth of mouse models for SARS-CoV-2 infection. Here we report the application of lentiviral vectors to generate hACE2 expression in mouse lung epithelial cells (LET1) as well as in interferon receptor knock-out (IFNAR1-/-) mice. Lenti-hACE2 transduction supported SARS-CoV-2 replication both in vitro and in vivo, simulating mild acute lung disease1. Gene expression analysis revealed two modes of immune responses to SARS-CoV-2 infection: one in response to the exposure of mouse lungs to SARS-CoV-2 particles in the absence of productive viral replication, and the second in response to a productive infection. This approach expands our knowledge on the role of type-1 interferon signaling in COVID-19 disease, and can be further implemented for a range of COVID-19 studies and drug development.
2021-12-07 | GSE186167 | GEO