Acute and convalescent serological data from Thai cohorts used in this analyses, colored by true infections status and prediction of infection during model testing.

Acute and convalescent serological data from Thai cohorts used in this analyses, colored by true infections status and prediction of infection during model testing.

<p><b>a)</b> Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. <b>b)</b> Immunoglobulin M (IgM) <b>c)</b> Immunoglobulin G (IgG) <b>d)</b> Ratio of IgM to IgG at both acute and convales...

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Bibliografske podrobnosti
Glavni avtor:	Marco Hamins-Puértolas (15238661) (author)
Drugi avtorji:	Darunee Buddhari (625855) (author), Henrik Salje (589157) (author), Angkana T. Huang (12262139) (author), Taweewun Hunsawong (798675) (author), Derek A.T. Cummings (14971341) (author), Stefan Fernandez (146590) (author), Aaron Farmer (4288318) (author), Surachai Kaewhiran (22683535) (author), Direk Khampaen (22683538) (author), Anon Srikiatkhachorn (110184) (author), Sopon Iamsirithaworn (260152) (author), Adam Waickman (118668) (author), Stephen J. Thomas (7105673) (author), Timothy Endy (150971) (author), Alan L. Rothman (6835178) (author), Kathryn B. Anderson (6835166) (author), Isabel Rodriguez-Barraquer (233670) (author)
Izdano:	2025
Teme:	Medicine Biotechnology Ecology Immunology Cancer Science Policy Infectious Diseases Virology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified since many cases linked immunosorbent assay independently utilize information hemagglutination inhibition assay globe &# 8217 currently implemented cut convalescent serum collected 87 &# 8211 82 &# 8211 real world data multiple serological assays infer infection events generate finalized interpretations bayesian mixture model prior standard interpretations pcr confirmed infections jointly model data gold standard ). f1 scores ). hai data consistently direct detection methods gold standard f1 scores infection detection eia ). including data individual assays new model igm data whether individuals transmission potential results provide recently experienced provide insight probabilistic framework population living point approaches pathogen systems kamphaeng phet interpreting results incorporating igg important role higher accuracy first test diagnostic process creating uncertainty combined metric already half 677 pairs
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Acute and convalescent samples from cohort study data for serological assays, colored by whether they are predicted as infections using Model 9.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

Acute and convalescent data for serological assays, colored by probability of.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

Acute and convalescent samples from cohort study data for serological assays, colored by the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent serum samples.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

Acute and convalescent data for serological assays, colored by probability of infection found during model testing for Model 9. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent sera samples.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

Relationship between estimated probability of a dengue virus infection (DENV) found during model testing for Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Izdano: (2025)

Distribution of paired serological data that fall into probability buckets when using Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Theoretical relationship between time since symptom onset and titers in one infected (red) and three uninfected (blue) individuals.
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Comparison between simulation based estimates for boosts in GM HAI, IgG, and IgM that is experienced after an infection event as a function of acute GM HAI, IgG, and IgM titers individuals under the varying levels of observational noise.
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Izdano: (2025)

Comparison of each accuracy metric (sensitivity, specificity, F1, and area under the curve for reporter operator curve (AUC ROC)) on each model. Models are defined by which assays were incorporated (i.e., HAI, IgG, and/or IgM). Bolded values represent the best performing model for the infection definition and metric of interest.
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Izdano: (2025)

Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.
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Izdano: (2025)

Comparison of accuracy metrics (sensitivity, specificity, and AUC ROC) of each model defined by what subset of data was used for training and testing.
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Model comparisons for simulated data with differing amounts of observational noise.
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Parameters found in Bayesian estimation framework, definition, and prior distribution.
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Relative observational noise by assay and dataset.
od: Marco Hamins-Puértolas (15238661)
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Parameters for forward simulation using serosim [38].
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Top-performing model configuration and performance by algorithm type.
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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
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The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
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The statistical description of the original data set of the patients (<i>n</i> = 162).
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Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction.
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ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
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The comparison of the accuracy score of the benchmark and the proposed models.
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The prediction accuracy of simple versus hybrid models.
od: Uğur Ejder (22683228)
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The accuracy score change matrix following model development.
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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
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Flow diagram of the proposed model.
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Flow diagram of the conversion of drugs into active substances.
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od: Sophia David (4172902)
Izdano: (2025)

<i>K. pneumoniae</i> genomes represented in amr.watch as of 31 March 2025 from the USA (A) and China (B) filtered by the most frequent ST from each country (ST258 and ST11, respectively).
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Proportion of genomes by year from the five critical-level priority pathogens carrying one or more AMR mechanisms for relevant antimicrobial classes, as displayed in the “AMR mechanism proportion over time” panel within amr.watch.
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Distribution of pathogen genomes represented in amr.watch as of 31 March 2025.
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Curated list of AMR mechanisms by pathogen from AMRFinderPlus that are reported in amr.watch.
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