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Related Experiment Video

Updated: Mar 15, 2026

Generating Whole Bacterial Genomes from Clinical Samples using a Target Enrichment Workflow
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Estimating original bacterial loads from delayed clinical samples: A methodological Modeling and empirical validation

Ahmed Dawood Al Mahrizi1, Fatima Mossolem2, Renald Blundell1

  • 1Faculty of Medicine & Surgery, University of Malta, Msida, Malta.

Journal of Microbiological Methods
|March 13, 2026
PubMed
Summary

The Al Mahrizi-Mossolem viability correction model (MM-VCM) accurately estimates original bacterial loads in clinical samples despite processing delays. This model corrects for temperature-dependent changes, improving diagnostic accuracy when resampling is not possible.

Keywords:
Bacterial load back-calculationBayesian uncertainty analysisColony-forming unitsInverse logistic growthMonte Carlo simulationViability correction model

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Area of Science:

  • Clinical Microbiology
  • Diagnostic Accuracy
  • Mathematical Modeling

Background:

  • Delayed processing of clinical samples (urine, blood, CSF) leads to inaccurate bacterial colony-forming unit (CFU) counts.
  • Variable storage and transit conditions distort initial bacterial load estimates, impacting timely and effective patient treatment.
  • Accurate quantification of bacterial load is critical for diagnosis and therapeutic monitoring.

Purpose of the Study:

  • To introduce and validate the Al Mahrizi-Mossolem viability correction model (MM-VCM) for correcting bacterial loads in delayed clinical samples.
  • To develop an inverse logistic growth-decay framework to back-calculate original bacterial concentrations (N₀) from delayed measurements (Nt).
  • To account for key factors influencing bacterial viability, including temperature-dependent growth, lag phases, decay rates, and carrying capacities.

Main Methods:

  • MM-VCM utilizes a closed-form equation integrating Gaussian-modulated doubling time into logistic dynamics.
  • Model parameters were literature-derived and applied to eight pathogen-matrix combinations (e.g., E. coli in urine/blood, N. meningitidis in CSF).
  • Validation employed extensive Monte Carlo and Bayesian simulations, alongside Sobol sensitivity analysis, and comparison with a multicenter clinical blood culture dataset.

Main Results:

  • Simulations showed exponential decline in estimated original loads (N₀) over 24 hours, with widening confidence intervals.
  • High probabilities (>0.99) of detecting significant bacterial loads (>10⁵ CFU/mL) were observed for delays up to 6 hours.
  • Temperature was identified as the primary driver of uncertainty; empirical validation showed good correspondence with observed trends for S. aureus and S. pneumoniae.

Conclusions:

  • MM-VCM provides an efficient method for pre-analytic correction of bacterial loads, crucial for diagnostics where resampling is impractical.
  • The model supports clinical laboratories, especially those handling CSF or high sample volumes, by enhancing diagnostic reliability.
  • Preliminary empirical validation supports the model's real-world applicability, particularly for certain bacterial species, warranting further investigation.