MRCS PART A · APPLIED BASIC SCIENCES
Antibiotic measurements: growth is not killing
Two bounded laboratory objectives and two original five-option questions. All organisms, compound Z and results are fictional. No clinical breakpoints, doses or prescribing advice. Sources checked and platform owner reviewed; not a complete microbiology course or calibrated assessment.
2 topics · 2 original SBAs
1 · Read the endpoint that was actually measured
Your task: Separate visible-growth inhibition from recovery of viable organisms.
What does the clear well establish in this model?
The specified visible-growth endpoint has been reached. It does not establish zero viable organisms.
Why include positive growth and sterility controls?
They test whether growth was possible and the medium uncontaminated; an uninterpretable control makes an apparently clear well unreliable.
Which concentration is the MIC here?
For this explicitly standard complete-inhibition endpoint, choose the lowest tested concentration without visible growth. EUCAST describes agent-specific exceptions; the fictional exercise excludes those exceptions and skipped wells.
Can a numerical MIC alone assign a clinical susceptibility category?
Not here: no organism-specific clinical breakpoint or exposure context is supplied. This exercise is not an S/I/R report or prescription.
2 · Compare concentration and duration separately
Your task: Interpret unchanged MIC alongside directly measured differences in bacterial killing time.
Can two cultures share a growth-inhibition endpoint yet differ in killing time?
Yes. The consensus distinguishes concentration-based growth resistance from longer survival at bactericidal exposure. A shared MIC does not specify the killing trajectory.
Does slower killing identify a molecular cause?
No. The fictional comparison gives a measured phenotype, not a mutation, dormancy mechanism, biofilm or clinical outcome.
What does a 99% reduction mean for one million starting viable units?
Ten thousand remain: 1,000,000 × 0.01. That is not sterilisation, and it is distinct from a 99.9% reduction, which leaves one thousand.
Can one survival count prove a persistent subpopulation?
No. A single count does not establish a heterogeneous time-kill pattern. Do not label persistence from the supplied endpoint alone.