Key Findings: 

Ni et al. investigated VBNC E. coli and P. aeruginosa across source water, tap water, and potable water collected from 54 source points over a period of six months in a megacity of eastern China, using propidium monoazide quantitative PCR (PMA-qPCR) combined with traditional culture methods.¹ They additionally developed multiple linear regression (MLR)- and artificial neural network (ANN)- based predictive models based on routine water quality parameters and performed quantitative microbial risk assessment (QMRA). 

• VBNC pathogens remain after standard treatment: Although water treatment reduced VBNC pathogen levels by 1–3 log units, VBNC E. coli and P. aeruginosa were still detected in tap and potable water at concentrations ranging from 10⁰ – 10² CFU/100 mL, with detection rates between 83.3% and 100%. 
• Potable water showed higher VBNC levels than tap water possibly due to seeding from faucets or water dispensers. 
• ANN-based modeling predicts VBNC levels more accurately compared to MLR based using conventional water quality indicators, with prediction accuracies of 88.9% for E. coli and 77.8% for P. aeruginosa. 
• QMRA indicated that annual infection risks and disease burdens from VBNC pathogens in both tap and potable water exceeded acceptable safety benchmarks.

Bigger Picture:

This study underscores that VBNC pathogens – dormant but viable microbes invisible to culture-based tests – can persist through conventional drinking-water treatment and pose quantifiable health risks. Water utilities and public health agencies must therefore augment monitoring strategies to include culture-independent techniques (such as PMA-qPCR) and predictive modelling to assess microbial safety more accurately. Moreover, predictive models such as ANN can help water managers anticipate VBNC pathogen trends based on routine water quality parameters. For risk assessors and regulators, these findings highlight a gap in current drinking water quality frameworks and suggest that VBNC pathogen monitoring may be essential for protecting public health.

References:

Key Findings: 

Schipper et al. (2025) evaluated 11 DNA extractions methods and 3 molecular assays, to optimize a Cryptosporidium detection system which was then assessed by real-time PCR and ddPCR using over 400 environmental (water and soil) and produce (root veggies, fruiting, and leafy greens) samples collected from farms and irrigation systems in South Africa.¹

• Improvements in sample concentration, inhibitor removal, and DNA extraction significantly increased oocyst recovery rates compared to conventional methods, including a >10-fold improvement in sensitivity in complex matrices like leafy greens and soil.
• None of the samples tested positive with real-time PCR, while ddPCR detected Cryptosporidium in 13.6% of water, 23.3% of soil, and 34.7% of fresh produce samples. 
  • Of the water types tested, surface water showed the highest contamination rate (28.6%).
  • Of the soil types tested, soil amended with both fertilizer and manure showed the highest contamination rate (45%).
• Among vegetables, roots were most affected (46.7%), followed by fruiting (40%) and leafy greens (30.15%).
• When integrated into existing surveillance frameworks, the method reduced false negatives and allowed consistent detection of low-level contamination.
• These findings also highlight the health risks of Cryptosporidium in food systems.

Bigger Picture:

Foodborne Cryptosporidium outbreaks remain largely undetected due to technical challenges in environmental testing. This work provides a validated, field-ready molecular toolkit for integrated monitoring of protozoan pathogens within the water–soil–plant–food nexus. By enabling harmonized detection from source water to edible crops, the approach offers a practical foundation for risk reduction strategies in produce production and irrigation management.

References:

1. Schipper et al. (2025). Optimized Molecular Detection of Cryptosporidium Within the Water-Soil-Plant-Food Nexus: Advancing Surveillance in Agricultural Systems. Journal of Food Protection.  Vol. 88, Issue 9: 100568.

Key Findings: 

120 of 276 hot water hospital water samples were contaminated as follows: 97 with L. pneumophila alone, 15 with L. anisa alone, 7 with L. pneumophila and L. anisa, and 1 with L. geestiana.

Different methods assessed singularly or in combination included:

A) direct plating 1 mL, BCYE medium;

B) concentration and elution with membrane filter, BCYE medium, acid treatment;

C) concentration and elution with membrane filter, BCYE medium, heat treatment;

D) concentration and elution with membrane filter, GVPC medium, acid treatment;

E) concentration and elution with membrane filter, GVPC medium, heat treatment; and

F) filtration 10 mL, GVPC medium.

• Single-method sensitivity ranged from 34%–76%.
• The best-performing single method involved concentration + heat + GVPC
• Combining three complementary methods captured 84% – 98% of positives; combining all six methods detected 100%.
• Heat was superior to acid in reducing competing flora and increasing recovery.
• GVPC (glycine–vancomycin–polymyxin–cycloheximide) agar consistently outperformed BCYE for selectivity and sensitivity.
• Techniques involving membrane concentration and elution were more sensitive than direct plating or simple filtration.
• Substantial inter-method variation reinforces the importance of standardized procedural selection for clinical and environmental surveillance.

Bigger Picture:

The results provide actionable data to guide laboratories toward a strategic, multi-method approach to Legionella testing – favoring membrane filtration, heat pre-treatment, and GVPC media for optimal recovery. This evidence supports refining ISO 11731:2017 implementation and could inform regulatory or accreditation updates requiring harmonized method selection. In hospital and long-term-care environments, where Legionnaires’ disease poses significant mortality risk, the study’s recommendations enhance both diagnostic sensitivity and public-health protection.

References:

1. Grandbastien et al. (2025). “Legionella detection and enumeration in water samples by ISO 11731-2017: which method is the most sensitive?” Applied and Environmental Microbiology, 0:e01147-25.