Digitalization, clinical microbiology and infectious diseases

Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures

Manual microscopy of Gram stains from positive blood cultures (PBCs) is crucial for diagnosing bloodstream infections but remains labor intensive, time consuming, and subjective. This study aimed to evaluate a scan and analysis system that combines fully automated digital microscopy with deep convolutional neural networks (CNNs) to assist the interpretation of Gram stains from PBCs for routine laboratory use. The CNN was trained to classify images of Gram stains based on staining and morphology into seven different classes: background/false-positive, Gram-positive cocci in clusters (GPCCL), Gram-positive cocci in pairs (GPCP), Gram-positive cocci in chains (GPCC), rod-shaped bacilli (RSB), yeasts, and polymicrobial specimens. A total of 1,555 Gram-stained slides of PBCs were scanned, pre-classified, and reviewed by medical professionals. The results of assisted Gram stain interpretation were compared to those of manual microscopy and cultural species identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The comparison of assisted Gram stain interpretation and manual microscopy yielded positive/negative percent agreement values of 95.8%/98.0% (GPCCL), 87.6%/99.3% (GPCP/GPCC), 97.4%/97.8% (RSB), 83.3%/99.3% (yeasts), and 87.0%/98.5% (negative/false positive). The assisted Gram stain interpretation, when compared to MALDI-TOF MS species identification, also yielded similar results. During the analytical performance study, assisted interpretation showed excellent reproducibility and repeatability. Any microorganism in PBCs should be detectable at the determined limit of detection of 105 CFU/mL. Although the CNN-based interpretation of Gram stains from PBCs is not yet ready for clinical implementation, it has potential for future integration and advancement.

MALDI Mass Spectrometry Imaging: A Potential Game-Changer in a Modern Microbiology

Nowadays, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is routinely implemented as the reference method for the swift and straightforward identification of microorganisms. However, this method is not flawless and there is a need to upgrade the current methodology in order to free the routine lab from incubation time and shift from a culture-dependent to an even faster independent culture system. Over the last two decades, mass spectrometry imaging (MSI) gained tremendous popularity in life sciences, including microbiology, due to its ability to simultaneously detect biomolecules, as well as their spatial distribution, in complex samples. Through this literature review, we summarize the latest applications of MALDI-MSI in microbiology. In addition, we discuss the challenges and avenues of exploration for applying MSI to solve current MALDI-TOF MS limits in routine and research laboratories.

Routine wastewater-based monitoring of antibiotic resistance in two Finnish hospitals: focus on carbapenem resistance genes and genes associated with bacteria causing hospital-acquired infections

BACKGROUND

Wastewater-based monitoring represents a useful tool for antibiotic resistance surveillance.

AIM

To investigate the prevalence and abundance of antibiotic resistance genes (ARGs) in hospital wastewater over time.

METHODS

Wastewater from two hospitals in Finland (HUS1 and HUS2) was monitored weekly for nine weeks (weeks 25-33) in summer 2020. A high-throughput real-time polymerization chain reaction (HT-qPCR) system was used to detect and quantify 216 ARGs and genes associated with mobile genetic elements (MGEs), integrons, and bacteria causing hospital-acquired infections (HAIs), as well as the 16S rRNA gene. Data from HT-qPCR were analysed and visualized using a novel digital platform, ResistApp. Eight carbapenem resistance genes (blaGES, blaKPC, blaVIM, blaNDM, blaCMY, blaMOX, blaOXA48, and blaOXA51) and three genes associated with bacteria causing HAIs (Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were studied.

FINDINGS

There was a significantly higher number of ARGs at both hospitals in weeks 27-30 (174-191 genes) compared to other sampling weeks (151-171 genes). Our analyses also indicated that the two hospitals, which used different amounts of antibiotics, had significantly different resistance gene profiles. Carbapenem resistance genes were more prevalent and abundant in HUS1 than HUS2. Across both hospitals, blaGES and blaVIM were the most prevalent and abundant. There was also a strong positive association between blaKPC and K. pneumoniae in HUS1 wastewater.

CONCLUSION

Routine wastewater-based monitoring using ResistApp can provide valuable information on the prevalence and abundance of ARGs in hospitals. This helps hospitals understand the spread of antibiotic resistance in hospitals and identify potential areas for intervention.

Toward strengthening active case finding for ending tuberculosis in India

Active case finding (ACF) is critical for the timely detection and treatment of diseases like tuberculosis (TB). ACF is crucial in India, where health-seeking behaviour across various populations is low and non-reporting is a barrier to breaking disease transmission cycles and eliminating the disease. As an integral part of India's national TB control programs, there is further scope for improvement in its design. This paper outlines a roadmap for ACF for TB in the country. It is based on current national strategic plans for public health monitoring and TB case identification. It suggests that ACF should be strengthened by developing it as a learning surveillance system involving a change from a siloed public health approach to a more comprehensive surveillance exercise. It defines operational requirements for establishing predictive capabilities and introducing feedback and learning in the system as a precondition. It highlights the importance of establishing proper processes, as well as extending and expanding on existing infrastructural and human resource, and inter-programmatic coordination and collaboration in the health sector.

Future developments in training

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of highly skilled and extensively trained specialists in clinical microbiology (CM) and infectious diseases (ID). Training curricula in CM and ID must constantly evolve to prepare trainees for future pandemics and to allow trainees to reach their full clinical and academic potential.

OBJECTIVES

In this narrative review, we aim to outline necessary future adaptations in CM and ID training curricula and identify current structural barriers in training with the aim of discussing possibilities to address these shortcomings.

SOURCES

We reviewed literature from PubMed and included selected books and online publications as appropriate. There was no time constraint on the included publications.

CONTENT

Drawing from the lessons learnt during the pandemic, we summarize novel digital technologies relevant to CM and ID trainees and highlight interdisciplinary teamwork and networking skills as important competencies. We centre CM and ID training within the One Health framework and discuss gender inequalities and structural racism as barriers in both CM and ID training and patient care.

IMPLICATIONS

CM and ID trainees should receive training and support developing skills in novel digital technologies, leadership, interdisciplinary teamwork and networking. Equally important is the need for equity of opportunity, with firm commitments to end gender inequality and structural racism in CM and ID. Policy-makers and CM and ID societies should ensure that trainees are better equipped to achieve their professional goals and are better prepared for the challenges awaiting in their fields.