Comparing the Efficacy of MALDI-TOF MS and Sequencing-Based Identification Techniques (Sanger and NGS) to Monitor the Microbial Community of Irrigation Water

Neisseria sicca Vertebral Osteomyelitis: A Case Report and Literature Review

Background: Culture-negative vertebral osteomyelitis presents a significant diagnostic challenge. Neisseria sicca (N. sicca) is a typically benign commensal organism of the upper respiratory tract that rarely causes invasive infections, warranting cautious interpretation if isolated in a single positive culture. This case study details a 62-year-old male diagnosed with vertebral osteomyelitis caused by N. sicca, examining diagnostic challenges, treatment, and outcomes. Methods: We conducted a comprehensive search on MEDLINE using the keywords "Neisseria sicca", "osteomyelitis", and "diskitis". An additional search excluding N. gonorrhea and N. meningitidis was also performed, revealing a total of four cases of N. sicca osteomyelitis in the literature. Results: A 62-year-old male with a history of hypertension, type 2 diabetes, atrial fibrillation, and previous L5-S1 spinal fusion presented with fever and back pain in May 2023. Initial imaging revealed T8-T9 vertebral osteomyelitis. Despite a six-week course of IV daptomycin, his symptoms worsened, prompting further evaluation. A CT-guided biopsy with comprehensive testing, including histopathology and microbial cultures, initially identified N. sicca. Due to its rarity, additional biopsies were conducted, confirming the infection. IV ceftriaxone was initiated, leading to significant pain improvement, and a subsequent MRI showed near resolution. The patient transitioned to oral cefuroxime, with follow-up confirming complete infection resolution by December 2023. Conclusions: This case underscores the importance of a structured diagnostic approach in culture-negative vertebral osteomyelitis to differentiate between commensal contamination and true infection. Repeated positive cultures of N. sicca from a sterile site confirmed its role as the causative agent. Early identification and targeted antibiotic therapy are critical to improving outcomes in rare cases of N. sicca vertebral osteomyelitis.

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

Integrating multi-wet laboratory diagnostics to study staphylococci in animals in Uganda

BACKGROUND

Several diagnostic environments in Uganda lack real-time, robust and high-throughput technologies for comprehensive typing of microbes, which is a setback to infectious disease surveillance. This study combined various wet laboratory diagnostics to understand the epidemiology of pathogenic staphylococci isolated from animals in Uganda and the implications for global health security priorities.

METHODS

A retrospective study was conducted employing records and pathogenic staphylococci (from animals) archived at the Central Diagnostic Laboratory (CDL), Makerere University, Uganda, between January 2012 and December 2019. The bacteria were speciated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and tested for virulence factors [beta lactamases, lecithinase, deoxyribonuclease (DNase), haemolysins] and resistance to ten antimicrobials of clinical and veterinary relevance. Tetracycline and methicillin resistance genes were also tested.

RESULTS

The prevalent diseases were mastitis in cattle and skin infections in dogs. Of the 111 staphylococci tested by MALDI-TOF MS, 79 (71.2%) were Staphylococcus aureus, 27 (24.3%) were Staphylococcus pseudintermedius and 5 (4.5%) were Staphylococcus schleiferi. All these strains expressed haemolysins. The prevalence of strains with lecithinase, penicillinase, cephalosporinase and DNase was 35.9% (14/39), 89.7% (35/39), 0.0% (0/39) and 87.2% (34/39), respectively. Staphylococci were primarily resistant to early penicillins (over 80%), tetracycline (57.7%), and chloramphenicol (46.2%). Minimal resistance was noted with cloxacillin (0.0%), ciprofloxacin (9.6%), and cefoxitin (3.8%). The prevalence of multidrug resistance (MDR) was 78.8% for general staphylococci, 82.2% for S. aureus, 73.1% for S. pseudintermedius, and 60.0% for S. schleiferi. Multidrug resistant staphylococci were significantly more prevalent in the cattle isolates than in the dog isolates (P < 0.05). The prevalence of methicillin-resistant staphylococci (MRS) tested by resistance to cefoxitin and mecA carriage was 3.8%. These four strains were all isolated from dog skin infections. The tetK gene was the most predominant (35.4%), followed by tetM (25.0%).

CONCLUSION

In resource-constrained settings, the approach of integrated diagnostics promises sustainable disease surveillance and the addressing of current capacity gaps. The emergence of MRS (zoonotic bacteria) in companion animals creates a likelihood of reduced treatment options for related human infections, a threat to global health.

Detection of Staphylococcus Isolates and Their Antimicrobial Resistance Profiles and Virulence Genes from Subclinical Mastitis Cattle Milk Using MALDI-TOF MS, PCR and Sequencing in Free State Province, South Africa

Staphylococcus species are amongst the bacteria that cause bovine mastitis worldwide, whereby they produce a wide range of protein toxins, virulence factors, and antimicrobial-resistant properties which are enhancing the pathogenicity of these organisms. This study aimed to detect Staphylococcus spp. from the milk of cattle with subclinical mastitis using MALDI-TOF MS and 16S rRNA PCR as well as screening for antimicrobial resistance (AMR) and virulence genes. Our results uncovered that from 166 sampled cows, only 33.13% had subclinical mastitis after initial screening, while the quarter-level prevalence was 54%. Of the 50 cultured bacterial isolates, MALDI-TOF MS and 16S rRNA PCR assay and sequencing identified S. aureus as the dominant bacteria by 76%. Furthermore, an AMR susceptibility test showed that 86% of the isolates were resistant to penicillin, followed by ciprofloxacin (80%) and cefoxitin (52%). Antimicrobial resistance and virulence genes showed that 16% of the isolates carried the mecA gene, while 52% of the isolates carried the Lg G-binding region gene, followed by coa (42%), spa (40%), hla (38%), and hlb (38%), whereas sea and bap genes were detected in 10% and 2% of the isolates, respectively. The occurrence of virulence factors and antimicrobial resistance profiles highlights the need for appropriate strategies to control the spread of these pathogens.

A Single-Laboratory Performance Evaluation of MALDI-TOF MS in Rapid Identification of Staphylococcus aureus, Cronobacter sakazakii, Vibrio parahaemolyticus, and Some Closely Related Bacterial Species of Public Health Importance

BACKGROUND

Staphylococcus is a genus of Gram-positive bacteria, known to cause food poisoning and gastrointestinal illness in humans. Additionally, the emergence of methicillin-resistant S. aureus (MRSA) strains has caused a major health care burden worldwide. Cronobacter is a group of Gram-negative bacteria that can survive in extreme dry conditions. Cronobacter sakazakii is known to contaminate powdered infant formula and cause life-threatening infections in neonates. Vibrio is a genus of human-pathogenic Gram-negative bacteria that can cause foodborne illness by consuming undercooked or raw seafood. Vibrio parahaemolyticus can cause serious gastrointestinal disease in humans. Thus, rapid identification of Staphylococcus spp., Cronobacter spp., and Vibrio spp. is crucial for the source tracking of contaminated food, as well as to measure the transmission dynamics of these bacterial pathogens causing foodborne diseases and outbreaks.

OBJECTIVE

This single-laboratory performance evaluation study used the VITEK MS system to evaluate the potential of MALDI-TOF MS technology for rapid identification of S. aureus-like, C. sakazakii-like, and V. parahaemolyticus-like isolates of public health importance.

METHOD

A total of 226 isolates recovered from various food, environmental surveillance samples, and other sources were identified by bioMérieux VITEK 2 and VITEK MS systems as Staphylococcus spp., Cronobacter spp., and Vibrio spp. Five American Type Culture Collection (ATCC) reference Gram-positive and Gram-negative bacterial isolates were also tested to complete the study. In addition, for some Staphylococcus spp. isolates, whole genome sequencing (WGS) and DNA sequencing of 16S rRNA partial region were also performed for species identification.

RESULTS

The VITEK MS system was able to provide species identification to all 96 isolates of Staphylococcus spp. and to all 29 isolates of Vibrio spp. examined with a high confidence value (99.9%). Similarly, species identification was observed for the majority of spots (245 of 303) for the 101 Cronobacter spp. isolates (∼82.0%) with a high confidence value (99.9%), and genus level identification was noticed for the rest of the Cronobacter spp. isolates (18.0%; 58 of the 303 spots) analyzed. Species identification data generated by VITEK 2 system were comparable to data obtained by the VITEK MS system.

CONCLUSIONS

The VITEK MS system is a reliable high-throughput platform that can rapidly identify Staphylococcus, Vibrio, and Cronobacter to the genus level, as well as S. aureus, C. sakazakii, V. parahaemolyticus, and other closely related foodborne isolates and bacterial isolates from additional sources, in most cases.

HIGHLIGHTS

The VITEK MS system can be used in the rapid genus and species identification of human-pathogenic Staphylococcus spp., Cronobacter spp., and Vibrio spp. isolates.

Bacterial contamination of medical face mask wearing duration and the optimal wearing time

Introduction

Bacterial contamination is a critical parameter for how long a medical mask will be worn.

Methods

In this study, we used the pour plate method to observe the total bacteria counts in used medical face masks. The bacterial community analysis was detected using bio-Mass spectrometry technology and 16SrRNA gene sequencing technology. The wearing time of the mask from 0.5 hours to 5 hours were studied.

Results

These results shown that the total number of bacteria on the inside surface of the mask were higher than the outside. The total number of bacteria on the inner surface of masks worn for 0.5 h, 1 h 2 h, 4 h and 5 h was 69 CFU/m2,91.3 CFU/m2, 159.6 CFU/m2, 219 CFU/m2, and 879 CFU/m2, respectively. The total number of bacteria on the outside surface of masks worn for 0.5 h, 1 h 2 h, 4 h and 5 h was 60 CFU/m2, 82.7 CFU/m2, 119.8 CFU/m2, 200 CFU/m2, and 498 CFU/m2, respectively. The bacterial abundance obtained from bio-Mass spectrometry were consistent with the results of 16SrRNA sequencing. Both the methods discovered the maximum number of Neisseria followed by Corynebacterium species in mask worn 5 hours. The top 100 bacteria isolated from inside and outside surface of mask belong to 11 phyla.

Conclusions

We analyzed bacterial penetration efficiency of the bacteria that were detected both on the inside and outside surface of the masks. In the top 10 bacteria, no bacteria were detected both inside and outside the mask worn for four hours, while 6 bacteria species were detected on the inside and outside of the mask after wearing for five hours. Bacterial penetration rates ranged from 0.74% to 99.66% for masks worn continuously for five hours, and the penetration rate of four strains exceeded 10% in the top 10 colonies. We recommend timely replacement of masks worn for more than four hours.

Determination of Pathogens by Electrophoretic and Spectrometric Techniques

In modern medical diagnostics, where analytical chemistry plays a key role, fast and accurate identification of pathogens is becoming increasingly important. Infectious diseases pose a growing threat to public health due to population growth, international air travel, bacterial resistance to antibiotics, and other factors. For instance, the detection of SARS-CoV-2 in patient samples is a key tool to monitor the spread of the disease. While there are several techniques for identifying pathogens by their genetic code, most of these methods are too expensive or slow to effectively analyze clinical and environmental samples that may contain hundreds or even thousands of different microbes. Standard approaches (e.g., culture media and biochemical assays) are known to be very time- and labor-intensive. The purpose of this review paper is to highlight the problems associated with the analysis and identification of pathogens that cause many serious infections. Special attention was paid to the description of mechanisms and the explanation of the phenomena and processes occurring on the surface of pathogens as biocolloids (charge distribution). This review also highlights the importance of electromigration techniques and demonstrates their potential for pathogen pre-separation and fractionation and demonstrates the use of spectrometric methods, such as MALDI-TOF MS, for their detection and identification.