Four isothermal recombinase polymerase amplification (RPA) assays were developed for fast in-field identification ofBacillus anthracis. The RPA assays targeted three specific sequences (i.e., the BA_5345 chromosomal marker, the lethal factorlef[from pXO1], and the capsule-biosynthesis-relatedcapA[from pXO2]) and a conserved sequence in the adenylate cyclase gene (adk) for theBacillus cereusgroup.B. anthracis-specific RPA assays were tested first with purified genomic DNAs (n= 60), including 11 representatives ofB. anthracis, and then with soil (n= 8) and white powder (n= 8) samples spiked with inactivatedB. anthracisspores and/or other biological agents. The RPA assays were also tested in another laboratory facility, which blindly provided DNA and lysate samples (n= 30, including 20B. anthracisstrains). RPA assays displayed 100% specificity and sensitivity. The hands-off turnaround times at 42°C ranged from 5 to 6 min for 10²genomic copies. The analytical sensitivity of each RPA assay was ∼10 molecules per reaction. In addition, the BA_5345 andadkRPA assays were assessed under field conditions with a series of surface swabs (n= 13, including 11 swabs contaminated withB. thuringiensisspores) that were blindly brought to the field laboratory by a chemical, biological, radiological, and nuclear (CBRN) sampling team. None of the 13 samples, except the control, tested positive forB. anthracis, and all samples that had been harvested from spore-contaminated surfaces tested positive with theadkRPA assay. All threeB. anthracis-specific RPA assays proved suitable for rapid and reliable identification ofB. anthracisand therefore could easily be used by first responders under field conditions to quickly discriminate between a deliberate release ofB. anthracisspores and a hoax attack involving white powder.IMPORTANCEIn recent decades, particularly following the 11 September 2001 and Amerithrax attacks, the world has experienced attempts to sow panic and chaos in society through thousands of white-powder copycats using household powders to mimic real bioterrorism attacks. In such circumstances, field-deployable detection methods are particularly needed to screen samples collected from the scene. The aim is to test the samples directly using a fast and reliable assay for detection of the presence ofB. anthracis. While this would not preclude further confirmatory tests from being performed in reference laboratories, it would bring useful, timely, and relevant information to local crisis managers and help them make appropriate decisions without having to wait for quantitative PCR results (with turnaround times of a few hours) or phenotypic identification and sequencing (with turnaround times of a few days). In the current investigation, we developed a set of isothermal RPA assays for the rapid screening and identification ofB. anthracisin powders and soil samples, with the purpose of discriminating a deliberate release ofB. anthracisspores from a hoax attack involving white powder; this would also apply to dispersion by spraying of aerosolized forms ofB. anthracis. Further work is now ongoing to confirm the first observations and validate the on-site use of these assays by first responders.

The Biological Light Fieldable Laboratory for Emergencies (B-LiFE) is a box-based modular laboratory with the capacity to quickly deploy on-site in cases of uncontrolled spread of infectious disease. During the 2014–2015 West Africa Ebola outbreak, this tent laboratory provided diagnostic support to the N’Zerekore Ebola Treatment Center (ETC), Guinea, for three months. One of the objectives of B-LiFE deployment was to contribute, as much as possible, to national capacity building by training local scientists. Two Guinean biologists were selected according to their basic biological knowledge and laboratory skills among 50 candidate trainees, and were integrated into the team through “just-in-time training” (JiTT), which helped the biologists acquire knowledge and laboratory skills beyond their expertise. The JiTT program was conducted according to standard laboratory procedures, in line with international biosafety guidelines adapted to field conditions. Supervised acquisition of field-laboratory practices mainly focused on biochemical testing and Ebola viral load quantification using routine PCR-based detection, including the Biofire FilmArray^® system (BFA), a novel, as yet non-validated, automated assay for diagnostic testing of Ebola virus disease at the time of B-LiFE deployment. During the JiTT, the two biologists were closely involved in all laboratory activities, including BFA validation and biosafety procedures. Meanwhile, this successful JiTT enhanced the B-LiFE in-field operational capacity and contributed to national capacity building. A post-training evaluation and contacts were organised to assess the evolution and technical skills gained by the two researchers during the B-LiFE mission. At the end of the B-LiFE mission, both biologists were enrolled in follow-on programmes to curb the epidemic spreading in Africa. These results demonstrate that during infectious disease outbreaks or major crises, the JiTT approach can rapidly expand access to critical diagnostic testing and train local staff to do so.

Coronavirus disease 2019 (COVID-19) is an acute infectious disease caused by the novel coronavirus (SARS-CoV-2) identified in 2019. The COVID-19 outbreak continues to have devastating consequences for human lives and the global economy. The B-LiFe mobile laboratory in Piedmont, Italy, was deployed for the surveillance of COVID-19 cases by large-scale testing of first responders. The objective was to assess the seroconversion among the regional civil protection (CP), police, health care professionals, and volunteers. The secondary objective was to detect asymptomatic individuals within this cohort in the light of age, sex, and residence. In this paper, we report the results of serological testing performed by the B-LiFe mobile laboratory deployed from 10 June to 23 July 2020. The tests included whole blood finger-prick and serum sampling for detection of SARS-CoV-2 spike receptor-binding domain (S-RBD) antibodies. The prevalence of SARS-CoV-2 antibodies was approximately 5% (294/6013). The results of the finger-prick tests and serum sample analyses showed moderate agreement (kappa = 0.77). Furthermore, the detection rates of serum antibodies to the SARS-CoV-2 nucleocapsid protein (NP) and S-RBD among the seroconverted individuals were positively correlated (kappa = 0.60), at least at the IgG level. Seroprevalence studies based on serological testing for the S-RBD protein or SARS-CoV-2 NP antibodies are not sufficient for diagnosis but might help in screening the population to be vaccinated and in determining the duration of seroconversion.

The accurate and rapid identification of bacterial pathogens is essential in clinical setups and medical biodefense. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry has emerged as a powerful tool for fast and reliable microbial identification. This study assesses the performance of eight Machine Learning (ML) and two Deep Learning (DL) models trained using 5-fold cross validation in classifying microorganisms in a series of experiments based on MALDI-TOF mass spectra ( n = 255) generated internally from seven cultured bacteria and five viral agents. The results showed that up to seven models achieved consistently robust classification across various tasks, including binary classification of viruses from bacteria and Gram-positive from Gram-negative bacteria, as well as multi-class classification of individual bacterial and viral species. Subsequently, we cross-validated three from our top-performing models, Extra Trees Classifier, Support Vector Classifier and 1-D Convolutional Neural Network, on Gram type and multi-class classification of individual bacterial species against an external dataset selected from a large MALDI-TOF MS database of highly pathogenic bacteria (curated by Robert Koch Institute). Among these, the Extra Trees Classifier consistently achieved the highest average accuracy and F1-score in both Gram type classification and species-level identification, demonstrating superior generalization across datasets. Its ensemble architecture proved particularly effective in capturing subtle spectral patterns associated with microbial cell wall composition and protein expression profiles. These results underscore the strong potential of this model to enhance MALDI-TOF-based classification frameworks in clinical microbiology and biodefense applications.