Reproducibility of African giant pouched rats detecting Mycobacterium tuberculosis

Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats

BACKGROUND

Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity.

OBJECTIVE

During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats.

METHODS

APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO).

RESULTS

The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model.

CONCLUSIONS

The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats.

Scent detection of Brucella abortus by African giant pouched rats (Cricetomys ansorgei)

BACKGROUND

Brucellosis is a contagious zoonosis caused by bacteria of the genus Brucella. While the disease has been eradicated in most developed countries, it remains endemic in sub-Saharan Africa where access to reliable diagnostics is limited. African giant pouched rats (Cricetomys ansorgei) have been trained to detect the scent of Mycobacterium tuberculosis to increase case detection in sub-Saharan Africa. Given the similar diagnostic challenges facing brucellosis and tuberculosis, we explored the feasibility of training African giant pouched rats to detect Brucella.

RESULTS

After 3 months of training, rats reliably identified cultured Brucella, achieving an average sensitivity of 93.56% (SD = 0.650) and specificity of 97.65% (SD = 0.016). Rats readily generalized to novel, younger Brucella cultures that presumably generated a weaker volatile signal and correctly identified at least one out of three fecal samples spiked with Brucella culture during a final test of feasibility.

DISCUSSION

To our knowledge, these experiments are the first to demonstrate Brucella emits a unique odor profile that scent detection animals can be trained to identify. Importantly, cultured E. coli samples were included throughout training and test to ensure the rats learned to specifically identify Brucella bacteria rather than any bacteria in comparison to bacteria-free culture medium. E. coli controls therefore served a crucial function in determining to what extent Brucella abortus emits a unique odor signature. Further research is needed to determine if a Brucella-specific volatile signature is present within clinical samples. If confirmed, the present results suggest trained rats could serve as a valuable, novel method for the detection of Brucella infection.

Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics

Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.

Breath biomarkers associated withnontuberculosis mycobacteriadisease status in persons with cystic fibrosis: a pilot study

Pulmonary infections caused by mycobacteria cause significant mortality and morbidity in the human population. Diagnosing mycobacterial infections is challenging. An infection can lead to active disease or remain indolent with little clinical consequence. In patients with pulmonarynontuberculosis mycobacteria(PNTM) identification of infection and diagnosis of disease can take months to years. Our previous studies showed the potential diagnostic power of volatile molecules in the exhaled breath samples to detect active pulmonaryM. tuberculosisinfection. Herein, we demonstrate the ability to detect the disease status of PNTM in the breath of persons with cystic fibrosis (PwCF). We putatively identified 17 volatile molecules that could discriminate between active-NTM disease (n= 6), indolent patients (n= 3), and those patients who have never cultured an NTM (n= 2). The results suggest that further confirmation of the breath biomarkers as a non-invasive and culture-independent tool for diagnosis of NTM disease in a larger cohort of PwCF is warranted.

Sensitive and robust chemical detection using an olfactory brain-computer interface

When it comes to detecting volatile chemicals, biological olfactory systems far outperform all artificial chemical detection devices in their versatility, speed, and specificity. Consequently, the use of trained animals for chemical detection in security, defense, healthcare, agriculture, and other applications has grown astronomically. However, the use of animals in this capacity requires extensive training and behavior-based communication. Here we propose an alternative strategy, a bio-electronic nose, that capitalizes on the superior capability of the mammalian olfactory system, but bypasses behavioral output by reading olfactory information directly from the brain. We engineered a brain-computer interface that captures neuronal signals from an early stage of olfactory processing in awake mice combined with machine learning techniques to form a sensitive and selective chemical detector. We chronically implanted a grid electrode array on the surface of the mouse olfactory bulb and systematically recorded responses to a large battery of odorants and odorant mixtures across a wide range of concentrations. The bio-electronic nose has a comparable sensitivity to the trained animal and can detect odors on a variable background. We also introduce a novel genetic engineering approach that modifies the relative abundance of particular olfactory receptors in order to improve the sensitivity of our bio-electronic nose for specific chemical targets. Our recordings were stable over months, providing evidence for robust and stable decoding over time. The system also works in freely moving animals, allowing chemical detection to occur in real-world environments. Our bio-electronic nose outperforms current methods in terms of its stability, specificity, and versatility, setting a new standard for chemical detection.

Visual Analytics of Tuberculosis Detection Rat Performance

The diagnosis of tuberculosis (TB) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. Trained African giant pouched rats are the scent TB detection technology for operational research. The adoption of this technology is beneficial to countries with a high TB burden due to its cost-effectiveness and speed than microscopy. However, rats with some factors perform better. Thus, more insights on factors that may affect performance is important to increase rats' TB detection performance. This paper intends to provide understanding on the factors that influence rats TB detection performance using visual analytics approach. Visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. Three algorithms such as Decision tree, Random Forest and Naive Bayes were used to predict the factors that influence rats TB detection performance. Hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. The algorithms were validated using the same test data to check their prediction accuracy. The accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. However, their accuracies difference is small. The study findings may help rats TB trainers, researchers in rats TB and Information systems, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.

Breath can discriminate tuberculosis from other lower respiratory illness in children

Pediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.

The Many Hosts of Mycobacteria 8 (MHM8): A conference report

Mycobacteria are important causes of disease in human and animal hosts. Diseases caused by mycobacteria include leprosy, tuberculosis (TB), nontuberculous mycobacteria (NTM) infections and Buruli Ulcer. To better understand and treat mycobacterial disease, clinicians, veterinarians and scientists use a range of discipline-specific approaches to conduct basic and applied research, including conducting epidemiological surveys, patient studies, wildlife sampling, animal models, genetic studies and computational simulations. To foster the exchange of knowledge and collaboration across disciplines, the Many Hosts of Mycobacteria (MHM) conference series brings together clinical, veterinary and basic scientists who are dedicated to advancing mycobacterial disease research. Started in 2007, the MHM series recently held its 8th conference at the Albert Einstein College of Medicine (Bronx, NY). Here, we review the diseases discussed at MHM8 and summarize the presentations on research advances in leprosy, NTM and Buruli Ulcer, human and animal TB, mycobacterial disease comorbidities, mycobacterial genetics and 'omics, and animal models. A mouse models workshop, which was held immediately after MHM8, is also summarized. In addition to being a resource for those who were unable to attend MHM8, we anticipate this review will provide a benchmark to gauge the progress of future research concerning mycobacteria and their many hosts.

Sniffing animals as a diagnostic tool in infectious diseases

BACKGROUND

Scents and odours characterize some microbes when grown in the laboratory, and experienced clinicians can diagnose patients with some infectious diseases based on their smell. Animal sniffing is an innate behaviour, and animals' olfactory acuity is used for detecting people, weapons, bombs, narcotics and food.

OBJECTIVES

We briefly summarized current knowledge regarding the use of sniffing animals to diagnose some infectious diseases and the potential use of scent-based diagnostic instruments in microbiology.

SOURCES

Information was sought through PubMed and extracted from peer-reviewed literature published between January 2000 and September 2019 and from reliable online news. The search terms 'odour', 'scent', 'bacteria', 'diagnostics', 'tuberculosis', 'malaria' and 'volatile compounds' were used.

CONTENT

Four major areas of using sniffing animals are summarized. Dogs have been used to reliably detect stool associated with toxigenic Clostridioides difficile and for surveillance. Dogs showed high sensitivity and moderate specificity for detecting urinary tract infections in comparison to culture, especially for Escherichia coli. African giant pouched rats showed superiority for diagnosing tuberculosis over microscopy, but inferiority to culture/molecular methods. Several approaches for detecting malaria by analysing host skin odour or exhaled breath have been explored successfully. Some microbial infections produce specific volatile organic compounds (VOCs), which can be analysed by spectrometry, metabolomics or other analytical approaches to replace animal sniffing.

IMPLICATIONS

The results of sniffing animal studies are fascinating, and animal sniffing can provide intermediate diagnostic solutions for some infectious diseases. Lack of reproducibility, and cost of animal training and housing are major drawbacks for wider implementation of sniffing animals. The ultimate goal is to understand the biological background of this animal ability and to characterize the specific VOCs that animals are recognizing. VOC identification, improvement of odour sampling methods and development of point-of-care instruments could allow implementation of scent-based tests for major human pathogens.

Ophthalmic examination findings and intraocular pressures in wild-caught African giant pouched rats (Cricetomys spp.)

PURPOSE

To report ophthalmic examination findings and intraocular pressures (IOPs) in wild-caught African giant pouched rats (Cricetomys ansorgei and gambianus) from Tanzania and Ghana.

PROCEDURES

After being placed under general anesthesia for examination, slit-lamp biomicroscopy before and after pharmacologic mydriasis and indirect ophthalmoscopy was performed. Eyes were fluorescein stained and IOPs measured by rebound tonometry using the TonoVet^® .

RESULTS

Thirty-two sexually mature pouched rats (64 eyes) were examined, including 16 males and 16 females. The mean IOP (± standard deviation) was 7.7 (±2.9) mmHg. Fluorescein staining was negative in all eyes. One or more ocular abnormalities were detected in 21 pouched rats (35 eyes). These ocular lesions included the following: lens opacities (n = 23 eyes), persistent pupillary membranes (n = 5), chorioretinal scarring (n = 3), corneal vascularization (n = 2), palpebral margin defect with focal trichiasis (n = 2), phthisis bulbi (n = 1), and posterior synechiae (n = 1). Lens opacities included incipient anterior cortical opacities (n = 7), immature cataract (n = 6), incipient nuclear opacities (n = 5), punctate pigment on anterior lens capsule (n = 2 eyes), incipient suture tip opacities (n = 2), and hypermature cataract (n = 1).

CONCLUSIONS

Ocular abnormalities were common in the evaluated population of giant pouched rats; however, most of the detected lesions were mild and believed to have minimal impact on vision. Rebound tonometry with the TonoVet^® was a reliable and simple technique to measure IOPs in the anesthetized pouched rats.