COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15\%-20\% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-$p$-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: LR, KNN, SVM, MLP, CNN, LSTM and Resnet50. Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.Comment: This paper has been accepted in "Computers in Medicine and Biology" and currently under productio

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Objective: The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments. Approach: We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines (SVM), k-nearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN). Main Results: Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the 90\% sensitivity at 70\% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test. Significance: The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden.Comment: This paper has been accepted in Physiological Measurement (2021

BDNF Val66Met and DRD2 Taq1A polymorphisms interact to influence PTSD symptom severity: A preliminary investigation in a South African population

BACKGROUND: We evaluated the role that selected variants in serotonin transporter (5-HTT), dopamine receptor 2 (DRD2) and brain-derived neurotrophic factor (BDNF) genes play in PTSD symptom severity in an at-risk population. We also investigated the interaction between the genetic variants to determine whether these variables and the interactions between the variables influenced the severity of PTSD symptoms. METHODS: PTSD symptoms were quantitatively assessed using the Davidson Trauma Scale (DTS) in 150 participants from an at-risk South African population. All participants were genotyped for the 5-HTTLPR, DRD2 Taq1A and BDNF Val66Met polymorphisms. Gene–gene interactions were investigated using various linear models. All analyses were adjusted for age, gender, major depressive disorder diagnosis, level of resilience, level of social support and alcohol dependence. RESULTS: A significant interaction effect between DRD2 Taq1A and BDNF Val66Met variants on DTS score was observed. On the background of the BDNF Val66Val genotype, DTS score increased significantly with the addition of a DRD2 Taq1A A1 allele. However, on the BDNF Met66 allele background, the addition of an A1 allele was found to reduce total DTS score. CONCLUSIONS: This study provides preliminary evidence for an epistatic interaction between BDNF Val66Met and DRD2 Taq1A polymorphisms on the severity of PTSD symptoms, where both too little and too much dopamine can result in increased PTSD symptom severity.Web of Scienc

A landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic

Abstract Background Atypical Beijing genotype Mycobacterium tuberculosis strains are widespread in South Africa and have acquired resistance to up to 13 drugs on multiple occasions. It is puzzling that these strains have retained fitness and transmissibility despite the potential fitness cost associated with drug resistance mutations. Methods We conducted Illumina sequencing of 211 Beijing genotype M. tuberculosis isolates to facilitate the detection of genomic features that may promote acquisition of drug resistance and restore fitness in highly resistant atypical Beijing forms. Phylogenetic and comparative genomic analysis was done to determine changes that are unique to the resistant strains that also transmit well. Minimum inhibitory concentration (MIC) determination for streptomycin and bedaquiline was done for a limited number of isolates to demonstrate a difference in MIC between isolates with and without certain variants. Results Phylogenetic analysis confirmed that two clades of atypical Beijing strains have independently developed resistance to virtually all the potent drugs included in standard (pre-bedaquiline) drug-resistant TB treatment regimens. We show that undetected drug resistance in a progenitor strain was likely instrumental in this resistance acquisition. In this cohort, ethionamide (ethA A381P) resistance would be missed in first-line drug-susceptible isolates, and streptomycin (gidB L79S) resistance may be missed due to an MIC close to the critical concentration. Subsequent inadequate treatment historically led to amplification of resistance and facilitated spread of the strains. Bedaquiline resistance was found in a small number of isolates, despite lack of exposure to the drug. The highly resistant clades also carry inhA promoter mutations, which arose after ethA and katG mutations. In these isolates, inhA promoter mutations do not alter drug resistance, suggesting a possible alternative role. Conclusion The presence of the ethA mutation in otherwise susceptible isolates from ethionamide-naïve patients demonstrates that known exposure is not an adequate indicator of drug susceptibility. Similarly, it is demonstrated that bedaquiline resistance can occur without exposure to the drug. Inappropriate treatment regimens, due to missed resistance, leads to amplification of resistance, and transmission. We put these results into the context of current WHO treatment regimens, underscoring the risks of treatment without knowledge of the full drug resistance profile

MDR M. tuberculosis outbreak clone in Eswatini missed by Xpert has elevated bedaquiline resistance dated to the pre-treatment era.

BACKGROUND: Multidrug-resistant (MDR) Mycobacterium tuberculosis complex strains not detected by commercial molecular drug susceptibility testing (mDST) assays due to the RpoB I491F resistance mutation are threatening the control of MDR tuberculosis (MDR-TB) in Eswatini. METHODS: We investigate the evolution and spread of MDR strains in Eswatini with a focus on bedaquiline (BDQ) and clofazimine (CFZ) resistance using whole-genome sequencing in two collections ((1) national drug resistance survey, 2009-2010; (2) MDR strains from the Nhlangano region, 2014-2017). RESULTS: MDR strains in collection 1 had a high cluster rate (95%, 117/123 MDR strains) with 55% grouped into the two largest clusters (gCL3, n = 28; gCL10, n = 40). All gCL10 isolates, which likely emerged around 1993 (95% highest posterior density 1987-1998), carried the mutation RpoB I491F that is missed by commercial mDST assays. In addition, 21 (53%) gCL10 isolates shared a Rv0678 M146T mutation that correlated with elevated minimum inhibitory concentrations (MICs) to BDQ and CFZ compared to wild type isolates. gCL10 isolates with the Rv0678 M146T mutation were also detected in collection 2. CONCLUSION: The high clustering rate suggests that transmission has been driving the MDR-TB epidemic in Eswatini for three decades. The presence of MDR strains in Eswatini that are not detected by commercial mDST assays and have elevated MICs to BDQ and CFZ potentially jeopardizes the successful implementation of new MDR-TB treatment guidelines. Measures to limit the spread of these outbreak isolates need to be implemented urgently

Molecular characterization of the drug resistant tuberculosis epidemic in the Eastern Cape, South Africa

Thesis (PhD)--Stellenbosch University, 2015ENGLISH ABSTRACT : South Africa has a high burden of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB), with the Eastern Cape (EC) being one of the worst affected provinces in the country. This study provides the first in-depth analysis of the molecular epidemiology of drug-resistant TB in the EC. A convenience sample of drug-sensitive and drug-resistant isolates was collected over one year by the National Health Laboratory Services in Port Elizabeth. These isolates were characterized by various molecular techniques. Our results were compared to data from three additional provinces, to contextualise the population structure of MDR-TB strains. Each province had a distinct population structure. The population structure of XDR-TB cases in the Western Cape was significantly influenced by strains originating from the EC. A high degree of clustering of drug resistance mutation patterns was detected in each setting, suggestive of transmission. Clustering was particularly pronounced in the EC, with 93% of pre-XDR and XDR-TB isolates belonging to the Atypical Beijing genotype. We showed that this genotype was programmatically selected through a weakened MDR-TB regimen that failed to recognise inhA defined ethionamide resistance. This weakened regimen has facilitated transmission and is the underlying cause of mortality. We propose that existing molecular assays which detect inhA mutations should be used to identify patients at risk of XDR-TB and to adjust treatment. Through spoligotyping, restriction fragment length polymorphism typing and mutation analysis we demonstrated that the EC Atypical Beijing isolates evolved from a common progenitor, giving rise to two sub-groups, each with unique features, including mutations that confer resistance to up to 11 anti-TB drugs. This finding was supported by whole genome sequencing (WGS) and RNA sequencing demonstrating close relatedness and suggests the emergence and spread of totally drug-resistant TB in the EC. We showed that isolates harbouring the rrs A1401G mutation displayed a decreased susceptibility to capreomycin, thereby questioning the utility of this drug in the treatment of XDR-TB when amikacin resistance was already noted. Importantly, strains harbouring the rpoB516 mutation were shown to be susceptible to rifabutin, despite low-level resistance to rifampicin (RIF). Therefore the use of rifabutin in the EC may improve therapeutic success and limit transmission of XDR-TB. WGS was used to investigate molecular features that may confer a selective advantage to the EC Atypical Beijing genotype strains. These analyses revealed that all represented Atypical Beijing genotype strains – including those diagnosed as pan-susceptible – harboured a mutation in ethA, conferring phenotypic ethionamide resistance. This surprising finding may explain the apparent increased ability of the Atypical Beijing genotype strains to develop higher drug-resistance in the context of an ethionamide-containing MDR-TB treatment regimen. It is unclear why some strains additionally acquire inhA promoter mutations. This requires further investigation. A large number of genes were shown by RNAseq to be differentially regulated, however, their influence on the physiological properties of the bacillus remain to be determined. Together these findings have challenged the use of standardised MDR-TB treatment without comprehensive DST. This view is now widely recognised but has not influenced the South African TB guidelines (2014) which promote treatment of RIF resistance without relevant knowledge of drug resistance. We propose that the effective treatment of highly resistant TB can only be achieved with the development of new drugs, new drug combinations and comprehensive rapid DST.AFRIKAANSE OPSOMMING : Suid-Afrika het ‘n hoë voorkoms van multi-middelweerstandige (MDR) en uiters middelweerstandige (XDR) tuberkulose (TB), veral in die Oos-Kaap. Hierdie studie bied die eerste in-diepte analise van die molekulêre epidemiologie van middelweerstandige TB in die Oos-Kaap. ‘n Gerieflikheidsteekproef wat oor een jaar geneem is en bestaan het uit middelsensitiewe sowel as middelweerstandige isolate is van die National Health Laboratory Services in Port Elizabeth ontvang. Hierdie isolate is deur verskeie molekulêre metodes gekarakteriseer. Ons resultate is vergelyk met data van drie addisionele provinsies om die populasiesamestelling van MDR-TB stamme in konteks te plaas. Elke provinsie het ‘n unieke populasiesamestelling getoon. Die populasiesamestelling van XDR-TB gevalle in die Wes-Kaap is beduidend deur stamme van die Oos-Kaap beïnvloed. ‘n Hoë mate van groepering van weerstandigheidspatrone is in elke provinsie gevind, wat dui op transmissie. Groepering was besonder duidelik in die Oos-Kaap, waar 93% van pre-XDR en XDR-TB isolate van die Atipiese Beijing genotipe was. Ons het getoon dat hierdie genotipe programmaties geselekteer is deur ‘n suboptimale MDR-TB behandelingsregime wat nie inhA-gedefiniëerde ethionamied weerstandigheid in ag neem nie. Hierdie ondoeltreffende behandelingsregime het transmissie gefasiliteer en is die onderliggende oorsaak van mortaliteit. Ons stel voor dat bestaande molekulêre toetse gebruik word wat inhA mutasies opspoor om XDR-TB risiko-pasiënte te identifiseer en hul behandeling dienooreenkomstig aan te pas. Ons het gedemonstreer dat twee sub-groepe van Oos-Kaap Atipiese Beijing isolate ontwikkel het uit ‘n gemene voorsaat, elk met unieke eienskappe, insluitend mutasies wat weerstandigheid teen tot 11 middels veroorsaak. Hierdie bevinding word gerugsteun deur heelgenoom volgordebepaling en ribonukleïensuur volgordebepaling en dui op die ontluiking en verspreiding van algeheel middelweerstandige TB in die Oos-Kaap. Ons het getoon dat isolate wat die rrs A1401G mutasie het, verminderde vatbaarheid vir capreomisien het, en dit bevraagteken die bruikbaarheid van hierdie middel in die behandeling van XDR-TB waar amikasienweerstandigheid teenwoordig is. Van belang is dat stamme wat die rpoB516 mutasie het, vatbaar is vir rifabutien, ten spyte van weerstandigheid teen rifampisien. Die gebruik van rifabutien kan dus die uitkomste van XDR-TB pasiënte in die Oos-Kaap verbeter, en ook transmissie beperk. Heelgenoom volgordebepaling is gebruik om molekulêre eienskappe te ondersoek wat moontlik ‘n selektiewe voordeel kan bied aan die Oos-Kaapse Atipiese Beijing genotipe stamme. Ons het getoon dat alle verteenwoordigde Atipiese Beijing genotipe stamme – insluitend dié wat as algeheel middelsensitief gediagnoseer is – ‘n ethA mutasie het wat ethionamied weerstandigheid veroorsaak. Dit mag die oënskynlike verhoogde vermoë van die Atipiese Beijing genotipe stamme om hoër weerstandigheid te ontwikkel verklaar. Verder het ribonukleïensuur volgordebepaling getoon dat ‘n groot aantal gene verskillend gereguleer is. Hierdie verskille moet verder ondersoek word om die invloed daarvan op die fisiologiese eienskappe van die bacillus te verklaar. Hierdie bevindinge betwis die gebruik van gestandardiseerde MDR-TB behandeling in die afwesigheid van omvattende middelsensitiwiteitstoetse. Hierdie siening word tans algemeen aanvaar, en tog het dit nie die Suid-Afrikaanse TB-riglyne (2014), wat behandeling van rifampisienweerstandigheid sonder die relevante kennis van middelweerstandigheid voorstaan, beïnvloed nie. Ons stel voor dat die effektiewe behandeling van hoogs weerstandige TB net bereik kan word deur die ontwikkeling van nuwe middels, nuwe kombinasies van middels en vinnige, omvattende middelsensitiwiteitstoetse

Programmatically selected multidrug-resistant strains drive the emergence of extensively drug-resistant tuberculosis in South Africa

South Africa shows one of the highest global burdens of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB). Since 2002, MDR-TB in South Africa has been treated by a standardized combination therapy, which until 2010 included ofloxacin, kanamycin, ethionamide, ethambutol and pyrazinamide. Since 2010, ethambutol has been replaced by cycloserine or terizidone. The effect of standardized treatment on the acquisition of XDR-TB is not currently known.; We genetically characterized a random sample of 4,667 patient isolates of drug-sensitive, MDR and XDR-TB cases collected from three South African provinces, namely, the Western Cape, Eastern Cape and KwaZulu-Natal. Drug resistance patterns of a subset of isolates were analyzed for the presence of commonly observed resistance mutations.; Our analyses revealed a strong association between distinct strain genotypes and the emergence of XDR-TB in three neighbouring provinces of South Africa. Strains predominant in XDR-TB increased in proportion by more than 20-fold from drug-sensitive to XDR-TB and accounted for up to 95% of the XDR-TB cases. A high degree of clustering for drug resistance mutation patterns was detected. For example, the largest cluster of XDR-TB associated strains in the Eastern Cape, affecting more than 40% of all MDR patients in this province, harboured identical mutations concurrently conferring resistance to isoniazid, rifampicin, pyrazinamide, ethambutol, streptomycin, ethionamide, kanamycin, amikacin and capreomycin.; XDR-TB associated genotypes in South Africa probably were programmatically selected as a result of the standard treatment regimen being ineffective in preventing their transmission. Our findings call for an immediate adaptation of standard treatment regimens for M/XDR-TB in South Africa

Automatic Tuberculosis and COVID-19 cough classification using deep learning

We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory disease, have cough as a predominant symptom and claim thousands of lives each year. The cough audio recordings were collected at both indoor and outdoor settings and also uploaded using smartphones from subjects around the globe, thus contain various levels of noise. This cough data include 1.68 hours of TB coughs, 18.54 minutes of COVID-19 coughs and 1.69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50. These three deep architectures were also pre-trained on 2.14 hours of sneeze, 2.91 hours of speech and 2.79 hours of noise for improved performance. The class-imbalance in our dataset was addressed by using SMOTE data balancing technique and using performance metrics such as F1-score and AUC. Our study shows that the highest F1-scores of 0.9259 and 0.8631 have been achieved from a pre-trained Resnet50 for two-class (TB vs COVID-19) and three-class (TB vs COVID-19 vs healthy) cough classification tasks, respectively. The application of deep transfer learning has improved the classifiers' performance and makes them more robust as they generalise better over the cross-validation folds. Their performances exceed the TB triage test requirements set by the world health organisation (WHO). The features producing the best performance contain higher order of MFCCs suggesting that the differences between TB and COVID-19 coughs are not perceivable by the human ear. This type of cough audio classification is non-contact, cost-effective and can easily be deployed on a smartphone, thus it can be an excellent tool for both TB and COVID-19 screening

Epistasis between antibiotic resistance mutations drives the evolution of extensively drug-resistant tuberculosis

Background and objectives: Multidrug resistant (MDR) bacteria are a growing threat to global health. Studies focusing on single antibiotics have shown that drug resistance is often associated with a fitness cost in the absence of drug. However, little is known about the fitness cost associated with resistance to multiple antibiotics. Methodology: We used Mycobacterium smegmatis as a model for human tuberculosis (TB) and an in vitro competitive fitness assay to explore the combined fitness effects and interaction between mutations conferring resistance to rifampicin (RIF) and ofloxacin (OFX); two of the most important first- and second-line anti-TB drugs, respectively. Results: We found that 4 out of 17 M. smegmatis mutants (24%) resistant to RIF and OFX showed a statistically significantly higher or lower competitive fitness than expected when assuming a multiplicative model of fitness effects of each individual mutation. Moreover, 6 out of the 17 double drug-resistant mutants (35%) had a significantly higher fitness than at least one of the corresponding single drug-resistant mutants. The particular combinations of resistance mutations associated with no fitness deficit in M. smegmatis were the most frequent among 151 clinical isolates of MDR and extensively drug-resistant (XDR) Mycobacterium tuberculosis from South Africa. Conclusions and implications: Our results suggest that epistasis between drug resistance mutations in mycobacteria can lead to MDR strains with no fitness deficit, and that these strains are positively selected in settings with a high burden of drug-resistant TB. Taken together, our findings support a role for epistasis in the evolution and epidemiology of MDR- and XDR-TB