Comparison of common machine learning models for classification of tuberculosis using transcriptional biomarkers from integrated datasets

Sex-specific blood-derived RNA biomarkers for childhood tuberculosis

Confirmatory diagnosis of childhood tuberculosis (TB) remains a challenge mainly due to its dependence on sputum samples and the paucibacillary nature of the disease. Thus, only ~ 30% of suspected cases in children are diagnosed and the need for minimally invasive, non-sputum-based biomarkers remains unmet. Understanding host molecular changes by measuring blood-based transcriptomic markers has shown promise as a diagnostic tool for TB. However, the implication of sex contributing to disease heterogeneity and therefore diagnosis remains to be understood. Using publicly available gene expression data (GSE39939, GSE39940; n = 370), we report a sex-specific RNA biomarker signature that could improve the diagnosis of TB disease in children. We found four gene biomarker signatures for male (SLAMF8, GBP2, WARS, and FCGR1C) and female pediatric patients (GBP6, CELSR3, ALDH1A1, and GBP4) from Kenya, South Africa, and Malawi. Both signatures achieved a sensitivity of 85% and a specificity of 70%, which approaches the WHO-recommended target product profile for a triage test. Our gene signatures outperform most other gene signatures reported previously for childhood TB diagnosis.

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

Machine learning in the loop for tuberculosis diagnosis support

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.

Cocrystals of tuberculosis antibiotics: Challenges and missed opportunities

Cocrystals have been extensively used to improve the physicochemical properties and bioavailability of active pharmaceutical ingredients. Cocrystals of anti-tuberculosis medications are among those commonly reported. This review provides a summary of the tuberculosis antibiotic cocrystals reported in the literature, providing the main results on current tuberculosis medications utilized in cocrystals. Moreover, anti-tuberculosis cocrystals limitations and advantages are described, including evidence for enhanced solubility, stability and effect. Opportunities to enhance anti-tuberculosis medications and fixed dose combinations using cocrystals are given. Several cocrystal pairs are suggested to enhance the effectiveness of anti-tuberculosis drugs.

Robustifying genomic classifiers to batch effects via ensemble learning

MOTIVATION

Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such 'batch effects' often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here, we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods.

RESULTS

We provide a systematic comparison between these two strategies using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed that in independent validation, while merging followed by batch adjustment provides better discrimination at low level of heterogeneity, our ensemble learning strategy achieves more robust performance, especially at high severity of batch effects. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the article and in its online supplementary material. Processed data is available in the Github repository with implementation code, at https://github.com/zhangyuqing/bea_ensemble.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Synthesis and Characterization of Nano-Sized 4-Aminosalicylic Acid-Sulfamethazine Cocrystals

Drug–drug cocrystals are formulated to produce combined medication, not just to modulate active pharmaceutical ingredient (API) properties. Nano-crystals adjust the pharmacokinetic properties and enhance the dissolution of APIs. Nano-cocrystals seem to enhance API properties by combining the benefits of both technologies. Despite the promising opportunities of nano-sized cocrystals, the research at the interface of nano-technology and cocrystals has, however, been described to be in its infancy. In this study, high-pressure homogenization (HPH) and high-power ultrasound were used to prepare nano-sized cocrystals of 4-aminosalysilic acid and sulfamethazine in order to establish differences between the two methods in terms of cocrystal size, morphology, polymorphic form, and dissolution rate enhancement. It was found that both methods resulted in the formation of form I cocrystals with a high degree of crystallinity. HPH yielded nano-sized cocrystals, while those prepared by high-power ultrasound were in the micro-size range. Furthermore, HPH produced smaller-size cocrystals with a narrow size distribution when a higher pressure was used. Cocrystals appeared to be needle-like when prepared by HPH compared to those prepared by high-power ultrasound, which had a different morphology. The highest dissolution enhancement was observed in cocrystals prepared by HPH; however, both micro- and nano-sized cocrystals enhanced the dissolution of sulfamethazine.

An artificial neural network approach for predicting hypertension using NHANES data

This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to large clinical data sets may provide a meaningful data-driven approach to categorize patients for population health management, and support in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart. Data was obtained from the National Health and Nutrition Examination Survey from 2007 to 2016. This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patients, and (30.29%) hypertensive patients. The results indicate a sensitivity of 40%, a specificity of 87%, precision of 57.8% and a measured AUC of 0.77 (95% CI [75.01-79.01]). This paper showed results that are to some degree more effectively than a previous study performed by the authors using a statistical model with similar input features that presents a calculated AUC of 0.73. This classification model can be used as an inference agent to assist the professionals in diseases of the heart field, and can be implemented in applications to assist population health management programs in identifying patients with high risk of developing hypertension.