High-throughput and computational approaches for diagnostic and prognostic host tuberculosis biomarkers

High-throughput techniques strive to identify new biomarkers that will be useful for the diagnosis, treatment, and prevention of tuberculosis (TB). However, their analysis and interpretation pose considerable challenges. Recent developments in the high-throughput detection of host biomarkers in TB are reported in this review

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

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

Using gene and microRNA expression in the human airway for lung cancer diagnosis

Lung cancer surpasses all other causes of cancer-related deaths worldwide. Gene-expression microarrays have shown that differences in the cytologically normal bronchial airway can distinguish between patients with and without lung cancer. In research reported here, we have used microRNA expression in bronchial epithelium and gene expression in nasal epithelium to advance biological understanding of the lung-cancer "field of injury" and develop new biomarkers for lung cancer diagnosis. MicroRNAs are known to mediate the airway response to tobacco smoke exposure but their role in the lung-cancer-associated field of injury was previously unknown. Microarrays can measure microRNA expression; however, they are probe-based and limited to detecting annotated microRNAs. MicroRNA sequencing, on the other hand, allows the identification of novel microRNAs that may play important biological roles. We have used microRNA sequencing to discover novel microRNAs in the bronchial epithelium. One of the predicted microRNAs, now known as miR-4423, is associated with lung cancer and airway development. This finding demonstrates for the first time a microRNA expression change associated with the lung-cancer field of injury and microRNA mediation of gene expression changes within that field. The National Lung Screening Trial showed that screening high-risk smokers using CT scans decreases lung-cancer-associated mortality. Nodules were detected in over 20% of participants; however, the overwhelming majority of screening-detected nodules were non-malignant. We therefore need biomarkers to determine which screening-detected nodules are benign and do not require further invasive testing. Given that the lung-cancer-associated field of injury extends to the bronchial epithelium, our group hypothesized that the field of injury may extend farther up in the airway. Using gene expression microarrays, we have identified a nasal epithelium gene-expression signature associated with lung cancer. Using samples from the bronchial epithelium and the nasal epithelium, we have established that there is a common lung-cancer-associated gene-expression signature throughout the airway. In addition, we have developed a nasal epithelium gene-expression biomarker for lung cancer together with a clinico-genomic classifier that includes both clinical factors and gene expression. Our data suggests that gene expression profiling in nasal epithelium might serve as a non-invasive approach for lung cancer diagnosis and screenin

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Catheter ablation for ventricular tachycardia in patients with cardiac sarcoidosis: a systematic review

AIMS: Cardiac sarcoidosis (CS) is associated with a poor prognosis. Important features of CS include heart failure, conduction abnormalities, and ventricular arrhythmias. Ventricular tachycardia (VT) is often refractory to antiarrhythmic drugs (AAD) and immunosuppression. Catheter ablation has emerged as a treatment option for recurrent VT. However, data on the efficacy and outcomes of VT ablation in this context are sparse. METHODS AND RESULTS: A systematic search was performed on PubMed, EMBASE, and Cochrane database (from inception to September 2016) with included studies providing a minimum of information on CS patients undergoing VT ablation: age, gender, VT cycle length, CS diagnosis criteria, and baseline medications. Five studies reporting on 83 patients were identified. The mean age of patients was 50 ± 8 years, 53/30 (males/females) with a maximum of 56 patients receiving immunosuppressive therapy, mean ejection fraction was 39.1 ± 3.1% and 94% had an implantable cardioverter defibrillator in situ. The median number of VTs was 3 (2.6–4.9)/patient, mean cycle length of 360 ms (326–400 ms). Hundred percent of VTs received endocardial ablation, and 18% required epicardial ablation. The complication rates were 4.7–6.3%. Relapse occurred in 45 (54.2%) patients with an incidence of relapse 0.33 (95% confidence interval 0.108–0.551, P < 0.004). Employing a less stringent endpoint (i.e. freedom from arrhythmia or reduction of ventricular arrhythmia burden), 61 (88.4%) patients improved following ablation. CONCLUSIONS: These data support the utilization of catheter ablation in selected CS cases resistant to medical treatment. However, data are derived from observational non-controlled case series, with low-methodological quality. Therefore, future well-designed, randomized controlled trials, or large-scale registries are required

Mass Cytometry Identifies Distinct Lung CD4+ T Cell Patterns in Löfgren’s Syndrome and Non-Löfgren’s Syndrome Sarcoidosis

Sarcoidosis is a granulomatous disorder of unknown etiology, characterized by accumulation of activated CD4+ T cells in the lungs. Disease phenotypes Löfgren’s syndrome (LS) and “non-LS” differ in terms of clinical manifestations, genetic background, HLA association, and prognosis, but the underlying inflammatory mechanisms largely remain unknown. Bronchoalveolar lavage fluid cells from four HLA-DRB1*03+ LS and four HLA-DRB1*03− non-LS patients were analyzed by mass cytometry, using a panel of 33 unique markers. Differentially regulated CD4+ T cell populations were identified using the Citrus algorithm, and t-stochastic neighborhood embedding was applied for dimensionality reduction and single-cell data visualization. We identified 19 individual CD4+ T cell clusters differing significantly in abundance between LS and non-LS patients. Seven clusters more frequent in LS patients were characterized by significantly higher expression of regulatory receptors CTLA-4, PD-1, and ICOS, along with low expression of adhesion marker CD44. In contrast, 12 clusters primarily found in non-LS displayed elevated expression of activation and effector markers HLA-DR, CD127, CD39, as well as CD44. Hierarchical clustering further indicated functional heterogeneity and diverse origins of T cell receptor Vα2.3/Vβ22-restricted cells in LS. Finally, a near-complete overlap of CD8 and Ki-67 expression suggested larger influence of CD8+ T cell activity on sarcoid inflammation than previously appreciated. In this study, we provide detailed characterization of pulmonary T cells and immunological parameters that define separate disease pathways in LS and non-LS. With direct association to clinical parameters, such as granuloma persistence, resolution, or chronic inflammation, these results provide a valuable foundation for further exploration and potential clinical application

Feature evaluation of accelerometry signals for cough detection

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector