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BACKGROUND: The progression from Mycobacterium tuberculosis infection to active tuberculosis (TB) disease varies among individuals, and identifying biomarkers to predict progression is crucial for guiding interventions. In this study, we aimed to determine plasma immune biomarker profiles in healthy household contacts of index pulmonary TB (PTB) patients who either progressed to TB or remained as non-progressors. METHODS: A cohort of household contacts of adults with PTB was enrolled, consisting of 15 contacts who progressed to TB disease and 15 non-progressors. Plasma samples were collected at baseline, 4 months, and 12 months to identify predictive TB progression markers. RESULTS: Our findings revealed that individuals in the progressor group exhibited significantly decreased levels of IFNγ, IL-2, TNFα, IL1α, IL1ß, IL-17A, and IL-1Ra at baseline, months 4 and 12. In contrast, the progressor group displayed significantly elevated levels of IFNα, IFNß, IL-6, IL-12, GM-CSF, IL-10, IL-33, CCL2, CCL11, CXCL8, CXCL10, CX3CL1, VEGF, Granzyme-B and PDL-1 compared to the non-progressor group at baseline, months 4 and 12. ROC analysis identified IFNγ, GM-CSF, IL-1Ra, CCL2 and CXCL10 as the most promising predictive markers, with an AUC of ≥90. Furthermore, combinatorial analysis demonstrated that GM-CSF, CXCL10 and IL-1Ra, when used in combination, exhibited high accuracy in predicting progression to active TB disease. CONCLUSIONS: Our study suggests that a specific set of plasma biomarkers GM-CSF, CXCL10 and IL-1Ra, can effectively identify household contacts at significant risk of developing TB disease. These findings have important implications for early intervention and preventive strategies in TB-endemic regions.

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Autoimmune uveitis, a severe inflammatory condition of the eye, poses significant challenges due to its complex pathophysiology and the critical balance between protective and detrimental immune responses. Central to this balance are microglia, the resident immune cells of the central nervous system, whose roles in autoimmune uveitis are multifaceted and dynamic. This review article delves into the dual nature of microglial functions, oscillating between neuroprotective and neurotoxic outcomes in the context of autoimmune uveitis. Initially, we explore the fundamental aspects of microglia, including their activation states and basic functions, setting the stage for a deeper understanding of their involvement in autoimmune uveitis. The review then navigates through the intricate mechanisms by which microglia contribute to disease onset and progression, highlighting both their protective actions in immune regulation and tissue repair, and their shift towards a pro-inflammatory, neurotoxic profile. Special emphasis is placed on the detailed pathways and cellular interactions underpinning these dual roles. Additionally, the review examines the potential of microglial markers as diagnostic and prognostic indicators, offering insights into their clinical relevance. The article culminates in discussing future research directions, and the ongoing challenges in translating these findings into effective clinical applications. By providing a comprehensive overview of microglial mechanisms in autoimmune uveitis, this review underscores the critical balance of microglial activities and its implications for disease management and therapy development.

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Non-coding RNAs that are small in size, called microRNAs (miRNAs), exert a consequence in neutralizing gene activity after transcription. The nervous system is a massively expressed organ, and an expanding body of research reveals the vital functions that miRNAs play in the brain's growth and neural activity. The significant benefit of miRNAs on the development of the central nervous system is currently shown through new scientific methods that concentrate on targeting and eradicating vital miRNA biogenesis pathways the elements involving Dicer and DGCR8. Modulation of miRNA has been associated with numerous essential cellular processes on neural progenitors, like differentiation, proliferation, and destiny determination. Current research discoveries that emphasize the significance of miRNAs in the complex process of brain development are included in this book. The miRNA pathway plays a major role in brain development, its operational dynamics, and even diseases. Recent studies on miRNA-mediated gene regulation within neural discrepancy, the circadian period and synaptic remodeling are signs of this. We also discussed how these discoveries may affect our comprehension of the fundamental processes behind brain diseases, highlighting the novel therapeutic opportunities miRNAs provide for treating various human illnesses.

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INTRODUCTION: This research hoped to explore the molecular mechanism of neutrophil extracellular traps (NETs) on glioblastoma multiforme (GBM) progression, and develop a promising prognostic signature for GBM based on NETs-related genes (NETGs). MATERIAL AND METHODS: Gene expression data and clinical data of GBM tumour samples were downloaded from TCGA and CGGA databases. NETs-related molecular subtypes were explored by using ConsensusClusterPlus. The NETGs with a prognostic value were identified, and then a prognostic model was constructed using LASSO Cox regression. The predicted performance of the prognostic model was evaluated using TCGA training and CGGA validation cohorts. Moreover, independent prognostic indicators were identified by univariate and multivariate analysis to generate the nomogram model. The sensitivities for antitumor drugs and immunotherapy were predicted. Finally, hub genes in the prognostic model were validated using qPCR analysis. RESULTS: GBM patients were divided into two molecular subtypes with significant differences in tumour microenvironment (TME) score, survival, and immune infiltration. A NETGs signature was constructed based on seven genes (CPPED1, F3, G0S2, MME, MMP9, MAPK1, and MPO), which had a high value for predicting prognosis. A nomogram was constructed by two independent prognostic factors (age and risk score), which could be used to predict 1-, 2-, and 3-year survival probability of GBM. Patients in the high-risk group were more sensitive to bicalutamide, gefitinib, and dasatinib; patients in the low-risk group were associated with poor response to immunotherapy. The validation of the six genes in the prognostic model was consistent with the results of bioinformatics analysis. CONCLUSIONS: The NETs-based prognostic model and nomogram proposed in this study are promising prognostic prediction tools for GBM, which may provide new ideas for the development of precise tumour targeted therapy.

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Introduction Exposure to dust due to stone quarrying can cause severe respiratory ailments. Besides lung problems, research shows that exposure to quarry dust may also increase the risk of health problems affecting the heart, liver, kidney, central nervous system, and other organs. Despite the fact that a lot of studies have been reported on the respiratory system, our aim was to explore the evidence on the association between occupational exposure to quarry dust and its effect on renal health. Methodology This study was conducted on 75 quarry workers and 45 healthy matched controls were recruited from Allukuzhi village. Blood samples were collected and their kidney parameters were assessed in Hitech Diagnostics, Kanchipuram. Data were analyzed using ANOVA and strength of association was determined by Pearson correlation at significance p=0.05*.  Results The obtained results showed a significant increase in the level of creatinine (1.02±0.31), urea (24.62±8.52), and uric acid (5.13±1.31) in quarry workers upon the duration of exposure to quarry dust compared with control subjects (p < 0.05*). Conclusion The results of this study suggest a significant correlation between exposure to quarry dust and its reduced renal function. This could suggest that the quarry work site should have proper hazard control measures and safety precautionary equipment for the workers. Also, to be educated about the importance of the safety measures which have to be practiced in order to protect them from occupational hazards.

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Abstract: Colon cancer presents a complex pathophysiological landscape, which poses a significant challenge to the precise prediction of patient prognosis and treatment response. However, the emergence of omics sciences such as genomics, transcriptomics, proteomics, and metabolomics has provided powerful tools to identify molecular alterations and pathways involved in colon cancer development and progression. To address the lack of literature exploring the intersection of omics sciences, precision medicine, and colon cancer, we conducted a comprehensive search in ScienceDirect and PubMed databases. We included systematic reviews, reviews, case studies, clinical studies, and randomized controlled trials that were published between 2015-2023. To refine our search, we excluded abstracts and non-English studies. This review provides a comprehensive summary of the current understanding of the latest developments in precision medicine and omics sciences in the context of colon cancer. Studies have identified molecular subtypes of colon cancer based on genomic and transcrip-tomic profiles, which have implications for prognosis and treatment selection. Furthermore, precision medicine (which involves tailoring treatments, based on the unique molecular characteristics of each patient's tumor) has shown promise in improving outcomes for colon cancer patients. Omics sciences and precision medicine hold great promise for identifying new therapeutic targets and developing more effective treatments for colon cancer. Although not strictly designed as a systematic review, this review provides a readily accessible and up-to-date summary of the latest developments in the field, highlighting the challenges and opportunities for future research.

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Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

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Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson's, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used.

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BACKGROUND: Hepatocellular carcinoma is the most common malignant liver tumor in adults and thermal ablation and transarterial embolization are important methods of therapy. Thermal ablation can be used in early stages. Methods based on the transarterial approach, especially transarterial chemoembolization, play an important role in intermediate stage diseases. The success of procedures depends not only on the biological nature and the size of the tumor, on the technical design of the procedure and on the patient's response to treatment, but also on the molecular changes associated with these procedures. In addition to classic predictive and prognostic factors including age, patient comorbidities, Child-Pugh score, tumor characteristics, presence of large surrounding vessels, and portal vein thrombosis, molecular prognostic and predictive factors (serum biomarkers) are often mentioned in studies. Currently, only a-fetoprotein is routinely used as a prognostic biomarker; however, there are studies referring to new serum biomarkers that can potentially help to classical markers and imaging methods to determine the cancer prognosis and predict the success of therapy. These biomarkers most often include g-glutamyltranspeptidase, des- g-carboxyprothrombin, some types of microRNAs, inflammatory and hypoxic substances, whose serum levels are changed by the intervention therapies. Evaluation of these molecules could lead to the optimization of the medical intervention (choice of therapy method, timing of treatment) or change the management of patient follow-up after interventions. Although several biomarkers have shown promising results, most serum biomarkers still require validation in phase III studies. PURPOSE: The aim of this work is to present a comprehensive overview of classical and molecular biomarkers that could potentially help in the prognostic stratification of patients and better predict the success and effect of radiological intervention methods.

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BACKGROUND: The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE: In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS: We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS: We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION: The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.

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The major cause (more than 90%) of all cancer-related deaths is metastasis, thus its prediction can critically affect the survival rate. Metastases are currently predicted by lymph-node status, tumor size, histopathology and genetic testing; however, all these are not infallible, and obtaining results may require weeks. The identification of new potential prognostic factors will be an important source of risk information for the practicing oncologist, potentially leading to enhanced patient care through the proactive optimization of treatment strategies. Recently, the new mechanobiology-related techniques, independent of genetics, based on the mechanical invasiveness of cancer cells (microfluidic, gel indentation assays, migration assays etc.), demonstrated a high success rate for the detection of tumor cell metastasis propensity. However, they are still far away from clinical implementation due to complexity. Hence, the exploration of novel markers related to the mechanobiological properties of tumor cells may have a direct impact on the prognosis of metastasis. Our concise review deepens our knowledge of the factors that regulate cancer cell mechanotype and invasion, and incites further studies to develop therapeutics that target multiple mechanisms of invasion for improved clinical benefit. It may open a new clinical dimension that will improve cancer prognosis and increase the effectiveness of tumor therapies.

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Colorectal cancer is a common malignancy, and the incidence and mortality rates continue to rise. An important factor in the emergence of inflammation-induced colorectal carcinogenesis is elevated cyclooxygenase-2. Prostaglandin E2 (PGE2) over-production is frequently equated with cyclooxygenase-2 gene over-expression. PGE2 can be assessed by measuring the level of prostaglandin's main metabolite, PGE-M, in urine. Colorectal adenoma is a precancerous lesion that can lead to colorectal cancer. We conducted research to evaluate the association between urinary levels of the PGE-M and the risk of colorectal adenomas. In a western Chinese population, we identified 152 cases of adenoma and 152 controls patients without polyps. Adenoma cases were categorized into control, low-risk and high-risk groups. There was no significant change in PGE-M levels, between the control group and the low-risk adenoma group. In the high-risk group, the PGE-M levels were 23% higher than the control group. When compared to people with the lowest urine PGE-M levels (first quartile), people with greater urinary PGE-M levels had a higher chance of developing high-risk colorectal adenomas, with an adjusted odds ratio (95% CI) of 1.65 (0.76-3.57) in the fourth quartile group, (p= 0.013). We conclude urinary PGE-M is associated with the risk of developing high-risk adenomas. Urinary PGE-M level may be used as a non-invasive indicator for estimating cancer risk.

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Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models.

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The biological pathways involved in lesion formation after an acute ischemic stroke (AIS) are poorly understood. Despite successful reperfusion treatment, up to two thirds of patients with large vessel occlusion remain functionally dependent. Imaging characteristics extracted from DWI and T2-FLAIR follow-up MR sequences could aid in providing a better understanding of the lesion constituents. We built a fully automated pipeline based on a tree ensemble machine learning model to predict poor long-term functional outcome in patients from the MR CLEAN-NO IV trial. Several feature sets were compared, considering only imaging, only clinical, or both types of features. Nested cross-validation with grid search and a feature selection procedure based on SHapley Additive exPlanations (SHAP) was used to train and validate the models. Considering features from both imaging modalities in combination with clinical characteristics led to the best prognostic model (AUC = 0.85, 95%CI [0.81, 0.89]). Moreover, SHAP values showed that imaging features from both sequences have a relevant impact on the final classification, with texture heterogeneity being the most predictive imaging biomarker. This study suggests the prognostic value of both DWI and T2-FLAIR follow-up sequences for AIS patients. If combined with clinical characteristics, they could lead to better understanding of lesion pathophysiology and improved long-term functional outcome prediction.

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Chronic kidney disease (CKD) is a condition that can be caused due to any etiology leading to structural damage to the kidney, which can be measured by a decrease in estimated glomerular filtration rate (eGFR) and the presence of damage biomarkers for more than three months. This article has discussed the causal relationship between atrial fibrillation (AF) and CKD, a few of them being inflammation, renin-angiotensin-aldosterone system (RAAS) activation, anemia, and uremia associated with CKD. This review mentioned the clinical impact of the presence of AF in CKD patients. The presence of AF in CKD patients aggravates the renal dysfunction, which in turn adds to the generation of AF. This article explores the various pharmacological and interventional treatment modalities, including antiarrhythmics, anticoagulants, and cardiac ablation, and their complications, leading to restricted usage in CKD patients.

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BACKGROUND AND OBJECTIVE: Eye-movement trajectories are rich behavioral data, providing a window on how the brain processes information. We address the challenge of characterizing signs of visuo-spatial neglect from saccadic eye trajectories recorded in brain-damaged patients with spatial neglect as well as in healthy controls during a visual search task. METHODS: We establish a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements. We use traditional machine learning algorithms together with deep convolutional networks (both 1D and 2D) to automatically analyze eye trajectories. RESULTS: Our top-performing machine learning models classified neglect patients vs. healthy individuals with an Area Under the ROC curve (AUC) ranging from 0.83 to 0.86. Moreover, the 1D convolutional neural network scores correlated with the degree of severity of neglect behavior as estimated with standardized paper-and-pencil tests and with the integrity of white matter tracts measured from Diffusion Tensor Imaging (DTI). Interestingly, the latter showed a clear correlation with the third branch of the superior longitudinal fasciculus (SLF), especially damaged in neglect. CONCLUSIONS: The study introduces new methods for both the pre-processing and the classification of eye-movement trajectories in patients with neglect syndrome. The proposed methods can likely be applied to other types of neurological diseases opening the possibility of new computer-aided, precise, sensitive and non-invasive diagnostic tools.

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BACKGROUND AND OBJECTIVES: Multiple Sclerosis (MS) is a neurological disease associated with various and heterogeneous clinical characteristics. Given its complex nature and its unpredictable evolution over time, there isn't an established and exhaustive clinical protocol (or tool) for its diagnosis nor for monitoring its progression. Instead, different clinical exams and physical/psychological evaluations need to be taken into account. The Expanded Disability Status Scale (EDSS) is the most used clinical scale, but it suffers from several limitations. Developing computational solutions for the identification of bio-markers of disease progression that overcome the downsides of currently used scales is crucial and is gaining interest in current literature and research. METHODS: This Review focuses on the importance of approaching MS diagnosis and monitoring by investigating correlations between cognitive impairment and clinical data that refer to different MS domains. We review papers that integrate heterogeneous data and analyse them with statistical methods to understand their applicability into more advanced computational tools. Particular attention is paid to the impact that computational approaches can have on personalized-medicine. RESULTS: Personalized medicine for neuro-degenerative diseases is an unmet clinical need which can be addressed using computational approaches able to efficiently integrate heterogeneous clinical data extracted from both private and publicly available electronic health databases. CONCLUSIONS: Reliable and explainable Artificial Intelligence are computational approaches required to understand the complex and demonstrated interactions between MS manifestations as well as to provide reliable predictions on the disease evolution, representing a promising research field.

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Introduction Tuberculosis-associated obstructive pulmonary disease (TOPD), anxiety, and depression are significant public health problems worldwide and their prevalence is common. These diseases interfere with physical, psychosocial, and economic well-being, resulting in unemployment, prolonged hospitalization, abstinence from working, and isolation. Subjects and methods This is a single-center, cross-sectional cohort, observational study conducted in a tertiary care hospital over six years to understand spirometry, laboratory profiles, as well as the impact on overall health, daily life, and perceived well-being in patients with TOPD. Result The sample size of the study was 73 patients. A total of 43 (58.5%) patients had depression with an average St. George's Respiratory Questionnaire for chronic obstructive pulmonary disease (SGRQ-C) score of 67.5, and 16 (21.9%) patients had anxiety with an average SGRQ-C score of 78.9. In the patients who scored higher on the Hamilton Depression Rating Scale (HAM-D), there was a significant correlation between Hamilton Anxiety Rating Scale (HAM-A) and HAM-D scores, as well as C-reactive protein (CRP) levels and WBC counts. In 16 (21.9%) of the patients with moderate to severe anxiety, there was a statistically significant negative correlation between higher HAM-A scores and lower WBC counts. Anxiety, depression, CRP level, WBC count, and serum fibrinogen did not show a significant correlation with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) groups-based assessment of TOPD severity. A high serum fibrinogen level did not correlate with a high HAM-D score, nor did a high CRP level correlate with a high HAM-A score. Conclusion Psychiatric comorbidities like depression are associated with increased inflammation in chronic diseases like TOPD, but no definitive biomarker has been identified and further studies are required to identify suitable biomarkers.

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BACKGROUND: The current paradigm is that fibrosis promotes electrophysiological disorders and drives atrial fibrillation (AF). In this current study, we investigated the relation between the degree of fibrosis in human atrial tissue samples of controls and patients in various stages of AF and the degree of electrophysiological abnormalities. METHODS: The degree of fibrosis was measured in the atrial tissue and serum of patients in various stages of AF and the controls. Hereto, picrosirius and H&E staining were performed to quantify degree of total, endo-perimysial fibrosis, and cardiomyocyte diameter. Western blot quantified fibrosis markers: neural cell adhesion molecule, tissue inhibitor of metalloproteinase, lysyl oxidase, and α-smooth muscle actin. In serum, the ratio carboxyl-terminal telopeptide of collagen/matrix-metalloproteinase1 was determined. High-resolution epicardial mapping evaluated low-voltage areas and conduction abnormalities. RESULTS: No significant differences were observed in the degree of fibrosis between the groups. Finally, no significant correlation-absolute nor spatial-was observed between all electrophysiological parameters and histological fibrosis markers. CONCLUSIONS: No differences in the degree of fibrosis were observed in patients from various stages of AF compared to the controls. Moreover, electrophysiological abnormalities did not correlate with any of the fibrosis markers. The findings indicate that fibrosis is not the hallmark of structural remodeling in AF.

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Breathomics is the future of non-invasive point-of-care devices. The field of breathomics can be split into the isolation of disease-specific volatile organic compounds (VOCs) and their detection. In the present work, an array of five quartz tuning fork (QTF)-based sensors modified by polymer with nanomaterial additive has been utilized. The array has been used to detect samples of human breath spiked with ∼0.5 ppm of known VOCs namely, acetone, acetaldehyde, octane, decane, ethanol, methanol, styrene, propylbenzene, cyclohexanone, butanediol, and isopropyl alcohol which are bio-markers for certain diseases. Polystyrene was used as the base polymer and it was functionalized with 4 different fillers namely, silver nanoparticles-reduced graphene oxide composite, titanium dioxide nanoparticles, zinc ferrite nanoparticles-reduced graphene oxide composite, and cellulose acetate. Each of these fillers enhanced the selectivity of a particular sensor towards a certain VOC compared to the pristine polystyrene-modified sensor. Their interaction with the VOCs in changing the mechanical properties of polymer giving rise to change in the resonant frequency of QTF is used as sensor response for detection. The interaction of functionalized polymers with VOCs was analyzed by FTIR and UV-vis spectroscopy. The collective sensor response of five sensors is used to identify VOCs using an ensemble classifier with 92.8% accuracy of prediction. The accuracy of prediction improved to 96% when isopropyl alcohol, ethanol, and methanol were considered as one class.

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