The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

BACKGROUND: Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. AIM OF REVIEW: This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. KEY SCIENTIFIC CONCEPTS OF REVIEW: We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study.

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests.

BACKGROUND: Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients. METHODS: Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively. RESULTS: Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality. CONCLUSIONS: It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry.

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis.

Paracoccidioidomycosis (PCM) is a systemic mycosis with high incidence in Latin America, caused by species of the genus Paracoccidioides spp. Brazil is considered to be the endemic center of this disease, which is identified as the eighth cause of mortality from chronic infectious disease in the country. There are several specific diagnostic methods in PCM, such as microbiological, immunological, histopathological, and molecular. However, the standard laboratory diagnosis depends mostly on fungus direct observation - the gold standard of PCM diagnosis. The implementation of new technologies, such as Fourier Transform Infrared (FTIR), can contribute to the clinical diagnosis trial of this disease. Here, we evaluated a new strategy for the diagnosis of PCM by using blood serum FTIR spectra from 20 patients with PCM and 20 healthy individuals. Machine learning algorithms were able to provide an overall accuracy of 91.67% by using Cubic SVM in the PCA data from FTIR results.

A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning.

Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

Uric acid electrochemical sensing in biofluids based on Ni/Zn hydroxide nanocatalyst.

A highly sensitive sensor for quantification of uric acid (UA) directly in body fluids (saliva and sweat) is reported, working at a potential as low as 0.0 V vs Ag/AgCl. New mixed hydroxide materials exhibiting stable electrocatalytic responses from alkaline to acidic media were prepared, their structure was thoroughly characterized, and the electrochemical properties of the modified FTO (fluorine-doped tin oxide) electrodes were evaluated for UA determination by cyclic voltammetry, chronoamperometry, and batch injection analysis. A very low limit of detection (2.3 × 10-8 mol L-1) with good repeatability (RSD = 3.2% for 30 successive analyses) was achieved based on a fast and simple BIA procedure. Finally, α-Ni0.75Zn0.25(OH)2 screen-printed electrodes (SPE) were developed for the measurement of UA directly in real saliva and sweat samples, without interference of ascorbic acid, acetaminophen, lactate, and glucose at their typical concentrations present in those body fluids, revealing high potential for application as disposable sensors in biological systems. Graphical abstract.

1H NMR-based metabolite profiling for biomarker identification.

The metabolome is affected by individual physiologic and pathophysiologic states as well as the environment. Metabolomics has become crucial approach for clinical studies, providing a better understanding of disease mechanisms. The expansion of analytical methods aiming at performing detailed analysis of biofluids has led to the characterization of many disease biomarkers. NMR provides faster and more comprehensive assessment of the biological samples in human models. NMR-based profiling studies aimed at identifying biomarkers for specific diseases has significantly increased over the last few years. These have attempted to correlate human pathophysiology with alterations in the metabolic profile of common biofluids such as urine, plasma and serum. In this context, NMR-based untargeted metabolomics has become an important adjunct for the identification of biomarkers in disease research, not only for early diagnosis purposes, but also for therapy prediction, precise prognosis or monitoring of disease progression. This review critically discusses recent findings of urine (non-invasive) and blood serum and plasma (minimally invasive) metabolomics, focusing on key role of metabolites and their use as potential biomarkers for diagnostic purposes.

Alteration of Sphingolipids in Biofluids: Implications for Neurodegenerative Diseases.

Sphingolipids (SL) modulate several cellular processes including cell death, proliferation and autophagy. The conversion of sphingomyelin (SM) to ceramide and the balance between ceramide and sphingosine-1-phosphate (S1P), also known as the SL rheostat, have been associated with oxidative stress and neurodegeneration. Research in the last decade has focused on the possibility of targeting the SL metabolism as a therapeutic option; and SL levels in biofluids, including serum, plasma, and cerebrospinal fluid (CSF), have been measured in several neurodegenerative diseases with the aim of finding a diagnostic or prognostic marker. Previous reviews focused on results from diseases such as Alzheimer's Disease (AD), evaluated total SL or species levels in human biofluids, post-mortem tissues and/or animal models. However, a comprehensive review of SL alterations comparing results from several neurodegenerative diseases is lacking. The present work compiles data from circulating sphingolipidomic studies and attempts to elucidate a possible connection between certain SL species and neurodegeneration processes. Furthermore, the effects of ceramide species according to their acyl-chain length in cellular pathways such as apoptosis and proliferation are discussed in order to understand the impact of the level alteration in specific species. Finally, enzymatic regulations and the possible influence of insulin resistance in the level alteration of SL are evaluated.