Antioxidant, antimicrobial and healing properties of an extract from coffee pulp for the development of a phytocosmetic.

Consumer demand for natural, chemical-free products has grown. Food industry residues, like coffee pulp, rich in caffeine, chlorogenic acid and phenolic compounds, offer potential for pharmaceutical and cosmetic applications due to their antioxidant, anti-inflammatory, and antibacterial properties. Therefore, the objective of this work was to develop a phytocosmetic only with natural products containing coffee pulp extract as active pharmaceutical ingredient with antioxidant, antimicrobial and healing activity. Eight samples from Coffea arabica and Coffea canephora Pierre were analyzed for caffeine, chlorogenic acid, phenolic compounds, tannins, flavonoids, cytotoxicity, antibacterial activity, and healing potential. The Robusta IAC-extract had the greatest prominence with 192.92 µg/mL of chlorogenic acid, 58.98 ± 2.88 mg GAE/g sample in the FRAP test, 79.53 ± 5.61 mg GAE/g sample in the test of total phenolics, was not cytotoxic, and MIC 3 mg/mL against Staphylococcus aureus. This extract was incorporated into a stable formulation and preferred by 88% of volunteers. At last, a scratch assay exhibited the formulation promoted cell migration after 24 h, therefore, increased scratch retraction. In this way, it was possible to develop a phytocosmetic with the coffee pulp that showed desirable antioxidant, antimicrobial and healing properties.

Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment.

As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions. KEY MESSAGES: Skin imprints complement plasma in disease metabolomics. The annotated markers have a role in immunomodulation and metabolic diseases. Skin imprints outperformed plasma samples at assessing disease severity. Skin imprints have potential as non-invasive sampling strategy for COVID-19.

Metabolomics in chronic hepatitis C: Decoding fibrosis grading and underlying pathways.

BACKGROUND: Chronic Hepatitis C (CHC) affects 71 million people globally and leads to liver issues such as fibrosis, cirrhosis, cancer, and death. A better understanding and prognosis of liver involvement are vital to reduce morbidity and mortality. The accurate identification of the fibrosis stage is crucial for making treatment decisions and predicting outcomes. Tests used to grade fibrosis include histological analysis and imaging but have limitations. Blood markers such as molecular biomarkers can offer valuable insights into fibrosis. AIM: To identify potential biomarkers that might stratify these lesions and add information about the molecular mechanisms involved in the disease. METHODS: Plasma samples were collected from 46 patients with hepatitis C and classified into fibrosis grades F1 (n = 13), F2 (n = 12), F3 (n = 6), and F4 (n = 15). To ensure that the identified biomarkers were exclusive to liver lesions (CHC fibrosis), healthy volunteer participants (n = 50) were also included. An untargeted metabolomic technique was used to analyze the plasma metabolites using mass spectrometry and database verification. Statistical analyses were performed to identify differential biomarkers among groups. RESULTS: Six differential metabolites were identified in each grade of fibrosis. This six-metabolite profile was able to establish a clustering tendency in patients with the same grade of fibrosis; thus, they showed greater efficiency in discriminating grades. CONCLUSION: This study suggests that some of the observed biomarkers, once validated, have the potential to be applied as prognostic biomarkers. Furthermore, it suggests that liquid biopsy analyses of plasma metabolites are a good source of molecular biomarkers capable of stratifying patients with CHC according to fibrosis grade.

Tomato classification using mass spectrometry-machine learning technique: A food safety-enhancing platform.

Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.

Comparative metabolomic profiling of women undergoing in vitro fertilization procedures reveals potential infertility-related biomarkers in follicular fluid.

Infertility is a worldwide concern, affecting one in six couples throughout their reproductive period. Therefore, enhancing the clinical tools available to identify the causes of infertility may save time, money, and emotional distress for the involved parties. This study aims to annotate potential biomarkers in follicular fluid that are negatively affecting pregnancy outcomes in women suffering infertility-related diseases such as endometriosis, tuboperitoneal factor, uterine factor, and unexplained infertility, using a metabolomics approach through high-resolution mass spectrometry. Follicular fluid samples collected from women who have the abovementioned diseases and managed to become pregnant after in vitro fertilization procedures [control group (CT)] were metabolically compared with those from women who suffer from the same diseases and could not get pregnant after the same treatment [infertile group (IF)]. Mass spectrometry analysis indicated 10 statistically relevant differential metabolites in the IF group, including phosphatidic acids, phosphatidylethanolamines, phosphatidylcholines, phosphatidylinositol, glucosylceramides, and 1-hydroxyvitamin D3 3-D-glucopyranoside. These metabolites are associated with cell signaling, cell proliferation, inflammation, oncogenesis, and apoptosis, and linked to infertility problems. Our results indicate that understanding the IF's metabolic profile may result in a faster and more assertive female infertility diagnosis, lowering the costs, and increasing the probability of a positive pregnancy outcome.

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients.

The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.

Efficacy and safety of HD-tDCS and respiratory rehabilitation for critically ill patients with COVID-19 The HD-RECOVERY randomized clinical trial.

BACKGROUND AND PURPOSE: Acute Respiratory Distress Syndrome (ADRS) due to coronavirus disease 2019 (COVID-19) has been associated with muscle fatigue, corticospinal pathways dysfunction, and mortality. High-Definition transcranial Direct Current Stimulation (HD-tDCS) may be used to attenuate clinical impairment in these patients. The HD-RECOVERY randomized clinical trial was conducted to evaluate the efficacy and safety of HD-tDCS with respiratory rehabilitation in patients with moderate to severe ARDS due to COVID-19. METHODS: Fifty-six critically ill patients were randomized 1:1 to active (n = 28) or sham (n = 28) HD-tDCS (twice a day, 30-min, 3-mA) plus respiratory rehabilitation for up to 10 days or until intensive care unit discharge. The primary outcome was ventilator-free days during the first 28 days, defined as the number of days free from mechanical ventilation. Furthermore, secondary outcomes such as delirium, organ failure, hospital length of stay and adverse effects were investigated. RESULTS: Active HD-tDCS induced more ventilator-free days compared to sham HD-tDCS. Patients in the active group vs in the sham group experienced lower organ dysfunction, delirium, and length of stay rates over time. In addition, positive clinical response was higher in the active vs sham group. There was no significant difference in the prespecified secondary outcomes at 5 days. Adverse events were similar between groups. CONCLUSIONS: Among patients with COVID-19 and moderate to severe ARDS, use of active HD-tDCS compared with sham HD-tDCS plus respiratory rehabilitation resulted in a statistically significant increase in the number of ventilator-free days over 28 days. HD-tDCS combined with concurrent rehabilitation therapy is a safe, feasible, potentially add-on intervention, and further trials should examine HD-tDCS efficacy in a larger sample of patients with COVID-19 and severe hypoxemia.

Differentially expressed plasmatic microRNAs in Brazilian patients with Coronavirus disease 2019 (COVID-19): preliminary results.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is known that host microRNAs (miRNAs) can be modulated to favor viral infection or to protect the host. Herein, we report preliminary results of a study aiming at identifying differentially expressed plasmatic miRNAs in Brazilian patients with COVID-19. METHODS AND RESULTS: miRNAs were extracted from the plasma of eight patients with COVID-19 (four patients with mild COVID-19 and four patients with severe/critical COVID-19) and four healthy controls. Patients and controls were matched for sex and age. miRNA expression levels were detected using high-throughput sequencing. Differential miRNA expression and enrichment analyses were further evaluated. A total of 18 miRNAs were differentially expressed between patients with COVID-19 and controls. miR-4433b-5p, miR-6780b-3p, miR-6883-3p, miR-320b, miR-7111-3p, miR-4755-3p, miR-320c, and miR-6511a-3p were the most important miRNAs significantly involved in the PI3K/AKT, Wnt/ß-catenin, and STAT3 signaling pathways. Moreover, 42 miRNAs were differentially expressed between severe/critical and mild patients with COVID-19. miR-451a, miR-101-3p, miR-185-5p, miR-30d-5p, miR-25-3p, miR-342-3p, miR-30e-5p, miR-150-5p, miR-15b-5p, and miR-29c-3p were the most important miRNAs significantly involved in the Wnt/ß-catenin, NF-κß, and STAT3 signaling pathways. CONCLUSIONS: If validated by quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) in a larger number of participants, the miRNAs identified in this study might be used as possible biomarkers for the diagnosis and severity of COVID-19.

Metabolic alterations in Strongyloidiasis stool samples unveil potential biomarkers of infection.

Strongyloidiasis, a parasitosis caused by Strongyloides stercoralis in humans, is a very prevalent infection in tropical or subtropical areas. Gaps on public health strategies corroborates to the high global incidence of strongyloidiasis especially due to challenges involved on its diagnosis. Based on the lack of a gold-standard diagnostic tool, we aimed to present a metabolomic study for the assessment of stool metabolic alterations. Stool samples were collected from 25 patients segregated into positive for strongyloidiasis (n = 10) and negative control (n = 15) and prepared for direct injection high-resolution mass spectrometry analysis. Using metabolomics workflow, 18 metabolites were annotated increased or decreased in strongyloidiasis condition, from which a group of 5 biomarkers comprising caprylic acid, mannitol, glucose, lysophosphatidylinositol and hydroxy-dodecanoic acid demonstrated accuracy over 89% to be explored as potential markers. The observed metabolic alteration in stool samples indicates involvement of microbiota remodeling, parasite constitution, and host response during S. stercoralis infection.

Effect of in vitro digestion on the bioaccessibility and bioactivity of phenolic compounds in fractions of Eugenia pyriformis fruit.

Uvaia is a Brazilian fruit species that has great economic and nutritional potential, in addition to being a good source of compounds of biological interest. In this study, we evaluated for the first time the influence of in vitro gastrointestinal digestion on the bioaccessibility and bioactivity of phenolic compounds from two fractions of uvaia (edible and seed). It was observed that the content of total phenolic compounds was about 3 times higher in the seed (undigested extract), but reduced significantly after intestinal digestion (-50.08%). In turn, the total flavonoid content was about 5 times higher in the undigested seed extract. After intestinal digestion, the flavonoid content increased in the edible fraction (+109.49%) and decreased in the uvaia seed (-70.20%). The heatmap analysis showed that after intestinal digestion, there was an increase in the relative intensity of the flavonoids, while phenolic acids reduced their intensity. The antioxidant capacity of the undigested extract was 4-7 times greater for the seed, but decreased after intestinal digestion (-8.04-27.23%), while the antioxidant capacity of the edible fraction increased by 72.12-107.89% in this same stage of digestion. Thus, the content of phenolic compounds and antioxidant capacity were higher in the uvaia seed, and the bioaccessibility of the bioactive compounds in this fruit were dependent on the fraction and digestive phase evaluated. These results can contribute to the establishment of uvaia as a novel ingredient for preparations with functional claims.

Gas6 drives Zika virus-induced neurological complications in humans and congenital syndrome in immunocompetent mice.

Zika virus (ZIKV) has the ability to cross placental and brain barriers, causing congenital malformations in neonates and neurological disorders in adults. However, the pathogenic mechanisms of ZIKV-induced neurological complications in adults and congenital malformations are still not fully understood. Gas6 is a soluble TAM receptor ligand able to promote flavivirus internalization and downregulation of immune responses. Here we demonstrate that there is a correlation between ZIKV neurological complications with higher Gas6 levels and the downregulation of genes associated with anti-viral response, as type I IFN due to Socs1 upregulation. Also, Gas6 gamma-carboxylation is essential for ZIKV invasion and replication in monocytes, the main source of this protein, which was inhibited by warfarin. Conversely, Gas6 facilitates ZIKV replication in adult immunocompetent mice and enabled susceptibility to transplacental infection. Our data indicate that ZIKV promotes the upregulation of its ligand Gas6, which contributes to viral infectivity and drives the development of severe adverse outcomes during ZIKV infection.

Metabolic shift of chronic myeloid leukemia patients under imatinib-pioglitazone regimen and discontinuation.

The Estudo de Descontinuação de Imatinibe após Pioglitazona (EDI-PIO) is a single-center, longitudinal, prospective, phase 2, non-randomized, open, clinical trial (NCT02852486, August 2, 2016 retrospectively registered) for the discontinuation of imatinib after concomitant use of pioglitazone, being the first of its kind in a Brazilian population with chronic myeloid leukemia. Due to remaining of leukemic quiescent cells that are not affected by tyrosine kinase inhibitors, it has been suggested the use of pioglitazone, a PPARγ agonist, together with imatinib as a strategy for the maintenance of deep molecular response. The clinical benefit to this association is still controversial, and the metabolic alteration along this process remains unclear. Therefore, we applied a metabolomic protocol using high-resolution mass spectrometry to profile plasmatic metabolic response of a prospective cohort of ten individuals under discontinuation of imatinib and pioglitazone protocol. By comparing patients under pioglitazone and imatinib treatment with imatinib monotherapy and discontinuation phase, we were able to annotate 41 and 36 metabolites, respectively. The metabolic alterations observed during imatinib-pioglitazone combined therapy are associated with an extensive lipid remodeling, with activation of ß-oxidation pathway, in addition to the presence of markers that suggest mitochondrial dysfunction.

Evaluation of antioxidant capacity, fatty acid profile, and bioactive compounds from buritirana (Mauritiella armata Mart.) oil: A little-explored native Brazilian fruit.

Buritirana (Mauritiella armata Mart.) is a fruit species native to the Amazon and Cerrado region, belonging to the Arecaceae family. It has high nutritional and functional potential, yet little explored. In this study, we evaluated for the first time the overall yield, behavior of total carotenoids in the extraction kinetics, fatty acid profile, bioactive compounds, and the antioxidant capacity of the oil from buritirana fractions obtained by supercritical CO2. The highest extraction yield was found in the pulp and whole without seed at 60 °C (18.06 ± 0.40 and 14.55 ± 1.10 g 100 g-1 of the freeze-dried sample (fdw), respectively), and in the peel at 40 °C (8.31 ± 0.73 g 100 g-1 fdw). During the extraction kinetics, the pulp had the highest yields of oil (41.57%) and total carotenoids (8.34 mg g-1) after 61 min at 40 °C. The antioxidant potential, fatty acid profile, and α-tocopherol content were dependent on both fraction and temperature, with oleic acid being the main fatty acid. The oil from the whole fraction without seed had the largest number (20) of identified phenolic compounds. The extraction at 60 °C reduced the relative intensity of most compounds in the whole without seed and pulp. Moreover, it increased the intensity of the compounds in the peel. These results suggest that buritirana is a good oil source with great bioactive potential to produce new products with functional claims.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

Chemical characterization of Eugenia stipitata: A native fruit from the Amazon rich in nutrients and source of bioactive compounds.

Eugenia stipitata is a fruit native to the Brazilian Amazonian region, belonging to the Myrtaceae family whose chemical composition has been little evidenced. In this study, we evaluated for the first time the nutritional composition, bioactive compounds and antioxidant properties of two fractions of this fruit. It was observed that the edible fraction had a higher content of minerals such as K, Ca and Mg (827.66 ± 14.51; 107.16 ± 1.54; and 75.65 ± 1.28 mg 100 g-1 dw, respectively), sucrose (38.01 ± 2.94 mg g-1 dw), fructose (17.58 ± 0.80 mg g-1 dw), and maltotetraose (1.63 ± 0.09 mg g-1 dw). In this same fraction, about 30 volatile compounds were found, mainly biciclo(3.2.1)octan-3-one, 6 (2-hydroxyethyl)-, endo-; butanoic acid, 2-methyl-, hexyl ester and p-ocimene. In turn, the seed had the highest number of compounds identified by ESI-LTQ-MS/MS (including vanillic acid, gallic acid hexoside, catechin hexoside, luteolin hexoside, among others), higher content of phenolics (142.43 ± 0.82 mg GAE g-1 dw), flavonoids (43.73 ± 0.23 mg CE g-1 dw), and antioxidant capacity (139.59 ± 2.47; 447.94 ± 2.70; and 100.07 ± 10.50 µM TE g-1 dw for DPPH, ABTS, and ORAC, respectively). These results suggest that Eugenia stipitata has excellent nutritional value and great functional potential, and may contribute to a greater commercial exploitation of this fruit, not only in food, but also in the pharmaceutical and cosmetic industries.

Influence of high-intensity ultrasound on color, chemical composition and antioxidant properties of araçá-boi pulp.

In this study, we evaluated the influence of the ultrasound application on five levels of energy density (1000; 3000; 5000 and 7000 J g-1) compared to two pasteurization techniques (70 °C/5 min and 94 °C/0.5 min) on color parameters, polyphenoloxidase activity, chemical composition, and antioxidant properties of araçá-boi pulp. Ultrasound caused changes in the parameters brightness/darkness, hue angle, and total color difference, but did not change chroma, yellowness/blueness, color index, and yellow index. Moreover, this technique was efficient for inactivating polyphenoloxidase. Ultrasound at 7000 J g-1 was responsible for an increase in soluble solids (16%), vitamin C (46.5%), phenolics (15.65%), flavonoids (50%) and antioxidant capacity in relation to untreated pulp, while ultrasound at 5000 J g-1 increased the relative intensity of compounds of biological interest. Thus, ultrasound can be considered as a promising technique to maintain the shelf life, without drastically affecting the nutritional and functional qualities of this fruit.

Distribution of nutrients and functional potential in fractions of Eugenia pyriformis: An underutilized native Brazilian fruit.

Uvaia is a Brazilian native species whose fruit has few studies on the nutritional composition and antioxidant properties. In this study, we evaluated for the first time the proximate composition, mineral content, carbohydrate profile, identification of organic compounds, and determination of antioxidant properties in two fractions of this fruit (edible fraction and seed). Edible fraction showed the highest content of ash, lipids, proteins, total fibers, minerals mainly K and Mg (1557.61 and 124.40 mg 100 g-1, respectively), and carbohydrates such as fructose, sucrose, glucose (123.08; 64.40; and 42.39 mg g-1, respectively), and maltotetraose (G4). From the ESI-LTQ-XL-MS/MS analysis, it was possible to identify 22 compounds in the edible fraction and 16 compounds in the uvaia seed, including organic acids, phenolic acids and flavonoids. On the other hand, uvaia seed had the highest content of total phenolics, flavonoids and antioxidant capacity. These results suggest that this fruit has great potential to be used in industry, with emphasis on making food with functional claims.

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses.

Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life.IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients' blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients' condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.

Does leukotriene F4 play a major role in the infection mechanism of Candida sp.?

Candidiasis is the most common fungal infection affecting hospitalized patients, especially immunocompromised and critical patients. Limitations regarding the assertive diagnosis of both Candidemia and Candidiasis not only impairs the introduction of effective treatments but also lays a heavy financial burden over the health system. Furthermore, it is still challenging to ascertain whether diagnostic methods are accurate and whether treatment is effective for patients with Candidemia. These constraints come from the uncertainty of the pathophysiological mechanism by which the pathogen establishes the opportunistic infection. Additionally, it is the reason why some patients present positive blood culture results, and others do not, and why it is very difficult during clinical routines to prove Candidemia or invasive candidiasis. Taking into account the current situation, this contribution proposes two markers that may help to understand the mechanisms of infection by the pathogen: Leukotriene F4 and 5,6-dihydroxy-eicosatetraenoic. These two lipids putatively modulate the host's immune response, and the initial data presented in this contribution suggest that these lipids allow the opportunistic infection to be installed. The study was carried out using an omics-based platform using direct-infusion high-resolution mass spectrometry and allied with bioinformatics tools to provide accurate and reliable results for biomarker candidates screening.

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.