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.

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.

An LDI-MSI approach for targeted and untargeted differentiation and assessment of pharmaceutical formulations.

Falsified, counterfeit and adulterated medicines are an endemic problem worldwide that results in both monetary and health-related losses. Developing fast and reliable methods that are able to present a timely result based on the drug's spectral profile is an effort that is sure to benefit those involved in the whole distribution chain. We propose herein a Laser Desorption/Ionization imaging-based method that provides simple and minimal sample preparation; this method is capable of providing specific markers that characterize adulterations, using as proof of concept one of the most adulterated drug products for oral use, sildenafil. Our approach is able to provide quality markers, which can be applied in the fast screening of any product within the same molecular class. This same strategy may be a useful alternative to provide accurate measurements with high specificity for unraveling contaminants and/or byproducts in virtually any given pharmaceutical product.

The role of lipids in the inception, maintenance and complications of dengue virus infection.

Dengue fever is a viral condition that has become a recurrent issue for public health in tropical countries, common endemic areas. Although viral structure and composition have been widely studied, the infection phenotype in terms of small molecules remains poorly established. This contribution providing a comprehensive overview of the metabolic implications of the virus-host interaction using a lipidomic-based approach through direct-infusion high-resolution mass spectrometry. Our results provide further evidence that lipids are part of both the immune response upon Dengue virus infection and viral infection maintenance mechanism in the organism. Furthermore, the species described herein provide evidence that such lipids may be part of the mechanism that leads to blood-related complications such as hemorrhagic fever, the severe form of the disease.

Migration from plastic packaging into meat.

Migration is a known phenomenon defined as the partitioning of chemical compounds from the packaging into food, and depends on several factors. Migration assays are generally time-consuming and require specific conditions in order to investigate the behavior of the packaging in different situations. Furthermore, these tests are often performed with food simulants, since the determination of migration under real conditions is highly impaired. Several methodologies have been designed to carry out this study, but an ideal approach should be capable of assessing the migration of compounds in real samples, providing fast and reliable results. Within this context, mass spectrometry can be considered a suitable and versatile technique that shows great potential to accurately characterize several contaminants in food by migration. Thus, in this work we present a mass spectrometry-based application for the detection of several compounds from plastic, directly from vacuum-packed meat samples. This preliminary and simple workflow can be easily applied in routine analyses for either quality control purposes or in the prospection of other potential bioactive contaminants in food.

Evaluating the effects of the adulterants in milk using direct-infusion high-resolution mass spectrometry.

Milk is an extremely complex food, capable of providing essential nutrients as well as being an important source of energy, and high-quality proteins and fats. Due to advances in technology, and to meet the increasing demand, production costs have increased, turning milk into a target of adulterations. Routine methods usually applied to certify the quality of the milk are restricted to microbiological tests, and assays that attest the nutritional composition within the expected values. However, potentially harmful byproducts generated by adulterating substances in general are not detected through these methodologies. In this contribution, we simulated the adulteration of freshly produced milk samples with four adulterants whose use already had reported for extended shelf life: formaldehyde, hydrogen peroxide, sodium hydroxide, and sodium hypochlorite. These samples were submitted to direct-infusion high-resolution mass spectrometry analysis and multivariate statistical analysis. This approach allows the characterization of a series of molecules modified by the adulterants, what demonstrates how these species affect the nutritious characteristics of this product.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

New Approach of QuEChERS and GC-MS Triple-Quadrupole for the Determination of Ethyl Carbamate Content in Brazilian cachaças.

Cachaça is a popular spirit produced in Brazil, obtained by distillation of fermented sugar cane. Among the contaminants arising from production, ethyl carbamate is a carcinogenic compound that occurs naturally in fermented foods and beverages; in Brazil, the maximum limit established by current legislation is 150 µg L-1. Quality control is usually performed using gas chromatography; however, robustness and reproducibility of quantitative results may be severely impaired, as the addition of 6-30 g L-1 of sucrose is a common procedure for taste standardization, directly interfering in the results. This work describes the development of a novel method to improve ethyl carbamate quantification in cachaças using a new approach of QuEChERS extraction based on salting-out phenomenon, to effectively separate ethanol from sugar-containing water. Eighteen different brands of cachaça were analyzed. The proposed methodology was able to eliminate components that contaminate the sample flow path in the gas chromatography system, while improving precision and accuracy by using a triple-quadrupole approach, in comparison with the methodology usually employed: direct analysis of cachaça samples with no sample prep. Results indicate that this approach is more effective due to the removal of sugar content, with no impact in costs per analysis.

A Metabolomic Overview of Follicular Fluid in Cows.

Follicular fluid (FF) protects the oocyte against proteolysis and extrusion during ovulation, providing an appropriate microenvironment that favors proper embryonic development; thereby, FF plays a key role in embryo quality. Being directly related to cattle breeding, studying FF is extremely important in livestock science to measure cattle fertility. This may eventually help to assess the quality of both meat and milk, products widely consumed worldwide. There is an important commercial interest in the evaluation and characterization of compounds present in the FF of livestock that present greater likelihood of pregnancy. Mass spectrometry is a great ally for this type of analysis and can provide quick and efficient screening for molecular markers in biological samples. The present study demonstrated the potential of high-resolution mass spectrometry in analyzing FF samples from two distinct groups of Nellore cows (Bos indicus): high and low fertility, as determined by the number of oocytes produced. We were able to delineate markers of interest for each group, which may ultimately be related to biochemical pathways that lead to higher or lower reproductive performance.

A Lipidomics Approach in the Characterization of Zika-Infected Mosquito Cells: Potential Targets for Breaking the Transmission Cycle.

Recent outbreaks of Zika virus in Oceania and Latin America, accompanied by unexpected clinical complications, made this infection a global public health concern. This virus has tropism to neural tissue, leading to microcephaly in newborns in a significant proportion of infected mothers. The clinical relevance of this infection, the difficulty to perform accurate diagnosis and the small amount of data in literature indicate the necessity of studies on Zika infection in order to characterize new biomarkers of this infection and to establish new targets for viral control in vertebrates and invertebrate vectors. Thus, this study aims at establishing a lipidomics profile of infected mosquito cells compared to a control group to define potential targets for viral control in mosquitoes. Thirteen lipids were elected as specific markers for Zika virus infection (Brazilian strain), which were identified as putatively linked to the intracellular mechanism of viral replication and/or cell recognition. Our findings bring biochemical information that may translate into useful targets for breaking the transmission cycle.

Early developmental stages of Ascaris lumbricoides featured by high-resolution mass spectrometry.

Ascaris lumbricoides is responsible for a highly disseminated helminth parasitic disease, ascariosis, a relevant parasitosis that responds for great financial burden on the public health system of developing countries. In this work, metabolic fingerprinting using high-resolution mass spectrometry (HRMS) was employed to identify marker molecules from A. lumbricoides in different development stages. We have identified nine biomarkers, such as pheromones and steroidal prohormones in early stages, among other molecules in late development stages, making up four molecules for fertilized eggs, four marker molecules for first larvae (L1) and one marker molecule for third larvae (L3). Therefore, our findings indicate that this approach is suitable for biochemical characterization of A. lumbricoides development stages. Moreover, the straightforward analytical method employed, with almost no sample preparation from a complex matrix (feces) using high-resolution mass spectrometry, suggests that it is possible to seek for an easier and faster way to study animal molding processes.

High-throughput analysis by SP-LDI-MS for fast identification of adulterations in commercial balsamic vinegars.

Balsamic vinegar (BV) is a typical and valuable Italian product, worldwide appreciated thanks to its characteristic flavors and potential health benefits. Several studies have been conducted to assess physicochemical and microbial compositions of BV, as well as its beneficial properties. Due to highly-disseminated claims of antioxidant, antihypertensive and antiglycemic properties, BV is a known target for frauds and adulterations. For that matter, product authentication, certifying its origin (region or country) and thus the processing conditions, is becoming a growing concern. Striving for fraud reduction as well as quality and safety assurance, reliable analytical strategies to rapidly evaluate BV quality are very interesting, also from an economical point of view. This work employs silica plate laser desorption/ionization mass spectrometry (SP-LDI-MS) for fast chemical profiling of commercial BV samples with protected geographical indication (PGI) and identification of its adulterated samples with low-priced vinegars, namely apple, alcohol and red/white wines.