Survival in patients undergoing surgical resection for brain metastasis from lung cancer and utility of different prognostic scales.

Brain metastases (BM) from lung cancer are among the most common intracranial tumors. Several studies have published scales to estimate the survival of patients with BM. Routine access to molecular diagnostics and modern oncologic treatments, including targeted therapy and immunotherapy, is limited in low- and middle-income countries (LMICs); therefore, incorporating them into recent prognostic scales may diminish the reliability of the scales in LMICs. This retrospective study aimed to determine the survival of 55 patients who were surgically treated for BM from lung cancer at a Brazilian public tertiary teaching hospital between 2012 and 2022. We determined clinical factors associated with survival, and compared observed survival rates with the estimated survival on prognostic scales. The mean overall survival (OS) was 9.3 months (range:0.2-76.5). At univariate analysis, female sex and improved postoperative Karnofsky performance status (KPS) score were associated with longer survival. The median survival did not differ between groups when classified using the Graded Prognostic Assessment (GPA)-2008, Lung-molecular GPA-2017, and Lung-GPA-2021 scales. According to the Diagnosis-Specific (DS)-GPA-2012 scale, there was a significant difference between the groups. In the multivariate Cox regression survival analysis, a higher DS-GPA-2012 and improved postoperative KPS score remained significantly associated with longer survival. In conclusion, this cohort showed a mean OS of < 1 year. Improved KPS score after surgery was associated with increased survival. This cohort DS-GPA scale demonstrated the highest concordance with observed survival, indicating its potential as a valuable tool for patient stratification in surgical treatment decision-making in LMICs.

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.

Skin imprinting in silica plates: a potential diagnostic methodology for leprosy using high-resolution mass spectrometry.

Leprosy is a chronic infectious disease caused by Mycobacterium leprae, which primarily infects macrophages and Schwann cells, affecting skin and peripheral nerves. Clinically, the most common form of identification is through the observation of anesthetic lesions on skin; however, up to 30% of infected patients may not present this clinical manifestation. Currently, the gold standard diagnostic test for leprosy is based on skin lesion biopsy, which is invasive and presents low sensibility for suspect cases. Therefore, the development of a fast, sensible and noninvasive method that identifies infected patients would be helpful for assertive diagnosis. The aim of this work was to identify lipid markers in leprosy patients directly from skin imprints, using a mass spectrometric analytical strategy. For skin imprint samples, a 1 cm(2) silica plate was gently pressed against the skin of patients or healthy volunteers. Imprinted silica lipids were extracted and submitted to direct-infusion electrospray ionization high-resolution mass spectrometry (ESI-HRMS). All samples were differentiated using a lipidomics-based data workup employing multivariate data analysis, which helped electing different lipid markers, for example, mycobacterial mycolic acids, inflammatory and apoptotic molecules were identified as leprosy patients' markers. Otherwise, phospholipids and gangliosides were pointed as healthy volunteers' skin lipid markers, according to normal skin composition. Results indicate that silica plate skin imprinting associated with ESI-HRMS is a promising fast and sensible leprosy diagnostic method. With a prompt leprosy diagnosis, an early and effective treatment could be feasible and thus the chain of leprosy transmission could be abbreviated.

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.

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.

Molecular signatures associated with prostate cancer cell line (PC-3) exposure to inactivated Zika virus.

The recent outbreak of Zika virus (ZIKV) infection associated with microcephaly cases has elicited much research on the mechanisms involved in ZIKV-host cell interactions. It has been described that Zika virus impairs cell growth, raising a hypothesis about its oncolytic potential against cancer cells. ZIKV tumor cell growth inhibition was later confirmed for glioblastoma. It was also demonstrated that an inactivated ZIKV prototype (ZVp) based on bacterial outer membrane vesicles has antiproliferative activity upon other cancer cell lines, such as PC-3 prostate cancer cell. This study aims at understanding the pathways that might be involved with the antiproliferative effect of Zika virus against prostate cancer cells. A metabolomic approach based on high-resolution mass spectrometry analysis led to the identification of 21 statistically relevant markers of PC-3 cells treated with ZVp. The markers were associated with metabolic alterations that trigger lipid remodeling, endoplasmic reticulum stress, inflammatory mediators, as well as disrupted porphyrin and folate metabolism. These findings highlight molecular signatures of ZVp-induced response that may be involved on cellular pathways triggered by its antiproliferative effect. To our knowledge, this is the first reported metabolomic assessment of ZIKV effect on prostate cancer cells, a promising topic for further research.

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.

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 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.

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.