Search

Abstract Objectives Electronic nicotine delivery systems (e‐cigarette, pod, and vape) are currently among the tobacco consumption of adolescents and young adults. The aim is to show oral mucosa and saliva alterations related to vape. Material and Methods A vape‐user patient, presenting a white plaque in the posterior region of the hard palate, underwent clinical examination, sialometry, pH evaluation, and excisional biopsy of the white lesion. Molecular changes in saliva and vape liquid were analyzed by vibrational spectroscopy. Results The histopathological analyses showed hyperparakeratosis without dysplasia. Formaldehyde, ketones, and aromatic hydrocarbon species were identified in e‐cig liquid by the FTIR. Conclusions The use of vape may be related to the development of hyperkeratotic lesions in the oral mucosa as well as significantly modify the patient's salivary patterns as the vape liquid presents carcinogenic and cytotoxic components in its composition.

Abstract Background Structural equation models (SEM) are used to model multiple traits and the casual links among them. The number of different causal structures that can be used to fit a SEM is typically very large, even when only a few traits are studied. In recent applications of SEM in quantitative genetics mixed model settings, causal structures were pre-selected based on prior beliefs alone. Alternatively, there are algorithms that search for structures that are compatible with the joint distribution of the data. However, such a search cannot be performed directly on the joint distribution of the phenotypes since causal relationships are possibly masked by genetic covariances. In this context, the application of the Inductive Causation (IC) algorithm to the joint distribution of phenotypes conditional to unobservable genetic effects has been proposed. Methods Here, we applied this approach to five traits in European quail: birth weight (BW), weight at 35 days of age (W35), age at first egg (AFE), average egg weight from 77 to 110 days of age (AEW), and number of eggs laid in the same period (NE). We have focused the discussion on the challenges and difficulties resulting from applying this method to field data. Statistical decisions regarding partial correlations were based on different Highest Posterior Density (HPD) interval contents and models based on the selected causal structures were compared using the Deviance Information Criterion (DIC). In addition, we used temporal information to perform additional edge orienting, overriding the algorithm output when necessary. Results As a result, the final causal structure consisted of two separated substructures: BW→AEW and W35→AFE→NE, where an arrow represents a direct effect. Comparison between a SEM with the selected structure and a Multiple Trait Animal Model using DIC indicated that the SEM is more plausible. Conclusions Coupling prior knowledge with the output provided by the IC algorithm allowed further learning regarding phenotypic causal structures when compared to standard mixed effects SEM applications.

Abstract Phenotypic traits may exert causal effects between them. For example, on the one hand, high yield in dairy cows may increase the liability to certain diseases and, on the other hand, the incidence of a disease may affect yield negatively. Likewise, the transcriptome may be a function of the reproductive status in mammals and the latter may depend on other physiological variables. Knowledge of phenotype networks describing such interrelationships can be used to predict the behavior of complex systems, e.g. biological pathways underlying complex traits such as diseases, growth and reproduction. Structural Equation Models (SEM) can be used to study recursive and simultaneous relationships among phenotypes in multivariate systems such as genetical genomics, system biology, and multiple trait models in quantitative genetics. Hence, SEM can produce an interpretation of relationships among traits which differs from that obtained with traditional multiple trait models, in which all relationships are represented by symmetric linear associations among random variables, such as covariances and correlations. In this review, we discuss the application of SEM and related techniques for the study of multiple phenotypes. Two basic scenarios are considered, one pertaining to genetical genomics studies, in which QTL or molecular marker information is used to facilitate causal inference, and another related to quantitative genetic analysis in livestock, in which only phenotypic and pedigree information is available. Advantages and limitations of SEM compared to traditional approaches commonly used for the analysis of multiple traits, as well as some indication of future research in this area are presented in a concluding section.

As abelhas são os polinizadores mais importantes do mundo. As ameaças à saúde das abelhas continuam a aumentar a nível mundial, contribuindo para a diminuição da sua produtividade. As abelhas são suscetíveis a uma enorme variedade de agentes patogénicos, incluindo vírus, bactérias, fungos, protozoários e ácaros. Este trabalho teve como objetivo apresentar uma compilação das principais doenças parasitárias, bacterianas, fúngicas e virais das abelhas em Portugal, descrevendo os agentes etiológicos envolvidos, a sua patogenia, os sinais clínicos, o diagnóstico, o tratamento e o controlo. A implementação de medidas sanitárias e boas práticas de maneio é fundamental para a prevenção e controlo das doenças, e constitui um dos principais desafios da apicultura moderna.

Abstract It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated with low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported that NP-, S-, RBD- specific IgA, IgG, and IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-transform infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19-ill (mild or oligosymptomatic) individuals (82 healthcare workers volunteers in “Instituto de Infectologia Emilio Ribas”, São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 µL of dried biofluid was compared to signal-to-cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702–1785 $$\hbox {cm}^{-1}$$ cm - 1 spectral window (carbonyl C=O vibration) is a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive sub-populations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods ( $$>0.85$$ > 0.85 ). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

Abstract Renal injury caused by renal ischemia and reperfusion strongly influences heart morphology, electrophysiology, and redox unbalance. The so-called cardiorenal syndrome is an important class of dysfunction since heart and kidneys are responsible for hemodynamic stability and organ perfusion through a complex network. In the present work we investigate the vibrational spectral features probed by Fourier-Transform Raman (FT-Raman) spectroscopy due to physiological alterations induced by renal ischemic reperfusion aiming to detect molecular markers related to progression of acute to chronic kidney injury and mortality predictors as well. C57BL/6J mice were subjected to unilateral occlusion of the renal pedicle for 60 min and reperfusion for 5, 8, and 15 days. Biopsies of heart and kidney tissues were analyzed. Our findings indicated that cysteine/cystine, fatty acids, methyl groups of Collagen, α-form of proteins, Tyrosine, and Tryptophan were modulated during renal ischemia and reperfusion process. These changes are consistent with fibroblast growth factors and Collagen III contents changes. Interestingly, Tyrosine and Tryptophan, precursor molecules for the formation of uremic toxins such as indoxyl sulfate and p-cresyl sulfate were also modulated. They are markers of kidney injury and their increase is strongly correlated to cardiovascular mortality. Regarding this aspect, we notice that monitoring the Tyrosine and Tryptophan bands at 1558, 1616, and 1625 cm−1 is a viable and and advantageous way to predict fatality in cardiovascular diseases both “in vivo” or “in vitro”, using the real-time, multiplexing, and minimally invasive advantages of FT-Raman spectroscopy.

Oral squamous cell carcinoma (OSCC) represents 90% of oral malignant neoplasms. The search for specific biomarkers for OSCC is a very active field of research contributing to establishing early diagnostic methods and unraveling underlying pathogenic mechanisms. In this work we investigated the salivary metabolites and the metabolic pathways of OSCC aiming find possible biomarkers. Salivary metabolites samples from 27 OSCC patients and 41 control individuals were compared through a gas chromatography coupled to a mass spectrometer (GC-MS) technique. Our results allowed identification of pathways of the malate-aspartate shuttle, the beta-alanine metabolism, and the Warburg effect. The possible salivary biomarkers were identified using the area under receiver-operating curve (AUC) criterion. Twenty-four metabolites were identified with AUC > 0.8. Using the threshold of AUC = 0.9 we find malic acid, maltose, protocatechuic acid, lactose, 2-ketoadipic, and catechol metabolites expressed. We notice that this is the first report of salivary metabolome in South American oral cancer patients, to the best of our knowledge. Our findings regarding these metabolic changes are important in discovering salivary biomarkers of OSCC patients. However, additional work needs to be performed considering larger populations to validate our results.

Copyright of Brazilian Dental Science is the property of Brazilian Dental Science Journal-ICT-UNESP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)