Highlights The COVID-19 crisis was studied regarding food, using different information sources. Google searches regarding COVID-19 were analysed. The most-watched videos pretended to explain and provide advice for COVID-19. Frequency of Twitter words changed from stocking food to future uncertainty. During strict lockdown, Spanish consumers bought food items perceived as healthier.

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BackgroundChanges in DNA methylation of crucial cancer genes including tumor suppressors can occur early in carcinogenesis, being potentially important early indicators of cancer. The objective of this study was to examine a multiplexed approach to assess the methylation of tumor suppressor genes as tumor stratification and clinical outcome prognostic biomarkers for lung cancer.MethodsA multicandidate probe panel interrogated DNA for aberrant methylation status in 18 tumor suppressor genes in lung cancer using a methylation-specific multiplex ligation-dependent probe amplification assay (MS-MLPA). Lung cancer cell lines (n = 7), and primary lung tumors (n = 54) were examined using MS-MLPA.ResultsGenes frequently methylated in lung cancer cell lines including SCGB3A1, ID4, CCND2 were found among the most commonly methylated in the lung tumors analyzed. HLTF, BNIP3, H2AFX, CACNA1G, TGIF, ID4 and CACNA1A were identified as novel tumor suppressor candidates methylated in lung tumors. The most frequently methylated genes in lung tumors were SCGB3A1 and DLC1 (both 50.0%). Methylation rates for ID4, DCL1, BNIP3, H2AFX, CACNA1G and TIMP3 were significantly different between squamous and adenocarcinomas. Methylation of RUNX3, SCGB3A1, SFRP4, and DLC1 was significantly associated with the extent of the disease when comparing localized versus metastatic tumors. Moreover, methylation of HTLF, SFRP5 and TIMP3 were significantly associated with overall survival.ConclusionsMS-MLPA can be used for classification of certain types of lung tumors and clinical outcome prediction. This latter is clinically relevant by offering an adjunct strategy for the clinical management of lung cancer patients.

Tumor suppressor gene methylation allowed for histopathological and clinical stratification. Urine methylation has noninvasive usefulness not only for diagnostic assessment but also as independent bladder cancer prognosticators.

DNA methylation of tumor suppressor genes (TSGs) represents a frequent and early epigenetic event with potential applications for cancer detection and disease evolution. Our aim was to examine the stratification and prognostic biomarker role of the methylation of a novel panel of TSGs in bladder cancer. The methylation status of 18 TSGs was evaluated in bladder cancer cells (n=14) and paraffin-embedded primary bladder tumors (n=61), using a methylation-specific multiplex ligation-dependent probe amplification assay (MS-MLPA). Recurrence, progression, and disease-specific survival were analyzed using univariate and multivariate Cox models. PRDM2, HLTF, ID4, DLC1, BNIP3, H2AFX, CACNA1G, TGIF, and CACNA1A were discovered methylated in bladder cancer. The methylation of RUNX3 (p=0.026), TWIST1 (p=0.009), SFRP4 (p=0.002), and CCND2 (p=0.027) correlated to tumor stage. Univariate analyses indicated prognostic associations for recurrence (DLC1, SFRP5, H2AFX, CACNA1G), progression (DLC1, SFRP5, CACNA1G), disease-specific (PRDM2, DLC1, SFRP5, CACNA1G, and TIMP3), and overall survival (SFRP5 and TIMP3). In multivariate analyses, several TSGs remained as independent prognosticators for recurrence (SFRP5, H2AFX), progression (CACNA1G), and disease-specific survival (SFRP5). Thus, a novel set of TSGs was identified, frequently methylated in bladder cancer cells and tumors. TSG methylation allowed histopathologic and outcome stratification using paraffin-embedded tumors. This is clinically relevant by offering a strategy for the management of patients affected with uroepithelial neoplasias in pathology routine laboratories.

There is an increasing demand for gluten-free products, with the texture being a critical aspect. The aim of this work was to study the food bolus properties of gluten-free breads in relation to the dynamics of sensations perceived during its consumption. In this study, five-commercial gluten-free breads and two regular breads were analysed for their texture, crumb structure, and moisture content. Bread bolus particle size after three chews, bolus characteristics at the swallowing point, and oral activity were determined. The dynamics of textural sensations during bread consumption was evaluated using the temporal dominance of sensations (TDS) technique. Texture and structure properties vary among gluten-free breads being some of them close to regular breads (crumb with more and smaller cells that shows low hardness and high springiness) that lead to different inmouth breakdown and TDS patterns. At the beginning, harder breads with low springiness values resulted in hard dominant sensations, in contrast, breads with low hardness and high springiness values were perceived soft and spongy. Breads that fragmented into a greater number of small size particles created crumbly and sandy sensations, characteristic of gluten-free breads with large air cell sizes. Compact sensation appeared in breads with low saliva uptake during bolus formation, while pasty and sticky sensations were related to a cohesive and adhesive bolus, respectively. Not only structure and mechanical properties, but also its oral behaviour in terms of fragmentation and bolus formation can fully explain the dynamics of texture perception of gluten-free breads.

Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like "gluten-free". As such, 16,386 tweets in Spanish containing terms "sin-gluten" and "gluten-free" were collected. A subset of 3,000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information about gluten-free, showing users share information about products, occasions, social situations, and places but also to product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza) occupied most tweets, and according to the related terms were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products and separated co-occurrence networks allowed better identification.

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