The staining effect of different mouthwashes containing nanoparticles on dental enamel.

Background: This study aimed to evaluate the effects of several mouthwashes containing nanoparticles on discoloration of dental enamel, and compare the results with that of 0.2% chlorhexidine (CHX). Material and Methods: Sixty intact premolars were randomly assigned to six groups. A spectrophotometer was used to measure the color of the teeth (T1) according to the CIELAB system. The specimens in groups 1 to 4 were then immersed in colloidal solutions containing nanoTiO2 (Group 1), nanoZnO (Group 2), nanoAg (Group 3) and nanoCuO (Group 4). In groups 5 and 6, a 0.2% CHX mouthwash and distilled water were used as positive and negative controls, respectively. After 24 hours of immersion, color determination was repeated (T2). The third color assessment was accomplished after brushing (T3). The L, a, and b values were recorded and the color change (∆E) between different stages was calculated. Results: ANOVA revealed significant between-group differences in the color change between T1 and T2 stages, as well as between T1 and T3 time points ( p <0.05), whereas the color change between T2 and T3 was not signifi - cantly different among the study groups ( p =0.09). ∆ET1-T3 was significantly lower in the specimens immersed in distilled water or CHX as compared to the nanoparticle-containing mouthwashes ( p <0.05). The highest ∆E value pertained to the specimens immersed in nanoZnO-containing solution. The TiO2 nanoparticles caused the lowest staining among the tested nanoparticles. Conclusions: The mouthwashes containing nanoparticles produced comparable or even greater enamel discoloration compared to CHX. Brushing had little effect on removal of induced stain

Bentonite Flocculation (BF) Method Evaluation comparing to Elisa, Indirect Immunofluorescence (IFA) and Latex Agglutination (LA) in Diagnosis of Toxoplasmosis

ABSTRACT Toxoplasmosis is a common disease among human and livestock with a worldwide distribution. This disease wil

Rehabilitation Educators′ Perceptions of Clinical Education Challenges in Iran: Is COVID-19 Having Redundant Effects?

Background: Clinical education is a core component of the curriculum of undergraduate rehabilitation students. Nevertheless, this field more than any other field of education has many shortcomings that should be addressed.Methods: The aim of this study was to explore the challenges of clinical education from the perspective of rehabilitation educators with particular focus on the new challenges created bythe COVID-19 outbreak. This qualitative study was conducted through purposeful sampling. Semi-structured interviews were conducted with 12 rehabilitation clinical educators of Ahvaz Jundishapur University of Medical Sciences, Iran. Data analysis was accomplished according to conventional content analysis. To prove the trustworthiness of the data, credibility, dependability, confirmability, and transferability were assessed.Results: Through data analysis, 240 initial codes were extracted in three main categories and nine sub-categories, indicating redundant challenges imposed by COVID-19 comprising restricted clinical resources (inadequate patient number and diversity, inadequate equipment, limited clinical space, inadequate manpower), an inefficient clinical education system (poor management of clinical education programs, insufficient clinical evaluation), and personal and professional characteristics of the students (lack of students’ practical knowledge, lack of motivation, fear).Conclusion: The results of this study provide deeper insight into the perceptions and experiences of rehabilitation educators regarding clinical education challenges. At present, an accidental and unexpected problematic event (COVID-19 pandemic) has inflicted detrimental effects on various aspects of clinical rehabilitation sciences. It is necessary to implement changes in future plans which include adaptations for COVID-19

NuClick : a deep learning framework for interactive segmentation of microscopic images

Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because it often requires expert knowledge, particularly in medical imaging domain where labels are the result of a time-consuming analysis made by one or more human experts. As nuclei, cells and glands are fundamental objects for downstream analysis in computational pathology/cytology, in this paper we propose NuClick, a CNN-based approach to speed up collecting annotations for these objects requiring minimum interaction from the annotator. We show that for nuclei and cells in histology and cytology images, one click inside each object is enough for NuClick to yield a precise annotation. For multicellular structures such as glands, we propose a novel approach to provide the NuClick with a squiggle as a guiding signal, enabling it to segment the glandular boundaries. These supervisory signals are fed to the network as auxiliary inputs along with RGB channels. With detailed experiments, we show that NuClick is applicable to a wide range of object scales, robust against variations in the user input, adaptable to new domains, and delivers reliable annotations. An instance segmentation model trained on masks generated by NuClick achieved the first rank in LYON19 challenge. As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images

Serendipitous meta-transcriptomics : the fungal community of Norway Spruce (Picea abies)

After performing de novo transcript assembly of >1 billion RNA-Sequencing reads obtained from 22 samples of different Norway spruce (Picea abies) tissues that were not surface sterilized, we found that assembled sequences captured a mix of plant, lichen, and fungal transcripts. The latter were likely expressed by endophytic and epiphytic symbionts, indicating that these organisms were present, alive, and metabolically active. Here, we show that these serendipitously sequenced transcripts need not be considered merely as contamination, as is common, but that they provide insight into the plant’s phyllosphere. Notably, we could classify these transcripts as originating predominantly fromDothideomycetes and Leotiomycetes species, with functional annotation of gene families indicating active growth and metabolism, with particular regards to glucose intake and processing, as well as gene regulation.S1 Fig. Samples collected from Norway spruce. For each sample a brief description and sample ID are shown below a representative image of the associated plant tissue, while the sampling date is shown above.S2 Fig. Bioinformatics workflow of RNA data processing. We assembled reads from all samples into a single assembly (left column), computed Tau scores, GC content, and mapped the transcripts to the genome as well as to the Uniref90 protein database. For enriching for fungal transcripts (right column), we applied GC content and expression breadth filters to the reads and assembly respectively, clustered sequences by similarity, and performed functional annotation as well as phylogenetic analyses.S3 Fig. Putative taxonomic characterization of transcripts via protein alignments. Bar plot showing the number of transcripts by taxonomy (super)kingdoms. Parent summarises taxons hierarchically higher than the represented (super)kingdoms, NA summarises transcripts with no sequence similarity in the UniRef90 database. The number of transcripts is indicated at the top of every bar.S4 Fig. Taxonomic class and phylum of the fungal transcripts. (a) Number of transcripts per fungal phylum. The phylum are sorted by abundance top to bottom with Ascomycota (n = 81,181) and Basidiomycota (n = 4,839) being the most represented; the remaining phyla varying from n = 11 to n = 2. (b) A graph of the taxonomic hierarchy from species to phylum of the fungal transcripts, showing the broad species diversity of the largest clusters: Ascomycota (bottom) and Basidiomycota (top). (c) Similar to (a) for the fungal classes, with the Eurotiomycetes and Dothideomycetes classes being over-represented among the fungal transcripts. (d) Similar to (b) for the fungal classes (n = 24).S5 Fig. Characterisation of transcripts lacking taxonomic assignment by their GMAP alignments to the P. abies genome. (a) Boxplot of the tau scores for the no taxon transcripts split based on their GMAP alignments to the P. abies genome. The tau score ranges from 1 for complete specificity to 0 for equal expression in all samples. The transcripts having a GMAP alignment in the genome (99% of the GMAP hits cover 80% of the transcripts with at least a 90% identity) show a wide tau score distribution indicative of the presence of ubiquitously expressed transcripts as well as that of more tissue-specific transcripts. The transcripts having no GMAP alignment show a distribution typical of only tissue-specific expression (mean tau score of 0.98). (b) Percentage GC density distribution of the no taxon transcripts split based on their GMAP alignments to the P. abies genome. Transcripts having a GMAP alignment to the genome present a GC distribution typical of the P. abies transcripts. The transcripts without a GMAP alignment show a distribution enriched for higher percentage GC, similar to that of fungi. The shoulder observed under the peak of transcripts with GMAP alignments may indicate transcripts where the assembly contained gaps or created chimeras. (c) Scatterplot of log2 FPKM expression values vs. the percentage GC content for the transcripts with a GMAP alignment. Colouring indicates density, which is shaded from yellow (high) to blue (low). The expression of transcripts with a GMAP alignment resembles that of the Embryophita phylum. (d) Scatterplot of log2 FPKM expression values vs. the percentage GC content for transcripts with a GMAP alignment. Colouring as in (c). The expression of transcripts with no GMAP alignment resembled that of the fungal kingdom.S6 Fig. Phylogeny built on four nuclear genes. Shown are maximum-likelihood phylogenies based on fungal nucleotide sequences assembled from the spruce samples in context of known sequences, with highest sequence similarity to: (a) phosphoenolpyruvate carboxykinase; (b) NADP-dependent medium chain alcohol dehydrogenase; (c) beta lactamase; and (d) unspecific lipid transporter. Only branch with support values > 0.9 are shown. While clusters with more representative sequences yield better branch support (a, b), placement of clusters with fewer sequences is less certain (c, d). However, in all cases, at least one sequence is grouped with Dothideomycetes, and for (a,b) with Leotiomycetes.S1 Table. Sample IDs, description, and ENA submission IDs. Correspondence between the sample IDs as described in Nystedt et al., (2013), this manuscript and the ENA are shown in columns one to three. The fourth column contains a succinct description of the samples, refer to Nystedt et al., (2013) for full details.http://www.plosone.orgam201

An Improved Canine Genome and a Comprehensive Catalogue of Coding Genes and Non-Coding Transcripts

The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog ∼175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional ∼3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to ∼20,700 high-confidence protein coding loci, we found ∼4,600 antisense transcripts overlapping exons of protein coding genes, ∼7,200 intergenic multi-exon transcripts without coding potential, likely candidates for long intergenic non-coding RNAs (lincRNAs) and ∼11,000 transcripts were reported by two different library construction methods but did not fit any of the above categories. Of the lincRNAs, about 6,000 have no annotated orthologs in human or mouse. Functional analysis of two novel transcripts with shRNA in a mouse kidney cell line altered cell morphology and motility. All in all, we provide a much-improved annotation of the canine genome and suggest regulatory functions for several of the novel non-coding transcripts