AIM: Establishment of ruminal bacterial community in dairy calves. METHODS AND RESULTS: Rumen bacterial community was analysed on 6 calves bred according to commercial practices from day one to ...weaning at day 83 of age, using 454 16S rRNA‐based pyrosequencing. Samples taken at day 1 did not produce amplicons. Analysis of data revealed a three‐stage implantation process with a progressive but important shift of composition. At day 2, the bacterial community was mainly composed of Proteobacteria (70%) and Bacteroidetes (14%), and Pasteurellaceae was the dominant family (58%). The bacterial community abruptly changed between days 2 and 3, and until day 12, dominant genera were Bacteroides (21%), Prevotella (11%), Fusobacterium (5%) and Streptococcus (4%). From 15 to 83 days, when solid food intake rapidly increased, Prevotella became dominant (42%) and many genera strongly decreased or were no longer detected. A limited number of bacteria genera correlated with feed intake, rumen volatile fatty acids and enzymatic activities. CONCLUSION: The ruminal bacterial community is established before intake of solid food, but solid food arrival in turn shapes this community. SIGNIFICANCE AND IMPACT OF THE STUDY: This study provides insight into the establishment of calves’ rumen bacterial community and suggests a strong effect of diet.
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and ...histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Abstract
Introduction:
Current AASM classification of hypopnea as obstructive (H-OB) is based on identification of flattening of inspiratory airflow, chest-abdominal paradox, or snoring. If none are ...present a hypopnea is classified as central (H-CEN). We hypothesized that surface chest wall EMG (CW-EMG, right 8th intercostal space), as a reflection of inspiratory effort, would be useful for hypopnea classification and AASM criteria validation.
Methods:
25 Consecutive adult positive airway pressure (PAP) titration studies with at least 10 hypopneas (including 3 putative central hypopneas) and an adequate CW-EMG signal were analyzed. The EMG signal was processed to remove ECG artifact, rectified and integrated. The integrated EMG signal (EF) was used to reflect effort. Five randomly chosen hypopneas from each patient were analyzed. An observer blind to CW-EMG and EF signals classified the hypopneas as OB or CEN based on AASM criteria. Inspiratory deflections in PAP flow (F) and EF were scaled based on pre-event breathing and a resistance (RES = EF/F) was calculated (pre-event breath RES = 1). An average RES for breaths in the first and second half of the hypopneas was calculated (odd number of breaths, middle breath included in both halves). The same observer classified hypopneas based ONLY on the smoothed flow (eliminating flattening), EF signal, and RES values. The two classifications (AASM and EF) were compared.
Results:
Events by AASM criteria: 68 H-OB and 32 H-CEN. The RES 1st half event (mean ± SD) was OB: 3.6 ± 3.4 versus CEN: 1.24 ± 0.7, P < 0.001 and 2nd half event was OB: 9.2 ± 8.0 versus CEN: 1.35 ± 0.7, P < 0.001. The RES ratio (RES 2nd /RES 1st half hypopnea) was OB: 3.3 ± 3.3 versus CEN 1.15 ± 0.3, P< 0.001. Agreement AASM/EF classifications: Kappa= 0.76, % agreement 89%.
Conclusion:
OB hypopneas had a greater resistance in both halves of the event than CEN hypopneas and the second half a larger relative RES (2nd half/1st half). There was good agreement between classification based on EF and AASM criteria. CW-EMG may be useful to classify hypopneas as obstructive or central.
Support (If Any):
None
A two-marker combination of plastid rbcL and matK has previously been recommended as the core plant barcode, to be supplemented with additional markers such as plastid trnH–psbA and nuclear ribosomal ...internal transcribed spacer (ITS). To assess the effectiveness and universality of these barcode markers in seed plants, we sampled 6,286 individuals representing 1,757 species in 141 genera of 75 families (42 orders) by using four different methods of data analysis. These analyses indicate that (i) the three plastid markers showed high levels of universality (87.1–92.7%), whereas ITS performed relatively well (79%) in angiosperms but not so well in gymnosperms; (ii) in taxonomic groups for which direct sequencing of the marker is possible, ITS showed the highest discriminatory power of the four markers, and a combination of ITS and any plastid DNA marker was able to discriminate 69.9–79.1% of species, compared with only 49.7% with rbcL + matK; and (iii) where multiple individuals of a single species were tested, ascriptions based on ITS and plastid DNA barcodes were incongruent in some samples for 45.2% of the sampled genera (for genera with more than one species sampled). This finding highlights the importance of both sampling multiple individuals and using markers with different modes of inheritance. In cases where it is difficult to amplify and directly sequence ITS in its entirety, just using ITS2 is a useful backup because it is easier to amplify and sequence this subset of the marker. We therefore propose that ITS/ITS2 should be incorporated into the core barcode for seed plants.
Microbial Geography of the Oral Cavity Simón-Soro, Á.; Tomás, I.; Cabrera-Rubio, R. ...
Journal of dental research,
07/2013, Letnik:
92, Številka:
7
Journal Article
Recenzirano
We aimed to determine the bacterial diversity of different oral micro-niches and to assess whether saliva and plaque samples are representative of oral microbial composition. We took minute samples ...from each surface of the individual teeth and gingival crevices of two healthy volunteers (112 samples per donor), as well as samples from the tongue dorsum and non-stimulated and stimulated saliva. DNA was extracted from 67 selected samples of each donor, and the 16S rRNA gene was amplified by PCR and pyrosequenced to obtain, on average, over 2,700 reads per sample, which were taxonomically assigned to obtain a geographic map of bacterial diversity at each tooth and sulcus location. Analysis of the data shows considerable differences in bacterial composition between teeth at different intra-oral locations and between surfaces of the same tooth. The most pronounced differences were observed in incisors and canines, where genera like Streptococcus were found at 40% to 70% on the vestibular surfaces but were almost absent on the lingual sides. Saliva samples, especially non-stimulated saliva, were not representative of supra-and subgingival plaque in the two individuals tested. We suggest that more precise sampling is required for the proper determination of oral microbial composition and to relate that diversity to epidemiological, clinical, and etiological parameters.
There is substantial evidence supporting the role of certain oral bacteria species in the onset and progression of periodontitis. Nevertheless, results of independent-culture diagnostic methods ...introduced about a decade ago have pointed to the existence of new periodontal pathogens. However, the data of these studies have not been evaluated together, which may generate some misunderstanding on the actual role of these microorganisms in the etiology of periodontitis. The aim of this systematic review was to determine the current weight of evidence for newly identified periodontal pathogens based on the results of “association” studies. This review was conducted and reported in accordance with the PRISMA statement. The MEDLINE, EMBASE, and Cochrane databases were searched up to September 2013 for studies (1) comparing microbial data of subgingival plaque samples collected from subjects with periodontitis and periodontal health and (2) evaluating at least 1 microorganism other than the already-known periodontal pathogens. From 1,450 papers identified, 41 studies were eligible. The data were extracted and registered in predefined piloted forms. The results suggested that there is moderate evidence in the literature to support the association of 17 species or phylotypes from the phyla Bacteroidetes, Candidatus Saccharibacteria, Firmicutes, Proteobacteria, Spirochaetes, and Synergistetes. The phylum Candidatus Saccharibacteria and the Archaea domain also seem to have an association with disease. These data point out the importance of previously unidentified species in the etiology of periodontitis and might guide future investigations on the actual role of these suspected new pathogens in the onset and progression of this infection.
JEL Classification System
Journal of economic literature,
03/2019, Letnik:
57, Številka:
1
Journal Article
Recenzirano
The categories listed below are used to classify books, book reviews, journal articles, and dissertations indexed in JEL, JEL on CD, and EconLit. New changes to the classification system appear as ...soon as possible on www.econlit.org . The JEL classification system may be used freely for scholarly purposes. We suggest the following format: “JEL: A10, B10, etc.”
Study Objective: To determine the prognostic significance of a new score (CO-RABS), formulated by our Institute to classify the covid patients into mild, moderate, severe cases and also to compare it ...with the conversion AIIMS based classification. Patients and Methods: This is a retrospective study in which we have collected data from the medical records of patients who were admitted in our Hospital with covid infection during 2nd and 3rd waves of the pandemic. We have taken Comorbidities (CO), Respiratory rate (R), Age (A), Blood pressure (B) and SpO2 (S) of the patients at the time of admission to calculate an overall score (Abbreviated as CO-RABS). Basing on this score, the patients were classified into mild, moderate and severe cases. We then compared our CO-RABS score based classification with AIIMS classification using a statistical software. Results: We studied 727 patients (440 men, 287 women) and 99 patients died due to covid related complications. The ability to predict the prognosis was higher for our newly formulated CO-RABS score when compared to AIIMS classification. (AUC of CO-RABS 0.88 vs 0.82 of AIIMS; p < 0.05). Conclusion: The ability of CO-RABS score to predict the prognosis of covid infection is higher than that of AIIMS/ICMR classification. Hence it can be used as a supportive tool in the covid management protocol along with all the other conversion modes of treatment.
Aim
The microbial differences between peri‐implantitis and periodontitis in the same subjects were examined using 16S rRNA gene clone library analysis and real‐time polymerase chain reaction.
...Materials and methods
Subgingival plaque samples were taken from the deepest pockets of peri‐implantitis and periodontitis sites in six subjects. The prevalence of bacteria was analysed using a 16S rRNA gene clone library and real‐time polymerase chain reaction.
Results
A total of 333 different taxa were identified from 799 sequenced clones; 231 (69%) were uncultivated phylotypes, of which 75 were novel. The numbers of bacterial taxa identified at the sites of peri‐implantitis and periodontitis were 192 and 148 respectively. The microbial composition of peri‐implantitis was more diverse when compared with that of periodontitis. Fusobacterium spp. and Streptococcus spp. were predominant in both peri‐implantitis and periodontitis, while bacteria such as Parvimonas micra were only detected in peri‐implantitis. The prevalence of periodontopathic bacteria was not high, while quantitative evaluation revealed that, in most cases, prevalence was higher at peri‐implantitis sites than at periodontitis sites.
Conclusions
The biofilm in peri‐implantitis showed a more complex microbial composition when compared with periodontitis. Common periodontopathic bacteria showed low prevalence, and several bacteria were identified as candidate pathogens in peri‐implantitis.
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