syphilitic chancres of the mouth three cases

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New Avoparcin-like Molecules from the Avoparcin Producer Amycolatopsis coloradensis ATCC 53629

Amycolatopsis coloradensis ATCC 53629 is the producer of the glycopeptide antibiotic avoparcin. While setting up the production of the avoparcin complex, in view of its use as analytical standard, we uncovered the production of a to-date not described ristosamynil-avoparcin. Ristosamynil-avoparcin is produced together with α-and β-avoparcin (overall indicated as the avoparcin complex). Selection of one high producer morphological variant within the A. coloradensis population, together with the use of a new fermentation medium, allowed to increase productivity of the avoparcin complex up to 9 g/L in flask fermentations. The selected high producer displayed a non-spore forming phenotype. All the selected phenotypes, as well as the original unselected population, displayed invariably the ability to produce a complex rich in ristosamynil-avoparcin. This suggested that the original strain deposited was not conforming to the description or that long term storage of the lyovials has selected mutants from the original population

Platelet-Rich Plasma combined with a sterile 3D polylactic acid scaffold for postoperative management of complete hoof wall resection for keratoma in four horses.

Keratoma is a non-malignant horse tumour that grows in the space between the horn of the hoof and the distal phalanx. Keratoma causes lameness in the horse, and surgical excision is the treatment of choice. Four horses underwent removal of a keratoma by complete hoof wall resection. The remaining wound was treated with Platelet-Rich Plasma (PRP) combined with a sterile 3D polylactic acid scaffold. The PRP was applied at 3, 6, 9, 12, 15 and 18 days postoperatively. The surgical site was cleaned with gauzes and swabs soaked in Ringer’s lactate solution before applying PRP and the foot bandage. Healthy granulation tissue developed at 6-21 days postoperatively. The hoof wall defect was completely filled with new hoof wall within 6-8 months after surgery. All horses returned to their previous exercise level and no recurrence of lameness was reported by the owner

Distributed deep reinforcement learning resource allocation scheme for industry 4.0 Device-To-Device scenarios

This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.This work has been partially funded by Junta de Andalucía (projects EDEL4.0:UMA18-FEDERJA-172 and PENTA:PY18-4647) and Universidad de Málaga (I Plan Propio de Investigación, Transferencia y Divulgación Científica). Ramoni Adeogun is supported by the Danish Council for Independent Research, grant no. DFF 9041-00146B. The authors would like to express their profound gratitude to Nokia Standardization Aalborg and Aalborg University for funding the first author’s research stay. The authors thank Assoc. Prof. Gilberto Beradinelli for his comments on the manuscript

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions

Early, incomplete, or preclinical autoimmune systemic rheumatic diseases and pregnancy outcome

OBJECTIVE: To evaluate the impact of preclinical systemic autoimmune rheumatic disorders on pregnancy outcome. METHODS: In this longitudinal cohort study, patients were enrolled during the first trimester of pregnancy if they reported having had connective tissue disorder symptoms, were found to be positive for circulating autoantibodies, and on clinical evaluation were judged to have a preclinical or incomplete rheumatic disorder. The incidence of fetal growth restriction (FGR), preeclampsia, and adverse pregnancy outcomes in patients with preclinical rheumatic disorders was compared with that in selected controls, after adjustment for confounders by penalized logistic regression. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. RESULTS: Of 5,232 women screened, 150 (2.9%) were initially diagnosed as having a suspected rheumatic disorder. After a mean\u2009\ub1\u2009SD postpartum follow-up of 16.7\u2009\ub1\u20095.5 months, 64 of these women (42.7%) had no clinically apparent rheumatic disease and 86 (57.3%) had persistent symptoms and positive autoantibody results, including 10 (6.7%) who developed a definitive rheumatic disease. The incidences of preeclampsia/FGR and of small for gestational age (SGA) infants were 5.1% (23 of 450) and 9.3% (42 of 450), respectively, among controls, 12.5% (8 of 640) (OR 2.7 [95% CI 1.1-6.4]) and 18.8% (12 of 64) (OR 2.2 [95% CI 1.1-4.5]), respectively, among women with no clinically apparent disease, and 16.3% (14 of 86) (OR 3.8 [95% CI 1.9-7.7]) and 18.6% (16 of 86) (OR 2.3 [95% CI 1.2-4.3]), respectively, among those with persisting symptoms at follow-up. Mean\u2009\ub1\u2009SD umbilical artery Doppler pulsatility indices were higher among women with no clinically apparent disease (0.95\u2009\ub1\u20090.2) and those with persisting symptoms (0.96\u2009\ub1\u20090.21) than in controls (0.89\u2009\ub1\u20090.12) (P\u2009=\u20090.01 and P\u2009<\u20090.001, respectively). CONCLUSION: In our study population, preclinical rheumatic disorders were associated with an increased risk of FGR/preeclampsia and SGA. The impact of these findings and their utility in screening for FGR/preeclampsia need to be confirmed in population studies

Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative

Background and objective Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. Materials and methods We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. Results: We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. Conclusions: We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years