Mean percentage of resistant isolates rose at 1 month and decreased at 6 months in saliva and plaque samples in test group (P<0.05) but remained unchanged in control group. Percentage of resistant isolates of Gemella morbillorum and Eubacterium saburreum increased significantly at 6 months in both groups. Antibiotic resistance by Aggregatibacter actinomycetemcomitans, Tannerella forsythia, and Porphyromonas gingivalis was either absent or infrequent. Minocycline microspheres result in transient selection of minocycline resistant species in saliva and subgingival plaque samples. Minocycline microspheres result in transient selection of minocycline resistant species in saliva and subgingival plaque samples. Radiotherapy (RT) enables conservative surgery for soft tissue sarcoma (STS). RT can be delivered either pre-operatively (PreRT) or postoperatively (PORT), yet in some patients, neither approach is fully satisfactory (e.g., urgent surgery or wound healing risk prevents PreRT, yet PORT alone cannot cover the entire surgical field). We hypothesized that, in such situations, low-dose PreRT (LD-PreRT) would decrease the risk of intraoperative tumor seeding and thus permit PORT to a reduced volume (covering the high-risk tumor bed but not all surgically manipulated tissues). We identified a single-institution retrospective cohort of 78 patients treated with LD-PreRT (10-30 Gy), resection, and PORT between 1980 and 2018. At a median follow-up of 8.2 years, 8-year overall survival (OS) was 65.9%, disease-free survival (DFS) 50.5%, and local control (LC) 76.7%; in 45 patients with extremity/superficial trunk (E/ST) STS, 8-year LC was 80.9%. Both before and after propensity score adjustment, there were no differences in OS, DFS, or LC between this cohort and a separate cohort of 394 STS (221 E/ST-STS) patients treated with surgery and PORT alone. In patients for whom neither PreRT nor PORT alone is optimal, LD-PreRT may prevent intraoperative tumor seeding and enable PORT to a reduced volume while preserving oncologic outcomes. In patients for whom neither PreRT nor PORT alone is optimal, LD-PreRT may prevent intraoperative tumor seeding and enable PORT to a reduced volume while preserving oncologic outcomes.This article analyses, the replacement of sucrose in muffins with nine different combinations of isomaltulose and oligofructose. Being a structural isomer of sucrose with approx. 50% of sucrose sweetness, isomaltulose is non-cariogenic and with a low glycemic profile but having the same calories as sucrose. Oligofructose is composed of fructose polymers, with a reduced caloric value and prebiotic effect. Specifically, height, percentage of alveoli, water content, Aw , mechanical, and optical properties have been measured along with a sensory evaluation. The results showed that all combinations of sweeteners gave place to softer muffins than control ones. Moreover, isomaltulose caused a darkening of the products likely due to an enhancement of the Maillard reactions. The highest amount of isomaltulose and the absence of sucrose meant the worst score in sweetness and flavor due to the low sweetening powder of isomaltulose.Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised machine learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 and March 2019 (n = 2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n = 187). Here, we show that regarding area under the curve (AUC) and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 (0.92-0.94) and F-measure of 0.72 (0.68-0.75). https://www.selleckchem.com/products/vu0463271.html This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information. Some maternal characteristics indicate worse prognosis in pregnant women with coronavirus disease 2019 (COVID-19). To describe the prevalence of endocrine disorders in pregnancies involving COVID-19, and its impact on maternal outcomes. Search terms were "pregnancy" and "COVID-19". PubMed, Embase, medRxiv, and Cochrane worksheet from February to July 2020 were searched. Articles describing endocrine disorders in pregnancies with and without COVID-19 involvement were considered. We performed meta-analyses of prevalence using random-effect models and estimated relative risk and 95% confidence intervals (CI) of maternal outcomes relative to presence of endocrine disorders. Articles included (n=141) were divided into three data sets individual (119 articles, 356 women), case series (17 articles, 1064 women), and national registries (7 articles, 10178 women). Prevalence of obesity ranged from 16% to 46% and hyperglycemia in pregnancy (HIP) ranged from 8% to 12%. In data set 1, HIP and obesity were risk factors for severe disease in crude and age-adjusted models, although not for intensive care unit admission. In data from two national registries, risk of dying was 5.62 (95% CI 0.30-105.95) in women with diabetes and 2.26 (95% CI 1.03-4.96) in those with obesity. Obesity and HIP were prevalent in pregnant women with severe COVID-19. Obesity and HIP were prevalent in pregnant women with severe COVID-19.