A more palatable alternative to oral rehydration therapy for kids

Review of: Freedman SB, Willan AR, Boutis K, et al. Effect of dilute apple juice and preferred fluids vs electrolyte maintenance solution on treatment failure among children with mild gastroenteritis: a randomized clinical trial. JAMA. 2016;315:1966-1974.Stength of recommendation: B: Based on a single, good quality randomized controlled trial.A more palatable alternative to oral rehydration therapy for kids. Parents no longer need to struggle to get their kids to drink electrolyte solutions during episodes of mild gastroenteritis; apple juice works just as well. Practice changer: Recommend that parents give half-strength apple juice to children greater than or equal to 24 months of age who are minimally dehydrated following a case of simple viral gastroenteritis. The juice reduces the need for further interventions better than oral hydration therapy

The Unfolded Protein Response Protects from Tau Neurotoxicity In Vivo

The unfolded protein response is a critical system by which the cell handles excess misfolded protein in the secretory pathway. The role of the system in modulating the effects of aggregation prone cytosolic proteins has received less attention. We use genetic reporters to demonstrate activation of the unfolded protein response in a transgenic Drosophila model of Alzheimer's disease and related tauopathies. We then use loss of function genetic reagents to support a role for the unfolded protein response in protecting from tau neurotoxicity. Our findings suggest that the unfolded protein response can ameliorate the toxicity of tau in vivo

Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images

Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster R-CNN) for threat recognition in carry-on luggage using X-ray images. This is part of the Transportation Security Administration's (TSA's) mission to protect air travelers in the United States. While more training data with threats may reliably improve performance for this class of deep algorithm, it is expensive to stage in realistic contexts. By contrast, data from the real world can be collected quickly with minimal cost. In this paper, we present a semi-supervised approach for threat recognition which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. The data sources described earlier make two "domains": a hand-collected data domain of images with threats, and a real-world domain of images assumed without threats. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. Without this penalty a Convolutional Neural Network (CNN) can learn to identify domains based on superficial characteristics, and minimize a supervised loss function without improving its ability to recognize objects. For the hand-collected data, only object proposals and image features from backgrounds are used. The losses for these domain-adaptive discriminators are added to the Faster R-CNN losses of images from both domains. This can reduce threat detection false alarm rates by matching the statistics of extracted features from hand-collected backgrounds to real world data. Performance improvements are demonstrated on two independently-collected datasets of labeled threats

Expanded Access as a source of real-world data: An overview of FDA and EMA approvals

Aims: To identify, characterize and compare all Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals that included real-world data on efficacy from expanded access (EA) programmes. Methods: Cross-sectional study of FDA (1955–2018) and EMA (1995–2018) regulatory approval documentation. We automated searching for terms related to EA in 22,506 documents using machine learning techniques. We included all approvals where EA terms appeared in the regulatory documentation. Our main outcome was the inclusion of EA data as evidence of clinical efficacy. Characterization was based on approval date, disease area, orphan designation and whether the evidence was supportive or pivotal. Results: EA terms appeared in 693 out of 22,506 (3.1%) documents, which referenced 187 approvals. For 39 approvals, data from EA programmes were used to inform on clinical efficacy. The yearly number of approvals with EA data increased from 1.25 for 1993–2013 to 4.6 from 2014–2018. In 13 cases, these programmes formed the main evidence for approval. Of these, patients in EA programmes formed over half (median 71%, interquartile range: 34–100) of the total patient population available for efficacy evaluation. Almost all (12/13) approvals were granted orphan designation. In 8/13, there were differences between regulators in approval status and valuation of evidence. Strikingly, 4 treatments were granted approval based solely on efficacy from EA. Conclusion: Sponsors and regulators increasingly include real-world data from EA programmes in the efficacy profile of a treatment. The indications of the approved treatments are characterized by orphan designation and high unmet medical need

One year survival with poorly differentiated metastatic pancreatic carcinoma following chemoembolization with gemcitabine and cisplatin.

While hepatic arterial chemoembolization is efficacious for a number of malignancies, there is scant data regarding treatment of pancreatic adenocarcinoma. We report a complete radiographic response at one year from diagnosis of metastatic pancreatic carcinoma. Gemcitabine/cisplatin based chemoembolization may be of potential benefit for patients with liver-dominant metastases from pancreatic carcinoma. Given the typical survival of 6 months or less in this patient group with standard therapies, further research is warranted

Liquid chromatography at critical conditions (LCCC): Capabilities and limitations for polymer analysis

This paper investigates liquid chromatography at critical condition (LCCC) for polymer analysis. Based on controversial claims on the separation of cyclic polymers from linear analogues in the literature, the efficiency of LCCC for separation and purity analysis is questioned. Polyisobutylene (PIB) and poly(3,6-dioxa-1,8-octanedithiols) (polyDODT) were used for the study. The structure of low molecular weight cyclic and linear polyDODT was demonstrated by MALDI-ToF. NMR did not show the presence of thiol end groups in higher molecular weight PIB-disulfide and polyDODT samples, so they were considered cyclic polymers. When a low molecular weight polyDODT oligomer with only traces of cycles, as demonstrated by MALDI-ToF, was mixed with an M_n = 27 K g/mol cyclic sample, LCCC did not detect the presence of linear oligomers at 6 wt%. Based on the data presented here, it can be concluded that the LCCC method is not capable of measuring <6 wt% linear contamination so earlier claims for cyclic polystyrene (PS) samples purified by LCCC having <3% linear contaminants are questioned