The pediatric emergency department: assessment of clinical activity and interaction with local health authorities]

A perspective study was carried out at the Emergency Department of the Gaslini Institute on a sample of the children admitted from May to September 1990. The study aimed to describe types and modalities of admission. "G. Gaslini" Institute is a multidisciplinary children's hospital: 45% of patients admitted in the hospital come from the southern regions of Italy, while the admission at the Emergency Department were local in 82%. Among all children considered, 2080 (71.9% were admitted on request of their parents, who did not ask previously any physician; of those cases, 356 (17%) were hospitalized, 474 (25%) were either examined by specialist or treated and then discharged. In the remaining cases, no intervention was necessary. As a consequence, 58% of children were admitted without any clinically plausible reason. Parents' anxiety clearly played an important role in most cases. 840 children were addressed to the Emergency Department by a physician, 652 (22.3%) by the doctor in charge, 213 (32.7%) of which were sent back home by the doctor on duty, who did not think it necessary any treatment. Of the 120 (4.1%) children sent by different Emergency Departments, 95 (79.1%) were hospitalized. The reason why physicians addressed quite a large number of children to the Emergency Department, when they didn't need any intervention remain nucleo. Their diagnosis, in fact, agreed with those of the doctor on duty in 83% of cases. In conclusion, it is difficult to imagine a diagnostic or therapeutic problem which needs specific structures. Cooperative programs with physicians working in our region will be carried out in order to control, where possible, admission to Emergency Departments.     

The activities of a pediatric emergency department. The emergency problems]

The authors reviewed records of admission at the Fist Ais-Emergency Service of "G. Gaslini" Children's Hospital, data referring both to in and outpatients. First of all we took into account epidemiological data analysing occurrence and types of diseases; at the same time a demographic study, which aimed to show a decrease in the child population in Genova, was performed. Secondly we compared these data with the real number of admitted patients: collected data showed that this service has been used excessively.     

Outcome in ulcerative colitis after switch from adalimumab/golimumab to infliximab: A multicenter retrospective study

Background

Anti-TNF therapies infliximab (IFX), adalimumab (ADA), and golimumab (GOL) are approved for treating moderate to severe ulcerative colitis (UC). In UC, only the switch from IFX to ADA has been investigated, reaching no more than 10–43% remission rates at 12 months.

呼和浩特地区 RhD 阴性献血者表型库的建立及应用

目的：了解该地区 RhD 阴性献血者表型分布,建立 RhD 阴性表型数据库。方法 RhD 初筛阴性的554份标本采用间接抗球蛋白试验确认。排除重复献血者后,剩余标本为366份,对其进行 Rh 血型表型分析。结果 RhD 阴性表型分布特征为ccdee（56．28％）＞ Ccdee （29．51％）＞ ccdEe （7．38％）＞ CcdEe （3．55％）＞ CCdee （3．01％）＞ ccdEE （0．27％）,未检出 CcdEE 、CCdEe 、CCdEE 型。结论建立 RhD 阴性表型数据库,有利于为 RhD 阴性及含有 Rh 同种抗体的患者提供相匹配血液,满足其急救用血,且对科学管理 RhD 阴性血源和合理运用 RhD 阴性血液有重要意义,为该地区尤其蒙古族 RhD 阴性血型积累了资料。     

On instabilities of deep learning in image reconstruction and the potential costs of AI

Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.