Search

Informed by a conceptual framework on boundary crossing, this qualitative case study explored how a student teacher engaged in professional learning through recursive boundary crossing between her field school and the university programme in a U.S. context. The findings revealed the power of boundary crossing as a cyclical, intense, and transformative learning mechanism that helped the student teacher connect teaching knowledge and experiences acquired at different sites and facilitated her reflective practice and self-transformation. However, the lack of communication between the university and the field school caused a cognitive disturbance and emotional challenges for the student teacher. The study concludes with practical implications for reforming teaching practicum to maximise the potential of boundary crossing for teacher learning. [ABSTRACT FROM AUTHOR]

To explore the role of chronic liver disease (CLD) in COVID-19. A total of 1439 consecutively hospitalized patients with COVID-19 from one large medical center in the United States from March 16, 2020 to April 23, 2020 were retrospectively identified. Clinical characteristics and outcomes were compared between patients with and without CLD. Postmortem examination of liver in 8 critically ill COVID-19 patients was performed. There was no significant difference in the incidence of CLD between critical and non-critical groups (4.1% vs 2.9%, p = 0.259), or COVID-19 related liver injury between patients with and without CLD (65.7% vs 49.7%, p = 0.065). Postmortem examination of liver demonstrated mild liver injury associated central vein outflow obstruction and minimal to moderate portal lymphocytic infiltrate without evidence of CLD. Patients with CLD were not associated with a higher risk of liver injury or critical/fatal outcomes. CLD was not a significant comorbid condition for COVID-19. [ABSTRACT FROM AUTHOR]

Teachers who teach in cross-cultural settings play a vital role in nurturing students' appreciation of cultural diversity but they tend to experience tremendous challenges in their early years of teaching. To draw a dynamic portrayal of those challenges, this study primarily collected and analyzed 521 reflective journals written by 14 Chinese language teachers throughout their first 2 years of teaching in U.S. public schools. The study found that the teachers experienced a wide range of challenges in managing classrooms, developing curricular materials, crafting instructional strategies, assessing students, catering to learner differences, and collaborating with others. The seriousness of the challenges varied across areas and transformed differently over time. Cultural difference, school context, and teacher's background and ongoing learning collectively contributed to the formation and transformation of the challenges. This paper concludes with practical implications on how to prepare and support teachers to work in cross-cultural settings. [ABSTRACT FROM AUTHOR]

• A computer-aided diagnosis (CAD) system was proposed to diagnose the lung cancer in endobronchial ultrasound (EBUS) images. • In this study, a CAD system using the convolutional neural network (CNN) was proposed to automatically differentiate benign and malignant lesions for early detecting lung cancer. • The results of our CAD system show that the accuracy, sensitivity, specificity, and the area under ROC achieved 85.4%, 87.0%, 82.1%, and 0.8705, respectively. • Our study has demonstrated that CAD system based on the CNN architecture could provide an automatic method to differentiate benign and malignant lesions in patients with lung cancer. In the United States, lung cancer is the leading cause of cancer death. The survival rate could increase by early detection. In recent years, the endobronchial ultrasonography (EBUS) images have been utilized to differentiate between benign and malignant lesions and guide transbronchial needle aspiration because it is real-time, radiation-free and has better performance. However, the diagnosis depends on the subjective judgment from doctors. In some previous studies, which using the grayscale image textures of the EBUS images to classify the lung lesions but it belonged to semi-automated system which still need the experts to select a part of the lesion first. Therefore, the main purpose of this study was to achieve full automation assistance by using convolution neural network. First of all, the EBUS images resized to the input size of convolution neural network (CNN). And then, the training data were rotated and flipped. The parameters of the model trained with ImageNet previously were transferred to the CaffeNet used to classify the lung lesions. And then, the parameter of the CaffeNet was optimized by the EBUS training data. The features with 4096 dimension were extracted from the 7th fully connected layer and the support vector machine (SVM) was utilized to differentiate benign and malignant. This study was validated with 164 cases including 56 benign and 108 malignant. According to the experiment results, applying the classification by the features from the CNN with transfer learning had better performance than the conventional method with gray level co-occurrence matrix (GLCM) features. The accuracy, sensitivity, specificity, and the area under ROC achieved 85.4% (140/164), 87.0% (94/108), 82.1% (46/56), and 0.8705, respectively. From the experiment results, it has potential ability to diagnose EBUS images with CNN. [ABSTRACT FROM AUTHOR]

The chloride in water frequently exceeds the standard; directly quoting foreign water-quality criteria (WQC) or standards will inevitably reduce the scientific value of the water-quality standard (WQS) in China. Additionally, this may lead to the under- or overprotection of water bodies. This study summarized the sources, distribution, pollution status, and hazards of chloride in China's water bodies. Additionally, we compared and analyzed the basis for setting WQS limits for chloride in China; we systematically analyzed the basis for setting the WQC for chloride in foreign countries, especially the United States. Finally, we collected and screened data on the toxicity of chloride to aquatic organisms; we also used the species sensitivity distribution (SSD) method to derive the WQC value for chloride, which is 187.5 mg·L -1 . We put forward a recommended value for freshwater WQS for chloride in China: less than 200 mg·L -1 . The study of a freshwater WQC for chloride is not only a key point of environmental research, but also an urgent demand to ensure water ecological protection in China. The results of this study are of great significance for the environmental management of chloride, protection of aquatic organisms, and risk assessment, especially for the revision of WQSs.

Objective: Pimavanserin, a serotonin 5-HT 2 antagonist, is indicated for treatment of hallucinations and delusions associated with Parkinson's disease psychosis. In premarketing trials in patients with Parkinson's disease psychosis, 11% of patients died during open-label pimavanserin treatment. Antipsychotics, which are used off-label in Parkinson's disease psychosis, increase mortality in dementia patients. The authors compared mortality with pimavanserin and atypical antipsychotics in a large database., Methods: This was a retrospective new-user cohort study of Medicare beneficiaries with Parkinson's disease initiating pimavanserin (N=3,227) or atypical antipsychotics (N=18,442) from April 2016 to March 2019. All-cause mortality hazard ratios and 95% confidence intervals were estimated for pimavanserin compared with atypical antipsychotics, using segmented proportional hazards regression over 1-180 and 181+ days of treatment. Potential confounding was addressed through inverse probability of treatment weighting (IPTW)., Results: Pimavanserin users had a mean age of approximately 78 years, and 45% were female. Before IPTW, some comorbidities were more prevalent in atypical antipsychotic users; after IPTW, comorbidities were well balanced between groups. In the first 180 days of treatment, mortality was approximately 35% lower with pimavanserin than with atypical antipsychotics (hazard ratio=0.65, 95% CI=0.53, 0.79), with approximately one excess death per 30 atypical antipsychotic-treated patients; however, during treatment beyond 180 days, there was no additional mortality advantage with pimavanserin (hazard ratio=1.05, 95% CI=0.82, 1.33). Pimavanserin showed no mortality advantage in nursing home patients., Conclusions: Pimavanserin use was associated with lower mortality than atypical antipsychotic use during the first 180 days of treatment, but only in community-dwelling patients, not nursing home residents.

Background: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19., Methods: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists., Findings: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001)., Interpretation: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19., Funding: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health., Competing Interests: Declaration of interests HXB reports grants from Brown University, Amazon Web Service, Radiological Society of North America, and National Cancer Institute of the National Institute of Health, during the conduct of the study. XF currently works in Carina Medical, a for-profit organisation that develops clinical products, outside of the submitted work. KC reports grants from National Institute of Biomedical Imaging and Bioengineering and National Cancer Institute of the National Institute of Health, during the conduct of the study. YF reports grants from National Institute of Health, during the conduct of the study. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)