Notes

Photocatalytic, self-cleaning and anti-bacterial properties associated with Cu(2) doped TiO2.
Aligning the national standards to these studies revealed that specific standards were underrepresented compared to others. Conclusions Results of this systematic review suggest that there is some association between curricular models and student outcomes, although results reported for specific student populations, particularly K-2nd grade, were lacking. Furthermore, specific student outcomes related to the national standards were underrepresented compared to others. Researchers should take a standards-based approach when deciding which student outcomes to measure and should further explore the associations between curricular models and student outcomes across grade levels.
Building well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform.

Using two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments.

The cyclically trained ANN showed a 3% increase in performance, a significant improvement across most learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.Cancer surveillance is a field focused on collection of data to evaluate the burden of cancer and apply public health strategies to prevent and control cancer in the community. A key challenge facing the cancer surveillance community is the number of manual tasks required to collect cancer surveillance data, thereby resulting in possible delays in analysis and use of the information. To modernize and automate cancer data collection and reporting, the Centers for Disease Control and Prevention is planning, developing, and piloting a cancer surveillance cloud-based computing platform (CS-CBCP) with standardized electronic reporting from laboratories and health-care providers. With this system, automation of the cancer case collection process and access to real-time cancer case data can be achieved, which could not be done before. Furthermore, the COVID-19 pandemic has illustrated the importance of continuity of operations plans, and the CS-CBCP has the potential to provide such a platform suitable for remote operations of central cancer registries.
In an effort to promote cost-conscious, high-quality, and patient-centered care in the palliative radiation of painful bone metastases, the National Quality Forum (NQF) formed measure 1822 in 2012, which recommends the use of one of the four dose-fractionation schemes (30 Gy in 10 fractions, 24 Gy in 6 fractions, 20 Gy in 5 fractions, or 8 Gy in a single fraction). We investigated whether a custom electronic health record (EHR) alert system improved quality measure compliance among 88 physicians at a large academic center and institutional network.

In March 2018, a multiphase alert system was embedded in a custom web-based EHR. Prior to a course of palliative bone radiation, the alert system notified the user of NQF 1822 recommendations and, once prescription was completed, either affirmed compliance or advised a change in treatment schedule. Rates of compliance were evaluated before and after implementation of alert system.

Of 2,399 treatment courses, 86.5% were compliant with NQF 1822 recommendations.ect the diversity of clinical scenarios.Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Representing this increasingly granular knowledge is a significant challenge. As users and managers of a freely available chemotherapy drug and regimen database, we describe the changes we have made to accommodate these challenges. These include the development of a domain ontology and increased granularity in the classification of cancer types on the website.Skull base osteomyelitis (SBO) is an infection of the temporal, sphenoid, or occipital bone that can be a challenge to diagnose because of its nonspecific symptoms, long clinical course, and radiologic findings that mimic those of other entities. The authors review this unusual infection on the basis of six proven cases. The diagnosis of SBO should be made according to four points a high index of clinical suspicion, radiologic evidence of infection, repeated biopsies that are negative for malignancy, and positive results of microbiologic tests. SBO typically manifests clinically in patients with diabetes and recurrent otitis externa; the infection usually extends inferiorly to the compact bone of the infratemporal fossa, affecting the lower cranial nerve foramina. Several image-based techniques should be used to diagnose SBO. CT is the best option for evaluating bone erosion and demineralization, MRI can help delineate the anatomic location and extent of disease, and nuclear imaging is useful for confirming bone infection with high sensitivity. However, the standard diagnostic procedure for SBO is for patients to undergo repeated biopsies to rule out malignancy, with histopathologic signs of infection and detection of microorganisms in the biopsied bone or soft tissue indicating SBO. The ability to diagnose SBO can be increased by identifying patients at risk, recognizing the most important causes and routes of infection, describing the main radiologic findings, and always considering the differential diagnosis. ©RSNA, 2021.Dual-energy CT (DECT) is a tremendous innovation in CT technology that allows creation of numerous imaging datasets by enabling discrete acquisitions at more than one energy level. The wide range of images generated from a single DECT acquisition provides several benefits such as improved lesion detection and characterization, superior determination of material composition, reduction in the dose of iodine, and more robust quantification. Technological advances and the proliferation of various processing methods have led to the availability of diverse vendor-based DECT approaches, each with a different acquisition and image reconstruction process. The images generated from various DECT scanners differ from those from conventional single-energy CT because of differences in their acquisition techniques, material decomposition methods, image reconstruction algorithms, and postprocessing methods. DECT images such as virtual monochromatic images, material density images, and virtual unenhanced images have different imaging appearances, texture features, and quantitative capabilities. This heterogeneity creates challenges in their routine interpretation and has certain associated pitfalls. Some artifacts such as residual iodine on virtual unenhanced images and an appearance of pseudopneumatosis in a gas-distended bowel loop on material-density iodine images are specific to DECT, while others such as pseudoenhancement seen on virtual monochromatic images are also observed at single-energy CT. Recognizing the potential pitfalls associated with DECT is necessary for appropriate and accurate interpretation of the results of this increasingly important imaging tool. Online supplemental material is available for this article.©RSNA, 2021.A multidisciplinary team evaluated and improved the MRI processes within the authors' integrated health care system, with the aim to increase patient access to MRI. The authors created a SMART (specific, measurable, achievable, relevant, and time-based) goal of decreasing the average number of days to wait for MRI examination by 50%, from 15 to 7.5 days, while also creating capacity to meet demand for same-day and next-day MRI appointment requests. The current performance metrics and processes were compared with available benchmarking and best practice data. Several work groups were created to empower and support frontline teams to identify and capture improvement opportunities. Across all MRI processes, teams focused on creating standard work, advancing practice to top of scope, removing waste, improving communication, reducing rework, and improving patient experience. Patient access to MRI was monitored, measured as the average number of days to wait from the time of scheduling to the MRI examination and time to the third-available appointment. The authors also monitored secondary outcomes (patient satisfaction, throughput metrics) and a balancing measure (technical repeat examination rates). The access improved after intervention the average number of days to wait for MRI access decreased from 14.2 days to 5.8 days after the intervention (-8.4 days, -59.2%, P less then .0001) and third-available appointment decreased from 18 days to 0 days. Ten to 20 same-day and next-day appointments became routinely available. The throughput metrics improved, and balancing measures were not changed. This project resulted in significant improvements in patient access to MRI examinations. The findings demonstrate the value of a multidisciplinary team applying comprehensive improvement strategies to increase patient access to complex services, such as MRI. ©RSNA, 2021.Rheumatoid arthritis (RA) is one of the most common chronic systemic inflammatory diseases and the most common chronic inflammatory arthritis. Classically a progressive symmetric polyarthritis, RA is characterized by inflammation, erosions, bone loss, and joint destruction. ZYVADFMK Up to half of patients with RA exhibit extra-articular manifestations (EAMs), which may precede articular disease and are more common in patients with seropositive RA (patients with detectable serum levels of rheumatoid factor and/or anticitrullinated peptide antibodies). Cardiovascular and pulmonary EAMs are the largest contributors to morbidity and mortality in RA and may be especially devastating. Imaging has a significant role in diagnosing these EAMs and assessing response to treatment. Although treatment with disease-modifying antirheumatic drugs has redefined the natural history of RA and helped many patients achieve low disease activity, patients are at risk for treatment-related complications, as well as infections. The clinical features of drug-induced lung disease and infection can overlap considerably with those of EAMs, presenting a diagnostic challenge. Radiologists, by recognizing the imaging characteristics and evolution of these various processes, are essential in diagnosing and distinguishing among EAMs, treatment-related complications, and unrelated processes and formulating an appropriate differential diagnosis. Moreover, recognizing these disease processes at imaging and contextualizing imaging findings with clinical information and laboratory and pathologic findings can facilitate definitive diagnosis and proper treatment. The authors review the articular and extra-articular thoracic imaging manifestations of RA, including cardiovascular, respiratory, and pleural diseases, as well as treatment-related complications and common infections. ©RSNA, 2021.
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