Wireless localisation of breast lesions with MagSeed. A radiological perspective of 300 cases.

OBJECTIVES: The purpose of this article is to review the technical and radiological aspects of MagSeed® localisation, to assess its accuracy based on post-localisation mammograms and excision specimen X-rays and to discuss the radiological experience of our institutions. METHODS: Two-year data were collected retrospectively from three NHS boards from the West of Scotland. A total of 309 MagSeeds® were inserted under mammographic or ultrasonographic guidance in 300 women with unifocal, multifocal and/or bilateral breast lesions at the day of surgery or up to 30 days prior to it. Radiological review of post-localisation mammograms and intraoperative specimen X-rays as well as a review of the surgical outcomes were performed to assess the accuracy and efficacy of the method. Our experience relating to the technique's strengths and downsides were also noted. RESULTS: The MagSeeds® were inserted on average 7.2 days before surgery. The localisation technique was straight forward for the radiologists. In 99% of the cases, the MagSeed® was successfully deployed and 100% of the successfully localised lesions were excised at surgery. There was no difference in the accuracy of the localisation whether this was mammographically or ultrasonographically guided. On post-localisation mammograms, the MagSeed® was radiologically accurately positioned in 97.3% of the cases. No delayed MagSeed® migration was observed. On the specimen X-rays, the lesion was centrally positioned in 45.1%, eccentric within more than 1 mm from the margin in 35.7% and in 14.8% it was at the specimen's margin. The re-excision rate was 18.3%. CONCLUSION: The MagSeed® is an accurate and reliable localisation method in breast conserving surgery with good surgical outcomes. ADVANCES IN KNOWLEDGE: To our knowledge, the radiological aspects of MagSeed® localisation have not been widely described in peer-reviewed journals thus far.

Mechanistic understanding of the performance of personalized 3D-printed cardiovascular polypills: A case study of patient-centered therapy.

The 3D printing has become important in drug development for patient-centric therapy by combining multiple drugs with different release characteristics in a single polypill. This study explores the critical formulation and geometric variables for tailoring the release of Atorvastatin and Metoprolol as model drugs in a polypill when manufactured via pressure-assisted-microextrusion 3D printing technology. The effects of these variables on the extrudability of printing materials, drug release and other quality characteristics of polypills were studied employing a definitive screening design. The extrudability of printing materials was evaluated in terms of flow pressure, non-recoverable strain, compression rate, and elastic/plastic flow. The extrudability results helped in defining an operating space free of printing defects. The Atorvastatin compartment of polypill consisted of mesh-shaped layers while Metoprolol compartment consisted of a core surrounded by a release controlling shell with a hydrophobic septum between the two compartments. The results indicated that both the formulation and geometric variables govern the drug release of the polypill. Specifically, the use of HPMC E3 matrix, and a 2 mm distance between the strands at a weaving angle of 90° were critical in achieving the desired immediate-release profile of Atorvastatin. The core and shell design primarily determined the desired extended-release profile of Metoprolol. The carbopol and HPMC K100 concentration of 1% in the core and 10% in the shell and the number of shell layers in Metoprolol compartment were critical for achieving the desired Metoprolol dissolution. Polymer and Metoprolol content of the shell and shell-thickness affected the mechanical strength of the polypills. In conclusion, the 3D printing provides the flexibility for independently tailoring the release of different drugs in the same dosage form for patient centric therapy, and both the formulation and geometric parameters need to be optimized to achieve desired drug release.

Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments.

Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid .