Citations

Background: The AIDS and Cancer Specimen Resource (ACSR) is a network of four regional biospecimen repositories and a technical core in the United States and South Africa. Its mission is to acquire, store, and distribute HIV-associated malignancy specimens and related clinical data to support translational research. At the outset of the COVID-19 pandemic, it became apparent that existing ACSR Standard Operating Procedures (SOPs) were not sufficient to ensure long-term maintenance and integrity of inventories during periods of extended shutdown. The ACSR needed an administrative SOP for situations pertaining to epidemics/pandemics. The ACSR Quality Working Group (QWG), comprised of representatives from each of the five ACSR sites and an external member who directs a large university medical center biorepository, addressed the issue. Methods: To understand the individual problems the sites faced, questions were developed to query each of the six QWG sites' contingency plans to cover this type of emergency, the amount of work allowed onsite and by whom, the challenges sites experienced, and the lessons learned to assist with future similar situations, while remaining consistent with the existing IRB protocols. Results: Reported challenges spanned all activities of classical biobanks and differed within the geographical locations of the sites and the local COVID-19 infection rate. Review of the responses to the questions revealed that the general shutdown of society external to the biorepositories presented them with a homogeneous collection of problems, limitations, and needs. This led to creating an SOP that addresses planning for pandemic emergencies, scaling down of activities, shutting down, and reopening plans. Conclusions: The ACSR QWG sites now have a structured response SOP for their sites, including guidance on how to develop and implement an emergency shutdown and reopening plan. The complete SOP is publicly available on the ACSR website.

AbstractBackgroundMinimally invasive treatments as soft tissue filler injections can enhance the appearance of the jawline. This prospective, single‐center study investigated aesthetic outcome, patient satisfaction, adverse events, and volume changes after jawline contouring using standardized reporting scales and objectifiable 3D surface analysis.MethodsA total of 30 patients (1 male and 29 females, mean age: 57.2 (±8.7) years) were investigated. Patients underwent jawline augmentation using a highly cross‐linked hyaluronic acid–based soft tissue filler. Three‐dimensional surface imaging was performed after 2 weeks, and 3, 6, 9, and 12 months. Furthermore, the aesthetic results and the occurrence of complications were investigated after two weeks, and 3, 6, 9, and 12 months.ResultsThe surface‐volume coefficient (SVC) had an average of 1.10 ± 0.2 after 14 days, 0.95 ± 0.1 after 3 months, 0.83 ± 0.1 after 6 months, 0.74 ± 0.1 after 9 months, and 0.63 ± 0.1 after 12 months. A significant correlation was revealed between time of measurement and measured SVC with rp = −0.761, p < 0.001. Multivariate analysis revealed a significant difference between the measured SVC and the different time points of measurement with p < 0.001. The data revealed strong aesthetic improvement with results most often reported as “very much improved” according to the 5‐point GAIS after 3, 6, and 9 months, both by the investigator and by the patients. A 12‐month follow‐up analysis showed “much improved” results in a majority of cases.ConclusionThe result of this investigation showed that jawline enhancement using minimally invasive soft tissue filler injections produces durable, safe results that are generally rated as very satisfying from a patient's and investigator's perspective over a time period of 12 months.

4-Aminopyridine is a potential therapeutic option for a subgroup of patients with KCNA2 -encephalopathy and gain-of-function variants.

Abstract Background Clinical and molecular subcategories of inflammatory bowel disease (IBD) are needed to discover mechanisms of disease and predictors of response and disease relapse. We aimed to develop a study of a prospective adult research cohort with IBD (SPARC IBD) including longitudinal clinical and patient-reported data and biosamples. Methods We established a cohort of adults with IBD from a geographically diverse sample of patients across the United States with standardized data and biosample collection methods and sample processing techniques. At enrollment and at time of lower endoscopy, patient-reported outcomes (PRO), clinical data, and endoscopy scoring indices are captured. Patient-reported outcomes are collected quarterly. The quality of clinical data entry after the first year of the study was assessed. Results Through January 2020, 3029 patients were enrolled in SPARC, of whom 66.1% have Crohn’s disease (CD), 32.2% have ulcerative colitis (UC), and 1.7% have IBD-unclassified. Among patients enrolled, 990 underwent colonoscopy. Remission rates were 63.9% in the CD group and 80.6% in the UC group. In the quality study of the cohort, there was 96% agreement on year of diagnosis and 97% agreement on IBD subtype. There was 91% overall agreement describing UC extent as left-sided vs extensive or pancolitis. The overall agreement for CD behavior was 83%. Conclusion The SPARC IBD is an ongoing large prospective cohort with longitudinal standardized collection of clinical data, biosamples, and PROs representing a unique resource aimed to drive discovery of clinical and molecular markers that will meet the needs of precision medicine in IBD.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.