Dermatology Residency Applicants’ Concerns Due to COVID-19

Background: The COVID-19 pandemic caused substantial disruptions to medical education. We hypothesized that these disruptions may affect students applying to competitive residencies such as dermatology. Methods: A 24-question IRB-exempt, online, anonymous survey was distributed to 240 eligible medical students via social media to assess dermatology residency applicants’ concerns due to the COVID-19 pandemic and to collect student-proposed solutions to assist with the virtual application cycle. Eligibility consisted of US MD students applying to dermatology residency in the 2020-2021 or 2021-2022 application cycles. Statistical significance was calculated using the Chi-squared procedure, T-test/ANOVA, Mann Whitney test, and Kruskil Wallis tests with post-hoc Tukey and Mann Whitney tests. Results: Of 240 eligible students, 77 students attempted the survey whereas 69 students completed it (response rate 32%, completion rate 89%). Salient findings include students without home dermatology departments (WHD; n=24) who demonstrated concerns about research project changes (p=0.00). Students under-represented in medicine (UIM; n=34) concerned about lack of in-person interviews (p=0.00; p=0.04). MS3 (n=14), concerned about test cost (p=0.00), and effect on test scores because of COVID-19 (p=0.03). WHD and UIM applicants showed concerns about changes in clinical experiences (p=0.03, p= 0.03, respectively). Lastly, WHD, UIM and MS3 expressed concerns about obtaining quality recommendation letters (p=0.00, p=0.04, and p=0.03, respectively). Conclusion: Our findings demonstrate the various concerns shared by dermatology residency applicants because of the COVID-19 pandemic. The suggested solutions offered by participants in our study can be used to improve processes for candidates in the 2021-2022 residency application cycle.

Evaluation of point-of-care algorithms to detect diabetes during screening for latent TB infection

BACKGROUND: Individuals with both diabetes mellitus (DM) and TB infection are at higher risk of progressing to TB disease.OBJECTIVE: To determine DM prevalence in populations at high risk for latent TB infection (LTBI) and to identify the most accurate point-of-care (POC) method for DM screening.METHODS: Adults aged ≥25 years were recruited at health department clinics in Hawaii and Arizona, USA, and screened for LTBI and DM. Screening methods for DM included self-report, random blood glucose (RBG), and POC hemoglobin A1c (HbA1c). Using HbA1c ≥6.5% or self-reported history as the gold standard for DM, we compared test strategies to determine the most accurate method while keeping test costs low.RESULTS: Of 472 participants, 13% had DM and half were unaware of their diagnosis. Limiting HbA1c testing to ages ≥30 years with a RBG level of 120–180 mg/dL helped identify most participants with DM (sensitivity 85%, specificity 99%) at an average test cost of US$2.56 per person compared to US$9.56 per person using HbA1c for all patients.CONCLUSION: Self-report was insufficient to determine DM status because many participants were previously undiagnosed. Using a combination of POC RBG and HbA1c provided an inexpensive option to assess DM status in persons at high risk for LTBI.

Test Scheduling and Test Time Reduction for SoC by Using Enhanced Firefly Algorithm

System-on-Chip (SoC) is an integration of electronic components and billions of transistors. Defects due to the base material is caused during the manufacturing of components. To overcome these issues testing of chips is necessary but total cost increases because of increasing test time. The main issues to be considered during testing of SoC are the time taken for testing and accessibility of core. Effective test scheduling should be done to minimize testing time. In this paper, an effective test scheduling mechanism to minimize testing time is proposed. The test time reduction causes test cost reduction. The Enhanced Firefly algorithm is used in this paper to minimize test time. Enhanced Firefly algorithm gives a better result than Ant colony and Firefly algorithms in terms of test time reduction thereby reduction test cost takes place.

Understanding the barriers to pooled SARS-CoV-2 testing in the United States

Pooled testing for SARS-CoV-2 detection is instrumental for increasing test capacity while decreasing test cost, key factors for sustainable, long-term surveillance measures. While numerous pooled approaches have been described, uptake by labs has been limited. We surveyed 90 US labs to understand the barriers to implementing pooled testing.

Identifying the Optimal Subsets of Test Items through Adaptive Test for Cost Reduction of ICs

With the growing complexity of integrated circuits (ICs), more and more test items are required in testing. However, the large number of invalid items (which narrowly pass the test) continues to increase the test time and, consequently, test costs. Aiming to address the problems of long test time and reduced test item efficiency, this paper presents a method which combines a fast correlation-based filter (FCBF) and a weighted naive Bayesian model which can identify the most effective items and make accurate quality predictions. Experimental results demonstrate that the proposed method reduces test time by around 2.59% and leads to fewer test escapes compared with the recently adopted test methods. The study shows that the proposed method can effectively reduce the test cost without jeopardizing test quality excessively.