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Rapid development of COVID-19 has resulted in a massive shift from traditional to online teaching. This review aims to evaluate the effectiveness of distance learning on anatomy and surgical training. This systematic review was conducted in line with the PRISMA statement and current methodological literature. The databases CINAHL, Cochrane, EMBASE and Pubmed were searched using the search terms "Distant learning" OR "Distance learning" AND "Anatomy OR Surgery". 182 non-duplicate studies were identified. 20 studies were included for qualitative analysis. 10 studies evaluated students' performance with distance learning. 3 studies suggested that students' learning motivation improved with distance learning pedagogy. 5 studies found improved student performance with distance learning (performance or task completion time) when compared to conventional physical method. While 2 other studies found non-inferior student performance. 10 studies evaluated students' feedback on distance learning. Most feedbacks were positive, with flexibility, efficiency, increased motivation and better viewing angles as the most-liked features of distance teaching. 4 studies pointed out some limitations of distance learning, including the lack of personal contact with tutor, poor network and reduced student concentration. 7 studies evaluated tutors' feedback on distance learning. Tutors generally liked online platforms for the ease of tracking silent students, monitoring performance and updating fast-changing knowledge. Yet the lack of hands-on experience for students, technical issues and high costs are the main concerns for tutors. In conclusion, distance learning is a feasible alternative for anatomy and surgical teaching.

Objectives There are concerns for COVID-19 vaccination in causing thyroid dysfunction and triggering thyroid autoimmunity. Also, data on the impact of pre-existing thyroid autoimmunity on COVID-19 vaccination efficacy are limited. We evaluated the impact of COVID-19 vaccination on thyroid function and antibodies, and the influence of pre-existing thyroid autoimmunity on neutralizing antibody (NAb) responses. Methods Adults without history of COVID-19 or thyroid disorders who received COVID-19 vaccination between 14 June 2021 and 8 August 2021 at three vaccination centers were recruited. All participants received two doses of vaccines. Thyroid-stimulating hormone (TSH), free thyroxine (fT4), free triiodothyronine (fT3), anti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-Tg) antibodies were measured at baseline and 8 weeks after the first dose of vaccination. Post-vaccination NAb against SARS-CoV-2 receptor-binding domain was measured. Results In total, 215 individuals were included (129 BNT162b2 [60%] and 86 CoronaVac [40%] recipients): mean age 49.6 years, 37.2% men, and 12.1% positive for anti-TPO/anti-Tg at baseline. After vaccination, TSH levels did not change (p=0.225), but fT4 slightly increased (from 12. 0±1.1 to 12.2±1.2 pmol/L, p Conclusion COVID-19 vaccination was associated with a modest increase in anti-thyroid antibody titers. Anti-TPO increase was greater among BNT162b2 recipients. However, there was no clinically significant thyroid dysfunction 8 weeks post-vaccination. NAb responses were not influenced by pre-existing thyroid autoimmunity. Our results provided important reassurance to people to proceed to COVID-19 vaccination. Presentation: No date and time listed

In 2020, the COVID-19 pandemic changed the landscape for out-of-school-time STEM programs. The LEAH Knox Scholars (LKS) program is a five-week, in-person molecular biology research experience for low-income high school youth of color who are underrepresented in the sciences. The program is organized and implemented by Health Resources in Action, a nonprofit public health organization in partnership with Massachusetts Institute of Technology (MIT). In response to the pandemic, all 2020 LKS program activities shifted from in-person to virtual engagement using the Zoom platform. To assess program implementation, qualitative data and quantitative data were collected through four virtual focus groups with youth participants and six key informant interviews with LKS staff, as well as pre-and post-surveys. Discussions explored program successes, challenges, opportunities for improvement, and perceived program impact. Key findings from these discussions identified several successes of the virtually adapted program including: relationship building between youth and staff, the teaching of life skills via a workshop format, and a the implementation of a hands-on research curriculum. Barriers to program implementation included technological challenges such as limited internet capability and “Zoom fatigue”; and limitations in assessing student engagement via the virtual format. Key learnings from this case study have relevant applications for STEM initiatives that need to adapt to the new environment post COVID-19. The findings of this experience demonstrate that “at-home” research experiences are not only possible in a pandemic but can be effective in preparing high school youth for STEM-based careers through hands-on experiences in the sciences in a virtual format. Such efforts are important, especially in adverse circumstances, to actively eliminate disparities and promote equity and inclusion in STEM fields.

We present a deep learning-based high-throughput cytometer to detect rare cells in whole blood using a cost-effective and light-weight design. This system uses magnetic-particles to label and enrich the target cells. Then, a periodically-alternating magnetic-field creates time-modulated diffraction patterns of the target cells that are recorded using a lensless microscope. Finally, a custom-designed convolutional network is used to detect and classify the target cells based on their modulated spatio-temporal patterns. This cytometer was tested with cancer cells spiked in whole blood to achieve a limit-of-detection of 10 cells/mL. This compact, cost-effective and high-throughput cytometer might serve diagnostics needs in resource-limited-settings.

We present a high-throughput and cost-effective computational cytometer for rare cell detection, where the target cells are specifically labeled with magnetic particles and exhibit an oscillatory motion under a periodically-changing magnetic field. The time-varying diffraction patterns of the oscillating cells are then captured with a holographic imaging system and are further classified by a customized pseudo-3D convolutional network. To evaluate the performance of our technique, we detected serially-diluted MCF7 cancer cells that were spiked in whole blood, achieving a limit of detection (LoD) of 10 cells per 1 mL of whole blood.

We present a cost-effective and high-throughput computational cytometer using a magnetically-modulated lensless imaging technique and deep learning-based classification, to rapidly detect rare cells in whole blood, achieving a detection limit of 10 cells/mL.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications., Deep Learning Cytometry: Magnetically modulating rare cells for high-throughput detection Rare cells of medical significance can be detected in blood by a high-throughput imaging technique that analyzes movements of magnetically-labelled target cells using deep-learning. Analysis of magnetically-modulated light interference using an artificial neural network enhances the ability of the system to specifically and sensitively detect the tagged cells as they respond to a periodically changing magnetic field. Researchers in the USA, led by Aydogan Ozcan at the University of California, Los Angeles, demonstrated the technique using rare cancer cells added to blood. They achieved a detection limit as low as ten cells per milliliter. The optical system uses a holographic process called speckle imaging, which is much more compact and achieves higher throughput than traditional methods. It holds significant potential for the rapid analysis of blood and other bodily fluids to diagnose and monitor disease.