Impact of the COVID-19 Pandemic on the Severity and Early Postoperative Outcomes of Acute Appendicitis.

Background The coronavirus disease 2019 (COVID-19) pandemic caused changes in surgical practice. For acute appendicitis (AA), measures to control the pandemic might hinder patients from seeking medical care timely, resulting in increasing severity, postoperative complications, and mortality. This study aimed to investigate whether the COVID-19 pandemic had a negative impact on the severity and postoperative outcomes of patients with AA. Methodology We retrospectively reviewed medical records of AA patients treated operatively at Nhan Dan Gia Dinh Hospital hospital from June 1st to September 30th in three consecutive years: pre-pandemic (2019)/Group 1, minor waves (2020)/Group 2, and major wave (2021)/Group 3 (2021). Data were collected focusing on the duration of symptoms, severity of AA, time from admission to operation, postoperative complications, and mortality. Results There were 1,055 patients, including 452 patients in Group 1, 409 in Group 2, and 194 in Group 3. The overall number of patients decreased mainly in non-complicated AA. The percentages of hospital admission after 24 hours gradually increased (20.8%, 27.9%, and 43.8%, p < 0.05). The percentages of complicated AA in Group 2 and Group 3 were statistically higher than in Group 1 (39% and 55% vs. 31%, p < 0.05). Waiting time for operation increased to five hours during the major wave. Laparoscopic appendectomy was performed in 98-99% of AA patients during the pandemic, with an early postoperative complication rate of 5-9% and a mortality rate of 0.2-1%. Conclusions Although the percentages of hospital admission after 24 hours and complicated AA increased, laparoscopic appendectomy was still feasible and effective and should be maintained as the standard management for AA during the COVID-19 pandemic.

Generating future fundus images for early age-related macular degeneration based on generative adversarial networks.

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is one of the most common diseases that can lead to blindness worldwide. Recently, various fundus image analyzing studies are done using deep learning methods to classify fundus images to aid diagnosis and monitor AMD disease progression. But until now, to the best of our knowledge, no attempt was made to generate future synthesized fundus images that can predict AMD progression. In this paper, we developed a deep learning model using fundus images for AMD patients with different time elapses to generate synthetic future fundus images. METHOD: We exploit generative adversarial networks (GANs) with additional drusen masks to maintain the pathological information. The dataset included 8196 fundus images from 1263 AMD patients. A proposed GAN-based model, called Multi-Modal GAN (MuMo-GAN), was trained to generate synthetic predicted-future fundus images. RESULTS: The proposed deep learning model indicates that the additional drusen masks can help to learn the AMD progression. Our model can generate future fundus images with appropriate pathological features. The drusen development over time is depicted well. Both qualitative and quantitative experiments show that our model is more efficient to monitor the AMD disease as compared to other studies. CONCLUSION: This study could help individualized risk prediction for AMD patients. Compared to existing methods, the experimental results show a significant improvement in terms of tracking the AMD stage in both image-level and pixel-level.

High-throughput assessment of mutations generated by genome editing in induced pluripotent stem cells by high-resolution melting analysis.

BACKGROUND AND AIMS: Genome editing of induced pluripotent stem cells (iPSCs) holds great potential for both disease modeling and regenerative medicine. Although clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 provides an efficient and precise genome editing tool, iPSCs are especially difficult to transfect, resulting in a small percentage of cells carrying the desired correction. A high-throughput method to identify edited clones is required to reduce the time and costs of such an approach. METHODS: Here we assess high-resolution melting analysis (HRMA), a simple and efficient real-time polymerase chain reaction-based method, and compare it with more commonly used assays. RESULTS AND CONCLUSIONS: Our data show that HRMA is a robust and highly sensitive method, allowing the cost-effective and time-saving screening of genome-edited iPSCs. Samples can be prepared directly from 96-well microtiter plates for high-throughput analysis, and amplicons can be further analyzed with downstream techniques for further confirmation, if needed.