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Background:. Informed consent is a fundamental pillar of patient rights and is an essential part of good clinical practice. In 2019, the International Confederation of Plastic Surgery Societies launched a survey to collect feedback on informed consent practices, with an aim to develop an international guideline for cosmetic surgery Methods:. A 15-question survey was sent to delegates of the International Confederation of Plastic Surgery Societies for dissemination to their national society members. The survey comprised a range of quantitative and qualitative questions. Descriptive and thematic analysis was performed. Results:. There were 364 respondents. Over half of the respondents reported no local informed consent policy, whereas others noted national society, specialist college, or government policies. The majority of respondents believed that the performing surgeon should be responsible for obtaining informed consent with at least two face-to-face consultations. Most respondents agreed with a cooling-off period (duration based on procedure type and use of high-risk devices). Regarding cosmetic breast augmentation, the majority of respondents felt that the performing surgeon should be responsible for postoperative management, including cases that occur as part of surgical tourism. Some respondents incorporate financial consent as part of their informed consent practice. Most supported the development of an international informed consent guideline. Conclusions:. Informed consent should result from face-to-face consultations with the performing surgeon. There should be a minimum cooling-off period. Postoperative surveillance should be available in all settings. The findings of this survey will help inform an international standardized informed consent guideline for cosmetic surgery.

Background:. Coronavirus disease 2019 (COVID-19) pandemic-related changes may have led to changes in immediate breast reconstruction (IBR) rates. We aimed to evaluate these changes before, during, and after the initial wave of COVID-19. Methods:. We retrospectively reviewed women who underwent mastectomy with or without IBR from January 1 to September 30, 2019 and from January 1 to September 30, 2020, and compared demographic, clinical, and surgical variables between defined time periods. Results:. A total of 202 mastectomies were included. Fewer patients underwent IBR during the initial surge of COVID-19 (surge period) compared with the months before (presurge period; 38.46% versus 70.97%, P = 0.0433). When comparing the postsurge period with a year before (postsurge control), fewer patients underwent reconstruction even after the initial surge had passed (53.13% versus 81.25%, P = 0.0007). Those who underwent IBR were older than the year before (59.34 versus 53.06, P = 0.0181). The median number of postoperative visits in the postsurge period was 8.50 (interquartile range: 6–12) compared with 14 (interquartile range: 8–20.50) in the year before (P = 0.0017). The overall incidences of complications and unanticipated resource utilization were also significantly lower in the postsurge period compared with the year before [5.88% versus 30.77% (P = 0.0055), and 14.71% versus 28.85% (P = 0.0103), respectively]. Conclusions:. IBR rates were lower even after the initial surge than at the year before. Furthermore, during the pandemic, IBR patients were older, had fewer follow-up visits, and fewer reported complications.

Argonaute (AGO) proteins associate with small RNAs to direct their effector function on complementary transcripts. The nematode Caenorhabditis elegans contains an expanded family of 19 functional AGO proteins, many of which have not been fully characterized. In this work, we systematically analyzed every C. elegans AGO using CRISPR-Cas9 genome editing to introduce GFP::3xFLAG tags. We have characterized the expression patterns of each AGO throughout development, identified small RNA binding complements, and determined the effects of ago loss on small RNA populations and developmental phenotypes. Our analysis indicates stratification of subsets of AGOs into distinct regulatory modules, and integration of our data led us to uncover novel stress-induced fertility and pathogen response phenotypes due to ago loss.

Background:. Little is known about the demographics and ambitions of plastic surgery trainees and if these differ between regions. This study sought opinion from current and recently graduated plastic surgery trainees to map demographics, training structure, and ambitions of plastic surgery trainees worldwide. Methods:. A cross-sectional study was designed and administered by the international trainee organization International Confederation of Societies of Plastic Surgery Trainees. A questionnaire of 45 questions was distributed digitally through several international channels using the REDCap platform. Results:. A total of 290 junior plastic surgeons, of whom 124 (42.8%) were women, from all seven International Confederation of Societies of Plastic Surgery regions, participated in this study. Of the trainees, 21% have emigrated, and 75% expressed a desire to undertake a part of their training abroad. The most common length of training in plastic surgery is 5 years. There is a difference in working hours between regions, where more than 80-hour work weeks are most common in Asia (24.1%), and work weeks of less than 40 hours are most common in Middle East (30.8%). A majority of trainees (85%) reported a research interest, and we found a negative correlation between the extent of research ambition and reported clinical workload. Conclusions:. We present here the first international investigation of trainee experiences of plastic surgery training. We show that training structure and organization vary between institutions, and that plastic surgery trainees report a strong interest in international training as well as in research.

Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the “black-box” nature of DL towards model transparency.