COVID-19 and The Surgical Neonate

COVID-19 pandemic in 2020-2021 affected millions of people including children. Though uncommon, there are few reports of COVID in neonates also. COVID is primarily managed by pediatricians;however, they are involved when providing anesthesia to these neonates for surgery. The role of anesthetists, besides during surgery, has proven to be vital in COVID pandemic for their expertise in airway and ventilatory management, also putting them to the highest risk of exposure. Various testing methods are available, and TrueNAT and RTPCR have emerged as most reliable. Most neonates remain asymptomatic or have mild symptoms;however, RTPCR testing should be done in all at least 72 h of preoperative. Utmost care should be taken during the preoperative evaluation, and in the perioperative period, goal is to prevent transmission of COVID to noninfected HCW involved in the perioperative period, to other newborns and neonates, and also to avoid increasing the severity of the diseases in the positive neonates, while keeping in mind the vulnerability of these babies in combination with their surgical disease and the changing neonatal physiology. COVID care protocols should be followed at all times. Anesthetic considerations remain the same as described in other chapters in the book, in newborns and neonates, both term and preterm. All OT personnel need to don the PPE, which can be problematic especially for the anesthetist, as it restricts the normal unhindered movements, use of stethoscope for chest auscultation for heart rate, respiration, and ETT positioning. Hence, one needs to be very meticulous in IV line and ET placement and their securing to prevent accidental dislodging during positioning and under the drapes. All disposable and non-disposable equipment used for the covid positive baby, should be adequately treated or discarded, as the case may be, after each surgery. Only emergency surgery should be undertaken in COVID-positive neonates to prevent high postoperative morbidity and mortality. There is not much data available in neonates, and most guidelines have been introduced for children and adolescents. Neonatal care has emerged from the experience of the anesthesiologist and from extrapolation of the available pediatric guidelines. Here, we will be discussing COVID in neonates and anesthetic management in COVID-positive neonates undergoing surgery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Intellectual Property Management in Healthcare Using Robotic Process Automation During COVID-19

In the current scenario, when the whole world is under stress due to the upcoming variant of the Novel Corona Virus, the Healthcare system, earlier practiced in the traditional mode, has been replaced in Digital mode with various scientific innovations like robotics which shows new thinking of perception among the people how it works in reality. Robotic Process Automation referred to as RPA, plays a significant role in today's healthcare system. The authors, through this chapter, seek to discuss the advancement of Robotic Process Automation and its link with Intellectual Property Management elaborately. The chapter begins with the authors tracing recent developments of Robotics in Healthcare through automation during COVID-19 as it has significant inception. Secondly, the authors also elaborate on how the management of IP assets created out of such a digital revolution due to accelerated and ramped-up innovation comes into the picture quite significantly. It shall enable the readers to gradually grasp the connection between the two fields. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.