Antibody glycan quality predicted from CHO cell culture media markers and machine learning.

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.

A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial.

Social media analyses have become increasingly popular among health care researchers. Social media continues to grow its user base and, when analyzed, offers unique insight into health problems. The process of obtaining data for social media analyses varies greatly and involves ethical considerations. Data extraction is often facilitated by software tools, some of which are open source, while others are costly and therefore not accessible to all researchers. The use of software for data extraction is accompanied by additional challenges related to the uniqueness of social media data. Thus, this paper serves as a tutorial for a simple method of extracting social media data that is accessible to novice health care researchers and public health professionals who are interested in pursuing social media research. The discussed methods were used to extract data from Facebook for a study of maternal perspectives on sudden unexpected infant death.

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic.

This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative Work-Life Balance model, the article provides fresh insights for future research.

Dutch tweets before, during, and after a black lives matter demonstration in Amsterdam: Expert annotated data, protocol, and labelling tool.

The Netherlands police are looking for measures to examine sentiment on social media related to protest demonstrations. While models exist to detect more subtle expressions of sentiment within tweets, models trained in the Dutch language are scarce. Being able to predict sentiment development during protests is relevant for parties like the Dutch government and the police to get more insight to when and where potential law enforcement is needed for public order and safety. Therefore, to analyse sentiment before, during, and after protest demonstrations, data was collected with tweets related to a Black Lives Matter protest that took place in Amsterdam during the COVID-19 pandemic. All tweets have been manually labelled by a dedicated open-source intelligence (OSINT) team within the Netherlands police following an established protocol. Both the data and the protocol are available, and interesting for researchers in natural language processing, topic detection, sentiment analysis, and protests analysis. The developed labelling tool for the labelling process is publicly available.

Analyzing online forums to identify information needs and knowledge gaps in patients with left ventricular assist devices: a qualitative study.

AIM: To explore the knowledge and unmet informational needs of candidates for left ventricular assist devices (LVADs), as well as of patients, caregivers, and family members, by analyzing social media data from the MyLVAD.com website. METHODS AND RESULTS: A qualitative content analysis method was employed, systematically examining and categorizing forum posts and comments published on the MyLVAD.com website from March 2015 to February 2023. The data was collected using an automated script to retrieve threads from MyLVAD.com, focusing on genuine questions reflecting information and knowledge gaps. The study received approval from an ethics committee. The research team developed and continuously updated categorization matrices to organize information into categories and subcategories systematically. From 856 posts and comments analyzed, 435 contained questions representing informational needs, of which six main categories were identified: clothing, complications/adverse effects, LVAD pros and cons, self-care, therapy, and recent LVAD implantation. The self-care category, which includes managing the driveline site and understanding equipment functionality, was the most prominent, reflecting nearly half of the questions. Other significant areas of inquiry included complications/adverse effects and the pros and cons of LVAD. CONCLUSION: The analysis of social media data from MyLVAD.com reveals significant unmet informational needs among LVAD candidates, patients, and their support networks. Unlike traditional data, this social media-based research provides an unbiased view of patient conversations, offering valuable insights into their real-world concerns and knowledge gaps. The findings underscore the importance of tailored educational resources to address these unmet needs, potentially enhancing LVAD patient care.

Media depictions of primary care teleconsultation safety: a thematic analysis of UK newspapers.

BACKGROUND: The COVID-19 pandemic necessitated the widespread roll-out of teleconsultations across primary care services in the UK. The media's depiction of remote consultations, especially regarding their safety, is not well established. These insights are important: newspapers' coverage of healthcare-related news can influence public perception, national policy, and clinicians' job satisfaction. AIM: To explore how the national newspapers in the UK depicted both the direct and indirect consequences of the remote-first approach on patient safety. DESIGN AND SETTING: We performed thematic analysis of newspaper articles that discussed patient safety in primary care teleconsultations, which were published between 21 January 2021 and 22 April 2022. METHOD: We identified relevant articles using the LexisNexis Academic UK database. We categorised data from these articles into codes before developing these into emergent themes through an iterative process. RESULTS: Across the 57 articles identified, the main safety concern identified was missed and/or delayed diagnoses over tele-appointment(s), while isolated cases of inappropriate prescribing were also reported. The media reported that the transition to a remote-first approach reduced the accessibility to primary care appointments for some groups (especially patients with lower digital literacy or access) and heightened the burden on other healthcare services; in particular, there were reports of patient care being compromised across NHS emergency departments. CONCLUSION: The print media predominantly reported negative impacts of remote consultations on patient safety, particularly involving missed and/ or delayed diagnoses. Our work highlights the importance of further exploration into the safety of remote consultations, and the impact of erroneous media reporting on policies and policymakers.

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey.

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

BaitBuster-Bangla: A comprehensive dataset for clickbait detection in Bangla with multi-feature and multi-modal analysis.

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications.

Drivers of vulnerability to medicine smuggling and combat strategies: a qualitative study based on online news media analysis in Iran.

BACKGROUND: Medicine smuggling poses a serious public health threat, limiting patients' safe and timely access to this essential resource. Thus, this study aims to identify the factors contributing to the vulnerability to medicine smuggling and propose effective strategies to combat this issue in Iran. METHODS: An analysis of news media was conducted using qualitative content analysis. News text items related to medicine smuggling were retrieved from various online news sources between March 21, 2017, and May 21, 2023. To select health-oriented and general online news stations, news agencies, and newspapers, the purposeful sampling method with a maximum variation strategy was used. The selected sources included Mehr News Agency, Khabar Online, Islamic Consultative Assembly News Agency (ICANA), Islamic Republic News Agency (IRNA), Iranian Students News Agency (ISNA), Hamshahri, Donya-e-Eqtesad newspapers, Webda, Sepid Online, and Iran's Food and Drug Administration News Agency (IFDANA). All data coding was manually done using Microsoft Excel software version 2016. RESULTS: A total of 277 news articles were found to meet the established criteria for inclusion. The analysis revealed four main themes, each with several sub-themes, that shed light on the factors that drive vulnerability and the strategies to combat medicine smuggling. These themes are the economic environment, government and stewardship, information technology systems, and socio-cultural factors. The economic environment emerged as the most significant theme, encompassing medicine selection, reimbursement, and procurement, all of which affect the smuggling of pharmaceuticals in Iran. CONCLUSION: To combat medicine smuggling, it is important to adjust policies based on the identified vulnerabilities. Effective strategies to reverse pharmaceutical smuggling include capacity building of pharmaceutical manufacturing companies, implementing regulated and enhanced supervisory and rulemaking policies, strengthening health insurance, improving e-infrastructure, and increasing public awareness through collaborative approaches involving various stakeholders within and outside the health system.

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.

BACKGROUND: The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as "X") is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. OBJECTIVE: This study used Twitter as the primary source of data to study the behavioral characteristics and real-time emotional projections of individuals identifying with autism spectrum disorder (ASD). We aimed to improve the rigor of ASD analytics research by using the digital footprint of an individual to study the linguistic patterns of individuals with ASD. METHODS: We developed a machine learning model to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users' self-identification with autism using "#ActuallyAutistic" and a separate control group to identify linguistic markers associated with ASD traits. To construct the data set, we targeted English-language tweets using the search query "#ActuallyAutistic" posted from January 1, 2014, to December 31, 2022. From these tweets, we identified unique users who used keywords such as "autism" OR "autistic" OR "neurodiverse" in their profile description and collected all the tweets from their timeline. To build the control group data set, we formulated a search query excluding the hashtag, "-#ActuallyAutistic," and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We also illustrate the utility of the data set through common natural language processing tasks such as sentiment analysis and topic modeling. RESULTS: Our tweet classifier reached a 73% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. This is a promising start, demonstrating the potential for effective digital phenotyping studies and large-scale intervention using text data mined from social media. CONCLUSIONS: Textual differences in social media communications can help researchers and clinicians conduct symptomatology studies in natural settings.

Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis.

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

Who Is Invited to the Metaverse? Analyzing Media Coverage of an Emerging Virtual World.

As a new technology awaiting widespread immersive usage, public awareness and understanding of the metaverse is likely significantly shaped by its coverage in the media. This study explored how the metaverse is framed in U.S. news media coverage, including who the media targets as metaverse users, and reflects on how this could shape public attitudes and engagement with the metaverse. Specifically, this study asked: which people and institutions are included and excluded from media coverage of the metaverse? To answer this question, a systematic content analysis of 526 U.S. news articles was conducted, drawing from three media databases. Findings reveal that the media frames the metaverse as a corporate space for those with buying power: investors, technology experts, and consumers. Users without buying power and users from marginalized groups were rarely considered in media coverage. Despite this, most coverage of the metaverse was descriptive, with only 11 percent of articles critiquing this space. These findings raise broad questions about commodification, exclusion, and inequality in the metaverse.

The social diagnoses of digital addictions: Technophobic ambivalences, the limits of the natural and imperatives of self-governance in the information age.

The contemporary visibility of 'digital addictions' to online gaming, watching pornography, social media and so on suggests the discovery of some new form of technologically facilitated disease. Yet, what we actually see in the symptoms of these various behaviours when described as 'addictions' are a series of social problems that range from the interpersonal to the sociocultural. In the current article, I work to step outside of the individualising tendency of an addiction taxonomy to instead view digital addictions as a process of social diagnosis. In this way, digital addictions are understood as a reaction to historically and socioculturally informed forces. Specifically, I contend that a social diagnosis of the digital addiction concept tells us a great deal about contemporary cultural anxiety towards the ubiquity of digital media in our social worlds as it rubs up against concerns for productivity, socially lauded ideas of ostensibly 'natural' behaviours and worries about self-governance and self-control. I conclude with a series of pertinent questions about digital technologies, which are elided-if not actively foreclosed-within an addiction framework and which can better be made sense of by understanding digital addictions as a process of social diagnosis rather than the expression of a new kind of Internet borne illness.

Identifying the communication of burnout syndrome on the Twitter platform from the individual, organizational, and environmental perspective.

Addressing the escalating prevalence of burnout syndrome, which affects individuals across various professions and domains, is becoming increasingly imperative due to its profound impact on personal and professional aspects of employees' lives. This paper explores the intersection of burnout syndrome and human resource management, recognizing employees as the primary assets of organizations. It emphasizes the growing importance of nurturing employee well-being, care, and work-life balance from a human resource management standpoint. Employing social media analysis, this study delves into Twitter-based discourse on burnout syndrome, categorizing communication into three distinct dimensions: individual, organizational, and environmental. This innovative approach provides fresh insights into interpreting burnout syndrome discourse through big data analysis within social network analysis. The methodology deployed in this study was predicated upon the enhanced Social Media Analysis based on Hashtag Research framework and frequency, topic and visual analysis were conducted. The investigation encompasses Twitter communication from January 1st, 2019, to July 31st, 2022, comprising a dataset of 190,770 tweets. Notably, the study identifies the most frequently used hashtags related to burnout syndrome, with #stress and #mentalhealth leading the discussion, followed closely by #selfcare, #wellbeing, and #healthcare. Moreover, a comprehensive analysis unveils seven predominant topics within the discourse on burnout syndrome: organization, healthcare, communication, stress and therapy, time, symptoms, and leadership. This study underscores the evolving landscape of burnout syndrome communication and its multifaceted implications for individuals, organizations, and the broader environment, shedding light on the pressing need for proactive interventions. In organizations at all levels of management, the concept of burnout should be included in the value philosophy of organizations and should focus on organizational aspects, working hours and work-life balance for a healthier working environment and well-being of employees at all levels of management.

Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches.

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users' sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts.

Fight Against the Mandatory COVID-19 Immunity Passport on Twitter: Natural Language Processing Study.

BACKGROUND: To contain and curb the spread of COVID-19, the governments of countries around the world have used different strategies (lockdown, mandatory vaccination, immunity passports, voluntary social distancing, etc). OBJECTIVE: This study aims to examine the reactions produced by the public announcement of a binding political decision presented by the president of the French Republic, Emmanuel Macron, on July 12, 2021, which imposed vaccination on caregivers and an immunity passport on all French people to access restaurants, cinemas, bars, and so forth. METHODS: To measure these announcement reactions, 901,908 unique tweets posted on Twitter (Twitter Inc) between July 12 and August 11, 2021, were extracted. A neural network was constructed to examine the arguments of the tweets and to identify the types of arguments used by Twitter users. RESULTS: This study shows that in the debate about mandatory vaccination and immunity passports, mostly "con" arguments (399,803/847,725, 47%; χ26=952.8; P<.001) and "scientific" arguments (317,156/803,583, 39%; χ26=5006.8; P<.001) were used. CONCLUSIONS: This study shows that during July and August 2021, social events permeating the public sphere and discussions about mandatory vaccination and immunity passports collided on Twitter. Moreover, a political decision based on scientific arguments led citizens to challenge it using pseudoscientific arguments contesting the effectiveness of vaccination and the validity of these political decisions.

A Media Analysis of the COVID-19 Tobacco Sales Ban in South Africa.

The South African government introduced a nationwide lockdown in March 2020 to mitigate the spread of COVID-19. Among other restrictions, the government banned the sale of tobacco products. The ban lasted for nearly five months. We performed a Google search using the keywords smok*, puff*, lockdown, tobacco, and cigarette* for articles published in English from 23 March 2020 to 18 December 2020. This yielded 441 usable online media articles. We identified and categorised the main arguments made by proponents and opponents of the tobacco sales ban. Three themes were identified: medical, legal, and economic/financial. Legal aspects were covered in 48% of articles, followed by economic (34%), and medical aspects (18%). The media was generally ambivalent about the tobacco sales ban during the first five weeks of lockdown. Sentiment subsequently turned against the ban because the medical rationale was not well communicated by the government. There was limited empirical evidence of a link between smoking and contracting COVID-19, and the sales ban was ineffective since most smokers still purchased cigarettes. Policy framing in the media plays an important role in how the public receives the policy. Any future tobacco control policy intervention should be better considered, especially within the context that cigarettes are easily accessed on the illicit market in South Africa.

A targeted liquid chromatography mass spectrometry method for routine monitoring of cell culture media components for bioprocess development.

The analysis of cell culture media (CCM) components is critical for understanding cell growth kinetics and overall product quality during biomanufacturing. Given the diverse physical and chemical nature of CCM compounds present at a wide range of concentrations, there is an increasing demand for single-platform analytical assays with exceptional specificity and sensitivity. This study presents a targeted LC-MS/MS method for the identification and quantitation of 110 CCM analytes is presented, where target metabolites are monitored over an 20-min gradient. The analyte panel constitutes amino acids, vitamins, organic acids, nucleic acids, carbohydrates, and lipids. The method employs isotopically labeled standards to enable specific and accurate relative quantitation of CCM compounds based on physicochemical properties and retention time. Quantitation is performed on a triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. The method demonstrates strong linearity with an R2 of ≥0.99 with three orders of linear dynamic range and inter-day and intra-day precision with a%CV of <10% for spiked-in quality control samples. We also present three case studies to demonstrate method applicability in the bioprocessing space for developing vaccines and biologics.

A topic models analysis of the news coverage of the Omicron variant in the United Kingdom press.

BACKGROUND: The COVID-19 pandemic has caused numerous casualties, overloaded hospitals, reduced the wellbeing of many and had a substantial negative economic impact globally. As the population of the United Kingdom was preparing for recovery, the uncertainty relating to the discovery of the new Omicron variant on November 24 2021 threatened those plans. There was thus an important need for sensemaking, which could be provided, partly, through diffusion of information in the press, which we here examine. METHOD: We used topic modeling, to extract 50 topics from close to 1,500 UK press articles published during a period of approximately one month from the appearance of Omicron. We performed ANOVAs in order to compare topics between full weeks, starting on week 48 of 2021. RESULTS: The three topics documenting the new variant (Omicron origins, Virus mutations, News of a new variant) as well as mentions of vaccination excluding booster, Scotlands First minister statement (Communications) travel bans and mask wearing (Restrictions) and the impact of market and investing (Domains and events) decreased through time (all ps < .01). Some topics featured lower representation at week two or three with higher values before and after: Government's Scientific Advisory Group for Emergencies recommendations (Communications), Situation in the US, Situation in Europe (Other countries and regions), all ps < .01. Several topics referring to symptoms and cases-e.g., rises of infections, hospitalisations, the pandemic the holidays, mild symptoms and care; restrictions and measures-e.g., financial help, Christmas and Plan B, restrictions and New Year; and domains of consequences and events-e.g., such as politics, NHS and patients, retail sales and airlines, featured increasing representation, (all ps < .01). Other topics featured less regular or non-significant patterns. CONCLUSION: Changes in sensemaking in the press closely matched the changes in the official discourse relating to Omicron and reflects the trajectory of the infection and its local consequences.

Gene editing achieves consistently higher favorability in social and traditional media than GMOs.

While GMOs have been the subject of negative discourse over a long time period, it is possible that newer breeding technologies like gene editing are viewed more favorably. We present data for a 5-year period between January 2018 and December 2022, showing that in content specific to agricultural biotechnology, gene editing achieves consistently higher favorability ratings than GMOs in both social and traditional English-language media. Our sentiment analysis shows that favorability is especially positive in social media, with close to 100% favorability achieved in numerous monthly values throughout our 5 years of analysis. We believe that the scientific community can therefore be cautiously optimistic based on current trends that gene editing will be accepted by the public and be able to achieve its promise of making a substantial contribution to future food security and environmental sustainability worldwide. However, there are some recent indications of more sustained downward trends, which may be a cause for concern.