Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023.

Objective: To investigate societal perceptions of ketamine's use in depression therapy by analysing Twitter posts from January 1, 2010 to April 1, 2023. Methods: Using Twitter as the social media platform of choice, and employing search terms based on (depression OR depressed OR depressive) AND (ketamine OR esketamine OR Spravato), we collected English-language tweets from January 1, 2010, to April 1, 2023. Using unsupervised machine learning and natural language processing (NLP) techniques, including Bidirectional Encoder Representations from Transformers (BERT) and BERTopic, the study identified prevalent topics surrounding public chatter around the use of ketamine in depression treatment. Manual thematic analyses further refined these topics into themes. Results: Out of an initial dataset of 99,405 tweets, after removing duplicate tweets, re-tweets and tweets posted by organizations over Twitter, 18,899 unique tweets from presumably individual users were analysed. Analysis of temporal trends revealed a shift in public attitudes, particularly after the United States Food and Drug Administration (FDA)'s 2019 approval of ketamine for depression. Three major themes emerged: a changing regulatory landscape, cautious optimism, and personal experiences with the drug. There was an initial spike in discussions post-FDA approval in 2019. Thereafter, cautious optimism (Theme 2) decreased among the general public, with more personal accounts (Theme 3) highlighting the potential benefits for some treatment-resistant patients. Limitations of the study include Twitter's inherent biases towards younger, English-speaking demographics. Conclusion: In summary, the public's multifaceted perception leans towards a hopeful stance on ketamine's therapeutic potential for depression.

Examining the non-linear effects of education on Alzheimer's and non-Alzheimer's disease: Sex-stratified analysis of 17,671 older persons.

BACKGROUND: Higher educational attainment is strongly associated with a reduced risk of neurocognitive disorders (NCDs). However, the literature is not yet clear on (1) the minimum years of compulsory education that would sufficiently mitigate the risk of NCDs, and (2) whether educational attainment has differential effects across sexes. To bridge these gaps, this study sought to model potential non-linear relationship between years of education and NCD risk, across men and women. METHODS: The study recruited 17,671 participants from Alzheimer's Disease Centres across United States, aged≥50 years and had normal cognition at baseline. Participants were followed up almost annually, and underwent standardized assessments to diagnose various aetiologies of NCDs. Cox proportional hazard regression was conducted to examine the relationship between years of education and NCD risk, stratified by sexes. Years of education were modelled using restricted cubic spline. RESULTS: Lesser years of education were associated with higher NCD risk in both sexes (specifically, when <12 years of education; HR 1.80-3.48), yet with key differences across sexes. In women, a linear relationship was observed whereby increasing years of education reduced NCD risk correspondingly; but in men, the relationship was non-linear whereby adding years of education beyond 12 years did not reduce NCD risk further. Men of lower educational attainment were at risk of both Alzheimer's Disease (AD; HR up to 3.45) and non-AD (HR up to 2.57), while women of lower educational attainment were only at risk of AD (HR up to 2.11). CONCLUSIONS: Educational attainment alters NCD risk differently across sexes, emphasizing the relevance of sex-specific research to better understand the moderating role of sex on NCD risk. The findings also highlight the need to reconsider public policies related to equitable access to education, as well as traditional assumptions on the minimum years of compulsory education.

Examining Public Messaging on Influenza Vaccine over Social Media: Unsupervised Deep Learning of 235,261 Twitter Posts from 2017 to 2023.

Although influenza vaccines are safe and efficacious, vaccination rates have remained low globally. Today, with the advent of new media, many individuals turn to social media for personal health questions and information. However, misinformation may be rife, and health communications may be suboptimal. This study, therefore, aimed to investigate the public messaging related to influenza vaccines by organizations over Twitter, which may have a far-reaching influence. The theoretical framework of the COM-B (capacity, opportunity, and motivation component of behavior) model was used to interpret the findings to aid the design of messaging strategies. Employing search terms such as "flu jab", "flu vaccine", "influenza vaccine", and '" influenza jab", tweets posted in English and by organizations from 1 January 2017 to 1 March 2023 were extracted and analyzed. Using topic modeling, a total of 235,261 tweets by organizations over Twitter were grouped into four main topics: publicizing campaigns to encourage influenza vaccination, public education on the safety of influenza vaccine during pregnancy, public education on the appropriate age to receive influenza vaccine, and public education on the importance of influenza vaccine during pregnancy. Although there were no glaring pieces of misinformation or misconceptions, the current public messaging covered a rather limited scope. Further information could be provided about influenza and the benefits of vaccination (capability), promoting community, pharmacist-led influenza vaccination, and other avenues (opportunity), and providing greater incentivization and support for vaccination (motivation).

Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts.

Several countries are witnessing significant increases in influenza cases and severity. Despite the availability, effectiveness and safety of influenza vaccination, vaccination coverage remains suboptimal globally. In this study, we examined the prevailing negative sentiments related to influenza vaccination via a deep learning analysis of public Twitter posts over the past five years. We extracted original tweets containing the terms 'flu jab', '#flujab', 'flu vaccine', '#fluvaccine', 'influenza vaccine', '#influenzavaccine', 'influenza jab', or '#influenzajab', and posted in English from 1 January 2017 to 1 November 2022. We then identified tweets with negative sentiment from individuals, and this was followed by topic modelling using machine learning models and qualitative thematic analysis performed independently by the study investigators. A total of 261,613 tweets were analyzed. Topic modelling and thematic analysis produced five topics grouped under two major themes: (1) criticisms of governmental policies related to influenza vaccination and (2) misinformation related to influenza vaccination. A significant majority of the tweets were centered around perceived influenza vaccine mandates or coercion to vaccinate. Our analysis of temporal trends also showed an increase in the prevalence of negative sentiments related to influenza vaccination from the year 2020 onwards, which possibly coincides with misinformation related to COVID-19 policies and vaccination. There was a typology of misperceptions and misinformation underlying the negative sentiments related to influenza vaccination. Public health communications should be mindful of these findings.

Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022.

Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand.

Examining the Public Messaging on 'Loneliness' over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade.

Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as Twitter and Reddit as well as online forums, e.g., loneliness forums, to seek advice and solace regarding their health and well-being. The present study therefore aimed to investigate the public messaging on loneliness via an unsupervised machine learning analysis of posts made by organisations on Twitter. We specifically examined tweets put out by organisations (companies, agencies or common interest groups) as the public may view them as more credible information as opposed to individual opinions. A total of 68,345 unique tweets in English were posted by organisations on Twitter from 1 January 2012 to 1 September 2022. These tweets were extracted and analysed using unsupervised machine learning approaches. BERTopic, a topic modelling technique that leverages state-of-the-art natural language processing, was applied to generate interpretable topics around the public messaging of loneliness and highlight the key words in the topic descriptions. The topics and topic labels were then reviewed independently by all study investigators for thematic analysis. Four key themes were uncovered, namely, the experience of loneliness, people who experience loneliness, what exacerbates loneliness and what could alleviate loneliness. Notably, a significant proportion of the tweets centred on the impact of the COVID-19 pandemic on loneliness. While current online interactions are largely descriptive of the complex and multifaceted problem of loneliness, more targeted prosocial messaging appears to be lacking to combat the causes of loneliness brought up in public messaging.

Effect of Probiotic Supplementation on Gut Microbiota in Patients with Major Depressive Disorders: A Systematic Review.

There is accumulating evidence on the beneficial effects of probiotic supplementation for patients with depressive disorders. However, prior reviews on the topic have largely focused on clinical effectiveness with limited emphasis on the underlying mechanisms of action and effects of probiotics on gut microbiota. In accordance with PRISMA guidelines, a systematic literature search of Medline, EMBASE and the Cochrane Library using combinations of the key words, ("depress*" OR "MDD" OR "suicide"), ("probiotic" OR "Lactobacillus" OR "Bifidobacterium") AND ("gut" OR "gut micr*" OR "microbiota"), as well as grey literature was performed. We found seven clinical trials involving patients with major depressive disorder (MDD). The small number of studies and heterogeneous sources of data precluded meta-analysis. Most trials (other than one open-label trial) had a low-to-moderate risk of bias, which was largely due to a lack of control for the effects of diet on gut microbiota. Probiotic supplementation yielded only modest effects on depressive symptoms and there were no consistent effects on gut microbiota diversity, and in most instances, no significant alterations in gut microbiota composition were observed after four to eight weeks of probiotic intervention. There is also a lack of systematic reporting on adverse events and no good longer-term data. Patients with MDD may require a longer time to show clinical improvement and the microbial host environment may also need longer than eight weeks to produce significant microbiota alterations. To advance this field, further larger-scale and longer-term studies are required.

Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts.

The World Health Organization (WHO) started the initiative on healthy ageing from 2016 to 2020, which has now continued into the United Nations (UN) Decade of Healthy Ageing 2021-2030. Research into healthy ageing and healthy ageing communities have emphasized that the concept of healthy ageing encompasses a plurality of views and has multiple dimensions. Anchored in a transdisciplinary approach, the present report thus aimed to investigate public perceptions of healthy ageing via a deep analysis of social media posts on Twitter. Original tweets, containing the terms "Healthy Ageing" OR "healthy aging" OR "healthyageing" OR "healthyaging", and posted in English between 1 January 2012 and 30 June 2022 were extracted. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition was applied to select for individual users. Topic modelling, specifically BERTopic was used to generate interpretable topics and descriptions pertaining to the concept of healthy ageing. Subsequently, manual thematic analysis was performed by the study investigators, with independent reviews of the topic labels and themes. A total of 63,809 unique tweets were analyzed and clustered semantically into 16 topics. The public perception of healthy ageing could be broadly grouped into three themes: (1) healthy diet and lifestyle, (2) maintaining normal bodily functions and (3) preventive care. While most perceptions dovetail WHO's definition, there are some points regarding skin appearances, beauty and aging that should be closely considered in the design of initiatives in the UN Decade of Healthy Ageing and beyond.

Examining the Validity and Utility of Montreal Cognitive Assessment Domain Scores for Early Neurocognitive Disorders.

OBJECTIVES: Montreal Cognitive Assessment (MoCA) total scores have been widely used to identify individuals with neurocognitive disorders (NCDs), but the utility of its domain-specific scores have yet to be thoroughly interrogated. This study aimed to validate MoCA's 6 domain-specific scores (ie, Memory, Language, Attention, Executive, Visuospatial, and Orientation) with conventional neuropsychological tests and explore whether MoCA domain scores could discriminate between different etiologies in early NCDs. DESIGN: Baseline data of a cohort study. SETTING AND PARTICIPANTS: Study included 14,571 participants recruited from Alzheimer's Disease Centers across United States, aged ≥50 years, with global Clinical Dementia Rating of ≤1, and mean age of 71.8 ± 8.9 years. METHODS: Participants completed MoCA, conventional neuropsychological tests, and underwent standardized assessments to diagnose various etiologies of NCDs. Partial correlation coefficient was used to examine construct validity between Z scores of neuropsychological tests and MoCA domain scores, whereas multinomial logistic regression examined utility of domain scores to differentiate between etiologies of early NCDs. RESULTS: MoCA domain scores correlated stronger with equivalent constructs (r = 0.15-0.43, P < .001), and showed divergence from dissimilar constructs on neuropsychological tests. Participants with Alzheimer's disease were associated with greater impairment in Memory, Attention, Visuospatial, and Orientation domains (RRR = 1.13-1.55, P < .001). Participants with Lewy body disease were impaired in Attention and Visuospatial domains (RRR = 1.21-1.47, P < .001); participants with frontotemporal lobar degeneration were impaired in Language, Executive, and Orientation domains (RRR = 1.25-1.75, P < .01); and participants with Vascular disease were impaired in Attention domain (RRR = 1.14, P < .001). CONCLUSIONS AND IMPLICATIONS: MoCA domain scores approximate well-established neuropsychological tests and can be valuable in discriminating different etiologies of early NCDs. Although MoCA domain scores may not fully substitute neuropsychological tests, especially in the context of diagnostic uncertainties, they can complement MoCA total scores as part of systematic evaluation of early NCDs and conserve the use of neuropsychological tests to patients who are more likely to require further assessments.

Effects of Humanitude care on people with dementia and caregivers: A scoping review.

AIMS AND OBJECTIVES: This study aimed to comprehensively review the research literature to provide an overview of the effects of Humanitude on people with dementia and their caregivers. BACKGROUND: Humanitude is a relationship-centred and compassionate care approach that focuses on improving the communication between people with dementia and their caregivers. There is a lack of updated and comprehensive synthesis on the evidence of the effects of Humanitude in dementia care. DESIGN AND METHODS: This paper adopted the scoping review framework by Arksey and O'Malley. We searched through the following databases: Pubmed, CINAHL, EMBASE, PsycINFO, ProQuest, Scopus and Web of Science from its inception to 3 September 2021. Three investigators independently screened the titles and abstracts and assessed the full-text articles for eligibility. The PRISMA-ScR checklist was included in this scoping review. RESULTS: We retrieved 1317 articles from databases and grey sources. Eleven studies were included after the screening. The synthesised results suggest that Humanitude can reduce agitation and psychological symptoms and improve the general well-being of people with dementia. Humanitude also has positive effects in improving care communication, empathy, job satisfaction and reducing burnout among caregivers. CONCLUSION: Humanitude shows the potential for positive effects on people with dementia and their caregivers. However, most studies did not include a comparator group and could not provide rigorous findings as compared to randomised controlled trials. There is a need for randomised controlled studies to demonstrate the effectiveness of Humanitude on people with dementia and their caregivers. RELEVANCE FOR CLINICAL PRACTICE: This paper reviewed the literature on all types of publications that examine the use of Humanitude in people with dementia and their caregivers. Thus, it provided an up-to-date overview of the effects of Humanitude to inform clinical practice.

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period.

Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to "COVID-19", "suicide", or "self-harm". An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments' responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.

Functional mobility decline and incident mild cognitive impairment and early dementia in community-dwelling older adults: the Singapore Longitudinal Ageing Study.

BACKGROUND: Motor and gait disturbances are evident in early Alzheimer and non-Alzheimer dementias and may predict the likelihood of mild cognitive impairment (MCI) or progression to dementia. OBJECTIVE: We investigated the Timed-Up-and-Go (TUG) measure of functional mobility in predicting cognitive decline and incident MCI or early dementia (MCI-dementia). DESIGN: Prospective cohort study with 4.5 years follow-up. SETTING: Population based. PARTICIPANTS: 2,544 community-dwelling older adults aged 55+ years. METHODS: Participants with baseline data on TUG, fast gait speed (GS), knee extension strength (KES) and performance-oriented mobility assessment (POMA) gait and balance were followed up for cognitive decline (Mini-Mental State Exam; MMSE drop of ≥2, among 1,336 dementia-free participants) and incident MCI-dementia (among 1,208 cognitively normal participants). Odds ratio (OR) and 95% confidence intervals (95% CI) were adjusted for age, sex, education, smoking, physical, social and productive activity, multi-morbidity, metabolic syndrome and MMSE. RESULTS: Per standard deviation increase in TUG, POMA, GS and KES were significantly associated with incident MCI-dementia: TUG (OR = 2.84, 95% CI = 2.02-3.99), GS (OR = 2.17, 95% CI = 1.62-2.91), POMA (OR = 1.88, 95% CI = 1.22-2.92) and KES (OR = 1.52, 95% CI = 1.15-2.02). Adjusted OR remained significant only for TUG (OR = 1.52, 95% CI = 1.01-2.31) and GS (OR = 1.53, 95% CI = 1.08-2.16). Areas under the curve (AUC) for TUG (AUC = 0.729, 95% CI = 0.671-0.787) were significantly greater than GS (AUC = 0.683, 95% CI = 0.619-0.746), KES (AUC = 0.624, 95% CI = 0.558-0.689) and POMA (AUC = 0.561, 95% CI = 0.485-0.637). Similar associations with cognitive decline were significant though less pronounced, and adjusted ORs remained significant for TUG, GS and POMA. CONCLUSION: Functional mobility decline precedes incident MCI and early dementia. The TUG appears to be especially accurate in predicting the future risks of adverse cognitive outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03405675. Registered 23 January 2018 (retrospectively registered).

One-Carbon Metabolism Biomarkers and Risks of Incident Neurocognitive Disorder among Cognitively Normal Older Adults.

There is a lack of evidence supporting an association between folate and vitamin B12 exposure with cognitive outcomes. We examined serum folate and vitamin B12 and plasma homocysteine in 690 cognitively-normal adults (aged ≥ 55) from the Singapore Longitudinal Aging Study (SLAS-2) followed-up over 4.5 years on incident neurocognitive disorder (NCD): mild cognitive impairment (MCI) and dementia. At follow-up, 5.7% (39) of participants developed NCD (34 MCI and 5 dementia). Comparing with those who remained cognitively-normal, participants progressed to NCD had significantly lower mean baseline vitamin B12 (420 [SD ± 221] vs. 510 [SD ± 290] pmol/L, p = 0.026), higher homocysteine (14.6 [SD ± 4.2] vs. 12.9 [SD ± 4.3], p = 0.018) and lower one-carbon index (Z-scores: -0.444 [SD ± 0.819] vs. -0.001 [SD ± 0.990], p = 0.006). Adjusted for confounders, significant associations with incident NCD were found for lower vitamin B12 (per-SD OR = 2.10, 95%CI = 1.26-3.52), higher homocysteine (per-SD OR = 1.96, 95%CI = 1.18-3.24) and lower one-carbon index (per-SD OR = 1.67, 95%CI = 1.06-2.64). Folate was not significantly associated with progression to NCD. Notably, low B12 in the presence of high folate was significantly associated with incident NCD (adjusted OR = 3.81, 95%CI = 1.04-13.9). Low B12, high homocysteine, low B12 in the presence of high folate, and a one-carbon index of hypo-methylation were independently associated with progression to NCD among cognitively normal.

Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period.

Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public's negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.

Subjective cognitive decline, APOE e4 allele, and the risk of neurocognitive disorders: Age- and sex-stratified cohort study.

OBJECTIVE: Subjective cognitive decline and APOE e4 allele (APOE4) are known predictors of mild cognitive impairment and dementia (mild cognitive impairment/dementia), with recent evidence showing interaction between subjective cognitive decline and APOE4 in amplifying the risk of mild cognitive impairment/dementia. However, the literature is unclear whether the interaction effect is seen across various age and sex strata. This study examined the interaction between subjective cognitive decline and APOE4-across different age and sex strata-on the risk of mild cognitive impairment/dementia. METHODS: This cohort study included 16,221 participants aged ⩾50 years and had normal cognition at baseline. Participants were evaluated for subjective cognitive decline and APOE4 at baseline, and followed-up almost annually for mild cognitive impairment/dementia (median follow-up = 4.5 years). Interaction effects were examined in Cox regression using Relative Excess Risk due to Interaction, stratified by age (⩽70 vs >70 years) and sex. RESULTS: Subjective cognitive decline and APOE4 were independently associated with mild cognitive impairment/dementia (hazard ratio: 1.4-1.8), with the highest risk when subjective cognitive decline and APOE4 co-occurred (hazard ratio: 2.6). APOE4 amplified the association between subjective cognitive decline and mild cognitive impairment/dementia in older women (Relative Excess Risk due to Interaction 1.0; 95% confidence interval = [0.3, 1.6]), but not in other age or sex strata. Among older women, half of them developed mild cognitive impairment/dementia by 12.1 years in the absence of subjective cognitive decline or APOE4. This duration shortened to 8.1-10.3 years in the presence of either subjective cognitive decline or APOE4, and to 4.4 years in the presence of both subjective cognitive decline and APOE4. Interaction effect among older women remained consistent when alternate outcomes were used (i.e. mild cognitive impairment and dementia due to Alzheimer's disease; dementia; and Alzheimer's dementia) (Relative Excess Risk due to Interaction 1.2-2.5). CONCLUSIONS: APOE4 amplifies the association between subjective cognitive decline and neurocognitive disorders in older women, with the findings suggesting the need for further research to delineate underlying neurobiology. APOE4 may potentially have a role in facilitating further risk stratification of older women with subjective cognitive decline in clinical practice.

How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds.

BACKGROUND: Loneliness is a public health problem that is expected to rise during the COVID-19 pandemic, given the widespread policy of quarantine. The literature is unclear whether loneliness during COVID-19 is similar to those of non-pandemic seasons. This study examined the expression of loneliness on Twitter during COVID-19 pandemic, and identified key areas of loneliness across diverse communities. METHODS: Twitter was searched for feeds that were:(1) in English; (2) posted from May 1, 2020 to July 1, 2020; (3) posted by individual users (not organisations); and (4) contained the words 'loneliness' and 'COVID-19'. A machine-learning approach (Topic Modeling) identified key topics from the Twitter feeds; Hierarchical Modeling identified overarching themes. Variations in the prevalence of the themes were examined over time and across the number of followers of the Twitter users. RESULTS: 4492 Twitter feeds were included and classified into 3 themes: (1) Community impact of loneliness during COVID-19; (2) Social distancing during COVID-19 and its effects on loneliness; and (3) Mental health effects of loneliness during COVID-19. The 3 themes demonstrated temporal variations. Particularly in Europe, Theme 1 showed a drastic reduction over time, with a corresponding rise in Theme 3. The themes also varied across number of followers. Highly influential users were more likely to talk about Theme 3 and less about Theme 2. CONCLUSIONS: The findings reflect close-to-real-time public sentiments on loneliness during the COVID-19 pandemic and demonstrated the potential usefulness of social media to keep tabs on evolving mental health issues. It also provides inspiration for potential interventions to address novel problems-such as loneliness-during COVID-19 pandemic.

Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts.

BACKGROUND: Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. OBJECTIVE: The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. METHODS: Twitter was searched for tweets related to "COVID-19" and "vaccine" over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). RESULTS: Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. CONCLUSIONS: The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.

Neuropsychiatric symptoms in early stage of Alzheimer's and non-Alzheimer's dementia, and the risk of progression to severe dementia.

BACKGROUND: Neuropsychiatric symptoms (NPSs) in early dementia have been suggested to predict a higher risk of dementia progression. However, the literature is not yet clear whether the risk is similar across Alzheimer's dementia (AD) and non-Alzheimer's dementia (non-AD), as well as across different NPSs. This study examined the association between NPSs in early dementia and the risk of progression to severe dementia, specifically in AD and non-AD, as well as across various NPSs. METHOD: This cohort study included 7,594 participants who were ≥65 years and had early dementia (global Clinical Dementia Rating [CDR] = 1). Participants completed Neuropsychiatric-Inventory-Questionnaire at baseline and were followed-up almost annually for progression to severe dementia (global CDR = 3) (median follow-up = 3.5 years; interquartile range = 2.1-5.9 years). Cox regression was used to examine progression risk, stratified by AD and non-AD. RESULTS: The presence of NPSs was associated with risk of progression to severe dementia, but primarily in AD (HR 1.4, 95% confidence interval [CI]: 1.1-1.6) and not in non-AD (HR 0.9, 95% CI: 0.5-1.5). When comparing across various NPSs, seven NPSs in AD were associated with disease progression, and they were depression, anxiety, apathy, delusions, hallucinations, irritability and motor disturbance (HR 1.2-1.6). In contrast, only hallucinations and delusions were associated with disease progression in non-AD (HR 1.7-1.9). CONCLUSIONS: NPSs in early dementia-especially among individuals with AD-can be useful prognostic markers of disease progression. They may inform discussion on advanced care planning and prompt clinical review to incorporate evidence-based interventions that may address disease progression.

Trajectories of early-onset rhinitis in the Singapore GUSTO mother-offspring cohort.

BACKGROUND: The natural history of childhood rhinitis is not well described. OBJECTIVE: This study aimed to identify different rhinitis trajectories in early childhood and their predictors and allergic associations. METHODS: Rhinitis symptoms were ascertained prospectively from birth until 6 years using standardized questionnaires in 772 participants. Rhinitis was defined as one or more episodes of sneezing, runny and/or blocked nose >2 weeks duration. Latent trajectories were identified using group-based modelling, and their predictive risk factors and allergic associations were examined. RESULTS: Three rhinitis trajectory groups were identified: 7.6% (n = 59) were termed early transient rhinitis, 8.6% (n = 66) late transient rhinitis, and 6.6% (n = 51) persistent rhinitis. The remaining 77.2% (n = 596) were classified as non-rhinitis/reference group. Early transient rhinitis subjects were more likely of Indian ethnicity, had siblings, reported childcare attendance, early wheezing and eczema in the first 3 years of life. Late transient rhinitis was associated with antenatal exposure to smoking, higher maternal education levels, and wheezing at age 36-72 months. Persistent rhinitis was associated with male gender, paternal and maternal history of atopy, eczema, and house dust mite sensitization. CONCLUSIONS & CLINICAL RELEVANCE: Risk factors for early transient rhinitis involve a combination of genetic and early environmental exposures, whereas late transient rhinitis may relate to maternal factors and early respiratory infections independent of atopy. In contrast, persistent rhinitis is strongly associated with atopic risk and likely represents the typical trajectory associated with allergic disorders. Allergic rhinitis symptoms may commence as early as the first year of life and may inform development of early interventive strategies.