Comparative analysis of symptom profile and risk of death associated with infection by SARS-CoV-2 and its variants in Hong Kong.

The recurrent multiwave nature of coronavirus disease 2019 (COVID-19) necessitates updating its symptomatology. We characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. With the COVID-19 cases reported up to August 25, 2022 in Hong Kong, an iterative multitier text-matching algorithm was developed to identify symptoms from free text. Multivariate regression was used to measure associations between variants, symptom development, death, and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Overall, 70.9% (54 450/76 762) of cases were symptomatic with 102 symptoms identified. Intrinsically, the wild-type and delta variant caused similar symptoms among unvaccinated symptomatic cases, whereas the wild-type and omicron BA.2 subvariant had heterogeneous patterns, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting, and sore throat) more frequent in the BA.2 cohort. With ≥2 vaccine doses, BA.2 was more likely than delta to cause fever among symptomatic cases. Fever, blocked nose, pneumonia, and shortness of breath remained jointly predictive of death among unvaccinated symptomatic elderly in the wild-type-to-omicron transition. Number of vaccine doses required for reducing occurrence varied by symptoms. We substantiate that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom-based case identification, increased weighing on symptoms predictive of death in outcome prediction, individual-based risk assessment in care homes, and incorporating free-text symptom reporting.

Transforming nursing with large language models: from concept to practice.

Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aiding in patient education, diagnostic assistance, treatment recommendations, and administrative task efficiency. While these advancements signal promising strides in healthcare, integrated LLMs are not without challenges, particularly artificial intelligence hallucination and data privacy concerns. Methodologies such as prompt engineering, temperature adjustments, model fine-tuning, and local deployment are proposed to refine the accuracy of LLMs and ensure data security. While LLMs offer transformative potential, it is imperative to acknowledge that they cannot substitute the intricate expertise of human professionals in the clinical field, advocating for a synergistic approach in patient care.

The importance of transparency: Declaring the use of generative artificial intelligence (AI) in academic writing.

The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.

Extracting symptoms from free-text responses using ChatGPT among COVID-19 cases in Hong Kong.

OBJECTIVES: To investigate the feasibility and performance of Chat Generative Pretrained Transformer (ChatGPT) in converting symptom narratives into structured symptom labels. METHODS: We extracted symptoms from 300 deidentified symptom narratives of COVID-19 patients by a computer-based matching algorithm (the standard), and prompt engineering in ChatGPT. Common symptoms were those with a prevalence >10% according to the standard, and similarly less common symptoms were those with a prevalence of 2-10%. The precision of ChatGPT was compared with the standard using sensitivity and specificity with 95% exact binomial CIs (95% binCIs). In ChatGPT, we prompted without examples (zero-shot prompting) and with examples (few-shot prompting). RESULTS: In zero-shot prompting, GPT-4 achieved high specificity (0.947 [95% binCI: 0.894-0.978]-1.000 [95% binCI: 0.965-0.988, 1.000]) for all symptoms, high sensitivity for common symptoms (0.853 [95% binCI: 0.689-0.950]-1.000 [95% binCI: 0.951-1.000]), and moderate sensitivity for less common symptoms (0.200 [95% binCI: 0.043-0.481]-1.000 [95% binCI: 0.590-0.815, 1.000]). Few-shot prompting increased the sensitivity and specificity. GPT-4 outperformed GPT-3.5 in response accuracy and consistent labelling. DISCUSSION: This work substantiates ChatGPT's role as a research tool in medical fields. Its performance in converting symptom narratives to structured symptom labels was encouraging, saving time and effort in compiling the task-specific training data. It potentially accelerates free-text data compilation and synthesis in future disease outbreaks and improves the accuracy of symptom checkers. Focused prompt training addressing ambiguous descriptions impacts medical research positively.

Temporal variations of vaccine hesitancy amid the COVID-19 outbreaks in Hong Kong.

To inform the dynamic adjustments of vaccination campaigns, this study examined the transitions among vaccine hesitancy profiles over the COVID-19 pandemic progression and their predictors and outcomes. The transition patterns among hesitancy profiles over three periods were identified using a latent transition analysis with individuals from a longitudinal cohort study since the emergence of COVID-19 in Hong Kong. Four profiles (i.e., skeptics, apathetics, fence-sitters, and believers) emerged consistently over time. From Period 1 (third and fourth pandemic waves) to Period 2 (dormant period, vaccine rollout), 14.17% of believers became fence-sitters (ambivalization), and 12.11% of fence-sitters became apathetics (apathetization). From Period 2 to Period 3 (omicron surge and vaccine mandates), 20.21% of believers became fence-sitters. Lower trust in government predicted a transition to skepticism, whereas higher trust predicted the opposite. Staying as believers was associated with decreased hygienic and social distancing behavior. The stable hesitancy profiles amid the rapid vaccine uptake suggest that structural factors rather than personal agency may drive the surge. Ambivalization and apathetization may signal disengagement in preventive behaviors. Trust in the government is crucial in the pandemic response. Public health interventions may improve compliance with guidelines and prevent skepticism and apathy.

Revisiting Vaccine Hesitancy in Residential Care Homes for the Elderly for Pandemic Preparedness: A Lesson from COVID-19.

Residents in residential care homes for the elderly (RCHEs) are at high risk of severe illnesses and mortality, while staff have high exposure to intimate care activities. Addressing vaccine hesitancy is crucial to safeguard vaccine uptake in this vulnerable setting, especially amid a pandemic. In response to this, we conducted a cross-sectional survey to measure the level of vaccine hesitancy and to examine its associated factors among residents and staff in RCHEs in Hong Kong. We recruited residents and staff from 31 RCHEs in July-November 2022. Of 204 residents, 9.8% had a higher level of vaccine hesitancy (scored ≥ 4 out of 7, mean = 2.44). Around 7% of the staff (n = 168) showed higher vaccine hesitancy (mean = 2.45). From multi-level regression analyses, higher social loneliness, higher anxiety, poorer cognitive ability, being vaccinated with fewer doses, and lower institutional vaccination rates predicted residents' vaccine hesitancy. Similarly, higher emotional loneliness, higher anxiety, being vaccinated with fewer doses, and working in larger RCHEs predicted staff's vaccine hesitancy. Although the reliance on self-report data and convenience sampling may hamper the generalizability of the results, this study highlighted the importance of addressing the loneliness of residents and staff in RCHEs to combat vaccine hesitancy. Innovative and technology-aided interventions are needed to build social support and ensure social interactions among the residents and staff, especially amid outbreaks.

Nursing education in the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet?

This article discusses the challenges and implications of artificial intelligence powered chatbot (AI-Chatbots) in nursing education. Chat Generative Pre-trained Transformer (ChatGPT) is an AI-Chatbot that can engage in detailed dialog and pass qualification tests in various fields. It can be applied for drafting course materials and administrative paperwork. Students can use it for personalized self-paced learning. AI-Chatbot technology can be applied in problem-based learning for hands-on practice experiences. There are concerns about over-reliance on the technology, including issues with plagiarism and limiting critical thinking skills. Educators must provide clear guidelines on appropriate use and emphasize the importance of critical thinking and proper citation. Educators must proactively adjust their curricula and pedagogy. AI-Chatbot technology could transform the nursing profession by aiding and streamlining administrative tasks, allowing nurses to focus on patient care. The use of AI-Chatbots to socially assist patients and for therapeutic purposes in mental health shows promise in improving well-being of patients, and potentially easing shortage and burnout for healthcare workers. AI-Chatbots can help nursing students and researchers to overcome technical barriers in nursing informatics, increasing accessibility for individuals without technical background. AI-Chatbot technology has potential in easing tasks for nurses, improving patient care, and enhancing nursing education.

Amplifying research influence through the social network, open access publishing, and international collaboration: A mediation analysis on nursing studies literature.

INTRODUCTION: Research impact and influence are commonly measured quantitatively by citation count received by research articles. Many institutes also use citation count as one of the factors in faculty performance appraisal and candidate selection of academic positions. Various strategies were recommended to amplify and accelerate research influence, particularly citation counts, by bringing research articles to a wider reach for potential readers. However, no prior empirical study was conducted to examine and valid effects of those strategies on nursing studies. This study examines and verifies the direct effects and mediation effects of some strategies, namely, the use of Twitter, international collaboration, the use of ResearchGate, and open access publishing, for amplifying the citation of research and review articles in nursing studies. DESIGN: Cross-sectional study design. METHODS: Articles published in top nursing journals in 2016 were identified in PUBMED and the citation metrics for individual articles until 2021 were extracted from Scopus. The primary outcome was the citation count of the article, while the tweet count on Twitter of the article was considered a mediator. The predictors included paper type, the total number of authors, the proportion of authors with a ResearchGate account in the article, funding support, open-accessed article, and the number of different countries stated in the authors' affiliation. A mediation analysis was conducted to examine the predictors' direct and indirect effects (i.e., via tweet count) on the citation count of the article. RESULTS: A total of 2210 articles were included in this study, of which 223 (10.1%) were review articles. The median (IQR) number of Scopus citations, tweets, countries, and percentage of authors with ResearchGate accounts were 12 (6-21), 2 (0-6), 1 (1-1), and 75% (50%-100%) respectively. In the mediation analysis, tweet count, article type, number of countries, percentage of authors with a ResearchGate account, and journal impact factors in 2014 were positively associated with the Scopus citation count. The effects of article type, open access, and journals' impact factors in 2014 on Scopus citation count were mediated by the tweet count. CONCLUSION: This study provides empirical support for some strategies researchers may employ to amplify the citation count of their research articles. The methodology of our study can be extended to compare research influence between entities (e.g., across countries or institutes). CLINICAL RELEVANCE: The citation refers to the research work cited by peers and is one of the indicators for research impact. Higher citations implied the research work is read and used by others, therefore, understanding the associated factors with higher citations is critical.

The way forward to achieve high COVID-19 vaccination and revaccination coverage in a city amid a period of tranquility.

Background: Amid the current COVID-19 pandemic, there is an urgent need for both vaccination and revaccination ("boosting"). This study aims to identify factors associated with the intention to receive a booster dose of the coronavirus (COVID-19) vaccine among individuals vaccinated with two doses and characterize their profiles in Hong Kong, a city with a low COVID-19 incidence in the initial epidemic waves. Among the unvaccinated, vaccination intention is also explored and their profiles are investigated. Methods: From December 2021 - January 2022, an online survey was employed to recruit 856 Hong Kong residents aged 18 years or over from an established population-based cohort. Latent class analysis and multivariate logistic regression modeling approaches were used to characterize boosting intentions. Results: Of 638 (74.5%) vaccinated among 856 eligible subjects, 42.2% intended to receive the booster dose. Four distinct profiles emerged with believers having the highest intention, followed by apathetics, fence-sitters and skeptics. Believers were older and more likely to have been vaccinated against influenza. Older age, smoking, experiencing no adverse effects from a previous COVID-19 vaccination, greater confidence in vaccines and collective responsibility, and fewer barriers in accessing vaccination services were associated with higher intentions to receive the booster dose. Of 218 unvaccinated, most were fence-sitters followed by apathetics, skeptics, and believers. Conclusion: This study foretells the booster intended uptake lagging initial vaccination across different age groups and can help refine the current or future booster vaccination campaign. Given the fourth COVID-19 vaccine dose may be offered to all adults, strategies for improving boosting uptake include policies targeting young adults, individuals who experienced adverse effects from previous doses, fence-sitters, apathetics, and the general public with low trust in the health authorities.

Path to normality: Assessing the level of social-distancing measures relaxation against antibody-resistant SARS-CoV-2 variants in a partially-vaccinated population.

Introduction: Two years into the coronavirus 2019 (COVID-19) pandemic, populations with less built-up immunity continued to devise ways to optimize social distancing measures (SDMs) relaxation levels for outbreaks triggered by SARS-CoV-2 and its variants to resume minimal economics activities while avoiding hospital system collapse. Method: An age-stratified compartmental model featuring social mixing patterns was first fitted the incidence data in second wave in Hong Kong. Hypothetical scenario analysis was conducted by varying population mobility and vaccination coverages (VCs) to predict the number of hospital and intensive-care unit admissions in outbreaks initiated by ancestral strain and its variants (Alpha, Beta, Gamma, Delta and Omicron). Scenarios were "unsustainable" if either of admissions was larger than the maximum of its occupancy. Results: At VC of 65%, scenarios of full SDMs relaxation (mean daily social encounters prior to COVID-19 pandemic = 14.1 contacts) for outbreaks triggered by ancestral strain, Alpha and Beta were sustainable. Restricting levels of SDMs was required such that the optimal population mobility had to be reduced to 0.9, 0.65 and 0.37 for Gamma, Delta and Omicron associated outbreaks respectively. VC improvement from 65% to 75% and 95% allowed complete SDMs relaxation in Gamma-, and Delta-driven epidemic respectively. However, this was not supported for Omicron-triggered epidemic. Discussion: To seek a path to normality, speedy vaccine and booster distribution to the majority across all age groups is the first step. Gradual or complete SDMs lift could be considered if the hybrid immunity could be achieved due to high vaccination coverage and natural infection rate among vaccinated or the COVID-19 case fatality rate could be reduced similar to that for seasonal influenza to secure hospital system sustainability.

Estimation of local transmissibility in the early phase of monkeypox epidemic in 2022.

OBJECTIVES: To estimate the basic reproductive number (Ro) to help us understand and control the spread of monkeypox in immunologically naive populations. METHODS: Using three highest incidence populations including England, Portugal, and Spain as examples as of 18 June 2022, we employed the branching process with a Poisson likelihood and gamma-distributed serial interval to fit daily reported case data of monkeypox to estimate Ro. Sensitivity analyses were performed by varying mean serial interval from 6.8 to 12.8 days. RESULTS: The median posterior estimates of Ro for monkeypox in the three study populations were statistically >1 (England: Ro = 1.60 [95% (credible interval) CrI, 1.50-1.70]; Portugal: Ro = 1.40 [95% CrI, 1.20-1.60]; Spain: Ro = 1.80 [95% CrI, 1.70-2.00]). Ro estimates varied over 1.30 to 2.10, depending on the serial interval. DISCUSSION: The updated Ro estimates across different populations will inform policy makers' plans for public health control measures. Currently, monkeypox has a sustainable outbreak potential and may challenge healthcare systems, mainly due to declines in the population level immunity to Orthopoxviruses since the cessation of routine smallpox vaccination. Smallpox vaccination has been shown to be effective in protecting (≤85% effectiveness) against monkeypox infection in earlier times. So early postexposure vaccination is currently being offered in an attempt to control its spread.

Effect of prior outbreak work experience to future outbreak responses for nurses in Hong Kong: A cross-sectional study.

Background: During the early phase of the Coronavirus Disease 2019 (COVID-19) epidemic, health care workers had elevated levels of psychological distress. Historical exposure to disease outbreak may shape different pandemic responses among experienced health care workers. Aim: Considering the unique experience of the 2003 SARS outbreak in Hong Kong, this study examined the association between prior epidemic work experience and anxiety levels, and the mediating role of perceived severity of COVID-19 and SARS in nurses. Methods: In March 2020, a cross-sectional survey targeting practising nurses in Hong Kong was conducted during the early phase of the COVID-19 epidemic. The interrelationships among participants' work experience during the SARS outbreak, perceived severity of SARS and COVID-19, and anxiety level were elucidated using structural equation model (SEM). Findings: Of 1,061 eligible nurses, a majority were female (90%) with a median age of 39 years (IQR = 32-49). A significant and negative indirect association was identified between SARS experience and anxiety levels (B=-0.04, p=0.04) in the SEM with a satisfactory fitness (CFI=0.95; RMSEA=0.06). SARS-experienced nurses perceived SARS to be less severe (B=-0.17, p=0.01), translated an equivalent perception to COVID-19 (B=1.29, p<0.001) and resulted in a lower level of anxiety (B=0.19, p<0.001). Conclusions: The less vigorous perception towards the severity of SARS and COVID-19 may explain SARS-experienced nurses' less initial epidemic-induced anxiety. The possible role of outbreak-experienced nurses in supporting outbreak-inexperienced nurses, both emotionally and technically, should be considered when an epidemic commences. Interventions aiming to facilitate the understanding of emerging virus should also be in place.

Intramolecular Interactions Enhance the Potency of Gallinamide A Analogues against Trypanosoma cruzi.

Gallinamide A, a metabolite of the marine cyanobacterium Schizothrix sp., selectively inhibits cathepsin L-like cysteine proteases. We evaluated the potency of gallinamide A and 23 synthetic analogues against intracellular Trypanosoma cruzi amastigotes and the cysteine protease, cruzain. We determined the co-crystal structures of cruzain with gallinamide A and two synthetic analogues at ∼2 Å. SAR data revealed that the N-terminal end of gallinamide A is loosely bound and weakly contributes in drug-target interactions. At the C-terminus, the intramolecular π-π stacking interactions between the aromatic substituents at P1' and P1 restrict the bioactive conformation of the inhibitors, thus minimizing the entropic loss associated with target binding. Molecular dynamics simulations showed that in the absence of an aromatic group at P1, the substituent at P1' interacts with tryptophan-184. The P1-P1' interactions had no effect on anti-cruzain activity, whereas anti-T. cruzi potency increased by ∼fivefold, likely due to an increase in solubility/permeability of the analogues.

Profiling vaccine believers and skeptics in nurses: A latent profile analysis.

BACKGROUND: A tailored immunization program is deemed more successful in encouraging vaccination. Understanding the profiles of vaccine hesitancy constructs in nurses can help policymakers in devising such programs. Encouraging vaccination in nurses is an important step in building public confidence in the upcoming COVID-19 and influenza vaccination campaigns. OBJECTIVES: Using a person-centered approach, this study aimed to reveal the profiles of the 5C psychological constructs of vaccine hesitancy (confidence, complacency, constraints, calculation, and collective responsibility) among Hong Kong nurses. DESIGN: Cross-sectional online survey. SETTINGS: With the promotion of a professional nursing organization, we invited Hong Kong nurses to complete an online survey between mid-March and late April 2020 during the COVID-19 outbreak. PARTICIPANTS: 1,193 eligible nurses (mean age = 40.82, SD = 10.49; with 90.0% being female) were included in the analyses. METHODS: In the online survey, we asked the invited nurses to report their demographics, COVID-19-related work demands (including the supply of personal protective equipment, work stress, and attitudes towards workplace infection control policies), the 5C vaccine hesitancy components, seasonal influenza vaccine uptake history, and the COVID-19 vaccine uptake intention. Latent profile analysis was employed to identify distinct vaccine hesitancy antecedent subgroups. RESULTS: Results revealed five profiles, including "believers" (31%; high confidence, collective responsibility; low complacency, constraint), "skeptics" (11%; opposite to the believers), "outsiders" (14%; low calculation, collective responsibility), "contradictors" (4%; high in all 5C constructs), and "middlers" (40%; middle in all 5C constructs). Believers were less educated, reported more long-term illnesses, greater work stress, higher perceived personal protective equipment sufficiency, and stronger trust in government than skeptics. They were older and had higher perceived personal protective equipment sufficiency than middlers. Also, believers were older and had greater work stress than outsiders. From the highest to the lowest on vaccination uptake and intention were believers and contradictors, then middlers and outsiders, and finally skeptics. CONCLUSION: Different immunization programs can be devised based on the vaccine hesitancy profiles and their predictors. Despite both profiles being low in vaccination uptake and intention, our results distinguished between outsiders and skeptics regarding their different levels of information-seeking engagement. The profile structure reveals the possibilities in devising tailored interventions based on their 5C characteristics. The current data could serve as the reference for the identification of individual profile membership and future profiling studies. Future endeavor is needed to examine the generalizability of the profile structure in other populations and across different study sites. Tweetable abstract: Covid-19 vaccine hesitancy profiles of Hong Kong nurses (believers, sceptics, outsiders, contradictors and middlers) highlight the importance of tailored vaccine campaigns.

Potent Anti-SARS-CoV-2 Activity by the Natural Product Gallinamide A and Analogues via Inhibition of Cathepsin L.

Cathepsin L is a key host cysteine protease utilized by coronaviruses for cell entry and is a promising drug target for novel antivirals against SARS-CoV-2. The marine natural product gallinamide A and several synthetic analogues were identified as potent inhibitors of cathepsin L with IC50 values in the picomolar range. Lead molecules possessed selectivity over other cathepsins and alternative host proteases involved in viral entry. Gallinamide A directly interacted with cathepsin L in cells and, together with two lead analogues, potently inhibited SARS-CoV-2 infection in vitro, with EC50 values in the nanomolar range. Reduced antiviral activity was observed in cells overexpressing transmembrane protease, serine 2 (TMPRSS2); however, a synergistic improvement in antiviral activity was achieved when combined with a TMPRSS2 inhibitor. These data highlight the potential of cathepsin L as a COVID-19 drug target as well as the likely need to inhibit multiple routes of viral entry to achieve efficacy.