Education is derived 'Educatum' a Latin word, combination of 'e' and 'duco'. 'e' means 'out of' or 'from inside' and 'duco' means 'to lead out' - means to lead out of what is there inside the mind and soul of learner. Medical education has undergone significant changes in the last few decades due to the technological explosion, and medical students need to be exposed in appropriate and calculated manner at that stage of education
Interdisciplinary research, a process of knowledge integration, is vital for scientific advancements. It remains unclear whether prestigious journals that are highly impactful lead in disseminating interdisciplinary knowledge. In this paper, by constructing topic-level correlation networks based on publications, we evaluated the interdisciplinarity of more and less prestigious journals in medicine. We found research from prestigious medical journals tends to be less interdisciplinary than research from other medical journals. We also established that cancer-related research is the main driver of interdisciplinarity in medical science. Our results indicate a weak tendency for differences in topic correlations between more and less prestigious journals to be co-located. Accordingly, we identified that interdisciplinarity in prestigious journals mainly differs from interdisciplinarity in other journals in areas such as infections, nervous system diseases and cancer. Overall, our results suggest that interdisciplinarity in science could benefit from prestigious journals easing rigid disciplinary boundaries.
The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fall short, leading to delayed and sometimes ineffective diagnoses and treatments. Quantum Computing (QC) and Quantum Machine Learning (QML) offer transformative advancements with the potential to revolutionize medicine. This paper summarizes areas where QC promises unprecedented computational power, enabling faster, more accurate diagnostics, personalized treatments, and enhanced drug discovery processes. However, integrating quantum technologies into precision medicine also presents challenges, including errors in algorithms and high costs. We show that mathematically-based techniques for specifying, developing, and verifying software (formal methods) can enhance the reliability and correctness of QC. By providing a rigorous mathematical framework, formal methods help to specify, develop, and verify systems with high precision. In genomic data analysis, formal specification languages can precisely (1) define the behavior and properties of quantum algorithms designed to identify genetic markers associated with diseases. Model checking tools can systematically explore all possible states of the algorithm to (2) ensure it behaves correctly under all conditions, while theorem proving techniques provide mathematical (3) proof that the algorithm meets its specified properties, ensuring accuracy and reliability. Additionally, formal optimization techniques can (4) enhance the efficiency and performance of quantum algorithms by reducing resource usage, such as the number of qubits and gate operations. Therefore, we posit that formal methods can significantly contribute to enabling QC to realize its full potential as a game changer in precision medicine.
Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean conditions on the phi-3 SLM scores. We found low-moderate significant correlations between the scores from the SLM and human evaluators for sports injury texts with low filtering (\r{ho} = 0.3413, p < 0.001) and medicine/health texts with high filtering (\r{ho} = 0.3854, p < 0.001), and low significant correlation for medicine/health texts with low filtering (\r{ho} = 0.2255, p < 0.001). There was negligible, insignificant correlation for sports injury-related texts with high filtering (\r{ho} = 0.0318, p = 0.4466).
Artificial intelligence in medicine is built to serve the average patient. By minimizing error across large datasets, most systems deliver strong aggregate accuracy yet falter at the margins: patients with rare variants, multimorbidity, or underrepresented demographics. This average patient fallacy erodes both equity and trust. We propose a different design: a multi-agent ecosystem for N-of-1 decision support. In this environment, agents clustered by organ systems, patient populations, and analytic modalities draw on a shared library of models and evidence synthesis tools. Their results converge in a coordination layer that weighs reliability, uncertainty, and data density before presenting the clinician with a decision-support packet: risk estimates bounded by confidence ranges, outlier flags, and linked evidence. Validation shifts from population averages to individual reliability, measured by error in low-density regions, calibration in the small, and risk--coverage trade-offs. Anticipated challenges include computational demands, automation bias, and regulatory fit, addressed through caching strategies, consensus checks, and adaptive trial frameworks. By moving from monolithic models to orchestrated intelligence, this approach seeks to align medical AI with the first principle of medicine: care that is transparent, equitable, and centered on the individual.
Artificial intelligence technology plays a crucial role in recommending prescriptions for traditional Chinese medicine (TCM). Previous studies have made significant progress by focusing on the symptom-herb relationship in prescriptions. However, several limitations hinder model performance: (i) Insufficient attention to patient-personalized information such as age, BMI, and medical history, which hampers accurate identification of syndrome and reduces efficacy. (ii) The typical long-tailed distribution of herb data introduces training biases and affects generalization ability. (iii) The oversight of the 'monarch, minister, assistant and envoy' compatibility among herbs increases the risk of toxicity or side effects, opposing the 'treatment based on syndrome differentiation' principle in clinical TCM. Therefore, we propose a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph diffusion guidance, namely TCM-HEDPR. Specifically, we pre-train symptom representations using patient-personalized prompt sequences and apply prompt-oriented contrastive learning for data augmentation. Furthermore, we employ a KG-guided homogeneous graph diffusion method integrated with a self-attention mechanism to globally capture the non-linear symptom-herb relationship. Lastly, we design a heterogeneous graph hierarchical network to integrate herbal dispensing relationships with implicit syndromes, guiding the prescription generation process at a fine-grained level and mitigating the long-tailed herb data distribution problem. Extensive experiments on two public datasets and one clinical dataset demonstrate the effectiveness of TCM-HEDPR. In addition, we incorporate insights from modern medicine and network pharmacology to evaluate the recommended prescriptions comprehensively. It can provide a new paradigm for the recommendation of modern TCM.
It has been established that service in the police is a specific type of public service with characteristic features. Therefore, the essence of this type of public and state service lies in the fact that it is a unique administrative and official activity, formalized in a separate system of bodies - the National Police, which is entrusted with performing a number of tasks and functions aimed at ensuring internal security in the state , establishing a high level of public order, serving the people and providing other types of police assistance. Service in the National Police is carried out by persons who meet the requirements and are appointed to the appropriate position, endowed with exclusive powers of a state-imperious nature, which allows them to carry out the functions assigned to them. Taking into account the peculiarities of the functioning of a service-oriented state, it is proposed to consider the activities of such bodies from the point of view of their provision of public services aimed at ensuring public security.
Abstract Background Digital therapeutics (DTx) is a treatment option that uses computer software to provide evidence-based interventions for medical disorders. DTx platforms are digital services that facilitate interactions among stakeholders of DTx treatment within a standardized structure. However, there is still a lack of overall awareness regarding the effectiveness and usage of DTx and DTx platforms. This study aimed to investigate insomnia patients’ recognition, thoughts, feelings, and demands for conventional treatments versus DTx for insomnia. Methods Nine participants, aged 19–50 years, who had experience with professional medical interventions for insomnia, were recruited through purposive sampling. Two online focus group interviews, each lasting 1.5 h, were conducted. The interview questions focused on difficulties encountered during conventional treatment, inadequate recognition of DTx, and concerns and demands regarding DTx and its platform. The data were analyzed using thematic analysis. Results The participants reported subjective difficulties associated with receiving conventional treatment, including concerns about drug side effects and dependence, social stigma, and lack of perceived necessity for treatment. They expressed concerns about DTx, such as cost-effectiveness, evidence on efficacy, and concerns about breach of personal information. Additionally, their demands included convenience of use, reduction in social stigma related to the use of DTx, compatibility of DTx with other healthcare systems, and enhanced communication with healthcare providers when using DTx platforms. Conclusions The focus group highlighted the need for increased awareness, demonstrated efficacy, cost-effectiveness, cybersecurity measures, and accessibility of insomnia DTx and its platforms. Tailored approaches considering patient characteristics are crucial for widespread adoption of insomnia DTx and its platforms.
Randi Hovden Borge, Håkon A Johannessen, Knut Inge Fostervold
et al.
OBJECTIVES: Several studies have found higher sickness absence in shared and open workspaces than in private offices, but little is known about why these differences occur. We propose and test job control as a potential mechanism underlying observed differences in the risk of physician-certified sickness absence between private offices and shared and open workspaces. METHODS: We conducted a counterfactual mediation analysis using observational survey data from a nationally representative sample of Norwegian employees merged with prospective data from national registries (N=5512). The registry data included information about whether participants had any physician-certified sickness absence the year following the survey. Models were adjusted for age, sex, education level, occupation group, executive/leadership responsibility, and time spent on office work. RESULTS: We found significantly higher sickness absence risk in conventional [risk ratio (RR) 1.12, 95% confidence interval (CI) 1.01‒1.25] and non-territorial (RR 1.20, 95% 1.04‒1.37) open-plan and non-territorial shared-room offices (RR 1.29, 95% CI 1.13‒1.48) compared to private offices. Natural indirect effects due to job control were statistically significant in all contrasts and accounted for 19–34% of total effects depending on contrast. CONCLUSIONS: Findings were in line with hypothesized relationships and suggest that job control may be a mechanism underlying observed differences in sickness absence across office concepts. Future studies should continue to explore potential mechanisms linking shared and open workspaces to higher sickness absence and other unfavorable outcomes in the workplace, particularly with study designs that provide stronger basis for causal inference.
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have unlocked significant potential to enhance the quality and efficiency of medical care. By introducing a novel way to interact with AI and data through natural language, LLMs offer new opportunities for medical practitioners, patients, and researchers. However, as AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified. These concerns about AI safety have emerged as the most significant obstacles to the adoption of AI in medicine. In response, this review examines emerging risks in AI utilization during the LLM era. First, we explore LLM-specific safety challenges from functional and communication perspectives, addressing issues across data collection, model training, and real-world application. We then consider inherent safety problems shared by all AI systems, along with additional complications introduced by LLMs. Last, we discussed how safety issues of using AI in clinical practice and healthcare system operation would undermine trust among patient, clinicians and the public, and how to build confidence in these systems. By emphasizing the development of safe AI, we believe these technologies can be more rapidly and reliably integrated into everyday medical practice to benefit both patients and clinicians.
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research.
This study investigates the impact of disinformation on public policies. Using 28 sets of keywords in eight databases, a systematic review was carried out following the Prisma 2020 model (Page et al., 2021). After applying filters and inclusion and exclusion criteria to 4,128 articles and materials found, 46 publications were analyzed, resulting in 23 disinformation impact categories. These categories were organized into two main axes: State and Society and Actors and Dynamics, covering impacts on State actors, society actors, State dynamics and society dynamics. The results indicate that disinformation affects public decisions, adherence to policies, prestige of institutions, perception of reality, consumption, public health and other aspects. Furthermore, this study suggests that disinformation should be treated as a public problem and incorporated into the public policy research agenda, contributing to the development of strategies to mitigate its effects on government actions.
Shannon L. Walston, Hiroshi Seki, Hirotaka Takita
et al.
Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.
The practices of contemporary clinical decision-making and care rely heavily on racial biological essentialism, which is a set of ideas originating in modern science that describes populations as comprising distinct subpopulations with unique sets of essential, heritable characteristics and propensities (i.e., races) purportedly due to their biology.1 Racial biological essentialism exaggerates the relevance of biology to health inequities and promotes the misuse of race (e.g., ?Black race?) in clinical decision-making, care, and research. Decades of research and scholarship2?4 (e.g., human genome project which inadvertently established that more genetic variation exists within each U.S. racial category than between them) have shown that race is fundamentally not biological. A substantial body of evidence clarifies that race is a sociopolitical, not biological construct.5 Nevertheless, the harmful, unscientific practices of racial biological essentialism persist, which helps explain why the misuse of race in clinical decision-making, research, and education remains pervasive.
For many years, medical trainees, health equity scholars, and public health physicians have explained how race consciousness (i.e., racism consciousness), which is the understanding of race as a sociopolitical construct, provides a more useful understanding for medicine and public health than racial biological essentialism does.6 This work has taken many forms, including the de-implementation of race-based algorithms used as clinical decision-making tools. In 2020, the Ways and Means Committee in the U.S. House of Representatives asked professional societies across medical disciplines to rethink their use of race-based clinical algorithms. The Committee sent a ?Request for Information? (RFI) to medical professional societies endorsing the elimination of race-based clinical algorithms. The study findings and RFI responses were captured in a 2021 report and captured responses from the professional societies as well as recommendations to improve clinical decision-making.7 The Agency for Healthcare Research and Quality (AHRQ) is also taking on this issue. At the time of this publication, AHRQ is undertaking a systematic review to provide Congress and the public responses to key questions on the impact of race-based clinical algorithms on health outcomes, and what can be done to address and/or mitigate racial bias on the development, validation, etc., of clinical algorithms.8
Since 2021, several professional societies have updated their positions on the inclusion of race in clinical algorithms within their respective specialties. The National Kidney Foundation and the American Society of Nephrology officially endorsed an estimated glomerular filtration rate (e-GFR) calculator without a race variable.9 The American College of Obstetrics and Gynecology no longer endorses a vaginal birth after caesarean calculator that uses race.10 Most recently, the American Thoracic Society issued updated recommendations in spirometry testing and the race-neutral reference equation for all patients, irrespective of race.11
As research continues to elucidate the harms and any benefits from including race in clinical algorithms, the urgency to address race-based algorithms is only intensifying. For instance, 35% of Americans suffering from renal failure are Black, while only representing 13% of the population.12 Many social and clinical factors contribute to this stark inequity, including the misuse of race to modify the e-GFR score, which is used in the diagnosis and treatment of chronic kidney disease (CKD). Race and other social factors have been linked to the e-GFR and other statistics, which has been associated with disproportionate suffering due to (CKD) and its sequalae among Black populations (e.g., higher rates of end-stage CKD diagnosis and lower rates of kidney transplantation eligibility among Black populations).13
Health equity experts agree to the implementation of nonrace-based clinical algorithms that cannot be subjected to the over 10-year timeframe typical for medical research and its adoption into practice.14 To meet the urgency of this moment, the NYC Department of Health and Mental Hygiene launched the Coalition to End Racism in Clinical Algorithms (CERCA). This coalition is a citywide initiative consisting of both safety-net hospitals and academic medical centers representing all five boroughs of NYC. Participation in CERCA requires that each coalition member commit to de-implement at least one race-based algorithm. Members are also required to furnish work, evaluation, and patient engagement plans regarding their de-implementation of race-based algorithms.15,16
In the summer of 2023, the NYC Department of Health and Mental Hygiene hosted the first annual New York City Anti-racism in Medical Education Symposium in partnership with the Josiah Macy Jr. Foundation, the American Academy of Medical Colleges, and the Fund for Public Health NYC. This symposium aimed to identify key stakeholders involved in anti-racism and curriculum development at NYC medical schools and understand the depth and breadth of anti-racism praxis incorporated into their educational programming.16
This special issue of Health Equity hopes to contribute to this growing body of knowledge regarding the de-implementation efforts needed to holistically address and eradicate race essentialism from practice and education. Specifically, this issue highlights scholarship in the following areas:
Historical origins of race adjustment in medicine, clinical decision-making tools, and artificial intelligence tools in medicine;
Current activities, successes, and challenges around removal of race from clinical decision-making tools at the institution- and system-level and its impact on patient outcomes;
City, state, and federal policies and policy analysis supporting removal of race adjustment from clinical decision-making tools; and
Programs, interventions, and policies intended to interrupt or end algorithmic racial discrimination in medicine and health care.
We hope this publication captures the latest work in addressing race-based medicine and facilitate the adaptation and implementation of initiatives to correct and mitigate the harmful effects of racial discrimination in health care. As we chart the next steps of this movement?which include equitable transplantation access, federal changes in Medicaid and Medicare policy on use of race-based algorithms, biases in artificial intelligence, application of public health critical race praxis (PHCRP) in research, to name a few emerging areas?remaining abreast of current and needed work will be essential to realizing a more equitable, just, and healthy society.
According to PHCRP, which is a health equity offshoot of Critical Race Theory, the first step toward advancing health equity is to acknowledge how the conventions of our field help reinforce inequities however well-intentioned our efforts may be. The use of arbitrary race corrections in clinical algorithms relies on and reifies racial biological determinism. It undermines the ability of clinicians to uphold their commitment to beneficence, nonmaleficence, and justice in the provision of care. Failure to uphold them harms minoritized patients and communities through, for instance, delayed or missed diagnoses and the exacerbation of racialized stigmata. A substantial body of scholarship and research now exists to promote more equitable clinical decision-making and care. Consistent with the PHCRP principle of disciplinary self-critique, this special issue documents the continued misuse of race in clinical algorithms. It also offers constructive alternatives that can be implemented immediately.17,18
Celebrities can significantly influence the public towards any desired outcome. In a bid to tackle an infectious disease, a leader (government) exploits such influence towards motivating a fraction of public to get vaccinated, sufficient enough to ensure eradication. The leader also aims to minimize the vaccinated fraction of public (that ensures eradication) and use minimal incentives to motivate the influencers; it also controls vaccine-supply-rates. Towards this, we consider a three-layered Stackelberg game, with the leader at the top. A set of influencers at the middle layer are involved in a stochastic vaccination game driven by incentives. The public at the bottom layer is involved in an evolutionary game with respect to vaccine responses. We prove the disease can always be eradicated once the public is sufficiently sensitive towards the vaccination choices of the influencers -- with a minimal fraction of public vaccinated. This minimal fraction depends only on the disease characteristics and not on other aspects. Interestingly, there are many configurations to achieve eradication, each configuration is specified by a dynamic vaccine-supply-rate and a number -- this number represents the count of the influencers that needs to be vaccinated to achieve the desired influence. Incentive schemes are optimal when this number equals all or just one; the former curbs free-riding among influencers while the latter minimizes the dependency on influencers.
Dimitris Bertsimas, Angelos G. Koulouras, Georgios Antonios Margonis
We propose a prognostic stratum matching framework that addresses the deficiencies of Randomized trial data subgroup analysis and transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data by correcting the estimated probabilities of the outcome under a treatment through a novel two-step process. These probabilities are then used to train Optimal Policy Trees (OPTs), which are decision trees that optimally assign treatments to subgroups of patients based on their characteristics. This facilitates the creation of clinically intuitive treatment recommendations. We applied our framework to observational data of patients with gastrointestinal stromal tumors (GIST) and validated the OPTs in an external cohort using the sensitivity and specificity metrics. We show that these recommendations outperformed those of experts in GIST. We further applied the same framework to randomized clinical trial (RCT) data of patients with extremity sarcomas. Remarkably, despite the initial trial results suggesting that all patients should receive treatment, our framework, after addressing imbalances in patient distribution due to the trial's small sample size, identified through the OPTs a subset of patients with unique characteristics who may not require treatment. Again, we successfully validated our recommendations in an external cohort.
Alexander Spangher, Emilio Ferrara, Ben Welsh
et al.
Journalists must find stories in huge amounts of textual data (e.g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us understand coverage patterns and help us build assistive tools. Yet, this is challenging because very few labelled links exist, language use between corpora is very different, and text may be covered for a variety of reasons. In this work we focus on news coverage of local public policy in the San Francisco Bay Area by the San Francisco Chronicle. First, we gather news articles, public policy documents and meeting recordings and link them using probabilistic relational modeling, which we show is a low-annotation linking methodology that outperforms other retrieval-based baselines. Second, we define a new task: newsworthiness prediction, to predict if a policy item will get covered. We show that different aspects of public policy discussion yield different newsworthiness signals. Finally we perform human evaluation with expert journalists and show our systems identify policies they consider newsworthy with 68% F1 and our coverage recommendations are helpful with an 84% win-rate.
Rodolphe Jantzen, Rodolphe Jantzen, Mathieu Maltais
et al.
IntroductionThe objective of this study was to characterize the combinations of demographic and socioeconomic characteristics associated to the unwillingness to receive the COVID-19 vaccines during the 2021 Quebec's vaccination campaign.Materials and MethodsIn March-June 2021, we conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. After comparing the vaccinated and unvaccinated participants, we investigated vaccine hesitancy among participants who were unvaccinated. For identifying homogeneous groups of individuals with respect to vaccine hesitancy, we used a machine learning approach based on a hybrid tree-based model.ResultsAmong the 6,105 participants of the vaccine cohort, 3,553 (58.2%) had at least one dose of COVID-19 vaccine. Among the 2,552 participants, 221 (8.7%) did not want to be vaccinated (91) or were uncertain (130). The median age for the unvaccinated participants was 59.3 years [IQR 54.7–63.9]. The optimal hybrid tree-based model identified seven groups. Individuals having a household income lower than $100,000 and being born outside of Canada had the highest rate of vaccine hesitancy (28% [95% CI 19.8–36.3]). For those born in Canada, the vaccine hesitancy rate among the individuals who have a household income below $50,000 before the pandemic or are Non-retired was of 12.1% [95% CI 8.7–15.5] and 10.6% [95% CI 7.6–13.7], respectively. For the participants with a high household income before the pandemic (more than $100,000) and a low level of education, those who experienced a loss of income during the pandemic had a high level of hesitancy (19.2% [8.5–29.9]) whereas others who did not experience a loss of income had a lower level of hesitancy (6.0% [2.8–9.2]). For the other groups, the level of hesitancy was low of around 3% (3.2% [95% CI 1.9–4.4] and 3.4% [95% CI 1.5–5.2]).DiscussionPublic health initiatives to tackle vaccine hesitancy should take into account these socio-economic determinants and deliver personalized messages toward people having socio-economic difficulties and/or being part of socio-cultural minorities.
BackgroundEnvironmental pollutants can affect N6-methyladenosine (m6A) level in the body, but the change of m6A level in kidney after being exposed to cadmium (Cd) and the molecular mechanism of renal injury need to be further studied. ObjectiveTo analyze the associations of m6A modification and methyltransferases/demethylases with microRNA-21 (miR-21) and transforming growth factor- β1 (TGF - β1) in kidney of rats exposed to Cd. MethodsTwenty-four SPF male SD rats were divided into 4 groups, with 6 rats in each group, and were exposed to Cd by subcutaneous injection of 2.0, 1.0, and 0.5 mg·kg−1 cadmium chloride (CdCl2) and equal volume of normal saline for 2 weeks, 7 d a week, respectively. The levels of N-acetyl-β-D-glucosidase (UNAG) and albumin (UALB) in urine, and the levels of m6A methylation and TGF-β1 in kidney were detected by enzyme-linked immunosorbent assay (ELISA). The level of blood urea nitrogen (BUN) was measured by urease method. The levels of renal oxidative stress indicators such as malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) were detected by total bile acid method, water-soluble tetrazolium asssay, and colorimetric method respectively. The relative levels of TGF-β1, methyltransferases, and demethylases in kidney were measured by reverse transcription-polymerase chain reaction. The expression of miR-21 in kidney was detected by fluorescent quantitative polymerase chain reaction. ResultsAfter 2 weeks of exposure to Cd, the body weights of rats in the 2.0 and 1.0 mg·kg−1 cadmium chloride groups decreased, and the ratio of kidney/body weight and the levels of BUN, UNAG, and TGF-β1 mRNA and protein increased in the 2.0 mg·kg−1 cadmium chloride group (P<0.05). The expression levels of m6A modification, methyltransferases METTL3, METTL14, Wilms’ tumor 1-associated protein (WTAP), and miR-21 were increased both in the 2.0 and 1.0 mg·kg−1 cadmium chloride groups, with significant differences compared with the control group (P<0.05). The results of correlation analysis showed that the m6A modification level was negatively correlated with SOD (r=−0.4489, P<0.05) and GSH-Px (r=−0.4874, P<0.05), METTL3 was negatively correlated with MDA (r=−0.5158, P<0.05), while there was a positive correlation between FTO and GSH-Px (r=0.4802, P<0.05). In addition, miR-21 was positively correlated with METTL3 (r=0.7491), METTL14 (r=0.6157), and WTAP (r=0.6660) (P<0.05), TGF-β1 was positively correlated with METTL3 (r=0.5025, P<0.05) but negatively correlated with FTO (r=−0.5634, P<0.05) . ConclusionCd can induce m6A methylation and up-regulation of METTL3, METTL14, WTAP, and miR-21 expression levels in rat kidney tissues, indicating that m6A and miR-21 may be associated with Cd-induced renal fibrosis.