Aim: In this study, it was aimed to investigate the effect of obesity on autonomy, principle of respect for autonomy (PRA) and quality of life (QOL), in other words, whether obese patients and non-obese individuals differ in terms of autonomy, PRA and QOL. Materyal Methods: The data were collected from Nutrition and Diet polyclinics in public institutions and organizations in Eskisehir /Türkiye. 708 volunteers participated in the study, of which 354 were from the case group and 354 from the control group. A survey including questions about sociodemographic characteristics, autonomy and PRA, as well as Obesity and Weight Loss Quality of Life Scale (OWLQOL) were administered to the participants. In the evaluation of the data, descriptive analyzes were made, Kruskal Wallis H, Mann Whitney U, chi-square independence tests were used.Results: A statistically significant difference was found between obesity and OWLQOL score in favor of the control group (p
History of medicine. Medical expeditions, Miscellaneous systems and treatments
Densely annotated medical image datasets that capture not only diagnostic labels but also the underlying reasoning behind these diagnoses are scarce. Such reasoning-related annotations are essential for developing and evaluating explainable AI (xAI) models that reason similarly to radiologists: making correct predictions for the right reasons. To address this gap, we introduce FunnyNodules, a fully parameterized synthetic dataset designed for systematic analysis of attribute-based reasoning in medical AI models. The dataset generates abstract, lung nodule-like shapes with controllable visual attributes such as roundness, margin sharpness, and spiculation. The target class is derived from a predefined attribute combination, allowing full control over the decision rule that links attributes to the diagnostic class. We demonstrate how FunnyNodules can be used in model-agnostic evaluations to assess whether models learn correct attribute-target relations, to interpret over- or underperformance in attribute prediction, and to analyze attention alignment with attribute-specific regions of interest. The framework is fully customizable, supporting variations in dataset complexity, target definitions, class balance, and beyond. With complete ground truth information, FunnyNodules provides a versatile foundation for developing, benchmarking, and conducting in-depth analyses of explainable AI methods in medical image analysis.
Objective To develop an LLM based realtime compound diagnostic medical AI interface and performed a clinical trial comparing this interface and physicians for common internal medicine cases based on the United States Medical License Exam (USMLE) Step 2 Clinical Skill (CS) style exams. Methods A nonrandomized clinical trial was conducted on August 20, 2024. We recruited one general physician, two internal medicine residents (2nd and 3rd year), and five simulated patients. The clinical vignettes were adapted from the USMLE Step 2 CS style exams. We developed 10 representative internal medicine cases based on actual patients and included information available on initial diagnostic evaluation. Primary outcome was the accuracy of the first differential diagnosis. Repeatability was evaluated based on the proportion of agreement. Results The accuracy of the physicians' first differential diagnosis ranged from 50% to 70%, whereas the realtime compound diagnostic medical AI interface achieved an accuracy of 80%. The proportion of agreement for the first differential diagnosis was 0.7. The accuracy of the first and second differential diagnoses ranged from 70% to 90% for physicians, whereas the AI interface achieved an accuracy rate of 100%. The average time for the AI interface (557 sec) was 44.6% shorter than that of the physicians (1006 sec). The AI interface ($0.08) also reduced costs by 98.1% compared to the physicians' average ($4.2). Patient satisfaction scores ranged from 4.2 to 4.3 for care by physicians and were 3.9 for the AI interface Conclusion An LLM based realtime compound diagnostic medical AI interface demonstrated diagnostic accuracy and patient satisfaction comparable to those of a physician, while requiring less time and lower costs. These findings suggest that AI interfaces may have the potential to assist primary care consultations for common internal medicine cases.
Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick
et al.
Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.
Editions published by scientific societies are of significant interest, as they typically served a reporting function. However, certain periodicals also functioned as tools for promoting the activities and ideas of the society. In Siberia during the late 19th and early 20th centuries, one such organization was the Society of Naturalists and Physicians, founded at the Imperial Tomsk University. Members of the society published an annual journal Trudy Tomskogo Obshchestva Estestvoispytateley i Vrachey featuring scientific articles, notes, reports, and presentations. This annual publication simultaneously served as a reporting body and an instrument of public education. The aim of this article is to determine the genre and thematic specifics of the yearbook Trudy Tomskogo Obshchestva Estestvoispytateley i Vrachey. The study examines its issues from 1889 to 1895. The research methods employed include a comprehensive analysis of the issues, genre analysis, and a historicaltypological method. The study identifies the genre and thematic characteristics of the journal and concludes that it contributed to the popularization of scientific knowledge. Firstly, the journal’s genre diversity encompasses the publication of abstracts of presentations, reports (of expeditions and research), scientific travel essays (geological and geographical descriptions), and scientific communications. The specific nature of scientific travel essays warrants particular attention. A key feature of such materials is the symbiosis of scientific and journalistic styles. Furthermore, the authors implemented the educational function most explicitly through essays, abstracts of presentations, and notes related to current events (materials on epidemics, statistical data, information on prevention, etc.). The visual presentation of information represents another important specific feature of the Trudy publications. Illustrations could be integrated within a given material or function as independent items. This is evident in the examples of medical anatomical illustrations, archaeological drawings, images of discoveries, maps, and so on. Secondly, the study identified the themes and proportion of publications in the yearbook: medicine (47.5%), geography and geology (22.5%), history and archeology (11.3%), botany (5%), and other (13.7%) – presentations, speeches of members of the society. The study also revealed that the authors raised issues of current relevance to the local population: the yearbook regularly featured articles on Tomsk Governorate, urban life (infrastructure issues, historical materials, epidemic problems, etc.), and the territories of Western Siberia and Siberia in general (articles addressed topics related to medicine, such as epidemics, statistics, reports). This broad range of topics allowed the publication to reach the largest possible number of interested readers, and the illustrative material served to simplify complex scientific information. In conclusion, Trudy Tomskogo Obshchestva Estestvoispytateley i Vrachey was a publication that transcended the boundaries of a “dry” reporting collection. The yearbook published interesting articles, speeches by members of the society, scientific essays and presentations that were characterized by a popular scientific narration of research, discoveries and expeditions.
O artigo analisa as reações dos católicos vinculados às associações leigas na cidade do Salvador, no período da gripe espanhola (1918) e da varíola (1919). Os jornais foram as principais fontes utilizadas para a identificação das festas e dos ritos, tanto dos praticados para pedir a intercessão dos santos quanto daqueles que foram suspensos em função da necessidade de isolamento social. Apesar de ambas as doenças serem transmissíveis e do curto espaço de tempo entre as duas epidemias, a análise das fontes evidenciou diferentes reações dos fiéis quanto às medidas de proteção e busca da cura.
The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images based on existing sets of real medical images. However, the exact image set size required to efficiently train such a GAN is unclear. In this work, we experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images, given the dataset's distribution of image complexities. We analyze statistical metrics based on delentropy, an image complexity measure rooted in Shannon's entropy in information theory. For our pipeline, we conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes. Across both GANs, general performance improved with increasing training set size but suffered with increasing complexity.
Yihao Liu, Jiaming Zhang, Andres Diaz-Pinto
et al.
The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation. In this work, we introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.
The current mainstream multi-modal medical image-to-image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel-wise aligned training data to constrain the model optimization. However, obtaining pixel-wise aligned multi-modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi-modal medical image-to-image translation method that can generate reliable translation results without the need for pixel-wise aligned data. This work aims to develop a novel medical image-to-image translation model that is independent of pixel-wise aligned data (MITIA), enabling reliable multi-modal medical image-to-image translation under the condition of misaligned training data. The proposed MITIA model utilizes a prior extraction network composed of a multi-modal medical image registration module and a multi-modal misalignment error detection module to extract pixel-level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle-consistent GAN model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well-aligned datasets. Subsequently, we compared the proposed method with six other state-of-the-art image-to-image translation methods. The results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state-of-the-art methods, both on misaligned data and aligned data.
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities.
Abstract The debate on the restitution of African cultural heritage has brought greater attention to the history of colonial violence, especially to the dispatch of so‐called ‘punitive’ expeditions in the late nineteenth and early twentieth centuries. Expanding knowledge on the genealogy of this particularly brutal form of military campaign, this article explores the historical semantics of the German term ‘Strafexpedition’ and its contextual use in the organ of militarist colonial propaganda at the time in Imperial Germany, the Deutsches Kolonialblatt. Through a content analysis of the occurrence of the term and its correlate, this study aims to bridge the fields of semantics and colonial historiography and lays the groundwork for a macro‐history of events of spoliation and plunder in German colonial contexts.
Photo by Sean Pollock on Unsplash ABSTRACT Bioethics and Corporate Social Responsibility (CSR) were born out of similar concerns, such as the reaction to scandal and the restraint of irresponsible actions by individuals and organizations. However, these fields of knowledge are seldom explored together. This article attempts to explain the motives behind the gap between bioethics and CSR, while arguing that their shared agenda – combined with their contrasting principles and goals – suggests there is potential for fruitful dialogue that enables the actualization of bioethical agendas and provides a direction for CSR in health-related organizations. INTRODUCTION Bioethics and Corporate Social Responsibility (CSR) seem to be cut from the same cloth: the concern for human rights and the response to scandal. Both are tools for the governance of organizations, shaping how power flows and decisions are made. They have taken the shape of specialized committees, means of stakeholder inclusion at deliberative forums, compliance programs, and internal processes. It should be surprising, then, that these two fields of study and practice have developed separately, only recently re-approaching one another. There have been displays of this reconnection both in academic and corporate spaces, with bioethics surfacing as part of the discourse of CSR and compliance initiatives. However, this is still a relatively timid effort. Even though the bioethics-CSR divide presents mostly reasonable explanations for this difficult relationship between the disciplines, current proposals suggest there is much to be gained from a stronger relationship between them. This article explores the common history of bioethics and corporate social responsibility and identifies their common features and differences. It then explores the dispute of jurisdictions due to professional and academic “pedigree” and incompatibilities in the ideological and teleological spheres as possible causes for the divide. The discussion turns to paths for improving the reflexivity of both disciplines and, therefore, their openness to mutual contributions. I. Cut Out of the Same Cloth The earliest record of the word “bioethics” dates back to 1927 as a term that designates one’s ethical responsibility toward not only human beings but other lifeforms as well, such as animals and plants.[1] Based on Kantian ethics, the term was coined as a response to the great prestige science held at its time. It remained largely forgotten until the 1970s, when it resurfaced in the United States[2] as the body of knowledge that can be employed to ensure the responsible pursuit and application of science. The resurgence was prompted by a response to widespread irresponsible attitudes toward science and grounded in a pluralistic perspective of morality.[3] In the second half of the twentieth century, states and the international community assumed the duty to protect human rights, and bioethics became a venue for discussing rights.[4] There is both a semantic gap and a contextual gap between these two iterations, with some of them already being established. Corporate social responsibility is often attributed to the Berle-Dodd debate. The discussion was characterized by diverging views on the extent of the responsibility of managers.[5] It was later settled as positioning the company, especially the large firm, as an entity whose existence is fomented by the law due to its service to the community. The concept has evolved with time, departing from a largely philanthropic meaning to being ingrained in nearly every aspect of a company’s operations. This includes investments, entrepreneurship models, and its relationship to stakeholders, leading to an increasing operationalization and globalization of the concept.[6] At first sight, these two movements seem to stem from different contexts. Despite the difference, it is also possible to tell a joint history of bioethics and CSR, with their point of contact being a generalized concern with technological and social changes that surfaced in the sixties. The publishing of Silent Spring in 1962 by Rachel Carson exemplifies this growing concern over the sustainability of the ruling economic growth model of its time by commenting on the effects of large-scale agriculture and the use of pesticides in the population of bees, one of the most relevant pollinators of crops consumed by humans. The book influenced both the author responsible for the coining bioethics in the 1971[7] and early CSR literature.[8] By initiating a debate over the sustainability of economic models, the environmentalist discourse became a precursor to vigorous social movements for civil rights. Bioethics was part of the trend as it would be carried forward by movements such as feminism and the patients’ rights movement.[9] Bioethics would gradually move from a public discourse centered around the responsible use of science and technology to academic and government spaces.[10] This evolution led to an increasing emphasis on intellectual rigor and governance. The transformation would unravel the effort to take effective action against scandal and turn bioethical discourse into governance practices,[11] such as bioethics and research ethics committees. The publication of the Belmont Report[12] in the aftermath of the Tuskegee Syphilis Experiment, as well as the creation of committees such as the “God Committee,”[13] which aimed to develop and enforce criteria for allocating scarce dialysis machines, exemplify this shift. On the side of CSR, this period represents, at first, a stronger pact between businesses and society due to more stringent environmental and consumer regulations. But afterward, a joint trend emerged: on one side, the deregulation within the context of neoliberalism, and on the other, the operationalization of corporate social responsibility as a response to societal concerns.[14] The 1990s saw both opportunities and crises that derived from globalization. In the political arena, the end of the Cold War led to an impasse in the discourse concerning human rights,[15] which previously had been split between the defense of civil and political rights on one side and social rights on the other. But at the same time, agendas that were previously restricted territorially became institutionalized on a global scale.[16] Events such as the European Environment Agency (1990), ECO92 in Rio de Janeiro (1992), and the UN Global Compact (2000) are some examples of the globalization of CSR. This process of institutionalization would also mirror a crisis in CSR, given that its voluntarist core would be deemed lackluster due to the lack of corporate accountability. The business and human rights movement sought to produce new binding instruments – usually state-based – that could ensure that businesses would comply with their duties to respect human rights.[17] This rule-creation process has been called legalization: a shift from business standards to norms of varying degrees of obligation, precision, and delegation.[18] Bioethics has also experienced its own renewed identity in the developed world, perhaps because of its reconnection to public and global health. Global health has been the object of study for centuries under other labels (e.g., the use of tropical medicine to assist colonial expeditions) but it resurfaced in the political agenda recently after the pandemics of AIDS and respiratory diseases.[19] Bioethics has been accused from the inside of ignoring matters beyond the patient-provider relationship,[20] including those related to public health and/or governance. Meanwhile, scholars claimed the need to expand the discourse to global health.[21] In some countries, bioethics developed a tight relationship with public health, such as Brazil,[22] due to its connections to the sanitary reform movement. The United Kingdom has also followed a different path, prioritizing governance practices and the use of pre-established institutions in a more community-oriented approach.[23] The Universal Declaration on Bioethics and Rights followed this shift toward a social dimension of bioethics despite being subject to criticism due to its human rights-based approach in a field characterized by ethical pluralism.[24] This scenario suggests bioethics and CSR have developed out of similar concerns: the protection of human rights and concerns over responsible development – be it economic, scientific, or technological. However, the interaction between these two fields (as well as business and human rights) is fairly recent both in academic and business settings. There might be a divide between these fields and their practitioners. II. A Tale of Jurisdictions It can be argued that CSR and business and human rights did not face jurisdictional disputes. These fields owe much of their longevity to their roots in institutional economics, whose debates, such as the Berle-Dodd debate, were based on interdisciplinary dialogue and the abandonment of sectorial divisions and public-private dichotomies.[25] There was opposition to this approach to the role of companies in society that could have implications for CSR’s interdisciplinarity, such as the understanding that corporate activities should be restricted to profit maximization.[26] Yet, those were often oppositions to CSR or business and human rights themselves. The birth of bioethics in the USA can be traced back to jurisdictional disputes over the realm of medicine and life sciences.[27] The dispute unfolded between representatives of science and those of “society’s conscience,” whether through bioethics as a form of applied ethics or other areas of knowledge such as theology.[28] Amid the civil rights movements, outsiders would gain access to the social sphere of medicine, simultaneously bringing it to the public debate and emphasizing the decision-making process as the center of the medical practice.[29] This led to the emergence of the b
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.
Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect 5 real medical datasets, including 2 public medical image datasets and 3 private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.
Saikat Roy, Gregor Koehler, Michael Baumgartner
et al.
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their relative effectiveness to open interpretation. We introduce Transformer Ablations that replace the Transformer blocks with plain linear operators to quantify this effectiveness. With experiments on 8 models on 2 medical image segmentation tasks, we explore -- 1) the replaceable nature of Transformer-learnt representations, 2) Transformer capacity alone cannot prevent representational replaceability and works in tandem with effective design, 3) The mere existence of explicit feature hierarchies in transformer blocks is more beneficial than accompanying self-attention modules, 4) Major spatial downsampling before Transformer modules should be used with caution.
Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei
et al.
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks. Over the last years, vision transformers (ViTs) have appeared as a competitive alternative to CNNs, yielding impressive levels of performance in the natural image domain, while possessing several interesting properties that could prove beneficial for medical imaging tasks. In this work, we explore the benefits and drawbacks of transformer-based models for medical image classification. We conduct a series of experiments on several standard 2D medical image benchmark datasets and tasks. Our findings show that, while CNNs perform better if trained from scratch, off-the-shelf vision transformers can perform on par with CNNs when pretrained on ImageNet, both in a supervised and self-supervised setting, rendering them as a viable alternative to CNNs.
The Genomic Observatories Network, Rosa M Alcazar, Maria Alvarez
et al.
Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.
This article re-examines from a new perspective the efforts of James Smith (1771–1841), a Maryland doctor, to eradicate smallpox in the United States. As one of the few successful cowpox inoculators at the turn of the nineteenth century, Smith recognized the necessity for a public vaccine institution that could ensure the safe production and continuous preservation and circulation of vaccine matter. Thus, he devoted himself to creating statewide and national vaccine institutions funded by the state and federal governments. He established the National Vaccine Institution (NVI), but despite his efforts, the NVI existed only a short time from 1813 to 1822. Previous studies on Smith have focused on the 1813 Vaccination Act (An Act to Encourage Vaccination) and the NVI, and have evaluated them as failed projects or historically missed opportunities. However, this kind of approach does not justly place the act and institutions within Smith’s larger plan and do not fully discuss the role of the NVI in his system of promoting vaccination in the United States. This article analyzes how he responded to the problems hindering cowpox vaccination, including spurious vaccine, failed vaccination, and low public acceptance of cowpox vaccine. In doing so, this study shows that Smith attempted to establish a universal and systematic vaccination system connecting citizens, government, and medical personnel through the NVI, as well as ensuring a safe and regular supply of vaccine.