Reexamining science journalism through the constructivist lens of Science and Technology Studies (STS), the present paper argues that this perspective promotes a more responsible approach to reporting scientific discoveries in medicine. The dominant anti-constructivist, realist approach often results in what we term "dramatic modalization," which attributes greater facticity and universality to scientific findings than they actually possess at the time of publication, leading to significant moral consequences.To illustrate this, we will first explore the STS perspective as a framework for understanding the construction of facts in practice. Next, through a discourse analysis of two historical cases in medical journalism—the MMR-autism link and the depression-serotonin connection—we will demonstrate that the realist media coverage of these cases engaged in dramatic modalization, resulting in tangible moral repercussions. We hereby propose an alternative STS model for science journalism in medicine, arguing that it offers a more morally responsible approach.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Karbala journal of Medicine Manager, Athraa Mohammed Mahdi, Atheer Hameid Odda
et al.
Background: RA is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Monocyte chemoattractant protein-1 (MCP-1) plays a key role in recruiting immune cells, contributing to both physiological immune responses and pathological conditions such as RA. The aim of this study is to evaluate the diagnostic and prognostic role of serum MCP-1 levels in patients with rheumatoid arthritis, particularly in relation to comorbid conditions such as diabetes mellitus. Methods: A case-control study was conducted in Kerbala province between November 2024 and March 2025. It included 90 participants, comprising 20 rheumatoid arthritis (RA) patients with diabetes mellitus, 25 RA patients without diabetes, and 45 apparently healthy individuals as controls. Body mass index (BMI) was calculated, and family medical history was recorded. Laboratory measurements included serum levels of MCP-1 using Enzyme-Linked Immunosorbent Assay (ELISA), rheumatoid factor (RF), anti-cyclic citrullinated peptide (ACCP), and erythrocyte sedimentation rate (ESR). Results: ACCP and RF levels were significantly elevated in RA patients, especially those with diabetes, compared to both non-diabetic RA patients and healthy controls. Obesity was also more prevalent among diabetic RA patients. ROC curve analysis showed excellent diagnostic performance of MCP-1 in distinguishing RA patients from controls, with high sensitivity and specificity. Conclusions: The findings indicate that MCP-1, ACCP, and RF were valuable biomarkers for RA diagnosis and disease assessment, particularly in patients with metabolic comorbidities such as diabetes and obesity.
A. Andreeva, Inna G. Isupova, I. M. Kobelev
et al.
Today, traumatology and orthopedics are among the most important areas of medical care. Pirogov, Turner, Vreden, Chaklin, and Rozanov are traditionally recognized as the founders of these fields. Another figure who dedicated his life to the study of traumatology and orthopedics and deserves inclusion in this list is Nikolay N. Priorov. This work aims to present the most comprehensive information on the professional path of Academician Priorov as one of the founders of Russian traumatology and orthopedics, in honor of the 140th anniversary of his birth. The methodological foundation of the article includes a systemic approach based on the principles of historicism, objectivity, and scientific rigor, as well as general scientific methods (such as generalization, analysis, synthesis, and induction). The study is based on archival documents, scientific data, books, and articles. Priorov achieved outstanding success as a teacher, scientist, and physician. He held the titles of Doctor of Medical Sciences, Professor, Head of the Department of Traumatology and Orthopedics at several institutions, Academician of the USSR Academy of Medical Sciences, Honored Scientist of the Russian Soviet Federative Socialist Republic, Deputy Minister of Health of the USSR, founder of the Moscow and All-Union Societies of Traumatologists and Orthopedists, and a member of several international medical societies. One of his major achievements was the establishment of the Institute of Prosthetics and Treatment, which was later named after him. For 40 years, Academician Priorov served as its permanent director of this institution. Today, it is known as the Priorov National Medical Research Center for Traumatology and Orthopedics under the Ministry of Health of Russia. Of particular note is Priorov’s role as Chief Surgeon in the hospital directorates of the People’s Commissariat of Health during the Great Patriotic War. His work focused on organizing the treatment and rehabilitation of the wounded and disabled, with an emphasis on providing prosthetics and orthopedic devices as essential components of comprehensive rehabilitation. His experience in military medicine remains highly relevant today. For his contributions, Priorov was awarded two Orders of Lenin, the Order of the Red Star, the Order of the Badge of Honor, and numerous medals. This article provides new biographical details about Priorov, including his expeditions to Novaya Zemlya, Vaygach Island, and Yugorsky Shar, as well as the history of the foundation of the Central Institute of Traumatology and Orthopedics. This feature sets it apart from previous publications devoted to this outstanding scientist.
As a rule, physicians’ reputation significantly influences public confidence in the medical profession. Unfortunately, the societal perception of physicians in contemporary Iran appears to be negatively impacted. Therefore, the present study aimed to analyze and elucidate the fundamental causes of this phenomenon.This qualitative study employed content analysis of semi-structured interviews conducted in 2022. The study population consisted of 6 physicians, 6 nurses and 12 patients in the the affiliated hospitals in Kerman University of Medical Sciences selected through purposive sampling. Extraction of the main themes followed the Graneheim and Lundman approach, and data management was facilitated through MAXQDA 20. The study identified five themes encapsulating the causes for damage to physicians’ reputation: physicians' relationship with patients, physicians' relationship with the community, physicians' relationship with the medical profession, challenges within medical practice, and challenges related to medical education. Within these themes, a total of 38 subthemes emerged.The primary drivers that seem to damage physicians’ reputation include: non-effective communication, negative public attitudes toward certain physicians and medical centers due to malpractice, illegitimate relationships of physicians, gaps in physicians’ skills, insufficient education, and ethical lapses.It was concluded that several infrastructural elements negatively impact physicians' reputation. Consequently, it is recommended to monitor the professional behaviors, practices and relationships of physicians, while scrutinizing the medical education system.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex medical scenarios. In this study, we introduced Test-Time Generative Augmentation (TTGA), a novel augmentation strategy specifically tailored for medical image segmentation at inference time. Different from conventional augmentation strategies that suffer from excessive randomness or limited flexibility, TTGA leverages a domain-fine-tuned generative model to produce contextually relevant and diverse augmentations tailored to the characteristics of each test image. Built upon diffusion model inversion, a masked null-text inversion method is proposed to enable region-specific augmentations during sampling. Furthermore, a dual denoising pathway is designed to balance precise identity preservation with controlled variability. We demonstrate the efficacy of our TTGA through extensive experiments across three distinct segmentation tasks spanning nine datasets. Our results consistently demonstrate that TTGA not only improves segmentation accuracy (with DSC gains ranging from 0.1% to 2.3% over the baseline) but also offers pixel-wise error estimation (with DSC gains ranging from 1.1% to 29.0% over the baseline). The source code and demonstration are available at: https://github.com/maxiao0234/TTGA.
Matthias Christenson, Cove Geary, Brian Locke
et al.
The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.
Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://github.com/FengheTan9/MambaMIM.
The Royal Philanthropic Vaccine Expedition is considered in the history of medicine as the first international health expedition aimed at the global elimination of a contagious disease: smallpox. However, the initiatives carried out in this way before the arrival of the Balmis Expedition, by surgeons from the Spanish Navy, are less well known. Thus, the main objective of this research work is to offer an overview of the different anti-variolic vaccination initiatives prior to the campaign financed by the Spanish crown from these health facilities. Using the heuristic and hermeneutic method, our article is based on primary sources contrasted with specialised literature. The results obtained are presented in a narrative style from each of the surgeons identified as decisive in the implementation of the vaccine, thus providing a divergent and unpublished historiographic approach. As the facts described show, before the arrival of Balmis the vaccine substance was introduced in those countries thanks to the initiative of various surgeons: in Puerto Rico by Francisco Oller; in Cartagena and Santa Marta in Colombia by Ángel Hidalgo; in Venezuela by Alonso Ruiz; in Cuba by Tomás Romay and Bernardo de Cózar; in the Viceroyalty of New Granada (Colombia) by Lorenzo Vergés; in Guatemala by Miguel José Monzón and José María Ledesma; in the Viceroyalty of New Spain by Alejandro García Arboleya and Antonio Serrano; in Peru by Pedro Belomo; in Río de la Plata by Cristóbal Martín de Montúfar; in the Chilean region of Coquimbo by José María Gómez; and in the Philippines by Cristóbal Regidor. Finally, it should be noted that these surgeons and the approach presented are part of a historiography based on the personal actions of professionals trained, for the most part, at the Medical–Surgical School of Cadiz.
In this research, I aimed to recognize the historical meaning of installing the medical education center, ‘Uihak 醫學’, during the Silla 新羅 dynasty. ‘Uihak’ was installed in 692, in the first year of King Hyoso 孝昭 ’s rule. ‘Uihak’ was founded by using various Chinese medical classics as its textbooks for medical education, such as the Classic of Plain Questions 素問經.The wooden prescriptions excavated from Anapji 雁鴨池, which is thought to have been created in the middle of the 8th century, and the Chinese medical book Prescriptions for Universal Benefit 廣利方, which the envoy of Silla tried to acquire in 803, reflect the idea on medicine during that period in Silla. By this time, the field of medicine began to develop the idea to discern the locations and mechanism of disease patterns by centering on the viscera and bowels 臟腑 while making use of the herbal prescriptions based on various drugs. This means that clinical medicine founded upon the medical education achieved in ‘Uihak’ was being realized in the medical fields as well. According to the Chronicles of the Three States 三國史記, for the illness of Queen Sunduk 善德 in 636, medicine, praying, and the method of esoteric Buddhism 密敎 was tried out as a means of her cure. Comparatively, for the treatment of the first rank Chunggong 忠公 in 822, the Kingdom’s representative doctor 國 醫 with professional medical knowledge was sought out to fine a cure. The analyses of the human disease, diagnosis, treatment method, etc., given by the kingdom’s representative doctor were identical to those recommended in the medical textbooks used in ‘Uihak’. As such, we can posit that his academic background was ‘Uihak’ and the education given there.The Classic of Materia Medica 本草經, which was also used in ‘Uihak’, was a book professionally centered on the drug branch of medicine. The Classic of Materia Medica is a terminology referring to various books on drugs, including the Shennong’s Classic of Materia Medica 神農本草經, the Variorum of the Classic of Materia Medica 本草經集注, the Newly Revised Materia Medica 新修本草, etc. Thus, we cannot specify what the classic of Materia Medica actually taught, based on only its terminology. However, based on the wooden prescriptions excavated from Anapji, and from the terminology of drugs recorded in the drug trading document Purchase List for Silla goods 買新羅物解 preserved in Shosoin 正倉院 of Japan, we can hypothesize that in the middle of the 8th century, the Newly Revised Materia Medica was indeed being circulated. Based on these evidences, we can also hypothesize that Silla was part of the network of drug trading that encompassed the entire region of Asia.After unifying the Korean peninsula, the Kingdom of Silla actively adopted the medical educational system of Tang 唐 China. By using the obtained medical knowledge, Silla cured illnesses and used the medical knowledge on various drugs recorded in the Newly Revised Materia Medica to pursue trade with China, Japan, and other countries. Through the installation of ‘Uihak’, the same medicine has now begun to be officially used in East Asia, including Silla.
Medical imaging is an essential tool for diagnosing and treating diseases. However, lacking medical images can lead to inaccurate diagnoses and ineffective treatments. Generative models offer a promising solution for addressing medical image shortage problems due to their ability to generate new data from existing datasets and detect anomalies in this data. Data augmentation with position augmentation methods like scaling, cropping, flipping, padding, rotation, and translation could lead to more overfitting in domains with little data, such as medical image data. This paper proposes the GAN-GA, a generative model optimized by embedding a genetic algorithm. The proposed model enhances image fidelity and diversity while preserving distinctive features. The proposed medical image synthesis approach improves the quality and fidelity of medical images, an essential aspect of image interpretation. To evaluate synthesized images: Frechet Inception Distance (FID) is used. The proposed GAN-GA model is tested by generating Acute lymphoblastic leukemia (ALL) medical images, an image dataset, and is the first time to be used in generative models. Our results were compared to those of InfoGAN as a baseline model. The experimental results show that the proposed optimized GAN-GA enhances FID scores by about 6.8\%, especially in earlier training epochs. The source code and dataset will be available at: https://github.com/Mustafa-AbdulRazek/InfoGAN-GA.
Hicham Messaoudi, Ahror Belaid, Douraied Ben Salem
et al.
Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and being trained on databases made of millions of natural images. However, progress in medical image analysis has been hindered by limited annotated data and acquisition constraints. These limitations are even more pronounced given the volumetry of medical imaging data. In this paper, we introduce an efficient way to transfer the efficiency of a 2D classification network trained on natural images to 2D, 3D uni- and multi-modal medical image segmentation applications. In this direction, we designed novel architectures based on two key principles: weight transfer by embedding a 2D pre-trained encoder into a higher dimensional U-Net, and dimensional transfer by expanding a 2D segmentation network into a higher dimension one. The proposed networks were tested on benchmarks comprising different modalities: MR, CT, and ultrasound images. Our 2D network ranked first on the CAMUS challenge dedicated to echo-cardiographic data segmentation and surpassed the state-of-the-art. Regarding 2D/3D MR and CT abdominal images from the CHAOS challenge, our approach largely outperformed the other 2D-based methods described in the challenge paper on Dice, RAVD, ASSD, and MSSD scores and ranked third on the online evaluation platform. Our 3D network applied to the BraTS 2022 competition also achieved promising results, reaching an average Dice score of 91.69% (91.22%) for the whole tumor, 83.23% (84.77%) for the tumor core, and 81.75% (83.88%) for enhanced tumor using the approach based on weight (dimensional) transfer. Experimental and qualitative results illustrate the effectiveness of our methods for multi-dimensional medical image segmentation.
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks
et al.
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesis, image restoration, reconstruction and translation, and optimization of imaging systems. However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging. In this work, canonical SIMs that simulate realistic vessels in angiography images are employed to evaluate procedures for establishing SIMs using GANs. The GAN-based SIM is compared to the canonical SIM based on its ability to reproduce those statistics that are meaningful to the particular medically realistic SIM considered. It is shown that evaluating GANs using classical metrics and medically relevant metrics may lead to different conclusions about the fidelity of the trained GANs. This work highlights the need for the development of objective metrics for evaluating GANs.
K. Weitzel, Madeline Alexander, B. Bernhardt
et al.
Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.
of the theoretical stances of most current women historians writing about the Progressive period. The postmodern world, if that is where we are, exhibits an interest in gender, identity, power, diversity, race, and much more, but not stressing, as MacNamara does in the sections on feminism, conflict between men and women. Women in leadership positions at the turn of the twentieth century had barely begun an interest in gender, but organized for political and legal rights. MacNamara ends his book in despair: the success of the birth control movement has produced below-replacement fertility rates in the industrialized West, while developing nations have falling, but still higher birth rates. The same imbalance holds for liberals and conservatives. Democratic liberalism, he believes, will not survive. Starvation, forced migration, and war are inevitable, and indeed he can imagine no escape. It is plausible—look at the Proud Boys and their ilk. Still, cooperative schemes might prevail, food production may increase, birth rates might fall faster than imagined. Conversely, death rates may rise, life expectancy fall, epidemics spread, pollution go unaddressed. These are important issues.
The Oxford English Dictionary defines precision medicine as "medical care designed to optimize efficiency or therapeutic benefit for particular groups of patients, especially by using genetic or molecular profiling." It is not an entirely new idea: physicians from ancient times have recognized that medical treatment needs to consider individual variations in patient characteristics. However, the modern precision medicine movement has been enabled by a confluence of events: scientific advances in fields such as genetics and pharmacology, technological advances in mobile devices and wearable sensors, and methodological advances in computing and data sciences. This chapter is about bandit algorithms: an area of data science of special relevance to precision medicine. With their roots in the seminal work of Bellman, Robbins, Lai and others, bandit algorithms have come to occupy a central place in modern data science ( Lattimore and Szepesvari, 2020). Bandit algorithms can be used in any situation where treatment decisions need to be made to optimize some health outcome. Since precision medicine focuses on the use of patient characteristics to guide treatment, contextual bandit algorithms are especially useful since they are designed to take such information into account. The role of bandit algorithms in areas of precision medicine such as mobile health and digital phenotyping has been reviewed before (Tewari and Murphy, 2017; Rabbi et al., 2019). Since these reviews were published, bandit algorithms have continued to find uses in mobile health and several new topics have emerged in the research on bandit algorithms. This chapter is written for quantitative researchers in fields such as statistics, machine learning, and operations research who might be interested in knowing more about the algorithmic and mathematical details of bandit algorithms that have been used in mobile health.
Vishwesh Nath, Dong Yang, Bennett A. Landman
et al.
Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points that would be beneficial to the model, unlike passive machine learning. The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set. Multiple frameworks for active learning combined with deep learning have been proposed, and the majority of them are dedicated to classification tasks. Herein, we explore active learning for the task of segmentation of medical imaging data sets. We investigate our proposed framework using two datasets: 1.) MRI scans of the hippocampus, 2.) CT scans of pancreas and tumors. This work presents a query-by-committee approach for active learning where a joint optimizer is used for the committee. At the same time, we propose three new strategies for active learning: 1.) increasing frequency of uncertain data to bias the training data set; 2.) Using mutual information among the input images as a regularizer for acquisition to ensure diversity in the training dataset; 3.) adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). The results indicate an improvement in terms of data reduction by achieving full accuracy while only using 22.69 % and 48.85 % of the available data for each dataset, respectively.
Gustavo Rodrigues Rocha, Dean Rickles, Florian J. Boge
It will be presented in this chapter a historical account of the consistent histories interpretation of quantum mechanics based on primary and secondary literature. Firstly, the formalism of the consistent histories approach will be outlined. Secondly, the works by Robert Griffiths and Roland Omnès will be discussed. Griffiths' seminal 1984 paper, the first physicist to have proposed a consistent-histories interpretation of quantum mechanics, followed by Omnès' 1990 paper, were instrumental to the consistent-histories model based on Boolean logic. Thirdly, Murray Gell-Mann and James Hartle's steps to their own version of consistent-histories approach, motivated by a cosmological perspective, will then be described and evaluated. Gell-Mann and Hartle understood that spontaneous decoherence could path the way to a concrete physical model to Griffiths' consistent histories. Moreover, the collective biography of these figures will be put in the context of the role played by the Santa Fe Institute, co-founded by Gell-Mann in 1984 in Santa Fe, New Mexico, where Hartle is also a member of the external faculty.