Hasil untuk "History of medicine. Medical expeditions"

F. Collins

Лідія Ковалець

Мета. У цьому дослідженні розглядається корпус олклорних та етнографічних текстів, опублікованих на сторінках чернівецького щомісячного дитячого журналу «Українська ластівка» («Українська ластівка») протягом 1933-1940 років, коли Буковина перебувала під румунською окупацією, і видання слугувало важливою платформою для ознайомлення дітей з українською культурною спадщиною. Редакційна стратегія журналу базувалася на глибокому розумінні того, що традиційна національна культура може слугувати основою для формування української національної ідентичності серед молодого покоління буковинців. Новизна дослідження. Зміст видання охоплював усі основні жанри усної словесної творчості: календарно-обрядову поезію, героїчно-епічні пісні, ліричні твори, казки, пареміографію та дитячі ігри. Особливо значним було систематичне представлення календарно-обрядової поезії. Перший же випуск журналу відкрився різдвяною колядкою «Нова радість стала…» («Нова радість прийшла…»), сповненою надії та духовного піднесення. У дослідженні також розглядаються жанри пареміографії, представлені в журналі, з їхньою орієнтацією на інтелектуальний та морально-етичний розвиток дітей. Окрім фольклорних текстів, журнал містив етнографічні матеріали, що описували традиційні звичаї, ремесла та повсякденні практики. Таким чином, послідовно підкреслювалася цінність української літератури та народної культури загалом для педагогічних цілей. Методологічні інновації – інтеграція розваг та освіти в процеси читання, виховання дітей та всебічного розвитку їхньої національної ідентичності, участь школярів та вчителів у збиранні, публікації та збереженні фольклору, адаптація традиційних матеріалів до нових реалій – залишаються актуальними навіть в епоху глобалізації. Висновки. Після дослідження можна зробити висновок про літературне та моральне значення текстів Чернівецького дитячого журналу «Українська ластівка» (1933‑1940). Редактори виявили сміливість, додавши нові строфи з прямими посиланнями на «скуту землю» та молитвами за свободу. Казки, опубліковані в журналі, відігравали важливу роль: вони служили засобом морального повчання, розваги та передачі культури. Чарівні казки, такі як «Кирило Кожум'яка», пов'язували юних буковинців з українською історичною свідомістю, тоді як байки про тварин давали важливі моральні уроки. Українська пісенна традиція також виконувала освітні та повчальні функції через історичні приклади, такі як «Пісня про зруйнування Січі» та «Максим Козак Залізняк», забезпечуючи емоційний зв'язок з національним історичним досвідом, тоді як ліричні пісні також давали естетичне виховання

Mikhail G. Katz

Infinitesimals have seen ups and downs in their tumultuous history. In the 18th century, d'Alembert set the tone by describing infinitesimals as chimeras. Some adversaries of infinitesimals, including Moigno and Connes, picked up on the term. We highlight the work of Cauchy, Noël, Poisson and Riemann. We also chronicle reactions by Moigno, Lamarle and Cantor, and signal the start of a revival with Peano.

Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou et al.

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Oksana Melnychuk, I. Khmeliar, Natalia Perekhodko et al.

The study explores the role of Information and Communication Technologies (ICT) in fostering interdisciplinary connections within medical education, specifically through the development of integrative lessons. The article examines the significance of interdisciplinary connections in medical education, focusing specifically on working out integrative lessons (e. g. “The history and science of antibiotics”) as a means to promote awareness of the importance of integrative approach in connection to modern digital technologies. By incorporating artificial intelligence (AI) adaptive tools and digital methodologies, this research demonstrates how integrating perspectives from different subjects (English, Ukrainian, Chemistry, History of Medicine) into medical curricula can improve critical thinking, contextual awareness, and deeper engagement among students. This research contributes to the effectiveness of medical education by supporting the integrated development of key medical concepts across disciplines. This study involved undergraduate medical students enrolled at Rivne Medical Academy. The experimental group employed innovative educational technologies – AI adaptive tools, including Google Scholar for accessing academic literature, Padlet for collaboratively constructing digital timelines, Grammarly for improving the quality of written assignments, Google Slides for interactive presentations, and virtual simulations via Google Expeditions to explore historical medical settings. Additionally, tools like Quizlet and Google Forms were used for formative assessments to reinforce learning outcomes. The qualitative and quantitative results demonstrate that these tools not only facilitate a comprehensive understanding of the topic of the lesson and related medical concepts, such as the history of antibiotics, but also empower students to make meaningful connections across disciplines, including language, chemistry, and the history of medicine. The study highlights the importance of an interdisciplinary approach in cultivating well-rounded healthcare professionals who can appreciate the historical context of scientific discoveries. This approach enables students to develop the skills to think critically, work collaboratively, and engage deeply demonstrating a range of competencies within practical dimensions of medical science.

Jocelyn Zimmerman

Abstract:In 1774, East India Company Governor-General Warren Hastings commissioned an expedition to Tibet. Much has been written about the mission, but little is known about Alexander Hamilton, who joined as assistant-surgeon. Hamilton described Tibetan eye surgery as both “further behind Europeans” and “more successful than the one we follow.” Using comparative methods and deep contextualization, this article reads the contradictions in Hamilton’s writings alongside eighteenth-century Tibetan ophthalmology to reveal an Enlightenment-era tension between seeking new knowledge and substantiating Britain’s progress narrative. Hamilton and the following two British medical men in Tibet—Robert Saunders and Thomas Manning—were aware that claims of British medical superiority were unfounded. Yet, their willful ignorance of Tibetan medicine resulted in the non-transfer of knowledge, facilitating the rise of imperial confidence.

Moramay Lopez-Alonso

This paper examines how variations in the height and health of Mexicans during the second half of the twentieth century reflect the evolution of economic inequality, as its effects have repercussions on the health and nutritional conditions of the population. The average height of Mexican adults had a modest increase with respect to the possibilities of human plasticity. These anthropometric variations were the result of the incorporation of advances in science and technology leading to improved standards of living among the population. Body changes were impacted by dietary habits, urbanization, and government policies supporting food production and distribution.

Fabíola Rohden

Este artigo analisa tensões e disputas entre o campo da ginecologia e da cirurgia plástica estética, especialidades autorizadas a realizar a cirurgia estética genital feminina no Brasil. Utiliza material documental, incluindo artigos científicos desde a década de 1990, e sites institucionais. Enquanto ginecologistas têm se mantido mais cautelosos com a prática, defendendo sua realização apenas quando há indicações funcionais, cirurgiões/ãs plásticos/as têm sido mais influentes na disseminação do procedimento, privilegiando a dimensão estética. Argumenta-se que, para além de disputas entre campos profissionais, esse fenômeno precisa ser entendido à luz da crescente ênfase no aprimoramento de si, via recursos biomédicos, e dos imperativos de gênero.

Nicholas Konz, Richard Osuala, Preeti Verma et al.

Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e.g., Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensitivity to image corruptions and adversarial attacks, feature interpretability, and correlation with radiologist-perceived image quality. Additionally, we address key gaps in the literature by presenting an extensive framework for the multifaceted evaluation of image similarity metrics in medical imaging -- including the first large-scale comparative study of generative models for medical image translation -- and release an accessible codebase to facilitate future research. Our results are supported by thorough experiments spanning a variety of datasets, modalities, and downstream tasks, highlighting the broad potential of FRD for medical image analysis.

Tao Chen, Chenhui Wang, Zhihao Chen et al.

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.

A. Uzhanov

In the second part of the article, based on the documents of the State Institute of Experimental Endocrinology of the People's Commissariat of Health of the RSFSR 1925-1940, information on the chronology of the development in our country of a specialized state medical institution in the field of care for patients with disorders of the endocrine glands is disclosed. Materials on the deployment of the industry in Soviet Russia for the production and export of endocrine preparations from the endocrine glands of animals, including domestic insulin for the treatment of diabetes mellitus, are of considerable value. The efforts of the state, the medical industry, and the State Institute of Experimental Endocrinology in the fight against one of the ominous mass diseases in the RSFSR and the USSR — endemic goiter, which in modern vocabulary is called iodine deficiency diseases, are revealed. Of great scientific interest is the work of the Insulin and Endocrine Committees and commissions established by them, including the creation of standards in the production of domestic insulin preparations, as well as the Goiter Commission under the Scientific Medical Council of the People's Commissariat of Health on the organization of the first epidemiological expeditions to different regions of the country to identify pathological iodine deficiency is of interest, which became an important historical stage on the way to saving the nation from cretinism and other thyroid diseases foci of goiter. The research conducted on the territory of Kabardino-Balkaria on the introduction of the so-called Swiss model for overcoming. Many aspects currently being implemented in federal and regional programs for the prevention and treatment of iodine deficiency diseases were first worked out in the 20-30s of the last century.

Galina O. Andreeva, M. M. Odinak, V. N. Tsygan et al.

The article presents the history of nascence traditional oriental medicine in Russia during XVIII–XIX centuries. The first information about oriental medicine was brought to Russia in the XVIII century by doctors, who visited Mongolia and China as members of embassy expeditions. The first decades of the XVIII century can be considered as beginning of a systematic study oriental treatment methods. It was possible thanks to the many years efforts of the employees of the Russian ecclesiastical mission in Beijing. This organization from 1715 to 1864 years served religious, diplomatic and scientific functions. An invaluable contribution to the study of Chinese medicine was made by the leaders of the mission. Major role belongs to Nikita Yakovlevich Bichurin (father Iakinf), archimandrite of the IX mission. He was fluent in Chinese, studied the primary sources of medical literature, translated significant treatises into Russian, and taught Chinese to the mission staff. The head of the X mission, Pavel Ivanovich Kamensky, compiled a Chinese-Russian medical dictionary, reorganized the mission, and insisted on the need to introduce the position of a doctor among the staff. Starting from 1821, doctors O.P. Voitsekhovsky, P.E. Kirillov, A.A. Tatarinov, S.I. Bazilevsky and P.A. Kornievsky, graduates of the Imperial Medical and Surgical (Military Medical) Academy worked as physician of the X–XIV missions. Doctors continued to study the theoretical concepts of Chinese medicine, philosophical and cultural traditions that underlie healthcare. In addition to medical work, in accordance with the instructions of the Medical Council at the Ministry of Foreign Affairs, they explored epidemiology, healthcare organization and the process of training doctors in China, analyzed Eastern approaches in the diagnosis and treatment of diseases, pharmacopoeia, used of herbal remedies, methods of prevention and health maintenance. The scientific approach, knowledge of the Chinese language, and a long stay in the country allowed them to lay the foundations of Oriental medicine in Russian, acquaint medical community with the methods of treatment and prevention of diseases adopted in China, introduce acupuncture, moxa, the use of new types of herbal remedies, enrich the collections of medicinal plants.

Алла Ткач, Максим Ткач

The functioning of the Ukrainian language takes on special importance in the conditions of a full-scale Russian-Ukrainian war. Language is an important component of national identity, a powerful productive tool that strengthens the nation and enriches national culture. The purpose of the article is to describe and systematize innovative processes in the modern national lexical system of the Ukrainian language, which developed during the Russian-Ukrainian war. The article presents new resources of language thinking - neologisms and occasionalisms, which verbalize the current events of the wartime in Ukraine and remind of the close connection between history and language. An overview of linguistic studies on language as a weapon was made; the need for a new nomination is substantiated; examples are considered and the main functions of neolexes are defined; the emotional and expressive component in such word formation is noted; attention is focused on the active functioning of neologisms in the language; the prospects for the study of neologisms against the background of modern events in Ukrainian society and the world are outlined. Research methods. For the scientific interpretation of actual material, we use such general scientific methods as observation and analysis of linguistic material. We use the descriptive method, methods of component and linguistic stylistic analysis. Conclusions. The factual material of the article proves that the word is a special weapon on the language front. The formation and active functioning of new lexical units in military discourse is a kind of reflection of contemporary events; a vivid manifestation of the vocabulary of the Ukrainian language and the high intellectual potential of the Ukrainian people; attesting to the dynamism, continuity and uniqueness of language as a living organism.

Sina Yarmoradian, Mehrdad Shahraki, Sadra Amirpour Haradasht

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Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu et al.

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. While the performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. We report the following findings: (1) SAM's performance based on single prompts highly varies depending on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation performance appears to be better for well-circumscribed objects with prompts with less ambiguity and poorer in various other scenarios such as the segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4) SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings. (5) When multiple-point prompts are provided iteratively, SAM's performance generally improves only slightly while other methods' performance improves to the level that surpasses SAM's point-based performance. We also provide several illustrations for SAM's performance on all tested datasets, iterative segmentation, and SAM's behavior given prompt ambiguity. We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others. SAM has the potential to make a significant impact in automated medical image segmentation in medical imaging, but appropriate care needs to be applied when using it.

Emma A. M. Stanley, Raissa Souza, Anthony Winder et al.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

Leonid I. Gurvits

Space Very Long Baseline Interferometry is a radio astronomy technique distinguished by a record-high angular resolution reaching single-digit microseconds of arc. The paper provides a brief account of the history of developments of this technique over the period 1960s-2020s.

Yongsong Huang, Wanqing Xie, Mingzhen Li et al.

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.

S. K. M Shadekul Islam, MD Abdullah Al Nasim, Ismail Hossain et al.

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.

Sergey Primakov, Elizaveta Lavrova, Zohaib Salahuddin et al.

Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment. Data curation and pre-processing of medical images are critical steps in the quantitative medical image analysis that can have a significant impact on the resulting model performance. In this paper, we introduce a precision-medicine-toolbox that allows researchers to perform data curation, image pre-processing and handcrafted radiomics extraction (via Pyradiomics) and feature exploration tasks with Python. With this open-source solution, we aim to address the data preparation and exploration problem, bridge the gap between the currently existing packages, and improve the reproducibility of quantitative medical imaging research.