Hasil untuk "History of medicine. Medical expeditions"

Антоній МОЙСЕЙ

В рамках проєкту RESTORY – грантової програми Європейської Комісії в рамках програми «Горизонт Європа» (HORIZON-CL2-2023-HERITAGE-01-№ 101132781) – було проведено комплексне дослідження формування та розвитку шкільної мережі в передмісті Чернівців, Роші. Метою дослідження є реконструкція розвитку системи державних (початкових) шкіл у Роші в періоди австрійського та румунського правління (1816–1918; 1918–1940), а також за часів румунської військової адміністрації (1941–1944), враховуючи інституційні трансформації, матеріальну інфраструктуру, викладацький склад, кількість учнів та роль місцевої громади у підтримці освіти. Дослідження зосереджене, зокрема, на Рошанській школі (сьогодні гімназія № 10), школі в Манастириськах, школах у Роші-Цецині та Роші-Стинці (сьогодні гімназія № 2), а також німецькій школі, розташованій поблизу кавалерійських казарм (сьогодні гімназія № 8). На відміну від попередніх публікацій, які переважно розглядали історію окремих навчальних закладів, у цій статті аналізуються школи Роші як єдина локальна освітня система, що розвивалася в різних просторових місцях та за різних адміністративних режимів. Актуальність дослідження полягає в необхідності суттєвого розширення існуючих знань про освітній простір Роші як багатокомпонентне явище, що має як наукове, так і практичне значення для сучасного освітнього середовища мікрорайону. Наукова новизна. Дослідження базується на великому корпусі раніше неопублікованих архівних матеріалів Державного архіву Чернівецької області, зокрема: Фонд 1 Районна адміністрація Буковини, Чернівці (справки № 875, 3639); Фонд 3 Обласне управління Буковини (файли № 1991, 3126, 5612); Фонд 39 Чернівецький міський магістрат (файли № 535, 820, 5460, 5518, 5810); та Фонд 43 Чернівецька міська рада (файли № 116, 1225, 2703, 2931, 5640, 6823, 8557, 8735, 12722, 12727). Особливу наукову цінність мають матеріали, зібрані під час історико-етнографічних польових досліджень, проведених у Роші у 2024–2025 роках. Особливе значення мають фонди архіву (музею) Чернівецької гімназії № 10, зокрема фотодокументи кінця ХІХ – першої половини ХХ століття, пов’язані зі шкільними будівлями та організацією навчального процесу. Деякі з цих матеріалів були надані дослідницькій групі вчителькою Корнелією Медвідь, яка з 1990-х років систематично збирає історичні та етнографічні матеріали від мешканців передмістя Роша. В результаті дослідження вперше з'ясовано обставини створення окремих шкіл; реконструйовано умови, за яких приватні та орендовані приміщення використовувалися для освітніх цілей; визначено імена директорів шкіл та вчителів, їхню конфесійну та мовну приналежність, а також чисельність та соціальний склад учнівського колективу. Вперше на основі звітів про перевірку та матеріалів Чернівецького міського магістрату систематизовано статистичні дані про кількість учнів та педагогічний колектив у школах Роші. Вперше в науковий обіг введено плани та плани шкільних будівель, зокрема креслення, підготовлені архітектором В. Греку в 1939 році, а також оригінальні таблиці та схеми, що містять дані про кількість учнів, етнічний склад та керівництво школи. Методологічна основа дослідження базується на комплексному аналізі архівних джерел та історіографії, що дозволяє з'ясувати особи засновників та меценатів школи, умови функціонування школи до будівництва спеціально побудованих приміщень, склад персоналу, кількість учнів та національну структуру, а також обставини будівництва, ремонту та оренди шкільних приміщень. Висновки. Дослідження демонструє, що розвиток шкільної освіти в Роші був системним та динамічним, формувався взаємодією демографічного зростання в передмісті, адміністративних та правових трансформацій та ресурсних можливостей місцевої громади. Освітня інфраструктура розвивалася завдяки постійним переговорам між місцевими жителями та владою, а також завдяки практиці адаптації та оренди приміщень, проведення ремонтів та поступового розширення інституційного потенціалу у відповідь на зростання кількості учнів. В результаті дослідження вперше створено цілісний науковий корпус джерел та візуалізацій (схем, таблиць, планів та фотодокументів), а також проведено аналітичну реконструкцію, яка дозволяє зрозуміти шкільну мережу Роші як складну багаторівневу систему, інтегровану в соціальний, культурний та адміністративний простір Чернівців з ХІХ до першої половини ХХ століття.

Oksana Melnychuk, Inesa Khmeliar, Nataliia 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.

Jingyun Chen, Yading Yuan

Federated Learning (FL) presents a promising avenue for collaborative model training among medical centers, facilitating knowledge exchange without compromising data privacy. However, vanilla FL is prone to server failures and rarely achieves optimal performance on all participating sites due to heterogeneous data distributions among them. To overcome these challenges, we propose Gossip Contrastive Mutual Learning (GCML), a unified framework to optimize personalized models in a decentralized environment, where Gossip Protocol is employed for flexible and robust peer-to-peer communication. To make efficient and reliable knowledge exchange in each communication without the global knowledge across all the sites, we introduce deep contrast mutual learning (DCML), a simple yet effective scheme to encourage knowledge transfer between the incoming and local models through collaborative training on local data. By integrating DCML with other efforts to optimize site-specific models by leveraging useful information from peers, we evaluated the performance and efficiency of the proposed method on three publicly available datasets with different segmentation tasks. Our extensive experimental results show that the proposed GCML framework outperformed both centralized and decentralized FL methods with significantly reduced communication overhead, indicating its potential for real-world deployment. Upon the acceptance of manuscript, the code will be available at: https://github.com/CUMC-Yuan-Lab/GCML.

Maxim V. Polyakov, Danila S. Sirotin

This article presents a study on the influence of antenna parameters in medical microwave radiometers on brightness temperature. A series of computational experiments was conducted to analyse the dependence of brightness temperature on antenna characteristics. Various antenna parameters and their effect on the distribution of electromagnetic fields in biological tissues were examined. It was demonstrated that considering the antenna mismatch parameter is crucial when modelling the brightness temperature of biological tissues, contributing about 2 percent to its formation. The depth range of brightness temperature measurement was determined. The dependence of brightness temperature on the antenna diameter and frequency was established. The findings of this study can be applied to improve medical microwave radiometers and enhance their efficiency in the early diagnosis of various diseases.

Jaime Sevilla, Nikolay Babakov, Ehud Reiter et al.

In this paper, we propose a model for building natural language explanations for Bayesian Network Reasoning in terms of factor arguments, which are argumentation graphs of flowing evidence, relating the observed evidence to a target variable we want to learn about. We introduce the notion of factor argument independence to address the outstanding question of defining when arguments should be presented jointly or separately and present an algorithm that, starting from the evidence nodes and a target node, produces a list of all independent factor arguments ordered by their strength. Finally, we implemented a scheme to build natural language explanations of Bayesian Reasoning using this approach. Our proposal has been validated in the medical domain through a human-driven evaluation study where we compare the Bayesian Network Reasoning explanations obtained using factor arguments with an alternative explanation method. Evaluation results indicate that our proposed explanation approach is deemed by users as significantly more useful for understanding Bayesian Network Reasoning than another existing explanation method it is compared to.

Kasra Borazjani, Naji Khosravan, Leslie Ying et al.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging. However, existing works on multi-modal FL often presume that all data-collecting institutions have access to all data modalities. This oversimplified approach neglects institutions that have access to only a portion of data modalities within the system. In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We shed light on the challenges associated with varying convergence speeds observed across different data modalities within our FL system. Subsequently, we propose a solution to tackle these challenges by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL. To show the superiority of our method, we conduct experiments using The Cancer Genome Atlas program (TCGA) datalake considering different cancer types and three modalities of data: mRNA sequences, histopathological image data, and clinical information. Our results further unveil the impact and severity of class-based vs type-based heterogeneity across institutions on the model performance, which widens the perspective to the notion of data heterogeneity in multi-modal FL literature.

Shogo Watanabe

This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with respect to clinical and medicine content that includes the variety of diseases, drugs, and examinations. Using a carefully designed pre-processing, a specialized morphological analyzer and tokenizer, this small and light-weight model performed not only to generate text but also indicated the feasibility of understanding clinical and medicine text. In comparison to other large language models, a fine-tuning NCVC-slm-1 demonstrated the highest scores on 6 tasks of total 8 on JMED-LLM. According to this result, SLM indicated the feasibility of performing several downstream tasks in the field of clinical and medicine. Hopefully, NCVC-slm-1 will be contributed to develop and accelerate the field of clinical and medicine for a bright future.

Zachary A Stoebner, Daiwei Lu, Seok Hee Hong et al.

Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models -- U-Net, U-Net++, and DenseNet -- to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90\%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds.

Xuanang Xu, Hannah H. Deng, Tianyi Chen et al.

Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a round-robin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code is publicly available at https://github.com/DIAL-RPI/FedCross.

Chen Chen, Matin Yarmand, Varun Singh et al.

Contouring is an indispensable step in Radiotherapy (RT) treatment planning. However, today's contouring software is constrained to only work with a 2D display, which is less intuitive and requires high task loads. Virtual Reality (VR) has shown great potential in various specialties of healthcare and health sciences education due to the unique advantages of intuitive and natural interactions in immersive spaces. VR-based radiation oncology integration has also been advocated as a target healthcare application, allowing providers to directly interact with 3D medical structures. We present VRContour and investigate how to effectively bring contouring for radiation oncology into VR. Through an autobiographical iterative design, we defined three design spaces focused on contouring in VR with the support of a tracked tablet and VR stylus, and investigating dimensionality for information consumption and input (either 2D or 2D + 3D). Through a within-subject study (n = 8), we found that visualizations of 3D medical structures significantly increase precision, and reduce mental load, frustration, as well as overall contouring effort. Participants also agreed with the benefits of using such metaphors for learning purposes.

Carlos Henrique Paiva, Gabriele Carvalho Freitas

Resumo A história do Programa Nacional de Serviços Básicos de Saúde (Prev-saúde) se inicia em 1979, na articulação entre os Ministérios da Saúde, da Previdência e Assistência Social, do Interior e da Economia e a Organização Pan-americana da Saúde. Teve como objetivo reorganizar os serviços básicos de saúde em suas conexões com os demais níveis assistenciais. Internacionalmente, inscrevia-se no movimento deflagrado pela Conferência de Alma-Ata, de setembro de 1978. Em termos nacionais, representava tanto um acúmulo de conhecimento sobre organização dos serviços quanto um movimento que se adequava, em parte, à agenda da reforma sanitária brasileira. O Prev-saúde representou um conjunto de proposições para a reorganização da saúde que, naquele contexto, era consenso técnico entre burocracias e lideranças da reforma da saúde.

Bogatov Egor, Korenev Artem, Mikhailov Ilya

One of the variants for systematizing the activities of the historian of mathematics is proposed, as well as a scheme for organizing research and search work in the preparation of scientific articles and reports on the history of science.

Mingquan Lin, Jacob Wynne, Yang Lei et al.

Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.

Rongguang Wang, Pratik Chaudhari, Christos Davatzikos

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift challenge and has limited the widespread clinical adoption of machine learning models. Harmonization methods which aim to learn a representation of data invariant to these differences are the prevalent tools to address domain shift, but they typically result in degradation of predictive accuracy. This paper takes a different perspective of the problem: we embrace this disharmony in data and design a simple but effective framework for tackling domain shift. The key idea, based on our theoretical arguments, is to build a pretrained classifier on the source data and adapt this model to new data. The classifier can be fine-tuned for intra-site domain adaptation. We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task. We demonstrate substantial improvements in both intra-site domain adaptation and inter-site domain generalization on large-scale real-world 3D brain MRI datasets for classifying Alzheimer's disease and schizophrenia.

Ivan Volkov, Gleb Radchenko, Andrey Tchernykh

As the population grows, the need for a quality level of medical services grows correspondingly, so does the demand for information technology in medicine. The concept of "Smart Healthcare" offers many approaches aimed at solving the acute problems faced by modern healthcare. In this paper, we review the main problems of modern healthcare, analyze existing approaches and technologies in the areas of digital twins, the Internet of Things and mobile medicine, determine their effectiveness in solving the set problems, consider the technologies that are used to monitor and treat patients and propose the concept of the Smart Healthcare platform.

Halyna KUTASEVYCH, Nataliya YAKUBOVSKA, Maryna SMIRNOVA

The relevance of the study is the interaction, determined by the need to focus on one of the significant challenges in distance learning. Recent studies have shown that the effectiveness of distance learning directly relies on the degree of interactivity, which is especially important for learning French. The purpose of the proposed study is to highlight the main aspects of interaction in distance learning of French in synchronous and asynchronous forms. The novelty is that for the first time, the three dimensions of interaction in distance French course are determined: interaction teacher - students, interaction students -students, and interaction students - content. The work used the method of critical analysis of scientific sources and the method of scientific observation of the pedagogical process. Conclusions: Creating interaction in the classroom is essential to student learning and the overall success and effectiveness of distance education. Distance French courses offer interaction among 14 Ibidem. 15 Grebennikova I. L'interaction médiatisée à travers le chat comme dispositif sociotechnique Computer-based interaction via chat as socitech device, 2008, Vol 1, P. 20-30 in French. 16 Puentedura R. SAMR: A brief introduction [in English]. URL: http://hippasus.com/blog/archives/227 51 Kutasevych H., Yakubovska N., Smirnova M. To conduct distance learning French courses: what is the place for interaction? peers, among teacher and students, among students and content by using different tools in synchronous and asynchronous forms of learning. The use of the latest information and communication technologies ensures the interactive interaction in synchronous and asynchronous forms of education. The rational combination of such technologies with already existing teaching methods is able to become the key to the effective formation of foreign language French-speaking competence among students.

Luciana Murari

Resumo O artigo analisa as narrativas de viagem ao interior de Mato Grosso e Goiás publicadas em 1935 e 1936 pelo explorador paulista Hermano Ribeiro da Silva, que obtiveram considerável sucesso editorial e impacto no meio letrado brasileiro. Concentramo-nos em suas ideias sobre a relação entre o ambiente do Brasil Central e o homem sertanejo, sobre as potencialidades de exploração econômica da região e sobre o papel do Estado na condução de iniciativas capazes de promover sua incorporação efetiva à nacionalidade. Buscamos também compreender a fundamentação de seu discurso em conceitos e esquemas científicos genéricos dotados de poder retórico e argumentativo.

Valentyn MALTSEV

The article outlines the problem of the place of the distich as a strophic unit in the binary opposition ―strophic – astrophic‖ verse, as well as considers several options for diversification from a clear two-verse structure with a constant double rhyme. Research methods: formal-statistical, comparative and descriptive. Scientific novelty. Analysis of the material and poetic research allows us to conclude that there is every reason to interpret the vast majority of poetic texts with a continuous double rhyme as strophic two-verse derivations. At the same time, in such texts can be noticed different ways of enrichment, diversification of such a simple strophic structure. Conclusions. There are cases of ―division‖ of the two-verses into replicas of two different charac- ters, which does not destroy the strophic structure in dramatic works with a two-verse strophic organization. Somewhat blur this structure of the text, in which occasionally appears ―tercet‖ and larger pieces of text per rhyme (either due to neglect and the ap- pearance of ―extra‖, additional line, the clause of which forms a triple rhyme with the clauses of the neighboring distich, or deliber- ately creating a kind of aesthetic effect ―stringing‖ additional rhyming clauses). It was also quite common practice to introduce additional regular internal rhymes (mostly in the caesura position), which significantly enrich the sound. Perspectives for further re- search of the stanza level of ancient Ukrainian syllabics are seen in a detailed study of the stanza of syllabic verse, including the con- text of its correlation with intonation and semantic division, as well as in the attraction of various strophic forms to certain syllabic proportions.

Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein et al.

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice.

Andrey Yu. Starikovskiy, Dinara R. Usmanova

In this brief report we propose a new design of a medical mask with a plasma layer, which provides both additional air filtration from microdrops, bacteria and viruses due to the electrostatic effect and self-disinfecting of surfaces by a pulsed barrier discharge. The key features of the mask are the mutual arrangement of the layers, the direction of air flows and the synchronization of the discharge with respiration, which ensures the safe wearing of the mask and high degree of protection against pathogenic microorganisms.