Hasil untuk "History of medicine. Medical expeditions"

Celil Atılgan, Osman Açıkgöz, Ayla Açıkgöz et al.

Amaç: Tıp Fakültesi öğrencilerinin birinci sınıfta bilim ve meslek olarak tıpla tanışmalarının sağlanması ve insani bilimlerden uzaklaşmalarının önlenmesi amacıyla Dokuz Eylül Üniversitesi Üniversitesi Tıp Fakültesi’nde 2019-2020 eğitim-öğretim yılında "Bilim, Sanat ve Tıp" başlıklı dersler müfredata eklenmiştir. Bu çalışmanın amacı, söz konusu derslere ilişkin öğrenci görüşlerini değerlendirmek ve bu derslerin öğrencilerde kitap okuma alışkanlığına ve isteğine olan etkilerini belirlemektir. Yöntem: Dönem 2, 3, 4 ve 5 öğrencilerinden 166’sı araştırmaya katılmıştır. Hazırlanan bir anketle öğrencilerin sosyodemografik bilgileri ve dersler hakkındaki görüşleri alınmıştır. Bulgular: Öğrencilerin dersler hakkındaki olumlu görüşleri %75-90 arasında değişmekteydi. Öğrencilerin %11’i dersin kaldırılması gerektiğini, %64’ü dersin seçmeli olması gerektiğini, %48’i bu dersin ikinci sınıfta da olması gerektiğini düşünüyordu. Öğrencilerin tıp fakültesine girmeden önce ay başına düşen okudukları kitap sayısına göre anketin yapıldığı dönemde ay başına düşen okudukları kitap sayısının azaldığı saptandı (p<0,05). Sonuçlar: Öğrenciler genel olarak ders hakkında olumlu görüşe sahipti. Öğrenciler derslerin kitap okuma isteklerini artırdığını belirtmiş olmakla birlikte ilerleyen yılarda okudukları kitap sayısı azalmıştı. Öğrencilerin kitap okuma alışkanlıkları inceleyen araştırmalarla bu durumun nedenleri ortaya konabilir.

Leida Fernández Prieto, José Ramón Marcaida López

Saniye Merve Cengiz, Sevde Nihal Yongacı, Kübra Arslan Çarpar

Amaç: Gelişen teknolojiyle birlikte dijital tasarımların ve üretimlerin ortodontik tedavilerde kullanımı sonucu popülaritesi artan şeffaf plaklar, yüksek oranda tercih edilen bir tedavi seçeneği olarak karşımıza çıkmaktadır. Bu anket çalışmasında diş hekimliği fakültesinde ortodonti pratik ve teorik eğitimini alan 4. ve 5. sınıf öğrencileri ile son 2 sene içerisinde mezun olmuş diş hekimlerinin şeffaf plak tedavileri hakkındaki bilgi düzeylerinin değerlendirilmesi amaçlanmaktadır. Gereç ve Yöntemler: Çalışmaya diş hekimliği 4. ve 5. sınıf öğrencileri ile son 2 sene içerisinde mezun olmuş diş hekimleri dahil edildi. Katılımcılara şeffaf plaklar ile ilgili 25 sorudan oluşan bir anket uygulandı. Bulgular: Çalışmaya toplamda 219 birey katıldı. Katılımcıların 96’sı 4. sınıf öğrencisi, 75’i 5. sınıf öğrencisi ve 48’i 2023-2024 yıllarında mezun olmuş diş hekimlerinden oluşmaktadır. Yapılan istatistiksel analizler sonucunda pekiştirme apareyi ve tedaviyi uygulaması gereken hekim grubuna dair verilen yanıtlarda farklı yaş gruplarına göre istatistiksel olarak anlamlı farklılıklar bulunmuştur (p<0,05). Plakların takılıp çıkarılabilmesi, plaklarla uyunabilmesi, plakta beslenmeyle oluşan renklenme ve plaklarla ağız bakımına dair verilen yanıtlarda cinsiyet gruplarına göre istatistiksel olarak anlamlı farklılıklar bulunmuştur (p<0,05). Şeffaf plakların nereden/kimden duyulduğu, plaklarla uyunabilmesi, ataşmanların amacı ve uygulanması ile protetik yaklaşımlara dair verilen yanıtlarda farklı eğitim düzeylerine göre istatistiksel olarak anlamlı farklılıklar bulunmuştur (p<0,05). Sonuç: Gelişen teknolojiyle birlikte popülaritesi artan şeffaf plak tedavilerine dair bilgi düzeylerinde anlamlı farklılıklar olduğu görülmüştür. Şeffaf plak tedavilerinin etkinliğinin arttırılmasında, materyalin tanınması ve tüm özelliklerinin anlaşılabilmesi için bilgi düzeyinin arttırılması gerekmektedir.

Doğancan Çavmak

Objective: Intensive care units are one of the most complex and critical service production areas that incur high levels of resource consumption in hospitals. This study aims to analyze the cost structures of intensive care units, and to examine cost containment strategies that can be implemented in this area. Methods: The study is a systematic literature review, employing the PRISMA flow diagram. The review was carried out in two stages using the Web of Science database, focusing on the cost structures of units and cost reduction strategies. Results: The study synthesized finding from 14 papers to examine the topic. The results of the 10 studies on the cost structure indicated that the cost structures of intensive care units are mostly composed of labor, medical consumables, and equipment expenses. Among the cost control methods, quality improvement, lean management, and value streams were detected to be frequently used approaches.Conclusion: Given that intensive care units are high-cost service areas, it will be beneficial to examine them considering cost management approaches.

YunHe Su, Zhengyang Lu, Junhui Liu et al.

This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.

Dou Liu, Ying Long, Sophia Zuoqiu et al.

Effective physician-patient communications in pre-diagnostic environments, and most specifically in complex and sensitive medical areas such as infertility, are critical but consume a lot of time and, therefore, cause clinic workflows to become inefficient. Recent advancements in Large Language Models (LLMs) offer a potential solution for automating conversational medical history-taking and improving diagnostic accuracy. This study evaluates the feasibility and performance of LLMs in those tasks for infertility cases. An AI-driven conversational system was developed to simulate physician-patient interactions with ChatGPT-4o and ChatGPT-4o-mini. A total of 70 real-world infertility cases were processed, generating 420 diagnostic histories. Model performance was assessed using F1 score, Differential Diagnosis (DDs) Accuracy, and Accuracy of Infertility Type Judgment (ITJ). ChatGPT-4o-mini outperformed ChatGPT-4o in information extraction accuracy (F1 score: 0.9258 vs. 0.9029, p = 0.045, d = 0.244) and demonstrated higher completeness in medical history-taking (97.58% vs. 77.11%), suggesting that ChatGPT-4o-mini is more effective in extracting detailed patient information, which is critical for improving diagnostic accuracy. In contrast, ChatGPT-4o performed slightly better in differential diagnosis accuracy (2.0524 vs. 2.0048, p > 0.05). ITJ accuracy was higher in ChatGPT-4o-mini (0.6476 vs. 0.5905) but with lower consistency (Cronbach's $α$ = 0.562), suggesting variability in classification reliability. Both models demonstrated strong feasibility in automating infertility history-taking, with ChatGPT-4o-mini excelling in completeness and extraction accuracy. In future studies, expert validation for accuracy and dependability in a clinical setting, AI model fine-tuning, and larger datasets with a mix of cases of infertility have to be prioritized.

Anbang Du, Michael Head, Markus Brede

Interdisciplinary research is critical for innovation and addressing complex societal issues. We characterise the interdisciplinary knowledge structure of PubMed research articles in medicine as correlation networks of medical concepts and compare the interdisciplinarity of articles between high-ranking (impactful) and less high-ranking (less impactful) medical journals. We found that impactful medical journals tend to publish research that are less interdisciplinary than less impactful journals. Observing that they bridge distant knowledge clusters in the networks, we find that cancer-related research can be seen as one of the main drivers of interdisciplinarity in medical science. Using signed difference networks, we also investigate the clustering of deviations between high and low impact journal correlation networks. We generally find a mild tendency for strong link differences to be adjacent. Furthermore, we find topic clusters of deviations that shift over time. In contrast, topic clusters in the original networks are static over time and can be seen as the core knowledge structure in medicine. Overall, journals and policymakers should encourage initiatives to accommodate interdisciplinarity within the existing infrastructures to maximise the potential patient benefits from IDR.

Mariam Shamal, Hossein Hassani

Access to Kurdish medicine brochures is limited, depriving Kurdish-speaking communities of critical health information. To address this problem, we developed a specialized Machine Translation (MT) model to translate English medicine brochures into Sorani Kurdish using a parallel corpus of 22,940 aligned sentence pairs from 319 brochures, sourced from two pharmaceutical companies in the Kurdistan Region of Iraq (KRI). We trained a Statistical Machine Translation (SMT) model using the Moses toolkit, conducting seven experiments that resulted in BLEU scores ranging from 22.65 to 48.93. We translated three new brochures to improve the evaluation process and encountered unknown words. We addressed unknown words through post-processing with a medical dictionary, resulting in BLEU scores of 56.87, 31.05, and 40.01. Human evaluation by native Kurdish-speaking pharmacists, physicians, and medicine users showed that 50% of professionals found the translations consistent, while 83.3% rated them accurate. Among users, 66.7% considered the translations clear and felt confident using the medications.

Ensieh Madani, Ali Dizani, Saeedeh Saeedi Tehrani et al.

Ethical dilemmas are among the most important ethical problems in medicine. With the advent of COVID-19, the moral problems of physicians have taken on new dimensions as the specific features of this disease pose additional ethical challenges that require particular solutions. One common way to solve ethical dilemmas is to use ethical decision making models. One of the most recent models in ethics of technology is the “ethical cycle” developed by Ibo van de Poel. By describing and comparing several models, this paper examines the application of the ethical cycle to physicians' ethical problems and medical ethics. This model can help solve complex problems in consultations and ethics committee meetings because it is comprehensive and covers various aspects of the discussion.In this model, first the ethical problem is formulated and analyzed and then the potential options for action are proposed.Subsequently, by referring to ethical theories and professional codes of conduct in the medical field, as well as applying the method of "reflective equilibrium," an ethical decision is reached. This decision is specific to each case and may not necessarily be the best solution for other individuals or situations

Fadhil muhaldeen, Abdullah Raoof

Background: Coronavirus disease 2019 (COVID-19) is a new emerging disease caused by SARS-CoV-2, first discovered in Wuhan, China, in December 2019. Infected patients of all age groups with associated medical diseases such as chronic kidney disease (CKD) stage five -end stage renal disease (ESRD) on hemodialysis are likely to have a higher risk of developing severe COVID-19 compared to patients without disease. The study aimed to address morbidity and mortality related to SARS-CoV2 infection in hemodialysis patients at the Kirkuk dialysis centre.Methods: A prospective observational study that enrolled 385 COVID-19 patients with CKD-stage 5 (ESRD), who were on a regular hemodialysis program in the dialysis center in Kirkuk city for a period of 6 months.Results: In hemodialysis patients, the incidence of SARS-CoV2 infection was 80 (20.75%), among these 80 patients, 43(53.75%) required hospital admission due to the severity of the disease, 32 (40%) were admitted to the intensive care unit and received ventilation, 19 patients (23.75%) died from complications related to SARS-CoV-2.Conclusion: There is a significant incidence of hospital admission and the need for ventilation among patients, as well as a notable mortality rate was observed in COVID-19 patients undergoing hemodialysis, with specific risk factors such as bronchial asthma, the presence of AV fistula, and type 2 diabetes mellitus contributing to a higher mortality percentage.

Stefano Woerner, Arthur Jaques, Christian F. Baumgartner

While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often the scarcity of large, diverse, and well-annotated datasets. Medical images vary in format, size, and other parameters and therefore require extensive preprocessing and standardization, for usage in machine learning. Addressing these challenges, we introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, all of which are standardized to the same format and readily usable in PyTorch or other ML frameworks. We perform a technical validation of MedIMeta, demonstrating its utility through fully supervised and cross-domain few-shot learning baselines.

Vincent Olesen, Nina Weng, Aasa Feragen et al.

Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups - such as those based on sex, age, or disease subtype - as well as previously unknown and unlabeled groups. Furthermore, the root cause of such observed performance disparities is often challenging to uncover, hindering mitigation efforts. In this paper, to address these issues, we leverage Slice Discovery Methods (SDMs) to identify interpretable underperforming subsets of data and formulate hypotheses regarding the cause of observed performance disparities. We introduce a novel SDM and apply it in a case study on the classification of pneumothorax and atelectasis from chest x-rays. Our study demonstrates the effectiveness of SDMs in hypothesis formulation and yields an explanation of previously observed but unexplained performance disparities between male and female patients in widely used chest X-ray datasets and models. Our findings indicate shortcut learning in both classification tasks, through the presence of chest drains and ECG wires, respectively. Sex-based differences in the prevalence of these shortcut features appear to cause the observed classification performance gap, representing a previously underappreciated interaction between shortcut learning and model fairness analyses.

Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez et al.

The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an Area Under the Curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chaksu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge. Our code is available at https://github.com/TheoSourget/MMC_Masking and https://github.com/TheoSourget/MMC_Masking_EyeFundus

Діана Ротар, Олена Бліндер, Анна Гуменна et al.

Розвиток інформаційного суспільства передбачає широке використання інформаційно-комунікаційних технологій в освіті, що визначається багатьма факторами. Перехід на широке використання онлайн-форм навчання в умовах карантину чи війни змушує вчителів опановувати та постійно вдосконалювати навички використання інформаційно-комунікаційних технологій. Мета. Проаналізувати літературні дані про методи та умови використання інформаційно-комунікаційних технологій. Методи дослідження. Моніторинг наукової літератури щодо організації спілкування з іноземними студентами за допомогою інформаційно-комунікаційних технологій. Результати досліджень. Узагальнено принципи використання інформаційно-комунікаційних технологій у процесі організації спілкування з іноземними студентами, уточнено важливість їх використання залежно від можливостей сприйняття студентами інформації; Визначено пріоритетні цифрові програми Google (Gmail, Meet, YouTube, Диск, Документи, Електронні таблиці, Презентації, Календар, Чат, Форми, Групи, Перекладач тощо); акцентовано увагу на доцільності та формі їх використання; продемонстрували вміння оцінювати та подавати інформацію учням. Висновки. Використання інформаційно-комунікаційних технологій дає можливість вирішувати такі актуальні проблеми: використовувати новітні досягнення інформаційних технологій в освіті; вдосконалювати навички самостійної роботи студентів в інформаційних базах даних, мережі Інтернет; інтенсифікувати навчання, покращити засвоєння знань учнями, зробити процес навчання більш цікавим і змістовним. Перспективи подальших досліджень. Аналіз результатів використання інформаційно-комунікаційних технологій у процесі організації спілкування з іноземними студентами

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

Niketha Sabesan, Nivethitha, J. N Shreyah et al.

Web scraping is a technique that allows us to extract data from websites automatically. in the field of medicine, web scraping can be used to collect information about medical procedures, treatments, and healthcare providers. this information can be used to improve patient care, monitor the quality of healthcare services, and identify areas for improvement. one area where web scraping can be particularly useful is in medical ministrations. medical ministrations are the actions taken to provide medical care to patients, and web scraping can help healthcare providers identify the most effective ministrations for their patients. for example, healthcare providers can use web scraping to collect data about the symptoms and medical histories of their patients, and then use this information to determine the most appropriate ministrations. they can also use web scraping to gather information about the latest medical research and clinical trials, which can help them stay up-to-date with the latest treatments and procedures.

Bo Yu, Hechang Chen, Chengyou Jia et al.

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes.

Jesutofunmi A. Omiye, Haiwen Gui, Shawheen J. Rezaei et al.

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine.

Galina Sinkevich

A short history of Russian researches in Chinese astronomy in 19-20 centuries

Mounir Laroussi

This mini review is to introduce the readers of Plasma to the field of plasma medicine. This is a multidisciplinary field of research at the intersection of physics, engineering, biology and medicine. Plasma medicine is only about two decades old, but the research community active in this emerging field has grown tremendously in the last few years. Today, research is being conducted on a number of applications including wound healing and cancer treatment. Although a lot of knowledge has been created and our understanding of the fundamental mechanisms that play important roles in the interaction between low temperature plasma and biological cells and tissues has greatly expanded, much remains to be done to get a thorough and detailed picture of all the physical and biochemical processes that enter into play.