Engineering Medicine Research Articles

AT THE CROSSROADS OF TECHNOLOGY AND MEDICINE: PROSPECTS OF AUTOMATION IN MEDICAL PRACTICE WITH THE USE OF NEURAL NETWORKS

This article delves into the exciting possibilities that arise when technology is applied to the field of medicine. It explores how automation can streamline various processes, leading to more efficient and accurate healthcare. One focus area is the potential for replacing direct interaction between doc-tors and patients with automated systems. By utilizing advanced neural networks, diagnostic and treatment processes can becomautomated, leading to faster and more precise outcomes. For exam-ple, AI-powered systems can analyze medical images and detect abnormalities with incredible accu-racy, aiding doctors in making diagnoses. Additionally, virtual assistants and chat bots can provide personalized healthcare advice, reducing the need for in-person consultations for minor concerns. These advancements not only enhance efficiency but also have potential to improve access to healthcare, especially in remote areas. Embracing technology in medicine opens up a lot of possibilities for better patient care and overall healthcare outcomes.

Considerations for applying emerging technologies in paediatric laboratory medicine.

Emerging technology in laboratory medicine can be defined as an analytical method (including biomarkers) or device (software, applications, and algorithms) that by its stage of development, translation into broad routine clinical practice, or geographical adoption and implementation has the potential to add value to clinical diagnostics. Paediatric laboratory medicine itself may be considered an emerging area of specialisation that is established relatively recently following increased appreciation and understanding of the unique physiology and healthcare needs of the children. Through four clinical (neonatal hypoglycaemia, neonatal hyperbilirubinaemia, sickle cell disorder, congenital adrenal hyperplasia) and six technological (microassays, noninvasive testing, alternative matrices, next generation sequencing, exosome analysis, machine learning) illustrations, key takeaways of application of emerging technology for each area are summarised. Additionally, nine key considerations when applying emerging technology in paediatric laboratory medicine setting are discussed.

HTA model for laboratory medicine technologies: overview of approaches adopted in some international agencies.

The Health Technology Assessment (HTA) Working Group of the Emerging Technology Division of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) aims to develop a methodological approach for producing structured HTA information for laboratory medicine technologies. This approach seeks to support decision-making processes at the country, regional, and/or hospital levels regarding the introduction of specific technologies. The focus of this model will primarily be ondefining assessment elements within the domains of 'organizational aspects' and 'costs and economic evaluations', potentially differentiated by the type of diagnostic technology (e.g., genetic tests, molecular tests). To achieve this project's goal, a literature review and examination ofwebsites of international HTA agencies have been conducted. The research aims to identify multidisciplinary methodological approaches used to assess laboratory diagnostic technologies and to pinpoint the domains and assessment elements utilized. Wefound 7 methodological articles describing methodological approaches adopted toassess laboratory diagnostic technologies. Among the HTA organizations considered, 23 reports were found, of which 7 were produced by the European Network of HTA (EUnetHTA), 4 by the National Institute for Health and Care Excellence Diagnostic Assessment Program (NICE DAP), and 12 by other HTA agencies. The EUnetHTA reports wererapid collaborative assessments covering various domains, while the NICE DAP reports focused on diagnostic guidances, including descriptions of technologies, clinical need and practice, diagnostic tests, accuracy, effectiveness, and cost-effectiveness. Finally, a survey targeting laboratory professionals will be conducted to introduce assessment elements, differentiated by the type of diagnostic technology, primarily for organizational and economic domains.

Влияние современных симуляционных тренажеров на развитие навыков командной работы: согласованных действий и коммуникации

<p style="text-align: justify;">The article presents a review of a number of modern foreign studies of simulators and their role in the development of teamwork skills. The analysis of some trending learning theories and methodologies, which are used by both national and foreign researchers in the implementation of training programs on simulators of professional activity is carried out. The results of studies demonstrating the influence of computer simulators on the development of teamwork skills, including aspects of coordinated actions and effective communication are presented. Various methods and means used by foreign researchers to improve the effectiveness of simulation-based training are described. The importance of using simulation-based training in various professional fields, such as medicine, aviation, space programs and information technology, to ensure a high level of training of specialists is substantiated. It is shown that modern simulation-based simulators offer training methods that are maximally close to real professional activity, which contributes to the formation of highly qualified specialists in professional fields that require high development of teamwork skills.</p>

Cognitive Brain of Homo sapiens: Stress, Emotions, Health, Hormones, Longevity

Replication of new discoveries in the field of genetics and epigenetics, the scientific union of neurobiology and neurophysiology of the quantum brain HOMO SAPIENS of the 21st century, the achievements of biophysics, hybrid neuroimaging and nuclear medicine, the introduction of nutritionology and bioelementology technologies, the 5P of MEDICINE and 5G TECHNOLOGY, compliance of art medicine and modern psychoanalysis, - will allow control the cognitive brain of a 21st century HUMAN. Scientific progress and 33 years of experimental experience of biophysics in the field of neurogeriatrics and neurocardiology, neuroendocrinology and psychoneuroimmunology - made it possible to argue that "nerve cells are restored," the quantum human brain is CONTROLLED, high-quality and healthy LONGEVITY HOMO SAPIENS of the 21st century is achievable. Children who experience chronic stressors are vulnerable to lifelong emotional and physical health problems. Cognitive and mental health affects a person's ability to cope with stress, maintain a relationship paradigm, make healthy choices and lead a fulfilling life. An increase in the expected and average life expectancy of a person is realizable/achievable/feasible when creating conditions/tools/algorithms/technologies for active and high-quality longevity.

Heterogenous biological network multi-task learning model for ncRNA-disease-drug association prediction

Traditional medical research is characterised by lengthy duration, significant financial investment, and substantial risk of failure. In response to these challenges, network medicine, combined with medicine and computer technology, has become an important development direction, and computational methods have been proposed to predict potential associations. However, most of the current computational methods focus on single-potential association prediction tasks, which face issues of association sparsity and weak generalisation ability. To address these challenges, we developed a heterogeneous biological network multi-task learning model (HBNMM). Unlike previous methods based on bipartite graphs, HBNMM constructs a complex heterogeneous biological network, including ncRNA-disease-drug association networks and diverse similarity networks. HBNMM applies graph attention networks to aggregate node neighbourhood information and acquire node feature embeddings, and is then trained with a multi-task learning strategy to simultaneously predict potential ncRNA-disease, ncRNA-drug, and drug-disease associations. As a result, the HBNMM achieves an excellent performance that is higher than that of the state-of-the-art models. Furthermore, five case studies supported by experiments showed powerful predictive ability for drug discovery and disease treatment.

The Influence of Three-Dimensionally Printed Polymer Materials as Trusses and Shell Structures on the Mechanical Properties and Load-Bearing Capacity of Reinforced Concrete.

Three-dimensional printing technology (3D printing) is becoming a more and more popular technology for aerospace, biology, medicine, mechanics, civil and other engineering fields. In civil engineering, researchers and engineers attempt to print construction materials such as concrete using 3D-printing technology. This study aims to investigate the reinforcement of concrete beams with 3D printing. To achieve this, fused deposition modeling (FDM) technology as a printing method and polylactic acid (PLA) as a material were selected. Two types of geometries were chosen to find the optimal mechanical behavior of concrete: high-performance concrete (HPC) reinforced with four types of trusses (Pratt, Howe, Warren, and Warren with vertical) and ultra-high-performance concrete (UHPC) reinforced with a hyperboloid shell structure. The compressive and tensile strengths of reinforced UHPC were examined by a three-point bending test, and reinforced HPC was examined by a four-point bending test. The results of the experiments show that hyperboloid shell structures can absorb energy, although the strength of reinforced UHPC is reduced. For example, there was a decrease of over 20% in the compressive strength and 41% in the flexural strength, but the ductility was raised. Adding the hyperboloid shell structure improved the deformability of the UHPC. When Warren and Howe trusses were added to the HPC as reinforcements, the flexural strength improved by over 26% and 4.3%, respectively. The overall results of this study show that the concrete reinforced with 3D-printed trusses was better than that with a hyperboloid shell structure.

Technologies of regenerative medicine as a method of treatment of male infertility: literature review

The problem of infertility has recently become quite widespread all over the world, having a negative impact on couples themselves and society as a whole. The male factor of infertility accounts for about 50% of cases, in this regard, a large number of studies conducted are aimed at finding new methods of treating this disease. However, the accepted modern methods of treatment, including assisted reproductive technologies, are currently not effective enough. Recently, much attention has been devoted to the use of regenerative medicine technologies in the treatment of diseases of the reproductive system of men and women due to its significant potential. This review presents existing experimental and clinical studies that studied the effect, safety and efficacy of stem cells of various kinds of origin, induced pluripotent stem cells, exosomes, spermatogonial stem cells, platelet rich plasma in the treatment of various types of male infertility.

P-290 Isolation and characterization of extracellular vesicles from human endometrial organoids

Abstract Study question Do human endometrial organoids produce extracellular vesicles (EVs)? Summary answer Endometrial organoids release EVs (ORG-EVs) that we successfully and comprehensively characterize according to guidelines. What is known already EVs play a crucial role in cellular communication, garnering increasing attention in physiological and pathological processes. Although the enormous potential of EVs has been extensively demonstrated across a large variety of research fields, the promise held by EVs derived from human endometrial organoids is still largely unexplored. Indeed, nowadays there is limited research available on the presence, role, and function of EVs derived from organoids of female reproductive tissue samples. This underscores the importance of adopting standardized methods for isolating, characterizing, and reporting findings, which is crucial for advancing in this promising and innovative, yet challenging, research field. Study design, size, duration This study involved establishing organoid cultures from endometrial biopsies of healthy women (n = 10) of reproductive age, who underwent ovarian surgery between January and October 2023. ORG-EVs were isolated using differential ultracentrifugation. Characterization of ORG-EVs, including assessment of their biophysical and biochemical properties, adhered to guidelines established by the International Society for Extracellular Vesicles (ISEV). Participants/materials, setting, methods Endometrial specimens from 10 women were employed to establish organoid cultures. ORG-EVs were isolated from spent medium through differential ultracentrifugation. Morphology, size distribution and concentration analysis were performed using Transmission Electron Microscopy (TEM) and Nanoparticle Tracking Analysis (NTA). Western blotting was used to confirm the presence of EVs markers. Main results and the role of chance TEM analysis confirmed the presence of isolated ORG-EVs, elucidating their morphology and structural integrity. NTA revealed that ORG-EVs had an average size of 160.4 ± 6.2 nm (mean ± SD) and a mean concentration of 5.2E+11 ± 2.1E+10 particles/ml (mean ± SD). Western blot analysis further confirmed the vesicular nature of the assessed particles, demonstrating positivity for standard EV markers such as tetraspanins (CD63 and CD9), and soluble proteins (ALIX and TSG101), while excluding the endoplasmic reticulum-resident protein calnexin (negative control). Additionally, ORG-EVs exhibited a positive signal for mucin1 (MUC1), a glycoprotein present on the surface of endometrial epithelial cells, thereby confirming their cellular origin. Limitations, reasons for caution This is a pilot study, characterized by a small sample size requiring larger cohorts to validate results and, at the same time, to undertake deeper exploration of ORG-EVs’ molecular cargo. Emphasizing the critical need for standardized isolation and characterization methods is paramount. Wider implications of the findings Endometrial organoids enhance EVs production, providing a powerful tool for reproductive aspects studies, and improving regenerative medicine and artificial reproductive technology. These initial findings propel further exploration, unveiling of ORG-EVs’ molecular cargo aiming to elucidate their potential role in human female reproductive tissues and mechanism related to embryo implantation process. Trial registration number not applicable

Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.

Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD. This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP). The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model. This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.

Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for Differentiating Thoracic Pathologies.

Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs. Methodology This study implemented convolution neural networks to develop a deep learning model capable of differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19. There were 3,063 normal chest X-rays, 3,098 pneumonia chest X-rays, 2,920 COVID-19 chest X-rays, 2,214 chest X-rays, and 554 tuberculosis chest X-rays from Kaggle that were used for training and validation.The model was trained to recognize patterns within the chest X-rays to efficiently recognize these diseases within patients to be treated on time. Results The results indicated a success rate of 98.34% incorrect detections, exemplifying a high degree of accuracy. There are limitations to this study. Training models require hundreds to thousands of samples, and due to potential variability in image scanning equipment and techniques from which the images are sourced, the model could have learned to interpret external noise and unintended details which can adversely impact accuracy. Conclusions Further studies that implement more universal database-sourced images with similar image scanning techniques, assess diverse but related medical conditions, and the utilization of repeat trials can help assess the reliability of the model.These results highlight the potential of machine learning algorithms for disease detection with chest X-rays.

Экспертная оценка индикаторов и показателей для разработки модели цифровой зрелости первичной медико-санитарной помощи.

The research is devoted to the development of a model of digital maturity of primary health care in the Russian Federation. Through expert evaluation, key indicators and indicators of digital maturity were identified, which made it possible to create a comprehensive model for assessing the digital development of medical organizations. The main attention is paid to the functional capabilities of medical information systems and information and communication technologies that ensure high quality and accessibility of medical care. Aim: to conduct an expert assessment of indicators and indicators for the development of a model of digital maturity of primary health care. This includes an analysis of existing indicators, identification of key indicators, their classification and assessment of significance for the formation of an objective model for assessing the digital maturity of medical organizations. Materials and methods. For a systematic assessment of the digital maturity of primary health care, a questionnaire was developed containing 6 indicators and 52 indicators reflecting various aspects of the digital maturity of the subjects of the Russian Federation. Regulatory documents, methodological guidelines and recommendations from experts in the field of medicine and information technology were studied. Experts assessed the importance of each indicator on a scale from 0 to 10 points, and the results of the assessments were classified to form the final model. Results. The expert assessment showed that the functionality of the medical information system is evaluated higher than the equipment of information and communication technologies. Key indicators, such as access to research results and telemedicine consultations, received high marks, indicating their importance for the digital maturity of medical organizations. The recommendations include updating the digital infrastructure, developing the functionality of the medical information system and improving the level of training of medical personnel. Conclusions: the study showed that in order to achieve a high level of digital maturity of PHC, it is necessary to pay attention to both the technical equipment and the development of the IIA functionality. An integrated approach, including updating the infrastructure and improving the digital literacy of medical personnel, will significantly improve the quality and accessibility of medical care. The proposed recommendations can serve as a basis for further planning of measures to increase the digital maturity of primary health care in the Russian Federation.

International Legal Approaches to the Issue of Admissibility of the Use of Genomic Technologies in Medicine

The purpose of the paper is to determine the main international standards and positions of international intergovernmental and non-governmental organizations regarding the use of genomic technologies in medicine, which are reflected in their documents. At the universal level, there is no international treaty containing uniform rules in this area, but there are many documents of international organizations (such as WHO, UNESCO) that set out the relevant standards. At the regional level, the experience of the Council of Europe in the field of legal regulation of genomic medicine plays a significant role. National approaches to the legal regulation of genomic medicine vary. In such conditions, the practice of international non-governmental organizations in the field of biomedicine and genetic technologies plays a significant role. They set standards and represent the positions of the biomedical community.Based on the analysis of international acts, it can be concluded that the general position of the international biomedical community is that genome editing technologies can be widely used for therapeutic and scientific purposes, provided that there is no criterion for transmitting genes to descendants. Genome editing should be carried out in accordance with international professional and ethical standards, including the principles of benefit over harm and informed consent. Discussions about the admissibility of hereditary genome editing continue. The prevailing approach of international organizations is that there are currently no reasonable standards for the safety and effectiveness of this technology, while research itself is encouraged.

MODELING THE IMPACT OF LASER IRRADIATION ON TEMPERATURE CHANGES IN BIOLOGICAL TISSUES

Modeling the impact of laser irradiation (MILI) on biological tissues plays a crucial role in research and the development of laser technologies in biology and medicine. MILI opens a wide spectrum of knowledge for scientists and medical professionals, providing insights into how laser radiation interacts with different types of tissues and organs and determining optimal parameters to achieve the desired outcome. Models can account for various physical processes occurring during irradiation, such as energy absorption, heat propagation, changes in tissue structure, and functions. This enables the prediction of tissue responses to different laser exposure regimes, including intensity, wavelength, and exposure time. In medicine, modeling is used to determine optimal laser exposure parameters for specific diseases or procedures. For example, MILI helps identify the optimal radiation dose during laser therapy or surgical interventions. Additionally, modeling allows for the assessment of potential risks associated with laser radiation, such as tissue overheating or side effects. This, in turn, enhances the development of safer and more effective laser application methods. Moreover, optimizing laser irradiation parameters is relevant for minimizing the risk of damage to surrounding tissues. MILI can be used to develop new laser technologies and devices aimed at improving the outcomes of laser applications in medicine. It also facilitates the faster and more efficient implementation of new methods and technologies into clinical practice. Thus, modeling the impact of laser irradiation on temperature changes in biological tissues plays a vital role in enhancing understanding of processes in biological tissues and contributes to the development of new methods and technologies, as well as the optimization of laser application for achieving effective treatment outcomes and patient safety.

Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease

BackgroundThe high prevalence, morbidity and mortality, and disease heterogeneity of chronic obstructive pulmonary disease (COPD) result in the scattered data derived from patient visits in different medical units. The huge cost of integrating the scattered data for analysis and modeling, as well as the legal demand for patient privacy protection lead to the emergence of data island. ObjectivesOn the premise of protecting patient privacy, integrating scattered data of patients from different medical units for high-quality modeling is beneficial to promoting the development of digital health. Based on this, we develop a distributed COPD disease diagnosis system termed COPD average federated learning (COPD_AVG_FL) using FedAvg. MethodsFirst, to build the COPD_AVG_FL, the clinical data of COPD patients from the real world is collected and the data pre-processing is performed to clean the incorrect data, outlier samples and missing values. Then, a classical federated learning architecture is designed as COPD_AVG_FL. Finally, to evaluate the established COPD_AVG_FL system, we develop Centralized Machine Learning (CML). ConclusionsOur results suggest that, with the assistance of COPD_AVG_FL, the absolute improvement rates are 13.4% (accuracy), 13.3% (precision), 12.8% (recall), 13.1% (F1-Score) and 12.9% (AUC) on the test data, respectively. The decoupling between model training and raw training data protects the patients' privacy, and helps to securely integrate more COPD data from different medical units to generate a more comprehensive model COPD_AVG_FL. This approach promotes the landing of wise information technology of medicine for COPD in the real clinical world. Code for our model will be made available at https://github.com/Cczhh/COPD_AVG_FL/tree/master.

Tuning Tumor Targeting and Ratiometric Photoacoustic Imaging by Fine-Tuning Torsion Angle for Colorectal Liver Metastasis Diagnosis.

Photoacoustic (PA) tomography is an emerging biomedical imaging technology for precision cancer medicine. Conventional small-molecule PA probes usually exhibit a single PA signal and poor tumor targeting that lack the imaging reliability. Here, we introduce a series of cyanine/hemicyanine interconversion dyes (denoted Cy-HCy) for PA/fluorescent dual-mode probe development that features optimized ratiometric PA imaging and tunable tumor-targeting ability for precise diagnosis and resection of colorectal cancer (CRC). Importantly, Cy-HCy can be presented in cyanine (inherent tumor targeting and long NIR PA wavelength) and hemicyanine (poor tumor targeting and short NIR PA wavelength) by fine-tuning torsion angle and the ingenious transformation between cyanine and hemicyanine through regulation optically tunable group endows the NIR ratiometric PA and tunable tumor-targeting properties. To demonstrate the applicability of Cy-HCy dyes, we designed the first small-molecule tumor-targeting and NIR ratiometric PA probe Cy-HCy-H2S for precise CRC liver metastasis diagnosis, activated by H2S (a CRC biomarker). Using this probe, we not only visualized the subcutaneous tumor and liver metastatic cancers in CRC mouse models but also realized PA and fluorescence image-guided tumor excision. We expect that Cy-HCy will be generalized for creating a wide variety of inherently tumor-targeting NIR ratiometric PA probes in oncological research and practice.

From Nature to Technology: Exploring the Potential of Plant-Based Materials and Modified Plants in Biomimetics, Bionics, and Green Innovations.

This review explores the extensive applications of plants in areas of biomimetics and bioinspiration, highlighting their role in developing sustainable solutions across various fields such as medicine, materials science, and environmental technology. Plants not only serve essential ecological functions but also provide a rich source of inspiration for innovations in green nanotechnology, biomedicine, and architecture. In the past decade, the focus has shifted towards utilizing plant-based and vegetal waste materials in creating eco-friendly and cost-effective materials with remarkable properties. These materials are employed in making advancements in drug delivery, environmental remediation, and the production of renewable energy. Specifically, the review discusses the use of (nano)bionic plants capable of detecting explosives and environmental contaminants, underscoring their potential in improving quality of life and even in lifesaving applications. The work also refers to the architectural inspirations drawn from the plant world to develop novel design concepts that are both functional and aesthetic. It elaborates on how engineered plants and vegetal waste have been transformed into value-added materials through innovative applications, especially highlighting their roles in wastewater treatment and as electronic components. Moreover, the integration of plants in the synthesis of biocompatible materials for medical applications such as tissue engineering scaffolds and artificial muscles demonstrates their versatility and capacity to replace more traditional synthetic materials, aligning with global sustainability goals. This paper provides a comprehensive overview of the current and potential uses of living plants in technological advancements, advocating for a deeper exploration of vegetal materials to address pressing environmental and technological challenges.

Undifferentiated carcinoma with osteoclast-like giant cells of the pancreas: a narrative review.

Undifferentiated carcinoma with osteoclast-like giant cells of the pancreas (UCOGCP) is a rare pancreatic tumor that accounts for less than 1% of all pancreatic malignancies. The characteristic pathological manifestation of UCOGCP is the presence of osteoclast-like giant cells (OGCs) distributed among pleomorphic undifferentiated tumor cells. UCOGCP can occur either alone or in association with other types of pancreatic tumors. At present, there is no unified consensus or guideline for the diagnosis and treatment of UCOGCP, and most of the literature are individual case reports. With the accumulation in the number of clinical cases and the development of precision medicine technology, the understanding of UCOGCP is also deepening. Researchers have begun to recognize that UCOGCP is a pancreatic tumor with distinctive clinical and molecular characteristics. In this review, we focus on the latest research status and future exploration directions in the diagnosis, treatment, and prognosis of UCOGCP.

3D-Printed Microdevices: From Design to Applications

3D printing represents an emerging technology in several fields, including engineering, medicine, and chemistry [...]