Advances In Medical Technology Research Articles

Optimizing ventilatory support in ARDS: A comprehensive guide for ICU nurses on patient care

The emergence of COVID-19 as a global pandemic has placed an unprecedented strain on healthcare systems worldwide, highlighting a critical shortage of trained healthcare professionals equipped to manage such a crisis. This deficit poses a significant challenge to the delivery of healthcare services, particularly in the context of intensive care units (ICUs) where the use of advanced medical technologies, such as ventilators, is paramount. For nurses with limited experience in ICU settings, navigating the complexities of invasive positive pressure ventilation (IPPV), a principal method of mechanical ventilation for critically ill patients can be daunting. This article aims to serve as a comprehensive guide for nurses less familiar with the intricacies of managing patients on IPPV, offering insights and strategies to enhance their competence and confidence in this critical aspect of patient care.

Seasonal Variation in Short-Term Ambient Air Pollutants and ST-Elevation Myocardial Infarction Admissions: An Innovative Exploration of Air Pollution’s Health Consequences

Seasonal Variation in Short-Term Ambient Air Pollutants and ST-Elevation Myocardial Infarction Admissions: An Innovative Exploration of Air Pollution’s Health Consequences

Advanced tumor electric fields therapy: A review of innovative research and development and prospect of application in glioblastoma.

Glioblastoma multiforme (GBM) is an aggressive malignant tumor with a high mortality rate and is the most prevalent primary intracranial tumor that remains incurable. The current standard treatment, which involves surgery along with concurrent radiotherapy and chemotherapy, only yields a survival time of 14-16 months. However, the introduction of tumor electric fields therapy (TEFT) has provided a glimmer of hope for patients with newly diagnosed and recurrent GBM, as it has been shown to extend the median survival time to 20 months. The combination of TEFT and other advanced therapies is a promising trend in the field of GBM, facilitated by advancements in medical technology. In this review, we provide a concise overview of the mechanism and efficacy of TEFT. In addition, we mainly discussed the innovation of TEFT and our proposed blueprint for TEFT implementation. Tumor electric fields therapy is an effective and highly promising treatment modality for GBM. The full therapeutic potential of TEFT can be exploited by combined with other innovative technologies and treatments.

Efficacy of a Single Day Extracorporeal Membrane Oxygenation Training Course for Critical Care Air Transport Team Eligible Personnel.

Extracorporeal membrane oxygenation (ECMO) is an advanced medical technology that is used to treat respiratory and heart failure. The U.S. military has used ECMO in the care of combat casualties during Operation Enduring Freedom and Operation Iraqi Freedom as well as in the treatment of patients during the recent Coronavirus Disease 2019 pandemic. However, few Military Health System personnel have training and experience in the use of ECMO therapy. To address this dearth of expertise, we developed and evaluated an accelerated ECMO course for military medical personnel. To compare the efficacy of an accelerated ECMO course for Military Health System critical care teams. Seventeen teams, each consisting of a physician and nurse, underwent a 5-h accelerated ECMO course. Similar to our previous live-tissue ECMO training program (phases I and II), each team watched prerecorded ECMO training lectures. Subjects then practiced priming the ECMO circuit, cannulating ECMO, initiating ECMO, and correcting common complications on an ECMO simulation model. An added component to this phase III project included transportation and telemedicine consultation availability. Training success was evaluated via knowledge and confidence assessments, and observation of each team attempting to initiate ECMO on a Yorkshire swine patient model, transport the patient model, and troubleshoot complications with the support of telemedicine consultation when desired. Seventeen teams successfully completed the course. All seventeen teams (100%) successfully placed the swine on veno-arterial ECMO. Of those, 15 teams successfully transitioned to veno-arterial-venous ECMO. The knowledge assessments of physicians and nurses increased by 12.2% from pretest (mean of 62.1%, SD 10.4%) to posttest (mean of 74.4%, SD 8.2%), P < .0001; their confidence assessments increased by 41.1% from pretest (mean of 20.1%, SD 11.8%) to posttest (mean of 61.2%, SD 18.6%). An abbreviated 1-day lecture and hands-on task-trainer-based ECMO course resulted in a high rate of successful skill demonstration and improvement of physicians' and nurses' knowledge assessments and confidence levels, similar to our previous live-tissue training program. When compared to our previous studies, the addition of telemedicine and patient transportation to this study did not affect the duration or performance of procedures.

A biomechanical analysis of novel endovascular implants for aortic valve replacement and ascending aortic repair

Advances in medical technology have enabled minimally invasive treatment of type A aortic dissection with accompanying aortic regurgitation. Implants include endovascular stent grafts (ESG) and heart valve substitute (HVS) modules. Traditional implants can be divided into two types based on the assembly relationship between ESG and HVS: separated z-shaped implants (SZ) and separated diamond-shaped implants (SD). This study proposes a novel linked diamond-shaped implant (LD). To evaluate the safety and effectiveness of this new implant, finite element simulation models were created to assess the risks of endoleak, migration, and vascular wall rupture under annulus displacement load. After the SZ, SD, and LD implants were grafted in virtual release method, all the implants can cover tear-entry located in the ascending aorta, but space distance (δ) which exposed to blood was 14.5, 13.1, and 7.4 mm, respectively; the maximum areas of contact gap was 76.5, 51.5 and 6.3 mm2; the maximum migration distance (ΔL1) were 1.27, 1.06, and 0.1 mm; the maximum stress on ascending aorta was 0.19, 0.24, and 0.51 MPa, which were lower than failure stress (0.9 MPa). This study showed that both SZ and SD implants had minimal effects on the ascending aorta; however, higher risks were associated with implant migration and proximal endoleak. In contrast, the LD implant can simplify the surgical procedure, has a lower risk of endoleak and migration, and limited stress stimulation of the aorta. This study validated the feasibility and effectiveness of this novel implant using the finite element method, indicating its potential as a secure and reliable treatment option.

Analysis of the Association between Protein Intake and Disability-Adjusted Life Year Rates for Alzheimer's Disease in Japanese Aged over 60.

With advancements in medical technology, the structure of disease is shifting from acute illnesses to chronic conditions, such as Alzheimer's disease (AD). Consequently, there is an escalating need for evaluations that discourse on the potential effects on healthy life years, as well as disease onset. We aimed to evaluate the associations with AD disability-adjusted life year (AD-DALY) rates and protein intake by sex and age group. For the analysis, we used representative values for males and females in their 60s and aged over 70, extracted from the public dataset of the Global Burden of Disease Study and the National Health and Nutrition Survey in Japan, covering the years 1990 to 2019. In order to evaluate the association between AD-DALY rates and protein intake, we analyzed correlations and stratified multiple regression models. Additionally, we simulated alterations in AD-DALY rates associated with changes in protein intake by utilizing stratified multiple regression models. AD-DALY rates and protein intake indicated significant negative correlations across all sex and age groups. In stratified multiple regression models, significant associations were found between higher protein intake and lower AD-DALY rates in females. In the simulation, when protein intake was increased to 1.5 g/kg/day, AD-DALY rates decreased by 5-9 percent compared with 2019. However, the association between intake of animal and plant protein and AD-DALY rates were found to vary based on sex and age group. The present study suggests the possibility to improve AD-DALY rates by increasing population average protein intake levels in a recommended range.

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

Challenges and Strategies for Medical Education in the New Era: An Analysis

Medical education, serving as the cornerstone for cultivating future healthcare talent, directly impacts the efficiency and quality of medical services. With the rapid advancement of medical technology and the growing demand for public health, medical education is facing unprecedented challenges and pressures. Optimizing resource allocation, updating educational content and methods, and strengthening soft skills training have become focal points for reform in medical education. This paper aims to explore key issues within medical education and propose effective improvement strategies, providing references and guidance for the development of medical education.

Poland’s Breast Self-Exams: Empowering Awareness

Introduction and purpose: Breast cancer poses a significant global health challenge, especially in Poland, where it is the most prevalent cancer among women. Despite advancements in medical technology, individual awareness and proactive health practices, such as Breast Self-Examination (BSE), are crucial for early detection. This study focuses on BSE practices in the Tarnów district of Poland, a small city facing unique healthcare challenges, to understand the prevalence, knowledge, and attitudes toward BSE. The aim of study was to comprehensively investigate BSE practices among women in the Tarnów district, considering socio-demographic factors.&#x0D; Material and Methods: Anonymous questionnaires assessed socio-demographic characteristics, attitudes toward BSE, frequency of BSE performance, knowledge on BSE, and sources of information. The survey involved 100 Polish women in gynecologist and general practitioner offices in Tarnów County. Results were analyzed in relation to age, education, and place of residence.&#x0D; Results: While 90% of women were familiar with BSE, 10% had never heard of it. While 29% performed BSE monthly, 18% never conducted the examination. Knowledge gaps were identified, particularly regarding optimal BSE practices. Information sources included the Internet (44%) and gynecologists (28%). Notably, 65% expressed a willingness to participate in BSE training.&#x0D; Conclusions: In conclusion, the study provides valuable insights into BSE practices among women in the Tarnów district, emphasizing the need for targeted interventions to encourage regular BSE. Knowledge gaps and the willingness of women to participate in training present opportunities for healthcare initiatives. The study also highlights the need for broader health education campaigns to dispel myths and enhance awareness, particularly regarding breast cancer affecting both genders. Limitations include a small sample size and geographic specificity to Tarnów district.

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing

BackgroundDespite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC.MethodsThe gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity.ResultsThe mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells.ConclusionADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

Unraveling the Enigma of Aortic Dissection: From Genetics to Innovative Therapies.

Aortic dissection (AD) presents a critical medical emergency characterized by a tear in the aortic wall, necessitating prompt recognition and management to mitigate catastrophic complications. Despite advancements in medical technology and therapeutic interventions, AD remains a formidable challenge, often resulting in severe morbidity and mortality. This narrative review provides a comprehensive overview of AD, encompassing its clinical presentation, diagnostic modalities, and management strategies, while also exploring emerging trends and innovations in its management. Genetic predispositions significantly influence AD pathogenesis, with over 30 contributory genes identified, emphasizing the importance of genetic screening and counseling. Classification systems such as Stanford and DeBakey, alongside their revised counterparts, aid in categorizing AD and guiding treatment decisions. Advancements in diagnostic imaging, including transesophageal echocardiography and computed tomography angiography, have enhanced diagnostic precision, augmented by artificial intelligence and machine learning algorithms. Pharmacological innovations focus on optimizing medical therapy, while surgical and endovascular approaches offer minimally invasive treatment options. Hybrid procedures and aortic valve-sparing techniques broaden treatment avenues, while bioresorbable stent grafts hold promise for tissue regeneration. Collaborative efforts and ongoing research are essential to address remaining challenges and improve outcomes in managing AD. This review contributes to the understanding of AD's complexity and facilitates informed decision-making in clinical practice, underscoring the imperative for continued innovation and research in AD management.

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images.

PRAGMALINGUISTIC STUDY OF INNOVATIVE PROCESSES IN MEDICAL TERMINOLOGY

This article examines the pragmatic and linguistic aspects of everyday processes in medical terminology, providing detailed information on pragma linguistics, which is considered a very relevant field in linguistics. Medical terminology is a highly specialized field that is constantly evolving to keep up with advances in medical science and technology. The study of innovative processes in medical terminology includes the analysis of how new terms are created, adopted, and integrated into the medical lexicon. This article examines the role of pragmatics in shaping the use and understanding of medical terminology, as well as the linguistic strategies used to create and disseminate new terms within the medical community.

Application of vibration analysis for medical diagnosis

The ability to interpret vibration signals in the biomedical field offers a promising path toward continuous improvement of medical devices. By examining the revolutions per minute profile, analysts can identify any deviations or anomalies in the vibration patterns at different speeds. This information can help identify potential faults or imbalances within the rotating machinery. With a comprehensive understanding of the revolutions per minute profile, analysts can make informed decisions regarding maintenance and repairs. Besides, the analysis of the order of vibration signals represents an essential pillar of biomedical engineering, bringing an innovative and in-depth perspective to the development of medical devices, and contributing to the continued advancement of medical technology and healthcare. Integrating vibration analysis into preventive maintenance practices can help ensure the reliability of medical equipment, reduce potential risks to patients, and contribute to the advancement of healthcare quality.

VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis

Eye problems can lead to vision loss and have a significant impact on daily life, underscoring the critical importance of early diagnosis and treatment to prevent further damage and complications. A comprehensive segmentation-classification framework – VisionDeep-AI is developed in the proposed work for retinal vessel segmentation and multi-class classification on given fundus images. A weighted bi-directional feature pyramid network and U-Net backbone architecture-based customized deep-learning model is built to segment blood vessels which enhances feature extraction and multi-scale feature fusion. The architectural design comprises an end-to-end encoder-decoder network featuring six-depth layers with varying resolutions, enabling the extraction of high-level descriptors and lower-level, fine-grained characteristics. Furthermore, a multi-modal deep feature fusion architecture is developed for the multi-class classification of fundus images in four different categories by integrating features from segmented vessel images and raw fundus images, thereby accommodating more diversified information. A thorough analysis of VisionDeep-AI has been done on a comprehensive colour fundus image dataset. To ensure reliable results, data augmentation was done to prevent the over-fitting of the model and to enhance its generalization capability. The segmentation model performed exceptionally well and achieved a high accuracy of 97.73% and a dice coefficient of 89.90% in blood vessel segmentation from fundus images. Furthermore, multi-modal deep feature-fused classification architecture achieved a test accuracy of 81.50%, with a specificity of 93.83%. These findings showcase the robustness, generalizability and efficiency of the proposed VisionDeep-AI framework for fundus image segmentation and retinal disease classification, contributing towards advanced medical technologies and diagnosis precision.

Decentralizing Health Care: History and Opportunities of Web3.

This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey

BackgroundThe aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences.MethodsA cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist’s and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents’ electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions.ResultsAltogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen.ConclusionsIndividuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Development of a 3D Vascular Network Visualization Platform for One-Dimensional Hemodynamic Simulation.

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D-0D simulations. This platform covers the entire workflow, from modeling to dynamic 3D visualization of simulation results. Two case studies on, respectively, carotid stenosis and arterial remodeling demonstrate its utility in blood flow simulation applications.

Advantages of Nail Surgical Procedures Using a 3D Biomodel

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; 3D biomodels represent a cutting-edge advancement in medical imaging technology. The incorporation of 3D technologies in dermatology through the acquisition of onychological images, 3D reconstruction, and development of customized equipment to assist in surgeries demonstrated reduction in operating times and improved surgical outcomes. Additionally, the use of 3D printing in surgical simulation provided a safe environment for training and education. This article explores the application of 3D biomodels in dermatology, focusing on three clinical cases involving nail tumors. &lt;b&gt;&lt;i&gt;Case Presentation:&lt;/i&gt;&lt;/b&gt; In case 1, a glomus tumor was visualized in 3D, guiding the creation of a personalized surgical device. The minimally invasive surgery, facilitated by the biomodel, resulted in successful tumor removal. Case 2, featuring a subungual keratoacanthoma, utilized 3D biomodels for conservative surgery planning, anatomical comprehension, and patient communication. Case 3 involved a longitudinal groove, where biomodels aided in precise lesion localization and surgical planning. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; The integration of virtual and physical anatomical biomodels proves valuable in surgical dermatology, contributing to enhanced treatment quality, patient safety, and medical education.

Analysis of the Significance of Key Points of Patient Interventional Psychological Care and Pain Care

Interventional therapy has become increasingly popular in clinical practice due to advancements in medical technology. However, patients often experience psychological and physiological pressure due to its invasive nature. The management of patient discomfort and tension is crucial to ensure effective treatment. Psychological and pain management are essential components of interventional therapy, as they significantly impact patient recovery and prognosis. This article discussed the importance of interventional psychological and pain care for patients, starting with the development and spread of interventional therapy. The significance of providing high-quality nursing services to patients and improving their quality of life was also discussed.