Microbiology reference laboratories perform a crucial role within public health systems. This role was especially evident during the COVID-19 pandemic. In this Viewpoint, we emphasise the importance of microbiology reference laboratories and highlight the types of digital data and expertise they provide, which benefit national and international public health. We also highlight the value of surveillance initiatives among collaborative international partners, who work together to share, analyse, and interpret data, and then disseminate their findings in a timely manner. Microbiology reference laboratories have substantial impact at regional, national, and international levels, and sustained support for these laboratories is essential for public health in both pandemic and non-pandemic times.
- MeSH
- COVID-19 * epidemiology MeSH
- Communicable Diseases MeSH
- Laboratories * economics MeSH
- Humans MeSH
- Microbiology MeSH
- Pandemics MeSH
- SARS-CoV-2 MeSH
- Public Health MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Clinical trials in oncology are important tools to identify and establish new effective drugs for cancer treatment. Since the development of the concept of precision oncology, a huge number of multi-centric biomarker-driven clinical trials have been performed and promoted by either academic institutions or pharmaceutical companies. In this scenario, the role of pathologists is essential in multiple aspects, with new challenges that should be addressed. In this position paper of the European Society of Pathology, the role of pathologists as contributors to the design of the clinical trial, as local collaborators, or as members of central review laboratories is discussed. Moreover, the paper emphasizes the important role of pathologists in guiding methods and criteria of tissue biomarker testing in the biomarker-driven clinical trials. The paper also addresses issues regarding quality control, training, and the possible role of digital pathology.
- MeSH
- Clinical Trials as Topic * MeSH
- Pathology, Clinical standards methods MeSH
- Humans MeSH
- Biomarkers, Tumor * analysis MeSH
- Neoplasms * pathology drug therapy MeSH
- Pathologists * MeSH
- Societies, Medical MeSH
- Research Design standards MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Geographicals
- Europe MeSH
BACKGROUND: Although neuromelanin-sensitive magnetic resonance imaging (NM-MRI) has been used to evaluate early neurodegeneration in Parkinson's disease, studies concentrating on the locus coeruleus (LC) in pre-dementia stages of dementia with Lewy bodies (DLB) are lacking. OBJECTIVES: The aims were to evaluate NM-MRI signal changes in the LC in patients with mild cognitive impairment with Lewy bodies (MCI-LB) compared to healthy controls (HC) and to identify the cognitive correlates of the changes. We also aimed to test the hypothesis of a caudal-rostral α-synuclein pathology spread using NM-MRI of the different LC subparts. METHODS: A total of 38 MCI-LB patients and 59 HCs underwent clinical and cognitive testing and NM-MRI of the LC. We calculated the contrast ratio of NM-MRI signal (LC-CR) in the whole LC as well as in its caudal, middle, and rostral MRI slices, and we compared the LC-CR values between the MCI-LB and HC groups. Linear regression analyses were performed to assess the relationship between the LC-CR and cognitive outcomes. RESULTS: The MCI-LB group exhibited a significant reduction in the right LC-CR compared to HCs (P = 0.021). The right LC-CR decrease was associated with impaired visuospatial memory in the MCI-LB group. Only the caudal part of the LC exhibited significant LC-CR decreases in MCI-LB patients compared to HCs on both sides (P < 0.0001). CONCLUSIONS: This is the first study that focuses on LC-CRs in MCI-LB patients and analyzes the LC subparts, offering new insights into the LC integrity alterations in the initial stages of DLB and their clinical correlates. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
- MeSH
- alpha-Synuclein metabolism MeSH
- Lewy Body Disease * diagnostic imaging pathology MeSH
- Cognitive Dysfunction * diagnostic imaging pathology physiopathology etiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Locus Coeruleus * diagnostic imaging pathology MeSH
- Magnetic Resonance Imaging * MeSH
- Neuropsychological Tests MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
PURPOSE OF REVIEW: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors. RECENT FINDINGS: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services. SUMMARY: In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.
- Publication type
- Journal Article MeSH
The COVID-19 Pandemic contributed to accelerating the process of using information and communication technologies and digital technologies in healthcare management and delivery within healthcare systems. At that time, the Czech healthcare system faced the same problems as other European systems and struggled with a temporary limitation of direct provision of healthcare services. It was solved by switching to telemedicine. The Czech healthcare system used telemedicine to a minimal extent until then. Despite adopting the law on healthcare digitisation, it is still one of the countries with a lower level of digitisation of healthcare processes. The article presents the results of an exploratory expert investigation focused on the implementation and development of telemedicine in the Czech Republic. The conducted research aimed to identify problems related to the implementation of telemedicine in practice, place them in the broader framework of the healthcare system and structure them, propose possible solutions, and identify the future challenges of telemedicine in the Czech Republic. We based our study on the results of a three-phase QUAL-QUAN-QUAL research. Data collection in the first phase took the form of individual semi-structured interviews with patients (25) with practical experience in the field of telemedicine, followed by the second quantitative phase of the questionnaire survey with patients (650). The third qualitative phase included semi-structured interviews with experts (17) with practical experience in telemedicine. The introduction and expansion of telemedicine require several fundamental changes. These include adjustments to the legislative environment and changes to the technological infrastructure, organisation of care and work. Several barriers have been identified at the healthcare system level, healthcare providers, healthcare professionals and patients.
- MeSH
- COVID-19 * epidemiology MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Pandemics * MeSH
- Delivery of Health Care organization & administration MeSH
- Surveys and Questionnaires MeSH
- Interviews as Topic MeSH
- SARS-CoV-2 MeSH
- Aged MeSH
- Telemedicine * organization & administration MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.
- MeSH
- Laboratories, Clinical MeSH
- Humans MeSH
- Surveys and Questionnaires MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
PURPOSE: The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. METHODS: Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. RESULTS: The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative T2*$$ {\mathrm{T}}_2^{\ast } $$ relaxometry analyses. CONCLUSION: Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.
- MeSH
- Algorithms * MeSH
- Artifacts * MeSH
- Phantoms, Imaging * MeSH
- Calibration MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Brain diagnostic imaging MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Reproducibility of Results MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
A novel Gram-stain-negative, strictly aerobic, rod-shaped, light-yellow-pigmented, and chemo-organoheterotrophic bacterium, designated DF-77T, was isolated from dense mats of filamentous algae collected in March 2004 at Okinawa in Japan. The microorganism grew at 0-2.0% NaCl concentrations (w/v), pH 6.0-9.0, and 20-30 °C. The 16S rRNA gene sequence-based phylogenetic tree demonstrated that the strain DF-77T is a novel member of the family Flavobacteriaceae and was greatly related to Flagellimonas nanhaiensis SM1704T with sequence similarity of 95.5%. The main fatty acids were iso-C15:1 G, iso-C15:0, and iso-C17:0 3-OH, and the only isoprenoid quinone was menaquinone-6. The dominant polar lipids were phosphatidylethanolamine, two unidentified aminolipids, an unidentified phosphoaminolipid, and four unidentified lipids. The genome size of strain DF-77T was 3.60 Mbp with a DNA G + C content of 47.5%. The average nucleotide identity (ANI) value between the genomes of strain DF-77T and its closely related species was 69.8-70.7%. The digital DNA - DNA hybridization (dDDH) value of strain DF-77T with the strain of F. nanhaiensis SM1704T was 16.8%. The genome of the strain DF-77T revealed that it encoded several genes involved in bio-macromolecule degradation, indicating a high potential for producing industrially useful enzymes. Consequently, the strain is described as a new species in the genus Flagellimonas, for which the name Flagellimonas algarum sp. nov., is proposed with the type strain DF-77T (= KCTC 72791T = NBRC 114251T).
- MeSH
- DNA, Bacterial genetics chemistry MeSH
- Flavobacteriaceae * classification isolation & purification genetics MeSH
- Phospholipids analysis MeSH
- Phylogeny MeSH
- Genome, Bacterial MeSH
- Nucleic Acid Hybridization MeSH
- Fatty Acids analysis MeSH
- RNA, Ribosomal, 16S genetics MeSH
- Sequence Analysis, DNA MeSH
- Bacterial Typing Techniques MeSH
- Vitamin K 2 analysis analogs & derivatives MeSH
- Base Composition MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Japan MeSH
PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value <0.05$$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.
- MeSH
- Algorithms * MeSH
- Aorta * diagnostic imaging MeSH
- Artifacts * MeSH
- Phantoms, Imaging MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Intracranial Aneurysm diagnostic imaging MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Brain diagnostic imaging MeSH
- Computer Simulation MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Signal-To-Noise Ratio * MeSH
- Reproducibility of Results MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
This conceptual study introduces the "virtual waiting room," an innovative, interactive, web-based platform designed to enhance the waiting experience in oncology by providing personalized, educational, and supportive content. Central to our study is the implementation of the circular entry model, which allows for non-linear navigation of health information, empowering patients to access content based on their immediate needs and interests. This approach respects the individual journeys of patients, acknowledging the diverse pathways through which they seek understanding and manage their health. The virtual waiting room is designed not only to support patients but also to facilitate stronger communication and shared understanding between patients, caregivers, and families. By providing a shared digital space, the platform enables caregivers and family members to access the same information and resources, thereby promoting transparency and collective knowledge. This shared access is crucial in managing the emotional complexities of oncology care, where effective communication can significantly impact treatment outcomes and patient well-being. Furthermore, the study explores how the circular entry model within the virtual waiting room can enhance patient autonomy and engagement by offering customized interactions based on user feedback and preferences. This personalized approach aims to reduce anxiety, improve health literacy, and prepare patients more effectively for clinical interactions. By transforming passive waiting into active engagement, the virtual waiting room turns waiting time into a meaningful, informative period that supports both the psychological and informational needs of patients and their support networks.
- MeSH
- Communication MeSH
- Medical Oncology * MeSH
- Humans MeSH
- Neoplasms * psychology therapy MeSH
- Narration MeSH
- Patient Education as Topic MeSH
- Health Literacy MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
