Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Gravitational Influence on Human Living Systems and the Evolution of Species on Earth.

Gravity constituted the only constant environmental parameter, during the evolutionary period of living matter on Earth. However, whether gravity has affected the evolution of species, and its impact is still ongoing. The topic has not been investigated in depth, as this would require frequent and long-term experimentations in space or an environment of altered gravity. In addition, each organism should be studied throughout numerous generations to determine the profound biological changes in evolution. Here, we review the significant abnormalities presented in the cardiovascular, immune, vestibular and musculoskeletal systems, due to altered gravity conditions. We also review the impact that gravity played in the anatomy of snakes and amphibians, during their evolution. Overall, it appears that gravity does not only curve the space-time continuum but the biological continuum, as well.

Personalised management of women with cervical abnormalities using a clinical decision support scoring system.

OBJECTIVES: To develop a clinical decision support scoring system (DSSS) based on artificial neural networks (ANN) for personalised management of women with cervical abnormalities. METHODS: We recruited women with cervical abnormalities and healthy controls that attended for opportunistic screening between 2006 and 2014 in 3 University Hospitals. We prospectively collected detailed patient characteristics, the colposcopic impression and performed a series of biomarkers using a liquid-based cytology sample. These included HPV DNA typing, E6&E7 mRNA by NASBA or flow cytometry and p16INK4a immunostaining. We used ANNs to combine the cytology and biomarker results and develop a clinical DSSS with the aim to improve the diagnostic accuracy of tests and quantify the individual's risk for different histological diagnoses. We used histology as the gold standard. RESULTS: We analysed data from 2267 women that had complete or partial dataset of clinical and molecular data during their initial or followup visits (N=3565). Accuracy parameters (sensitivity, specificity, positive and negative predictive values) were assessed for the cytological result and/or HPV status and for the DSSS. The ANN predicted with higher accuracy the chances of high-grade (CIN2+), low grade (HPV/CIN1) and normal histology than cytology with or without HPV test. The sensitivity for prediction of CIN2 or worse was 93.0%, specificity 99.2% with high positive (93.3%) and negative (99.2%) predictive values. CONCLUSIONS: The DSSS based on an ANN of multilayer perceptron (MLP) type, can predict with the highest accuracy the histological diagnosis in women with abnormalities at cytology when compared with the use of tests alone. A user-friendly software based on this technology could be used to guide clinician decision making towards a more personalised care.

Management and modeling of balance disorders using decision support systems: the EMBALANCE project.

In this work, we present the concept, the methodological ideas and the architecture of the EMBALANCE platform. EMBALANCE platform extends existing but generic and currently uncoupled balance modeling activities, leading to a multi-scale and patient-specific balance Hypermodel, which is incorporated to a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. Various data feed the intelligent system increasing the dimensionality and personalization of the system. Human Computer Interaction techniques are utilized in order to develop the required interfaces in a user-intuitive and efficient way, while interoperable web services enhance the accessibility and acceptance of the system. The platform will be validated using both retrospective as well as prospective experimental and clinical data. The final tool will be a powerful web-based platform provided to primary and secondary care physicians across specialties, levels of training and geographical boundaries, targeting wider clinical acceptance as well as the increased confidence in the developed DSS towards the early diagnostic evaluation, behaviour prediction and effective management planning of balance problems. Currently we focus and present the management and modeling of the balance disorders.

Miniature surgical robots in the era of NOTES and LESS: dream or reality?

Laparoscopy is an established method for the treatment of numerous surgical conditions. Natural orifice transluminal endoscopic surgery (NOTES) is a novel surgical technique that uses the natural orifices of the human body as entrances to the abdominal cavity. An alternative concept of minimally invasive approach to the abdominal cavity is to insert all the laparoscopic instruments through ports using a single small incision on the abdominal wall. A suggested name for this technique is laparoendoscopic single-site surgery (LESS). Considering the technical difficulties in NOTES and LESS and the progress in informatics and robotics, the use of robots seems ideal. The aim of this study is to investigate if there is at present, a realistic possibility of using miniature robots in NOTES or LESS in daily clinical practice. An up-to-date review on in vivo surgical miniature robots is made. A Web-based research of the English literature up to March 2013 using PubMed, Scopus, and Google Scholar as search engines was performed. The development of in vivo miniature robots for use in NOTES or LESS is a reality with great advancements, potential advantages, and possible application in minimally invasive surgery in the future. However, true totally NOTES or LESS procedures on humans using miniature robots either solely or as assistance, remain a dream at present.

A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports.

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data.

Tinnitus is a highly prevalent condition, affecting more than 1 in 7 adults in the EU and causing negative effects on sufferers' quality of life. In this study, we utilised data collected within the "UNITI" project, the largest EU tinnitus-related research programme. Initially, we extracted characteristics from both auditory brainstem response (ABR) and auditory middle latency response (AMLR) signals, which were derived from tinnitus patients. We then combined these features with the patients' clinical data, and integrated them to build machine learning models for the classification of individuals and their ears according to their level of tinnitus-related distress. Several models were developed and tested on different datasets to determine the most relevant features and achieve high performances. Specifically, seven widely used classifiers were utilised on all generated datasets: random forest (RF), linear, radial, and polynomial support vector machines (SVM), naive bayes (NB), neural networks (NN), and linear discriminant analysis (LDA). Results showed that features extracted from the wavelet-scattering transformed AMLR signals were the most informative data. In combination with the 15 LASSO-selected clinical features, the SVM classifier achieved optimal performance with an AUC value, sensitivity, and specificity of 92.53%, 84.84%, and 83.04%, respectively, indicating high discrimination performance between the two groups.

An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient's age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.

Enhancing Healthcare Through Telehealth Ecosystems: Impacts and Prospects.

This poster presents a comprehensive assessment of the transformative potential of telehealth ecosystems, integrating Internet of Things (IoT), Internet of Medical Things (IoMT), and Artificial Intelligence (AI) technologies. The study explores their impact on healthcare delivery and markets, emphasising the need for robust cybersecurity measures and technological integration. By facilitating continuous monitoring, personalised interventions, and improved patient outcomes, the integration of advanced technologies in telehealth ecosystems has the potential to revolutionise healthcare delivery and reduce healthcare costs. However, successful implementation and maximisation of their benefits require collaborative research and adherence to ethical and regulatory standards.

Acceptance of Artificial Intelligence in Supporting Cancer Patients.

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients' reluctancy by better informing them on the potential benefits of AI.

Training ChatGPT Models in Assisting Urologists in Daily Practice.

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer.

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein-protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients' response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Design and Virtual Workflow of a Patient Database for Clinical and Pharmaceutical Trials Part II: A Prototype for Endocrinology.

Background In previous work, we have reported that patient recruitment is closely related to electronic health records (EHR). As a result, the next step of investigation would lead to the implementation of research practices for using EHR in selecting patients for clinical trials. Towards that end, open-source software offers several integrated solutions that can meet the needs of an EHR and patient recruitment.  Aim In the present work, we have designed a prototype of a patient recruitment system using open-source tools. The proposed prototype can draw data from a patient management system and present selected patients based on specific criteria.  Methods For the objective of the present study, we have used the methodology described previously. In particular, we recorded numerous integrated solutions for EHR from the area of free and open-source software. Open Electronic Medical Records (OpenEMR) ranked first for functionality and second for usability efficiency. Hence, we relied on OpenEMR to design a prototype patient recruitment system. After the installation and commissioning of OpenEMR, we created appropriate test scenarios. Therefore, populated appropriate patient data in OpenEMR. PhpMyAdmin was installed and commissioned along with the OpenEMR installation. This tool is used to manage MySQL database systems. MySQL is the database system that programmers rely on to develop OpenEMR.  Results A prototype patient recruitment system was designed, which draws data from a view of the OpenEMR database to present results based on criteria.  Conclusions After the adaptation of the database and the design of the proposed solution, we concluded, based on the prototype results, that there is potential for developing an integrated patient recruitment management system. This management system can be based on the implementation of complex criteria and present results according to the needs of the end-user.

Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project.

Introduction: Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives: This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods: The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results: The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion: The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

A Modelling Framework for Evidence-Based Public Health Policy Making.

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Predicting the optimal therapeutic intervention for tinnitus patients using random forest regression: A preliminary study of UNITI's decision support system model.

Tinnitus is the conscious perception of a phantom sound in absence of an external or internal stimulus. More than 1 in 7 adults in the EU experience tinnitus and for a large proportion of them tinnitus is an intrusive, persistent, and disabling condition, which impairs their life quality. Therefore, tinnitus is posed as a major global burden, which requires a precision-medicine approach in terms of treatments that are tailored to individual patients, due to its high heterogeneity. UNITI is a research and innovation project which aims towards this goal, unifying treatments and interventions for tinnitus. In the context UNITI, a randomized controlled trial (RCT) is being conducted and all the participants' data will be utilized for the development of a clinical decision support system (CDSS). This CDSS will predict the optimal therapeutic intervention for a tinnitus patient based on their profile. In this paper, we present a preliminary study of the CDSS model development process. We describe the available input data, the pre-processing steps conducted, the algorithms tested to model the CDSS' prediction, the models' results, and the future work in the context of this project. The R2 score of the selected model is currently 0.65, indicating that its development process is in the right direction but further tuning and hyperparameter optimization is needed. Clinical Relevance- The proposed model will be integrated in a CDSS aiming at indicating the optimal treatment strategy for a tinnitus patient based their personal profile.

Electrophysiological differences in distinct hearing threshold level individuals with and without tinnitus distress.

Tinnitus is the perception of sound when no actual external noise is present. Tinnitus is highly prevalent, with more than 1 in 7 adults in the EU having tinnitus, and it causes negative effects on quality of life for many individuals. However, there is currently no cure for tinnitus and its pathophysiology and genesis are unknown. Auditory evoked potentials (AEPs) provide a non-invasive means by which the electrical signals evoked by the brain can be recorded, and constitute a useful indicator for the evaluation of auditory disorders such as tinnitus and hearing loss. The present study analyzed a total of 98 auditory middle evoked potential (AMLR) waveforms, a subtype of AEPs, from 49 participants with subjective tinnitus, attempting to identify differences in AMLR parameters between sufferers with and without tinnitus distress. The waveforms were divided into three categories according to the ear's hearing level, and comparisons were made between sufferers in the same hearing level category. The results of the analysis indicated some statistically significant differences in AMLR latencies and amplitudes between the compared groups. Clinical Relevance- Identification of the electro-physiological profile of subjective tinnitus sufferers based on the distress manifested by tinnitus using AMLRs.

A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation.

Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools' detection and annotation results, regarding the waves of interest, were then compared to the clinicians' manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals.

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.