Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment alternatives. Comprehending their biological mechanisms requires the examination of gene expression data, provides insights into transcriptional activity. Gene co-expression networks (GCNs) are essential for elucidating functional gene linkages within datasets, depicting genes as nodes and their interactions as edges. Such methods are typically employed for the discovery of potential biomarkers. However, the computational performance for analysing huge genomic datasets requires High-Performance Computing (HPC) technologies, such as GPGPU and distributed computing models, to improve scalability and efficiency. This study uses HPC-based GCN techniques to uncover potential biomarkers associated with the aggressiveness of LMS and MPNST. Through the comparison of co-expression networks from malignant tumor tissues and their normal or benign counterparts, we identify genes exhibiting differential expression patterns that may facilitate sarcoma growth. As a result, six potential biomarkers were identified in the study. This holistic approach improves our comprehension of STS biology while providing novel tools for biomarker identification and prospective therapeutic targeting.

The present study analyses a clinical cohort of patients with stage III non-small cell lung cancer (NSCLC), with a particular emphasis on the evaluation of the combined treatment of chemoradiotherapy and Durvalumab maintenance. The clinical efficacy of the treatment was assessed by applying survival analysis techniques (Kaplan-Meier and Cox proportional hazards models) and comparing the results with those of the PACIFIC trial. Furthermore, clinical subgroups were explored using machine learning techniques. The findings demonstrate a substantial benefit of immunotherapy on both overall survival (OS) and progression-free survival (PFS). The median OS for patients receiving Durvalumab reached 37.3 months versus 22.97 months for those without, with a 5-year survival rate of 47.9%. Median PFS improved from 12.83 to 22.10 months, with 5-year PFS rates of 47.47% vs. 29.06%. Furthermore, Clustering analysis revealed distinct clinical profiles, with the subgroup matching the PACIFIC trial scheme achieving the best outcomes.

Scoping reviews represent a powerful methodological tool for synthesizing knowledge across broad and interdisciplinary domains, particularly where technological advancements intersect with medical practice. Unlike traditional systematic reviews that rely on narrowly defined research questions and stringent inclusion criteria, scoping reviews allow for a wider scope of inquiry and the inclusion of diverse evidence types, including grey literature, technical reports, and early-stage research. This flexibility is particularly advantageous when assessing the impact of emerging technologies on medical protocols and interventions, where the pace of innovation often outstrips the availability of standardized clinical data. This paper explores the methodological advantages of scoping reviews in the context of investigating technological implementations in healthcare. As a practical illustration, we present a case study on the potential of artificial intelligence (AI) technologies in the treatment of insomnia. We outline the process of developing a scoping review protocol tailored to this topic and discuss how it supports a comprehensive understanding of the field, enables identification of knowledge gaps, and informs future research directions. The findings underscore the value of scoping reviews in mapping complex, evolving research landscapes and in facilitating interdisciplinary insights that may be missed by more narrowly focused review methodologies.

Businesses looking to expand into international markets need high-quality market research to make strategic decisions and develop effective market entry strategies. However, conducting extensive secondary market research that contains all the necessary data is very time-consuming and resource-intensive. In recent years, Artificial Intelligence (AI) has been widely used for marketing applications. Conducting market research and generating market entry strategies are functions that AI, especially Large Language Models (LLMs), could support businesses with. In this paper we propose a theoretical model of how an LLM could be trained to ensure the quality of the output by conducting secondary market research and, based on it, to generate realistic market entry strategy for decision making about international expansion. One of the key elements of the proposed pipeline is an adjustable scoring system which ensures input data reliability and transparency and, as a result, helps to provide a reliable output which could be used for strategic decision-making. Compared to traditional market research approaches, the proposed methodology offers significant improvements in speed, resource efficiency, and accessibility and is aimed to support the businesses in their expansion into new international markets.

Driving safety is an important matter that, if violated, can have serious consequences such as injury or even death. Several studies have shown that fatigue, and the drowsiness that often results from it, has a significant impact on road safety. It is therefore essential to detect the symptoms of fatigue at an early stage so that appropriate countermeasures can be taken before a dangerous situation arises. Various systems can be used to detect fatigue, including those that monitor physiological signals and look for specific patterns to detect changes and trigger appropriate action if necessary. Mobile systems, which offer greater flexibility, are the focus of some of these efforts. In order to facilitate a methodological approach in the development of new systems, it is imperative to obtain a systematic overview within the initial phase. This enables the identification of both established and promising approaches, as well as the identification of actual gaps that necessitate further research. The aim of this article is to set out a protocol for the conduct of a systematic review in order to be able to subsequently carry it out. This includes researching and defining features such as eligibility criteria, selecting appropriate databases, search strategies and, most importantly, defining the specific research question. The result of this work is a clear and methodologically prepared summary of the most important points to be considered when conducting the systematic review on the subject of ”Mobile Physiological Monitoring Systems for Driver Fatigue Detection”.

Sleep is a crucial aspect of human well-being, with significant implications for overall health and quality of life. In response to the growing concern over sleep-related issues and the need for innovative solutions, this paper presents ‘Sleep Sheep’, an innovative system designed to monitor sleep and promote healthy sleep habits. The motivation behind Sleep Sheep stems from recognizing the vital role sleep plays in our daily lives. Inadequate sleep has been associated with various health problems, including cognitive impairments, mood disorders, and compromised immune function. Thus, addressing sleep-related concerns has become a pressing priority. To achieve its objectives, Sleep Sheep utilizes a smartphone application monitored by a doctor, to collect and analyze comprehensive sleep data, including sleep duration, sleep stages, and sleep disturbances. The collected data is then processed using several algorithms to provide results, which demonstrate its potential to impact sleep quality and overall well-being. By providing users with personalized sleep reports and actionable recommendations, Sleep Sheep makes individuals aware that they should adopt healthier sleep practices. These reports and recommendations, in turn, can lead to improved sleep duration, enhanced sleep efficiency, and a better overall sleep experience. By fostering awareness about the importance of sleep and providing individuals with the tools for being monitored by their doctors and improving their sleep, Sleep Sheep has the potential to make a substantial impact on public health. Ultimately, this innovative system aims to contribute to the well-being and quality of life of individuals by encouraging healthy sleep habits and optimizing sleep outcomes.

The classification of sleep and wake states is of paramount importance in the context of sleep disorders. In order to detect and monitor disorders such as obstructive sleep apnea (OSA), it is essential to obtain the total sleep time (TST) so as to assess the severity of the patient’s sleep apnea. With the advent of new technologies for detecting events associated with sleep disorders, it is not always straightforward to calculate the sleep/wakefulness state. Consequently, this work presents the development of a deep learning model (a variant of U-Net) for the detection of sleep/wakefulness states. For this purpose, an engineering approach using Keras Tuner and the use of three signals with minimal processing was employed. The three signals, oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (AbdRes), were selected to ensure both patient comfort during signal collection and the possibility of using portable monitors. The models were trained and tested on data from polysomnography studies, namely the Sleep Heart Health Study (SHHS) and the Multiethnic Study of Atherosclerosis (MESA). The best performing model achieved results with 88% binary precision, 88% recall, 89% precision, 89% f1-score and Cohen’s Kappa of 0.74 for the SHHS test set. The model obtained 82% binary accuracy, 82% recall, 84% precision, 82% f1-score and 0.62 Cohen’s kappa for the MESA data set.

There are a large number of scientific publications that focus on the development and evaluation of artificial intelligence (AI) models for the detection of various pathologies in the field of sleep medicine. However, most of these publications do not show the process or methodology to be followed for the final deployment of these models in a complete diagnostic system (in terms of software and hardware). This is a major drawback when translating from the development or research environment to the real clinical setting. This work focuses on a methodology for deploying an AI model for sleep apnea detection with the end user in mind: the clinician. For the deployment, the transmission of data between the device, the cloud platform and the machine learning server, as well as the protocols used, were considered. In addition, the storage and visualization of the data has been taken into account so that it can be analyzed accurately by experts.

Non-invasive sleep monitoring holds significant promise for enhancing healthcare by offering insights into sleep quality and patterns. In this context, accurate detection of body position is crucial, as it provides essential information for diagnosing and understanding the causes of various sleep disorders, including sleep apnea. The aim of this work is to develop an efficient system for sleep position detection using a minimal number of FSR (Force Sensitive Resistor) sensors and advanced machine learning techniques. A hardware setup was developed incorporating 3 FSR sensors, on-board signal processing for frequency boundary filtering and gain adjustment, an ADC (Analog-to-digital converter), and a computing unit for data processing. The collected data was then cleaned and structured before applying various machine learning models, including Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and XGBoost. An experiment with 15 subjects in 4 different sleeping positions was conducted to evaluate the system. The SVC demonstrated notable performance with a test accuracy of 64%. Analysis of the results identified areas for future improvement, including better differentiation between similar positions. The study highlights the feasibility of using FSR sensors and machine learning for effective sleep position detection. However, further research is needed to improve accuracy and explore more advanced techniques. Future efforts will aim to integrate this approach into a comprehensive, unobtrusive sleep monitoring system, contributing to better healthcare services.

The FAST-Forward scheme is an adjuvant radiotherapy protocol for breast cancer treatment. In this study, we analyze a patient database treated with this scheme to demonstrate its non-inferiority to the standard protocol in terms of local tumor control and safety regarding toxicity. Our findings support the continued use of the FAST-Forward scheme in clinical practice. Additionally, we employ machine learning techniques to identify subgroups of patients with similar characteristics and uncover clinically relevant patterns. These algorithms have enabled us to define three distinct patient profiles based on treatment regimen, molecular subtype, and toxicity outcomes. Furthermore, we observed a correlation between maximum skin dose and long-term induration, as well as the impact of breast volume on short-term hyperpigmentation.

Driving behaviour is a critical factor in accidents today. Physiological factors have a significant impact on driving behaviour. A potential solution lies in vehicle services that benefit from sensing environmental conditions to improve road safety, such as collision avoidance routines in driver assistance systems. Stress, assessed subjectively or physiologically, influences decision making and behaviour, with implications for individuals and the economy. In this paper we present a novel approach to formulate a risk index by combining data from subjective self-reports and objective physiological measures (in particular heart rate). The model identifies stress tendencies in driving behaviour by monitoring behavioural and physiological markers. We present our evaluation results and explore potential ways to implement the model in vehicle systems and its implications for improving road safety. We discuss potential enhancements to improve driving safety and enable timely responses in situations with an increased risk of accidents due to stress or drowsiness.

The importance of sleep in the life of a human being to function in nowadays society is known from a large number of studies. A standard method for sleep analysis is polysomnography (PSG), which uses multiple sensors to measure and analyze multiple signals, allowing precise and detailed sleep analysis. Nonetheless, the cost of using PSG technologies is high in terms of complexity, personnel and time. To overcome these shortcomings, alternative solutions can be used to reduce costs and increase comfort for patients. The objective of this work is to design and develop a prototype of the software component of the sleep analysis system, taking into account the aspects of data flow, data storage and user interface in addition to data processing. The software components implemented and developed in the Morpheus System and described in this article comprise a usable platform capable of assisting custom research implementations in IoT.

Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used for sleep monitoring, but alternative methods can help to make sleep monitoring more portable in someone's home. This study conducted a series of experiments to investigate the use of pressure sensors placed under the bed as an alternative to PSG for monitoring heart rate and breathing during sleep. The following sets of experiments involved the addition of small rubber domes - transparent and black - that were glued to the pressure sensor. The resulting data were compared with the PSG system to determine the accuracy of the pressure sensor readings. The study found that the pressure sensor provided reliable data for extracting heart rate and respiration rate, with mean absolute errors (MAE) of 2.32 and 3.24 for respiration and heart rate, respectively. However, the addition of small rubber hemispheres did not significantly improve the accuracy of the readings, with MAEs of 2.3 bpm and 7.56 breaths per minute for respiration rate and heart rate, respectively. The findings of this study suggest that pressure sensors placed under the bed may serve as a viable alternative to traditional PSG systems for monitoring heart rate and breathing during sleep. These sensors provide a more comfortable and non-invasive method of sleep monitoring. However, the addition of small rubber domes did not significantly enhance the accuracy of the readings, indicating that it may not be a worthwhile addition to the pressure sensor system.

The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety.

The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.

Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

In the area of human digital twins, the designed model should be as close as possible to the reality. The weight variable is one of the interested parameters of these mathematical models, as well as its interaction with other vital signals. The aim of this paper is including the information gathered from weight sensors of a person in its digital twin. To do this it is described an algorithm that filters and forecasts the human weight to use it in indexes such as BMI. It has been applied to a real sample and the results obtained are good.

Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive. Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes. We present the literature review of non-invasive sound acquisition devices and techniques. The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope. Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.

Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm.

Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless technologies, data storage, and evaluation of RR intervals was developed. Some tests yelled runtimes up to 24 hours with wireless Lan activated and streaming.

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs.

Children younger than 3 years old are very special humans, their psychomotor and social development is very fast and parents and relatives would like to know every new detail (when, who, where, what, how and why) in real time. These news are difficult to remember and some kind of diary is needed. Here we propose a "prosthetic memory" based on Digital Object Memories applied to Web of Things using hidden NFC tags in children's clothes, mobile applications for smartphones and a central server to store the ontologized information.

In this paper, Bayesian‐Networks (BN) and Ant Colony Optimization (ACO) techniques are combined to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work is that the amount of pheromones released is variable. This amount is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required. By using ACO and BN, a fitness function, responsible of automatically selecting the next course in the learning graph, is defined. This is done by generating a path that maximizes the probability of each user's success in the course. Therefore, the path can change to improve learners’ average performance, taking into account the pedagogical weight of each learning unit and the social behavior of the system. Furthermore, a discrete dynamical system is obtained and its stability is studied. How to wrap an existing Learning Management System is also described in this work. Finally, an experiment compares this approach with the old on‐line learning system being used previously.

This paper presents a new way to combine two different approaches of artificial intelligence looking for the best path in a graph, ant colony optimization and Bayesian networks. The main objective is to develop a learning management system which will have the capability of adapting the learning path to the learnerpsilas needs in execution time, taking into account the pedagogical weight of each learning unit and the systempsilas social behavior.

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.