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Background: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity., Objective: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals., Methods: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition., Results: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F 1 -score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs., Conclusions: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions., (©Alberto De Ramón Fernández, Daniel Ruiz Fernández, Miguel García Jaén, Juan M. Cortell-Tormo. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 09.08.2024.)

Background and Objective: Mode of delivery is one of the issues that most concerns obstetricians. The caesarean section rate has increased progressively in recent years, exceeding the limit recommended by health institutions. Obstetricians generally lack the necessary technology to help them decide whether a caesarean delivery is appropriate based on antepartum and intrapartum conditions., Methods: In this study, we have tested the suitability of using three popular artificial intelligence algorithms, Support Vector Machines, Multilayer Perceptron and, Random Forest, to develop a clinical decision support system for the prediction of the mode of delivery according to three categories: caesarean section, euthocic vaginal delivery and, instrumental vaginal delivery. For this purpose, we used a comprehensive clinical database consisting of 25,038 records with 48 attributes of women who attended to give birth at the Service of Obstetrics and Gynaecology of the University Clinical Hospital "Virgen de la Arrixaca" in the Murcia Region (Spain) from January of 2016 to January 2019. Women involved were patients with singleton pregnancies who attended to the emergency room on active labour or undergoing a planned induction of labour for medical reasons., Results: The three implemented algorithms showed a similar performance, all of them reaching an accuracy equal to or above 90% in the classification between caesarean and vaginal deliveries and somewhat lower, around 87% between instrumental and euthocic., Conclusions: The results validate the use of these algorithms to build a clinical decision system to help gynaecologists to predict the mode of delivery., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer., Methods: We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed., Results: 380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%., Conclusions: The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study., (Copyright © 2021. Published by Elsevier B.V.)

The genetic admixture of Caribbean Hispanics provides an opportunity to discover novel genetic factors in Alzheimer disease (AD). We sought to identify genetic variants for AD through a family-based design using the Puerto Rican (PR) Alzheimer Disease Initiative (PRADI). Whole-genome sequencing (WGS) and parametric linkage analysis were performed for 100 individuals from 23 multiplex PRADI families. Variants were prioritized by minor allele frequency (<0.01), functional potential [combined annotation dependent depletion score (CADD) >10], and co-segregation with AD. Variants were further ranked using an independent PR case-control WGS dataset (PR10/66). A genome-wide significant linkage peak was found in 9p21 with a heterogeneity logarithm of the odds score (HLOD) >5.1, which overlaps with an AD linkage region from two published independent studies. The region harbors C9orf72, but no expanded repeats were observed in the families. Seven variants prioritized by the PRADI families also displayed evidence for association in the PR10/66 (p < 0.05), including a missense variant in UNC13B. Our study demonstrated the importance of family-based design and WGS in genetic study of AD., (Copyright © 2021. Published by Elsevier Inc.)

Ceramic materials based on naturally occurring clays are a low cost and environmentally friendly alternative to commercial polymer-based membranes in bioelectrochemical systems. In this work, ceramic membranes containing different amounts of iron oxide (1.06, 2.76 and 5.75 vol.%) and sintered at different temperatures (1100, 1200 and 1300 °C) have been elaborated and tested as separators in urine-fed microbial fuel cells (MFCs). The results reveal that the presence of iron oxide in the ceramic membrane composition increases the structural porosity and reduces the pore size for the three temperatures investigated. On the other hand, it was also observed that the iron content mitigates the negative effect of the high sintering temperature on the power performance of the MFCs. In the case of the ceramic membranes sintered at 1300 °C, power output improved ca. 10-fold when the iron oxide content in the membrane increased from 1.06 up to 5.75 vol.% (30.9 and 286.6 µW, respectively). Amongst the different combinations of iron phase content and sintering temperatures, the maximum power output was obtained by MFCs working with separators containing 5.75 vol. % of iron oxide and sintered at 1100 °C (1.045 mW). Finally, the system was stable for 65 days, which supports the long-term functionality of the different materials assessed., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors. Published by Elsevier Ltd.)

Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

Purpose: The collapsibility of the upper airway is a key factor in the pathogenesis of obstructive sleep apnea (OSA). The exact measurement of this parameter, typically performed by obtaining the critical pressure (Pcrit), has not been introduced into clinical practice. The techniques that are used to measure the upper airway resistance could provide information on its the collapsibility of the airway. The aim of this study was to associate resistance in the upper airway with the presence of OSA., Methods: Using a cross-sectional design with a control group, consecutive cases that were seen in the Chest Diseases Sleep Unit were recruited after undergoing nocturnal polysomnography. The upper airway collapsibility was determined based on the change in its resistance from the standing position to the supine position with a flow interruption (Rint) device by measuring the angle formed between the pressure/flow lines (PF angle) between the two positions., Results: The PF angle was greater in the OSA group than in the controls: 7.5° (4) vs. 4° (2) (P < .001), with a positive correlation between the width of the angle and the apnea-hypopnea index (AHI) (r: 0.28, P = .03). Placing the threshold level at 10°, a sensitivity of 25% was obtained, along with a specificity of 96%, for the diagnosis of OSA., Conclusion: The measurement of resistance in the upper airway using flow interruption (Rint) can predict the presence of moderate and severe OSA with high specificity but low sensitivity.

Business Process Management is a new strategy for process management that is having a major impact today. Mainly, its use is focused on the industrial, services, and business sector. However, in recent years, it has begun to apply for optimizing clinical processes. So far, no studies that evaluate its true impact on the healthcare sector have been found. This systematic review aims to assess the results of the application of Business Process Management methodology on clinical processes, analyzing whether it can become a useful tool to improve the effectiveness and quality of processes. We conducted a systematic literature review using ScienceDirect, Web of Science, Scopus, PubMed, and Springer databases. After the electronic search process in different databases, 18 articles met the pre-established requirements. The findings support the use of Business Process Management as an effective methodology to optimize clinical processes. Business Process Management has proven to be a feasible and useful methodology to design and optimize clinical processes, as well as to automate tasks. However, a more comprehensive follow-up of this methodology, better technological support, and greater involvement of all the clinical staff are factors that play a key role for the development of its true potential.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with a high global prevalence. The main scientific societies dedicated to the management of this disease have published clinical practice guidelines for quality practice. However, at present, there are important weaknesses in COPD diagnosis criteria that often lead to underdiagnosis or misdiagnosis., Objective: We sought to develop a new support system for COPD diagnosis. The system was designed to overcome the weaknesses detected in current guidelines with the goals of enabling early diagnosis, and improving the diagnostic accuracy and quality of care provided., Methods: We first analyzed the main clinical guidelines for COPD to detect weaknesses that exist in the current diagnostic process, and then proposed a redesign based on a business process management (BPM) strategy for its optimization. The BPM system acts as a backbone throughout the process of COPD diagnosis in this proposed approach. The newly developed support system was integrated into a health information system for validation of its use in a hospital environment. The system was qualitatively evaluated by experts (n=12) and patients (n=36)., Results: Among the 12 experts, 10 (83%) positively evaluated our system with respect to increasing the speed for making the diagnosis, helping in interpreting results, and encouraging opportunistic diagnosis. With an overall rating of 4.29 on a 5-point scale, 27/36 (75%) of patients considered that the system was very useful in providing a warning about possible cases of COPD. The overall assessment of the system was 4.53 on a 5-point Likert scale with agreement to extend its use to all primary care centers., Conclusions: The proposed system provides a functional method to overcome the weaknesses detected in the current diagnostic process for COPD, which can help foster early diagnosis, while improving the diagnostic accuracy and quality of care provided., (©Alberto De Ramón Fernández, Daniel Ruiz Fernández, Diego Marcos-Jorquera, Virgilio Gilart Iglesias. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.03.2020.)

Laparoscopy is an invasive surgical technique performed in abdominal surgery that provides faster recovery than conventional open surgeries. It requires to introduce a camera to observe the surgical maneuvers. However, during this intervention, the quality of the image may be reduced due to the creation of water vapor and carbon dioxide inside the pelvic-abdominal cavity. This phenomenon produces a nebulous image that causes interruptions during the surgical intervention. Removing this nebulous effect is a key factor to improve the vision of the surgeon. In this study, we have used a method based on the dark channel prior to remove the haze in video frames of laparoscopic surgeries to provide better quality images. The results have been positively evaluated by specialists using real video frames of laparoscopic surgeries, thus demonstrating that this method can be effective in improving the quality of the images without losing any detail of the original image.

Microbial fuel cells (MFCs) is a promising technology that is able to simultaneously produce bioenergy and treat wastewater. Their potential large-scale application is still limited by the need of optimising their power density. The aim of this study is to simulate the absolute power output by ceramic-based MFCs fed with human urine by using a fuzzy inference system in order to maximise the energy harvesting. For this purpose, membrane thickness, anode area and external resistance, were varied by running a 27-parameter combination in triplicate with a total number of 81 assays performed. Performance indices such as R 2 and variance account for (VAF) were employed in order to compare the accuracy of the fuzzy inference system designed with that obtained by using nonlinear multivariable regression. R 2 and VAF were calculated as 94.85% and 94.41% for the fuzzy inference system and 79.72% and 65.19% for the nonlinear multivariable regression model, respectively. As a result, these indices revealed that the prediction of the absolute power output by ceramic-based MFCs of the fuzzy-based systems is more reliable than the nonlinear multivariable regression approach. The analysis of the response surface obtained by the fuzzy inference system determines that the maximum absolute power output by the air-breathing set-up studied is 450  μ W when the anode area ranged from 160 to 200 cm 2 , the external loading is approximately 900 Ω and a membrane thickness of 1.6 mm, taking into account that the results also confirm that the latter parameter does not show a significant effect on the power output in the range of values studied., (© 2019 The Author(s).)

Background and Objective: Ectopic pregnancy is an important cause of morbidity and mortality worldwide. An early diagnosis, as well as the choice of the most suitable treatment for the patient is crucial to avoid possible complications. According to different factors an ectopic pregnancy must be treated from surgery, using a pharmacological treatment or following a conservative treatment. In this paper, a clinical decision support systems based on artificial intelligence algorithms has been developed to help clinicians to choose the initial treatment to be followed by the patient., Methods: A decision support system based on a three stages classifier has been developed. Each stage acts as a filter and allows re-evaluation of the classification made in the previous stage in order to find diagnostic errors. This classifier has been implemented and tested for four different aid algorithms: Multilayer Perceptron, Deep Learning, Support Vector Machine and Naives Bayes., Results: The results prove that the evaluated algorithms Support Vector Machine and Multilayer Perceptron can be useful to help gynecologists in their decisions about initial treatment, especially with Support Vector Machine that presents accuracy, sensitivity and specificity outcomes about 96.1%, 96% and 98%, respectively., Conclusions: According to the results, it is feasible to develop a clinical decision support system using the algorithms that present a higher precision. This system would help gynecologists to take the most accurate decision about the initial treatment, thus avoiding future complications., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Microbial fuel cells (MFCs) are an environment-friendly technology, which addresses two of the most important environmental issues worldwide: fossil fuel depletion and water scarcity. Modelling is a useful tool that allows us to understand the behaviour of MFCs and predict their performance, yet the number of MFC models that could accurately inform a scale-up process, is low. In this work, a three-factor three-level Box-Behnken design is used to evaluate the influence of different operating parameters on the performance of air-breathing ceramic-based MFCs fed with human urine. The statistical analysis of the 45 tests run shows that both anode area and external resistance have more influence on the power output than membrane thickness, in the range studied. The theoretical optimal conditions were found at a membrane thickness of 1.55 mm, an external resistance of 895.59 Ω and an anode area of 165.72 cm 2 , corresponding to a maximum absolute power generation of 467.63 μW. The accuracy of the second order model obtained is 88.6%. Thus, the three-factor three-level Box-Behnken-based model designed is an effective tool which provides key information for the optimisation of the energy harvesting from MFC technology and saves time in terms of experimental work.

Nowadays, in many countries, stress is becoming a problem that increasingly affects the health of people. Suffering stress continuously can lead to serious behavioral disorders such as anxiety or depression. Every person, in his daily routine, can face many factors which can contribute to increase his stress level. This paper describes a flexible and distributed model to monitor environmental variables associated with stress, which provides adaptability to any environment in an agile way. This model was designed to transform stress environmental variables in value added information (key stress indicator) and to provide it to external systems, in both proactive and reactive mode. Thus, this value-added information will assist organizations and users in a personalized way helping in the detection and prevention of acute stress cases. Our proposed model is supported by an architecture that achieves the features above mentioned, in addition to interoperability, robustness, scalability, autonomy, efficient, low cost and consumption, and information availability in real time. Finally, a prototype of the system was implemented, allowing the validation of the proposal in different environments at the University of Alicante., Competing Interests: The authors declare no conflict of interest in this article.

Olive oil is rich in several minor components like maslinic (MA) and oleanolic (OA) acids which have cardioprotective, antitumor, and anti-inflammatory properties. In order to assess the health benefits in humans provided by the olive oil triterpenes (MA and OA), suitable analytical methods able to quantify the low concentrations expected in human fluids are required. In this study, the LC-MS/MS quantification of both OA and MA in plasma and urine has been evaluated. The plasmatic method is based on the direct determination of the analytes. The urinary detection requires more sensitivity which was reached by derivatization with 2-picolylamine. Additionally, the urinary species present after MA and OA ingestion were evaluated by the direct detection of several phase II metabolites previously synthesized. Our results showed that OA is metabolized as both sulfate and glucuronide conjugates whereas MA is mainly excreted as glucuronide. Based on this information, the method for the urinary detection of MA and OA involved an enzymatic hydrolysis. Both plasmatic and urinary methods were validated with suitable precision and accuracy at all tested levels. Required sensitivity was achieved in both matrices. Up to our knowledge, this is the first method able to quantify the low concentration levels of triterpenes present in urine. Samples from two healthy volunteers who received virgin olive oils with different triterpenes content were analyzed. Some preliminary clues on the metabolic disposition of OA and MA after olive oil intake are provided., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Crohn's disease is a chronic pathology belonging to the group of inflammatory bowel diseases. Patients suffering from Crohn's disease must be supervised by a medical specialist for the rest of their lives; furthermore, each patient has its own characteristics and is affected by the disease in a different way, so health recommendations and treatments cannot be generalized and should be individualized for a specific patient. To achieve this personalization in a cost-effective way using technology, we propose a model based on different information flows: control, personalization, and monitoring. As a result of the model and to perform a functional validation, an architecture based on services and a prototype of the system has been defined. In this prototype, a set of different devices and technologies to monitor variables from patients and their environment has been integrated. Artificial intelligence algorithms are also included to reduce the workload related to the review and analysis of the information gathered. Due to the continuous and automated monitoring of the Crohn's patient, this proposal can help in the personalization of the Crohn's disease clinical process., Competing Interests: The authors declare no conflict of interest.

The reported prevalence of candidiasis in peristomal skin varies greatly. Very few studies exist that correlate the clinical findings around the peristomal skin to the mycology. In this study, the authors report on Candida species prevalence, clinical correlation, and mycology.

Apathy is one of the most common and debilitating nonmotor manifestations of Parkinson's disease (PD) and is characterized by diminished motivation, decreased goal-directed behavior, and flattened affect. Despite its high prevalence, its underlying mechanisms are still poorly understood, having been associated with executive dysfunction, and impaired emotional processing and decision making. Apathy, as a syndrome, has recently been associated with reduced activation in the ventral striatum, suggesting that early- to middle-stage Parkinson's disease patients with this manifestation may have a compromised mesocorticolimbic dopaminergic pathway and impaired incentive processing. To test this hypothesis, we measured the amplitude of the feedback-related negativity, an event-related brain potential associated with performance outcome valence, following monetary gains and losses in human PD patients (12 women) and healthy controls (6 women) performing a gambling task. Early- to middle-stage PD patients presenting clinically meaningful symptoms of apathy were compared with nonapathetic PD patients and healthy controls. Patients with cognitive impairment, depression, and other psychiatric disturbances were excluded. Results showed that the amplitude of the feedback-related negativity, measured as the difference wave in the event-related brain potential between gains and losses, was significantly reduced in PD patients with apathy compared with nonapathetic patients and healthy controls. These findings indicate impaired incentive processing and suggest a compromised mesocorticolimbic pathway in cognitively intact PD patients with apathy.

Aim: To elucidate the relationship between CYP2D6 genotype and risperidone pharmacokinetics and extrapyramidal symptoms we propose the APSEP pharmacogenetic clinical trial., Materials & Methods: Twenty-five healthy subjects were included in this randomized, placebo-controlled, single dose (risperidone 2.5 mg) crossover and double-blind clinical trial. Subjects were selected according to their CYP2D6 genotype and classified as: poor metabolizers (n = 8), extensive metabolizers (n = 10) and ultrarapid metabolizers (n = 7)., Results & Conclusion: Our study demonstrates that CYP2D6 predicted 65% of the risperidone metabolism variability. Moreover, its ability to predict actigraphy records is similar to the predictive power of pharmacokinetic parameters (24%). Our results also highlight the need for the development of pharmacogenetic predictors that take into account the complexity of pharmacokinetic and pharmacodynamic relationships.

Aim: This study aimed to elucidate the relationship between CYP2D6 genotype and haloperidol pharmacokinetics and induced extrapyramidal symptoms (EPSs)., Materials & Methods: Twenty five healthy subjects were included in this randomized, placebo-controlled, single-dose (5 mg) crossover and double-blind clinical trial, selected according to their CYP2D6 genotype and classified as poor metabolizers (n = 8), extensive metabolizers (n = 10) and ultrarapid metabolizers (n = 7)., Results & Conclusion: We confirm that CYP2D6 genotype partially determines haloperidol metabolism and the rate of EPSs measured as wakefulness activity by actigraphy. The best predictor of wakefulness activity was the model including haloperidol area under the plasma concentration-time curve, sex and tranquilization, which explained 48.3% of the total variance. However, other markers need to be identified in order to explain the observed variability of haloperidol response and to develop pharmacogenetic predictors of haloperidol-induced EPSs.