The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP).

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of "what's next" in the management of children with R/R ALL.

DDQN-based optimal targeted therapy with reversible inhibitors to combat the Warburg effect.

We cover the Warburg effect with a three-component evolutionary model, where each component represents a different metabolic strategy. In this context, a scenario involving cells expressing three different phenotypes is presented. One tumour phenotype exhibits glycolytic metabolism through glucose uptake and lactate secretion. Lactate is used by a second malignant phenotype to proliferate. The third phenotype represents healthy cells, which performs oxidative phosphorylation. The purpose of this model is to gain a better understanding of the metabolic alterations associated with the Warburg effect. It is suitable to reproduce some of the clinical trials obtained in colorectal cancer and other even more aggressive tumours. It shows that lactate is an indicator of poor prognosis, since it favours the setting of polymorphic tumour equilibria that complicates its treatment. This model is also used to train a reinforcement learning algorithm, known as Double Deep Q-networks, in order to provide the first optimal targeted therapy based on experimental tumour growth inhibitors as genistein and AR-C155858. Our in silico solution includes the optimal therapy for all the tumour state space and also ensures the best possible quality of life for the patients, by considering the duration of treatment, the use of low-dose medications and the existence of possible contraindications. Optimal therapies obtained with Double Deep Q-networks are validated with the solutions of the Hamilton-Jacobi-Bellman equation.

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances.

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools-namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton-Jacobi-Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.

Deep Reinforcement Learning for Attacking Wireless Sensor Networks.

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures.

Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack.

We study a CSMA/CA (Carrier Sense Medium Access with Collision Avoidance) wireless network where some stations deviate from the defined contention mechanism. By using Bianchi's model, we study how this deviation impacts the network throughput and show that the fairness of the network is seriously affected, as the stations that deviate achieve a larger share of the resources than the rest of stations. Previously, we modeled this situation using a static game and now, we use repeated games, which, by means of the Folk theorem, allow all players to have better outcomes. We provide analytical solutions to this game for the two player case using subgame perfect and correlated equilibria concepts. We also propose a distributed algorithm based on communicating candidate equilibrium points for learning the equilibria of this game for an arbitrary number of players. We validate approach using numerical simulations, which allows comparing the solutions we propose and discussing the advantages of using each of the methods we propose.

Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN.

In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.

Wireless Networks under a Backoff Attack: A Game Theoretical Perspective.

We study a wireless sensor network using CSMA/CA in the MAC layer under a backoff attack: some of the sensors of the network are malicious and deviate from the defined contention mechanism. We use Bianchi's network model to study the impact of the malicious sensors on the total network throughput, showing that it causes the throughput to be unfairly distributed among sensors. We model this conflict using game theory tools, where each sensor is a player. We obtain analytical solutions and propose an algorithm, based on Regret Matching, to learn the equilibrium of the game with an arbitrary number of players. Our approach is validated via simulations, showing that our theoretical predictions adjust to reality.

The Impacts of Exposure to Environmental Risk on Physical and Mental Health in a Small Geographic Community in Houston, TX.

Previous research has shown that communities with low average socioeconomic status (SES) and majority minority populations are more likely to be exposed to industrial buildings, waste facilities, and poor infrastructure compared to white communities with higher average SES. While some studies have demonstrated linkages between exposures to specific environmental contaminates within these communities and negative health outcomes, little research has analyzed the effects of environmental contaminants on the mental and physical health of these populations. A cross-sectional survey collected data from residents of Manchester, a small neighborhood in Houston, TX, that is characterized by industrial sites, unimproved infrastructure, nuisance flooding, and poor air quality. Our study (N = 109) utilized the 12 item Short Form Health Survey version 2 (SF12v2) to assess the general mental and physical health of the community. The community as a whole had reduced physical health scores compared to U.S. national averages. The time residents had lived in the neighborhood was also correlated with a reported reduction in physical health scores (r2 = 0.136; p-value <0.001). The association between time lived in the neighborhood and poorer health scores remained after adjusting for age, race, and gender (coef = -0.27, p-value <0.001). Mental health scores were within national averages and time spent living in the neighborhood did not appear to negatively impact respondent's mental health scores. These findings point to the need for more research to determine the potential for additive physical and mental health impacts in long-term residents in neighborhoods characterized by environmental justice issues.

Evaluating community and campus environmental public health programs.

The National Institute of Environmental Health Sciences' (NIEHS) Partnerships for Environmental Public Health (PEPH) program created the Evaluation Metrics Manual as a tool to help grantees understand how to map out their programs using a logic model, and to identify measures for documenting their achievements in environmental public health research. This article provides an overview of the manual, describing how grantees and community partners contributed to the manual, and how the basic components of a logic model can be used to identify metrics. We illustrate how the approach can be implemented, using a real-world case study from the University of Texas Medical Branch, where researchers worked with community partners to develop a network to address environmental justice issues.

Effect of hospital case-manager nurses on the level of dependence, satisfaction and caregiver burden in patients with complex chronic disease.

AIMS AND OBJECTIVES: To determine the medium-term effects of nurse case management on the dependence and satisfaction of patients with complex chronic disease and on caregiver burden. BACKGROUND: Caregiver exhaustion increases the readmission rate of highly dependent patients with complex chronic disease and their consumption of primary care resources. DESIGN: An observational and analytical cohort study was undertaken in multimorbid patients. METHODS: Data were gathered on Barthel Index and Caregiver Burden Index scores, primary care resource consumption, readmission and mortality rates, and patient satisfaction with care and care continuity. Results were compared between nurse case-managed (n = 62) and control (n = 193) multimorbid patients using univariate and bivariate analyses. RESULTS: The study included 255 patients with complex chronic disease (24·32% in management cohort vs. 75·68% in control cohort). The nurse case-managed group had significantly lower Barthel Index and higher Caregiver Burden Index scores and a significantly longer hospital stay. At 90 days postdischarge, no significant intergroup differences were observed in Barthel Index or Caregiver Burden Index scores, primary care resource consumption, readmission rate or mortality rate; the case-managed patients showed a significantly higher satisfaction level with their care and its continuity. CONCLUSIONS: Nurse case management prevents a postdischarge increase in the dependence of multimorbid patients and the burden of their caregivers. RELEVANCE TO CLINICAL PRACTICE: Application of nurse case management can reduce the readmission rate and primary care consumption of patients with chronic complex disease after their hospital stay and prevent an exacerbation of caregiver exhaustion.

Effectiveness of the 'sleep enhancement' nursing intervention in hospitalized mental health patients.

AIM: To determine the effectiveness of the 'sleep enhancement' nursing intervention (Nursing Interventions Classification) in patients hospitalized with mental illness and having a disturbed sleep pattern and to identify the possible effect of psycho-active medications on this disturbed sleep pattern. DESIGN: A quasi-experimental pretest-posttest type study without control group. METHOD: The study was conducted in all patients admitted to the mental health inpatient unit of University Hospital of Spain from 1 March 2007-31 May 2008. The effectiveness of the 'sleep enhancement' nursing intervention was measured using the Oviedo Sleep Questionnaire score and Nursing Outcome Classification sleep scores at admission and discharge. Psycho-active medication was considered an intervening variable and data were analysed by multivariate analysis of variance for repeated measures. RESULTS: The study included 291 patients. Consumption of psycho-active medications did not change between admission and discharge and was not statistically significantly different in the multivariate analysis of variance. Oviedo Sleep Questionnaire and Nursing Outcome Classification sleep scores at admission and discharge demonstrated significant sleep improvement after the nursing intervention. CONCLUSION: This nursing intervention could be implemented in patients admitted to a mental health inpatient unit with disturbed sleep pattern, regardless of their consumption of psycho-active medications.

Is the Scale for Measuring Motivational Interviewing Skills a valid and reliable instrument for measuring the primary care professionals motivational skills?: EVEM study protocol.

BACKGROUND: Lifestyle is one of the main determinants of people's health. It is essential to find the most effective prevention strategies to be used to encourage behavioral changes in their patients. Many theories are available that explain change or adherence to specific health behaviors in subjects. In this sense the named Motivational Interviewing has increasingly gained relevance. Few well-validated instruments are available for measuring doctors' communication skills, and more specifically the Motivational Interviewing. METHODS/DESIGN: The hypothesis of this study is that the Scale for Measuring Motivational Interviewing Skills (EVEM questionnaire) is a valid and reliable instrument for measuring the primary care professionals skills to get behavior change in patients. To test the hypothesis we have designed a prospective, observational, multi-center study to validate a measuring instrument. - SCOPE: Thirty-two primary care centers in Spain. -Sampling and Size: a) face and consensual validity: A group composed of 15 experts in Motivational Interviewing. b) Assessment of the psychometric properties of the scale; 50 physician- patient encounters will be videoed; a total of 162 interviews will be conducted with six standardized patients, and another 200 interviews will be conducted with 50 real patients (n=362). Four physicians will be specially trained to assess 30 interviews randomly selected to test the scale reproducibility. -Measurements for to test the hypothesis: a) Face validity: development of a draft questionnaire based on a theoretical model, by using Delphi-type methodology with experts. b) Scale psychometric properties: intraobservers will evaluate video recorded interviews: content-scalability validity (Exploratory Factor Analysis), internal consistency (Cronbach alpha), intra-/inter-observer reliability (Kappa index, intraclass correlation coefficient, Bland & Altman methodology), generalizability, construct validity and sensitivity to change (Pearson product-moment correlation coefficient). DISCUSSION: The verification of the hypothesis that EVEM is a valid and reliable tool for assessing motivational interviewing would be a major breakthrough in the current theoretical and practical knowledge, as it could be used to assess if the providers put into practice a patient centered communication style and can be used both for training or researching purposes. TRIALS REGISTRATION Dislip-EM study: NCT01282190 (ClinicalTrials.gov).