scholarly journals Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory

2017 ◽  
Vol 375 (2096) ◽  
pp. 20160285 ◽  
Author(s):  
Panayiotis Christodoulides ◽  
Yoshito Hirata ◽  
Elisa Domínguez-Hüttinger ◽  
Simon G. Danby ◽  
Michael J. Cork ◽  
...  
Keyword(s):  
Optimal Control ◽  
Atopic Dermatitis ◽  
Differential Evolution Algorithm ◽  
Treatment Strategies ◽  
Disease Status ◽  
Computational Design ◽  
Optimal Treatment ◽  
Computational Method ◽  
Mathematical Methods ◽  
Proactive Therapy

Atopic dermatitis (AD) is a common chronic skin disease characterized by recurrent skin inflammation and a weak skin barrier, and is known to be a precursor to other allergic diseases such as asthma. AD affects up to 25% of children worldwide and the incidence continues to rise. There is still uncertainty about the optimal treatment strategy in terms of choice of treatment, potency, duration and frequency. This study aims to develop a computational method to design optimal treatment strategies for the clinically recommended ‘proactive therapy’ for AD. Proactive therapy aims to prevent recurrent flares once the disease has been brought under initial control. Typically, this is done by using an anti-inflammatory treatment such as a potent topical corticosteroid intensively for a few weeks to ‘get control’, followed by intermittent weekly treatment to suppress subclinical inflammation to ‘keep control’. Using a hybrid mathematical model of AD pathogenesis that we recently proposed, we computationally derived the optimal treatment strategies for individual virtual patient cohorts, by recursively solving optimal control problems using a differential evolution algorithm. Our simulation results suggest that such an approach can inform the design of optimal individualized treatment schedules that include application of topical corticosteroids and emollients, based on the disease status of patients observed on their weekly hospital visits. We demonstrate the potential and the gaps of our approach to be applied to clinical settings. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

scholarly journals Current Insights into Immunology and Novel Therapeutics of Atopic Dermatitis

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1392
Author(s):  
Hidaya A. Kader ◽  
Muhammad Azeem ◽  
Suhib A. Jwayed ◽  
Aaesha Al-Shehhi ◽  
Attia Tabassum ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Skin Diseases ◽  
Current Knowledge ◽  
Treatment Strategies ◽  
Developed Countries ◽  
Food Allergies ◽  
Th2 Response ◽  
Therapeutic Outcomes ◽  
Environmental Agents ◽  
Novel Treatment Strategies

Atopic dermatitis (AD) is one of the most prevalent inflammatory disease among non-fatal skin diseases, affecting up to one fifth of the population in developed countries. AD is characterized by recurrent pruritic and localized eczema with seasonal fluctuations. AD initializes the phenomenon of atopic march, during which infant AD patients are predisposed to progressive secondary allergies such as allergic rhinitis, asthma, and food allergies. The pathophysiology of AD is complex; onset of the disease is caused by several factors, including strong genetic predisposition, disrupted epidermal barrier, and immune dysregulation. AD was initially characterized by defects in the innate immune system and a vigorous skewed adaptive Th2 response to environmental agents; there are compelling evidences that the disorder involves multiple immune pathways. Symptomatic palliative treatment is the only strategy to manage the disease and restore skin integrity. Researchers are trying to more precisely define the contribution of different AD genotypes and elucidate the role of various immune axes. In this review, we have summarized the current knowledge about the roles of innate and adaptive immune responsive cells in AD. In addition, current and novel treatment strategies for the management of AD are comprehensively described, including some ongoing clinical trials and promising therapeutic agents. This information will provide an asset towards identifying personalized targets for better therapeutic outcomes.

Avascular Osteonecrosis of the Talus: Current Treatment Strategies

2021 ◽  
pp. 107110072110510
Author(s):  
Hanci Zhang ◽  
Amanda N. Fletcher ◽  
Daniel J. Scott ◽  
James Nunley
Keyword(s):  
Risk Factors ◽  
Clinical Diagnosis ◽  
Treatment Strategies ◽  
Core Decompression ◽  
Bone Grafts ◽  
Current Treatment ◽  
Optimal Treatment ◽  
Patient Specific ◽  
Avascular Osteonecrosis ◽  
Current Trends

Avascular osteonecrosis (AVN) of the talus (AVNT) is a painful and challenging clinical diagnosis. AVNT has multiple known risk factors and etiologies and presents at different stages in severity. Given these unique factors, the optimal treatment solution has yet to be determined. Both joint-preserving and joint-sacrificing procedures are available, including core decompression and arthrodeses. Recently, new salvage and replacement techniques have been described including vascularized pedicle bone grafts and total talus replacement using patient-specific prosthesis; however, evidence remains limited. This review examines the current trends AVNT treatment and the emerging data behind these novel techniques.

scholarly journals Optimal control of acute myeloid leukaemia

2018 ◽  
Author(s):  
Jesse A Sharp ◽  
Alexander P Browning ◽  
Tarunendu Mapder ◽  
Kevin Burrage ◽  
Matthew J Simpson
Keyword(s):  
Optimal Control ◽  
Immune Response ◽  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Cell Model ◽  
Control Strategies ◽  
Treatment Strategies ◽  
Numerical Convergence ◽  
Stem Cell Model ◽  
Acute Myeloid

AbstractAcute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corresponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin’s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.

scholarly journals Substantiation of Using Pimecrolimus 1% Cream in Proactive Therapy of Children with Atopic Dermatitis

2021 ◽  
Vol 20 (5) ◽  
pp. 376-382
Author(s):  
Nikolay N. Murashkin ◽  
Roman A. Ivanov ◽  
Eduard T. Ambarchian ◽  
Roman V. Epishev ◽  
Alexander I. Materikin ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Maintenance Therapy ◽  
Skin Disease ◽  
Pediatric Population ◽  
Efficacy And Safety ◽  
Inflammatory Skin Disease ◽  
High Prevalence ◽  
Proactive Therapy ◽  
Term Maintenance

Atopic dermatitis (AtD) is multifactorial inflammatory skin disease with high prevalence in pediatric population. It is crucial to implement long-term maintenance therapy to prevent AtD exacerbations according to current clinical guidelines and expert reports. The article summarizes the results of the major studies on using pimecrolimus 1% cream. Its efficacy and safety in long-term proactive therapy of children with AtD are presented.

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