Personalized medicine support system : resolving conflict in allocation to risk groups and predicting patient molecular response to targeted therapy

Treatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies.Haneen Banjar, David Adelson, Fred Brown, and Tamara Leclerc

Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr

Modelling predictors of molecular response to frontline imatinib for patients with chronic myeloid leukaemia

BACKGROUND: Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. PRINCIPLE FINDINGS: The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.Haneen Banjar, Damith Ranasinghe, Fred Brown, David Adelson, Trent Kroger, Tamara Leclercq, Deborah White, Timothy Hughes, Naeem Chaudhr

Laser Vaporization of Mouth Lesions, an Overview

Lasers are utilized in dentistry as a therapeutic tool or as an auxiliary tool. The major purpose of employing lasers in dentistry is to overcome the difficulties that are currently observed in traditional dental treatment treatments. The laser is used in hard tissue applications such as caries prevention, bleaching, restorative removal and curing, cavity preparation, dentinal hypersensitivity, growth modulation, and diagnostics, whereas soft tissue applications include wound healing, removal of hyperplastic tissue to uncover impacted or partially erupted teeth, photodynamic therapy for malignancies, and photo-stimulation of herpetic lesions. Lasers' capacity to perform minimally invasive operations with minimum patient discomfort has proven effective in the patient delivery system in dentistry practice. The availability of lasers with various wavelengths has produced a surgical panacea, and laser technology has replaced traditional surgical techniques in many oral surgical operations

Cystic fibrosis: presentation with other diseases, the experience in Saudi Arabia

AbstractSimultaneous occurrence of Cystic fibrosis and other inherited diseases or congenital anomalies has been rare. This association has never been described before in the Arab population. In this report we describe the first report on cystic fibrosis in association with other diseases in the same patient such as sickle cell disease, Insulin dependant Diabetes mellitus, congenital adrenal hyperplasia, cardiac anomalies in twins and Ehler's Danlos syndrome. We also evaluate their effects on CF patients and review the literature in this aspect