Cancer risk among patients with multiple sclerosis: A cohort study in Isfahan, Iran

Background: Multiple sclerosis (MS), a central nervous system (CNS) autoimmune disorder, affects 2.3 million people around the world. Cancer kills around 7.5 million people annually. Both diseases have similar risks and intertwining molecular causes. Most studies focusing on MS and cancer have found an insignificant difference or reduction in the amount of cancer found in the MS community. Methods: We performed a cohort study using data from Isfahan Multiple Sclerosis Society (IMSS) and Isfahan cancer society and followed-up for 8 years on average (2006-2014). All of the 1718 MS patients were diagnosed according to McDonald's criteria, then standardized incidence ratio and the numbers of expected cancer case were calculated. Results: While patients had an insignificant change in cancer prevalence, men had fewer cancer cases and women showed an increased prevalence of cancer. Certain types of cancer proved statistically significant. Breast cancer, nervous system cancers, and lymphoma were elevated in the cohort. Conclusion: Our results support the hypothesis that MS significantly affects certain cancers in a protective or associative manner. All cancer rates, except breast cancer, cancers located in the nervous system, and lymphomas were reduced in cohort, suggesting that unregulated immune function may provide protective effects to MS patients against cancer

Magnetic Hyperthermia as an adjuvant cancer therapy in combination with radiotherapy versus radiotherapy alone for recurrent/progressive glioblastoma: a systematic review

Introduction: Hyperthermia therapy (HT) is a recognized treatment modality, that can sensitize tumors to the effects of radiotherapy (RT) and chemotherapy by heating up tumor cells to 40�45 °C. The advantages of noninvasive inductive magnetic hyperthermia (MH) over RT or chemotherapy in the treatment of recurrent/progressive glioma have been confirmed by several clinical trials. Thus, here we have conducted a systematic review to provide a concise, albeit brief, account of the currently available literature regarding this topic. Methods: Five databases, PubMed/Medline, Embace, Ovid, WOS, and Scopus, were investigated to identify clinical studies comparing overall survival (OS) following RT/chemotherapy versus RT/chemotherapy + MH. Results: Eleven articles were selected for this systematic review, including reports on 227 glioma patients who met the study inclusion criteria. The papers included in this review comprised nine pilot clinical trials, one non-randomized clinical trial, and one retrospective investigation. As the clinical trials suggested, MH improved OS in primary glioblastoma (GBM), however, in the case of recurrent glioblastoma, no significant change in OS was reported. All 11 studies ascertained that no major side effects were observed during MH therapy. Conclusion: Our systematic review indicates that MH therapy as an adjuvant for RT could result in improved survival, compared to the therapeutic outcomes achieved with RT alone in GBM, especially by intratumoral injection of magnetic nanoparticles. However, heterogeneity in the methodology of the most well-known studies, and differences in the study design may significantly limit the extent to which conclusions can be drawn. Thus, further investigations are required to shed more light on the efficacy of MH therapy as an adjuvant treatment modality in GBM. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models

Introduction: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients undergoing radiation therapy of abdominal cancers. Methods: 50 patients were evaluated for chronic kidney disease 12 months after completion of abdominal radiation therapy. At the first step, the region of interest was automatically extracted using deep learning models in computed tomography images. Afterward, a combination of radiomic and clinical features was extracted from the region of interest to build a radiomic signature. Finally, six popular classifiers, including Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, Random Forest, and Support Vector Machine, were used to predict chronic kidney disease. Evaluation criteria were as follows: accuracy, sensitivity, specificity, and area under the ROC curve. Results: Most of the patients (58) experienced chronic kidney disease. A total of 140 radiomic features were extracted from the segmented area. Among the six classifiers, Random Forest performed best with the accuracy and AUC of 94 and 0.99, respectively. Conclusion: Based on the quantitative results, we showed that a combination of radiomic and clinical features could predict chronic kidney radiation toxicities. The effect of factors such as renal radiation dose, irradiated renal volume, and urine volume 24-h on CKD was proved in this study. © 202

On patterns for decentralized control in self-adaptive systems

Self-adaptation is typically realized using a control loop. One prominent approach for organizing a control loop in self-adaptive systems is by means of four components that are responsible for the primary functions of self-adaptation: Monitor, Analyze, Plan, and Execute, together forming a MAPE loop. When systems are large, complex, and heterogeneous, a single MAPE loop may not be sufficient for managing all adaptation in a system, so multiple MAPE loops may be introduced. In self-adaptive systems with multiple MAPE loops, decisions about how to decentralize each of the MAPE functions must be made. These decisions involve how and whether the corresponding functions from multiple loops are to be coordinated (e.g., planning components coordinating to prepare a plan for an adaptation). To foster comprehension of self-adaptive systems with multiple MAPE loops and support reuse of known solutions, it is crucial that we document common design approaches for engineers. As such systematic knowledge is currently lacking, it is timely to reflect on these systems to: (a) consolidate the knowledge in this area, and (b) to develop a systematic approach for describing different types of control in self-adaptive systems. We contribute with a simple notation for describing interacting MAPE loops, which we believe helps in achieving (b), and we use this notation to describe a number of existing patterns of interacting MAPE loops, to begin to fulfill (a). From our study, we outline numerous remaining research challenges in this area