High resolution system for nanoparticles hyperthermia efficiency evaluation

A system to evaluate nanoparticles efficiency in hyperthermia applications is presented. The method allows a direct measurement of the power dissipated by the nanoparticles through the determination of the first harmonic component of the in quadrature magnetic moment induced by the applied field. The magnetic moment is measured by using an induction method. To avoid errors and reduce the noise signal a double in phase demodulation technique is used. To test the system viability we have measured nanowires, nanoparticles and copper samples of different volumes to prove by comparing experimental and modeled result

Resistance to antiangiogenic therapies by metabolic symbiosis in renal cell carcinoma PDX models and patients

Antiangiogenic drugs are used clinically for treatment of renal cell carcinoma (RCC) as a standard first-line treatment. Nevertheless, these agents primarily serve to stabilize disease, and resistance eventually develops concomitant with progression. Here, we implicate metabolic symbiosis between tumor cells distal and proximal to remaining vessels as a mechanism of resistance to antiangiogenic therapies in patient-derived RCC orthoxenograft (PDX) models and in clinical samples. This metabolic patterning is regulated by the mTOR pathway, and its inhibition effectively blocks metabolic symbiosis in PDX models. Clinically, patients treated with antiangiogenics consistently present with histologic signatures of metabolic symbiosis that are exacerbated in resistant tumors. Furthermore, the mTOR pathway is also associated in clinical samples, and its inhibition eliminates symbiotic patterning in patient samples. Overall, these data support a mechanism of resistance to antiangiogenics involving metabolic compartmentalization of tumor cells that can be inhibited by mTOR-targeted drugs

Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms.

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing

Expert Clinical Management of Inflammatory Immune-Related Arthritis in Patients with Cancer Receiving Immune Checkpoint Inhibitors

Immune checkpoint inhibitor therapy; Inflammatory arthritis; SurveyTeràpia amb inhibidors del punt de control immunitari; Artritis inflamatòria; EnquestaTerapia con inhibidores del punto de control inmunitario; Artritis inflamatoria; EncuestaIntroduction Treatment guidelines for immune-related inflammatory arthritis (irAE-IA) in patients with cancer receiving immune checkpoint inhibitors (ICIs) are vague with respect to the use of specific agents. Patients are usually referred to rheumatologists for treatment. We conducted a survey of expert rheumatologists to determine current practices. We also assessed experts’ views on the potential deleterious effects of various agents on tumor progression. Methods We conducted a survey of international experts in the treatment of irAE-IA, identified as members of collaborative scientific workgroups in this area. Experts were presented with a case of a patient with moderate irAE-IA and were asked about their preferred management including glucocorticoids, timing and initial choice of disease-modifying antirheumatic drugs (DMARDs), and perception of the deleterious effects of different agents on tumor progression. Results We approached 25 experts, of whom 19 (76%) responded. Most experts (63%) agreed on 20 mg or less of prednisone as initial dose. Experts selected methotrexate (41%) or tumor necrosis factor inhibitor (TNFi) (23%) as the initial DMARD if there was no improvement with corticosteroids; most experts (42%) would initiate DMARDs after 4 weeks. For patients whose initial DMARD therapy failed, the second choice was either a tumor necrosis factor inhibitor (TNFi) (38%) or interleukin-6 receptor antagonist (IL6ri) (33%). Experts were most concerned about the potential deleterious effects on tumor progression of abatacept and prednisone at doses of 20 mg or higher. Conclusion There was substantial heterogeneity in the initial management of irAE-IA. Further understanding of the pathophysiology of this immunotoxicity can assist in the classification of different presentations, selection of relevant outcomes, and planning of clinical trials to establish optimal therapeutic efficacy while minimizing potential deleterious effects of treatment on immune tumor responses

Feelings of contrast at test reduce false memory in the Deese/Roediger-McDermott paradigm

False memories in the Deese/Roediger-McDermott (DRM) paradigm are explained in terms of the interplay between error-inflating and error-editing (e.g., monitoring) mechanisms. In this study, we focused on disqualifying monitoring, a decision process that helps to reject false memories through the recollection of collateral information (i.e., recall-to-reject strategies). Participants engage in recall-to-reject strategies using one or two metacognitive processes: (1) applying the logic of mutual exclusivity or (2) experiencing feelings of contrast between studied items and unstudied lures. We aimed to provide, for the first time in the DRM literature, evidence favorable to the existence of a recall-to-reject strategy based on the experience of feelings of contrast. One hundred and forty participants studied six-word DRM lists (e.g., spy, hell, fist, fight, abduction, mortal), simultaneously associated with three critical lures (e.g., WAR, BAD, FEAR). Lists differed in their ease to identify their critical lures (extremely low-BAS lists vs. high-BAS lists). At recognition test, participants saw either one or the three critical lures of the lists. Participants in the three-critical-lure condition were expected to increase their monitoring, as they would experience stronger feelings of contrast than the participants in the one-critical-lure condition. Results supported our hypothesis, showing lower false recognition in the three-critical-lure condition than in the one critical-lure condition. Critically, in the three-critical-lure condition, participants reduced even more false memory when they could also resort to another monitoring strategy (i.e., identify-to-reject). These findings suggest that, in the DRM context, disqualifying monitoring could be guided by experiencing feelings of contrast between different types of words

Design and analysis of clustering algorithms for numerical, categorical and mixed data

In recent times, several machine learning techniques have been applied successfully to discover useful knowledge from data. Cluster analysis that aims at finding similar subgroups from a large heterogeneous collection of records, is one o f the most useful and popular of the available techniques o f data mining. The purpose of this research is to design and analyse clustering algorithms for numerical, categorical and mixed data sets. Most clustering algorithms are limited to either numerical or categorical attributes. Datasets with mixed types o f attributes are common in real life and so to design and analyse clustering algorithms for mixed data sets is quite timely. Determining the optimal solution to the clustering problem is NP-hard. Therefore, it is necessary to find solutions that are regarded as “good enough” quickly. Similarity is a fundamental concept for the definition of a cluster. It is very common to calculate the similarity or dissimilarity between two features using a distance measure. Attributes with large ranges will implicitly assign larger contributions to the metrics than the application to attributes with small ranges. There are only a few papers especially devoted to normalisation methods. Usually data is scaled to unit range. This does not secure equal average contributions of all features to the similarity measure. For that reason, a main part o f this thesis is devoted to normalisation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Editorial: Multidisciplinary management of cancer patients with immune-related adverse events from checkpoint inhibitors

Cancer; Immunotherapy; ToxicityCáncer; Inmunoterapia; ToxicidadCàncer; Immunoteràpia; Toxicita

Design and analysis of clustering algorithms for numerical, categorical and mixed data

In recent times, several machine learning techniques have been applied successfully to discover useful knowledge from data. Cluster analysis that aims at finding similar subgroups from a large heterogeneous collection of records, is one o f the most useful and popular of the available techniques o f data mining. The purpose of this research is to design and analyse clustering algorithms for numerical, categorical and mixed data sets. Most clustering algorithms are limited to either numerical or categorical attributes. Datasets with mixed types o f attributes are common in real life and so to design and analyse clustering algorithms for mixed data sets is quite timely. Determining the optimal solution to the clustering problem is NP-hard. Therefore, it is necessary to find solutions that are regarded as “good enough” quickly. Similarity is a fundamental concept for the definition of a cluster. It is very common to calculate the similarity or dissimilarity between two features using a distance measure. Attributes with large ranges will implicitly assign larger contributions to the metrics than the application to attributes with small ranges. There are only a few papers especially devoted to normalisation methods. Usually data is scaled to unit range. This does not secure equal average contributions of all features to the similarity measure. For that reason, a main part o f this thesis is devoted to normalisation