Radiochemotheraphy-induced oral mucositis: Ectoin solution as a new treatment.

Background: The current treatment for head and neck cancer involves radiotherapy, systemic therapy and surgery in a multidisciplinary approach. Unfortunately, cancer therapies can lead to local and systemic complications or side effects such as mucositis, which is the most common dose-dependent complication in the oral cavity and gastrointestinal tract. Mucositis can cause a considerably reduced quality of life in cancer patients already suffering from physical and psychological exhaustion. Moreover, radiotherapy interruptions due to toxicity can impact negatively in local control and survival. The main purpose of this study was to analyze patient satisfaction of Ectoin solution use in radiotherapy or radiochemotherapy-induced oral mucositis. Material and Methods: This is an institutional prospective analysis including 15 patients, conducted by two Spanish centers, between October 2019 and May 2020. Patients were treated with Ectoin solution during Radiotherapy and one month after the end of the treatment, three times per day. A seven-ítem Likert scale was used. We present our descriptive statistic regarding doctors and patients´s satisfaction. Results: Our results suggest that Ectoin solution relieves mucositis and is well tolerated by patients. Conclusions: We observed a favorable repercussion in the oral mucositis management and suggest a potential benefit of treating it. Key words:Radiotherapy, oral mucositis, head and neck cancer, ectoine, oral care.

Principles of analytical calibration/quantification for the separation sciences.

Calibration is an operation whose main objective is to know the metrological status of a measurement system. Nevertheless, in analytical sciences, calibration has special connotations since it is the basis to do the quantification of the amount of one or more components (analytes) in a sample, or to obtain the value of one or more analytical parameters related with that quantity. Regarding this subject, the aim of analytical calibration is to find an empiric relationship, called measurement function, which permits subsequently to calculate the values of the amount (x-variable) of a substance in a sample, from the measured values on it of an analytical signal (y-variable). In this paper, the metrological bases of analytical calibration and quantification are established and, the different work schemes and calibration methodologies, which can be applied depending on the characteristic of the sample (analyte+matrix) to analyse, are distinguished and discussed. Likewise, the different terms and related names are clarified. A special attention has been paid to those analytical methods which use separation techniques, in relation with its effect on calibration operations and later analytical quantification.

Identification of biological markers of sensitivity to high-clinical-risk-adapted therapy for patients with diffuse large B-cell lymphoma.

The aim of the project was to identify biological variables in high-clinical-risk patients with diffuse large B-cell lymphoma (DLBCL), treated with risk-adapted therapies. The study was performed in a series of high-clinical-risk patients with DLBCL treated with MegaCHOP or MegaCHOP + IFE followed by autologous stem-cell transplantation (ASCT). An initial reduced set of diagnostic tumoral samples was studied by gene expression profiling and gene-set-enrichment analysis. A set of potential biomarkers extracted from this study was then explored in tissue microarrays containing paraffin-diagnostic tissue from 50 patients. The statistical analysis identified 17 immunohistochemical markers associated with the clinical endpoints. A subsequent multivariate analysis identified FoxP3+ T-reg cells as an independent predictor of failure-free survival. Bcl6 expression, CG/ABC subclasses and IPI were found not to predict survival in this series. The increased presence of regulatory T-cells as a marker of adverse outcome highlights specific components of the tumoral microenvironment in the pathogenesis and treatment response prediction for high-clinical-risk patients with DLBCL.

Building an outcome predictor model for diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) patients are treated using relatively homogeneous protocols, irrespective of their biological and clinical variability. Here we have developed a protein-expression-based outcome predictor for DLBCL. Using tissue microarrays (TMAs), we have analyzed the expression of 52 selected molecules in a series of 152 DLBCLs. The study yielded relevant information concerning key biological aspects of this tumor, such as cell-cycle control and apoptosis. A biological predictor was built with a training group of 103 patients, and was validated with a blind set of 49 patients. The predictive model with 8 markers can identify the probability of failure for a given patient with 78% accuracy. After stratifying patients according to the predicted response under the logistic model, 92.3% patients below the 25 percentile were accurately predicted by this biological score as "failure-free" while 96.2% of those above the 75 percentile were correctly predicted as belonging to the "fatal or refractory disease" group. Combining this biological score and the International Prognostic Index (IPI) improves the capacity for predicting failure and survival. This predictor was then validated in the independent group. The protein-expression-based score complements the information obtained from the use of the IPI, allowing patients to be assigned to different risk categories.