Native Study of the Behaviour of Magnetite Nanoparticles for Hyperthermia Treatment during the Initial Moments of Intravenous Administration

Magnetic nanoparticles (MNPs) present outstanding properties making them suitable as therapeutic agents for hyperthermia treatments. Since the main safety concerns of MNPs are represented by their inherent instability in a biological medium, strategies to both achieve longterm stability and monitor hazardous MNP degradation are needed. We combined a dynamic approach relying on flow field flow fractionation (FFF)-multidetection with conventional techniques to explore frame-by-frame changes of MNPs injected in simulated biological medium, hypothesize the interaction mechanism they are subject to when surrounded by a saline, protein-rich environment, and understand their behaviour at the most critical point of intravenous administration. In the first moments of MNPs administration in the patient, MNPs change their surrounding from a favorable to an unfavorable medium, i.e., a complex biological fluid such as blood; the particles evolve from a synthetic identity to a biological identity, a transition that needs to be carefully monitored. The dynamic approach presented herein represents an optimal alternative to conventional batch techniques that can monitor only size, shape, surface charge, and aggregation phenomena as an averaged information, given that they cannot resolve different populations present in the sample and cannot give accurate information about the evolution or temporary instability of MNPs. The designed FFF method equipped with a multidetection system enabled the separation of the particle populations providing selective information on their morphological evolution and on nanoparticle– proteins interaction in the very first steps of infusion. Results showed that in a dynamic biological setting and following interaction with serum albumin, PP-MNPs retain their colloidal properties, supporting their safety profile for intravenous administration

Predicting second breast cancer among women with primary breast cancer using machine learning algorithms, a population-based observational study

Breast cancer survivors often experience recurrence or a second primary cancer. We developed an automated approach to predict the occurrence of any second breast cancer (SBC) using patient-level data and explored the generalizability of the models with an external validation data source. Breast cancer patients from the cancer registry of Zurich, Zug, Schaffhausen, Schwyz (N = 3213; training dataset) and the cancer registry of Ticino (N = 1073; external validation dataset), diagnosed between 2010 and 2018, were used for model training and validation, respectively. Machine learning (ML) methods, namely a feed-forward neural network (ANN), logistic regression, and extreme gradient boosting (XGB) were employed for classification. The best-performing model was selected based on the receiver operating characteristic (ROC) curve. Key characteristics contributing to a high SBC risk were identified. SBC was diagnosed in 6% of all cases. The most important features for SBC prediction were age at incidence, year of birth, stage, and extent of the pathological primary tumor. The ANN model had the highest area under the ROC curve with 0.78 (95% confidence interval [CI] 0.750.82) in the training data and 0.70 (95% CI 0.61-0.79) in the external validation data. Investigating the generalizability of different ML algorithms, we found that the ANN generalized better than the other models on the external validation data. This research is a first step towards the development of an automated tool that could assist clinicians in the identification of women at high risk of developing an SBC and potentially preventing it

Quality indicators of clinical cancer care for prostate cancer: a population-based study in southern Switzerland

Abstract Background Quality of cancer care (QoCC) has become an important item for providers, regulators and purchasers of care worldwide. Aim of this study is to present the results of some evidence-based quality indicators (QI) for prostate cancer (PC) at the population-based level and to compare the outcomes with data available in the literature. Methods The study included all PC diagnosed on a three years period analysis (01.01.2011–31.12.2013) in the population of Canton Ticino (Southern Switzerland) extracted from the Ticino Cancer Registry database. 13 QI, approved through the validated Delphi methodology, were calculated using the “available case” approach: 2 for diagnosis, 4 for pathology, 6 for treatment and 1 for outcome. The selection of the computed QI was based on the availability of medical documentation. QI are presented as proportion (%) with the corresponding 95% confidence interval. Results 700 PC were detected during the three-year period 2011–2013: 78.3% of them were diagnosed through a prostatic biopsy and for 72.5% 8 or more biopsy cores were taken. 46.5% of the low risk PC patients underwent active surveillance, while 69.2% of high risk PC underwent a radical treatment (radical prostatectomy, radiotherapy or brachytherapy) and 73.5% of patients with metastatic PC were treated with hormonal therapy. The overall 30-day postoperative mortality was 0.5%. Conclusions Results emerging from this study on the QoCC for PC in Canton Ticino are encouraging: the choice of treatment modalities seems to respect the international guidelines and our results are comparable to the scarce number of available international studies. Additional national and international standardisation of the QI and further QI population-based studies are needed in order to get a real picture of the PC diagnostic-therapeutic process progress through the definition of thresholds of minimal standard of care

Predicting second breast cancer among women with primary breast cancer using machine learning algorithms, a population-based observational study

Breast cancer survivors often experience recurrence or a second primary cancer. We developed an automated approach to predict the occurrence of any second breast cancer (SBC) using patient-level data and explored the generalizability of the models with an external validation data source. Breast cancer patients from the cancer registry of Zurich, Zug, Schaffhausen, Schwyz (N = 3213; training dataset) and the cancer registry of Ticino (N = 1073; external validation dataset), diagnosed between 2010 and 2018, were used for model training and validation, respectively. Machine learning (ML) methods, namely a feed-forward neural network (ANN), logistic regression, and extreme gradient boosting (XGB) were employed for classification. The best-performing model was selected based on the receiver operating characteristic (ROC) curve. Key characteristics contributing to a high SBC risk were identified. SBC was diagnosed in 6% of all cases. The most important features for SBC prediction were age at incidence, year of birth, stage, and extent of the pathological primary tumor. The ANN model had the highest area under the ROC curve with 0.78 (95% confidence interval [CI] 0.750.82) in the training data and 0.70 (95% CI 0.61-0.79) in the external validation data. Investigating the generalizability of different ML algorithms, we found that the ANN generalized better than the other models on the external validation data. This research is a first step towards the development of an automated tool that could assist clinicians in the identification of women at high risk of developing an SBC and potentially preventing it.ISSN:0020-7136ISSN:1097-021

Additional file 1: of Quality indicators of clinical cancer care for prostate cancer: a population-based study in southern Switzerland

List of QI assessed and selected for prostate cancer, according to the clinical domain. This file describes the full list of selected QI for prostate cancer. (PDF 142 kb

Valoración del tiempo en rango terapéutico en pacientes bajo tratamiento con antagonistas orales de la vitamina K en un centro de anticoagulación

Oral dicumarinic anticoagulants are used in various pathologies for the primary and secondary prevention of venous and arterial thromboembolic events.They have complex pharmacokinetics and pharmacodynamics resulting in a very narrow therapeutic range. Therefore, monitoring and strict control of anticoagulant therapy are essential. The efficacy of this medical treatment resides in a low percentage of ischemic and hemorrhagic episodes related to percentages of therapeutic interval time (TTR) associated with correctly controlled anticoagulation.This retrospective study was carried out on 495 adults population who attended the Hemostasis and Thrombosis Department of the Transfusion Medicine Service of the Italian Hospital for being under treatment with oral anticoagulants, from which the TTR values were obtained from their medical records calculated by the Rosendaal method.The purposes of this study are to determine the TTR to know the reached anticoagulation level and to assess whether age, gender, diagnosis and type of anticoagulant are associated with worse outcomes.The median (Me) TRT value from the total samples was 64.0% (IIC 53.0 - 77.0). The mean out of range time (either above or below the accepted values) was also calculated with a value of 36.0% No statistically significant differences were found in the levels of TTR among the different groups or in the total of patients in the following variables: gender, type of anticoagulant and age. On the other hand, a statistically significant difference was found in the TTR value of the group of patients with valve replacement vs. patients with other diagnoses.Knowing the TTR value is important, as it enables us to identify the level of anticoagulant care and toestablish new goals. Values obtained in this research are similar to the experience published by nationallyand internationally recognized anticoagulation sites and allow us to reconsider strategies to improvequality and performance of service.Los anticoagulantes orales, dicumarínicos, se utilizan en diversas patologías para la prevención primaria y secundaria de eventos tromboembólicos venosos y arteriales. Tienen una compleja farmacocinética y farmacodinamia resultando en un rango terapéutico muy estrecho. Por lo tanto, la monitorización y el estricto control de la terapia anticoagulante son fundamentales. La eficacia de estos fármacos reside en un porcentaje bajo de episodios isquémicos y hemorrágicos relacionados con porcentajes elevados de tiempo en rango terapéutico (TRT) asociados a una anticoagulación correctamente controlada.El presente es un trabajo retrospectivo que fue realizado sobre 495 adultos que concurrieron al Departamento de Hemostasia y Trombosis del Servicio de Medicina Transfusional del Hospital Italiano por estar bajo tratamiento con anticoagulantes orales de los cuales se obtuvieron los valores de TRT de sus historias clínicas calculados mediante el método de Rosendaal.Este estudio tiene como objetivos determinar el TRT para conocer el nivel de anticoagulación alcanzado y valorar si la edad, sexo, diagnóstico y tipo de anticoagulante se asocian con peores resultados.El TRT total de la muestra presentó una mediana (Me) de 64,0% (IIC 53,0 - 77,0). Se analizó también el porcentaje del tiempo en que los pacientes estuvieron fuera de rango ya sea por encima o por debajo de los valores aceptados, que fue del 36,0%. No se encontraron diferencias estadísticamente significativas en los niveles de TRT entre los distintos grupos ni en el total de pacientes en las siguientes variables: sexo, tipo de anticoagulante y edad. Por el contrario, se encontró diferencia estadísticamente significativa en el valor de TRT del grupo de pacientes con reemplazo valvular frente a pacientes con otros diagnósticos.Conocer el valor de TRT es importante, ya que nos permite identificar el nivel de atención anticoagulante y establecer nuevas metas. Los valores obtenidos en esta investigación se asemejan a la experiencia publicada por centros de anticoagulación reconocidos a nivel nacional e internacional y nos permiten replantear estrategias para la mejora en la calidad y desempeño del servicio

Impact of subtypes and comorbidities on breast cancer relapse and survival in population-based studies

OBJECTIVE To study the impact of subtypes and comorbidities on breast cancer (BC) relapse and survival in the heterogeneous patients of the real world. METHODS We identified patients diagnosed with BC between January 2003 and December 2005 from six population-based Swiss cancer registries. Clinicopathologic data was completed with information on locoregional and distant relapse and date and cause of death for over 10-years. We approximated BC subtypes using grade and the immunohistochemical panel for oestrogen, progesterone and human epidermal growth factor 2 (HER2) receptor status. We studied factors affecting relapse and survival. RESULTS Luminal A-like subtype represented 46% of all newly diagnosed BC (N = 1831), followed by luminal B-like (N = 1504, 38%), triple negative (N = 436, 11%) and HER2 enriched (N = 204, 5%). We observed regional disparities in subtype prevalence that contribute to explain regional differences in survival formerly described. Disease relapse and BC specific mortality differed by subtype and were lower for luminal A like tumours than for other subtypes for any stage at diagnosis. After a median follow-up of 10.9 years, 1311 (33%) had died, half of them 647 (16%) due to another disease, showing the importance of comorbidities. Omission of systemic therapies in selected patients was not associated with poorer BC specific survival, BC subtype and life expectancy playing a role. CONCLUSIONS Information on tumour subtype is necessary for an adequate interpretation of population-based BC studies. Measures of comorbidity or frailty help in the evaluation of quality of care in the highly heterogeneous patients of the real world