Activation of Antioxidant and Proteolytic Pathways in the Nigrostriatal Dopaminergic System After 3,4-Methylenedioxymethamphetamine Administration: Sex-Related Differences

3,4-Methylenedioxymethamphetamine (MDMA, “ecstasy”) is an amphetamine-related drug that may damage the dopaminergic nigrostriatal system. To investigate the mechanisms that sustain this toxic effect and ascertain their sex-dependence, we evaluated in the nigrostriatal system of MDMA-treated (4 × 20 mg/kg, 2 h apart) male and female mice the activity of superoxide dismutase (SOD), the gene expression of SOD type 1 and 2, together with SOD1/2 co-localization with tyrosine hydroxylase (TH)-positive neurons. In the same mice and brain areas, activity of glutathione peroxidase (GPx) and of β2/β5 subunits of the ubiquitin-proteasome system (UPS) were also evaluated. After MDMA, SOD1 increased in striatal TH-positive terminals, but not nigral neurons, of males and females, while SOD2 increased in striatal TH-positive terminals and nigral neurons of males only. Moreover, after MDMA, SOD1 gene expression increased in the midbrain of males and females, whereas SOD2 increased only in males. Finally, MDMA increased the SOD activity in the midbrain of females, without affecting GPx activity, decreased the β2/β5 activities in the striatum of males and the β2 activity in the midbrain of females. These results suggest that the mechanisms of MDMA-induced neurotoxic effects are sex-dependent and dopaminergic neurons of males could be more sensitive to SOD2- and UPS-mediated toxic effects

Quality control for next-generation liquefaction case histories

The Next-Generation Liquefaction (NGL) database is an open-source, global database of liquefaction and non-ground failure case-histories. The database is part of a multi-year research effort with the main goal of developing improved procedures to evaluate liquefaction susceptibility, triggering, and consequences. In NGL, a case-history is defined as the intersection of three components: (1) a site, (2) an earthquake event, and (3) post-earthquake observations. The NGL database hosts case-histories used to develop existing liquefaction models, as well as new data derived from recent earthquakes such as the 2010-2011 Canterbury earthquake sequence, the 2011 Tohoku-Oki earthquake, and the 2012 Emilia earthquake. The database also hosts lateral spread case-histories, and a substantial number of liquefaction sites characterized by the presence of co-located recording stations. All of the data present in the NGL database are reviewed by the NGL Database Working Group. The NGL formal vetting process is described for an example case-history

MKI67 (marker of proliferation Ki-67)

Review on MKI67 (marker of proliferation Ki-67), with data on DNA, on the protein encoded, and where the gene is implicated

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

Cell killing and resistance in pre-operative breast cancer chemotherapy

<p>Abstract</p> <p>Background</p> <p>Despite the recent development of technologies giving detailed images of tumours <it>in vivo</it>, direct or indirect ways to measure how many cells are actually killed by a treatment or are resistant to it are still beyond our reach.</p> <p>Methods</p> <p>We designed a simple model of tumour progression during treatment, based on descriptions of the key phenomena of proliferation, quiescence, cell killing and resistance, and giving as output the macroscopically measurable tumour volume and growth fraction. The model was applied to a database of the time course of volumes of breast cancer in patients undergoing pre-operative chemotherapy, for which the initial estimate of proliferating cells by the measure of the percentage of Ki67-positive cells was available.</p> <p>Results</p> <p>The analysis recognises different patterns of response to treatment. In one subgroup of patients the fitting implied drug resistance. In another subgroup there was a shift to higher sensitivity during the therapy. In the subgroup of patients where killing of cycling cells had the highest score, the drugs showed variable efficacy against quiescent cells.</p> <p>Conclusion</p> <p>The approach was feasible, providing items of information not otherwise available. Additional data, particularly sequential Ki67 measures, could be added to the system, potentially reducing uncertainty in estimates of parameter values.</p