Application of magnetically induced hyperthermia on the model protozoan Crithidia fasciculata as a potential therapy against parasitic infections

Magnetic hyperthermia is currently an EU-approved clinical therapy against tumor cells that uses magnetic nanoparticles under a time varying magnetic field (TVMF). The same basic principle seems promising against trypanosomatids causing Chagas disease and sleeping sickness, since therapeutic drugs available display severe side effects and drug-resistant strains. However, no applications of this strategy against protozoan-induced diseases have been reported so far. In the present study, Crithidia fasciculata, a widely used model for therapeutic strategies against pathogenic trypanosomatids, was targeted with Fe_{3}O_{4} magnetic nanoparticles (MNPs) in order to remotely provoke cell death using TVMFs. The MNPs with average sizes of d approx. 30 nm were synthesized using a precipitation of FeSO_{4}4 in basic medium. The MNPs were added to Crithidia fasciculata choanomastigotes in exponential phase and incubated overnight. The amount of uploaded MNPs per cell was determined by magnetic measurements. Cell viability using the MTT colorimetric assay and flow cytometry showed that the MNPs were incorporated by the cells with no noticeable cell-toxicity effects. When a TVMF (f = 249 kHz, H = 13 kA/m) was applied to MNP-bearing cells, massive cell death was induced via a non-apoptotic mechanism. No effects were observed by applying a TVMF on control (without loaded MNPs) cells. No macroscopic rise in temperature was observed in the extracellular medium during the experiments. Scanning Electron Microscopy showed morphological changes after TVMF experiments. These data indicate (as a proof of principle) that intracellular hyperthermia is a suitable technology to induce the specific death of protozoan parasites bearing MNPs. These findings expand the possibilities for new therapeutic strategies that combat parasitic infections.Comment: 9 pages, four supplementary video file

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient’s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient\u27s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Derivation of Del180 from sediment core log data\u27 Implications for millennial-scale climate change in the Labrador Sea

Sediment core logs from six sediment cores in the Labrador Sea show millennial-scale climate variability during the last glacial by recording all Heinrich events and several major Dansgaard-Oeschger cycles. The same millennial-scale climate change is documented for surface water δ18O records of Neogloboquadrina pachyderma (left coiled); hence the surface water δ18O record can be derived from sediment core logging by means of multiple linear regression, providing a paleoclimate proxy record at very high temporal resolution (70 years). For the Labrador Sea, sediment core logs contain important information about deepwater current velocities and also reflect the variable input of ice-rafted debris from different sources as inferred from grain-size analysis, the relation of density and P wave velocity, and magnetic susceptibility. For the last glacial, faster deepwater currents, which correspond to highs in sediment physical properties, occurred during iceberg discharge and lasted from several centuries to a few millennia. Those enhanced currents might have contributed to increased production of intermediate waters during times of reduced production of North Atlantic Deep Water. Hudson Strait might have acted as a major supplier of detrital carbonate only during lowered sea level (greater ice extent). During coldest atmospheric temperatures over Greenland, deepwater currents increased during iceberg discharge in the Labrador Sea, then surface water freshened shortly thereafter, while the abrupt atmospheric temperature rise happened after a larger time lag of ≥ 1 kyr. The correlation implies a strong link and common forcing for atmosphere, sea surface, and deep water during the last glacial at millennial timescales but decoupling at orbital timescales

Functional proteomics outlines the complexity of breast cancer molecular subtypes

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptorpositive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expressionbased probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score

Understanding The Correlation Of Libs And Acoustic Measurements Of Rocks And Soils Found In The Traverse Of The Perseverance Rover Across The Jezero Crater, Mars

The SuperCam instrument of the NASA MARS 2020 Perseverance rover combines a suite of atomic and molecular spectroscopies intended for an extensive description of rocks, soils and minerals in the surroundings of the landing site of the mission – the Jezero crater. The microphone installed on the SuperCam instrument allows the acquisition of acoustic signals resulting from the expansion of laser-induced plasmas towards the atmosphere. Apart from being affected by the propagation characteristics of the Mars atmosphere, the acoustic signal has an additional component related to the properties of the target including surface morphology, hardness, deformation parameters, and elasticity, among others. This information is currently being investigated as a complementary resource for characterization of the ablated material and may well supplement the LIBS data gathered from coincident laser shots. This talk will present SuperCam acoustic data of rocks and minerals found in the traverse of the Perseverance rover and will discuss its correlation with LIBS spectra.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Challenges and Lessons Learned from fabrication, testing and analysis of eight MQXFA Low Beta Quadrupole magnets for HL-LHC

By the end of October 2022, the US HL-LHC Accelerator Upgrade Project (AUP) had completed fabrication of ten MQXFA magnets and tested eight of them. The MQXFA magnets are the low beta quadrupole magnets to be used in the Q1 and Q3 Inner Triplet elements of the High Luminosity LHC. This AUP effort is shared by BNL, Fermilab, and LBNL, with strand verification tests at NHMFL. An important step of the AUP QA plan is the testing of MQXFA magnets in a vertical cryostat at BNL. The acceptance criteria that could be tested at BNL were all met by the first four production magnets (MQXFA03-MQXFA06). Subsequently, two magnets (MQXFA07 and MQXFA08) did not meet some criteria and were disassembled. Lessons learned during the disassembly of MQXFA07 caused a revision to the assembly specifications that were used for MQXFA10 and subsequent magnets. In this paper, we present a summary of: 1) the fabrication and test data of all the MQXFA magnets; 2) the analysis of MQXFA07/A08 test results with characterization of the limiting mechanism; 3) the outcome of the investigation, including the lessons learned during MQXFA07 disassembly; and 4) the finite element analysis correlating observations with test performance

LIBS and Acoustic Measurements of Rocks and Regolith Found in the Traverse of the Perseverance Rover Across the Jezero Crater, Mars

The SuperCam instrument of the NASA MARS 2020 Perseverance rover combines a suite of atomic and molecular spectroscopies intended for an extensive description of rocks, soils and minerals in the surroundings of the landing site of the mission – the Jezero crater. The microphone installed on the SuperCam instrument allows the acquisition of acoustic signals resulting from the expansion of laser-induced plasmas towards the atmosphere. Apart from being affected by the propagation characteristics of the Mars atmosphere, the acoustic signal has an additional component related to the properties of the target including surface morphology, hardness, deformation parameters, and elasticity, among others. This information is currently being investigated as a complementary resource for characterization of the ablated material and may well supplement the LIBS data gathered from coincident laser shots. This talk will present SuperCam acoustic data of rocks and minerals found in the traverse of the Perseverance rover and will discuss its correlation with LIBS spectra.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

The sound of geological targets on Mars from the absolute intensity of laser-induced sparks shock waves

Inspection of geological material is one of the main goals of the Perseverance rover during its journey across the landscape of the Jezero crater in Mars. NASA's rover integrates SuperCam, an instrument capable of performing standoff characterization of samples using a variety of techniques. Among those tools, SuperCam can perform laser-induced breakdown spectroscopy (LIBS) studies to elucidate the chemical composition of the targets of interest. Data from optical spectroscopy can be supplemented by simultaneously-produced laser-produced plasma acoustics in order to expand the information acquired from the probed rocks thanks to the SuperCam's microphone (MIC) as it can be synchronized with the LIBS laser. Herein, we report cover results from LIBS and MIC during Perseverance's first 380 sols on the Martian surface. We study the correlation between both recorded signals, considering the main intrasample and environmental sources of variation for each technique, to understand their behavior and how they can be interpreted together towards complimenting LIBS with acoustics. We find that louder and more stable acoustic signals are recorded from rock with compact surfaces, i.e., low presence loose particulate material, and harder mineral phases in their composition. Reported results constitute the first description of the evolution of the intensity in the time domain of shockwaves from laser-produced plasmas on geological targets recorded in Mars. These signals are expected contain physicochemical signatures pertaining to the inspected sampling positions. As the dependence of the acoustic signal recorded on the sample composition, provided by LIBS, is unveiled, the sound from sparks become a powerful tool for the identification of mineral phases with similar optical emission spectra.Many people helped with this project in addition to the co-authors, including hardware and operation teams, and we are most grateful for their support. This project was supported in the USA by NASA’s Mars Exploration Program and in France is conducted under the authority of CNES. Research funded by projects UMA18-FEDERJA-272 from Junta de Andalucía and PID2020-119185GB-I00 from Ministerio de Ciencia e Innovacion, of Spain. P.P. is grateful to the European Union’s Next Generation EU (NGEU) plan and the Spanish Ministerio de Universidades for his Margarita Salas fellowship under the program ′′Ayudas para la Recualificacion del Sistema Universitario Español′′. RCW was funded by JPL contract 1681089. A.U was funded by NASA Mars 2020 Participating Scientist program 80NSSC21K0330. Funding for open access charge: Universidad de Málaga / CBU