Investigation of activation cross-sections of proton induced nuclear reactions on natTl up to 42 MeV: review, new data and evaluation

Cross-sections of proton induced nuclear reactions on natural thallium have been studied for investigation of the production of the medical important 201Tl diagnostic radioisotope. The excitation functions of 204mPb, 203Pb, 202mPb, 201Pb, 200Pb, 199Pb, 202Tl (direct, cumulative), 201Tl (direct, cumulative), 200Tl(direct), and 203Hg were measured up to 42 MeV proton energy by stacked foil technique and activation method. The experimental data were compared with the critically analyzed experimental data in the literature, with the IAEA recommended data and with the results of model calculations by using the ALICE-IPPE, EMPIRE-II and TALYS codes

Lineage of origin in rhabdomyosarcoma informs pharmacological response

Lineage or cell of origin of cancers is often unknown and thus is not a consideration in therapeutic approaches. Alveolar rhabdomyosarcoma (aRMS) is an aggressive childhood cancer for which the cell of origin remains debated. We used conditional genetic mouse models of aRMS to activate the pathognomonic Pax3:Foxo1 fusion oncogene and inactivate p53 in several stages of prenatal and postnatal muscle development. We reveal that lineage of origin significantly influences tumor histomorphology and sensitivity to targeted therapeutics. Furthermore, we uncovered differential transcriptional regulation of the Pax3:Foxo1 locus by tumor lineage of origin, which led us to identify the histone deacetylase inhibitor entinostat as a pharmacological agent for the potential conversion of Pax3:Foxo1-positive aRMS to a state akin to fusion-negative RMS through direct transcriptional suppression of Pax3:Foxo1.Stem Cell and Regenerative Biolog

Investigation of the deuteron induced nuclear reaction cross sections on lutetium up to 50 MeV: review of production routes for 177Lu, 175Hf and 172Hf via charged particle activation

In a systematic study of light charged particle induced nuclear reactions we investigated the excitation functions of deuteron induced nuclear reactions on natural lutetium targets. Experimental excitation functions up to 50 MeV on high purity natLu were determined using the standard stacked foil activation technique. High resolution of-line gamma-ray spectrometry was applied to assess the activity of each foil. From the measured activity direct and/or cumulative elemental cross-section data for production of 171,172,173,175Hf, 171,172,173,174g,176m,177m,177gLu and 169Yb radioisotopes were determined. The experimental data were compared to results of the TALYS theoretical code taken from the TENDL databases and results of our calculations using the ALICE-IPPE-D and the EMPIRE-D codes. No earlier experimental data were found in the literature. Thick target yields for the investigated radionuclides were calculated from the measured excitation functions

Proton and deuteron induced reactions on natGa: Experimental and calculated excitation functions

Cross-sections for reactions on natGa, induced by protons (up to 65 MeV) and deuterons (up to 50 MeV), producing c-emitting radionuclides with half-lives longer than 1 h were measured in a stacked-foil irradiation using thin Ga–Ni alloy (70–30%) targets electroplated on Cu or Au backings. Excitation functions for generation of 68,69Ge, 66,67,68,72Ga and 65,69mZn on natGa are discussed, relative to the monitor reactions natAl(d,x)24,22Na, natAl(p,x)24,22Na, natCu(p,x)62Zn and natNi(p,x)57Ni. The results are compared to our earlier measurements, the scarce literature values and to the results of the code TALYS 1.6 (online database TENDL-2014)

New data on cross-sections of deuteron induced nuclear reactions on gold up to 50 MeV and comparison of production routes of medically relevant Au and Hg radioisotopes

Investigations of cross-sections of deuteron induced nuclear reactions on gold were extended up to 50 MeV by using the standard stacked foil irradiation technique and high resolution gamma-ray spectrometry. New cross-sections are reported for the 197Au(d,xn)197m,197g,195m,195g,193m,193gHg and 197Au(d,x)198m,198g,196m,196g,195,194Au nuclear reactions. The application for production of the medically relevant isotopes 198Au and 195m,195g,197m,197gHg is discussed, including the comparison with other charged particle induced production routes. The possible use of the 197Au(d,x)197m,197g,195m,193mHg and 196m,196gAu reactions for monitoring deuteron beam parameters is also investigated

Limitation of the long-lived (121)Te contaminant in production of (123)I through the (124)Xe(p,x) route

The 13.2 h half-life radioisotope (123)I is widely used in clinical nuclear medicine diagnosis. At present it is mostly produced in nca form by proton irradiation of highly enriched (124)Xe in dedicated gas target set-ups and relying on the decay chain (123)Cs-(123)Xe-(123)I. Depending on the irradiation conditions contamination with long-lived (121)Te, a daughter product of the co-produced rather short lived (121)I, occurs and can limit the useful shelf life of the (123)I solution. Excitation function of the (124)Xe(p,α)(121)I, (124)Xe(p,2n)(123)Cs and (124)Xe(p,2p)(123)Xe reactions are measured up to 35 MeV using the stacked gas cell technique and high-resolution γ-ray spectrometry. The experimental data were compared with the earlier literature values, with new results of the ALICE-IPPE and EMPIRE-II codes and with the data taken from the TENDL-2009 database. Existing discrepancies in cross-section data are largely solved and new recommended values are proposed. From fits to the new excitation curves integral (123)I batch yields and (121)Te contaminations for realistic production conditions are derived. Optimization of irradiation and cooling times and energy degradation in the target can strongly influence the contamination level

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Abstract Background Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. Methods Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. Results Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89–0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state. Conclusion CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 201