Iron deficiency reduces synapse formation in the Drosophila clock circuit

Iron serves as a critical cofactor for proteins involved in a host of biological processes. In most animals, dietary iron is absorbed in enterocytes and then disseminated for use in other tissues in the body. The brain is particularly dependent on iron. Altered iron status correlates with disorders ranging from cognitive dysfunction to disruptions in circadian activity. The exact role iron plays in producing these neurological defects, however, remains unclear. Invertebrates provide an attractive model to study the effects of iron on neuronal development since many of the genes involved in iron metabolism are conserved, and the organisms are amenable to genetic and cytological techniques. We have examined synapse growth specifically under conditions of iron deficiency in the Drosophila circadian clock circuit. We show that projections of the small ventrolateral clock neurons to the protocerebrum of the adult Drosophila brain are significantly reduced upon chelation of iron from the diet. This growth defect persists even when iron is restored to the diet. Genetic neuronal knockdown of ferritin 1 or ferritin 2, critical components of iron storage and transport, does not affect synapse growth in these cells. Together, these data indicate that dietary iron is necessary for central brain synapse formation in the fly and further validate the use of this model to study the function of iron homeostasis on brain development

NEMA NU 2-2018 performance evaluation of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT

A NEMA performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32-cm extended field-of-view. This study marks the introduction of the first-ever commercially available clinical digital bismuth germanate technology. Testing was performed in accordance with the NEMA NU2-2018 standard. A comparison was made with the performance of two other commercial GE scanners with extended fields-of-view. A digital lutetium yttrium orthosilicate system (Discovery MI - 6 ring) and a non-digital bismuth germanate system (Discovery IQ). For the Omni assessment, the tangential, radial, and axial spatial resolutions at 1 cm radial offset were measured as 3.76 mm, 3.73 mm, and 4.25 mm FWHM. The total system sensitivity to a line source at the center was 44.36 cps/kBq. The peak NECR was 501 kcps at 17.8 kBq/mL. The scatter fraction at NECR peak was 35.48%, and the maximum count-rate error at and below NEC peak was 5.5%. Sphere contrast recovery coefficients were from 52% (10 mm) to 93% (37 mm). The system does not use time of flight; thus, no assessment of timing resolution was made. The PET-CT co-registration accuracy was 2.4 mm. The performance of the Omni Legend surpassed that of the Discovery MI on all NEMA tests, except for assessments of background variability (image noise). Time of flight is associated with inherent improvements in signal-to-noise ratio. In lieu of time of flight capabilities, the Omni provides software corrections in the form of a pre-trained neural network (trained on non-ToF to ToF). With such corrections, average performance is competitive when compared to ToF systems. Further validation is required to optimize clinical imaging protocols and hyperparameters associated with such software corrections and to examine the effect of non-linear corrections with varying target size, particularly for real world, clinical scans

Vertical movements of shortfin mako sharks Isurus oxyrinchus in the western North Atlantic Ocean are strongly influenced by temperature

Although shortfin mako sharks Isurus oxyrinchus are regularly encountered in pelagic fisheries, limited information is available on their vertical distribution and is primarily restricted to cooler areas of their geographic range. We investigated the vertical movements of mako sharks across differing temperature regimes within the western North Atlantic by tagging 8 individuals with pop-up satellite archival tags off the northeastern United States and the Yucatan Peninsula, Mexico. Depth and temperature records across 587 d showed vertical movements strongly associated with ocean temperature. Temperatures150 m compared to only 1% in the coldest water columns. The sharks showed diel diving behavior, with deeper dives occurring primarily during the daytime (maximum depth: 866 m). Overall, sharks experienced temperatures between 5.2 and 31.1°C. When the opportunity was available, sharks spent considerable time in waters ranging from 22 to 27°C, indicating underestimation of the previously reported upper limit of the mako sharks’ preferred temperature. The preference for higher temperatures does not support endothermy as an adaption for niche expansion in mako sharks. The strong influence of thermal habitat on movement behavior suggests potentially strong impacts of rising ocean temperatures on the ecology of this highly migratory top predator

Phantom and clinical evaluation of the effect of full Monte Carlo collimator modelling in post-SIRT yttrium-90 Bremsstrahlung SPECT imaging

Background: Post-therapy SPECT/CT imaging of Y-90 microspheres delivered to hepatic malignancies is difficult, owing to the continuous, high-energy Bremsstrahlung spectrum emitted by Y-90. This study aimed to evaluate the utility of a commercially available software package (HybridRecon, Hermes Medical Solutions AB) which incorporates full Monte Carlo collimator modelling. Analysis of image quality was performed on both phantom and clinical images in order to ultimately provide a recommendation of an optimum reconstruction for post-therapy Y-90 microsphere SPECT/CT imaging. A 3D-printed anthropomorphic liver phantom was filled with Y-90 with a sphere-to-background ratio of 4:1 and imaged on a GE Discovery 670 SPECT/CT camera. Datasets were reconstructed using ordered-subsets expectation maximization (OSEM) 1-7 iterations in order to identify the optimal OSEM reconstruction (5 iterations, 15 subsets). Quantitative analysis was subsequently carried out on phantom datasets obtained using four reconstruction algorithms: the default OSEM protocol (2 iterations, 10 subsets) and the optimised OSEM protocol, both with and without full Monte Carlo collimator modelling. The quantitative metrics contrast recovery (CR) and background variability (BV) were calculated. The four algorithms were then used to retrospectively reconstruct 10 selective internal radiation therapy (SIRT) patient datasets which were subsequently blind scored for image quality by a consultant radiologist. Results: The optimised OSEM reconstruction (5 iterations, 15 subsets with full MC collimator modelling) increased the CR by 42% (p <0.001) compared to the default OSEM protocol (2 iterations, 10 subsets). The use of full Monte Carlo collimator modelling was shown to further improve CR by 14% (30 mm sphere, CR = 90%, p <0.05). The consultant radiologist had a significant preference for the optimised OSEM over the default OSEM protocol (p <0. 001), with the optimised OSEM being the favoured reconstruction in every one of the 10 clinical cases presented. Conclusions: OSEM (5 iterations, 15 subsets) with full Monte Carlo collimator modelling is quantitatively the optimal image reconstruction for post-SIRT 90Y Bremsstrahlung SPECT/CT imaging. The use of full Monte Carlo collimator modelling for correction of image-degrading effects significantly increases contrast recovery without degrading clinical image quality.Peer reviewe

Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules

OBJECTIVES: Investigate the effect of a novel Bayesian penalised likelihood (BPL) reconstruction algorithm on analysis of pulmonary nodules examined with 18F-FDG PET/CT, and to determine its effect on small, sub-10-mm nodules. METHODS: 18F-FDG PET/CTs performed for nodule evaluation in 104 patients (121 nodules) were retrospectively reconstructed using the new algorithm, and compared to time-of-flight ordered subset expectation maximisation (OSEM) reconstruction. Nodule and background parameters were analysed semi-quantitatively and visually. RESULTS: BPL compared to OSEM resulted in statistically significant increases in nodule SUV(max) (mean 5.3 to 8.1, p < 0.00001), signal-to-background (mean 3.6 to 5.3, p < 0.00001) and signal-to-noise (mean 24 to 41, p < 0.00001). Mean percentage increase in SUV(max) (%ΔSUV(max)) was significantly higher in nodules ≤10 mm (n = 31, mean 73 %) compared to >10 mm (n = 90, mean 42 %) (p = 0.025). Increase in signal-to-noise was higher in nodules ≤10 mm (224 %, mean 12 to 27) compared to >10 mm (165 %, mean 28 to 46). When applying optimum SUV(max) thresholds for detecting malignancy, the sensitivity and accuracy increased using BPL, with the greatest improvements in nodules ≤10 mm. CONCLUSION: BPL results in a significant increase in signal-to-background and signal-to-noise compared to OSEM. When semi-quantitative analyses to diagnose malignancy are applied, higher SUV(max) thresholds may be warranted owing to the SUV(max) increase compared to OSEM. KEY POINTS: • Novel Bayesian penalised likelihood PET reconstruction was applied for lung nodule evaluation. • This was compared to current standard of care OSEM reconstruction. • The novel reconstruction generated significant increases in lung nodule signal-to-background and signal-to-noise. • These increases were highest in small, sub-10-mm pulmonary nodules. • Higher SUV(max)thresholds may be warranted when using semi-quantitative analyses to diagnose malignancy

Analysis of photon scattering trends for material classification using artificial neural network models

In this project, we concentrate on using the Artificial Neural Network (ANN) approach to analyze the photon scattering trend given by specific materials. The aim of this project is to fully utilize the scatter components of an interrogating gamma-ray radiation beam in order to determine the types of material embedded in sand and later to determine the depth of the material. This is useful in a situation in which the operator has no knowledge of potentially hidden materials. In this paper, the materials that we used were stainless steel, wood and stone. These moderately high density materials are chosen because they have strong scattering components, and provide a good starting point to design our ANN model. Data were acquired using the Monte Carlo N-Particle Code, MCNP5. The source was a collimated pencil-beam projection of 1 MeV energy gamma rays and the beam was projected towards a slab of unknown material that was buried in sand. The scattered photons were collected using a planar surface detector located directly above the sample. In order to execute the ANN model, several feature points were extracted from the frequency domain of the collected signals. For material classification work, the best result was obtained for stone with 86.6% accurate classification while the most accurate buried distance is given by stone and wood, with a mean absolute error of 0.05