Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery.

Background and purpose: Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS). Materials and methods: Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients. Results: Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model. Conclusions: A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.

Cyanobacteria: miniature factories for green synthesis of metallic nanomaterials: a review.

Nanotechnology is one of the most promising and advanced disciplines of science that deals with synthesis, characterization and applications of different types of Nanomaterials (NMs) viz. nanospheres, nanoparticles, nanotubes, nanorods, nanowires, nanocomposites, nanoalloys, carbon dots and quantum dots. These nanosized materials exhibit different physicochemical characteristics and act as a whole unit during its transport. The unique characteristics and vast applications of NMs in diverse fields viz. electronics, agriculture, biology and medicine have created huge demand of different type of NMs. Conventionally physical and chemical methods were adopted to manufacture NMs which are expensive and end up with hazardous by-products. Therefore, green synthesis exploiting biological resources viz. algae, bacteria, fungi and plants emerged as a better and promising alternative due to its cost effective and ecofriendly approach and referred as nanobiotechnology. Among various living organisms, cyanobacteria have proved one of the most favourable bioresources for NMs biosynthesis due to their survival in diverse econiches including metal and metalloid contaminated sites and capability to withstand high levels of metals. Biosynthesis of metallic NMs is accomplished through bioreduction of respective metal salts by various capping agents viz. alkaloids, pigments, polysaccharides, steroids, enzymes and peptides present in the biological systems. Advancement in the field of Nanobiotechnology has produced large number of diverse NMs from cyanobacteria which have been used as antimicrobial agents against Gram positive and negative human pathogens, anticancer agents, luminescent nanoprobes for imaging of cells, antifungal agents against plant pathogens, nanocatalyst and semiconductor quantum dots in industries and in bioremediation in toxic pollutant dyes. In the present communication, we have reviewed cyanobacteria mediated biosynthesis of NMs and their applications in various fields.

COVID-19 in vaccinated versus unvaccinated hematologic malignancy patients.

The effect of vaccination on severity of subsequent COVID-19 in patients with hematologic malignancies (HMs) is unknown. In this single-center retrospective cohort study, we found no difference in severity of COVID-19 disease in vaccinated (n = 16) versus unvaccinated (n = 54) HM patients using an adjusted multiple logistic regression model. Recent anti-B-cell therapy was associated with more severe illness.

Biogenic synthesis and characterization of selenium nanoparticles and their applications with special reference to antibacterial, antioxidant, anticancer and photocatalytic activity.

Oxyanions of selenium, selenite (SeO3)2- and selenate (SeO4)2- are toxic to terrestrial and aquatic biota but few microorganisms including cyanobacteria are resistant to high levels of selenite. Cyanobacteria evade selenite toxicity through bioreduction and synthesis of selenium nanoparticles (SeNPs). In this study, extracellular biosynthesis of SeNPs (Se0) using cyanobacterium, Anabaena sp. PCC 7120 on exposure to sodium selenite and characterization was done by using UV-visible spectroscopy, SEM-EDX, TEM and FTIR analyses which confirmed spherical shape with size range of 5-50 nm diameter. These biogenic SeNPs demonstrated significant antibacterial and anti-biofilm activity against bacterial pathogens. Furthermore, these SeNPs showed high antioxidant activity at minimum concentration of 50 µg/mL and significant anti-proliferative activity against HeLa cell line with IC50 value of 5.5 µg/mL. The SeNPs also induced accumulation of cancer cells in the sub-G1 phase which was clearly observed in cellular and nuclear morphology. These biofabricated SeNPs also reduced and decolorized toxic methylene blue dye significantly through photocatalytic degradation. Therefore Anabaena sp. PCC 7120 may be employed as a green bioresource to synthesize SeNPs with potential applications in medicine and environmental bioremediation.

Joint total variation-based reconstruction of multiparametric magnetic resonance images for mapping tissue types.

Multispectral analysis of coregistered multiparametric magnetic resonance (MR) images provides a powerful method for tissue phenotyping and segmentation. Acquisition of a sufficiently varied set of multicontrast MR images and parameter maps to objectively define multiple normal and pathologic tissue types can require long scan times. Accelerated MRI on clinical scanners with multichannel receivers exploits techniques such as parallel imaging, while accelerated preclinical MRI scanning must rely on alternate approaches. In this work, tumor-bearing mice were imaged at 7 T to acquire k-space data corresponding to a series of images with varying T1-, T2- and T2*-weighting. A joint reconstruction framework is proposed to reconstruct a series of T1-weighted images and corresponding T1 maps simultaneously from undersampled Cartesian k-space data. The ambiguity introduced by undersampling was resolved by using model-based constraints and structural information from a reference fully sampled image as the joint total variation prior. This process was repeated to reconstruct T2-weighted and T2*-weighted images and corresponding maps of T2 and T2* from undersampled Cartesian k-space data. Validation of the reconstructed images and parameter maps was carried out by computing tissue-type maps, as well as maps of the proton density fat fraction (PDFF), proton density water fraction (PDwF), fat relaxation rate ( R2f*) and water relaxation rate ( R2w*) from the reconstructed data, and comparing them with ground truth (GT) equivalents. Tissue-type maps computed using 18% k-space data were visually similar to GT tissue-type maps, with dice coefficients ranging from 0.43 to 0.73 for tumor, fluid adipose and muscle tissue types. The mean T1 and T2 values within each tissue type computed using only 18% k-space data were within 8%-10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k-space data were within 3%-15% of GT values and showed good agreement with the expected values for the four tissue types.