An atypical heterotrimeric Gα and its interactome suggest an extra-large role in overcoming abiotic and biotic stress.

Canonical heterotrimeric G-proteins (G-proteins) are comprised of Gα, Gß, and Gγ subunits. G-proteins regulate multiple crucial plant growth and development processes, incorporating environmental responses. Besides Gα, Gß and Gγ, the discovery of atypical Gα subunits termed as extra-large G-proteins or extra-large GTP-binding proteins (XLGs) makes G-protein signaling unique in plants. The C-terminus of XLG shares similarities with the canonical Gα subunits; the N-terminus harbors a nuclear localization signal (NLS) and is rich in cysteine. The earlier explorations suggest XLG's role in flowering, the development of embryos and seedlings, root morphogenesis, stamen development, cytokinin-induced development, stomatal opening and regulation of rice grain filling. The XLGs are also known to initiate signaling cascades that prime plants against a variety of abiotic and biotic stresses. They are also engaged in controlling several agronomic parameters such as rice panicle length, grain filling, grain size, and biomass, highlighting their potential contribution to crop improvement. The present review explores the remarkable properties of non-canonical Gα subunits (XLGs) and reflects on the various developmental, abiotic and biotic stress signaling pathways controlled by them. Moreover, the bottleneck dilemma of how a tiny handful of XLGs control a multiplicity of stress-responsive activities is partially resolved in this review by addressing the interaction of XLGs with different interacting proteins. XLG proteins presented in this review can be exploited to gain access to highly productive and stress-tolerant plants.

Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD.

OBJECTIVES: Beyond total kidney and cyst volume (TCV), non-cystic tissue plays an important role in autosomal dominant polycystic kidney disease (ADPKD) progression. This study aims at presenting and preliminarily validating a diffusion MRI (DWI)-based TCV quantification method and providing evidence of DWI potential in characterising non-cystic tissue microstructure. METHODS: T2-weighted MRI and DWI scans (b = 0, 15, 50, 100, 200, 350, 500, 700, 1000; 3 directions) were acquired from 35 ADPKD patients with CKD stage 1 to 3a and 15 healthy volunteers on a 1.5 T scanner. ADPKD classification was performed using the Mayo model. DWI scans were processed by mono- and segmented bi-exponential models. TCV was quantified on T2-weighted MRI by the reference semi-automatic method and automatically computed by thresholding the pure diffusivity (D) histogram. The agreement between reference and DWI-based TCV values and the differences in DWI-based parameters between healthy and ADPKD tissue components were assessed. RESULTS: There was strong correlation between DWI-based and reference TCV (rho = 0.994, p < 0.001). Non-cystic ADPKD tissue had significantly higher D, and lower pseudo-diffusion and flowing fraction than healthy tissue (p < 0.001). Moreover, apparent diffusion coefficient and D values significantly differed by Mayo imaging class, both in the whole kidney (Wilcoxon p = 0.007 and p = 0.004) and non-cystic tissue (p = 0.024 and p = 0.007). CONCLUSIONS: DWI shows potential in ADPKD to quantify TCV and characterise non-cystic kidney tissue microstructure, indicating the presence of microcysts and peritubular interstitial fibrosis. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion. CLINICAL RELEVANCE STATEMENT: This study shows diffusion-weighted MRI (DWI) potential to quantify total cyst volume and characterise non-cystic kidney tissue microstructure in ADPKD. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion. KEY POINTS: • Diffusion magnetic resonance imaging shows potential to quantify total cyst volume in ADPKD. • Diffusion magnetic resonance imaging might allow to non-invasively characterise non-cystic kidney tissue microstructure. • Diffusion magnetic resonance imaging-based biomarkers significantly differ by Mayo imaging class, suggesting their possible prognostic value.

Magnetic resonance imaging T1- and T2-mapping to assess renal structure and function: a systematic review and statement paper.

This systematic review, initiated by the European Cooperation in Science and Technology Action Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease (PARENCHIMA), focuses on potential clinical applications of magnetic resonance imaging in renal non-tumour disease using magnetic resonance relaxometry (MRR), specifically, the measurement of the independent quantitative magnetic resonance relaxation times T1 and T2 at 1.5 and 3Tesla (T), respectively. Healthy subjects show a distinguishable cortico-medullary differentiation (CMD) in T1 and a slight CMD in T2. Increased cortical T1 values, that is, reduced T1 CMD, were reported in acute allograft rejection (AAR) and diminished T1 CMD in chronic allograft rejection. However, ambiguous findings were reported and AAR could not be sufficiently differentiated from acute tubular necrosis and cyclosporine nephrotoxicity. Despite this, one recent quantitative study showed in renal transplants a direct correlation between fibrosis and T1 CMD. Additionally, various renal diseases, including renal transplants, showed a moderate to strong correlation between T1 CMD and renal function. Recent T2 studies observed increased values in renal transplants compared with healthy subjects and in early-stage autosomal dominant polycystic kidney disease (ADPKD), which could improve diagnosis and progression assessment compared with total kidney volume alone in early-stage ADPKD. Renal MRR is suggested to be sensitive to renal perfusion, ischaemia/oxygenation, oedema, fibrosis, hydration and comorbidities, which reduce specificity. Due to the lack of standardization in patient preparation, acquisition protocols and adequate patient selection, no widely accepted reference values are currently available. Therefore this review encourages efforts to optimize and standardize (multi-parametric) protocols to increase specificity and to tap the full potential of renal MRR in future research.

Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume (TKV) is crucial for studying disease progression in ADPKD. However, automatic segmentation of polycystic kidneys is a challenging task due to severe alteration in the morphology caused by non-uniform cyst formation and presence of adjacent liver cysts. In this study, an automated segmentation method based on deep learning has been proposed for TKV computation on computed tomography (CT) dataset of ADPKD patients exhibiting mild to moderate or severe renal insufficiency. The proposed method has been trained (n = 165) and tested (n = 79) on a wide range of TKV (321.2-14,670.7 mL) achieving an overall mean Dice Similarity Coefficient of 0.86 ± 0.07 (mean ± SD) between automated and manual segmentations from clinical experts and a mean correlation coefficient (ρ) of 0.98 (p < 0.001) for segmented kidney volume measurements in the entire test set. Our method facilitates fast and reproducible measurements of kidney volumes in agreement with manual segmentations from clinical experts.

Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance.

Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC2)-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52 mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters.

First use of mini gamma cameras for intra-operative robotic SPECT reconstruction.

Different types of nuclear imaging systems have been used in the past, starting with pre-operative gantry-based SPECT systems and gamma cameras for 2D imaging of radioactive distributions. The main applications are concentrated on diagnostic imaging, since traditional SPECT systems and gamma cameras are bulky and heavy. With the development of compact gamma cameras with good resolution and high sensitivity, it is now possible to use them without a fixed imaging gantry. Mounting the camera onto a robot arm solves the weight issue, while also providing a highly repeatable and reliable acquisition platform. In this work we introduce a novel robotic setup performing scans with a mini gamma camera, along with the required calibration steps, and show the first SPECT reconstructions. The results are extremely promising, both in terms of image quality as well as reproducibility. In our experiments, the novel setup outperformed a commercial fhSPECT system, reaching accuracies comparable to state-of-the-art SPECT systems.