Diagnostic accuracy of whole spine magnetic resonance imaging in spinal tuberculosis validated through tissue studies.

INTRODUCTION: Conventional diagnosis of spinal tuberculosis (TB) is based on a combination of clinical features, laboratory tests and imaging studies, since none of these individual diagnostic features are confirmatory. Despite the high sensitivity of MRI findings in evaluating spinal infections, its efficacy in diagnosing spinal TB is less emphasized and remains unvalidated through tissue studies. METHODOLOGY: We reviewed consecutive patients evaluated for spondylodiscitis with documented clinical findings, MRI spine, and tissue analysis for histopathology, TB culture and genetic TB PCR. MRI features documented include location, contiguous/non-contiguous skip lesions, para/intraosseous abscess, subligamentous spread, vertebral collapse, abscess size/wall, disc involvement, end plate erosion and epidural abscess. Based on the results, patients were divided into two groups-CONFIRMED TB with positive culture/histopathology and NON-TB. The efficacy of MRI findings in accurately diagnosing spinal TB was compared between the two groups. RESULTS: Among 150 patients, 79 patients were TB positive, and 71 were TB negative. Three MRI parameters showed significant differences (p < 0.001), namely subligamentous spread (67/79, 84.8%), vertebral collapse > 50% (55/79, 69.6%) and large abscess collection with thin abscess wall (72/79, 91.1%) being strongly predictive of TB. Combination of MRI findings had a higher predictive value. 97.5% of TB positive patients had at least one of these three MRI features, 89.8% patients had any two and 58.2% had all three. CONCLUSION: Our study validated different MRI findings with tissue studies and showed spinal infections with large abscess with thin wall, subligamentous spread of abscess and vertebral collapse were highly suggestive of spinal tuberculosis. These slides can be retrieved under Electronic Supplementary Material.

Development of transgenic pigeonpea (Cajanus cajan. L Millsp) overexpressing citrate synthase gene for high phosphorus uptake.

Plants have developed several adaptive strategies to enhance the availability and uptake of phosphorus (P) from the soil under conditions of P deficiency. Exudation of organic acids like citrate is one of the important strategies. In this study, we developed transgenic pigeonpea (Cajanus cajan) over-expressing Dacus carota citrate synthase (DcCs) gene to increase the synthesis and exudation of citrate. Transgenic plants were generated through agro bacterium mediated in-planta transformation technique. Integration and expression of the transgene was confirmed by genomic Southern and RT-PCR analysis. We observed that the transgenic lines had more tissue P and chlorophyll content, and also citrate synthase content higher in the roots. Further, transgenic lines had more vigorous root system both under P sufficient and deficient conditions with more lateral roots and root hairs under P deficient conditions. We conclude that the transgenic pigeonpea plants have the capacity to acquire more P under P deficient conditions.

Fan beam CT image synthesis from cone beam CT image using nested residual UNet based conditional generative adversarial network.

A radiotherapy technique called Image-Guided Radiation Therapy adopts frequent imaging throughout a treatment session. Fan Beam Computed Tomography (FBCT) based planning followed by Cone Beam Computed Tomography (CBCT) based radiation delivery drastically improved the treatment accuracy. Furtherance in terms of radiation exposure and cost can be achieved if FBCT could be replaced with CBCT. This paper proposes a Conditional Generative Adversarial Network (CGAN) for CBCT-to-FBCT synthesis. Specifically, a new architecture called Nested Residual UNet (NR-UNet) is introduced as the generator of the CGAN. A composite loss function, which comprises adversarial loss, Mean Squared Error (MSE), and Gradient Difference Loss (GDL), is used with the generator. The CGAN utilises the inter-slice dependency in the input by taking three consecutive CBCT slices to generate an FBCT slice. The model is trained using Head-and-Neck (H&N) FBCT-CBCT images of 53 cancer patients. The synthetic images exhibited a Peak Signal-to-Noise Ratio of 34.04±0.93 dB, Structural Similarity Index Measure of 0.9751±0.001 and a Mean Absolute Error of 14.81±4.70 HU. On average, the proposed model guarantees an improvement in Contrast-to-Noise Ratio four times better than the input CBCT images. The model also minimised the MSE and alleviated blurriness. Compared to the CBCT-based plan, the synthetic image results in a treatment plan closer to the FBCT-based plan. The three-slice to single-slice translation captures the three-dimensional contextual information in the input. Besides, it withstands the computational complexity associated with a three-dimensional image synthesis model. Furthermore, the results demonstrate that the proposed model is superior to the state-of-the-art methods.