Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis.

BACKGROUND: Positron emission tomography-magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis. METHODS: Ten patients being treated with ano-rectal radiotherapy received a [Formula: see text]F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of [Formula: see text]. Equivalence margins of [Formula: see text] were used. RESULTS: Mean whole-image SUV differences were -0.02% (sCTAC) compared to -3.0% (MRAC), with larger differences in the bone regions (-0.5% to -16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients [Formula: see text]. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in [Formula: see text] were [Formula: see text] (± standard error, sCTAC) and [Formula: see text] (MRAC), and [Formula: see text] (sCTAC) and [Formula: see text] (MRAC) in [Formula: see text]. The sCTAC was statistically equivalent to CTAC within a [Formula: see text] equivalence margin for [Formula: see text] and [Formula: see text] ([Formula: see text] and [Formula: see text]), whereas the MRAC was not ([Formula: see text] and [Formula: see text]). CONCLUSION: Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

Exploring the Paradigm of Phyto-Nanofabricated Metal Oxide Nanoparticles: Recent Advancements, Applications, and Challenges.

The development of nanotechnology, in particular metal oxide nanoparticles, has captured immense scientific attention in the global arena due to their unique properties leading to their unique diverse applications. But the use of toxic precursors and high operational cost make existing methodologies inefficient for synthesising metal oxide nanoparticles (MONPs). Biogenic synthesis of MONPs has been hailed as a more sustainable approach for the synthesis of NPs due to its alignment with the principles of green chemistry. Microorganisms (bacteria, yeast, algae), animal sources (silk, fur, etc.), and plants are effective, low-cost, and eco-friendly means of synthesizing MONPs since they possess a high bio-reduction abilities to produce NPs of various shapes and sizes. The current review encompasses recent advancements in the field of plant-mediated MONP synthesis and characterisation. The detailed evaluation of various synthesis processes and parameters, key influencing factors affecting the synthesis efficiency and product morphology, practical applications with insight into the associated limitations and challenges presents a valuable database that will be helpful in developing alternative prospects and potential engineering applications.

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare. MATERIALS AND METHODS: ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n = 10), rectum (n = 4) and anus (n = 6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis. RESULTS: Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively. CONCLUSIONS: A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.

Troxerutin-Mediated Complement Pathway Inhibition is a Disease-Modifying Treatment for Inflammatory Arthritis.

Troxerutin (TXR) is a phytochemical reported to possess anti-inflammatory and hepatoprotective effects. In this study, we aimed to exploit the antiarthritic properties of TXR using an adjuvant-induced arthritic (AIA) rat model. AIA-induced rats showed the highest arthritis score at the disease onset and by oral administration of TXR (50, 100, and 200 mg/kg body weight), reduced to basal level in a dose-dependent manner. Isobaric tags for relative and absolute quantitative (iTRAQ) proteomics tool were employed to identify deregulated joint homogenate proteins in AIA and TXR-treated rats to decipher the probable mechanism of TXR action in arthritis. iTRAQ analysis identified a set of 434 proteins with 65 deregulated proteins (log2 case/control≥1.5) in AIA. Expressions of a set of important proteins (AAT, T-kininogen, vimentin, desmin, and nucleophosmin) that could classify AIA from the healthy ones were validated using Western blot analysis. The Western blot data corroborated proteomics findings. In silico protein-protein interaction study of tissue-proteome revealed that complement component 9 (C9), the major building blocks of the membrane attack complex (MAC) responsible for sterile inflammation, get perturbed in AIA. Our dosimetry study suggests that a TXR dose of 200 mg/kg body weight for 15 days is sufficient to bring the arthritis score to basal levels in AIA rats. We have shown the importance of TXR as an antiarthritic agent in the AIA model and after additional investigation, its arthritic ameliorating properties could be exploited for clinical usability.

Assessment of the bioaccumulation pattern of Pb, Cd, Cr and Hg in edible fishes of East kolkata Wetlands, India.

Waste water fed pisciculture is nowadays a common feature in aquaculture belts across the globe. East Kolkata Wetlands (EKW) a nature's wonder where waste water fed natural aquaculture beltis is active for more than 70 years now and is efficiently operating as a natural waste management system. The peri urban wetland is also a site of international importance and is listed in Ramsar. Field and lab-based investigations were carried out using three commonly edible carp variety of fishes such as Rohu (Labeorohita), Catla (Catlacatla) and Nile Tilapia (Oreochromisniloticus) collected from ponds (bheries) of the wetland located on the eastern fringes of Kolkata, India. The lab-based analysis revealed the presence of toxic metals such as Cr, Pb, Cd and Hg in the samples with the seasonal order of accumulation being monsoon > post-monsoon > winter > pre-monsoon in the successive years of 2016, 2017 and 2018. Bio-accumulation of toxic heavy metals in fishes follows the order Tilapia > Rohu > Catla where as the bioaccumulation pattern of toxic metals shows the trend Pb > Cd > Cr > Hg across all the seasons and years. The ambient media was also investigated to understand in detail the bioaccumulation pattern at different trophic levels of the ecosystem. Water and sediments were analyzed to evaluate the contamination of toxic heavy metals from point and non-point sources. Current study shows the observed bioaccumulation pattern of the toxic heavy metals in one of the fragile ecosystems that raises an important question of environmental safety in the food we intake on daily basis.

Low circulatory Fe and Se levels with a higher IL-6/IL-10 ratio provide nutritional immunity in tuberculosis.

Tuberculosis (TB) patients show dysregulated immunity, iron metabolism, and anemia. In this study, circulatory cytokines, trace metals, and iron-related proteins (hepcidin, ferroportin, transferrin, Dmt1, Nramp1, ferritin, ceruloplasmin, hemojuvelin, aconitase, and transferrin receptor) were monitored in case (active tuberculosis patients: ATB) and control (non-tuberculosis: NTB and healthy) study populations (n = 72, male: 100%, mean age, 42.94 years; range, 17-83 years). Using serum elemental and cytokine levels, a partial least square discriminate analysis model (PLS-DA) was built, which clustered ATB patients away from NTB and healthy controls. Based on the PLS-DA variable importance in projection (VIP) score and analysis of variance (ANOVA), 13 variables were selected as important biosignatures [IL-18, IL-10, IL-13, IFN-γ, TNF-α, IL-5, IL-12 (p70), IL-1ß, copper, zinc, selenium, iron, and aluminum]. Interestingly, low iron and selenium levels and high copper and aluminum levels were observed in ATB subjects. Low circulatory levels of transferrin, ferroportin, and hemojuvelin with higher ferritin and ceruloplasmin levels observed in ATB subjects demonstrate an altered iron metabolism, which partially resolved upon 6 months of anti-TB therapy. The identified biosignature in TB patients demonstrated perturbed iron homeostasis with anemia of inflammation, which could be useful targets for the development of host-directed adjunct therapeutics.

Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation.

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.

Growth performance of Ganoderma lucidum using billet method in Garhwal Himalaya, India.

Medicinal mushrooms have been used in various treatments from a very long time, among which, Ganoderma lucidum is one of the most important medicinal mushroom. It is cultivated worldwide to meet its ever-increasing demand in the market. It is generally cultivated by bed log (Sawdust) and wood log (billet) method. This study was an attempt to observe the growth performance of G. lucidum on poplar billets (Populus deltoides) in the Sherpur Village (Dehradun) and Manjgaun village (Tehri Garhwal) of Garhwal Himalaya, India. The farmers' field with empty house/ rooms having proper growing conditions especially humidity and light were used for the cultivation of G. lucidum. The G. lucidum spawn was inoculated in poplar wood billets and these billets were installed in well prepared soil. The results demonstrated that cropping cycle of G. lucidum was shorter (132-136 days) in Sherpur Village (Dehradun) as compared to Manjgaun village (141-145 days) in Tehri Garhwal. Further the results also revealed that yield was decreased in the subsequent flushes. In Village Sherpur, the fruiting bodies of G. lucidum were harvested between 64-66 days, 100-101  days and 135-136  days during first, second and third flush after the installation of billets, respectively. However; in village Manjgaun, the fruiting bodies of G. lucidum were harvested between 69 and 71 days, 107-108  days and 144-145 days in first, second and third after the installation of billets respectively. Warmer temperature in Village Sherpur resulted in the early emergence and development of the fruiting bodies as compared to village Manjgaun where pinhead and fruiting body development was delayed due to the lower temperature during cropping cycle.

Reproductive success in Zygogramma bicolorata: A role of post-insemination association of male and female.

Reproductive success is attained by various mechanisms in insects. Prolonged post insemination association is one such mechanism to increase the reproductive success. The present study was conducted to assess the role of post insemination association of mating partners on reproductive performance in Chrysomelidae beetle, Zygogramma bicolorata Pallister. The matings were disrupted at different time intervals and fecundity and percent egg viability of the females were recorded. In addition, the mounting attempts, mating attempts, time to commencement of mating and latent period were also recorded. It was hypothesized that: (1) the mounting and mating attempts would not exist, (2) copulation duration, would not affect the reproductive performance, and (3) the beetle would not exhibit the mate guarding behaviour. Interestingly, results revealed that 6.00 ± 1.3 and 6.59 ± 0.93 mounting and mating attempts are needed to establish successful mating. The results revealed that males improved their percent egg viability with a mating duration ranging from nearly 30-50 min. While fecundity increased with a mating duration of above 30 min and up to a duration of 60 min. This result concluded that males of this beetle display post copulatory mate guarding behaviour after 60 min in which male rides on female's back with his aedeagus inserted in the female genital tract.

RRBP1 rewires cisplatin resistance in oral squamous cell carcinoma by regulating Hippo pathway.

BACKGROUND: Chemoresistance is one of the major factors for treatment failure in OSCC. Identifying key resistance triggering molecules will be useful strategy for developing novel treatment methods. METHODS: To identify the causative factors of chemoresistance, we performed RNA sequencing and global proteomic profiling of human OSCC lines presenting with sensitive, early and late cisplatin-resistance patterns. RESULTS: From the common set of dysregulated genes from both the analysis, RRBP1 was identified to be upregulated in both early and late cisplatin-resistant cells with respect to the sensitive counterpart. Analysis of OSCC patient sample indicates that RRBP1 expression is upregulated in chemotherapy-non-responder tumours as compared to chemotherapy-responder tumours. Genetic (knockout) or pharmacological (Radezolid, represses expression of RRBP1) inhibition of RRBP1 restores cisplatin-mediated cell death in chemo-resistant OSCC. Mechanistically, RRBP1 regulates Yes-associated protein1 (YAP1), a key protein in the Hippo pathway to induce chemoresistance. The PDC xenograft data suggests that knockout of RRBP1 induces cisplatin-mediated cell death and facilitates a significant reduction of tumour burden. CONCLUSION: Overall, our data suggests that (I) RRBP1 is a major driver of cisplatin-resistance in OSCC, (II) RRBP1 regulates YAP1 expression to mediate cisplatin-resistance, (III) Radezolid represses RRBP1 expression and (IV) targeting RRBP1 reverses cisplatin-induced chemoresistance in advanced OSCC.

Intranasal immunization with peptide-based immunogenic complex enhances BCG vaccine efficacy in a murine model of tuberculosis.

Prime-boost immunization strategies are required to control the global tuberculosis (TB) pandemic, which claims approximately 3 lives every minute. Here, we have generated an immunogenic complex against Mycobacterium tuberculosis (M.tb), consisting of promiscuous T cell epitopes (M.tb peptides) and TLR ligands assembled in liposomes. Interestingly, this complex (peptide-TLR agonist-liposomes; PTL) induced significant activation of CD4+ T cells and IFN-γ production in the PBMCs derived from PPD+ healthy individuals as compared with PPD- controls. Furthermore, intranasal delivery of PTL significantly reduced the bacterial burden in the infected mice by inducing M.tb-specific polyfunctional (IFN-γ+IL-17+TNF-α+IL-2+) immune responses and long-lasting central memory responses, thereby reducing the risk of TB recurrence in DOTS-treated infected animals. The transcriptome analysis of peptide-stimulated immune cells unveiled the molecular basis of enhanced protection. Furthermore, PTL immunization significantly boosted the Bacillus Calmette-Guerin-primed (BCG-primed) immune responses against TB. The greatly enhanced efficacy of the BCG-PTL vaccine model in controlling pulmonary TB projects PTL as an adjunct vaccine against TB.

CMTM6 drives cisplatin resistance by regulating Wnt signaling through the ENO-1/AKT/GSK3ß axis.

Rewiring tumor cells to undergo drug-induced apoptosis is a promising way to overcome chemoresistance. Therefore, identifying causative factors for chemoresistance is of high importance. Unbiased global proteome profiling of sensitive, early, and late cisplatin-resistant oral squamous cell carcinoma (OSCC) lines identified CMTM6 as a top-ranked upregulated protein. Analyses of OSCC patient tumor samples demonstrated significantly higher CMTM6 expression in chemotherapy (CT) nonresponders as compared with CT responders. In addition, a significant association between higher CMTM6 expression and poorer relapse-free survival in esophageal squamous cell carcinoma, head and neck squamous cell carcinoma, and lung squamous cell carcinoma was observed from Kaplan-Meier plot analysis. Stable knockdown (KD) of CMTM6 restored cisplatin-mediated cell death in chemoresistant OSCC lines. Upon CMTM6 overexpression in CMTM6-KD lines, the cisplatin-resistant phenotype was rescued. The patient-derived cell xenograft model of chemoresistant OSCC displaying CMTM6 depletion restored the cisplatin-induced cell death and tumor burden substantially. The transcriptome analysis of CMTM6-KD and control chemoresistant cells depicted enrichment of the Wnt signaling pathway. We demonstrated that CMTM6 interaction with membrane-bound Enolase-1 stabilized its expression, leading to activation of Wnt signaling mediated by AKT-glycogen synthase kinase-3ß. CMTM6 has been identified as a stabilizer of programmed cell death ligand 1. Therefore, as CMTM6 facilitates tumor cells for immune evasion and mediates cisplatin resistance, it could be a promising therapeutic target for treating therapy-resistant OSCC.

Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data.

AIM: Attenuation correction using zero-echo time (ZTE) - magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer's disease (AD) by performing simulation study. METHODS: We recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient's specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference. RESULTS: Positron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (-0.0796 ± 0.0938 vs. -0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R 2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8-84.5%), respectively). CONCLUSION: For FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.

Essential amino acid profiling of the four lac hosts belonging to genus Flemingia: its implications on lac productivity.

The Indian lac insect (Kerria lacca), a hemipteran, phloem sap sucking sedentary insect is an important bioresource which thrives on tender twigs of more than 400 plant species belonging to various genera and families. The most common commercial host plants for lac cultivation are big trees hence cultivation was concentrated mainly to dense forests across the country till last decade. Recently, a new bushy host plant belonging to the genus Flemingia has been introduced so that lac can be cultivated on farmlands like other cash crops. The insect is sedentary and feeds on the phloem sap of the host plants, the only source of its nutrition. Interestingly, the biological attributes of the insect as well as the qualitative and quantitative production of lac is influenced by the host plant on which the insect feeds upon. The present study was thus aimed at deciphering the effect of phloem sap constituents obtained from four plant host taxa belonging to the same genus Flemingia viz. F. semialata, F. macrophylla, F. bracteata and F. chapar (essential amino acids only-EAAs) on lac productivity. Moreover, a newer method for phloem sap collection i.e. Dot-blot in addition to the facilitated exudation using EDTA was also investigated. Dot-blot method for phloem sap collection also came out to be a promising method for field studies; although slightly higher concentration of EAAs were obtained from EDTA method, thus the later was used for further analysis. Phloem sap of four plant host taxa belonging to the same genus Flemingia were qualitatively and quantitatively analysed for seven EAAs (Arginine, Glycine, Leucine, Methionine, Phenylalanine Tyrosine and Valine). Amino acid concentration regime and further analysis done using statistical tools (ANOVA and PCA) points out the EAA concentration in the phloem sap is in congruency with the lac production data obtained through previous studies as F. semialata > F. macrophylla > F. chapar > F. bracteata. The present study thus scientifically points out that F. semialata can be a promising plant for lac cultivation on the basis of higher EAA content as compared to the rest three.

COVID-19 pandemic: Insights into structure, function, and hACE2 receptor recognition by SARS-CoV-2.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a newly emerging, highly transmissible, and pathogenic coronavirus in humans that has caused global public health emergencies and economic crises. To date, millions of infections and thousands of deaths have been reported worldwide, and the numbers continue to rise. Currently, there is no specific drug or vaccine against this deadly virus; therefore, there is a pressing need to understand the mechanism(s) through which this virus enters the host cell. Viral entry into the host cell is a multistep process in which SARS-CoV-2 utilizes the receptor-binding domain (RBD) of the spike (S) glycoprotein to recognize angiotensin-converting enzyme 2 (ACE2) receptors on the human cells; this initiates host-cell entry by promoting viral-host cell membrane fusion through large-scale conformational changes in the S protein. Receptor recognition and fusion are critical and essential steps of viral infections and are key determinants of the viral host range and cross-species transmission. In this review, we summarize the current knowledge on the origin and evolution of SARS-CoV-2 and the roles of key viral factors. We discuss the structure of RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 and its significance in drug discovery and explain the receptor recognition mechanisms of coronaviruses. Further, we provide a comparative analysis of the SARS-CoV and SARS-CoV-2 S proteins and their receptor-binding specificity and discuss the differences in their antigenicity based on biophysical and structural characteristics.

The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer's diseases- A simulation study combining multi-center data and ADNI-data.

BACKGROUND: The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer's disease (AD) by using simulated images. METHODS: We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images. RESULTS: The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4-85.0%], 78.5% [range 72.9-83.3%,] and 86.1% [range 81.4-89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5-66.7%], 75.7% [range 66.7-81.8%,] and 59.0% [range 50.8-63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores. CONCLUSION: FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.