In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus.

INTRODUCTION: TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification. METHODS: Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics. RESULTS: Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition. DISCUSSION: Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory. HIGHLIGHTS: Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.

Correction: Sarwer et al. Green Synthesis and Characterization of Silver Nanoparticles Using Myrsine africana Leaf Extract for Their Antibacterial, Antioxidant and Phytotoxic Activities. Molecules 2022, 27, 7612.

The authors wish to make the following corrections to this paper [...].

Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery.

Importance: Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. Objective: To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. Design, Setting, and Participants: This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Exposure: Body composition derived from automated analysis of multislice abdominal CT scans. Main Outcomes and Measures: The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. Results: The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. Conclusions and Relevance: In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.

Background: Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN). Methods: Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach. Results: The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping. Conclusion: In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Correction: The fodder grass resources for ruminants: A indigenous treasure of local communities of Thal desert Punjab, Pakistan.

[This corrects the article DOI: 10.1371/journal.pone.0224061.].

Integrative analysis of the metabolome and transcriptome reveals the molecular regulatory mechanism of isoflavonoid biosynthesis in Ormosia henryi Prain.

Flavonoids are important components of many phytopharmaceuticals, however, most studies on flavonoids and isoflavonoids have been conducted on herbaceous plants of the family Leguminosae, such as soybean, and less attention has been paid to woody plants. To fill this gap, we characterized the metabolome and transcriptome of five plant organs of Ormosia henryi Prain (OHP), a woody Leguminosae plant with great pharmaceutical value. Our results indicate that OHP possesses a relatively high content of isoflavonoids as well as significant diversity, with greater diversity of isoflavonoids in the roots. Combined with transcriptome data, the pattern of isoflavonoid accumulation was found to be highly correlated with differential expression genes. Furthermore, the use of trait-WGCNA network analysis identified OhpCHSs as a probable hub enzyme that directs the downstream isoflavonoid synthesis pathway. Transcription factors, such as MYB26, MYB108, WRKY53, RAV1 and ZFP3, were found to be involved in the regulation of isoflavonoid biosynthesis in OHP. Our findings will be beneficial for the biosynthesis and utilization of woody isoflavonoids.

Neuroimaging feature extraction using a neural network classifier for imaging genetics.

BACKGROUND: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. RESULTS: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. CONCLUSIONS: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

Safety, tolerability, and effectiveness of the sodium-glucose cotransporter 2 inhibitor (SGLT2i) dapagliflozin in combination with standard chemotherapy for patients with advanced, inoperable pancreatic adenocarcinoma: a phase 1b observational study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Thus, there is an urgent need for safe and effective novel therapies. PDAC's excessive reliance on glucose metabolism for its metabolic needs provides a target for metabolic therapy. Preclinical PDAC models have demonstrated that targeting the sodium-glucose co-transporter-2 (SGLT2) with dapagliflozin may be a novel strategy. Whether dapagliflozin is safe and efficacious in humans with PDAC is unclear. METHODS: We performed a phase 1b observational study (ClinicalTrials.gov ID NCT04542291; registered 09/09/2020) to test the safety and tolerability of dapagliflozin (5 mg p.o./day × 2 weeks escalated to 10 mg p.o./day × 6 weeks) added to standard Gemcitabine and nab-Paclitaxel (GnP) chemotherapy in patients with locally advanced and/or metastatic PDAC. Markers of efficacy including Response Evaluation Criteria in Solid Tumors (RECIST 1.1) response, CT-based volumetric body composition measurements, and plasma chemistries for measuring metabolism and tumor burden were also analyzed. RESULTS: Of 23 patients who were screened, 15 enrolled. One expired (due to complications from underlying disease), 2 dropped out (did not tolerate GnP chemotherapy) during the first 4 weeks, and 12 completed. There were no unexpected or serious adverse events with dapagliflozin. One patient was told to discontinue dapagliflozin after 6 weeks due to elevated ketones, although there were no clinical signs of ketoacidosis. Dapagliflozin compliance was 99.4%. Plasma glucagon increased significantly. Although abdominal muscle and fat volumes decreased; increased muscle-to-fat ratio correlated with better therapeutic response. After 8 weeks of treatment in the study, partial response (PR) to therapy was seen in 2 patients, stable disease (SD) in 9 patients, and progressive disease (PD) in 1 patient. After dapagliflozin discontinuation (and chemotherapy continuation), an additional 7 patients developed the progressive disease in the subsequent scans measured by increased lesion size as well as the development of new lesions. Quantitative imaging assessment was supported by plasma CA19-9 tumor marker measurements. CONCLUSIONS: Dapagliflozin is well-tolerated and was associated with high compliance in patients with advanced, inoperable PDAC. Overall favorable changes in tumor response and plasma biomarkers suggest it may have efficacy against PDAC, warranting further investigation.

Body composition and lung cancer-associated cachexia in TRACERx.

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC.

Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks.

BACKGROUND: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain. METHODS: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated. RESULTS: Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities. CONCLUSION: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.

Physiological and transcriptomic responses to magnesium deficiency in Neolamarckia Cadamba.

Magnesium (Mg2+) is a critical component of chlorophyll and enzymes involved in various physiological and biochemical processes essential for plant growth, biomass accumulation, and photosynthesis. Mg2+ deficiency (MgD) is common in hot and rainy subtropical areas due to its easy loss from soil. Neolamarckia cadamba, an important tropical tree in South Asia, faces severe effects of MgD, however, the responses of N. cadamba to MgD stress remain unclear. In here, effects of N. cadamba under MgD stress were investigated. The study revealed that MgD had lower plant biomass, fresh and dry weight, root length, root volume, and surface area compared to CK (normal Mg2+). As treatment time increased, the leaves began to yellow, and lesions appeared. Chlorophyll a, chlorophyll b, and total chlorophyll content, along with fluorescence-related parameters and leaf photosynthetic capacity, were significantly reduced in MgD stress compared to CK treatment. Transcriptome analysis showed that transporters as well as transcription factors (TFs) from MYC (v-myc avian myelocytomatosis viral oncogene homolog), MYB (v-myb avian myeloblastosis viral oncogene homolog), bHLH (basic helix-loop-helix) and WRKY families were upregulated in leaves at 10 d of MgD stress, indicating that magnesium signaling transduction might be activated to compensate MgD. In addition, genes including chlorophyll(ide) b reductase (NYC1/NOL) chlorophyll/bacteriochlorophyll synthase (G4) and 7-hydroxymethyl chlorophyll a reductase synthesizing (HCAR) chlorophyll a and chlorophyll b were down-regulated in leaves, while those scavenging reactive oxygen species (ROS) were mainly up-regulated at 10 d of MgD stress. These results shed light on underlying MgD in N. cadamba.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum.

BACKGROUND: The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear. OBJECTIVE: To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points. METHODS: We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up. RESULTS: In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12. CONCLUSION: These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

White-matter abnormalities in presymptomatic GRN and C9orf72 mutation carriers.

A large proportion of familial frontotemporal dementia is caused by TAR DNA-binding protein 43 (transactive response DNA-binding protein 43 kDa) proteinopathies. Accordingly, carriers of autosomal dominant mutations in the genes associated with TAR DNA-binding protein 43 aggregation, such as Chromosome 9 open reading frame 72 (C9orf72) or progranulin (GRN), are at risk of later developing frontotemporal dementia. Brain imaging abnormalities that develop before dementia onset in mutation carriers may serve as proxies for the presymptomatic stages of familial frontotemporal dementia due to a genetic cause. Our study objective was to investigate brain MRI-based white-matter changes in predementia participants carrying mutations in C9orf72 or GRN genes. We analysed mutation carriers and their family member controls (noncarriers) from the University of British Columbia familial frontotemporal dementia study. First, a total of 42 participants (8 GRN carriers; 11 C9orf72 carriers; 23 noncarriers) had longitudinal T1-weighted MRI over ∼2 years. White-matter signal hypointensities were segmented and volumes were calculated for each participant. General linear models were applied to compare the baseline burden and the annualized rate of accumulation of signal abnormalities among mutation carriers and noncarriers. Second, a total of 60 participants (9 GRN carriers; 17 C9orf72 carriers; 34 noncarriers) had cross-sectional diffusion tensor MRI available. For each participant, we calculated the average fractional anisotropy and mean, radial and axial diffusivity parameter values within the normal-appearing white-matter tissues. General linear models were applied to compare whether mutation carriers and noncarriers had different trends in diffusion tensor imaging parameter values as they neared the expected age of onset. Baseline volumes of white-matter signal abnormalities were not significantly different among mutation carriers and noncarriers. Longitudinally, GRN carriers had significantly higher annualized rates of accumulation (estimated mean: 15.87%/year) compared with C9orf72 carriers (3.69%/year) or noncarriers (2.64%/year). A significant relationship between diffusion tensor imaging parameter values and increasing expected age of onset was found in the periventricular normal-appearing white-matter region. Specifically, GRN carriers had a tendency of a faster increase of mean and radial diffusivity values and C9orf72 carriers had a tendency of a faster decline of fractional anisotropy values as they reached closer to the expected age of dementia onset. These findings suggest that white-matter changes may represent early markers of familial frontotemporal dementia due to genetic causes. However, GRN and C9orf72 mutation carriers may have different mechanisms leading to tissue abnormalities.

Brain Imaging Abnormalities in Mixed Alzheimer's and Subcortical Vascular Dementia.

BACKGROUND: A large proportion of Alzheimer's disease (AD) patients have coexisting subcortical vascular dementia (SVaD), a condition referred to as mixed dementia (MixD). Brain imaging features of MixD presumably include those of cerebrovascular disease and AD pathology, but are difficult to characterize due to their heterogeneity. OBJECTIVE: To perform an exploratory analysis of conventional and non-conventional structural magnetic resonance imaging (MRI) abnormalities in MixD and to compare them to those observed in AD and SVaD. METHODS: We conducted a cross-sectional, region-of-interest-based analysis of 1) hyperintense white-matter signal abnormalities (WMSA) on T2-FLAIR and hypointense WMSA on T1-weighted MRI; 2) diffusion tensor imaging; 3) quantitative susceptibility mapping; and 4) effective transverse relaxation rate (R2*) in N = 17 participants (AD:5, SVaD:5, MixD:7). General linear model was used to explore group differences in these brain imaging measures. RESULTS: Model findings suggested imaging characteristics specific to our MixD group, including 1) higher burden of WMSAs on T1-weighted MRI (versus both AD and SVaD); 2) frontal lobar preponderance of WMSAs on both T2-FLAIR and T1-weighted MRI; 3) higher fractional anisotropy values within normal-appear white-matter tissues (versus SVaD, but not AD); and 4) lower R2* values within the T2-FLAIR WMSA areas (versus both AD and SVaD). CONCLUSION: These findings suggest a preliminary picture of the location and type of brain imaging characteristics associated with MixD. Future imaging studies may employ region-specific hypotheses to distinguish MixD more rigorously from AD or SVaD.

Effects of Myopia and Glaucoma on the Neural Canal and Lamina Cribrosa Using Optical Coherence Tomography.

PRCIS: Glaucoma was associated with axial bowing and rotation of Bruchs membrane opening (BMO) and anterior laminar insertion (ALI), skewed neural canal, and deeper anterior lamina cribrosa surface (ALCS). Longer axial length was associated with wider, longer, and more skewed neural canal and flatter ALCS. PURPOSE: Investigate the effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography. PATIENTS: 19 control (38 eyes) and 38 glaucomatous subjects (63 eyes). MATERIALS AND METHODS: Participants were imaged with swept-source optical coherence tomography, and the images were analyzed for the BMO and ALI dimensions, prelaminar neural canal dimensions, and ALCS depth. RESULTS: Glaucomatous eyes had more bowed and nasally rotated BMO and ALI, more horizontally skewed prelaminar neural canal, and deeper ALCS than the control eyes. Increased axial length was associated with a wider, longer, and more horizontally skewed neural canal and a decrease in the ALCS depth and curvature. CONCLUSION: Our findings suggest that glaucomatous posterior bowing or cupping of lamina cribrosa can be significantly confounded by the myopic expansion of the neural canal. This may be related to higher glaucoma risk associated with myopia from decreased compliance and increased susceptibility to IOP-related damage of LC being pulled taut.

AAV5-miHTT-mediated huntingtin lowering improves brain health in a Huntington's disease mouse model.

Huntingtin (HTT)-lowering therapies show great promise in treating Huntington's disease. We have developed a microRNA targeting human HTT that is delivered in an adeno-associated serotype 5 viral vector (AAV5-miHTT), and here use animal behaviour, MRI, non-invasive proton magnetic resonance spectroscopy and striatal RNA sequencing as outcome measures in preclinical mouse studies of AAV5-miHTT. The effects of AAV5-miHTT treatment were evaluated in homozygous Q175FDN mice, a mouse model of Huntington's disease with severe neuropathological and behavioural phenotypes. Homozygous mice were used instead of the more commonly used heterozygous strain, which exhibit milder phenotypes. Three-month-old homozygous Q175FDN mice, which had developed acute phenotypes by the time of treatment, were injected bilaterally into the striatum with either formulation buffer (phosphate-buffered saline + 5% sucrose), low dose (5.2 × 109 genome copies/mouse) or high dose (1.3 × 1011 genome copies/mouse) AAV5-miHTT. Wild-type mice injected with formulation buffer served as controls. Behavioural assessments of cognition, T1-weighted structural MRI and striatal proton magnetic resonance spectroscopy were performed 3 months after injection, and shortly afterwards the animals were sacrificed to collect brain tissue for protein and RNA analysis. Motor coordination was assessed at 1-month intervals beginning at 2 months of age until sacrifice. Dose-dependent changes in AAV5 vector DNA level, miHTT expression and mutant HTT were observed in striatum and cortex of AAV5-miHTT-treated Huntington's disease model mice. This pattern of microRNA expression and mutant HTT lowering rescued weight loss in homozygous Q175FDN mice but did not affect motor or cognitive phenotypes. MRI volumetric analysis detected atrophy in four brain regions in homozygous Q175FDN mice, and treatment with high dose AAV5-miHTT rescued this effect in the hippocampus. Like previous magnetic resonance spectroscopy studies in Huntington's disease patients, decreased total N-acetyl aspartate and increased myo-inositol levels were found in the striatum of homozygous Q175FDN mice. These neurochemical findings were partially reversed with AAV5-miHTT treatment. Striatal transcriptional analysis using RNA sequencing revealed mutant HTT-induced changes that were partially reversed by HTT lowering with AAV5-miHTT. Striatal proton magnetic resonance spectroscopy analysis suggests a restoration of neuronal function, and striatal RNA sequencing analysis shows a reversal of transcriptional dysregulation following AAV5-miHTT in a homozygous Huntington's disease mouse model with severe pathology. The results of this study support the use of magnetic resonance spectroscopy in HTT-lowering clinical trials and strengthen the therapeutic potential of AAV5-miHTT in reversing severe striatal dysfunction in Huntington's disease.

Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.

Bio-fabrication of Zinc Oxide nanoparticles to rescue Mung Bean against Cercospora leaf spot disease.

Plant disease management using nanotechnology is evolving continuously across the world. The purpose of this study was to determine the effect of different concentrations of green synthesized zinc oxide nanoparticles (ZnO NPs) using Trachyspermum ammi seed extract on Cercospora leaf spot disease in mung bean plants under in-vitro and in-planta conditions. Additionally, the effects on mung bean agronomic and physiological parameters were also assessed. The green synthesized ZnO NPs were characterized using UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Scanning electron microscopy (SEM). Green synthesized NPs were tested for their ability to inhibit fungal growth at five different concentrations under in-vitro experiment. After 7 days of inoculation, ZnO NPs (1200 ppm) inhibited mycelial growth substantially (89.86% ± 0.70). The in-planta experiment showed statistically significant result of disease control (30% ± 11.54) in response to 1200 ppm ZnO NPs. The same treatment showed statistically significant improvements in shoot length, root length, number of leaves, number of pods, shoot fresh weight (28.62%), shoot dry weight (85.18%), root fresh weight (38.88%), and root dry weight (38.88%) compared to the control. Our findings show that green synthesized ZnO NPs can control Cercospora canescens in mung bean, pointing to their use in plant disease control and growth enhancement.

Green synthesis and application of GO nanoparticles to augment growth parameters and yield in mungbean (Vigna radiata L.).

Plant growth promotion has long been a challenge for growers all over the world. In this work, we devised a green nanomaterial-assisted approach to boost plant growth. It has been reported that carbon nanomaterials are toxic to plants because they can inhibit the uptake of nutrients if employed in higher concentrations, however this study shows that graphene oxide (GO) can be used as a regulator tool to improve plant growth and stability. Graphene oxide in different concentrations was added to the soil of mungbean. It is proved that when a suitable amount of graphene oxide was applied, it had a good influence on plant growth by enhancing the length of roots and shoots, number of leaves, number of root nodules per plant, number of pods, and seeds per pod. We presume that the use of bio-fabricated graphene oxide as a strategy would make it possible to boost both plant growth and the significant increase in the number of seeds produced by each plant.