Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke.

Introduction: Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment. Methods: This study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients. Results: The results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively. Conclusion: The proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.

The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction.

Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions.

Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke.

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA). This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA,13 feature sets (F method ) were obtained from different feature selection algorithms. Furthermore, these 13 F method were validated in identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue. In identifying HA and NA, the composite score (CS) of the 13 F method ranged from 0.624 to 0.925. F Lasso in the 13 F method achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. The classification ability was relatively stable when the reference threshold (RT) was <0.25. Otherwise, when RT was >0.25, the performance will gradually decrease as its increases. These results showed that radiomics features extracted from the Lasso algorithms could accurately reflect cerebral blood flow changes and classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithms in the future.

A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke.

BACKGROUND: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. METHODS: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. RESULTS: In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of Ft-test in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.

Cerebral Venous Oxygen Saturation in Hypoperfusion Regions May Become a New Imaging Indicator to Predict the Clinical Outcome of Stroke.

To automatically and quantitatively evaluate the venous oxygen saturation (SvO2) in cerebral ischemic tissues and explore its value in predicting prognosis. A retrospective study was conducted on 48 AIS patients hospitalized in our hospital from 2015−2018. Based on quantitative susceptibility mapping and perfusion-weighted imaging, this paper measured the cerebral SvO2 in hypoperfusion tissues and its change after intraarterial rt-PA treatment. The cerebral SvO2 in different hypoperfusion regions between the favorable and unfavorable clinical outcome groups was analyzed using an independent t-test. Relationships between cerebral SvO2 and clinical scores were determined using the Pearson correlation coefficient. The receiver operating characteristic process was conducted to evaluate the accuracy of cerebral SvO2 in predicting unfavorable clinical outcomes. Cerebral SvO2 in hypoperfusion (Tmax > 4 and 6 s) was significantly different between the two groups at follow-up (p < 0.05). Cerebral SvO2 and its changes before and after treatment were negatively correlated with clinical scores. The positive predictive value, negative predictive value, accuracy, and area under the curve of the cerebral SvO2 were higher than those predicted by the ischemic core. Therefore, the cerebral SvO2 of hypoperfusion regions was a stronger imaging predictor of unfavorable clinical outcomes after stroke.

Quantitative Distribution of Cerebral Venous Oxygen Saturation and Its Prognostic Value in Patients with Acute Ischemic Stroke.

This study investigated the quantitative distribution of cerebral venous oxygen saturation (SvO2) based on quantitative sensitivity mapping (QSM) and determined its prognostic value in patients with acute ischemic stroke (AIS). A retrospective study was conducted on 39 hospitalized patients. Reconstructed QSM was used to calculate the cerebral SvO2 of each region of interest (ROI) in the ischemic hemisphere. The intraclass correlation coefficient (ICC) and Bland−Altman analysis were conducted to define the best resolution of the distribution map. The correlation between the cerebral SvO2 in hypoxic regions (SvO2ROI < 0.7) and clinical scores was obtained by Spearman and power analysis. The associations between cerebral SvO2 and unfavorable prognosis were analyzed using multivariate logistic regression. Excellent agreement was found between the cerebral SvO2 in hypoxic regions with a resolution of 7.18 × 7.18 × 1.6 mm3 and asymmetrically prominent cortical veins regions (ICC: 0.879 (admission), ICC: 0.906 (discharge)). The cerebral SvO2 was significantly negative with clinical scores (all |r| > 0.3). The cerebral SvO2 and its changes at discharge were significantly associated with an unfavorable prognosis (OR: 0.812 and 0.866). Therefore, the cerebral SvO2 in hypoxic regions measured by the quantitative distribution map can be used as an indicator for evaluating the early prognosis of AIS.

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome.

BACKGROUND: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. METHODS: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. RESULTS: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. CONCLUSIONS: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.

Molecular identification of a root apical cell-specific and stress-responsive enhancer from an Arabidopsis enhancer trap line.

BACKGROUND: Plant root apex is the major part to direct the root growth and development by responding to various signals/cues from internal and soil environments. To study and understand root system biology particularly at a molecular and cellular level, an Arabidopsis T-DNA insertional enhancer trap line J3411 expressing reporters (GFP) only in the root tip was adopted in this study to isolate a DNA fragment. RESULTS: Using nested PCR, DNA sequencing and sequence homology search, the T-DNA insertion site(s) and its flanking genes were characterised in J3411 line. Subsequently, a 2000 bp plant DNA-fragment (Ertip1) upstream of the insert position of the coding T-DNA was in silico analysed, revealing certain putative promoter/enhancer cis-regulatory elements. Cloning and transformation of this DNA fragment and its truncated segments tagged with or without 35S minimal promoter (35Smini), all of which were fused with a GFP or GUS reporter, allowed to detect GFP and GUS expression mediated only by Ertip1 + 35mini (PErtip1+35Smini) specifically in the Arabidopsis root tip region. The PErtip1+35Smini activity was further tested to be strong and stable under many different growth conditions but suppressed by cold, salt, alkaline pH and higher ammonium and phosphorus. CONCLUSION: This work describes a promising strategy to isolate a tissue-/cell-specific enhancer sequence from the enhancer trap lines, which are publically available. The reported synthetic promoter i.e. PErtip1+35Smini may provide a valuable and potent molecular-tool for comprehensive investigation of a gene function related to root growth and development as well as molecular engineering of root-architectural formation aiming to improve plant growth.

Molecular identification and functional characterization of GhAMT1.3 in ammonium transport with a high affinity from cotton (Gossypium hirsutum L.).

Ammonium (NH4 + ) represents a primary nitrogen source for many plants, its effective transport into and between tissues and further assimilation in cells determine greatly plant nitrogen use efficiency. However, biological components involved in NH4 + movement in woody plants are unclear. Here, we report kinetic evidence for cotton NH4 + uptake and molecular identification of certain NH4 + transporters (AMTs) from cotton (Gossypium hirustum). A substrate-influx assay using 15 N-isotope revealed that cotton possessed a high-affinity transport system with a Km of 58 µM for NH4 + . Sequence analysis showed that GhAMT1.1-1.3 encoded respectively a membrane protein containing 485, 509 or 499 amino acids. Heterologous functionality test demonstrated that GhAMT1.1-1.3 expression mediated NH4 + permeation across the plasma membrane (PM) of yeast and/or Arabidopsis qko-mutant cells, allowing a growth restoration of both mutants on NH4 + . Quantitative PCR measurement showed that GhAMT1.3 was expressed in roots and leaves and markedly up-regulated by N-starvation, repressed by NH4 + resupply and regulated diurnally and age-dependently, suggesting that GhAMT1.3 should be a N-responsive gene. Importantly, GhAMT1.3 expression in Arabidopsis improved plant growth on NH4 + and enhanced total nitrogen accumulation (∼50% more), conforming with the observation of 2-fold more NH4 + absorption by GhAMT1.3-transformed qko plant roots during a 1-h root influx period. Together with its targeting to the PM and saturated transport kinetics with a Km of 72 µM for NH4 + , GhAMT1.3 is suggested to be a high-affinity NH4 + permease that may play a significant role in cotton NH4 + acquisition and utilization, adding a new member in the plant AMT family.

An Arabidopsis PWI and RRM motif-containing protein is critical for pre-mRNA splicing and ABA responses.

The phytohormone abscisic acid (ABA) is important for growth, development and stress responses in plants. Recent research has identified ABA receptors and signalling components that regulate seed germination and stomatal closure. However, proteins that regulate ABA signalling remain poorly understood. Here we use a forward-genetic screen to identify rbm25-1 and rbm25-2, two Arabidopsis mutants with increased sensitivity to growth inhibition by ABA. Using RNA-seq, we found that RBM25 controls the splicing of many pre-mRNAs. The protein phosphatase 2C HAB1, a critical component in ABA signalling, shows a dramatic defect in pre-mRNA splicing in rbm25 mutants. Ectopic expression of a HAB1 complementary DNA derived from wild-type mRNAs partially suppresses the rbm25-2 mutant phenotype. We suggest that RNA splicing is of particular importance for plant response to ABA and that the splicing factor RBM25 has a critical role in this response.

The ureide-degrading reactions of purine ring catabolism employ three amidohydrolases and one aminohydrolase in Arabidopsis, soybean, and rice.

Several ureides are intermediates of purine base catabolism, releasing nitrogen from the purine nucleotides for reassimilation into amino acids. In some legumes like soybean (Glycine max), ureides are used for nodule-to-shoot translocation of fixed nitrogen. Four enzymes of Arabidopsis (Arabidopsis thaliana), (1) allantoinase, (2) allantoate amidohydrolase (AAH), (3) ureidoglycine aminohydrolase, and (4) ureidoglycolate amidohydrolase (UAH), catalyze the complete hydrolysis of the ureide allantoin in vitro. However, the metabolic route in vivo remains controversial. Here, in growth and metabolite analyses of Arabidopsis mutants, we demonstrate that these enzymes are required for allantoin degradation in vivo. Orthologous enzymes are present in soybean, encoded by one to four gene copies. All isoenzymes are active in vitro, while some may be inefficiently translated in vivo. Surprisingly, transcript and protein amounts are not significantly regulated by nitrogen fixation or leaf ureide content. A requirement for soybean AAH and UAH for ureide catabolism in leaves has been demonstrated by the use of virus-induced gene silencing. Functional AAH, ureidoglycine aminohydrolase, and UAH are also present in rice (Oryza sativa), and orthologous genes occur in all other plant genomes sequenced to date, indicating that the amidohydrolase route of ureide degradation is universal in plants, including mosses (e.g. Physcomitrella patens) and algae (e.g. Chlamydomomas reinhardtii).

Rice DUR3 mediates high-affinity urea transport and plays an effective role in improvement of urea acquisition and utilization when expressed in Arabidopsis.

• Despite the great agricultural and ecological importance of efficient use of urea-containing nitrogen fertilizers by crops, molecular and physiological identities of urea transport in higher plants have been investigated only in Arabidopsis. • We performed short-time urea-influx assays which have identified a low-affinity and high-affinity (K(m) of 7.55 µM) transport system for urea-uptake by rice roots (Oryza sativa). • A high-affinity urea transporter OsDUR3 from rice was functionally characterized here for the first time among crops. OsDUR3 encodes an integral membrane-protein with 721 amino acid residues and 15 predicted transmembrane domains. Heterologous expression demonstrated that OsDUR3 restored yeast dur3-mutant growth on urea and facilitated urea import with a K(m) of c. 10 µM in Xenopus oocytes. • Quantitative reverse-transcription polymerase chain reaction (qPCR) analysis revealed upregulation of OsDUR3 in rice roots under nitrogen-deficiency and urea-resupply after nitrogen-starvation. Importantly, overexpression of OsDUR3 complemented the Arabidopsis atdur3-1 mutant, improving growth on low urea and increasing root urea-uptake markedly. Together with its plasma membrane localization detected by green fluorescent protein (GFP)-tagging and with findings that disruption of OsDUR3 by T-DNA reduces rice growth on urea and urea uptake, we suggest that OsDUR3 is an active urea transporter that plays a significant role in effective urea acquisition and utilisation in rice.

Identification and characterization of proteins involved in rice urea and arginine catabolism.

Rice (Oryza sativa) production relies strongly on nitrogen (N) fertilization with urea, but the proteins involved in rice urea metabolism have not yet been characterized. Coding sequences for rice arginase, urease, and the urease accessory proteins D (UreD), F (UreF), and G (UreG) involved in urease activation were identified and cloned. The functionality of urease and the urease accessory proteins was demonstrated by complementing corresponding Arabidopsis (Arabidopsis thaliana) mutants and by multiple transient coexpression of the rice proteins in Nicotiana benthamiana. Secondary structure models of rice (plant) UreD and UreF proteins revealed a possible functional conservation to bacterial orthologs, especially for UreF. Using amino-terminally StrepII-tagged urease accessory proteins, an interaction between rice UreD and urease could be shown. Prokaryotic and eukaryotic urease activation complexes seem conserved despite limited protein sequence conservation for UreF and UreD. In plant metabolism, urea is generated by the arginase reaction. Rice arginase was transiently expressed as a carboxyl-terminally StrepII-tagged fusion protein in N. benthamiana, purified, and biochemically characterized (K(m) = 67 mm, k(cat) = 490 s(-1)). The activity depended on the presence of manganese (K(d) = 1.3 microm). In physiological experiments, urease and arginase activities were not influenced by the external N source, but sole urea nutrition imbalanced the plant amino acid profile, leading to the accumulation of asparagine and glutamine in the roots. Our data indicate that reduced plant performance with urea as N source is not a direct result of insufficient urea metabolism but may in part be caused by an imbalance of N distribution.