Phase I clinical trial of intracerebral injection of lentiviral-ABCD1 for the treatment of cerebral adrenoleukodystrophy.

This was a single-arm, multicenter, open-label phase I trial. Lentiviral vectors (LV) carrying the ABCD1 gene (LV-ABCD1) was directly injected into the brain of patients with childhood cerebral adrenoleukodystrophy (CCALD), and multi-site injection was performed. The injection dose increased from 200 to 1600 µL (vector titer: 1×109 transduction units per mL (TU/mL)), and the average dose per kilogram body weight ranges from 8 to 63.6 µL/kg. The primary endpoint was safety, dose-exploration and immunogenicity and the secondary endpoint was initial evaluation of efficacy and the expression of ABCD1 protein. A total of 7 patients participated in this phase I study and were followed for 1 year. No injection-related serious adverse event or death occurred. Common adverse events associated with the injection were irritability (71%, 5/7) and fever (37.2-38.5 â„ƒ, 57%, 4/7). Adverse events were mild and self-limited, or resolved within 3 d of symptomatic treatment. The maximal tolerable dose is 1600 µL. In 5 cases (83.3%, 5/6), no lentivirus associated antibodies were detected. The overall survival at 1-year was 100%. The ABCD1 protein expression was detected in neutrophils, monocytes and lymphocytes. This study suggests that the intracerebral injection of LV-ABCD1 for CCALD is safe and can achieve successful LV transduction in vivo; even the maximal dose did not increase the risk of adverse events. Furthermore, the direct LV-ABCD1 injection displayed low immunogenicity. In addition, the effectiveness of intracerebral LV-ABCD1 injection has been preliminarily demonstrated while further investigation is needed. This study has been registered in the Chinese Clinical Trial Registry (https://www.chictr.org.cn/, registration number: ChiCTR1900026649).

Neurometabolic topography and associations with cognition in Alzheimer's disease: A whole-brain high-resolution 3D MRSI study.

INTRODUCTION: Altered neurometabolism, detectable via proton magnetic resonance spectroscopic imaging (1H-MRSI), is spatially heterogeneous and underpins cognitive impairments in Alzheimer's disease (AD). However, the spatial relationships between neurometabolic topography and cognitive impairment in AD remain unexplored due to technical limitations. METHODS: We used a novel whole-brain high-resolution 1H-MRSI technique, with simultaneously acquired 18F-florbetapir positron emission tomography (PET) imaging, to investigate the relationship between neurometabolic topography and cognitive functions in 117 participants, including 22 prodromal AD, 51 AD dementia, and 44 controls. RESULTS: Prodromal AD and AD dementia patients exhibited spatially distinct reductions in N-acetylaspartate, and increases in myo-inositol. Reduced N-acetylaspartate and increased myo-inositol were associated with worse global cognitive performance, and N-acetylaspartate correlated with five specific cognitive scores. Neurometabolic topography provides biological insights into diverse cognitive dysfunctions. DISCUSSION: Whole-brain high-resolution 1H-MRSI revealed spatially distinct neurometabolic topographies associated with cognitive decline in AD, suggesting potential for noninvasive brain metabolic imaging to track AD progression. HIGHLIGHTS: Whole-brain high-resolution 1H-MRSI unveils neurometabolic topography in AD. Spatially distinct reductions in NAA, and increases in mI, are demonstrated. NAA and mI topography correlates with global cognitive performance. NAA topography correlates with specific cognitive performance.

Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla.

Introduction: Nicotinamide adenine dinucleotide (NAD) is a crucial molecule in cellular metabolism and signaling. Mapping intracellular NAD content of human brain has long been of interest. However, the sub-millimolar level of cerebral NAD concentration poses significant challenges for in vivo measurement and imaging. Methods: In this study, we demonstrated the feasibility of non-invasively mapping NAD contents in entire human brain by employing a phosphorus-31 magnetic resonance spectroscopic imaging (31P-MRSI)-based NAD assay at ultrahigh field (7 Tesla), in combination with a probabilistic subspace-based processing method. Results: The processing method achieved about a 10-fold reduction in noise over raw measurements, resulting in remarkably reduced estimation errors of NAD. Quantified NAD levels, observed at approximately 0.4 mM, exhibited good reproducibility within repeated scans on the same subject and good consistency across subjects in group data (2.3 cc nominal resolution). One set of higher-resolution data (1.0 cc nominal resolution) unveiled potential for assessing tissue metabolic heterogeneity, showing similar NAD distributions in white and gray matter. Preliminary analysis of age dependence suggested that the NAD level decreases with age. Discussion: These results illustrate favorable outcomes of our first attempt to use ultrahigh field 31P-MRSI and advanced processing techniques to generate a whole-brain map of low-concentration intracellular NAD content in the human brain.

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

PURPOSE: To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS: Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION: We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

[Retracted] Downregulation of CKS1B restrains the proliferation, migration, invasion and angiogenesis of retinoblastoma cells through the MEK/ERK signaling pathway.

Following the publication of the above paper, it has been drawn to the Editors' attention by a concerned reader that certain of the lumen formation assay data shown in Fig. 5A on p. 112 were strikingly similar to data appearing in different form in another article written by different authors at different research institute, which had already been published in the journal Biomedicine & Pharmacotherapy prior to the submission of this paper to International Journal of Molecular Medicine, and which has also subsequently been retracted. In view of the fact that the contentious data had already apparently been published previously, the Editor of International Journal of Molecular Medicine has decided that this paper should be retracted from the Journal. After having been in contact with the authors, they agreed with the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Molecular Medicine 44: 103­114, 2019; DOI: 10.3892/ijmm.2019.4183].

Arsenic effectively improves the degradation of fluorene by Rhodococcus sp. 2021 under the combined pollution of arsenic and fluorene.

The performance of bacterial strains in executing degradative functions under the coexistence of heavy metals/heavy metal-like elements and organic contaminants is understudied. In this study, we isolated a fluorene-degrading bacterium, highly arsenic-resistant, designated as strain 2021, from contaminated soil at the abandoned site of an old coking plant. It was identified as a member of the genus Rhodococcus sp. strain 2021 exhibited efficient fluorene-degrading ability under optimal conditions of 400 mg/L fluorene, 30 °C, pH 7.0, and 250 mg/L trivalent arsenic. It was noted that the addition of arsenic could promote the growth of strain 2021 and improve the degradation of fluorene - a phenomenon that has not been described yet. The results further indicated that strain 2021 can oxidize As3+ to As5+; here, approximately 13.1% of As3+ was converted to As5+ after aerobic cultivation for 8 days at 30 °C. The addition of arsenic could greatly up-regulate the expression of arsR/A/B/C/D and pcaG/H gene clusters involved in arsenic resistance and aromatic hydrocarbon degradation; it also aided in maintaining the continuously high expression of cstA that codes for carbon starvation protein and prmA/B that codes for monooxygenase. These results suggest that strain 2021 holds great potential for the bioremediation of environments contaminated by a combination of arsenic and polycyclic aromatic hydrocarbons. This study provides new insights into the interactions among microbes, as well as inorganic and organic pollutants.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

OBJECTIVE: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. METHODS: Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. RESULTS: The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. CONCLUSION: This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. SIGNIFICANCE: The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Optimization of 3D dynamic speech MRI: Poisson-disc undersampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis.

PURPOSE: To improve the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach. METHODS: For data acquisition, we used a Poisson-disc random under sampling scheme that reduced the undersampling coherence. For image reconstruction, we proposed a novel locally higher-rank partial separability model. This reconstruction model represented the oral and static regions using separate low-rank subspaces, therefore, preserving their distinct temporal signal characteristics. Regional optimized temporal basis was determined from the regional-optimized virtual coil approach. Overall, we achieved a better spatiotemporal image reconstruction quality with the potential of reducing total acquisition time by 50%. RESULTS: The proposed method was demonstrated through several 2-mm isotropic, 64 mm total thickness, dynamic acquisitions with 40 frames per second and compared to the previous approach using a global subspace model along with other k-space sampling patterns. Individual timeframe images and temporal profiles of speech samples were shown to illustrate the ability of the Poisson-disc under sampling pattern in reducing total acquisition time. Temporal information of sagittal and coronal directions was also shown to illustrate the effectiveness of the locally higher-rank operator and regional optimized temporal basis. To compare the reconstruction qualities of different regions, voxel-wise temporal SNR analysis were performed. CONCLUSION: Poisson-disc sampling combined with a locally higher-rank model and a regional-optimized temporal basis can drastically improve the spatiotemporal image quality and provide a 50% reduction in overall acquisition time.

The sources, properties, extraction, biosynthesis, pharmacology, and application of lycopene.

Lycopene is an important pigment with an alkene skeleton from Lycopersicon esculentum, which is also obtained from some red fruits and vegetables. Lycopene is used in the food field with rich functions and serves in the medical field with multiple clinical values because it has dual functions of both medicine and food. It was found that lycopene was mainly isolated by solvent extraction, ultrasonic-assisted extraction, supercritical fluid extraction, high-intensity pulsed electric field-assisted extraction, enzymatic-assisted extraction, and microwave-assisted extraction. Meanwhile, it was also obtained via 2 synthetic pathways: chemical synthesis and biosynthesis. Pharmacological studies revealed that lycopene has anti-oxidant, hypolipidemic, anti-cancer, immunity-enhancing, hepatoprotective, hypoglycemic, cardiovascular-protective, anti-inflammatory, neuroprotective, and osteoporosis-inhibiting effects. The application of lycopene mainly includes food processing, animal breeding, and medical cosmetology fields. It is hoped that this review will provide some useful information and guidance for future study and exploitation of lycopene.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.

High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.

Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.

The sulfonamide-resistance dihydropteroate synthase gene is crucial for efficient biodegradation of sulfamethoxazole by Paenarthrobacter species.

Sulfonamide antibiotics (SAs) are serious pollutants to ecosystems and environments. Previous studies showed that microbial degradation of SAs such as sulfamethoxazole (SMX) proceeds via a sad-encoded oxidative pathway, while the sulfonamide-resistant dihydropteroate synthase gene, sul, is responsible for SA resistance. However, the co-occurrence of sad and sul genes, as well as how the sul gene affects SMX degradation, was not explored. In this study, two SMX-degrading bacterial strains, SD-1 and SD-2, were cultivated from an SMX-degrading enrichment. Both strains were Paenarthrobacter species and were phylogenetically identical; however, they showed different SMX degradation activities. Specifically, strain SD-1 utilized SMX as the sole carbon and energy source for growth and was a highly efficient SMX degrader, while SD-2 did could not use SMX as a sole carbon or energy source and showed limited SMX degradation when an additional carbon source was supplied. Genome annotation, growth, enzymatic activity tests, and metabolite detection revealed that strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation and a pathway of protocatechuate degradation. A new sulfonamide-resistant dihydropteroate synthase gene, sul918, was identified in strain SD-1, but not in SD-2. Moreover, the lack of sul918 resulted in low SMX degradation activity in strain SD-2. Genome data mining revealed the co-occurrence of sad and sul genes in efficient SMX-degrading Paenarthrobacter strains. We propose that the co-occurrence of sulfonamide-resistant dihydropteroate synthase and sad genes is crucial for efficient SMX biodegradation. KEY POINTS: • Two sulfamethoxazole-degrading strains with distinct degrading activity, Paenarthrobacter sp. SD-1 and Paenarthrobacter sp. SD-2, were isolated and identified. • Strains SD-1 and SD-2 shared a sad-encoded oxidative pathway for SMX degradation. • A new plasmid-borne SMX resistance gene (sul918) of strain SD-1 plays a crucial role in SMX degradation efficiency.

Preliminary Development of an MRI Atlas for Application to Cleft Care: Findings and Future Recommendations.

OBJECTIVE: To introduce a highly innovative imaging method to study the complex velopharyngeal (VP) system and introduce the potential future clinical applications of a VP atlas in cleft care. DESIGN: Four healthy adults participated in a 20-min dynamic magnetic resonance imaging scan that included a high-resolution T2-weighted turbo-spin-echo 3D structural scan and five custom dynamic speech imaging scans. Subjects repeated a variety of phrases when in the scanner as real-time audio was captured. SETTING: Multisite institution and clinical setting. PARTICIPANTS: Four adult subjects with normal anatomy were recruited for this study. MAIN OUTCOME: Establishment of 4-D atlas constructed from dynamic VP MRI data. RESULTS: Three-dimensional dynamic magnetic resonance imaging was successfully used to obtain high quality dynamic speech scans in an adult population. Scans were able to be re-sliced in various imaging planes. Subject-specific MR data were then reconstructed and time-aligned to create a velopharyngeal atlas representing the averaged physiological movements across the four subjects. CONCLUSIONS: The current preliminary study examined the feasibility of developing a VP atlas for potential clinical applications in cleft care. Our results indicate excellent potential for the development and use of a VP atlas for assessing VP physiology during speech.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS: In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION: This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Vicatia thibetica de Boiss: Botany, Traditional Uses, Phytochemistry, Quantitative Analysis, and Pharmacology.

BACKGROUND: Vicatia thibetica de Boiss is a common Tibetan medicine used for both medicine and food, belonging to the family Apiaceae. This plant has the functions of dispelling wind, removing dampness, dispersing cold, and relieving pain. It has great development potential and application prospects in food development and medicinal value. METHODS: The related references on botany, traditional uses, phytochemistry, quantitative analysis, and pharmacology of V. thibetica de Boiss had been retrieved from both online and offline databases, including PubMed, ScienceDirect, Web of Science, Elsevier, Willy, SpringLink, SciFinder, Google Scholar, Baidu Scholar, ACS publications, SciHub, Scopus, and CNKI. RESULTS: V. thibetica de Boiss exerts nourishing, appetizing, and digestive effects according to the theory of Tibetan medicine. Phytochemical reports have revealed that V. thibetica de Boiss contains flavonoids, coumarins, sterols, and organic acids. Meanwhile, the quantitative analysis of the chemical constituents of V. thibetica de Boiss has been done by means of UPLC-Q-TOF-MS. It has also been found that V. thibetica de Boiss possesses multiple pharmacological activities, including anti-fatigue, anti-oxidant, anti-aging, and non-toxic activities. CONCLUSION: This paper has comprehensively summarized botany, traditional uses, phytochemistry, quantitative analysis, and pharmacology of V. thibetica de Boiss. It will not only provide an important clue for further studying V. thibetica de Boiss, but also offer an important theoretical basis and valuable reference for in-depth research and exploitation of this plant in the future.

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.

OBJECTIVE: The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS: Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS: In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION: The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE: Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Hippophae rhamnoides L.: A Comprehensive Review on the Botany, Traditional Uses, Phytonutrients, Health Benefits, Quality Markers, and Applications.

Hippophae rhamnoides L. (sea buckthorn), consumed as a food and health supplement worldwide, has rich nutritional and medicinal properties. Different parts of H. rhamnoides L. were used in traditional Chinese medicines for relieving cough, aiding digestion, invigorating blood circulation, and alleviating pain since ancient times. Phytochemical studies revealed a wide variety of phytonutrients, including nutritional components (proteins, minerals, vitamins, etc.) and functional components like flavonoids (1-99), lignans (100-143), volatile oils (144-207), tannins (208-230), terpenoids (231-260), steroids (261-270), organic acids (271-297), and alkaloids (298-305). The pharmacological studies revealed that some crude extracts or compounds of H. rhamnoides L. demonstrated various health benefits, such as anti-inflammatory, antioxidant, hepatoprotective, anticardiovascular disease, anticancer, hypoglycemic, hypolipidemic, neuroprotective, antibacterial activities, and their effective doses and experimental models were summarized and analyzed in this paper. The quality markers (Q-markers) of H. rhamnoides L. were predicted and analyzed based on protobotanical phylogeny, traditional medicinal properties, expanded efficacy, pharmacokinetics and metabolism, and component testability. The applications of H. rhamnoides L. in juice, wine, oil, ferment, and yogurt were also summarized and future prospects were examined in this review. However, the mechanism and structure-activity relationship of some active compounds are not clear, and quality control and potential toxicity are worth further study in the future.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.