Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping.

BACKGROUND: The modified Look-Locker inversion recovery (MOLLI) sequence is commonly used for myocardial T1 mapping. However, it acquires images with different inversion times, which causes difficulty in motion correction for respiratory-induced misregistration to a given target image. HYPOTHESIS: Using a generative adversarial network (GAN) to produce virtual MOLLI images with consistent heart positions can reduce respiratory-induced misregistration of MOLLI datasets. STUDY TYPE: Retrospective. POPULATION: 1071 MOLLI datasets from 392 human participants. FIELD STRENGTH/SEQUENCE: Modified Look-Locker inversion recovery sequence at 3 T. ASSESSMENT: A GAN model with a single inversion time image as input was trained to generate virtual MOLLI target (VMT) images at different inversion times which were subsequently used in an image registration algorithm. Four VMT models were investigated and the best performing model compared with the standard vendor-provided motion correction (MOCO) technique. STATISTICAL TESTS: The effectiveness of the motion correction technique was assessed using the fitting quality index (FQI), mutual information (MI), and Dice coefficients of motion-corrected images, plus subjective quality evaluation of T1 maps by three independent readers using Likert score. Wilcoxon signed-rank test with Bonferroni correction for multiple comparison. Significance levels were defined as P < 0.01 for highly significant differences and P < 0.05 for significant differences. RESULTS: The best performing VMT model with iterative registration demonstrated significantly better performance (FQI 0.88 ± 0.03, MI 1.78 ± 0.20, Dice 0.84 ± 0.23, quality score 2.26 ± 0.95) compared to other approaches, including the vendor-provided MOCO method (FQI 0.86 ± 0.04, MI 1.69 ± 0.25, Dice 0.80 ± 0.27, quality score 2.16 ± 1.01). DATA CONCLUSION: Our GAN model generating VMT images improved motion correction, which may assist reliable T1 mapping in the presence of respiratory motion. Its robust performance, even with considerable respiratory-induced heart displacements, may be beneficial for patients with difficulties in breath-holding. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-scale database consisting of 7039 brain T1-weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross-institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW-where "MX" is a mix of RW and nonuniform intensity normalization data and "RW" is raw data with basic preprocessing-did not require time-consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross-institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time-restricted applications, MX_RW represents a competitive alternative to FreeSurfer.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Byproduct formation during the biosynthesis of spinosyn A and evidence for an enzymatic interplay to prevent its formation.

Biosynthesis of spinosyn A in Saccharopolyspora spinosa involves a 1,4-dehydration followed by an intramolecular [4 + 2]-cycloaddition catalyzed by SpnM and SpnF, respectively. The cycloaddition also takes place in the absence of SpnF leading to questions regarding its mechanism of catalysis and biosynthetic role. Substrate analogs were prepared with an unactivated dienophile or an acyclic structure and found to be unreactive consistent with the importance of these features for cyclization. The SpnM-catalyzed dehydration reaction was also found to yield a byproduct corresponding to the C11 = C12 cis isomer of the SpnF substrate. This byproduct is stable both in the presence and absence of SpnF; however, relative production of the SpnM product and byproduct could be shifted in favor of the former by including SpnF or the dehydrogenase SpnJ in the reaction. This result suggests a potential interplay between the enzymes of spinosyn A biosynthesis that may help to improve the efficiency of the pathway.

Polymicrobial interaction between Lactobacillus and Saccharomyces cerevisiae: coexistence-relevant mechanisms.

The coordination of single or multiple microorganisms are required for the manufacture of traditional fermented foods, improving the flavour and nutrition of the food materials. However, both the additional economic benefits and safety concerns have been raised by microbiotas in fermented products. Among the fermented products, Lactobacillus and Saccharomyces cerevisiae are one of the stable microbiotas, suggesting their interaction is mediated by coexistence-relevant mechanisms and prevent to be excluded by other microbial species. Thus, aiming to guide the manufacture of fermented foods, this review will focus on interactions of coexistence-relevant mechanisms between Lactobacillus and S. cerevisiae, including metabolites communications, aggregation, and polymicrobial biofilm. Also, the molecular regulatory network of the coexistence-relevant mechanisms is discussed according to omics researches.

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

"One-step" characterization platform for pathogenic genetics of Staphylococcus aureus.

Staphylococcus aureus (S. aureus) is an important human pathogen causing a variety of life-threatening diseases. In recent years, the health problem caused by S. aureus contaminated food has become a global health problem. S. aureus can express various pathogenic factors, mainly used for adhesion, colonization, invasion and infection of the host. Therefore, rapid and accurate detection of virulence genes in S. aureus is necessary to prevent outbreaks caused by this pathogen. PCR is a useful tool for rapid detection of foodborne pathogens. The objective of this study was to detect the presence of major toxin genes in S. aureus, including sea, seb, sec, see, pvl and tsst, by using a PCR plate. Of the 13 strains tested, 12 (92.3%) were found to be positive for one or more toxin genes. This study realized the one-step detection of main toxin factors in S. aureus.

Analysis on the virulomes and resistomes of multi-drug resistance clinical Escherichia coli isolates, as well as the interactome with gut microbiome.

Escherichia coli is one of the most diverse microbial species. Pathogenic E. coli is capable of causing various diseases in humans, including several types of diarrhea, urinary tract infections, sepsis, and meningitis. This study focused on the antibiotic susceptibility profile and genomic analysis of a clinical E. coli Guangzhou-Eco330 isolated from a hospitalized 8-year-old female patient suffered from pulmonary infection in 2017. Susceptibility to 15 antibiotics were determined using Vitek2™ Automated Susceptibility System and Etest strips and interpreted based on CLSI guidelines. The genome was sequenced using Illumina Hiseq 2500 platform and assembled de novo using Velvet, followed by bioinformatics analysis. The genome has a length of 5,132,642 bp and contains 4989 predicted genes with an average GC content of 50.51%. The carriage of rfbE gene suggested the strain belonging to O157. In the genome, 70 non-coding RNAs, 50 repeat sequences, 18 transposons, 78 GIs, 9 CRISPRs, and 3 large prophages were identified. 37 PHI related genes and 108 virulence genes were determined to contribute to its pathogenicity. Specifically, the acquisition of multiple antibiotic resistance genes including blaCTX-M-55, blaOXA-10, blaCMY-48, tetB, and qnrS1 contributed to its resistance to penicillins, telracyclines, cephalosporin, and quinolones. The understanding of the genome may aid in further study on the clinical control of multi-drug resistance E. coli.

Resistome and virulome study on pathogenic Streptococcus agalactiae Guangzhou-SAG036.

Streptococcus agalactiae is considered as a leading case of bacterial infection among neonates. Although relative protection strategies have been performed in many high-income countries, resulting in a massive reduction in the occurrences of early-onset GBS disease, the late-onset disease has not affected. Here, the whole genome of S. agalactiae Guangzhou-SAG036 was sequenced by the Pacific Biosciences Sequel using the P4-C2 chemistry and the continuous long reads were used for de novo assembly using HGAP. Besides, genes prediction and multiply annotation were performed by comparing it with diverse databases. The whole genome has a length of 2,206,504 bp and contains 2162 predicted genes with an average G + C content of 35.85%. Based on the whole genome sequence, 2 large prophages, 20 virulence factors genes, and 8 antibiotic resistant genes were identified. MLST analysis revealed S. agalactiae Guangzhou-SAG036 was identified as ST-17. The virulence factors genes were identified with different functions including adherence, antiphagocytosis, spreading factor, immune evasion, invasion, toxin. Besides, the antibiotic-resistant genes may provide S. agalactiae with resistance to multi-drugs including erythromycin, streptomycin, azithromycin, spiramycin, ampicillin, kanamycin, cationic peptides, and tetracycline. Therefore, the infection of S. agalactiae Guangzhou-SAG036 ST-17 strain maybe caused by the complex virulence factors and multi-drugs resistance. These results contribute to further understand GBS epidemiology and surveillance targets.

Genomic analysis of a hop-resistance Lactobacillus brevis strain responsible for food spoilage and capable of entering into the VBNC state.

BACKGROUND: Lactobacillus brevis is a major contaminant of spoiled beer. And it was able to enter VBNC state and cause false negative detection, which poses a major challenge to the brewing industry. METHODS: The genomic DNA of L. brevis BM-LB13908 was extracted and purified to form a sequencing library that meets the quality requirements and was sequenced. The sequencing results were then screened and assembled to obtain the entire genome sequence of L. brevis. Predicted genes were annotated by GO database, KEGG pathway database and COG functional classification system. RESULTS: The final assembly yielded 275 scaffolds of a total length of 2 840 080 bp with a G + C content of 53.35%. There were 2357, 701, 1519 predicted genes with corresponding GO functional, COG functional, and KEGG biological pathway annotations, respectively. The genome of L. brevis BM-LB13908 contains hop resistance gene horA and multiple genes related to the formation of VBNC state. CONCLUSIONS: This report describes the draft genome sequence of L. brevis BM-LB13908, a spoilage strain isolated from finished beer sample. This study may support further study on L. brevis and other beer spoilage bacteria, and prevent and control beer spoilage caused by microorganisms.

Study on the virulome and resistome of a vancomycin intermediate-resistance Staphylococcus aureus.

Methicillin-resistant S. aureus (MRSA) has been considered a potential "Super Bugs", responsible for various infectious diseases. Vancomycin has been the most effective antibitic to treat MRSA originated infections. In this study, we aimed at investigating the genomic features of a vancomycin intermediate-resistance S. aureus strain Guangzhou-SauVS2 isolated from a female patient suffering from chronic renal function failure, emphasizing on its antimicrobial resistance and virulence determinants. The genome has a total length of 2,605,384 bp and the G+C content of 33.21%, with 2,239 predicted genes annotated with GO terms, COG categories, and KEGG pathways. Besides the carriage of vancomycin b-type resistance protein responsible for the vancomycin intermediate-resistance, S. aureus strain Guangzhou-SauVS2 showed resistance to ß-lactams, quinolones, macrolide, and tetracycline, due to the acquisition of corresponding antimicrobial resistance genes. In addition, virulence factors including adherence, antiphagocytosis, iron uptake, and toxin were determined, indicating the pathogenesis of the strain.

Identification of and solution for false D-dimer results.

BACKGROUND: Clinically, D-dimer (DD) levels are mainly used to exclude diseases such as deep venous thrombosis (DVT). In clinical testing, DD assays can be subjected to interference that may cause false results, which directly affect the clinical diagnosis. Our hypothesis was that the 95% confidence intervals (CIs) of the fibrin degradation product (FDP)/DD and fibrinogen (Fib)/DD ratios were used to identify these false results and corrected via multiple dilutions. METHODS: In total, 16 776 samples were divided into three groups according to the DD levels detected by Sysmex CS5100 and CA7000: Group A, DD ≥ 2.0 µg/mL fibrinogen equivalent unit (FEU); group B, 0.5 < DD < 2.0 µg/mL FEU; and group C, DD ≤ 0.5 µg/mL FEU. The 95% CIs of the FDP/DD and Fib/DD ratios were calculated. Six abnormal DD results were found according to the 95% CIs. For verification, we performed multiple dilutions, compared the results with those of other instruments, and tested the addition of heterophilic blocking reagent (HBR). RESULTS: The median and 95% CI of the FDP/DD ratio were 3.76 and 2.25-8.15 in group A, 5.63 and 2.86-10.58 in group B, 10.23 and 0.91-47.71 in groups C, respectively. For the Fib/DD ratio, the 95% CIs was 0.02-2.21 in group A, 0.68-8.15 in group B, and 3.82-55.27 in groups C. Six abnormal results were identified after multiple dilutions, by comparison with other detection systems, and after HBR addition. CONCLUSIONS: The FDP/DD ratio is more reliable for identifying false results. If the FDP/DD ratio falls outside the 95% CI, it should be verified by different methods.

What makes D-dimer assays suspicious-heterophilic antibodies?

BACKGROUND: Heterophilic antibodies are still an important source of interference in immunoassays, but reports of interference with D-dimers are rare. Are D-dimer level abnormalities, found in the clinic, caused by heterophilic antibodies as well, or are other mechanisms involved? We will elaborate on this issue through two different examples in this article. METHODS: Serum from two patients with significantly elevated levels of D-dimers were measured and compared by different methods, diluted, and dealt with heterophilic antibody blockers. At the same time, to retrieve the interference, we focused on the cause of D-dimer false positives and made a systematic review of the literature. RESULTS: The D-dimer values were normal (0.49 and 0.15 µg/mL) detected with different testing method and decreased after addition of heterophilic antibody blocking reagent. According to literature data, there were 66.7% (4/6) references showed the interference were heterophilic antibody. CONCLUSIONS: The influence of heterophilic antibodies on the measurement of D-dimers remains a big challenge. Different measuring instruments and methods may have significant differences in the measurement of D-dimers. By using a combination of instrumental methods for measuring, incorporating heterophilic antibody blockers, and combining with clinical performance and imaging data, most of the interference can be eliminated.

Structural basis for oligomerization and glycosaminoglycan binding of CCL5 and CCL3.

CC chemokine ligand 5 (CCL5) and CCL3 are critical for immune surveillance and inflammation. Consequently, they are linked to the pathogenesis of many inflammatory conditions and are therapeutic targets. Oligomerization and glycosaminoglycan (GAG) binding of CCL5 and CCL3 are vital for the functions of these chemokines. Our structural and biophysical analyses of human CCL5 reveal that CCL5 oligomerization is a polymerization process in which CCL5 forms rod-shaped, double-helical oligomers. This CCL5 structure explains mutational data and offers a unified mechanism for CCL3, CCL4, and CCL5 assembly into high-molecular-weight, polydisperse oligomers. A conserved, positively charged BBXB motif is key for the binding of CC chemokines to GAG. However, this motif is partially buried when CCL3, CCL4, and CCL5 are oligomerized; thus, the mechanism by which GAG binds these chemokine oligomers has been elusive. Our structures of GAG-bound CCL5 and CCL3 oligomers reveal that these chemokine oligomers have distinct GAG-binding mechanisms. The CCL5 oligomer uses another positively charged and fully exposed motif, KKWVR, in GAG binding. However, residues from two partially buried BBXB motifs along with other residues combine to form a GAG-binding groove in the CCL3 oligomer. The N termini of CC chemokines are shown to be involved in receptor binding and oligomerization. We also report an alternative CCL3 oligomer structure that reveals how conformational changes in CCL3 N termini profoundly alter its surface properties and dimer-dimer interactions to affect GAG binding and oligomerization. Such complexity in oligomerization and GAG binding enables intricate, physiologically relevant regulation of CC chemokine functions.

Changes in sensorimotor-related thalamic diffusion properties and cerebrospinal fluid hydrodynamics predict gait responses to tap test in idiopathic normal-pressure hydrocephalus.

OBJECTIVES: To compare diffusion tensor (DT)-derived indices from the thalamic nuclei and cerebrospinal fluid (CSF) hydrodynamic parameters for the prediction of gait responsiveness to the CSF tap test in early iNPH patients. METHODS: In this study, 22 patients with iNPH and 16 normal controls were enrolled with the approval of an institutional review board. DT imaging and phase-contrast magnetic resonance imaging were performed in patients and controls to determine DT-related indices of the sensorimotor-related thalamic nuclei and CSF hydrodynamics. Gait performance was assessed in patients using gait scale before and after the tap test. The Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were applied to compare group differences between patients and controls and assess the predictive performance of gait responsiveness to the tap test in the patients. RESULTS: Fractional anisotropy (FA) and axial diffusivity showed significant increases in the ventrolateral (VL) and ventroposterolateral (VPL) nuclei of the iNPH group compared with those of the control group (p < 0.05). The predictions of gait responsiveness of ventral thalamic FA alone (area under the ROC curve [AUC] < 0.8) significantly outperformed those of CSF hydrodynamics alone (AUC < 0.6). The AUC curve was elevated to 0.812 when the CSF peak systolic velocity and FA value were combined for the VPL nucleus, yielding the highest sensitivity (0.769) and specificity (0.778) to predict gait responses. CONCLUSIONS: Combined measurements of sensorimotor-related thalamic FA and CSF hydrodynamics can provide potential biomarkers for gait response to the CSF tap test in patients with iNPH. KEY POINTS: • Ventrolateral and ventroposterolateral thalamic FA may predict gait responsiveness to tap test. • Thalamic neuroplasticity can be assessed through DTI in idiopathic normal-pressure hydrocephalus. • Changes in the CST associated with gait control could trigger thalamic neuroplasticity. • Activities of sensorimotor-related circuits could alter in patients with gait disturbance. • Management of patients with iNPH could be more appropriate.

Structure of the Complex between a Heparan Sulfate Octasaccharide and Mycobacterial Heparin-Binding Hemagglutinin.

Heparin-binding hemagglutinin (HBHA) is a 199 amino acid virulence factor at the envelope of Mycobacterium tuberculosis that contributes to latent tuberculosis. The binding of HBHA to respiratory epithelial cells, which leads to extrapulmonary dissemination of the pathogen, is mediated by cell-surface heparan sulfate (HS). We report the structural characterization of the HBHA/HS complex by NMR spectroscopy. To develop a model for the molecular recognition, the first chemically synthesized uniformly 13 C- and 15 N-labeled HS octasaccharide and a uniformly 13 C- and 15 N-labeled form of HBHA were prepared. Residues 180-195 at the C-terminal region of HBHA show large chemical shift perturbation upon association with the octasaccharide. Molecular dynamics simulations conforming to the multidimensional NMR data revealed key electrostatic and even hydrophobic interactions between the binding partners that may aid in the development of agents targeting the binding event.

Atomic Details of the Interactions of Glycosaminoglycans with Amyloid-ß Fibrils.

The amyloid plaques associated with Alzheimer's disease (AD) comprise fibrillar amyloid-ß (Aß) peptides as well as non-protein factors including glycosaminoglycan (GAG) polysaccharides. GAGs affect the kinetics and pathway of Aß self-assembly and can impede fibril clearance; thus, they may be accessory molecules in AD. Here we report the first high-resolution details of GAG-Aß fibril interactions from the perspective of the saccharide. Binding analysis indicated that the GAG proxy heparin has a remarkably high affinity for Aß fibrils with 3-fold cross-sectional symmetry (3Q). Chemical synthesis of a uniformly (13)C-labeled octasaccharide heparin analogue enabled magic-angle spinning solid-state NMR of the GAG bound to 3Q fibrils, and measurements of dynamics revealed a tight complex in which all saccharide residues are restrained without undergoing substantial conformational changes. Intramolecular (13)C-(15)N dipolar dephasing is consistent with close (<5 Å) contact between GAG anomeric position(s) and one or more histidine residues in the fibrils. These data provide a detailed model for the interaction between 3Q-seeded Aß40 fibrils and a major non-protein component of AD plaques, and they reveal that GAG-amyloid interactions display a range of affinities that critically depend on the precise details of the fibril architecture.