Mapping enzyme activity in living systems by real-time mid-infrared photothermal imaging of nitrile chameleons.

Simultaneous spatial mapping of the activity of multiple enzymes in a living system can elucidate their functions in health and disease. However, methods based on monitoring fluorescent substrates are limited. Here, we report the development of nitrile (C≡N)-tagged enzyme activity reporters, named nitrile chameleons, for the peak shift between substrate and product. To image these reporters in real time, we developed a laser-scanning mid-infrared photothermal imaging system capable of imaging the enzymatic substrates and products at a resolution of 300 nm. We show that when combined, these tools can map the activity distribution of different enzymes and measure their relative catalytic efficiency in living systems such as cancer cells, Caenorhabditis elegans, and brain tissues, and can be used to directly visualize caspase-phosphatase interactions during apoptosis. Our method is generally applicable to a broad category of enzymes and will enable new analyses of enzymes in their native context.

Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms.

White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.

High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting.

PURPOSE: This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. METHODS: We developed 3D visualization of short transverse relaxation time component (ViSTa)-MRF, which combined ViSTa technique with MR fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multicompartment fitting that could introduce bias and/or noise from additional assumptions or priors. RESULTS: The in vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in vivo results of 1 mm- and 0.66 mm-isotropic resolution datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30× slower with lower SNR. Furthermore, we applied the proposed method to enable 5-min whole-brain 1 mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples. CONCLUSIONS: In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1 and 0.66 mm isotropic resolution in 5 and 15 min, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.

DTI-MR fingerprinting for rapid high-resolution whole-brain T1 , T2 , proton density, ADC, and fractional anisotropy mapping.

PURPOSE: This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time. METHODS: To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design. RESULTS: The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence. CONCLUSION: The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

Accelerating Cellular Uptake with Unnatural Amino Acid for Inhibiting Immunosuppressive Cancer Cells.

Targeting immunosuppressive metastatic cancer cells is a key challenge in therapy. We recently have shown that a rigid-rod aromatic, pBP-NBD, that responds to enzymes and kill immunosuppressive metastatic osteosarcoma (mOS) and castration resistant prostate cancer (CRPC) cells in mimetic bone microenvironment. However, pBP-NBD demonstrated moderate efficacy against CRPC cells. To enhance activity, we incorporated the unnatural amino acid L- or D-4,4'-biphenylalanine (L- or D-BiP) into pBP-NBD, drastically increasing cellular uptake and CRPC inhibition. Specifically, we inserted BiP into pBP-NBD to target mOS (Saos2 and SJSA1) and CRPC (VCaP and PC3) cells with overexpressed phosphatases. Our results show that the D-peptide backbone with an aspartate methyl diester at the C-terminal offers the highest activity against these immunosuppressive mOS and CRPC cells. Importantly, imaging shows that the peptide assemblies almost instantly enter the cells and accumulate primarily within the endoplasmic reticulum of Saos2, SJSA1, and PC3 cells and at the lysosomes of VCaP cells. By using BiP to boost cellular uptake and self-assembly within cancer cells, this work illustrates an unnatural hydrophobic amino acid as a versatile and effective residue to boost endocytosis of synthetic peptides for intracellular self-assembly.

Assessment of the Enzymatic Dephosphorylation Kinetics in the Assemblies of a Phosphopentapeptide that Forms Intranuclear Nanoribbons.

Although the formation of peptide assemblies catalyzed by alkaline phosphatase (ALP) has received increasing attention in inhibiting cancer cells, the detailed enzyme kinetics of the dephosphorylation of the corresponding phosphopeptide assemblies have yet to be determined. We recently discovered that assemblies from a phosphopentapeptide can form intracellular nanoribbons that kill induced pluripotent stem cells or osteosarcoma cells, but the kinetics of enzymatic dephosphorylation remain unknown. Thus, we chose to examine the enzyme kinetics of the dephosphorylation of the phosphopentapeptide [NBD-LLLLpY (1)] from concentrations below to above its critical micelle concentration (CMC). Our results show that the phosphopeptide exhibits a CMC of 75 µM in phosphate saline buffer, and the apparent Vmax and Km values of alkaline phosphatase catalyzed dephosphorylation are approximately 0.24 µM/s and 5.67 mM, respectively. Despite dephosphorylation remaining incomplete at 60 min in all the concentrations tested, dephosphorylation of the phosphopeptide at concentrations of 200 µM or above mainly results in nanoribbons, dephosphorylation at concentrations of CMC largely produces nanofibers, and dephosphorylation below the CMC largely generates nanoparticles. Moreover, the formation of nanoribbons correlates with the intranuclear accumulation of the pentapeptide. By providing the first examination of the enzymatic kinetics of phosphopeptide assemblies, this work further supports the notion that the assemblies of phosphopentapeptides can act as a new functional entity for controlling cell fates.

A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects.

Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.

Neural substrate underlying the learning of a passage with unfamiliar vocabulary and syntax.

Speech comprehension is a complex process involving multiple stages, such as decoding of phonetic units, recognizing words, and understanding sentences and passages. In this study, we identify cortical networks beyond basic phonetic processing using a novel passage learning paradigm. Participants learn to comprehend a story composed of syllables of their native language, but containing unfamiliar vocabulary and syntax. Three learning methods are employed, each resulting in some degree of learning within a 12-min learning session. Functional magnetic resonance imaging results reveal that, when listening to the same story, the classic temporal-frontal language network is significantly enhanced by learning. Critically, activation of the left anterior and posterior temporal lobe correlates with the learning outcome that is assessed behaviorally through, e.g. word recognition and passage comprehension tests. This study demonstrates that a brief learning session is sufficient to induce neural plasticity in the left temporal lobe, which underlies the transformation from phonetic units to the units of meaning, such as words and sentences.

Synergistic suppression of BDNF via epigenetic mechanism deteriorating learning and memory impairment caused by Mn and Pb co-exposure.

Microglia, the resident immune cells of the central nervous system (CNS), play a dual role in neurotoxicity by releasing the NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome and brain-derived neurotrophic factor (BDNF) in response to environmental stress. Suppression of BDNF is implicated in learning and memory impairment induced by exposure to manganese (Mn) or lead (Pb) individually. Methyl CpG Binding Protein 2 (MeCp2) and its phosphorylation status are related to BDNF suppression. Protein phosphatase2A (PP2A), a member of the serine/threonine phosphatases family, dephosphorylates substrates based on the methylation state of its catalytic C subunit (PP2Ac). However, the specific impairment patterns and molecular mechanisms resulting from co-exposure to Mn and Pb remain unclear. Therefore, the purpose of this study was to explore the effects of Mn and Pb exposure, alone and in combination, on inducing neurotoxicity in the hippocampus of mice and BV2 cells, and to determine whether simultaneous exposure to both metals exacerbate their toxicity. Our findings reveal that co-exposure to Mn and Pb leads to severe learning and memory impairment in mice, which correlates with the accumulation of metals in the hippocampus and synergistic suppression of BDNF. This suppression is accompanied by up-regulation of the epigenetic repressor MeCp2 and its phosphorylation status, as well as demethylation of PP2Ac. Furthermore, inhibition of PP2Ac demethylation using ABL127, an inhibitor for its protein phosphatase methylesterase1 (PME1), or knockdown of MeCp2 via siRNA transfection in vitro effectively increases BDNF expression and mitigates BV2 cell damage induced by Mn and Pb co-exposure. We also observe abnormal activation of microglia characterized by enhanced release of the NLRP3 inflammasome, Casepase-1 and pro-inflammatory cytokines IL-1ß, in the hippocampus of mice and BV2 cells. In summary, our experiments demonstrate that simultaneous exposure to Mn and Pb results in more severe hippocampus-dependent learning and memory impairment, which is attributed to epigenetic suppression of BDNF mediated by PP2A regulation.

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization.

In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization.

Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping.

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI.

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

PURPOSE: To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping. METHODS: In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. RESULTS: The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI. CONCLUSION: GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network.

PURPOSE: To reduce the total scan time of multiple postlabeling delay (multi-PLD) pseudo-continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D convolutional neural network (H-CNN) that estimates the arterial transit time (ATT) and cerebral blow flow (CBF) maps from the reduced number of PLDs as well as averages. METHODS: A total of 48 subjects (38 females and 10 males), aged 56-80 years, compromising a training group (n = 45) and a validation group (n = 3) underwent MRI including multi-PLD pCASL. We proposed an H-CNN to estimate the ATT and CBF maps using a reduced number of PLDs and a separately reduced number of averages. The proposed method was compared with a conventional nonlinear model fitting method using the mean absolute error (MAE). RESULTS: The H-CNN provided the MAEs of 32.69 ms for ATT and 3.32 mL/100 g/min for CBF estimations using a full data set that contains six PLDs and six averages in the 3 test subjects. The H-CNN also showed that the smaller number of PLDs can be used to estimate both ATT and CBF without significant discrepancy from the reference (MAEs of 231.45 ms for ATT and 9.80 mL/100 g/min for CBF using three of six PLDs). CONCLUSION: The proposed machine learning-based ATT and CBF mapping offers substantially reduced scan time of multi-PLD pCASL.

Increased Free Water in the Substantia Nigra in Asymptomatic LRRK2 G2019S Mutation Carriers.

BACKGROUND: The alteration of substantia nigra (SN) degeneration in populations at risk of Parkinson's disease (PD) is unclear. OBJECTIVE: We investigated free water (FW) values in the posterior SN (pSN) in asymptomatic LRRK2 G2019S mutation carriers. METHODS: We analyzed diffusion imaging data from 28 asymptomatic LRRK2 G2019S mutation carriers and 30 healthy controls (HCs), whereas 11 asymptomatic LRRK2 G2019S carriers and 11 HCs were followed up. FW values in the pSN were measured and compared between the groups. The relationship between longitudinal changes in FW in the pSN and dopamine transporter striatal binding ratio (SBR) was analyzed. RESULTS: FW values in the pSN were significantly elevated and kept increasing during follow-up in asymptomatic LRRK2 G2019S carriers. There was a negative correlation between FW changes in the left pSN and SBR changes in the left putamen. CONCLUSION: FW in the pSN has the potential to be a progression imaging marker of early dopaminergic degeneration in the population at risk of PD. © 2022 International Parkinson and Movement Disorder Society.

Free-Water Imaging of the Substantia Nigra in GBA Pathogenic Variant Carriers.

BACKGROUND: Pathogenic variants in the glucocerebrosidase gene (GBA) have been identified as the most common genetic risk factor for Parkinson's disease (PD). However, the features of substantia nigra damage in GBA pathogenic variant carriers remain unclear. OBJECTIVE: We aimed to evaluate the microstructural changes in the substantia nigra in non-manifesting GBA pathogenic variant carriers (GBA-NMC) and PD patients with GBA pathogenic variant (GBA-PD) with free-water imaging. METHODS: First, we compared free water values in the posterior substantia nigra between non-manifesting non-carriers (NMNC, n = 29), GBA-NMC (n = 26), and GBA-PD (n = 16). Then, free water values in the posterior substantia nigra were compared between GBA-PD and early- (n = 19) and late-onset (n = 40) idiopathic PD (iPD) patients. Furthermore, we examined whether the baseline free water values could predict the progressions of clinical symptoms. RESULTS: The free water values in the posterior substantia nigra were significantly higher in the GBA-NMC and GBA-PD groups compared to NMNC, and were significantly increased in the GBA-PD group than both early- and late-onset iPD. Free water values in the posterior substantia nigra could predict the progression of anxiety and cognitive decline in GBA-NMC and GBA-PD groups. CONCLUSIONS: We demonstrate that free water values are elevated in the substantia nigra and predict the development of non-motor symptoms in GBA-NMC and GBA-PD. Our findings demonstrate that a significant nigral impairment already exists in GBA-NMC, and nigral injury may be more severe in GBA-PD than in iPD. These results support that free-water imaging can as a potential early marker of substantia nigra damage. © 2023 International Parkinson and Movement Disorder Society.

Increased Free Water in the Putamen in Idiopathic REM Sleep Behavior Disorder.

BACKGROUND: It has been suggested that the loss of nigrostriatal dopaminergic axon terminals occurs before the loss of dopaminergic neurons in the substantia nigra (SN) in Parkinson's disease (PD). This study aimed to use free-water imaging to evaluate microstructural changes in the dorsoposterior putamen (DPP) of idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) patients, which is considered a prodromal stage of synucleinopathies. METHODS: Free water values in the DPP, dorsoanterior putamen (DAP), and posterior SN were compared between the healthy controls (n = 48), iRBD (n = 43) and PD (n = 47) patients. In iRBD patients, the relationships between baseline and longitudinal free water values and clinical manifestations or dopamine transporter (DAT) striatal binding ratio (SBR) were analyzed. RESULTS: Free water values were significantly higher in the DPP and posterior substantia nigra (pSN), but not in the DAP, in the iRBD and PD groups than in controls. In iRBD patients, free water values in the DPP were progressively increased and correlated with the progression of clinical manifestations and the striatal DAT SBR. Baseline free water in the DPP was negatively correlated with striatal DAT SBR and hyposmia and positively correlated with motor deficits. CONCLUSIONS: This study demonstrates that free water values in the DPP are increased cross-sectionally and longitudinally and associated with clinical manifestations and the function of the dopaminergic system in the prodromal stage of synucleinopathies. Our findings indicate that free-water imaging of the DPP has the potential to be a valid marker of early diagnosis and progression of synucleinopathies. © 2023 International Parkinson and Movement Disorder Society.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Autohydrolysis of Diglycine-Activated Succinic Esters Boosts Cellular Uptake.

Rapid cellular uptake of synthetic molecules remains a challenge, and the motif frequently employed to generate prodrugs, succinic ester, unfortunately lowers the efficacy of the desired drugs due to their slow ester hydrolysis and low cell entry. Here we show that succinic ester-containing diglycine drastically boosts the cellular uptake of supramolecular assemblies or prodrugs. Specifically, autohydrolysis of the diglycine-activated succinic esters turns the nanofibers of the conjugates of succinic ester and self-assembling motif into nanoparticles for fast cellular uptake. The autohydrolysis of diglycine-activated succinic esters and drug conjugates also restores the efficacy of the drugs. 2D nuclear magnetic resonance (NMR) suggests that a "U-turn" of diglycine favors intramolecular hydrolysis of diglycine-activated succinic esters to promote autohydrolysis. As an example of rapid autohydrolysis of diglycine-activated succinic esters for instant cellular uptake, this work illustrates a nonenzymatic bond cleavage approach to develop effective therapeutics for intracellular targeting.

Small molecule drug and biotech drug interaction prediction based on multi-modal representation learning.

BACKGROUND: Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs). As the number of biotechnology drugs (BioDs) increases, so makes the threat of interactions between SMDs and BioDs. However, few computational methods are available to predict their interactions. RESULTS: Considering the structural specificity and relational complexity of SMDs and BioDs, a novel multi-modal representation learning method called Multi-SBI is proposed to predict their interactions. First, multi-modal features are used to adequately represent the heterogeneous structure and complex relationships of SMDs and BioDs. Second, an undersampling method based on Positive-unlabeled learning (PU-sampling) is introduced to obtain negative samples with high confidence from the unlabeled data set. Finally, both learned representations of SMD and BioD are fed into DNN classifiers to predict their interaction events. In addition, we also conduct a retrospective analysis. CONCLUSIONS: Our proposed multi-modal representation learning method can extract drug features more comprehensively in heterogeneous drugs. In addition, PU-sampling can effectively reduce the noise in the sampling procedure. Our proposed method significantly outperforms other state-of-the-art drug interaction prediction methods. In a retrospective analysis of DrugBank 5.1.0, 14 out of the 20 predictions with the highest confidence were validated in the latest version of DrugBank 5.1.8, demonstrating that Multi-SBI is a valuable tool for predicting new drug interactions through effectively extracting and learning heterogeneous drug features.