Imaging and Brain

To explore the distribution and clearance of 99mTc labeled diethylenetriamine pentaacetic acid (99mTc-DTPA) in different brain regions of adult rats after administration through brain extracellular space (ECS) pathway. After the injection of a volume of 2 μL and radioactive activity of about 3.7 MBq (100 μCi) of 99mTc-DTPA into the caudate nucleus and thalamus of SD rats through stereotactic positioning of rat brain, the single photon emission computed tomography/computed tomography (SPECT/CT) for small animals was used for imaging at different time points, and the dyna-mic distribution and clearance of the tracer in the whole body were observed continuously. The SD rats were injected with 99mTc-DTPA into thalamus and caudate nucleus respectively for biological distribution in vivo. They were put to death 4 h later. Their blood and urine were collected. The brain, cerebellum, heart, liver, spleen, lung, and kidney were taken and weighed by γ counter to measure its radioactivity. SPECT/CT imaging results showed that after 99mTc-DTPA was administered through brain ECS, the radioactivity was concentrated in the brain, kidney and bladder. The tracer administered to the left caudate nucleus was preferentially drained to the right cerebellum, while the tracer administered to the right caudate nucleus was preferentially drained to the left cerebellum. There was a phenomenon of "contralateral cerebellar dominant drainage" in the caudate nucleus. The thalamic area preferentially drained to the ipsilateral cerebellum after administration. Four hours after administration via ECS, high radioactive uptake appeared in urine, cerebellum and brain, followed by blood and kidney. The radioactive uptake values of heart, liver, spleen and lung were low, which were mainly excreted through urinary system. Intracerebral ECS administration is a promising method of administration, but there are significant differences in distribution and clearance in different brain regions. This study further expands the content and significance of "ECS regions", and also provides an important theoretical foundation for the treatment of encephalopathy and the research of new drugs through brain ECS in the future.

The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously, we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Mars et al., 2018b). Here, we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.

Abstract A diverse heterogeneity of microglial cells was previously described in Alzheimer’s disease (AD) pathology, including dark microglia, a state characterized by ultrastructural markers of cellular stress. To provide novel insights into the roles of dark microglia during aging in the context of AD pathology, we performed a quantitative density and ultrastructural analysis of these cells using high-throughput scanning electron microscopy in the ventral hippocampus CA1 stratum lacunosum-moleculare of 20-month-old APP-PS1 vs C57BL/6J male mice. The density of dark microglia was significantly higher in APP-PS1 vs C57BL/6J mice, with these cells accounting for nearly half of all microglia observed near amyloid-beta (Aß) plaques. This dark microglial state interacted more with dystrophic neurites compared to other APP-PS1 microglia and possessed glycogen granules, associated with a metabolic shift toward glycolysis, which provides the first ultrastructural evidence of their presence in microglia. Dark microglia were further observed in aging human post-mortem brain samples showing similar ultrastructural features as in mouse. Overall, our results provide a quantitative ultrastructural characterization of a microglial state associated with cellular stress (i.e., dark microglia) that is primarily restricted near Aß plaques and dystrophic neurites. The presence of this microglial state in the aging human post-mortem brain is further revealed.

The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously, we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Mars et al., 2018b). Here, we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.

Alzheimer’s disease (AD) accounts for 50-70% of dementia cases, making it the most common type of dementia. Positron Emission Tomography (PET) has demonstrated the ability to diagnose dementia to an equivalent ability as a cognitive exam. Advances in computational power and deep learning have revolutionized quantitative analysis of medical images. A variational autoencoder (VAE) has previously been proposed for the identification of PET brain abnormalities by examining divergence of the reconstruction error from a normal dataset. Little work has examined the utility of training a multiclass VAE and its ability to faithfully reconstruct quantitatively acceptable images across the AD class range; in effect exploring the inclusiveness of the learned latent state representation across different disease states. We construct a VAE that is trained on a dataset containing scans of 94 patients with varying degrees / classes of neurode-generation. A ’leave one group out’ approach to model training allows the classes with the most pertinent features to be probed across the dataset. Metrics used to assess VAE performance were the peak signal-to-noise ratio (PSNR) and structured similarity index (SSIM) if the VAE reconstructed imags versus the ground truth. A decreased average PSNR, 0.82%, and SSIM, 3.97%, demonstrates poorer performance when the cognitively normal (CN) data is removed from the training dataset, suggesting the CN group feature representations aid in the faithful reconstruction of subsequent disease groups. Furthermore, it was observed that inclusion of the late mild cognitively impairment (LMCI) group reduces reconstructed quantitative accuracy across all classes. Removal of this class during training increases PSNR and SSIM across all groups, with increases of 0.19% and 0.42% respectively. This suggests that the LMCI group does not follow the posterior Gaussian distribution.

In contrast to shigatoxin-associated Escherichia coli (STEC) causing hemolytic uremic syndrome, STEC-unrelated infections associated with thrombotic microangiopathy are less characterized. Our retrospective study in a four-hospital institution of 530 consecutive patients with adjudicated thrombotic microangiopathies during the 2009-2016 period studied STEC-unrelated infections' epidemiology and major outcomes (death, acute dialysis, and major cardiovascular events). STEC-unrelated infection was present in 145 of 530 (27%) patients, thrombotic microangiopathies without infection were present in 350 of 530 (66%) patients, and STEC causing hemolytic and uremic syndrome was present in 35 of 530 (7%) patients. They (versus thrombotic microangiopathy without infection) were associated with age >60 years (36% versus 18%), men (53% versus 27%), altered consciousness (32% versus 11%), mean BP <65 mm Hg (21% versus 4%), lower hemoglobin and platelet count, and AKI (72% versus 49%). They were associated with more than one pathogen in 36 of 145 (25%) patients (either isolated [14%] or combined [86%] to other causes of thrombotic microangiopathy); however, no significant clinical or biologic differences were noted between the two groups. They were more frequently due to bacteria (enterobacteria [41%], Staphylococcus aureus [11%], and Streptococcus pneumonia [3%]) than viruses (Epstein-Barr [20%], cytomegalovirus [18%], influenza [3%], hepatitis C [1%], HIV [1%], and rotavirus [1%]). STEC-unrelated infections were independent risk factors for in-hospital death (odds ratio, 2.22; 95% confidence interval, 1.18 to 4.29), major cardiovascular event (odds ratio, 3.43; 95% confidence interval, 1.82 to 6.69), and acute dialysis (odds ratio, 3.48; 95% confidence interval, 1.78 to 7.03). Bacteria (versus other pathogens), and among bacteria, enterobacteria, presence of more than one bacteria, and E. coli without shigatoxin were risk factors for acute dialysis. Infections are frequent thrombotic microangiopathy triggers or causes, and they are mostly unrelated to STEC. Infections convey a higher risk of death and major complications. The most frequent pathogens were enterobacteria, S. aureus, Epstein-Barr virus, and cytomegalovirus. This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_09_07_CJN17511120.mp3.

The LUCA device combines clinical ultrasound, time-domain near infrared and diffuse correlation spectroscopies with the aim of improving thyroid cancer screening sensitivity and specificity. The preliminary clinical campaign on patients (n=31) with thyroid nodules and healthy controls (n=11) allowed the characterization of the precision of the instrument and demonstrated that using a couple of biomarkers the muscle-to-nodule contrast allows an area under the curve of 0.92 for single-nodule patients and 0.77 for all patients in differentiating benign and malignant nodules in a receiver operating characteristic curve. We will present the updated results from the ongoing study.

To improve the methods to synthesize and purify of optical-magnetic bimodal molecular probe of Gd-[4, 7-Bis-carboxymethyl-10-(2-fluorescein thioureaethyl)-1, 4, 7, 10-tetraaza-cyclododec-1-yl]-acetic acid complexes. Target compound (7), optical-magnetic bimodal molecular molecular probe, was synthesized by the use of 1, 4, 7, 10-tetraazacyclododecane (1) as starting material via substitution reaction, hydrolysis reaction, coupling reaction and complexation reaction with metal. The synthetic route of Gd-[4, 7-Bis-carboxymethyl-10-(2-fluoresceinthioureaethyl)-1, 4, 7, 10-tetraaza-cyclododec-1-yl]-acetic acid complexes was improved. The optical-magnetic bimodal molecular probes were synthesized by substitution reaction, hydrolysis reaction, coupling reaction and complex reaction with metal respectively. For the improved route, the total yield could reach 34.6% which was higher than the original route (18.0%). The structures of those compounds were identified by 1H nuclear magnetic resonance, 13C nuclear magnetic resonance, and mass spectrometry. The improved route could avoid the uncontrollable disadvantage of the substitution reaction, this process could reduce the formation of impurities and made the purification process easier, and in the aspect of purification and separation, the preparative high-performance liquid chromatography with less sample loading and high cost was improved to a column chromatography with many sample loads and being easy to operate. Therefore, the use of column chromatography could be more conducive to mass production of the optical-magnetic bimodal molecular molecular probe. The improved synthetic route improves the controllability of the reaction conditions and makes it easier to purify and separate the compounds. At the same time, the improved synthetic route can increase the total yield significantly. The optical-magnetic bimodal molecular probe can combine the living magnetic resonance imaging with the in vitro optical imaging to realize the dual synchronous detection of magneto-optics, so that the detection results of the living magnetic resonance imaging and the in vitro optical imaging are mutually verified. In other words, this synthetic optical-magnetic bimodal molecular probe will make the experimental results more accurate and reliable. In subsequent biological experimental studies, the optical-magnetic bimodal molecular probe can be applied to related research of brain structure and function, and the probe can be used for the brain-related diseases researches, such as brain tumors. after intravenous administration, and thus the optical-magnetic bimodal molecular probe can play an important role in medical treatment of brain tumors and cerebrovascular diseases.

This study investigates a variational method to determine the most representative shape of a set of knee kinematic curves with application to knee pathology classification. Although they provide essential information for pathology classification, knee kinematic curves are characterized by high intra-class variability and outliers are often present. As a result, a set of several measurement curves are acquired of any single individual which are then averaged before their use for pathology classification. Rather than using the average of an individual’s recorded measurement curves, this method determines a better representative curve by first correcting the data to account for outliers occurrence and class variability using a variational method. The correction is performed by simultaneous minimization of a set of objective functions, one for each curve in the measurement set, and consisting of a weighed sum of two terms: a data term of conformity of the corrected curve to the given curve, and a regularization term of proximity of the corrected curve to the mean of all the corrected curves to inhibit the influence of outliers in the set. Validation tests were performed to discriminate between knee osteoarthritis data (OA) and non-OA data. Using a support vector machine, the classification accuracy with the proposed representation was 86%, with 81% sensitivity and 90% specificity, compared to 83% accuracy for the standard representation by average, with 76% sensitivity and 90% specificity. The representation has also been tested within the OA category to distinguish the femero-tibial patholgy from the femero-patellar, giving 76% accuracy, with 76% sensitivity and 76% specificity, compared to 69% accuracy, with 62% sensitivity and 76% specificity. These significant improvements by the proposed method warrant its further investigation by application to other biomedical engineering pattern classification problems and datasets.