Initial Segment Research Articles

Microfluidic Platforms Promote Polarization of Human-Derived Retinal Ganglion Cells That Model Axonopathy.

Axons depend on long-range transport of proteins and organelles which increases susceptibility to metabolic stress in disease. The axon initial segment (AIS) is particularly vulnerable due to the high bioenergetic demand of action potential generation. Here, we prepared retinal ganglion cells derived from human embryonic stem cells (hRGCs) to probe how axonal stress alters AIS morphology. hRGCs were cultured on coverslips or microfluidic platforms. We assayed AIS specification and morphology by immunolabeling against ankyrin G (ankG), an axon-specific protein, and postsynaptic density 95 (PSD-95), a dendrite-specific protein. Using microfluidic platforms that enable fluidic isolation, we added colchicine to the axon compartment to lesion axons. We verified axonopathy by measuring the anterograde axon transport of cholera toxin subunit B and immunolabeling against cleaved caspase 3 (CC3) and phosphorylated neurofilament H (SMI-34). We determined the influence of axon injury on AIS morphology by immunolabeling samples against ankG and measuring AIS distance from soma and length. Based on measurements of ankG and PSD-95 immunolabeling, microfluidic platforms promote the formation and separation of distinct somatic-dendritic versus axonal compartments in hRGCs compared to coverslip cultures. Chemical lesioning of axons by colchicine reduced hRGC anterograde axon transport, increased varicosity density, and enhanced expression of CC3 and SMI-34. Interestingly, we found that colchicine selectively affected hRGCs with axon-carrying dendrites by reducing AIS distance from somas and increasing length, thus suggesting reduced capacity to maintain excitability. Thus, microfluidic platforms promote polarized hRGCs that enable modeling of axonopathy. Microfluidic platforms may be used to assay compartmentalized degeneration that occurs during glaucoma.

An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning

In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentation process based on marker-controlled watershed method. Next the feature reduction is a two-fold approach with principal component analysis applied to reduce the dimensionality of large feature space from convolutional neural network (CNN) models as first step. The second step is optimal feature subset selection using a swarm intelligence method referred as modified grey wolf optimization. Finally, the selected feature subset is fed to various machine learning classifiers. The experimental result reveals that the proposed algorithm outperforms the other state-of-the-art methods with classification accuracy of 96.56%, thus upholding the dependability of the approach.

Retinal ganglion cells adapt to ionic stress in experimental glaucoma.

Identification of early adaptive and maladaptive neuronal stress responses is an important step in developing targeted neuroprotective therapies for degenerative disease. In glaucoma, retinal ganglion cells (RGCs) and their axons undergo progressive degeneration resulting from stress driven by sensitivity to intraocular pressure (IOP). Despite therapies that can effectively manage IOP many patients progress to vision loss, necessitating development of neuronal-based therapies. Evidence from experimental models of glaucoma indicates that early in the disease RGCs experience altered excitability and are challenged with dysregulated potassium (K+) homeostasis. Previously we demonstrated that certain RGC types have distinct excitability profiles and thresholds for depolarization block, which are associated with sensitivity to extracellular K+. Here, we used our inducible mouse model of glaucoma to investigate how RGC sensitivity to K+ changes with exposure to elevated IOP. In controls, conditions of increased K+ enhanced membrane depolarization, reduced action potential generation, and widened action potentials. Consistent with our previous work, 4 weeks of IOP elevation diminished RGC light-and current-evoked responses. Compared to controls, we found that IOP elevation reduced the effects of increased K+ on depolarization block threshold, with IOP-exposed cells maintaining greater excitability. Finally, IOP elevation did not alter axon initial segment dimensions, suggesting that structural plasticity alone cannot explain decreased K+ sensitivity. Thus, in response to prolonged IOP elevation RGCs undergo an adaptive process that reduces sensitivity to changes in K+ while diminishing excitability. These experiments give insight into the RGC response to IOP stress and lay the groundwork for mechanistic investigation into targets for neuroprotective therapy.

Palmitoylation controls the stability of 190 kDa ankyrin-G in dendritic spines and is regulated by ZDHHC8 and lithium.

AnkG, encoded by the ANK3 gene, is a multifunctional scaffold protein with complex isoform expression: the 480 and 270 kDa isoforms have roles at the axon initial segment and node of Ranvier, whereas the 190 kDa isoform (AnkG-190) has an emerging role in the dendritic shaft and spine heads. All isoforms of AnkG undergo palmitoylation, a post-translational modification regulating protein attachment to lipid membranes. However, palmitoylation of AnkG-190 has not been investigated in dendritic spines. The ANK3 gene and altered expression of AnkG proteins are associated with a variety of neuropsychiatric and neurodevelopmental disorders including bipolar disorder and are implicated in the lithium response, a commonly used mood stabilizer for bipolar disorder patients, although the precise mechanisms involved are unknown. Here, we showed that Cys70 palmitoylation stabilizes the localization of AnkG-190 in spine heads and at dendritic plasma membrane nanodomains. Mutation of Cys70 impairs AnkG-190 function in dendritic spines and alters PSD-95 scaffolding. Interestingly, we find that lithium reduces AnkG-190 palmitoylation thereby increasing its mobility in dendritic spines. Finally, we demonstrate that the palmitoyl acyl transferase ZDHHC8, but not ZDHHC5, increases AnkG-190 stability in spine heads and is inhibited by lithium. Together, our data reveal that palmitoylation is critical for AnkG-190 localization and function and a potential ZDHHC8/AnkG-190 mechanism linking AnkG-190 mobility to the neuronal effects of lithium.

Staged endovascular recanalization for symptomatic atherosclerotic non-acutely occluded internal carotid artery

Symptomatic "non-acutely" occluded internal carotid artery (NAOICA) results in stroke, cognitive impairment, and hemicerebral atrophy through cerebral hypoperfusion and artery-to-artery embolism. Atherosclerosis is the main cause of NAOICA. Conventional one-stage endovascular recanalization showed effectiveness but was plagued by many challenges. This retrospective analysis reports the technical feasibility and outcomes of the staged endovascular recanalization in patients with NAOICA. Eight consecutive patients with atherosclerotic NAOICA and ipsilateral ischemic stroke within 3 months between January 2019 and March 2022 were retrospectively reviewed. The patients (all males, with a mean age of 64.6 years) underwent staged endovascular recanalization 13 to 56 days after documented occlusion by imaging techniques (mean 28.8 days); the mean follow-up period was 20 months (range: 6-28). The approach of the staged intervention was as follows. In the first stage, the occluded ICA was successfully recanalized using the simple small balloon dilation technique. In the second stage, angioplasty with a stent implant was performed with >50% residual stenosis in the initial segment or ≥70% residual stenosis in the C2-C5 segment. The technical success rate, the incidence of clinical adverse events (any stroke, death, cerebral hyperperfusion), and long-term in-stent stenosis (ISR) and reocclusion rates were evaluated. Technical success was achieved in seven patients, with early reocclusion occurring in one patient after the first-stage intervention. There were no adverse events observed within 30 days (0%), and long-term reocclusion and long-term ISR rates were both 14% (1/7). However, all patients developed iatrogenic arterial dissections during the first stage, indicating the challenge of reaching the true lumen through the occlusion site without damaging the intima. Based on the National Heart, Lung and Blood Institute (NHLBI) classification, there were two type A, four type B, three type C, and two type D dissections. The mean time interval between the two stages was 46.1 days (21-152 days). All type A and B dissections resolved spontaneously after ≥3 weeks of dual antiplatelet therapy, whereas most type C and all type D dissections did not heal spontaneously before the second stage. Also, one type C dissection led to reocclusion. This observation suggested that occlusions without flow limit and persistent vessel staining or extravasation could be clinically observed, while severe dissections (characterized as type C or greater) required prompt stenting rather than conservative treatment. Performing high-resolution MRI preoperatively to exclude fresh thrombus in the occluded vessel segment is indispensable in selecting appropriate candidates for endovascular recanalization. This could avoid downstream embolism during the interventional procedure. This retrospective study found that staged endovascular recanalization for symptomatic atherosclerotic NAOICA may be feasible with an acceptable technical success rate and a low complication rate in the selected candidates.

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier's parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.

TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images.

Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods.

BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.

BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field ofBUS. Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of-the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and typeeffects. Of the nine state-of-the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value>0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different toSk-U-Net. BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of-the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS-Set, which allows for a fully reproduciblebenchmark.

Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex.

The axon initial segment (AIS) is a highly specialized axonal compartment where the action potential is initiated. The heterogeneity of AISs has been suggested to occur between interneurons and pyramidal neurons (PyNs), which likely contributes to their unique spiking properties. However, whether the various characteristics of AISs can be linked to specific PyN subtypes remains unknown. Here, we report that in the prelimbic cortex (PL) of the mouse, two types of PyNs with axon projections either to the contralateral PL or to the ipsilateral basal lateral amygdala, possess distinct AIS properties reflected by morphology, ion channel expression, action potential initiation, and axo-axonic synaptic inputs from chandelier cells. Furthermore, projection-specific AIS diversity is more prominent in the superficial layer than in the deep layer. Thus, our study reveals the cortical layer- and axon projection-specific heterogeneity of PyN AISs, which may endow the spiking of various PyN types with exquisite modulation.

Heparan Sulfates Regulate Axonal Excitability and Context Generalization through Ca2+/Calmodulin-Dependent Protein Kinase II.

Our previous studies demonstrated that enzymatic removal of highly sulfated heparan sulfates with heparinase 1 impaired axonal excitability and reduced expression of ankyrin G at the axon initial segments in the CA1 region of the hippocampus ex vivo, impaired context discrimination in vivo, and increased Ca2+/calmodulin-dependent protein kinase II (CaMKII) activity in vitro. Here, we show that in vivo delivery of heparinase 1 in the CA1 region of the hippocampus elevated autophosphorylation of CaMKII 24 h after injection in mice. Patch clamp recording in CA1 neurons revealed no significant heparinase effects on the amplitude or frequency of miniature excitatory and inhibitory postsynaptic currents, while the threshold for action potential generation was increased and fewer spikes were generated in response to current injection. Delivery of heparinase on the next day after contextual fear conditioning induced context overgeneralization 24 h after injection. Co-administration of heparinase with the CaMKII inhibitor (autocamtide-2-related inhibitory peptide) rescued neuronal excitability and expression of ankyrin G at the axon initial segment. It also restored context discrimination, suggesting the key role of CaMKII in neuronal signaling downstream of heparan sulfate proteoglycans and highlighting a link between impaired CA1 pyramidal cell excitability and context generalization during recall of contextual memories.

UNVEILING THE CULTURAL SIGNIFICANCE OF ILLNESS AND HEALING: PERSPECTIVES FROM PSYCHOLOGICAL AND MEDICAL ANTHROPOLOGY

Psychological and medical anthropology converge in illuminating the symbiotic relationship between psychological well-being and physical health. Although numerous academic disciplines share an interest in these inquiries, but they approach them from distinct vantage points. The current study scrutinizes the historical evolution, methodologies employed, and fundamental theoretical frameworks underpinning of these two specialized branches of inquiry. The initial segment of this exposition serves to introduce the realm of psychological anthropology, furnishing a precise delineation of its scope, and critically assessing the field’s contributions toward augmenting the comprehension of human conduct vis-à-vis emotions, cognition, and psychological wellness. In the subsequent section, attention turns toward medical anthropology, delving into its purview, investigating the manifold facets of health disparities, cultural interpretations of maladies and convalescence, and the ramifications of healthcare systems upon individual well-being. Thereafter, arguments revolve around the probe between medical anthropology and psychological anthropology, penetrating their confluences and mutual enhancements. Cultural paradigms and rituals are also weighed in terms of their capacity to shape psychological equilibrium, and their potential repercussions upon health outcomes and healthcare dispensation. In the concluding segment the researcher contemplates the strides achieved by psychological and medical anthropology in advancing the cognizance of interplay amid cultures, society, and health, wherein these two spheres make headway in the direction of more culturally attuned healthcare methodologies and regulatory frameworks, notwithstanding the obstacles that lie ahead of them.

The Modular Gait Design of a Soft, Earthworm-like Locomotion Robot Driven by Ultra-Low Frequency Excitation

In complex and extreme environments, such as pipelines and polluted waters, gait programming has great significance for multibody segment locomotion robots. The earthworm-like locomotion robot is a representative multibody bionic robot, which has the characteristics of low weight, multibody segments, and excellent movement performance under the designed gait. The body segment cell can realize large deformation under ultra-low frequency excitation. The multibody segment robot can locomote under ultra-low frequency excitation with appropriate shifts. In this paper, a modular gait design principle for a soft, earthworm-like locomotion robot is proposed. The driven modules defined by modular gait generation correspond to the peristaltic wave transmissions of the excitation in the robot for different modular gait modes. A locomotion algorithm is presented to simulate the locomotion of the earthworm-like robot under different locomotion gaits. Moreover, the locomotion speeds are obtained for different modular gait modes. The results show that locomotion speed is related to the original state of the body segments and modular gaits. As the initial actuated segments and driven modules (which correspond to the excitation frequency and shift) increase, faster movement speeds can be realized, which resolves the speed saturation of the earthworm-like robot. The proposed modular gait design method gives a new gait generation principle for the improvement of the locomotion performance of soft, earthworm-like robots.

SUMOylation of NaV1.2 channels regulates the velocity of backpropagating action potentials in cortical pyramidal neurons.

Voltage-gated sodium channels located in axon initial segments (AIS) trigger action potentials (AP) and play pivotal roles in the excitability of cortical pyramidal neurons. The differential electrophysiological properties and distributions of NaV1.2 and NaV1.6 channels lead to distinct contributions to AP initiation and propagation. While NaV1.6 at the distal AIS promotes AP initiation and forward propagation, NaV1.2 at the proximal AIS promotes the backpropagation of APs to the soma. Here, we show the small ubiquitin-like modifier (SUMO) pathway modulates Na+ channels at the AIS to increase neuronal gain and the speed of backpropagation. Since SUMO does not affect NaV1.6, these effects were attributed to SUMOylation of NaV1.2. Moreover, SUMO effects were absent in a mouse engineered to express NaV1.2-Lys38Gln channels that lack the site for SUMO linkage. Thus, SUMOylation of NaV1.2 exclusively controls INaP generation and AP backpropagation, thereby playing a prominent role in synaptic integration and plasticity.

Image Augmentation based on Variational Autoencoder for Breast Tumor Segmentation

Breast tumor segmentation based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging is significant step for computable radiomics analysis of breast cancer. Manual tumor annotation is time-consuming process and involves medical acquaintance, biased, inclined to error, and inter-user discrepancy. A number of modern trainings have revealed the capability of deep learning representations in image segmentation. Here, we describe a 3D Connected-UNets for tumor segmentation from 3D Magnetic Resonance Imagings based on encoder-decoder architecture. Due to a restricted training dataset size, a variational auto-encoder outlet is supplementary to renovate the input image itself in order to identify the shared decoder and execute additional controls on its layers. Based on initial segmentation of Connected-UNets, fully connected 3D provisional unsystematic domain is used to enhance segmentation outcomes by discovering 2D neighbor areas and 3D volume statistics. Moreover, 3D connected modules evaluation is used to endure around large modules and decrease segmentation noise. The proposed method has been assessed on two widely offered datasets, explicitly INbreast and the curated breast imaging subset of digital database for screening mammography The proposed model has also been estimated using a private dataset. The experimental results show that the proposed model outperforms the state-of-the-art methods for breast tumor segmentation.

Overlapping Pellet Size Detection Method Based on Marker Watershed and GMM Image Segmentation

The particle size of pellets is an important parameter in steel big data, and the high density and high overlap rate of pellets bring a great challenge to particle size detection. To address this problem, a particle size intelligent detection algorithm with an improved watershed and a Gaussian mixture model (GMM) is proposed. First, the initial segmentation of the pellets and background is achieved by using adaptive binary segmentation, and then the secondary fine segmentation of the pellets and background is achieved by combining morphological operations such as skeleton extraction and marked watershed segmentation; then, the contour of the connected domain of pellets is calculated, and the non-overlapping pellets in the foreground and the overlapping pellets are filtered according to the roundness of their contours. Finally, the number of overlapping pellets is predicted by Gaussian reconstruction of the grayscale image of the overlapping pellets, and the number and granularity of the overlapping pellets are predicted by the Gaussian reconstruction of the overlapping pellets. The experimental results showed that the algorithm achieved a 91.98% segmentation accuracy in the experimental images. Compared with other algorithms, the algorithm can also effectively suppress the over-segmentation and under-segmentation problems, and it can effectively realize the pellet size detection of dense, overlapping pellets such as those on a pelletizing disk, which provides an effective technical means for the metallurgical performance analysis of pellet ore and intelligent pellet-making driven by big data.

Superpixel-Based Grain Segmentation in Sandstone Thin-Section

Mineral segmentation is an equally important and difficult task in the quantification of mineral composition. Difficulties come from the process of determining boundaries of distinctive mineral grains necessary for further analysis and mineral identification. Done by hand, the task is very time-consuming and higher accuracies are burdened with the possible human fatigue factor. The presented method is a fully automated solution to the problem that uses a superpixel approach and feature-based merging. The method is validated by comparison with the manual approach. Analyzed data consist of photos taken by a Nikon Eclipse LV100N POL polarizing microscope at 200× magnification, in transmitted light, with crossed polarizers. Images are first prepared by Gaussian filter and meanshift operations, then the initial segmentation is provided by the superpixel algorithm. Oversegmentation is resolved by feature-based merging. The last step consists of counting the individual grain boundaries and preparing the results as easily readable visual data.

Carbogen-Induced Respiratory Acidosis Blocks Experimental Seizures by a Direct and Specific Inhibition of NaV1.2 Channels in the Axon Initial Segment of Pyramidal Neurons.

Brain pH is a critical factor for determining neuronal activity, with alkalosis increasing and acidosis reducing excitability. Acid shifts in brain pH through the breathing of carbogen (5% CO2/95% O2) reduces seizure susceptibility in animal models and patients. The molecular mechanisms underlying this seizure protection remain to be fully elucidated. Here, we demonstrate that male and female mice exposed to carbogen are fully protected from thermogenic-triggered seizures. Whole-cell patch-clamp recordings revealed that acid shifts in extracellular pH (pHo) significantly reduce action potential firing in CA1 pyramidal neurons but did not alter firing in hippocampal inhibitory interneurons. In real-time dynamic clamp experiments, acidification reduced simulated action potential firing generated in hybrid model neurons expressing the excitatory neuron predominant NaV1.2 channel. Conversely, acidification had no effect on action potential firing in hybrid model neurons expressing the interneuron predominant NaV1.1 channel. Furthermore, knockdown of Scn2a mRNA in vivo using antisense oligonucleotides reduced the protective effects of carbogen on seizure susceptibility. Both carbogen-mediated seizure protection and the reduction in CA1 pyramidal neuron action potential firing by low pHo were maintained in an Asic1a knock-out mouse ruling out this acid-sensing channel as the underlying molecular target. These data indicate that the acid-mediated reduction in excitatory neuron firing is mediated, at least in part, through the inhibition of NaV1.2 channels, whereas inhibitory neuron firing is unaffected. This reduction in pyramidal neuron excitability is the likely basis of seizure suppression caused by carbogen-mediated acidification.SIGNIFICANCE STATEMENT Brain pH has long been known to modulate neuronal excitability. Here, we confirm that brain acidification reduces seizure susceptibility in a mouse model of thermogenic seizures. Extracellular acidification reduced excitatory pyramidal neuron firing while having no effect on interneuron firing. Acidification also reduced dynamic clamp firing in cells expressing the NaV1.2 channel but not in cells expressing NaV1.1 channels. In vivo knockdown of Scn2a mRNA reduced seizure protection of acidification. In contrast, acid-mediated seizure protection was maintained in the Asic1a knock-out mouse. These data suggest NaV1.2 channel as an important target for acid-mediated seizure protection. Our results have implications on how natural variations in pH can modulate neuronal excitability and highlight potential antiseizure drug development strategies based on the NaV1.2 channel.

AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography.

Automatic segmentation and differentiation of retinal arteriole and venule (AV), defined as small blood vessels directly before and after the capillary plexus, are of great importance for the diagnosis of various eye diseases and systemic diseases, such as diabetic retinopathy, hypertension, and cardiovascular diseases. Optical coherence tomography angiography (OCTA) is a recent imaging modality that provides capillary-level blood flow information. However, OCTA does not have the colorimetric and geometric differences between AV as the fundus photography does. Various methods have been proposed to differentiate AV in OCTA, which typically needs the guidance of other imaging modalities. In this study, we propose a cascaded neural network to automatically segment and differentiate AV solely based on OCTA. A convolutional neural network (CNN) module is first applied to generate an initial segmentation, followed by a graph neural network (GNN) to improve the connectivity of the initial segmentation. Various CNN and GNN architectures are employed and compared. The proposed method is evaluated on multi-center clinical datasets, including 3 ×3 mm2 and 6 ×6 mm2 OCTA. The proposed method holds the potential to enrich OCTA image information for the diagnosis of various diseases.

Utilizing micro-CT imaging to define collagen modeling in murine aorta.

We aim to demonstrate local aortic tissue biodynamics via collagen and elastin fiber remodeling in murine aorta at varying distension using microcomputed tomography (microCT). Ten week-old C57BL/6 mice were injected via tail vein with in-vivo collagen hybridizing peptide, which reforms the triple-helix structure of denatured collagen, and is visualized via fluorescein tag. Careful dissection and removal of the thoracoabdominal aorta was performed, which was pressurized with formalin at varying distensions. Samples were then prepared via heavy-metal staining, and fixed in epoxy resin. These were subsequently imaged at the microCT scanner at University of Chicago, as well as at the 2-BM-B beamline using the Advanced Photon Source at Argonne National Laboratory to obtain micron and sub-micron resolution images and thus provide multi-level views of the tissue. Clear visualization of distinct collagen and elastin layers was observed with microCT. The images also clearly illustrate the effect on individual collagen and elastin fiber organization created by overdistension and shear-stress on the aorta. Clear demonstration of the geometric structure of aortic elastin fibers was seen upon initial segmentation. Preliminary experiments demonstrate that microCT provides an excellent visualization of murine aorta at micron and submicron levels. Next steps will be to segment and mesh this data in order to simulate local tissue responses to stress via finite element analysis. This method will produce a multi-scale imaging model of aortic failure.

Identification, characterization and expression analysis of rLcn13, an epididymal lipocalin in rats.

As the essential tissue for sperm maturation and storage, the epididymis secretes a number of tissue-specific proteins to exert its functions. Among these proteins, epididymal lipocalins have been intensively studied because of their epididymis-specific expression pattern and clustered genomic organization. In this study, rLcn13, a member of the rat epididymal lipocalin family, is identified and elaborately characterized. The cDNA sequence of rLcn13 consists of 719 nucleotides and encodes a 176 amino-acid protein with a predicted N-terminal signal peptide of 19 amino acids. rLcn13 shares a similar genomic structure and predicted 3D protein structure with other lipocalin family members. A recombinant rLCN13 mature peptide of 157 amino acids is expressed and purified, which is used to raise a polyclonal antibody against rLCN13 with high specificity and sensitivity. Northern blot, western blot, and immunohistochemistry assays reveal that rLcn13 is an epididymis-specific gene which is expressed predominantly in the initial segment and proximal caput epididymis and influenced by androgen. The rLCN13 protein is modified by N-glycosylation and secreted into the epididymal lumen, and then binds to the acrosome region of the sperm. Our data demonstrate that rLcn13 exhibits a specific temporospatial expression pattern and androgen dependence, indicating its potential roles in sperm maturation.