Global Connectivity and Function of Descending Spinal Input Revealed by 3D Microscopy and Retrograde Transduction

The brain communicates with the spinal cord through numerous axon tracts that arise from discrete nuclei, transmit distinct functions, and often collateralize to facilitate the coordination of descending commands. This complexity presents a major challenge to interpreting functional outcomes from therapies that target supraspinal connectivity after injury or disease, while the wide distribution of supraspinal nuclei complicates the delivery of therapeutics. Here we harness retrograde viral vectors to overcome these challenges. We demonstrate that injection of AAV2-Retro to the cervical spinal cord of adult female mice results in highly efficient transduction of supraspinal populations throughout the brainstem, midbrain, and cortex. Some supraspinal populations, including corticospinal and rubrospinal neurons, were transduced with \u3e90% efficiency, with robust transgene expression within 3 d of injection. In contrast, propriospinal and raphe spinal neurons showed much lower rates of retrograde transduction. Using tissue clearing and light-sheet microscopy we present detailed visualizations of descending axons tracts and create a mesoscopic projectome for the spinal cord. Moreover, chemogenetic silencing of supraspinal neurons with retrograde vectors resulted in complete and reversible forelimb paralysis, illustrating effective modulation of supraspinal function. Retrograde vectors were also highly efficient when injected after spinal injury, highlighting therapeutic potential. These data provide a global view of supraspinal connectivity and illustrate the potential of retrograde vectors to parse the functional contributions of supraspinal inputs

Hepatic Ischemia and Reperfusion Injury and Trauma: Current Concepts

Context: Ischemia-reperfusion injury is a fascinating topic which has drawn a lot of interest in the last several years. Hepatic ischemia reperfusion injury may occur in a variety of clinical situations. These include transplantation, liver resection, trauma, and vascular surgery. Evidence Acquisition: The purpose of this review was to outline the molecular mechanisms underlying hepatic I/R injury and present the latest approaches, both surgical and pharmacological, regarding the prevention of it. A comprehensive electronic literature search in MEDLINE/PubMed was performed to identify relative articles published within the last 2 years. Results: The basic mechanism of hepatic ischemia – reperfusion injury is one of blood deprivation during ischemia, followed by the return of flow during reperfusion. It involves a complex series of events, such as mitochondrial deenergization, adenosine-5'-triphosphate depletion, alterations of electrolyte homeostasis, as well as Kupffer cell activation, oxidative stress changes and upregulation of proinflammatory cytokine signaling. The great number of variable pathways, with several mediators interacting with each other, leads to a high number of candidates for potential therapeutic intervention. As far as surgical approaches are concerned, the modification of existing clamping techniques and the ischemic preconditioning are the most promising techniques till recently. In the search for novel techniques of protecting against hepatic ischemia reperfusion injury, many different strategies have been used in experimental models. The biggest part of this research lies around antioxidant therapy, but other potential solutions have been explored as well. Conclusions: The management of hepatic trauma, in spite of the fact that it has become increasingly nonoperative, there still remains the possibility of hepatic resection in the hepatic trauma setting, especially in severe injuries. Hence, clinicians should be familiar with the concept of hepatic ischemia-reperfusion injury and respond appropriately and timely

Single Nuclei Analyses Reveal Transcriptional Profiles and Marker Genes for Diverse Supraspinal Populations

The mammalian brain contains numerous neurons distributed across forebrain, midbrain, and hindbrain that project axons to the lower spinal cord and work in concert to control movement and achieve homeostasis. Extensive work has mapped the anatomic location of supraspinal cell types and continues to establish specific physiological functions. The patterns of gene expression that typify and distinguish these disparate populations, however, are mostly unknown. Here, using adult mice of mixed sex, we combined retrograde labeling of supraspinal cell nuclei with fluorescence-activated nuclei sorting and single-nuclei RNA sequencing analyses to transcriptionally profile neurons that project axons from the brain to lumbar spinal cord. We identified 14 transcriptionally distinct cell types and used a combination of established and newly identified marker genes to assign an anatomic location to each. To validate the putative marker genes, we visualized selected transcripts and confirmed selective expression within lumbar-projecting neurons in discrete supraspinal regions. Finally, we illustrate the potential utility of these data by examining the expression of transcription factors that distinguish different supraspinal cell types and by surveying the expression of receptors for growth and guidance cues that may be present in the spinal cord. Collectively, these data establish transcriptional differences between anatomically defined supraspinal populations, identify a new set of marker genes of use in future experiments, and provide insight into potential differences in cellular and physiological activity across the supraspinal connectome

Abdominoperineal Resection for Rectal Cancer: Is the Pelvic Drain Externalization Site an Independent Risk Factor for Perineal Wound Healing?

Aim. The aim of this paper is to investigate if the insertion of the pelvic drainage tube via the perineal wound could be considered as an independent risk factor for perineal healing disorders, after abdominoperineal resection for rectal malignancy. Patients and Methods. The last two decades, 75 patients underwent elective abdominoperineal resection for malignancy. In 42 patients (56%), the pelvic drain catheter was inserted through the perineal wound (PW group), while in the remaining 33 (44%) through a puncture skin wound of the perineum (SW group). Patients' data with respect to age (P = 0.136), stage (P > 0.05), sex (P = 0.188) and comorbidity (P = 0.128) were similar in both groups. 25 patients (PW versus SW: 8 versus 17, P = 0.0026) underwent neoadjuvant radio/chemotherapy. Results. The overall morbidity rate was 36%, but a significant increase was revealed in PW group (52.4% versus 9%, P = 0.0007). In 33.3% of the patients in the PW group, perineal healing was delayed, while in the SW group, no delay was noted. Perineal healing disorders were revealed as the main source of increased morbidity in this group. Conclusion. The insertion of the pelvic drain tube through the perineal wound should be considered as an independent risk factor predisposing to perineal healing disorders

Challenges in Abdominal Organ Transplantation During the COVID-19 Pandemic

© Copyright © 2020 Esagian, Ziogas, Giannis, Hayat, Elias and Tsoulfas. As the coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic, abdominal organ transplantation programs are currently facing multiple challenges. Transplant candidates and recipients are considered high-risk populations for severe disease and death due to COVID-19 as a result of their numerous underlying comorbidities, advanced age and impaired immune function. Emerging reports of atypical and delayed clinical presentations in these patients generate further concerns for widespread disease transmission to medical personnel and the community. The striking similarities between COVID-19 and other outbreaks that took place over the past two decades, like Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome, highlight the severity of the situation and dictate that extra measures should be taken by the transplant programs to avoid adverse outcomes. Transplant organizations are currently calling for strict screening and isolation protocols to be established in all transplant programs, for both organ donors and recipients. As the situation escalates, more radical measures might be necessary, including a temporary hold on non-urgent transplantations, resulting in serious ethical dilemmas between the survival of these patients and the safety of the community. Further data about these special populations could result in more individualized guidelines for abdominal organ transplantation in the era of COVID-19

Brain-Wide Analysis of the Supraspinal Connectome Reveals Anatomical Correlates to Functional Recovery After Spinal Injury

The supraspinal connectome is essential for normal behavior and homeostasis and consists of numerous sensory, motor, and autonomic projections from brain to spinal cord. Study of supraspinal control and its restoration after damage has focused mostly on a handful of major populations that carry motor commands, with only limited consideration of dozens more that provide autonomic or crucial motor modulation. Here, we assemble an experimental workflow to rapidly profile the entire supraspinal mesoconnectome in adult mice and disseminate the output in a web-based resource. Optimized viral labeling, 3D imaging, and registration to a mouse digital neuroanatomical atlas assigned tens of thousands of supraspinal neurons to 69 identified regions. We demonstrate the ability of this approach to clarify essential points of topographic mapping between spinal levels, measure population-specific sensitivity to spinal injury, and test the relationships between region-specific neuronal sparing and variability in functional recovery. This work will spur progress by broadening understanding of essential but understudied supraspinal populations

Evaluating the educational environment of an international animal model-based wet lab course for undergraduate students

publisher: Elsevier articletitle: Evaluating the educational environment of an international animal model-based wet lab course for undergraduate students journaltitle: Annals of Medicine and Surgery articlelink: http://dx.doi.org/10.1016/j.amsu.2016.10.004 content_type: article copyright: © 2016 The Author(s). Published by Elsevier Ltd on behalf of IJS Publishing Group Ltd

COVLIAS 1.0: Lung segmentation in COVID-19 computed tomography scans using hybrid deep learning artificial intelligence models

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi-or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate