Outcomes of symptomatic penetrating aortic ulcer and intramural hematoma in the endovascular era.

BACKGROUND: Although endovascular technology has resulted in a paradigm shift in treatment, medical management remains the standard of care for penetrating aortic ulcer (PAU) and intramural hematoma (IMH). This study aimed to detail the short- and long-term outcomes of symptomatic PAU/IMH. METHODS: Institutional data on symptomatic PAU/IMH were gathered (2005-2020). The primary outcome was the composite of recurrent symptoms, radiographic progression, intervention, rupture, and death from related or unknown cause. Factors associated with the primary outcome were determined using a Fine-Gray model with death from an unrelated cause as a competing risk. RESULTS: A total of 83 symptomatic patients treated with medical management aside from ruptures and type A dissections: 21 isolated PAU, 30 isolated IMH, and 32 IMH and PAU. Adverse outcomes included symptom recurrence in 14 (16.9%), radiographic progression to dissection or saccular aneurysm in 17 (20.5%), surgery in 20 (24.1%) (17 thoracic endovascular aortic repair, 1 endovascular aortic repair, 1 frozen elephant trunk, and 1 open repair), and rupture in 4 (4.8%). Twenty-seven patients (32.5%) died during follow-up: 6 from IMH treatment complications, 8 from an unknown cause, and 13 from other causes. The 30-day, 1-year, and 5-year cumulative incidences of the primary outcome was 26.5% (95% confidence interval [CI], 16.9%-37.0%), 44.9% (95% CI, 32.8%-56.2%), and 57.5% (95% CI, 42.4%-69.9%), respectively. IMH with PAU was associated with a significantly higher risk of the primary outcome compared with isolated IMH (subdistribution hazard ratio, 2.21; 95% CI, 1.09-4.50; P = .027) and isolated PAU (subdistribution hazard ratio, 3.58; 95% CI, 1.44-8.88; P = .006). CONCLUSIONS: Complications from symptomatic PAU and IMH are frequent, with intervention, recurrent symptoms, radiographic progression, rupture, or death affecting 25% of patients at 30 days after diagnosis and almost one-half of patients 1 year after diagnosis. Given the high rate of adverse events in this population, investigation into a more aggressive interventional strategy may warranted, especially in patients with a combined IMH and PAU.

Implementation of a Consensus-Driven Quality Improvement Protocol to Decrease Length of Stay after Elective Carotid Endarterectomy.

BACKGROUND: National guidelines stipulate that postoperative length-of-stay (LOS) after elective carotid endarterectomy (CEA) should not exceed 1 day on average, yet perioperative care coordination gaps may limit the ability for institutions to achieve this goal. Internal review determined that increased LOS after CEA at our institution was frequently attributable to urinary retention or postoperative hypertension. We designed and implemented a quality improvement (QI) protocol aiming to better our institutional performance in postoperative LOS after CEA, consisting of 2 Plan-Do-Study-Act (PDSA) cycles. METHODS: In the first PDSA cycle, a division-wide standardized protocol was developed by which antihypertensive medications were managed preoperatively and through postoperative day (POD) 1. This protocol included dedicated patient outreach with instructions for at-home antihypertensive management through the morning of POD 0. Second, alpha-1-blockade was administered to all male patients preoperatively. All patients receiving an elective CEA performed at our institution by vascular surgeons were included in the protocol. The primary outcome measure was defined percent failure of the LOS >1 day metric, with raw LOS as a secondary outcome measure. Process measures included adherence to the antihypertensive medication protocol and adherence to preoperative alpha-1 blockade. Balance measures included documented intraoperative hypotension and 30-day readmission. Fisher's exact test was used to evaluate relationships between preintervention and postintervention cohorts and the outcome measure. Wilcoxon rank-sum tests were used to evaluate relationships between cohorts and total LOS. RESULTS: Baseline performance on the LOS >1 day metric after elective CEA was 58.3% in the 8 months prior to intervention, across 48 patients. Both PDSA interventions were implemented simultaneously. In the 12 months after intervention, 64 patients met protocol inclusion criteria, including 19 symptomatic patients (29.7%). Process measure success for preoperative antihypertensive regimen adherence was 89.8%. For males not chronically prescribed alpha-1 blockade preoperatively, process measure success for adherence to preoperative alpha-1 blockade was 78.8%. The intraoperative hypotension balance measure occurred in 1 patient (1.6%). Performance on the LOS >1 day outcome measure was improved to 32.8% (P = 0.01). Performance on the raw LOS outcome measure was similar between the preintervention cohort (median 2 days, interquartile range [IQR] 1-2) and postintervention cohort (median 1 day, IQR 1-2, P = 0.07). Performance on the 30-day readmission balance measure was similar between preintervention (6.3%) and postintervention cohorts (9.4%, P = 0.73). CONCLUSIONS: The consensus-driven development and implementation of a QI protocol to reduce postoperative LOS after CEA showed promising results in our institution, with approximately 40% improvement in the primary outcome measure. Wider efforts to improve LOS after CEA should include a focus on minimization of postoperative hypertension and urinary retention.

Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach.

The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients' health report and provides better treatment.

Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network.

Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra- and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.

An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. The emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. The proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. The simulation is conducted to test the efficacy of the model on various features present in a dataset. The results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset.

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications.

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks. The model is designed in such a way that it minimizes the delays and complexity while authenticating the networks in 5G networks. The attacks are mitigated using an evolutionary model when it is trained with the relevant attack datasets, and the model is validated to mitigate the attacks. The simulation is conducted to test the efficacy of the model, and the results of simulation show that the proposed method is effective in improving the handling and authentication and mitigation against various types of attacks in mobile health applications.

A systematic review of spinal cord ischemia prevention and management after open and endovascular aortic repair.

OBJECTIVE: Spinal cord ischemia (SCI) is one of the most devastating complications after descending thoracic aortic (DTA) and thoracoabdominal aortic (TAA) repairs. Patients who develop SCI have a poor prognosis, with mortality rates reaching 75% within the first year after surgery. Many factors have been shown to increase the risk of this complication, including the extent of TAA repair, length of aortic and collateral network coverage, embolization, and reduced spinal cord perfusion pressure. As a result, a variety of treatment strategies have been developed. We aimed to provide an up-to-date review of SCI rates with associated treatment algorithms from open and endovascular DTA and TAA repair. METHODS: Using PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines, a literature review with the MeSH (medical subject headings) terms "spinal cord ischemia," "spinal cord ischemia prevention and mitigation strategies," "spinal cord ischemia rates," and "spinal cord infarction" was performed in the Cochrane and PubMed databases to find all peer-reviewed studies of DTA and TAA repair with SCI complications reported. The search was limited to 2012 to 2021 and English-language reports. MeSH subheadings, including diagnosis, complications, physiopathology, surgery, mortality, and therapy, were used to further restrict the included studies. Studies were excluded if they were not of humans, had not pertained to SCI after DTA or TAA operative repair, and if the study had primarily discussed neuromonitoring techniques. Additionally, studies with <40 patients or limited information regarding SCI protection strategies were excluded. Each study was individually reviewed by two of us (S.L. and A.D.) to assess the type and extent of aortic pathology, operative technique, SCI protection or mitigation strategies, rates of overall and permanent SCI symptoms, associations with SCI on multivariate analysis, and mortality. RESULTS: Of the 450 studies returned by the MeSH search strategy, 41 met the inclusion criteria and were included in the final analysis. For the endovascular DTA repair patients, the overall SCI rates ranged from 0% to 10.6%, with permanent SCI symptoms ranging from 0% to 5.1%. The rate of overall SCI after endovascular and open TAA repair was 0% to 35%. The permanent SCI symptom rate was reported by only one study of open repair at 1.1%. The permanent SCI symptom rate after endovascular TAA repair was 2% to 20.5%. CONCLUSIONS: The present review has provided an up-to-date review of the current rates of SCI and the prevention and mitigation strategies used during DTA and TAA repair. We found that a multimodal approach, including a bundled institutional protocol, staging of multiple repairs, preservation of the collateral blood flow network, augmented spinal cord perfusion, selective cerebrospinal fluid drainage, and distal aortic perfusion during open TAA repairs, appears to be important in reducing the risk of SCI.

A systematic review of thromboelastography utilization in vascular and endovascular surgery.

OBJECTIVE: Thromboelastography (TEG) is diagnostic modality that analyzes real-time blood coagulation parameters. Clinically, TEG primarily allows for directed blood component resuscitation among patients with acute blood loss and coagulopathy. The utilization of TEG has been widely adopted in among other surgical specialties; however, its use in vascular surgery is less prominent. We aimed to provide an up-to-date review of TEG utilization in vascular and endovascular surgery. METHODS: Using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a literature review with the Medical Subject Headings (MeSH) terms "TEG and arterial events", "TEG and vascular surgery", "TEG and vascular", "TEG and endovascular surgery", "TEG and endovascular", "TEG and peripheral artery disease", "TEG and prediction of arterial events", "TEG and prediction of complications ", "TEG and prediction of thrombosis", "TEG and prediction of amputation", and "TEG and amputation" was performed in Cochrane and PubMed databases to identify all peer-reviewed studies of TEG utilization in vascular surgery, written between 2000 and 2021 in the English language. The free-text and MeSH subheadings search terms included diagnosis, complications, physiopathology, surgery, mortality, and therapy to further restrict the articles. Studies were excluded if they were not in humans or pertaining to vascular or endovascular surgery. Additionally, case reports and studies with limited information regarding TEG utilization were excluded. Each study was independently reviewed by two researchers to assess for eligibility. RESULTS: Of the 262 studies identified through the MeSH strategy, 15 studies met inclusion criteria and were reviewed and summarized. Literature on TEG utilization in vascular surgery spanned cerebrovascular disease (n = 3), peripheral arterial disease (n = 3), arteriovenous malformations (n = 1), venous thromboembolic events (n = 7), and perioperative bleeding and transfusion (n = 1). In cerebrovascular disease, TEG may predict the presence and stability of carotid plaques, analyze platelet function before carotid stenting, and compare efficacy of antiplatelet therapy after stent deployment. In peripheral arterial disease, TEG has been used to predict disease severity and analyze the impact of contrast on coagulation parameters. In venous disease, TEG may predict hypercoagulability and thromboembolic events among various patient populations. Finally, TEG can be utilized in the postoperative setting to predict hemorrhage and transfusion requirements. CONCLUSIONS: This systematic review provides an up-to-date summarization of TEG utilization in multiple facets of vascular and endovascular surgery.

Extraction of the molecular level biomedical event trigger based on gene ontology using radial belief neural network techniques.

Detection of molecular level biomedical event extraction plays a vital role in creating and visualizing the applications related to natural language processing. Cystic Fibrosis is an inherited genetic and debilitating pathology involving the respiratory and digestive systems. The excessive production of thick sticky mucus on the outside of the cells is the main consequence of such disease. This includes disease prevention and medical search to signify the occurrence and detection of event triggers, which is regarded as a proper step in an event extraction of molecular level in biomedical applications. In this model, use a rich set of extracted features to feed the machine learning classifier that helps in better extraction of events. The study uses an automatic feature selection and a classification model using Radial Belief Neural Network (RBNN) for the optimal detection of molecular biomedical event detection. The Radial Belief Neural Network (RBNN) is the proposed system is implemented and it is the classifier to give accurate result of the disease detection. These three algorithms are used to enhance the generalization performance and scalability of detecting the molecular event triggers. The validation is conducted on the cystic fibrosis event trigger based on the gene ontology bio system using the RBNN model with a lung molecular event-level extraction dataset. The extensive computation shows that the Radial Belief Neural Network (RBNN) is proposed to given the better performance results like Accuracy, Sensitivity, Specificity, F-measure and Execution time.

Does recipient work status pre-transplant affect post-heart transplant survival? A United Network for Organ Sharing database review.

BACKGROUND: Recipient-related factors, such as education level and type of health insurance, are known to affect heart transplantation outcomes. Pre-operative employment status has shown an association with survival in abdominal organ transplant patients. We sought to evaluate the effect of work status of heart transplant (HTx) recipients at the time of listing and at the time of transplantation on short- and long-term survival. METHODS: We evaluated the United Network for Organ Sharing (UNOS) registry for all adult HTx recipients from 2001 to 2014. Recipients were grouped based on their work status at listing and at heart transplantation. Kaplan-Meier estimates illustrated 30-day, 1-year, 5-year, and 10-year survival comparing working with non-working groups. The Cox proportional hazards regression model was applied to adjust for covariates that could potentially confound the post-transplantation survival analysis. RESULTS: Working at listing for HTx was not significantly associated with 30-day and 1-year survival. However, 5- and 10-year mortality were 14.5% working vs 19.8% not working (p < 0.0001) and 16% working vs 26% not working (p < 0.0001), respectively. Working at HTx appeared to be associated with a survival benefit at every time interval, with a trend toward improved survival at 30 days and 1 year and a significant association at 5 and 10 years. Kaplan-Meier analysis demonstrated a 5% and 10% decrease in 5- and 10-year mortality, respectively, for the working group compared with the group not working at transplantation. The Cox proportional hazards regression model showed that working at listing and working at transplantation were each associated with decreased mortality (hazard ratio [HR] = 0.8, 95% confidence interval [CI] 0.71 to 0.91; and HR = 0.76, 95% CI 0.65 to 0.89, respectively). CONCLUSIONS: This study is the first analysis of UNOS STAR data on recipient work status pre-HTx demonstrating: (1) an improvement in post-transplant survival for working HTx candidates; and (2) an association between working pre-HTx and longer post-HTx survival. Given that work status before HTx may be a modifiable risk factor for better outcomes after HTx, we strongly recommend that UNOS consider these important findings for moving forward this patient-centered research on work status. Working at listing and working at HTx are associated with long-term survival benefits. The association may be reciprocal, where working identifies less ill patients and also improves well-being. Consideration should be given to giving additional weight to work status during organ allocation. Work status may also be a modifiable factor associated with better post-HTx outcomes.

Synthesis, DNA binding and in-vitro cytotoxicity studies on novel bis-pyrazoles.

A new series of bis-pyrazoles 6a-t were synthesized from 3,5-dimethyl pyrazole using sequential approach. All these compounds were characterized by IR, 1H NMR, 13C NMR and mass spectral data. The interaction of newly synthesized bis-pyrazoles with DNA was investigated through molecular docking and absorption spectroscopic technique. Among all bis-pyrazoles compounds, the 6h compound showed lower conformational energy through in silico analysis. The interaction of each molecule in this series 6a-t with the various concentrations of DNA was examined through the UV-visible spectroscopic studies. The UV-visible spectroscopy studies on the specific binding of compound 6a, 6b, 6g, 6h, 6d, 6i, 6k, 6n, 6s with DNA have exhibited spectral shifts and the results were discussed. In further the compounds 6a-t were subjected to the in-vitro cytotoxicity studies against human pancreatic adenocarcinoma, human non-small cell lung carcinoma cell lines. Among the screened compounds, N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-1H-pyrazol-1-yl)-1H-pyrazol-5-yl)cyclobutane carboxamide and N-(5'-Isopropoxy-2'-isopropyl-3,5-dimethyl-2'H-[1,4'] bipyrazolyl-3'-yl)-dimethane sulfonamide were found as lead molecules since they have exhibited promising activity against both the cancer cell lines used in this study, whereas the compounds 4-(trifluoromethyl)-N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-2H-pyrrol-2-yl)-1H-pyrazol-5-yl)benzamide and 2,6-difluoro-N-(3-isopropoxy-1-isopropyl-4-(3,5-dimethyl-2H-pyrrol-2-yl)-1H-pyrazol-5-yl) benzamide were found to be active against the pancreatic cell line only. Rest all the other compounds were found to exhibit moderate to good activity towards both the cell lines.

Viral delivery of shRNA to amygdala neurons leads to neurotoxicity and deficits in Pavlovian fear conditioning.

The use of viral vector technology to deliver short hairpin RNAs (shRNAs) to cells of the nervous system of many model organisms has been widely utilized by neuroscientists to study the influence of genes on behavior. However, there have been numerous reports that delivering shRNAs to the nervous system can lead to neurotoxicity. Here we report the results of a series of experiments where adeno-associated viruses (AAV), that were engineered to express shRNAs designed to target known plasticity associated genes (i.e. Arc, Egr1 and GluN2A) or control shRNAs that were designed not to target any rat gene product for depletion, were delivered to the rat basal and lateral nuclei of the amygdala (BLA), and auditory Pavlovian fear conditioning was examined. In our first set of experiments we found that animals that received AAV (3.16E13-1E13 GC/mL; 1 µl/side), designed to knockdown Arc (shArc), or control shRNAs targeting either luciferase (shLuc), or nothing (shCntrl), exhibited impaired fear conditioning compared to animals that received viruses that did not express shRNAs. Notably, animals that received shArc did not exhibit differences in fear conditioning compared to animals that received control shRNAs despite gene knockdown of Arc. Viruses designed to harbor shRNAs did not induce obvious morphological changes to the cells/tissue of the BLA at any dose of virus tested, but at the highest dose of shRNA virus examined (3.16E13 GC/mL; 1 µl/side), a significant increase in microglia activation occurred as measured by an increase in IBA1 immunoreactivity. In our final set of experiments we infused viruses into the BLA at a titer of (1.60E+12 GC/mL; 1 µl/side), designed to express shArc, shLuc, shCntrl or shRNAs designed to target Egr1 (shEgr1), or GluN2A (shGluN2A), or no shRNA, and found that all groups exhibited impaired fear conditioning compared to the group which received a virus that did not express an shRNA. The shEgr1 and shGluN2A groups exhibited gene knockdown of Egr1 and GluN2A compared to the other groups examined respectively, but Arc was not knocked down in the shArc group under these conditions. Differences in fear conditioning among the shLuc, shCntrl, shArc and shEgr1 groups were not detected under these circumstances; however, the shGluN2A group exhibited significantly impaired fear conditioning compared to most of the groups, indicating that gene specific deficits in fear conditioning could be observed utilizing viral mediated delivery of shRNA. Collectively, these data indicate that viral mediated shRNA expression was toxic to neurons in vivo, under all viral titers examined and this toxicity in some cases may be masking gene specific changes in learning. Therefore, the use of this technology in behavioral neuroscience warrants a heightened level of careful consideration and potential methods to alleviate shRNA induced toxicity are discussed.

The production of viral vectors designed to express large and difficult to express transgenes within neurons.

BACKGROUND: Viral vectors are frequently used to deliver and direct expression of transgenes in a spatially and temporally restricted manner within the nervous system of numerous model organisms. Despite the common use of viral vectors to direct ectopic expression of transgenes within the nervous system, creating high titer viral vectors that are capable of expressing very large transgenes or difficult to express transgenes imposes unique challenges. Here we describe the development of adeno-associated viruses (AAV) and lentiviruses designed to express the large and difficult to express GluN2A or GluN2B subunits of the N-methyl-D-aspartate receptor (NMDA) receptor, specifically within neurons. RESULTS: We created a number of custom designed AAV and lentiviral vectors that were optimized for large transgenes, by minimizing DNA sequences that were not essential, utilizing short promoter sequences of 8 widely used promoters (RSV, EFS, TRE3G, 0.4αCaMKII, 1.3αCaMKII, 0.5Synapsin, 1.1Synapsin and CMV) and utilizing a very short (~75 bps) 3' untranslated sequence. Not surprisingly these promoters differed in their ability to express the GluN2 subunits, however surprisingly we found that the neuron specific synapsin and αCaMKII, promoters were incapable of conferring detectable expression of full length GluN2 subunits and detectable expression could only be achieved from these promoters if the transgene included an intron or if the GluN2 subunit transgenes were truncated to only include the coding regions of the GluN2 transmembrane domains. CONCLUSIONS: We determined that viral packaging limit, transgene promoter and the presence of an intron within the transgene were all important factors that contributed to being able to successfully develop viral vectors designed to deliver and express GluN2 transgenes in a neuron specific manner. Because these vectors have been optimized to accommodate large open reading frames and in some cases contain an intron to facilitate expression of difficult to express transgenes, these viral vectors likely could be useful for delivering and expressing many large or difficult to express transgenes in a neuron specific manner.

Adeno-associated viral serotypes produce differing titers and differentially transduce neurons within the rat basal and lateral amygdala.

BACKGROUND: In recent years, there has been an increased interest in using recombinant adeno-associated viruses (AAV) to make localized genetic manipulations within the rodent brain. Differing serotypes of AAV possess divergent capsid protein sequences and these variations greatly influence each serotype's ability to transduce particular cell types and brain regions. We therefore aimed to determine the AAV serotype that is optimal for targeting neurons within the Basal and Lateral Amygdala (BLA) since the transduction efficiency of AAV has not been previously examined within the BLA. This region is desirable to genetically manipulate due to its role in emotion, learning & memory, and numerous psychiatric disorders. We accomplished this by screening 9 different AAV serotypes (AAV2/1, AAV2/2, AAV2/5, AAV2/7, AAV2/8, AAV2/9, AAV2/rh10, AAV2/DJ and AAV2/DJ8) designed to express red fluorescent protein (RFP) under the regulation of an alpha Ca2+/calmodulin-dependent protein kinase II promoter (αCaMKII). RESULTS: We determined that these serotypes produce differing amounts of virus under standard laboratory production. Notably AAV2/2 consistently produced the lowest titers compared to the other serotypes examined. These nine serotypes were bilaterally infused into the rat BLA at the highest titers achieved for each serotype and at a normalized titer of 7.8E + 11 GC/ml. Twenty one days following viral infusion the degree of transduction was quantitated throughout the amygdala. These viruses exhibited differential transduction of neurons within the BLA. AAV2/7 exhibited a trend toward having the highest efficiency of transduction and AAV2/5 exhibited significantly lower transduction efficiency as compared to the serotypes examined. AAV2/5's decreased ability to transduce BLA neurons correlates with its significantly different capsid protein sequences as compared to the other serotypes examined. CONCLUSIONS: For laboratories producing their own recombinant adeno-associated viruses, the use of AAV2/2 is likely less desirable since AAV2/2 produces significantly lower titers than many other serotypes of AAV. Numerous AAV serotypes appear to efficiently transduce BLA neurons, with the exception of AAV2/5. Taking into consideration the ability of certain serotypes to achieve high titers and transduce BLA neurons well, in our hands AAV2/DJ8 and AAV2/9 appear to be ideal serotypes to use when targeting neurons within the BLA.

Amygdala nuclei critical for emotional learning exhibit unique gene expression patterns.

The amygdala is a heterogeneous, medial temporal lobe structure that has been implicated in the formation, expression and extinction of emotional memories. This structure is composed of numerous nuclei that vary in cytoarchitectonics and neural connections. In particular the lateral nucleus of the amygdala (LA), central nucleus of the amygdala (CeA), and the basal (B) nucleus contribute an essential role to emotional learning. However, to date it is still unclear to what extent these nuclei differ at the molecular level. Therefore we have performed whole genome gene expression analysis on these nuclei to gain a better understanding of the molecular differences and similarities among these nuclei. Specifically the LA, CeA and B nuclei were laser microdissected from the rat brain, and total RNA was isolated from these nuclei and subjected to RNA amplification. Amplified RNA was analyzed by whole genome microarray analysis which revealed that 129 genes are differentially expressed among these nuclei. Notably gene expression patterns differed between the CeA nucleus and the LA and B nuclei. However gene expression differences were not considerably different between the LA and B nuclei. Secondary confirmation of numerous genes was performed by in situ hybridization to validate the microarray findings, which also revealed that for many genes, expression differences among these nuclei were consistent with the embryological origins of these nuclei. Knowing the stable gene expression differences among these nuclei will provide novel avenues of investigation into how these nuclei contribute to emotional arousal and emotional learning, and potentially offer new genetic targets to manipulate emotional learning and memory.