Long-term Cre-mediated retrograde tagging of neurons using a novel recombinant pseudorabies virus

Brain regions contain diverse populations of neurons that project to different long-range targets. The study of these subpopulations in circuit function and behavior requires a toolkit to characterize and manipulate their activity in vivo. We have developed a novel set of reagents based on Pseudorabies Virus (PRV) for efficient and long-term genetic tagging of neurons based on their projection targets. By deleting IE180, the master transcriptional regulator in the PRV genome, we have produced a mutant virus capable of infection and transgene expression in neurons but unable to replicate in or spread from those neurons. IE180-null mutants showed no cytotoxicity, and infected neurons exhibited normal physiological function more than 45 days after infection, indicating the utility of these engineered viruses for chronic experiments. To enable rapid and convenient construction of novel IE180-null recombinants, we engineered a bacterial artificial chromosome (BAC) shuttle-vector system for moving new constructs into the PRV IE180-null genome. Using this system we generated an IE180-null recombinant virus expressing the site-specific recombinase Cre. This Cre-expressing virus (PRV-hSyn-Cre) efficiently and robustly infects neurons in vivo and activates transgene expression from Cre-dependent vectors in local and retrograde projecting populations of neurons in the mouse. We also generated an assortment of recombinant viruses expressing fluorescent proteins (mCherry, EGFP, ECFP). These viruses exhibit long-term labeling of neurons in vitro but transient labeling in vivo. Together these novel IE180-null PRV reagents expand the toolkit for targeted gene expression in the brain, facilitating functional dissection of neuronal circuits in vivo

Autism spectrum disorder in a community-based sample with neurodevelopmental problems in Lagos, Nigeria

Autism Spectrum Disorder (ASD) is a globally prevalent neurodevelopmental disorder for which early diagnosis and intervention is the mainstay of management. In the African continent, limited data is available regarding the non-clinic based samples. Lack of information available to caregivers and inadequate skilled manpower often limit early detection and access to the few available though under resourced services in the community. Community based screening can be an important drive to create awareness and improve information dissemination regarding services available for those living with this disorder. This is a descriptive cross-sectional study utilizing data obtained from participants of a community-based autism screening exercise. The surveillance exercise was part of the annual Orange Ribbon initiative for autism awareness and screening held in 2014. Data was obtained from 85 participants involved in the Autism Surveillance screening exercise within the Lagos community. Community public service radio announcements state wide and word of mouth were used to invite and enroll eligible participants to the screening and consultation exercise. A second stage screening and a brief sociodemographic questionnaire followed by a third stage clinical interview and evaluation using the Diagnostic and Statistical Manual of Mental Disorders - 5 Edition (DSM 5) were used. Appropriate consultation and referrals to services in the community were given. Participants had a mean age of 7.53 years (SD 4.35). Twenty-nine (34.5%) met the diagnosis of ASD. Other diagnosis included attention deficit hyperactivity disorder (ADHD), language and speech disorder, intellectual disability (8.3%) and learning disorders (9.5%). Main health concerns to caregivers were poor language development in all (100%), of which 11 (40.7%) were non-verbal; gaze avoidance was seen in 14 (48.3%) and challenging behavior in 12 (42.9%). Comorbidities included seizure disorders (3.4%) and ADHD (6.9%). Persons with autism had history of ASD behavior more often when compared to the other neurodevelopmental disorders and these findings were statistically significant. Referrals were given to caregivers to engage in services within the community. As seen in this study, community understanding of ASD is poor in such locations, in which many persons with other neurodevelopmental disorders are often presented as having autism. Caregivers in the study location are distressed by many symptoms associated with autism and their comorbid conditions. Currently there is an evident role for frequent large scale community based screening and autism awareness exercises possibly using inter-sectoral collaboration as a strategy.Yewande O. Oshodi, Andrew T. Olagunju, Motunrayo. A. Oyelohunnu, Elizabeth A. Campbell, Charles S. Umeh, Olatunji F. Aina, Wellington Oyibo, Folusho E.A. Lesi, Joseph D. Adeyem

Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network.  It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented.  For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations.  We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent.  We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines.  Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website

Interaction between Antimarial and Antibacterials as possible contributor to development of multi-drug resistant P. falciparum in a malaria endemic region

In recent times clinical outcomes following the use of the major anti malarial drugs such chloroquine and sulphadoxine – pyrimethamine, has become complicated and difficult to predict because of the increasing prevalence of multi-drug resistant P. falciparum malaria. Consequently, the armamentarium of sale and efficacious drugs required to wage a successful war against malaria is beginning to dwindle. Although newer chemotherapeutic strategies, such as the use of artemisinin combination regimen, have been employed to overcome this problem, there remains a need to safeguard the few compounds available from falling into the pitfalls arising from possible mistakes of the past. This paper reviews measures that have been take so far to tackle the malaria drug resistance problem and focuses on an aspect, which has remained largely under appreciated – the possible role of antimalarial-antibacterial interaction in the selection and spread of mutant strains of P. falciparum in a malaria endemic region where both classes of compounds are frequently co-administered. NQJHM Vol. 16 (1) 2005: pp. 30-3

Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings

Purpose: Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis.Approach: Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator.Result: The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference.Conclusion: Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Host-parasite interactio

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope-the Schistoscope-which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.Host-parasite interactio

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope-the Schistoscope-which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings

Good cllinical practice in clinical drug trials - What you need to know

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