Links between traumatic brain injury and ballistic pressure waves originating in the thoracic cavity and extremities

Identifying patients at risk of traumatic brain injury (TBI) is important because research suggests prophylactic treatments to reduce risk of long-term sequelae. Blast pressure waves can cause TBI without penetrating wounds or blunt force trauma. Similarly, bullet impacts distant from the brain can produce pressure waves sufficient to cause mild to moderate TBI. The fluid percussion model of TBI shows that pressure impulses of 15-30 psi cause mild to moderate TBI in laboratory animals. In pigs and dogs, bullet impacts to the thigh produce pressure waves in the brain of 18-45 psi and measurable injury to neurons and neuroglia. Analyses of research in goats and epidemiological data from shooting events involving humans show high correlations (r > 0.9) between rapid incapacitation and pressure wave magnitude in the thoracic cavity. A case study has documented epilepsy resulting from a pressure wave without the bullet directly hitting the brain. Taken together, these results support the hypothesis that bullet impacts distant from the brain produce pressure waves that travel to the brain and can retain sufficient magnitude to induce brain injury. The link to long-term sequelae could be investigated via epidemiological studies of patients who were gunshot in the chest to determine whether they experience elevated rates of epilepsy and other neurological sequelae

Serum microRNA array analysis identifies miR-140-3p, miR-33b-3p and miR-671-3p as potential osteoarthritis biomarkers involved in metabolic processes.

Background: MicroRNAs (miRNAs) in circulation have emerged as promising biomarkers. In this study, we aimed to identify a circulating miRNA signature for osteoarthritis (OA) patients and in combination with bioinformatics analysis to evaluate the utility of selected differentially expressed miRNAs in the serum as potential OA biomarkers. Methods: Serum samples were collected from 12 primary OA patients, and 12 healthy individuals were screened using the Agilent Human miRNA Microarray platform interrogating 2549 miRNAs. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the deregulated miRNAs. Expression levels of selected miRNAs were validated by quantitative real-time PCR (qRT-PCR) in all serum and in articular cartilage samples from OA patients (n = 12) and healthy individuals (n = 7). Bioinformatics analysis was used to investigate the involved pathways and target genes for the above miRNAs. Results: We identified 279 differentially expressed miRNAs in the serum of OA patients compared to controls. Two hundred and five miRNAs (73.5%) were upregulated and 74 (26.5%) downregulated. ROC analysis revealed that 77 miRNAs had area under the curve (AUC) > 0.8 and p < 0.05. Bioinformatics analysis in the 77 miRNAs revealed that their target genes were involved in multiple signaling pathways associated with OA, among which FoxO, mTOR, Wnt, pI3K/akt, TGF-β signaling pathways, ECM-receptor interaction, and fatty acid biosynthesis. qRT-PCR validation in seven selected out of the 77 miRNAs revealed 3 significantly downregulated miRNAs (hsa-miR-33b-3p, hsa-miR-671-3p, and hsa-miR-140-3p) in the serum of OA patients, which were in silico predicted to be enriched in pathways involved in metabolic processes. Target-gene analysis of hsa-miR-140-3p, hsa-miR-33b-3p, and hsa-miR-671-3p revealed that InsR and IGFR1 were common targets of all three miRNAs, highlighting their involvement in regulation of metabolic processes that contribute to OA pathology. Hsa-miR-140-3p and hsa-miR-671-3p expression levels were consistently downregulated in articular cartilage of OA patients compared to healthy individuals. Conclusions: A serum miRNA signature was established for the first time using high density resolution miR-arrays in OA patients. We identified a three-miRNA signature, hsa-miR-140-3p, hsa-miR-671-3p, and hsa-miR-33b-3p, in the serum of OA patients, predicted to regulate metabolic processes, which could serve as a potential biomarker for the evaluation of OA risk and progression.Peer reviewedFinal Published versio

BézierSketch: A Generative Model for Scalable Vector Sketches

The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process. The landmark SketchRNN provided breakthrough by sequentially generating sketches as a sequence of waypoints. However this leads to low-resolution image generation, and failure to model long sketches. In this paper we present B\'ezierSketch, a novel generative model for fully vector sketches that are automatically scalable and high-resolution. To this end, we first introduce a novel inverse graphics approach to stroke embedding that trains an encoder to embed each stroke to its best fit B\'ezier curve. This enables us to treat sketches as short sequences of paramaterized strokes and thus train a recurrent sketch generator with greater capacity for longer sketches, while producing scalable high-resolution results. We report qualitative and quantitative results on the Quick, Draw! benchmark.Comment: Accepted as poster at ECCV 202

Changes in Cross-Sectional Area of Spinal Canal and Vertebral Body Under 2 Years of Teriparatide Treatment: Results from the EUROFORS Study

The treatment of osteoporotic patients with teriparatide is associated with a significant increase in bone formation and gain of bone mass. The purpose of this post hoc analysis was to determine if the cross-sectional area (CSA) of the spinal canal and the vertebral body is affected by teriparatide treatment. Narrowing of the spinal canal might represent a safety problem, while widening of the vertebral CSA might improve mechanical stability. High-resolution computed tomography (HRCT) scans of vertebra T12 were obtained at baseline and after 6, 12, and 24 months of teriparatide treatment (20 μg/day) from 44 postmenopausal women with established osteoporosis participating in the prospective, randomized EUROFORS study. The CSA of the spinal canal did not decrease but increased marginally by 0.9% (2.6 mm2) over 24 months (P < 0.001), with a range from −0.5% (−2 mm2) to 3.1% (+8 mm2). Even when analyzing the spinal CSA on a slice-by-slice basis, no clinically relevant narrowing of the spinal canal was observed. For vertebral bodies, the CSA increased by 0.7% (5.7 mm2) over 24 months (P < 0.001), with a range from −0.4% (–3 mm2) to 1.6% (+14 mm2). Our data do not provide evidence for safety concerns regarding spinal canal narrowing. On the other hand, the increases observed for vertebral CSA apparently also only minimally contribute to the mechanical strengthening of the vertebral body under teriparatide treatment

Multiplex quantitative PCR for single-reaction genetically modified (GM) plant detection and identification of false-positive GM plants linked to Cauliflower mosaic virus (CaMV) infection.

BACKGROUND:Most genetically modified (GM) plants contain a promoter, P35S, from the plant virus, Cauliflower mosaic virus (CaMV), and many have a terminator, TNOS, derived from the bacterium, Agrobacterium tumefaciens. Assays designed to detect GM plants often target the P35S and/or TNOS DNA sequences. However, because the P35S promoter is derived from CaMV, these detection assays can yield false-positives from non-GM plants infected by this naturally-occurring virus. RESULTS:Here we report the development of an assay designed to distinguish CaMV-infected plants from GM plants in a single multiplexed quantitative PCR (qPCR) reaction. Following initial testing and optimization via PCR and singleplex-to-multiplex qPCR on both plasmid and plant DNA, TaqMan qPCR probes with different fluorescence wavelengths were designed to target actin (a positive-control plant gene), P35S, P3 (a CaMV-specific gene), and TNOS. We tested the specificity of our quadruplex qPCR assay using different DNA extracts from organic watercress and both organic and GM canola, all with and without CaMV infection, and by using commercial and industrial samples. The limit of detection (LOD) of each target was determined to be 1% for actin, 0.001% for P35S, and 0.01% for both P3 and TNOS. CONCLUSIONS:This assay was able to distinguish CaMV-infected plants from GM plants in a single multiplexed qPCR reaction for all samples tested in this study, suggesting that this protocol is broadly applicable and readily transferrable to any interested parties with a qPCR platform

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: The PALMS study protocol

Background Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. Methods/Design In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. Discussion This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions

The pharmacological regulation of cellular mitophagy

Small molecules are pharmacological tools of considerable value for dissecting complex biological processes and identifying potential therapeutic interventions. Recently, the cellular quality-control process of mitophagy has attracted considerable research interest; however, the limited availability of suitable chemical probes has restricted our understanding of the molecular mechanisms involved. Current approaches to initiate mitophagy include acute dissipation of the mitochondrial membrane potential (ΔΨm) by mitochondrial uncouplers (for example, FCCP/CCCP) and the use of antimycin A and oligomycin to impair respiration. Both approaches impair mitochondrial homeostasis and therefore limit the scope for dissection of subtle, bioenergy-related regulatory phenomena. Recently, novel mitophagy activators acting independently of the respiration collapse have been reported, offering new opportunities to understand the process and potential for therapeutic exploitation. We have summarized the current status of mitophagy modulators and analyzed the available chemical tools, commenting on their advantages, limitations and current applications

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?