53 research outputs found

    Identification of Nucleic Acid Binding Sites on Translin-Associated Factor X (TRAX) Protein

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    Translin and TRAX proteins play roles in very important cellular processes such as DNA recombination, spatial and temporal expression of mRNA, and in siRNA processing. Translin forms a homomeric nucleic acid binding complex and binds to ssDNA and RNA. However, a mutant translin construct that forms homomeric complex lacking nucleic acid binding activity is able to form fully active heteromeric translin-TRAX complex when co-expressed with TRAX. A substantial progress has been made in identifying translin sites that mediate its binding activity, while TRAX was thought not to bind DNA or RNA on its own. We here for the first time demonstrate nucleic acid binding to TRAX by crosslinking radiolabeled ssDNA to heteromeric translin-TRAX complex using UV-laser. The TRAX and translin, photochemically crosslinked with ssDNA, were individually detected on SDS-PAGE. We mutated two motifs in TRAX and translin, designated B2 and B3, to help define the nucleic acid binding sites in the TRAX sequence. The most pronounced effect was observed in the mutants of B3 motif that impaired nucleic acid binding activity of the heteromeric complexes. We suggest that both translin and TRAX are binding competent and contribute to the nucleic acid binding activity

    Thrombectomy Outcomes With General vs Nongeneral Anesthesia: A Pooled Patient-Level Analysis From the EXTEND-IA Trials and SELECT Study

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    BACKGROUND AND OBJECTIVES: The effect of anesthesia choice on endovascular thrombectomy (EVT) outcomes is unclear. Collateral status on perfusion imaging may help identify the optimal anesthesia choice. METHODS: In a pooled patient-level analysis of EXTEND-IA, EXTEND-IA TNK, EXTEND-IA TNK part II, and SELECT, EVT functional outcomes (modified Rankin Scale score distribution) were compared between general anesthesia (GA) vs non-GA in a propensity-matched sample. Furthermore, we evaluated the association of collateral flow on perfusion imaging, assessed by hypoperfusion intensity ratio (HIR) - Tmax \u3e 10 seconds/Tmax \u3e 6 seconds (good collaterals - HIR \u3c 0.4, poor collaterals - HIR ≥ 0.4) on the association between anesthesia type and EVT outcomes. RESULTS: Of 725 treated with EVT, 299 (41%) received GA and 426 (59%) non-GA. The baseline characteristics differed in presentation National Institutes of Health Stroke Scale score (median [interquartile range] GA: 18 [13-22], non-GA: 16 [11-20], DISCUSSION: GA was associated with worse functional outcomes after EVT, particularly in patients with poor collaterals in a propensity score-matched analysis from a pooled patient-level cohort from 3 randomized trials and 1 prospective cohort study. The confounding by indication may persist despite the doubly robust nature of the analysis. These findings have implications for randomized trials of GA vs non-GA and may be of utility for clinicians when making anesthesia type choice. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that use of GA is associated with worse functional outcome in patients undergoing EVT. TRIAL REGISTRATION INFORMATION: EXTEND-IA: ClinicalTrials.gov (NCT01492725); EXTEND-IA TNK: ClinicalTrials.gov (NCT02388061); EXTEND-IA TNK part II: ClinicalTrials.gov (NCT03340493); and SELECT: ClinicalTrials.gov (NCT02446587)

    Thrombectomy Outcomes With General vs Nongeneral Anesthesia: A Pooled Patient-Level Analysis From the EXTEND-IA Trials and SELECT Study

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    Background and Objectives The effect of anesthesia choice on endovascular thrombectomy (EVT) outcomes is unclear. Collateral status on perfusion imaging may help identify the optimal anesthesia choice. Methods In a pooled patient-level analysis of EXTEND-IA, EXTEND-IA TNK, EXTEND-IA TNK part II, and SELECT, EVT functional outcomes (modified Rankin Scale score distribution) were compared between general anesthesia (GA) vs non-GA in a propensity-matched sample. Furthermore, we evaluated the association of collateral flow on perfusion imaging, assessed by hypoperfusion intensity ratio (HIR) – Tmax \u3e 10 seconds/Tmax \u3e 6 seconds (good collaterals – HIR \u3c 0.4, poor collaterals – HIR ≥ 0.4) on the association between anesthesia type and EVT outcomes. Results Of 725 treated with EVT, 299 (41%) received GA and 426 (59%) non-GA. The baseline characteristics differed in presentation National Institutes of Health Stroke Scale score (median [interquartile range] GA: 18 [13–22], non-GA: 16 [11–20], p \u3c 0.001) and ischemic core volume (GA: 15.0 mL [3.2–38.0] vs non-GA: 9.0 mL [0.0–31.0], p \u3c 0.001). In addition, GA was associated with longer last known well to arterial access (203 minutes [157–267] vs 186 minutes [138–252], p = 0.002), but similar procedural time (35.5 minutes [23–59] vs 34 minutes [22–54], p = 0.51). Of 182 matched pairs using propensity scores, baseline characteristics were similar. In the propensity score–matched pairs, GA was independently associated with worse functional outcomes (adjusted common odds ratio [adj. cOR]: 0.64, 95% CI: 0.44–0.93, p = 0.021) and higher neurologic worsening (GA: 14.9% vs non-GA: 8.9%, aOR: 2.10, 95% CI: 1.02–4.33, p = 0.045). Patients with poor collaterals had worse functional outcomes with GA (adj. cOR: 0.47, 95% CI: 0.29–0.76, p = 0.002), whereas no difference was observed in those with good collaterals (adj. cOR: 0.93, 95% CI: 0.50–1.74, p = 0.82), pinteraction: 0.07. No difference was observed in infarct growth overall and in patients with good collaterals, whereas patients with poor collaterals demonstrated larger infarct growth with GA with a significant interaction between collaterals and anesthesia type on infarct growth rate (pinteraction: 0.020). Discussion GA was associated with worse functional outcomes after EVT, particularly in patients with poor collaterals in a propensity score–matched analysis from a pooled patient-level cohort from 3 randomized trials and 1 prospective cohort study. The confounding by indication may persist despite the doubly robust nature of the analysis. These findings have implications for randomized trials of GA vs non-GA and may be of utility for clinicians when making anesthesia type choice. Classification of Evidence This study provides Class III evidence that use of GA is associated with worse functional outcome in patients undergoing EVT. Trial Registration Information EXTEND-IA: ClinicalTrials.gov (NCT01492725); EXTEND-IA TNK: ClinicalTrials.gov (NCT02388061); EXTEND-IA TNK part II: ClinicalTrials.gov (NCT03340493); and SELECT: ClinicalTrials.gov (NCT02446587)

    Clinical practice recommendations for the detection and management of hyperglycemia in pregnancy from South Asia, Africa and Mexico during COVID‑19 pandemic

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    CITATION: Priya G, Bajaj S, Kalra B, Coetzee A, Kalra S, Dutta D, et al. Clinical practice recommendations for the detection and management of hyperglycemia in pregnancy from South Asia, Africa and Mexico during COVID‑19 pandemic. J Family Med Prim Care 2021;10:4350-63. doi.10.4103/jfmpc.jfmpc_653_21The original publication is available at: lww.comThe human coronavirus disease 2019 (COVID‑19) pandemic has affected overall healthcare delivery, including prenatal, antenatal and postnatal care. Hyperglycemia in pregnancy (HIP) is the most common medical condition encountered during pregnancy. There is little guidance for primary care physicians for providing delivery of optimal perinatal care while minimizing the risk of COVID‑19 infection in pregnant women. This review aims to describe pragmatic modifications in the screening, detection and management of HIP during the COVID‑ 19 pandemic. In this review, articles published up to June 2021 were searched on multiple databases, including PubMed, Medline, EMBASE and ScienceDirect. Direct online searches were conducted to identify national and international guidelines. Search criteria included terms to extract articles describing HIP with and/or without COVID‑19 between 1st March 2020 and 15th June 2021. Fasting plasma glucose, glycosylated hemoglobin (HbA1c) and random plasma glucose could be alternative screening strategies for gestational diabetes mellitus screening (at 24–28 weeks of gestation), instead of the traditional 2 h oral glucose tolerance test. The use of telemedicine for the management of HIP is recommended. Hospital visits should be scheduled to coincide with obstetric and ultrasound visits. COVID‑19 infected pregnant women with HIP need enhanced maternal and fetal vigilance, optimal diabetes care and psychological support in addition to supportive measures. This article presents pragmatic options and approaches for primary care physicians, diabetes care providers and obstetricians for GDM screening, diagnosis and management during the pandemic, to be used in conjunction with routine antenatal care.Publisher’s versio

    Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

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    Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

    Overview of Techniques for Lung Nodule Retrieval System

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    Biomedical Image Indexing and Retrieval Descriptors: A Comparative Study

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    AbstractThis paper focuses on the comparison of two new proposed pattern descriptors i.e., local mesh ternary pattern (LMeTerP) and directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the former descriptor encodes the gray scale relationship among the neighbors for a given center pixel with three selected directions of mess patterns which is generated from 2D image and later descriptor encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0̊, 45̊, 90̊ and 135̊) for a given center pixel in an image. The novelty of the proposed descriptors is that they use ternary patterns from images to encode more spatial structure information which lead to better retrieval. The experimental results demonstrate the superiority of the new techniques in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques (like LBP, LTP, LQEP, LMeP etc.) on three different types of benchmark biomedical databases
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