Πέμπτη 16 Φεβρουαρίου 2023

Small noncoding RNA interactome capture reveals pervasive, carbon source-dependent tRNA engagement of yeast glycolytic enzymes [ARTICLE]

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Small noncoding RNAs fulfill key functions in cellular and organismal biology, typically working in concert with RNA-binding proteins (RBPs). While proteome-wide methodologies have enormously expanded the repertoire of known RBPs, these methods do not distinguish RBPs binding to small noncoding RNAs from the rest. To specifically identify this relevant subclass of RBPs, we developed small noncoding RNA interactome capture (snRIC2C) based on the differential RNA-binding capacity of silica matrices (2C). We define the S. cerevisiae proteome of nearly 300 proteins that specifically binds to RNAs smaller than 200 nt in length (snRBPs), identifying informative distinctions from the total RNA-binding proteome determined in parallel. Strikingly, the snRBPs include most glycolytic enzymes from yeast. With further methodological developments using silica matrices, 12 tRNAs were identified as specific binders of the glycolytic enzyme GAPDH. We show that tRNA engagement of GAPDH is carbon source–dependent and regulated by the RNA polymerase III repressor Maf1, suggesting a regulatory interaction between glycolysis and RNA polymerase III activity. We conclude that snRIC2C and other 2C-derived methods greatly facilitate the study of RBPs, revealing previously unrecognized interactions.
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Incidence of Postoperative CSF Leaks in Class III Obese Patients Undergoing Middle Cranial Fossa Approach for Spontaneous CSF Leak Repair

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imageObjective To determine the safety and effectiveness of the middle cranial fossa (MCF) approach for spontaneous cerebrospinal fluid leak (sCSF-L) repair in class III obese patients. To also assess the need for prophylactic lumbar drain (LD) placement in this patient population. Study Design Retrospective cohort study. Setting Tertiary Academic Center. Patients All patients older than 18 years undergoing sCSF-L repair with an MCF approach. Intervention An MCF craniotomy for sCSF-L repair. Main outcome measure Rate of complications and postoperative leaks. Results There were no perioperative complications in 78.9% (56/71) of cases. The surgical complication rate was 12.5% (2/16), 10% (2/20), and 22.2% (6/27) in class I, class II, and class III obese patients. There was no statistically significant difference in complications among these three groups. The most common postoperative complication was a persistent CSF leak in the acute postoperative period with an overall rate of 9.9% (7/71) with six of the seven patients requiring postoperative LD placement. The percentage of postoperative CSF leaks in nonobese, class I, class II, and class III patients were 25% (2/8), 12.5% (2/16), 0% (0/20), and 11.1% (3/27), respectively. There was no statistically significant difference in the rate of postoperative CSF leaks among the four groups (chi-square, p = 0.48). In all cases, the acute postoperative CSF leaks resolved in the long term and did not require further surgical repair. Conclusions We determine that MCF craniotomy repair for sCSF-Ls is safe in patients with class III obesity, and the incidence of postoperative CSF leaks did not vary among other obesity classes. We also find that prophylactic placement of LDs is not routinely needed in this population.
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Motor Imagery EEG Decoding Based on Multi-Scale Hybrid Networks and Feature Enhancement

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Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals' advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.
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Surface Electromyography-Based Analysis of the Lower Limb Muscle Network and Muscle Synergies at Various Gait Speeds

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Gait movement is an important activity in daily human life. The coordination of gait movement is directly affected by the cooperation and functional connectivity between muscles. However, the mechanisms of muscle operation at different gait speeds remain unclear. Therefore, this study addressed the gait speed effect on the changes in co operative modules and functional connectivity between muscles. To this end, surface electromyography (sEMG) signals were collected from eight key lower extremity muscles of twelve healthy subjects walking on a treadmill at high, middle, and low motion speeds. Nonnegative matrix factorization (NNMF) was applied to the sEMG envelope and intermuscular coherence matrix, yielding five muscle synergies. Muscle functional networks were constructed by decomposing the intermuscular coherence matrix, revealing different layers of functional muscle networks across frequencies. In addition, the coupling strength between cooperative muscles grew with gait speed. Different coordination patterns among muscles with changes in gait speed related to the neuromuscular system regulation were identified.
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Fasting Plasma Glucose Increase and Neutrophil-to-Lymphocyte Ratio as Risk Predictors of Clinical Outcome of COVID-19 Pneumonia in Type 2 Diabetes Mellitus

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Exp Clin Endocrinol Diabetes
DOI: 10.1055/a-2009-6937

Aim of the study To evaluate fasting plasma glucose (FPG) increase and neutrophil-to-lymphocyte ratio (NLR) as risk predictors of severe clinical outcome of COVID-19 pneumonia in type 2 diabetes mellitus (T2DM) hospitalised patients. Patients and methods Type 2 diabetes mellitus (T2DM) patients hospitalised between March 2020 and February 2021 were studied retrospectively. The NLR ratio at admission and FPG increase (day 7, day with maximal FPG) were evaluated in association with the clinical progression of SARS-CoV-2 infection. Results Three hundred patients (165 men, 135 women) were included in the study. The mean age was 67.17±8.65 years. Severe COVID-19 pneumonia was diagnosed in 170 patients (56.7%). Fifty-four patients (18%) were intubated and 49 (16.3%) died. Greater increase in FPG (79.5 vs. 44.5 mg/dL for day 1–7, p<0.001; and 113.5 vs. 75 mg/dL for day 1-day with maximum glucose value, p<0.001) and higher NLR at admission (10.65 vs. 6.85) were seen in patients with need of high-flow oxygen compared to those without need, and they were associated with a higher probability of intubation and death. Conclusion FPG increase and NLR could be significant risk predictors of severe COVID-19 pneumonia in T2DM hospitalised patients.
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Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Article in Thieme eJournals:
Table of contents  |  Abstract  |  Full text
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Cerebral Small Vessel Disease in Elderly Patients with Sudden Sensorineural Hearing Loss

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imageBackground The cause of sudden sensorineural hearing loss (SSNHL) is uncertain in a significant number of patients. A vascular etiology has been proposed because SSNHL increases the risk of developing a stroke. Cardiovascular risk factors can cause cerebral small vessel disease (CSVD). The presence of CSVD in turn raises the risk of stroke. Aim The aim of this study was to compare the presence of CSVD and cardiovascular risk factors in elderly patients with idiopathic SSNHL (iSSNHL) to a control cohort. Method Patients with iSSNHL of 50 years and older were compared with a control cohort with patients suspected of trigeminal neuralgia or vestibular paroxysmia. The primary outcome was the difference in the number of white matter hyperintensities using the ordinal Fazekas scale. Secondary outcomes were the presence of brain infarctions on MRI and the difference in cardiovascular risk factors. Results In the SSNHL cohort, Fazekas score 2 was most frequently seen compared with Fazekas 1 in the control cohort. The distribution of Fazekas scores did not differ significantly. The sum of the Fazekas scores were 13,925 and 14,042 for iSSNHL and controls, respectively (p = 0.908). Brain infarctions were seen in 8 patients with iSSNHL (n = 118) and in 13 patients in the control cohort (n = 118) (p = 0.361). None of the cardiovascular risk factors were more frequently seen in the iSSNHL cohort. Conclusion Patients with iSSNHL did not exhibit more CSVD on MRI than controls. This result is in contrast with previous literature demonstrating a higher risk of stroke in patients with iSSNHL than in controls. A prospective analysis with a larger study population is therefore warranted.
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Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses—A Pictorial Review

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imageThe expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapi es, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.
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