Barrerajimenez6509

RNA-binding proteins are key regulators of cell identity and function, which underscores the need for unbiased and versatile protocols to identify and characterize novel protein-RNA interactions. Here, we describe a simple and cost-effective in vitro RNA immunoprecipitation (iv-RIP) method to assess the direct or indirect RNA-binding ability of any protein of interest. The versatility of this method relies on the adaptability of the immunoprecipitation conditions and the choice of the RNA, which exponentially broadens the range of potential applications. For complete details on the use and execution of this protocol, please refer to Guallar et al. (2020).A combination of immunotherapy and chemotherapy strategies could strengthen antitumor effects. This protocol elucidates a robust method via co-culturing drug pre-treated acute myeloid leukemia cells with CD3+ T cells, derived from leukoreduction system chambers, for in vitro and in vivo study. We optimized several aspects of the procedures, including timing of drug treatment, quantification of tumor cells, and approach of combination of CD3+ T cells with drug treatment in vivo. This enables the readouts of the interplay between drugs and T cells. For complete details on the use and execution of this protocol, please refer to Su et al. (2020).By using negatively charged Coomassie brilliant blue G-250 dye to induce a charge shift on proteins, blue native polyacrylamide gel electrophoresis (BN-PAGE) allows resolution of enzymatically active multiprotein complexes extracted from cellular or subcellular lysates while retaining their native conformation. BN-PAGE was first developed to analyze the size, composition, and relative abundance of the complexes and supercomplexes that form the mitochondrial respiratory chain and OXPHOS system. Here, we present a detailed protocol of BN-PAGE to obtain robust and reproducible results. For complete details on the use and execution of this protocol, please refer to Lobo-Jarne et al. Vafidemstat supplier (2018) and Timón-Gómez et al. (2020).The use of destabilizing domains (DDs) to conditionally control the abundance of a protein of interest (POI) through a small-molecule stabilizer has gained increasing traction both in vitro and in vivo. Yet there are specific considerations for the development and accurate control of user-defined POIs via DDs, as well as the identification of novel (and potentially synergistic) small-molecule stabilizers. Here, we describe a platform for achieving these goals. For complete details on the use and execution of this protocol, please refer to Ramadurgum et al. (2020).Seizures are a common emergency in the neonatal intesive care unit (NICU) among newborns receiving therapeutic hypothermia for hypoxic ischemic encephalopathy. The high incidence of seizures in this patient population necessitates continuous electroencephalographic (EEG) monitoring to detect and treat them. Due to EEG recordings being reviewed intermittently throughout the day, inevitable delays to seizure identification and treatment arise. In recent years, work on neonatal seizure detection using deep learning algorithms has started gaining momentum. These algorithms face numerous challenges first, the training data for such algorithms comes from individual patients, each with varying levels of label imbalance since the seizure burden in NICU patients differs by several orders of magnitude. Second, seizures in neonates are usually localized in a subset of EEG channels, and performing annotations per channel is very time-consuming. Hence models which make use of labels only per time periods, and not per channels, are preferable. In this work we assess how different deep learning models and data balancing methods influence learning in neonatal seizure detection in EEGs. We propose a model which provides a level of importance to each of the EEG channels - a proxy to whether a channel exhibits seizure activity or not, and we provide a quantitative assessment of how well this mechanism works. The model is portable to EEG devices with differing layouts without retraining, facilitating its potential deployment across different medical centers. We also provide a first assessment of how a deep learning model for neonatal seizure detection agrees with human rater decisions - an important milestone for deployment to clinical practice. We show that high AUC values in a deep learning model do not necessarily correspond to agreement with a human expert, and there is still a need to further refine such algorithms for optimal seizure discrimination.Guilt is a quintessential emotion in interpersonal interactions and moral cognition. Detecting the presence and measuring the intensity of guilt-related neurocognitive processes is crucial to understanding the mechanisms of social and moral phenomena. Existing neuroscience research on guilt has been focused on the neural correlates of guilt states induced by various types of stimuli. While valuable in their own right, these studies have not provided a sensitive and specific bio-marker of guilt suitable for use as an indicator of guilt-related neurocognitive processes in novel experimental settings. In a recent study, we identified a distributed Guilt-Related Brain Signature (GRBS) based on 2 independent functional MRI datasets. We demonstrated the sensitivity of GRBS in detecting a critical cognitive antecedent of guilt, namely one's responsibility in causing harm to another person, across participant populations from 2 distinct cultures (ie, Chinese and Swiss). We also showed that the sensitivity of GRBS did not generalize to other types of negative affective states (eg, physical and vicarious pain). In this commentary, we discuss the relevance of guilt in the broader scope of social and moral phenomena, and discuss how guilt-related biomarkers can be useful in understanding their psychological and neurocognitive mechanisms underlying these phenomena.Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and fatal neurodegenerative disorder for which there is no effective curative treatment available and minimal palliative care. Mutations in the gene encoding the TAR DNA-binding protein 43 (TDP-43) are a well-recognized genetic cause of ALS, and an imbalance in energy homeostasis correlates closely to disease susceptibility and progression. Considering previous research supporting a plethora of downstream cellular impairments originating in the histopathological signature of TDP-43, and the solid evidence around metabolic dysfunction in ALS, a causal association between TDP-43 pathology and metabolic dysfunction cannot be ruled out. Here we discuss how TDP-43 contributes on a molecular level to these impairments in energy homeostasis, and whether the protein's pathological effects on cellular metabolism differ from those of other genetic risk factors associated with ALS such as superoxide dismutase 1 (SOD1), chromosome 9 open reading frame 72 (C9orf72) and fused in sarcoma (FUS).