Notes

Credibility with the Montreal Mental Examination (MoCA) List Ratings: analysis with all the Cognitive Website Lots of your Seoul Neuropsychological Screening process Battery pack (SNSB).
This integrative scRNA-seq analysis revealed heterogeneity of cells of the periodontium and their cell type-specific markers, as well as their relationships with DF cells. Most importantly, our analysis identified a cementoblast-specific metagene that discriminate cementoblasts from alveolar bone osteoblasts, including Pthlh (encoding PTHrP) and Tubb3. RNA velocity analysis indicated that cementoblasts were directly derived from PTHrP+ DF cells in the early developmental stage and did not interconvert with other cell types. Further, CellPhoneDB cell-cell communication analysis indicated that PTHrP derived from cementoblasts acts on diversity of cells in the periodontium in an autocrine and paracrine manner. Collectively, our findings provide insights into the lineage hierarchy and intercellular interactions of cells in the periodontium at a single-cell level, aiding to understand cellular and molecular basis of periodontal tissue formation.Circulating biomarkers of drug-induced liver injury (DILI) have been a focus of research in hepatology over the last decade, and several novel DILI biomarkers that hold promise for certain applications have been identified. For example, glutamate dehydrogenase holds promise as a specific biomarker of liver injury in patients with concomitant muscle damage. It may also be a specific indicator of mitochondrial damage. Imidazole ketone erastin mouse In addition, microRNA-122 is sensitive for early detection of liver injury in acetaminophen overdose patients. However, recent events in the field of DILI biomarker research have provided us with an opportunity to step back, consider how biomarker discovery has been done thus far, and determine how to move forward in a way that will optimize the discovery process. This is important because major challenges remain in the DILI field and related areas that could be overcome in part by new biomarkers. In this short review, we briefly describe recent progress in DILI biomarker discovery and development, identify current needs, and suggest a general approach to move forward.Neuroelectrophysiology is an old science, dating to the 18th century when electrical activity in nerves was discovered. Such discoveries have led to a variety of neurophysiological techniques, ranging from basic neuroscience to clinical applications. These clinical applications allow assessment of complex neurological functions such as (but not limited to) sensory perception (vision, hearing, somatosensory function), and muscle function. The ability to use similar techniques in both humans and animal models increases the ability to perform mechanistic research to investigate neurological problems. Good animal to human homology of many neurophysiological systems facilitates interpretation of data to provide cause-effect linkages to epidemiological findings. Mechanistic cellular research to screen for toxicity often includes gaps between cellular and whole animal/person neurophysiological changes, preventing understanding of the complete function of the nervous system. Building Adverse Outcome Pathways (AOPs) wto provide the mechanistic underpinnings for biological changes. Establishment of linkages between changes in cellular physiology and those at the level of the AO will allow construction of biological pathways (AOPs) and allow development of higher throughput assays to test for changes to critical physiological circuits. To allow mechanistic/predictive toxicology of the nervous system to be protective of human populations, neuroelectrophysiology has a critical role in our future.Pain has been an area of growing interest in the past decade and is known to be associated with mental health issues. Due to the ambiguous nature of how pain is described in text, it presents a unique natural language processing (NLP) challenge. Understanding how pain is described in text and utilizing this knowledge to improve NLP tasks would be of substantial clinical importance. Not much work has previously been done in this space. For this reason, and in order to develop an English lexicon for use in NLP applications, an exploration of pain concepts within free text was conducted. The exploratory text sources included two hospital databases, a social media platform (Twitter), and an online community (Reddit). This exploration helped select appropriate sources and inform the construction of a pain lexicon. The terms within the final lexicon were derived from three sources-literature, ontologies, and word embedding models. This lexicon was validated by two clinicians as well as compared to an existing 26-term pain sub-ontology and MeSH (Medical Subject Headings) terms. The final validated lexicon consists of 382 terms and will be used in downstream NLP tasks by helping select appropriate pain-related documents from electronic health record (EHR) databases, as well as pre-annotating these words to help in development of an NLP application for classification of mentions of pain within the documents. The lexicon and the code used to generate the embedding models have been made publicly available.Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extraccuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.Optical clearing methods serve as powerful tools to study intact organs and neuronal circuits. We developed an aqueous clearing protocol, Fast 3D Clear, that relies on tetrahydrofuran for tissue delipidation and iohexol for clearing, such that tissues can be imaged under immersion oil in light-sheet imaging systems. Fast 3D Clear requires 3 days to achieve high transparency of adult and embryonic mouse tissues while maintaining their anatomical integrity and preserving a vast array of transgenic and viral/dye fluorophores. A unique advantage of Fast 3D Clear is its complete reversibility and thus compatibility with tissue sectioning and immunohistochemistry. Fast 3D Clear can be easily and quickly applied to a wide range of biomedical studies, facilitating the acquisition of high-resolution two- and three-dimensional images.
Abatacept was well tolerated by patients with early diffuse cutaneous systemic sclerosis in a phase 2, double-blind randomised trial, with potential efficacy at 12 months. We report here the results of an open-label extension for 6 months.

Patients (aged ≥18 years) with diffuse cutaneous systemic sclerosis of less than 3 years' duration from their first non-Raynaud's symptom were enrolled into the ASSET trial (A Study of Subcutaneous Abatacept to Treat DiffuseCutaneous Systemic Sclerosis), which is a double-blind trial at 22 sites in Canada, the UK, and the USA. Aftercompletion of 12 months of treatment with either abatacept or placebo, patients received a further 6 months ofabatacept (125 mg subcutaneous every week) in an open-label extension. The primary endpoint of the double-blind trial was modified Rodnan Skin Score (mRSS) at 12 months, which was reassessed at 18 months in the open-label extension. The primary analysis included all participants who completed the double-blind trial and received at leadouble-blind period in the abatacept group, which were related to scleroderma renal crisis; no deaths were recorded during the open-label extension.

During the 6-month open-label extension, no new safety signals for abatacept were identified in the treatment of diffuse cutaneous systemic sclerosis. Clinically meaningful improvements in mRSS and other outcome measures were observed in both the abatacept and placebo groups when patients transitioned to open-label treatment. These data support further studies of abatacept in diffuse cutaneous systemic sclerosis.

Bristol-Myers Squibb and National Institutes of Health.
Bristol-Myers Squibb and National Institutes of Health.Mass estimates of plastic pollution in the Great Lakes based on surface samples differ by orders of magnitude from what is predicted by production and input rates. It has been theorized that a potential location of this missing plastic is on beaches and in nearshore water. We incorporate a terrain dependent beaching model to an existing hydrodynamic model for Lake Erie which includes three dimensional advection, turbulent mixing, density driven sinking, and deposition into the sediment. When examining parameter choices, in all simulations the majority of plastic in the lake is beached, potentially identifying a reservoir holding a large percentage of the lake's plastic which in previous studies has not been taken into account. The absolute amount of beached plastic is dependent on the parameter choices. We also find beached plastic does not accumulate homogeneously through the lake, with eastern regions of the lake, especially those downstream of population centers, most likely to be impacted. This effort constitutes a step towards identifying sinks of missing plastic in large bodies of water.People spontaneously infer other people's psychology from faces, encompassing inferences of their affective states, cognitive states, and stable traits such as personality. These judgments are known to be often invalid, but nonetheless bias many social decisions. Their importance and ubiquity have made them popular targets for automated prediction using deep convolutional neural networks (DCNNs). Here, we investigated the applicability of this approach how well does it generalize, and what biases does it introduce? We compared three distinct sets of features (from a face identification DCNN, an object recognition DCNN, and using facial geometry), and tested their prediction across multiple out-of-sample datasets. Across judgments and datasets, features from both pre-trained DCNNs provided better predictions than did facial geometry. However, predictions using object recognition DCNN features were not robust to superficial cues (e.g., color and hair style). Importantly, predictions using face identification DCNN features were not specific models trained to predict one social judgment (e.g., trustworthiness) also significantly predicted other social judgments (e.g., femininity and criminal), and at an even higher accuracy in some cases than predicting the judgment of interest (e.g., trustworthiness). Models trained to predict affective states (e.g., happy) also significantly predicted judgments of stable traits (e.g., sociable), and vice versa. Our analysis pipeline not only provides a flexible and efficient framework for predicting affective and social judgments from faces but also highlights the dangers of such automated predictions correlated but unintended judgments can drive the predictions of the intended judgments.
The online version contains supplementary material available at 10.1007/s42761-021-00075-5.
The online version contains supplementary material available at 10.1007/s42761-021-00075-5.
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