Trigeminal neuralgia (TN) is a rare condition for which there are multiple treatment options available. To date, there has been difficulty in comparing the outcomes of treatment due to the variety of patient-reported outcome measures (PROMs) and their inadequate psychometric testing. The aim of this review was to assess the psychometric properties of PROMs used to date in TN and make recommendations for their use in future studies. Five electronic databases (MEDLINE, EMBASE, CINAHL Plus, PsycINFO, Health and Psychosocial Instruments) were searched for studies assessing the development of PROMs or their psychometric properties in TN studies. The records obtained were assessed independently by two reviewers for their methodological quality, following guidance from the Consensus-based Standards for the selection of Health Measurement Instruments (COSMIN). Six studies were identified and information on five PROMs (Brief Pain Inventory Facial (BPI-F), Visual Analogue Scale (VAS), Barrow Neurology Institute Pity of an objective outcome measure for pain or health related quality of life, psychometrically sound PROMs are essential for assessing medical and surgical treatment outcomes in TN.This review highlights the knowledge gap in the field of psychometrics of patient reported outcomes measures in the field of TN. Given the unavailability of an objective outcome measure for pain or health related quality of life, psychometrically sound PROMs are essential for assessing medical and surgical treatment outcomes in TN.Researchers seeking to generate genomic data for non-model organisms are faced with a number of trade-offs when deciding which method to use. The selection of reduced representation approaches versus whole genome resequencing will ultimately affect the marker density, sequencing depth, and the number of individuals that can multiplexed. These factors can affect researchers' ability to accurately characterize certain genomic features, such as landscapes of divergence-how FST varies across the genomes. To provide insight into the effect of sequencing method on the estimation of divergence landscapes, we applied an identical bioinformatic pipeline to three generations of sequencing data (GBS, ddRAD, and WGS) produced for the same system, the yellow-rumped warbler species complex. We compare divergence landscapes generated using each method for the myrtle warbler (Setophaga coronata coronata) and the Audubon's warbler (S. c. auduboni), and for Audubon's warblers with deeply divergent mtDNA resulting from mitochondrial introgression. We found that most high-FST peaks were not detected in the ddRAD data set, and that while both GBS and WGS were able to identify the presence of large peaks, WGS was superior at a finer scale. Comparing Audubon's warblers with divergent mitochondrial haplotypes, only WGS allowed us to identify small (10-20 kb) regions of elevated differentiation, one of which contained the nuclear-encoded mitochondrial gene NDUFAF3. We calculated the cost per base pair for each method and found it was comparable between GBS and WGS, but significantly higher for ddRAD. These comparisons highlight the advantages of WGS over reduced representation methods when characterizing landscapes of divergence. Circadian rhythms are involved not only in the repair and regeneration of the immune system, but may also be associated with regulation of inflammation and immune responses. Rev-erbα could constitute a link between immunity and circadian rhythms since it is a transcription factor that regulates circadian rhythms and has functions in multiple physiological and pathological processes. Decidual macrophages (dMφs) play crucial roles in immune balance at the maternal-fetal interface, and abnormal macrophage polarization is related to adverse pregnancy outcomes, such as infertility, recurrent spontaneous abortion, and preterm labor. However, whether Rev-erbα could modulate the polarization of macrophages is unknown. In this study, we analyzed the phenotype of dMφs and the expression of Rev-erbα in dMφs from normal pregnancies and miscarriages. The effect of Rev-erbα on macrophage polarization was evaluated by its knockdown or pharmacological activation. The mechanism by which the Rev-erbα agonist SR9009 regulathages and suggest a therapeutic target for pregnancy loss and pregnancy complications.Extant fold-switching proteins remodel their secondary structures and change their functions in response to environmental stimuli. These shapeshifting proteins regulate biological processes and are associated with a number of diseases, including tuberculosis, cancer, Alzheimer's, and autoimmune disorders. Thus, predictive methods are needed to identify more fold-switching proteins, especially since all naturally occurring instances have been discovered by chance. In response to this need, two high-throughput predictive methods have recently been developed. Here we test them on ORF9b, a newly discovered fold switcher and potential therapeutic target from the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Selleckchem CY-09 Promisingly, both methods correctly indicate that ORF9b switches folds. We then tested the same two methods on ORF9b1, the ORF9b homolog from SARS-CoV-1. Again, both methods predict that ORF9b1 switches folds, a finding consistent with experimental binding studies. Together, these results (a) demonstrate that protein fold switching can be predicted using high-throughput computational approaches and (b) suggest that fold switching might be a general characteristic of ORF9b homologs.The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed-effect group-level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior.