In our search for novel subtype-selective estrogen receptor (ER) ligands, we have examined various heterocyclic units as core structural elements. Here, we have investigated the fused, bicyclic pyrazolo[1,5-a]pyrimidine core, which is a system that allows for analogues to be readily assembled in a library-like fashion. This series of pyrazolo[1,5-a]pyrimidine ER ligands provided us with a new pharmacological profile for an ER ligand: compounds that are passive on both ERs, with a distinct potency selectivity in favor of ERbeta. The most distinctive ligand in this series, 2-phenyl-3-(4-hydroxyphenyl)-5,7-bis(trifluoromethyl)-pyrazolo[1,5-a]pyrimidine, was 36-fold selective for ERbeta in binding. Curiously, on the basis of molecular modeling, the ERbeta binding selectivity of compounds in this series appears to be derived from differing orientations that they adapt in the ligand binding pockets of ERalpha vs ERbeta. In transcription assays this pyrazolopyrimidine was fully effective as an ERbeta antagonist while exhibiting no significant activity on ERalpha. Thus, this ligand functions as a potency- and efficacy-selective ERbeta antagonist that would abrogate estrogen action through ERbeta with minimal effects on its activity through ERalpha; as such, it could be used to study the biological function of ERbeta.

Decreased neuronal dendrite branching and plasticity of the hippocampus, a limbic structure implicated in mood disorders, is thought to contribute to the symptoms of depression. However, the mechanisms underlying this effect, as well as the actions of antidepressant treatment, remain poorly characterized. Here, we show that hippocampal expression of neuritin, an activity-dependent gene that regulates neuronal plasticity, is decreased by chronic unpredictable stress (CUS) and that antidepressant treatment reverses this effect. We also show that viral-mediated expression of neuritin in the hippocampus produces antidepressant actions and prevents the atrophy of dendrites and spines, as well as depressive and anxiety behaviors caused by CUS. Conversely, neuritin knockdown produces depressive-like behaviors, similar to CUS exposure. The ability of neuritin to increase neuroplasticity is confirmed in models of learning and memory. Our results reveal a unique action of neuritin in models of stress and depression, and demonstrate a role for neuroplasticity in antidepressant treatment response and related behaviors.is a devastating and recurrent illness affecting up to 17% of the population, resulting in personal disability, increased rates of suicide, and socioeconomic loss (1). Moreover, currently available antidepressants are only effective in approximately one-third of patients with MDD and in up to two-thirds after multiple trials, and they take weeks to months to produce a response (2, 3). In addition, the mechanisms underlying the therapeutic actions of antidepressants are poorly understood. New targets beyond monoamine signaling are now emerging in both preclinical and clinical reports of MDD (4, 5). These studies have focused on key limbic brain structures, including the hippocampus, that are significantly altered by chronic stress and depression and that are known to regulate mood, anxiety, and cognition (6, 7). Hippocampal synaptic plasticity has received much attention in recent years because human imaging and rodent studies demonstrate that stress and depression are associated with decreased hippocampal volume and atrophy of neurons (6,8).Neuritin, also known as candidate plasticity gene 15 (CPG15), encodes a small, extracellular GPI-anchored protein critical for dendritic outgrowth, maturation, and axonal regeneration (9-13). Neuritin expression in the hippocampus is induced by neuronal activity following chemical-or electrical-induced seizures (9, 14, 15), ischemia (16), and exercise (5, 17). Neuritin has been implicated in the actions of BDNF (9, 18), which is up-regulated in the hippocampus by antidepressant treatment and is sufficient to produce antidepressant behavioral responses (19,20). Moreover, chronic antidepressant treatment has been shown to increase neuritin expression in rat brain (21). The current study was conducted to test the hypothesis that neuritin is a critical downstream mediator of antidepressant/BDNF-mediated plasticity and, conversely, that loss of neuritin could contribute to depre...

The vast bacteriophage population harbors an immense reservoir of genetic information. Almost 2000 phage genomes have been sequenced from phages infecting hosts in the phylum Actinobacteria, and analysis of these genomes reveals substantial diversity, pervasive mosaicism, and novel mechanisms for phage replication and lysogeny. Here, we describe the isolation and genomic characterization of 46 phages from environmental samples at various geographic locations in the U.S. infecting a single Arthrobacter sp. strain. These phages include representatives of all three virion morphologies, and Jasmine is the first sequenced podovirus of an actinobacterial host. The phages also span considerable sequence diversity, and can be grouped into 10 clusters according to their nucleotide diversity, and two singletons each with no close relatives. However, the clusters/singletons appear to be genomically well separated from each other, and relatively few genes are shared between clusters. Genome size varies from among the smallest of siphoviral phages (15,319 bp) to over 70 kbp, and G+C contents range from 45–68%, compared to 63.4% for the host genome. Although temperate phages are common among other actinobacterial hosts, these Arthrobacter phages are primarily lytic, and only the singleton Galaxy is likely temperate.

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BACKGROUND Colonic perfusion status can be assessed easily by indocyanine green (ICG) angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery. Recently, various parameter-based perfusion analysis have been studied for quantitative evaluation, but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure. Therefore, it can help improve the accuracy and consistency by artificial intelligence (AI) based real-time analysis microperfusion (AIRAM). AIM To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery. METHODS The ICG curve was extracted from the region of interest (ROI) set in the ICG fluorescence video of the laparoscopic colorectal surgery. Pre-processing was performed to reduce AI performance degradation caused by external environment such as background, light source reflection, and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit (CPU) PC. AI learning and evaluation were performed by dividing into a training patient group ( n = 50) and a test patient group ( n = 15). Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map (SOM) network. The predictive reliability of anastomotic complications in a trained SOM network is verified using test set. RESULTS AI-based risk and the conventional quantitative parameters including T 1/2 max , time ratio (TR), and rising slope (RS) were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern. When the ICG graph pattern showed stepped rise, the accuracy of conventional quantitative parameters decreased, but the AI-based classification maintained accuracy consistently. The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks. Statistical performance verifications were improved in the AI-based analysis. AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications. The F1 score of the AI-based method increased by 31% for T 1/2 max , 8% for TR, and 8% for RS. The processing time of AIRAM was measured as 48.03 s, which was suitable for real-time processing. CONCLUSION In conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.