The compound N-piperidinyl-[8-chloro-1-(2,4-dichlorophenyl)-1,4,5,6-tetrahydrobenzo [6,7]cyclohepta[1,2-c]pyrazole-3-carboxamide] (NESS 0327) was synthesized and evaluated for binding affinity toward cannabinoid CB 1 and CB 2 receptor. NESS 0327 exhibited a stronger selectivity for CB 1 receptor compared with, showing a much higher affinity for CB 1 receptor (K i ϭ 350 Ϯ 5 fM and 1.8 Ϯ 0.075 nM, respectively) and a higher affinity for the CB 2 receptor (K i ϭ 21 Ϯ 0.5 nM and 514 Ϯ 30 nM, respectively). Affinity ratios demonstrated that NESS 0327 was more than 60,000-fold selective for the CB 1 receptor, whereas SR 141716A only 285-fold. NESS 0327 alone did not produce concentration-dependent stimulation of guanosine 5Ј-O-(3-[ Interest in the pharmacology of cannabinoids (CBs) has rapidly increased after the cloning of cannabinoid receptors and the discovery of their endogenous ligand: arachidonylethanolamide (anandamide) (Devane et al., 1988(Devane et al., , 1992Munro et al., 1993). Two types of cannabinoid receptors, CB 1 and CB 2 , have been characterized, both of which have distinct anatomical distributions and ligand binding profiles. Cannabinoid CB 1 receptors are present in the central nervous system with the highest densities in the hippocampus, cerebellum, and striatum (Herkenham et al., 1990;Howlett, 1998), and to a lesser extent in several peripheral tissues. Cannabinoid CB 2 receptors seem to be predominantly located in peripheral tissues (Pertwee, 1997(Pertwee, , 1999Galiègue et al., 1995). Both receptors belong to the G protein-coupled family of receptors that negatively regulate adenylate cyclase and control the release of arachidonic acid (Howlett, 1995). Naturally occurring [⌬ 9 -tetrahydrocannabinol (⌬ 9 -THC) and ⌬ 8 -THC] and synthetic cannabinoid agonists CP 55,940, and WIN 55, produce a number of effects in mice (hypoactivity, catalepsy, hypothermia, and antinociception) that are collectively known as the tetrad of cannabinoidinduced behaviors (Abood and Martin, 1992;Compton et al., 1992Compton et al., , 1993. These behaviors are of a central origin and are thought to be mediated via the cannabinoid CB 1 receptor (Rinaldi-Carmona et al., 1994;Compton et al., 1996;Lichtman and Martin, 1997), whereas the CB 2 receptor may mediate some of the peripheral effects of ⌬ 9 -THC, such as immunosuppression (Martin, 1986).The cloning of CB 1 and CB 2 receptors and the subsequent development of selective tools have advanced the concept of Article, publication date, and citation information can be found at

We hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer. Bilateral masking procedures are applied for this purpose by using automatically detected anatomical landmarks. Changes in structural information of the extracted regions are investigated using spherical semivariogram descriptors and correlation-based structural similarity indices in the spatial and complex wavelet domains. The spatial distribution of grayscale values as well as of the magnitude and phase responses of multidirectional Gabor filters are used to represent the structure of mammographic density and of the directional components of breast tissue patterns, respectively. A total of 188 mammograms from the DDSM and mini-MIAS databases, consisting of 47 asymmetric cases and 47 normal cases, were analyzed. For the combined dataset of mammograms, areas under the receiver operating characteristic curves of 0.83, 0.77, and 0.87 were obtained, respectively, with linear discriminant analysis, the Bayesian classifier, and an artificial neural network with radial basis functions, using the features selected by stepwise logistic regression and leave-one-patient-out cross-validation. Two-view analysis provided accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively.

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Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors’ discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. the approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. the originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning convolutional neural network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. for each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories. Cell motility is fundamental for life, along the entire evolutionary tree, being involved in bacteria collective motion 1 , in the morphogenesis of pluricellular organisms 2 , in adult physiological process (such as tissue repair and immune cell trafficking) 3 and in some pathologies (such as cancer metastasis) 4-7. Nature evolved a variety of cell motility modes, single-cell or collective, mesenchymal or amoeboid, random or directed, etc. Yet, since the driving force of cell motility is always the active reorganization of the cellular cytoskeleton, it is reasonable to assume that some universal principles of cell motility behaviours have been conserved. We applied machine learning approach to explore this hypothesis exploiting Deep Learning (DL) architecture, by presenting a novel tool called Deep Tracking. DL is a recent machine learning framework 8 developed on the basis of the human brain machine. DL technique learns how to extract the "style" of an atlas of digital images (like the style from an atlas of an artist's paintings 9,10) in order to represent a given set of pictures in terms of most relevant quantitative descriptors (i.e., features) 8. We addressed the question of whether DL could be proficient in extracting the motility styles, i.e. the paintings drawn by cells while moving. Typically, cell motility experiments use time-lapse microscopy imaging (Fig. 1). Starting from the image stacks (Fig. 1A), video processing methods are used to track cell trajectories (see the description of the Cell Hunter tool 11,12 in Steps 2 and 3, Methods section) (Fig. 1B). The first step of our Deep Tracking method relies on the assembly of the individual cell tracks collected for e...

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The potential efficacy of GABA B receptor agonists in the treatment of pain, drug addiction, epilepsy, cognitive dysfunctions, and anxiety disorders is supported by extensive preclinical and clinical evidence. However, the numerous side effects produced by the GABA B receptor agonist baclofen considerably limit the therapeutic use of this compound. The identification of positive allosteric modulators (PAMs) of the GABA B receptor may constitute a novel approach in the pharmacological manipulation of the GABA B receptor, leading to fewer side effects. The present study reports the identification of two novel compounds, methyl 2-(1-adamantanecarboxamido)-4-ethyl-5-methylthiophene-3-carboxylate (COR627) and methyl 2-(cyclohexanecarboxamido)-4-ethyl-5-methylthiophene-3-carboxylate (COR628), which act as GABA B PAMs in 1) rat cortical membranes and 2) in vivo assay. Both compounds potentiated GABA-and baclofen-stimulated guanosine 5Ј-O-(3-[ 35 S]thio)-triphosphate binding to native GABA B receptors, while producing no effect when given alone. GABA concentration-response curves in the presence of fixed concentrations of COR627 and COR628 revealed an increase of potency of GABA rather than its maximal efficacy. In radioligand binding experiments [displacement of the GABA B receptor antago-, both COR627 and COR628 increased the affinity of high-and low-affinity binding sites for GABA, producing no effect when administered alone up to a concentration of 1 mM. In vivo experiments indicated that pretreatment with per se ineffective doses of COR627 and COR628 potentiated the sedative/hypnotic effect of baclofen. In conclusion, COR627 and COR628 may represent two additional tools for use in investigating the roles and functions of positive allosteric modulatory binding sites of the GABA B receptor.

Methamphetamine (METH) is a potent psychostimulant with neurotoxic properties. Heavy use increases the activation of neuronal nitric oxide synthase (nNOS), production of peroxynitrites, microglia stimulation, and induces hyperthermia and anorectic effects. Most METH recreational users also consume cannabis. Preclinical studies have shown that natural (Δ9-tetrahydrocannabinol, Δ9-THC) and synthetic cannabinoid CB1 and CB2 receptor agonists exert neuroprotective effects on different models of cerebral damage. Here, we investigated the neuroprotective effect of Δ9-THC on METH-induced neurotoxicity by examining its ability to reduce astrocyte activation and nNOS overexpression in selected brain areas. Rats exposed to a METH neurotoxic regimen (4×10 mg/kg, 2 hours apart) were pre- or post-treated with Δ9-THC (1 or 3 mg/kg) and sacrificed 3 days after the last METH administration. Semi-quantitative immunohistochemistry was performed using antibodies against nNOS and Glial Fibrillary Acidic Protein (GFAP). Results showed that, as compared to corresponding controls (i) METH-induced nNOS overexpression in the caudate-putamen (CPu) was significantly attenuated by pre- and post-treatment with both doses of Δ9-THC (−19% and −28% for 1 mg/kg pre- and post-treated animals; −25% and −21% for 3 mg/kg pre- and post-treated animals); (ii) METH-induced GFAP-immunoreactivity (IR) was significantly reduced in the CPu by post-treatment with 1 mg/kg Δ9-THC1 (−50%) and by pre-treatment with 3 mg/kg Δ9-THC (−53%); (iii) METH-induced GFAP-IR was significantly decreased in the prefrontal cortex (PFC) by pre- and post-treatment with both doses of Δ9-THC (−34% and −47% for 1 mg/kg pre- and post-treated animals; −37% and −29% for 3 mg/kg pre- and post-treated animals). The cannabinoid CB1 receptor antagonist SR141716A attenuated METH-induced nNOS overexpression in the CPu, but failed to counteract the Δ9-THC-mediated reduction of METH-induced GFAP-IR both in the PFC and CPu. Our results indicate that Δ9-THC reduces METH-induced brain damage via inhibition of nNOS expression and astrocyte activation through CB1-dependent and independent mechanisms, respectively.