MSO-GP: 3-D segmentation of large and complex conjoined tree structures.

Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.

Hit-to-lead optimization of 2-aminoquinazolines as anti-microbial agents against Leishmania donovani.

Visceral leishmaniasis is a potentially fatal disease caused by infection by the intracellular protist pathogens Leishmania donovani or Leishmania infantum. Present therapies are ineffective because of high costs, variable efficacy against different species, the requirement for hospitalization, toxicity and drug resistance. Detailed analysis of previously published hit molecules suggested a crucial role of 'guanidine' linkage for their efficacy against L. donovani. Here we report the design of 2-aminoquinazoline heterocycle as a basic pharmacophore-bearing guanidine linkage. The introduction of various groups and functionality at different positions of the quinazoline scaffold results in enhanced antiparasitic potency with modest host cell cytotoxicity using a physiologically relevant THP-1 transformed macrophage infection model. In terms of the ADME profile, the C7 position of quinazoline was identified as a guiding tool for designing better molecules. The good ADME profile of the compounds suggests that they merit further consideration as lead compounds for treating visceral leishmaniasis.

Intra-tumor ROS amplification by melatonin interferes in the apoptosis-autophagy-inflammation-EMT collusion in the breast tumor microenvironment.

Epidemiological as well as experimental studies have established that the pineal hormone melatonin has inhibitory effects on different types of cancers. Several mechanisms have been proposed for the anticancer activities of melatonin, but the fundamental molecular pathways still require clarity. We developed a mouse model of breast cancer using Ehrlich's ascites carcinoma (injected in the 4th mammary fat pad of female Swiss albino mice) and investigated the possibility of targeting the autophagy-inflammation-EMT colloquy to restrict breast tumor progression using melatonin as intervention. Contrary to its conventional antioxidant role, melatonin was shown to augment intracellular ROS and initiate ROS-dependent apoptosis in our system, by modulating the p53/JNK & NF-κB/pJNK expressions/interactions. Melatonin-induced ROS promoted SIRT1 activity. Interplay between SIRT1 and NF-κB/p65 is known to play a pivotal role in regulating the crosstalk between autophagy and inflammation. Persistent inflammation in the tumor microenvironment and subsequent activation of the IL-6/STAT3/NF-κB feedback loop promoted EMT and suppression of autophagy through activation of PI3K/Akt/mTOR signaling pathway. Melatonin disrupted NF-κB/SIRT1 interactions blocking IL-6/STAT3/NF-κB pathway. This led to reversal of pro-inflammatory bias in the breast tumor microenvironment and augmented autophagic responses. The interactions between p62/Twist1, NF-κB/Beclin1 and NF-κB/Slug were altered by melatonin to strike a balance between autophagy, inflammation and EMT, leading to tumor regression. This study provides critical insights into how melatonin could be utilized in treating breast cancer via inhibition of the PI3K/Akt/mTOR signaling and differential modulation of SIRT1 and NF-κB proteins, leading to the establishment of apoptotic and autophagic fates in breast cancer cells.

Correction: Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

[This corrects the article DOI: 10.1371/journal.pone.0286862.].

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output. Our two stage synergistic segmentation method, Deep-Fuzz, works especially well for overlapping objects, and achieves state-of-the-art performance in four public cell nuclei segmentation datasets. We also show through visual examples how our final output is better aligned with pathological insights, and thus more clinically interpretable.

Mitigating hERG Liability of Toll-Like Receptor 9 and 7 Antagonists through Structure-Based Design.

hERG is considered to be a primary anti-target in the drug development process, as the K+ channel encoded by hERG plays an important role in cardiac re-polarization. It is desirable to address the hERG safety liability during early-stage development to avoid the expenses of validating leads that will eventually fail at a later stage. We have previously reported the development of highly potent quinazoline-based TLR7 and TLR9 antagonists for possible application against autoimmune disease. Initial experimental hERG assessment showed that most of the lead TLR7 and TLR9 antagonists suffer from hERG liability rendering them ineffective for further development. The present study herein describes a coordinated strategy to integrate the understanding from structure-based protein-ligand interaction to develop non- hERG binders with IC50 >30 µM with retention of TLR7/9 antagonism through a single point change in the scaffold. This structure-guided strategy can serve as a prototype for abolishing hERG liability during lead optimization.

Arsenic-induced differential inflammatory responses in mouse thymus involves NF-κB/STAT-3 disruption, Treg bias and autophagy activation.

AIM: Arsenic contamination in drinking water is a world-wide public health concern. Sustained arsenic ingestion leads to immune alterations and subsequent development of inflammatory and autoimmune diseases; however, the underlying cellular and molecular intricacies of immunotoxicity remains uncharacterized. We aim to understand how exposure to arsenic at different concentrations affects the immune system differentially and whether arsenic-induced differential inflammation dictates altered T-regulatory cell bias and emphasize the role of autophagy in the pathway. MAIN METHODS: Swiss albino mice were exposed to environmentally relevant concentrations of arsenic in drinking water for 28 days. Examination of thymic cyto-architecture was done to evaluate thymic damage. ELISA was performed for key cytokines. Flow cytometry, western blotting, and immunostaining were performed for cell surface and intracellular proteins. Co-immunoprecipitation and transfection with siRNA were performed to examine the direct physical interactions between proteins. KEY FINDINGS: Our study distinctly demonstrates that arsenic-induced oxidative stress instigates NF-κB activation, which not only provokes pro-inflammatory responses, but also exhibits immune-suppressive activity depending on the dose of arsenic. Co-immunoprecipitation of NF-κBp65 and pSTAT-3 reveals that arsenic alters their physical interaction, thereby suppressing IL-6/STAT-3/IL-17A feedback loop. Flow cytometry and silencing studies demonstrate that NF-κB-driven Treg cell differentiation induces immune-suppression through FoxP3 up-regulation at the highest dose of arsenic and such immune-suppression is actively supported by NF-κB-driven autophagy activation. SIGNIFICANCE: Collectively, our findings reveal that exposure to arsenic differentially impacts the immune system and understanding the molecular cascade might provide direction for prevention/treatment of arsenic-induced inflammatory and autoimmune diseases.

Development, Optimization, and In Vivo Validation of New Imidazopyridine Chemotypes as Dual TLR7/TLR9 Antagonists through Activity-Directed Sequential Incorporation of Relevant Structural Subunits.

Undesirable activation of endosomal toll-like receptors TLR7 and TLR9 present in specific immune cells in response to host-derived ligands is implicated in several autoimmune diseases and other contexts of autoreactive inflammation, making them important therapeutic targets. We report a drug development strategy identifying a new chemotype for incorporating relevant structural subunits into the basic imidazopyridine core deemed necessary for potent TLR7 and TLR9 dual antagonism. We established minimal pharmacophoric features in the core followed by hit-to-lead optimization, guided by in vitro and in vivo biological assays and ADME. A ligand-receptor binding hypothesis was proposed, and selectivity studies against TLR8 were performed. Oral absorption and efficacy of lead candidate 42 were established through favorable in vitro pharmacokinetics and in vivo pharmacodynamic studies, with IC50 values of 0.04 and 0.47 µM against TLR9 and TLR7, respectively. The study establishes imidazopyridine as a viable chemotype to therapeutically target TLR9 and TLR7 in relevant clinical contexts.

Hit-to-lead optimization of novel phenyl imidazole carboxamides that are active against Leishmania donovani.

Visceral leishmaniasis is a potentially fatal disease caused by the parasitic protists, Leishmania donovani and L. infantum. Current treatments remain unsuitable due to cost, the need for hospitalization, variable efficacy against different species, toxicity and emerging resistance. Herein, we report the SAR exploration of the novel hit 4-Fluoro-N-(5-(4-methoxyphenyl)-1-methyl-1H-imidazole-2-yl)benzamide [1] previously identified from a high throughput screen against Trypanosoma brucei, Trypanosoma cruzi and Leishmania donovani. An extensive and informative set of analogues were synthesized incorporating key modifications around the scaffold resulting in improved potency, whilst the majority of compounds maintained low cytotoxicity against human THP-1 macrophages that are target cells for these pathogens. New lead compounds identified within this study also maintained desirable physicochemical properties, improved metabolic stability in vitro and displayed no significant mitotoxicity against HepG2 cell lines. This compound class warrants continued investigation towards development as a novel treatment for Visceral Leishmaniasis.

3dSpAn: An interactive software for 3D segmentation and analysis of dendritic spines.

Three-dimensional segmentation and analysis of dendritic spine morphology involve two major challenges: 1) how to segment individual spines from the dendrites and 2) how to quantitatively assess the morphology of individual spines. To address these two issues, we developed software called 3dSpAn (3-dimensional Spine Analysis), based on implementing a previously published method, 3D multi-scale opening algorithm in shared intensity space. 3dSpAn consists of four modules: a) Preprocessing and Region of Interest (ROI) selection, b) Intensity thresholding and seed selection, c) Multi-scale segmentation, and d) Quantitative morphological feature extraction. In this article, we present the results of segmentation and morphological analysis for different observation methods and conditions, including in vitro and ex vivo imaging with confocal microscopy, and in vivo observations using high-resolution two-photon microscopy. In particular, we focus on software usage, the influence of adjustable parameters on the obtained results, user reproducibility, accuracy analysis, and also include a qualitative comparison with a commercial benchmark. 3dSpAn software is freely available for non-commercial use at www.3dSpAn.org .

Systematic Optimization of Potent and Orally Bioavailable Purine Scaffold as a Dual Inhibitor of Toll-Like Receptors 7 and 9.

Several toll-like receptors (TLRs) reside inside endosomes of specific immune cells-among them, aberrant activation of TLR7 and TLR9 is implicated in myriad contexts of autoimmune diseases, making them promising therapeutic targets. However, small-molecule TLR7 and TLR9 antagonists are not yet available for clinical use. We illustrate here the importance of C2, C6, and N9 substitutions in the purine scaffold for antagonism to TLR7 and TLR9 through structure-activity relationship studies using cellular reporter assays and functional studies on primary human immune cells. Further in vitro and in vivo pharmacokinetic studies identified an orally bioavailable lead compound 29, with IC50 values of 0.08 and 2.66 µM against TLR9 and TLR7, respectively. Isothermal titration calorimetry excluded direct TLR ligand-antagonist interactions. In vivo antagonism efficacy against mouse TLR9 and therapeutic efficacy in a preclinical murine model of psoriasis highlighted the potential of compound 29 as a therapeutic candidate in relevant autoimmune contexts.

Structural Evolution and Translational Potential for Agonists and Antagonists of Endosomal Toll-like Receptors.

Toll-like receptors (TLRs) are members of a large family of evolutionarily conserved pattern recognition receptors (PRRs), which serve as key components of the innate immune system by playing a pivotal role in sensing "nonself" ligands. Endosomal TLRs (TLR3, TLR7, TLR8, and TLR9) can recognize pathogen-derived nucleic acid and initiate an innate immune response because they react against both self- and non-self-origin nucleic acid molecules. Accordingly, both receptor agonists and antagonists are potentially useful in disparate clinical contexts and thus are globally sought after. Recent research has revealed that agonists and antagonists share an overlapping binding region. This Perspective highlights rational medicinal chemistry approaches to elucidate the structural attributes of small molecules capable of agonism or antagonism or of elegantly switching between the two. The structural evolution of different chemotypes can provide the framework for the future development of endosomal TLR agonists and antagonists.

PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies.

BACKGROUND: Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. RESULTS: In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. CONCLUSIONS: In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

Protein-templated gold nanoclusters as specific bio-imaging probes for the detection of Hg(ii) ions in in vivo and in vitro systems: discriminating between MDA-MB-231 and MCF10A cells.

Gold nanoclusters (AuNCs) synthesized within a protein (Human Serum Albumin, HSA) template exhibited intense red luminescence accompanied by a quantum yield >10% and remarkable photo and cluster-core stability for a prolonged period (more than a year). These photoluminescent nanoclusters (NCs) were resistant to chemical and thermal perturbations but break down selectively and highly sensitively in the presence of mercury, Hg(ii), ions. The AuNCs were efficient in quantifying Hg(ii) ions in solution as well as bound to the hormone insulin. By exploiting the auto-fluorescence of these AuNCs, we demonstrated that our AuNCs were able to sense Hg(ii) ions at single-molecule sensitivity using Fluorescence Correlation Spectroscopy (FCS), highlighting a detection limit in the sub-nanomolar regime. The translational diffusion time of the AuNCs decreased significantly upon the interaction with Hg(ii) ions and resulted in the formation of smaller sized clusters. A cell viability study reveals the non-toxic nature of these nano-probes, which thus can be used for cell imaging. Interestingly, a cell line-based study reveals that the fluorescence intensity of AuNCs could be detected in cancerous MDA-MB-231 cells but not in non-cancerous breast-derived MCF10A cells. Further, time lapse fixed cell imaging by confocal microscopy revealed that the fluorescence of AuNCs could be quenched by Hg(ii) ions inside the MDA-MB-231 cells. Thus the objective of our study is to appraise the sensitive in vivo as well as in vitro detection of Hg(ii) ions using AuNCs as a probe.

The Mixture of Autoregressive Hidden Markov Models of Morphology for Dentritic Spines During Activation Process.

The dendritic spines play a crucial role in learning and memory processes, epileptogenesis, drug addiction, and postinjury recovery. The shape of the dendritic spine is a morphological key to understand learning and memory process. The classification of the dendritic spines is based on their shapes but the major questions are how the shapes changes in time, how the synaptic strength changes, and is there a correlation between shapes and synaptic strength? Because the changes of the classes by dendritic spines during activation are time dependent, the forward-directed autoregressive hidden Markov model (ARHMM) can be used to model these changes. It is also more appropriate to use an ARHMM directed backward in time. Thus, the mixture of forward-directed ARHMM and backward-directed ARHMM (MARHMM) is used to model time-dependent data related to the dendritic spines. In this article, we discuss (1) how to choose the initial probability vector and transition and dependence matrices in ARHMM and MARHMM for modeling the dendritic spines changes and (2) how to estimate these matrices. Many descriptors to classify dendritic spines in two-dimensional or/and three-dimensional (3D) are available. Our results from sensitivity analysis show that the classification that comes from 3D descriptors is closer to the truth, and estimated transition and dependence probability matrices are connected with the molecular mechanism of the dendritic spines activation.

Prophylactic Ketamine Treatment Promotes Resilience to Chronic Stress and Accelerates Recovery: Correlation with Changes in Synaptic Plasticity in the CA3 Subregion of the Hippocampus.

Ketamine is an N-methyl-d-aspartate receptor antagonist that has gained wide attention as a potent antidepressant. It has also been recently reported to have prophylactic effects in animal models of depression and anxiety. Alterations of neuroplasticity in different brain regions; such as the hippocampus; prefrontal cortex; and amygdala; are a hallmark of stress-related disorders; and such changes may endure beyond the treatment of symptoms. The present study investigated whether a prophylactic injection of ketamine has effects on structural plasticity in the brain in mice that are subjected to chronic unpredictable stress followed by an 8-day recovery period. Ketamine administration (3 mg/kg body weight) 1 h before stress exposure increased the number of resilient animals immediately after the cessation of stress exposure and positively influenced the recovery of susceptible animals to hedonic deficits. At the end of the recovery period; ketamine-treated animals exhibited significant differences in dendritic spine density and dendritic spine morphology in brain regions associated with depression compared with saline-treated animals. These results confirm previous findings of the prophylactic effects of ketamine and provide further evidence of an association between the antidepressant-like effect of ketamine and alterations of structural plasticity in the brain.

Author Correction: Quantitative 3-D morphometric analysis of individual dendritic spines.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Enhanced Luminescent Properties of Photo-Stable Copper Nanoclusters through Formation of "Protein-Corona"-Like Assemblies.

In this study, interactions of synthesized copper nanoclusters (CuNCs) with a model transport protein, human serum albumin (HSA), have been systematically investigated by using various spectroscopic approaches. The interactions give rise to the formation of "protein-corona" like assemblies and the luminescence properties (both steady-state and time-resolved) are enhanced due to gradual adsorption of the protein on the surface of the NCs. The associated thermodynamics and binding parameters have been estimated resorting to luminescent experimental techniques as well as isothermal titration calorimetry (ITC) studies, indicating that every NC is surrounded by (4±1) protein molecules. The adsorption of HSA on the surface of the NCs has been characterized by dynamic light scattering (DLS) and time-resolved anisotropy measurements. Finally, fluorescence correlation spectroscopy (FCS) data substantiate the emergence of new "protein-corona" like assemblies resulting in slower translational diffusion motions and concomitant rise of the hydrodynamic diameters.

Quantitative 3-D morphometric analysis of individual dendritic spines.

The observation and analysis of dendritic spines morphological changes poses a major challenge in neuroscience studies. The alterations of their density and/or morphology are indicators of the cellular processes involved in neural plasticity underlying learning and memory, and are symptomatic in neuropsychiatric disorders. Despite ongoing intense investigations in imaging approaches, the relationship between changes in spine morphology and synaptic function is still unknown. The existing quantitative analyses are difficult to perform and require extensive user intervention. Here, we propose a new method for (1) the three-dimensional (3-D) segmentation of dendritic spines using a multi-scale opening approach and (2) define 3-D morphological attributes of individual spines for the effective assessment of their structural plasticity. The method was validated using confocal light microscopy images of dendritic spines from dissociated hippocampal cultures and brain slices (1) to evaluate accuracy relative to manually labeled ground-truth annotations and relative to the state-of-the-art Imaris tool, (2) to analyze reproducibility of user-independence of the segmentation method, and (3) to quantitatively analyze morphological changes in individual spines before and after chemically induced long-term potentiation. The method was monitored and used to precisely describe the morphology of individual spines in real-time using consecutive images of the same dendritic fragment.