Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process. Today, the search for biological mechanisms at molecular scale can leverage an unprecedented amount of information. With the recent development of high-throughput technologies, data collection has received an enormous impulse that has radically changed the perspective toward molecular biology. The main protagonist of this shift is omic data—namely, experimental profiles with large coverage over multiple biological domains. Several levels of knowledge have become associated with emerging omic technologies –3. The mA fundamental tool for the inspection, interpretation, and exploitation of omic data is machine (and deep) learning, which has arguably fueled several leaps forward in recent research and is expected to increasingly drive it in the near future , 5. MachIn parallel, the increase in data and knowledge has also favored the development of mathematical models for biomolecular systems. Contrary to data-driven approaches, hypothesis-driven analysis of large-scale omic domains typically remains prohibitive given the difficulty in pinpointing the underlying biological mechanisms. There are, however, some exceptions. Among the various approaches, constraint-based modeling (CBM) of metabolism is receiving a huge impulse thanks to its wide scope and flexibility, enabling mechanistic insights into the genotype–phenotype environment relationship via integration with omic data . With reThese two computational frameworks have mostly been used in isolation, having distinct research communities associated with them. However, we believe that their complementary characteristics and common mathematical bases make them particularly suitable to be combined. Several works implemented this idea in various ways and were partially surveyed before , 17. NevWe therefore aim at providing a comprehensive and systematic catalog of existing interactions between CBM and machine learning while distinguishing between the various methodological and applicative aspects concerned. In general, the central idea is that GSMMs can be used to generate an additional omic layer: the so-called fluxomic data. The multiomic learning considered here then integrates this newly generated omic with the ones already available. For instance, concatenation of two datasets following normalization is a viable option. However, we will describe why this may not be the best approach in practice. Although it is outside of the scope of this work, we also remark that other computational techniques have successfully been used to build on CBM approaches and study the multiomic nature of various organisms. These include Bayesian  and metaIn the following sections, we first concisely summarize the rationale and scope of machine learning and CBM of metabolism. Next, we review and classify previous studies in which these two frameworks were combined. Finally, we discuss similarities and differences among their mathematical bases, evaluate the advantages and limitations of computationally generating omic information, and outline aspects that have not been explored so far. To distinguish among the different types of mathematical models considered, throughout the text we will use the term “data driven” to refer to machine and deep learning models, whereas “knowledge driven” will refer to constraint-based models. If the meaning is intended to be more general, we will simply use the term “biological model.” Overall, we show that mining and integrating experimental and GSMM-generated multiomic data with machine learning techniques can unveil unknown mechanisms in a sample-specific manner, hence identifying relevant targets for biotechnology and biomedicine. Compared with approaches applying machine learning to omic data directly, we believe that a multiview approach merging experimentally and GSMM-generated omic data can include key mechanistic information in an otherwise biology-agnostic learning process.The key problem in an increasingly omic-based biology is the difficulty in extracting knowledge from large and complex datasets. This task can be conveniently tackled through machine learning algorithms, many of which can be adapted to specific settings and omic types. A number of recent developments in the application of machine learning to problems in molecular biology and biomedicine have been critically analyzed in previous surveys, along with their limitations and challenges –9, 25–27A fundamental distinction in machine learning is between “supervised” and “unsupervised” learning approaches. In supervised learning, the goal is to predict one or more targets associated with a given sample. For instance, pathogenicity resulting from mutations can be predicted starting from the sequence as a continuous risk score or a discrete risk class. Broadly speaking, supervised learning methods can be subdivided into two main categories: classifiers, which aim to predict sample classes , and regressors, whose task is to estimate numerical quantities (such as pathogenicity risk level). Several methods, such as support vector machines (SVMs) or artificial neural networks (ANNs), can be used to solve both classification and regression problems.k-means and hierarchical clustering families, but many other algorithms are available with several applications [In contrast, unsupervised learning allows the exploration of data collections by deconstructing variation or correlations among samples. Unsupervised learning approaches are largely classified as either association algorithms, which uncover latent rules or trends in data, or clustering algorithms, which partition samples based on their inherent and often hidden characteristics. Owing to the large volume of omic data, its condensation or simplification can prove to be useful in order to facilitate its interpretation. The most popular approaches for data dimensionality reduction are (1) principal components analysis (PCA), which reduces data into low-dimensional representations summarizing maximum variance among variables; (2) factor analysis, which decomposes data based on latent relationships describing the correlation between variables; and (3) matrix factorization, which breaks down data matrices into denoised constituents. For instance, nonnegative matrix factorization (NMF) has been used to infer the ecological interaction networks of different gut microbial communities, starting from high-dimensional metagenomic samples . Finallyications .A single type of data usually offers a partial view on biological complexity and limits our understanding of it. Data-integration methods can facilitate the combined analysis of multiple omic datasets, which may be heterogeneous, in order to more closely represent genotype–phenotype relationships , 30–34. Most successful large-scale data-integration approaches are metadimensional methods, which simultaneously span multiple data sources and can cope with variable inputs . They arConcatenation-based integration  fuses muTransformation-based integration  convertsModel-based integration  generateAll these strategies are commonly applied to heterogeneous datasets obtained from different experimental sources. However, there are also computational methods for generating data on the omic levels for which empirical means are inadequate. In particular, we concentrate on CBM of metabolism, as described in the following section.Metabolism is one of the major biological components that coparticipates with the genotype in composing the phenotype. Metabolites can generate signals that are received at other omic levels, whereas metabolic feedbacks can compensate or modify genetic and environmental signals through complex nonintuitive routes , 40. UnfDespite these experimental difficulties, metabolism remains the domain in molecular biology with the vastest knowledge, accumulated over the past century. Reconstructions of entire metabolic reaction networks have immediately followed after completing the first genomes in the late 1990s , 45. GSMThe mathematical framework of GSMMs is grounded on two physical assumptions. The first assumption is mass and charge conservation, which guarantees that the total mass of produced substrates equals the total mass of those consumed. Second, the system must be at steady state, meaning that internal metabolite concentrations do not change over time. The steady-state assumption differentiates CBM from the modeling based on ordinary differential equations. The latter allows the study of metabolic systems in dynamical conditions, but it is computationally expensive and requires detailed knowledge of initial metabolic conditions and kinetic reaction coefficients. For these reasons, it is only feasible for small systems and therefore cannot capture long-range phenomena or general metabolic reprogramming. Conversely, GSMMs are restricted to steady-state conditions, but they can span the entire cellular metabolism or even multicellular communities .Modeling fluxes can be crucial for gaining a better understanding of both metabolic activity and wider biological phenomena . At a reIn a typical constraint-based metabolic model, fluxes are the variables whose values have to be determined. Because there are usually a greater number of reactions than metabolites in a GSMM, the problem is underdetermined—meaning that multiple solutions can satisfy it. In order to determine biologically meaningful solutions, it is often necessary to further refine the model by applying additional biological, physical, or chemical constraints. For example, these may account for enzyme capacity and promiscuity, spatial occupation, metabolite sequestration, and multiple levels of gene, transcript, and protein regulation . ConstraThe development of condition-specific or context-specific GSMMs constitutes a further data-integration framework, as shown in cat values) is modeled by pseudostoichiometric coefficients in this matrix. Constraining protein abundance in this way has the effect of significantly reducing flux variability and improving the accuracy of the predictions. Methodology and applications for condition-specific GSMMs have been reviewed in detail elsewhere [Transcriptional profiles are the most popular omic to build context-specific GSMMs via an array of methods utilizing different contextualizing criteria , 59. Swilsewhere , 66.The integration of CBM of metabolism with machine learning is based on two key ideas. The first is that genetic and environmental perturbations propagate in a nonlinear fashion through metabolic networks and assume patterns on a reaction flux level that may be used to gain mechanistic insights into several research questions. The second is that GSMMs can act as both an analytical framework to represent biological systems and generators of information to be mined. In other words, flux solutions obtained by a GSMM can be treated like additional numerical data (another omic layer) and analyzed via learning algorithms. With the knowledge-driven metabolic model being set, the information extracted from it may depend on the task of interest and on the variables deemed relevant. As a result, it is possible to leverage the whole array of techniques defined on CBM  . Abbreviations: ANN, artificial neural network; CBM, constraint-based modeling; CHO, Chinese hamster ovary; dFBA, dynamic FBA; FBA, flux balance analysis; FCA, flux coupling analysis; FVA, flux variability analysis; GLM, generalized linear model; kNN, The baseline case is when biological targets are predicted based solely on metabolic fluxes obtained from general-purpose GSMMs. The output of FBA or related techniques can then be fed to algorithms for supervised analysis without data integration being involved see .For instance, Sridhara and colleagues investigated whether bacterial growth conditions could be inferred from intracellular flux configurations . MultinoE. coli for the production of xylitol [In the context of human metabolism, integration of constraint-based models and machine learning has been shown to correctly identify side effects of inhibitory drugs with higher accuracy than baseline methods . Drug-sp xylitol . Also in xylitol . Based oImportantly, CBM and machine learning can be formulated as a joint problem by embedding stoichiometric constraints in a learning task. As an example of supervised method, a discriminant analysis technique based on metabolic network constraints—called dynamic elementary mode regression discriminant analysis (dynEMR-DA)—was defined to identify pathway activation patterns that best discriminate between experimental conditions . The metPseudomonas species living in the endosphere and rhizosphere and simulated 12 media formulations in order to generate predictive features. A cross comparison of SVM, ANN, and NMF suggested that metabolic flux features may be more predictive than purely genomic features.Expanding the analysis of fluxes to an ecological scale, DiMucci and colleagues developed an approach to predict interactions among bacterial species starting from temporal simulations of cocultures through dynamic FBA (dFBA) . A randoThe exploration and statistical characterization of fluxomic profiles extrapolated from a GSMM can be of interest to shed light on the underlying physiology. In the absence of a well-defined biological target, unsupervised machine learning approaches can generally characterize correlation or variation across multiple samples. This allows clustering metabolic states or describing them in terms of sparser sets of variables.This was first realized by Segre and colleagues, who exploited a GSMM to explore epistasis in yeast metabolism . The tasE. coli [Furthermore, dimensionality reduction techniques can be employed to deconstruct the entire flux space associated with constraint-based models, as done for E. coli . In thisFinally, as in the supervised scenario, the analysis of multiple flux profiles can benefit from constraining a learning objective with stoichiometric knowledge. Alternative hybrids of PCA and stoichiometric flux analysis, termed as principal elementary mode analysis (PEMA) and principal metabolic flux mode analysis (PMFA), extract flux modes generated by metabolic models that contribute the most significantly toward variance while penalizing deviations from the steady state , 78. TheWhen experimental data is available, it can be aggregated with CBM-generated fluxomes to build multiomic sets of features and predict targets of interest. Thanks to the peculiar advantages of each individual data-integration approach, there are multiple ways to combine them depending on the questions addressed and on the available resources. One-stage integration by machine learning methods is a possibility, as described in the Machine learning for multiomic data.E. coli. FBA-like approaches coupled with artificial gene deletions can efficiently estimate essential reactions, although this often requires precise knowledge of nutrient availability in a given condition. The essentiality is usually evaluated merely based on the biomass accumulation rate, which may be an imprecise estimator in some cases. Plaimas and colleagues [Saccharomyces cerevisiae [This strategy was first investigated to predict metabolic reaction essentiality in lleagues  investigrevisiae . A randorevisiae , who insrevisiae .k-nearest neighbor (kNN) model. A similar procedure was used to explore the molecular biology of aging [P. putida starting from corresponding gene expression measurements [However, the metabolic capabilities of a cell population vary according to environmental and genetic conditions. For the sake of prediction, it is therefore important that metabolic information extracted by GSMMs reflects the differences between these conditions. This can be achieved through the creation of condition-specific metabolic models (see the "Condition-specific constraint-based models" section). This constraint-based integration was used for the first time by Li and colleagues to predict novel drug-reaction interactions in cancer . They emof aging . Using tof aging . Applyinurements .Effectively learning from empirical omic profiles and associated GSMM-based metabolic states necessitates fully exploiting all the varieties of multiomic analysis methods. In this case, a two-stage integration can be achieved through the creation of condition-specific GSMMs and the subsequent machine learning–based data integration. This idea was used to predict the metabolic capabilities of Chinese hamster ovary (CHO) cells for diverse growth conditions . In the More sophisticated data-integration pipelines have also been developed. A study used a method similar to sparse-group LASSO to identify phenotypic extreme currents (ECs) based on a combination of metabolic network features and gene expression data . Extremek-means clustering on transcriptomic data and fluxomic profiles in order to characterize the aging process in human [Like in the supervised case, unsupervised algorithms can be applied on heterogeneous sets of experimental and GSMM-generated omic profiles. For instance, environmental condition–specific metabolic modeling was combined with statistical modeling by Angione and colleagues to estimate the metabolic pathway activation cascade triggered by different environmental stimuli . The metin human . SubsequMoreover, metabolism and GSMMs can be used as a basis to understand underlying genomic variation. The Gene Expression Latent Space Encoder (GEESE) is a recently proposed approach  in which13C metabolic flux analysis estimations with associated genetic and environmental information from a cohort of studies. In a second stage, the predicted flux outputs were adjusted to satisfy stoichiometric constraints using quadratic optimization in order to account for the flux balance and boost their accuracy.Besides analyzing fluxomes generated via CBM, machine learning can be combined with CBM itself to acquire novel fluxomic information. For instance, a suite of different machine learning algorithms  was used to directly predict fluxomic configurations starting from genetic and environmental factors . The traE. coli GSMM and achieving a better characterization of each culture phase. This strategy also allows estimating metabolic fluxes in conditions that are not directly accessible to FBA, such as in unsteady-state FBA (uFBA), in which multiple flux profiles associated with dynamic conditions can be predicted [E. coli GSMM, improving its predictions on proteome allocation compared with the integration of turnover rates measured in vitro [Although in the previous paragraphs we have presented examples of machine learning applications on FBA outputs, data mining can even be used as a preliminary step to gain additional constraints for CBM. For example, Brunk and colleagues  applied redicted . The undin vitro . It is iAs detailed in the previous section, a number of data- and knowledge-driven workflows can be devised depending on the research goals and on the available resources. Ideally, multiomic settings appear the most promising for effectively grasping meaningful biological patterns, not only because of the well-known advantages of data integration but also considering the complementary characteristics of experimental and GSMM-based data. In the "Advantages and limitations of expanding the multiomic array in silico" section, we first articulate this point, highlighting the strengths and limitations of both omic types. In the "Emerging applications" section, we outline important scenarios to which we believe this multiomic machine learning framework could be applied and which are largely or entirely unexplored so far.At the same time, many novel integrative methods could be developed given the variety of algorithms existing within the machine/deep learning and CBM frameworks. In particular, in the last section, we discuss two related aspects that we believe could inspire the design of novel integrative methods: the importance of interpretability in biological data-driven models and the connections of both CBM and machine learning to mathematical programming .Because of their generation process, fluxomic profiles obtained through a GSMM provide an alternative and mechanistic perspective on the underlying biology compared with traditional omics. Both possess complementary benefits and drawbacks in scientific and operational terms, as outlined in this section, which make them particularly suitable for integration.As previously pointed out, important differences exist first of all in terms of genetic coverage and prior knowledge . ExperimIf experimentally generated omics are the first step toward a comprehensive understanding of living systems, the use of condition-specific GSMMs can therefore help contextualize and interpret them on a large scale. This fusion can also help identify gaps or inconsistencies in knowledge-driven models and maintain a comprehensive biological scope. Likewise, errors arising during experimental measurements might be mitigated through constraint-based integration, also controlling for biological soundness.Furthermore, cost and time factors may motivate the integration of the two data types, albeit to varying degrees between constructing a GSMM or its condition-specific variants and the calculation of flux data. The initial building and curation of general-purpose knowledge-driven models can in fact be time-consuming and require up to months or even years, despite aid from computational pipelines . HoweverE. coli and S. cerevisiae [There are nevertheless unsolved issues involving, to some extent, all omic data types and posing major limitations to studies based on them. In particular, we mention the quality of estimated biological phenomena and related biases. As previously mentioned, experimental measurement is subject to intrinsic noise and uncertainty that has to be corrected through appropriate normalization, and small numbers of technical replicates may undermine the statistical significance of the observed signals. Additionally, traditional omics are affected by sampling or technology-specific systematic errors —in partirevisiae –109. Howrevisiae , 110.All these points are very important to bear in mind for a correct and meaningful analysis and interpretation of the underlying biology. Overall, knowledge-driven fluxomic data relies on strong assumptions that require cautious evaluation to ensure biological soundness. At the same time, experimental data generation has to deal with issues that in some cases risk undermining any scientific conclusions. For these reasons, signals obtained from both experimental and GSMM-based omic studies should always be thoroughly evaluated through careful study design, appropriate statistical methods, and independent data (when available), without omitting negative results in downstream reports , 112.Despite the challenges highlighted in the previous section, omic data analysis will probably remain fundamental in numerous contexts and spread to new ones. Given the complementary advantages of GSMM-generated fluxomes and other omic data, their integration therefore has the potential for many novel applicative scenarios. As long as steady-state metabolism is deemed relevant to the task at hand, CBM can be employed to extend the omic or multiomic data array, and machine learning techniques can be used to identify hidden patterns. For instance, metabolic engineering could enormously benefit from integrative biological models, which are more efficient and cost-effective than empirical trials  in termsE. coli, which is arguably the organism with the most highly curated GSMMs. However, for several eukaryotic organisms—including human—constraint-based models are now available with increasing scope and precision, which constitute a promising platform for integrative biological models. Initial studies have demonstrated this in the context of human aging and disorders [Our survey also shows that many previous studies dealt with bacterial systems—in particular, isorders , 88, as isorders , 83. TheMoreover, there are emerging research areas that are likely to require strong analytical and automation skills in the near future. In particular, we refer to those applications that require GSMMs of growing size, such as for cell populations and microbial communities. At present, CBM can be used to describe multiple cell types, tissues , or evenFinally, as mentioned previously, CBM is extensible to biomolecular domains other than metabolism. Efforts have particularly focused on integrated constraint-based models of gene expression and metabolism , 100. ThIn all these contexts, we believe that effectively combining machine learning and CBM allows achieving a richer and more meaningful mechanistic comprehension of inherently multiomic system. New integrative approaches are also expected to ultimately contribute to the progress of applicative fields such as biotechnology, bioengineering, and biomedicine.One last aspect of pressing importance concerns the trustworthiness of integrative data- and knowledge-driven models and their capacity to produce novel insights. Interpretability is a desirable property for any mathematical model, and it constitutes a particularly delicate and widespread criticality in machine learning. Indeed, most automatically generated models are complex and provide no direct explanation for their predictions. At the same time, interpreting results, generating hypotheses, and testing them is imperative to maintain scientific rigor . As discNote that interpretability does not uniquely depend on the data-driven model form but also on the input preprocessing. A neural network trained on intuitively meaningful features learns a data representation that can be visualized and reasoned upon more easily than a linear model trained on heavily processed features.It can be therefore argued that CBM constitutes a vehicle for obtaining biological knowledge in the form of coherent information equipped with mechanistic relations at a single-reaction level. From this point of view, the generation of flux data from a condition-specific GSMM can be regarded as an elaborate but transparent feature engineering step, in which a fluxome is the result of combining available omics with expert knowledge and mathematical optimization. Therefore, we believe that CBM could be the key to building more interpretable machine learning models—for instance, by providing variables of clear meaning , 72.Perhaps even more importantly, paths for building more interpretable and mechanistically meaningful biological models exist also on a methodological level. In particular, it may be useful to consider that both machine learning methods and FBA-based approaches are grounded in mathematical programming , even though from distinct points of view.In machine learning, optimization tasks target any cost function that is assumed to minimize the true predictive error and allow the final model to generalize. Like in traditional mathematical optimization research, this discipline seeks to formulate investigative questions in terms of tractable and scalable problems. In addition, other qualities such as easy implementation and interpretation are important, disregarding high accuracy and robustness across wide classes of problems . Many ofThis underlying connection can potentially be exploited to develop novel hybrid methods and provide a second way toward more transparent biological models. For instance, the learning problem can be formulated by integrating constraints borrowed from a knowledge-driven model. This idea has been already implemented both in the context of unsupervised fluxomic analysis , 78 and The use of machine and deep learning in computational and systems biology will keep growing in parallel with the rapid advancement of high-throughput omic technologies. However, extensions of current methodologies are needed to adapt to the heterogeneous, multidimensional nature of omic data. Here, we have explored the joint application of machine learning and genome-scale metabolic modeling in the context of multiomic analysis, evaluating strengths and pitfalls in developing hybrid methods that draw from both fields. Machine learning is a valuable tool for deconstructing biological complexity for the purposes of condensing high-volume multiomic datasets and extracting relevant outputs from them. In turn, CBM makes it possible to analyze metabolic activities associated with distinct properties or states specific to each cell, tissue, or community. This is achieved through multiomic data integration and the estimation of an additional (flux)omic layer that is closer to cellular phenotype.CBM can provide ways to inject mechanistic knowledge within novel multiview methods, aiding in the achievement of data- and knowledge-driven analysis of biological systems. Given the increasing recognition of the importance of metabolism and mechanism-aware omic data analysis in a range of biomedical and biotechnological problems, we envisage that this multiomic machine learning approach could be useful to researchers across computational biology.


Skeletal systems provide support, movement, and protection to the human body. It can be affected by several life suffering bone disorders such as osteoporosis, osteoarthritis, and bone cancers. It is not an easy job to treat bone disorders because of avascular cartilage regions. Treatment with non-specific drug delivery must utilize high doses of systemic administration, which may result in toxicities in non-skeletal tissues and low therapeutic efficacy. Therefore, in order to overcome such limitations, developments in targeted delivery systems are urgently needed. Although the idea of a general targeted delivery system using bone targeting moieties like bisphosphonates, tetracycline, and calcium phosphates emerged a few decades ago, identification of carrier systems like viral and non-viral vectors is a recent approach. Viral vectors have high transfection efficiency but are limited by inducing immunogenicity and oncogenicity. Although non-viral vectors possess low transfection efficiency they are comparatively safe. A number of non-viral vectors including cationic lipids, cationic polymers, and cationic peptides have been developed and used for targeted delivery of DNA, RNA, and drugs to bone tissues or cells with successful consequences. Here we mainly discuss such various non-viral delivery systems with respect to their mechanisms and applications in the specific targeting of bone tissues or cells. Moreover, we discuss possible therapeutic agents that can be delivered against various bone related disorders. The human body contains soft tissues and organs which are supported and protected by the skeletal system. The skeletal system is composed of bone cells, bone matrix, and bone minerals. Abnormalities in any of these parts may result in bone disorders like bone cancer, osteoarthritis, and osteoporosis . AlthougTo date, based on biochemical, molecular, and radiologic studies, more than 450 different types of skeletal dysplasia have been characterized . BecauseCurrently, a number of targeted delivery systems against bone disorders are available or are under consideration. Drugs can be delivered either by targeting the whole skeletal system or specific cellular locations in it. The pathogenesis of some diseases, including metabolic skeletal dysplasia, have been treated by targeting the entire skeletal system. However, specific targeting involves the delivery to the bone resorption cells, i.e., osteoclasts or osteoblasts; or bone formation cells, which is the focus of modern research . In ordeLiving cells do not receive naked DNA, RNA, and certain drugs deliberately because of their large sizes and hydrophilicity problems. Moreover, enzymes like nucleases may degrade naked DNA or RNA . TherefoIn this review, we mainly discuss about the different kinds of non-viral delivery systems, their mechanistic features, and applications in the form of bone targeting moieties in the treatment of bone-related disorders at the molecular level.Non-viral delivery system simply refers to the use of non-viral vectors for drugs, genes or RNA delivery into cells. Non-viral vectors are safer, less toxic non-immunogenic alternatives to the viral vectors. They are easy to design, develop, and are inexpensive. Many of the non-viral vectors have been extensively used for the treatment of bones, ranging from bone repair and regeneration to various serious bone disorders like bone cancer, osteoarthritis, and osteoporosis .Non-viral vectors are broadly classified as organic and inorganic vectors. Organic non-viral vectors include cationic lipids, polymers, and peptide-based vectors while those that are inorganic in nature are calcium phosphates, calcium carbonate, and metal nanoparticles. Hence, non-viral delivery systems can be categorized on the basis of the types of vectors they use into organic and inorganic delivery systems. In order to understand them in detail, they are discussed both in general and as well as with some specificities to bone as below.Since the middle 1980s, synthetically-prepared, positively-charged cationic lipids were considered effective delivery vehicles for proteins and nucleic acids. Before this, they were used for purposes like membrane fusion mechanisms and protein studies ,11. FelgOver the last ten years, a large number of cationic lipids have been developed and applied for the purpose of drug delivery especially nucleic acids. Some of them like lipofectamine and DOTAP are commercially available and frequently used as the gold standards in different cellular experiments for sufficient transfection efficiency . They alCationic lipids are comparatively less immunogenic, less toxic and easily producible. However, their transfection efficiency needs improvement. Moreover, they form aggregates in the blood causing toxicity and inflammatory response. But these drawbacks are treatable by designing ionizable cationic lipids with PEGlyated lipids in formulation. PEGlyation prevent the cationic lipids from opsonization by reticulo-endothelial system, and thus give the opportunity for prolonging in vivo circulation .BMP-7 gene to mesenchymal stem cells (MSCs). The BMP-7 gene actually codes for bone morphogenetic protein-7(BMP-7) that plays an important role in transforming mesenchymal stem cells (MSCs) into bone. An enhanced growth rate with extracellular matrix deposition and promoted alkaline phosphatase activity (ALP) was observed in transfected MSCs, thus suggesting the formation of osteoblasts-like cells. They concluded that their designed delivery tool can be used not only as efficient delivery system for BMP-7 but also as proliferating and bone forming cells for bone regeneration [3 and modified it with fluorescent naphthalimide, oleic acid and octadecylamine. We found that all of them showed good transfection efficiency to osteoblastic cell line MC3T3-E1, MG63, HeLa, and HEK293 cells, but the one modified with naphthalimide showed even higher efficiency than lipofectamine 2000. Most importantly, it was successfully applied for in-situ monitoring of cellular uptake, DNA transportation, and release through non-invasive fluorescence imaging. Hence, we concluded that it can be used as a multifunctional non-viral delivery system for treating various bone disorders related to osteoblasts in future [Cationic lipids have been used as successful delivery systems to different types of cells, including bone cells, endothelial cells, airway epithelial cells, muscle cells, placental cells, hepatocytes, tumors, and others. Recently, Attia and her colleagues developed a delivery system in the form of niosomes by using cationic lipid called 2,3-di(tetradecyloxy)propan-1-amine. They used these niosomes to deliver the neration . Xuelei neration . Moreoveneration -aneN3 ann future .Polymeric systems containing positive charges are known as cationic polymers. Being positively charged, they can bind with negatively-charged nucleic acids, proteins, and cell membranes through electrostatic interaction. When they are mixed with DNA they form complexes called polyplexes, usually more stable than lipoplexes . They arL-lysine (PLL), and poly dimethylaminoethylmethacrylate (PDMAEMA)   are the Polyethylenimine (PEI) is the most important and highly used cationic polymer, synthesized in both linear and branched form with various molecular weights. It consists of reactive amino groups, and thus can be modified chemically for desirable physiochemical properties. The positively charged amino group of PEI can bind with negatively charged molecules including drugs and nucleic acids. However, they are somewhat cytotoxic and non-degradable, but they have the ability to form small and enzymatically stable polyplexes leading to high transfection efficiency .Generally, cationic polymers with higher molecular weights and positive charges can act as efficient delivery systems but unfortunately they are limited due to their excessive cytotoxic effects . MoreoveVarious cationic peptides are under investigation for use as safe and sound vehicles for targeted nucleic acids and negatively charged drug delivery. As they are rich in positively-charged lysine and arginine residues, therefore, they can bind with DNA efficiently and condense it into small compact particles. They are comparatively more advantageous because they have the ability to interact electrostatically with negatively charged nucleic acid or drugs, protect and target it to specific cell receptors, disrupt endosomal membrane, and deliver the cargo into nuclear localization . Short pRecently, Mansure and his colleagues synthesized novel polymer–drug–peptide conjugates containing carboxymethylcellulose (CMC) conjugated by amide bonds with an anticancer drug called doxorubicin. In order to facilitate the targeting and internalization by cancer cells they dually functionalized the conjugate with L-arginine (R) and integrin-target receptor tripeptide (RGD). They tested and observed the bioconjugates against breast, bone, and brain cancer cell lines and reported them as ‘‘smart’’ delivery system to normal cells while highly toxic to cancer cells . Sun and2+ that form stable ionic complexes with negatively charged nucleic acids. These complexes are transferred to the cells through ion channel-mediated endocytosis. Although they possess low transfection efficiency, they are highly compatible and easily biodegradable. They are comparatively less toxic than commercial transfection reagents like Lipofectamine 2000, therefore they are considered as safe materials for transfection [In 1973, Graham and Van der Eb were the first to use calcium phosphates as non-viral vectors . Now thesfection . MoreoveCalcium phosphate nanoparticles can be used in the treatment of various bone disorders by carrying drugs like bisphosphonates and other related drugs to the target sites . RecentlIn the new era, nanotechnology has gained a lot of attention for developing new methods regarding diagnosis and treatment of diseases . VariousMetal nanoparticles especially gold nanoparticles (Au NPs) are of prime importance for use in various biomedical applications as they can be designed in different sizes and different shapes. They are non-toxic and highly biocompatible. Moreover, they are electrostatically charged and hence can be functionalized by other biomolecules like nucleic acids and drugs . They caNon-viral delivery systems, unlike viruses, do not have the ability of inserting genes directly into the cells’ nuclei. In order to reach and enter the cells, they always face some extracellular and intracellular obstacles both in vitro and in vivo. To date, complete mechanisms of non-viral delivery towards bone cells is still skeptical. However, according to the available literature, generally it can be divided into four stages including binding to cell surface, endocytosis and intracellular processing, endolysosomal escape, and nuclear entry and expression. As different types of non-viral vectors may follow slightly different mechanisms, therefore, for the sake of easiness, we will refer to the most extensively used non-viral vectors, i.e., cationic lipids and cationic polymers in terms of DNA delivery to bone cells and try to understand the mechanisms. Cationic lipids and cationic polymers are positively charged; therefore, they have the ability to bind with negatively charged DNA and cell surfaces through electrostatic interactions. Upon mixing with DNA, cationic lipids form complexes called lipoplexes while cationic polymers form polyplexes. Usually, complexes are assembled with a minimal surplus positive charge to allow them to bind with the negatively charged cell surfaces. High charge ratios lead to the formation of small complexes while larger aggregates are formed when the net charge is near to zero .Generally, cell surfaces are negatively charged because of the presence of proteoglycans with different glycosaminoglycan chains including dermatan, heparin, and chondroitin sulfates. Especially heparan sulfate proteoglycans (HSPGs) are assumed as the most prominent interacting regions for cationic delivery vectors . MoreoveIn general, polyplexes face degradative enzymes and negatively charged serum proteins in vivo. If cationic polymers fail to condense and compact, the DNA properly degradative enzymes will digest it . SimilarEndocytosis is the process through which biomolecules and other cargo enter into the cells. Generally, it can be divided into two categories, i.e., phagocytosis and pinocytosis. Phagocytosis refers to engulfing large solid particles while pinocytosis for taking up solutes, fluid, and smaller particles. Unlike phagocytosis, pinocytosis generally occurs in all types of cells, and therefore its related pathways are of prime importance in delivery. The most prominent pinocytosis pathways include clathrin-mediated endocytosis, caveolin-mediated endocytosis, clathrin- and caveolin-independent endocytosis and macropinocytosis  Figure . All of Clathrin-dependent endocytosis pathway exploits receptors including transferrin receptors and clathrin-coated pits. Polymeric vectors first bind to these receptors, are taken up, assemble in vesicles, and are moved to the early endosome for facing acidified environment, early endosome to late endosome, and finally degraded in lysosomes . WhereasEndolysosomal escape is considered to be the one of the most important factors that imitate the delivery efficiency of non-viral vectors. The internalized molecules after internalization through endocytosis are situated in endosomes. They should be released into cytoplasm otherwise they will be destroyed by the acidic environment of endosomes. Moreover, endosomes may fuse with lysosomes and hence degraded . TherefoIn order to get successful endosomal escape, different strategies have been adopted for different types of complexes. Some lipoplexes consist of DOPE (dioleoyl phosphatidylethanolamine) which is basically a pH-sensitive fusiogenic lipid that form stable lipid bilayers at neutral pH but transits to inverted hexagonal structures at acidic pH. Because of the acidic environment inside endosomes, DOPE assumes an inverted hexagonal structure that tightly binds and disturbs the stability of endosomal membrane, and thus releases the contents into the cytosol . SimilarAfter successful endolysosomal escape, the lipo and polyplexes should move through cytosol towards nuclear membrane. Although some naked DNAs can pass through nuclear membrane by diffusion, it is difficult for the larger DNA molecules to do so. Moreover, nucleases which are situated in cytoplasm may degrade the DNA and thus reduce the transfection efficiency . TherefoGenerally, two types of pathways, i.e., cell-dividing dependent and cell-dividing-independent pathways, are considered to be responsible for nuclear entry of DNAs alone or in the form of non-viral vectors . Large mIn order to avoid the risks of toxicity to non-targeted tissues and low therapeutic efficiency bones are targeted with a specific delivery system. Targeting moiety works as an important part in targeted delivery system as they help in carrying the cargo towards its target. They are categorized as bone specific and bone cell specific moieties. The bone specific moieties target the whole skeleton  while boIn 1969, Fleisch and his colleagues  for the Due to safety measures the optimal administration of bisphosphonate is under investigation. Its prolonged usage may have negative effects on bone mass. Because of long term usage of BPs, some of the patients suffering from osteoporosis have been detected with disorders of osteonecrosis of the jaw and atypical femoral fracture . In one Apart from systemic administration, calcium phosphate is considered to be an appropriate biomaterial for BP’s delivery to bone. Calcium phosphates are the main parts of bones; therefore, they are comparatively more compatible and degradable. Combination of calcium phosphate with bisphosphonates like alendronate may act as a local implant drug for promoting bone formation in an osteoporotic model . FaucheuNanoparticles usage for BP targeting is another approach for drug delivery to bone. BPs targeting moieties can be attached to nanoparticles to carry it to the bone sites. Nanoparticles have the advantage of carrying huge amounts of a drug, protecting it from degradation, and extending its circulation time to target the particles to bone . New tecStreptomyces genus of bacteria or developed synthetically using isolated compounds from it. Traditionally they are exploited against many bacterial infections but they can also be utilized for bone targeting because of their binding affinity with hydroxyapatite of bone [Tetracycline constitutes a family of broad-spectrum antibiotics that were first discovered in the 1940s. They are isolated either directly from the  of bone . Wang an of bone . Recentl of bone . MoreoveTo date, many oligopeptide conjugated drugs have been utilized against several diseases like osteoporosis, musculoskeletal diseases, infection diseases, and cancers. In contrast to polypeptides, oligopeptides contain a small number of amino acids (maximum 10–50). Nowadays, oligopeptides are considered among the potent classes of molecules for nanotechnology applications. Oligopeptides have been reported as materials having strong binding affinity to hydroxyapetite which is the main component of bone . Park anOsteoblasts, also called as bone forming cells are most commonly found on outer surfaces of bones. They are always kept in balance to osteoclasts through homeostasis to maintain total bone mass. Any imbalance in bone formation by osteoblasts or resorption by osteoclasts can lead to severe bone disorders . UsuallyPlekho1 gene by successfully delivered siRNA to osteoblasts [6). They used these liposomes as delivery systems for siRNAs to target casein kinase-2 interacting proteins-1 which are encoded by Plekho1 genes. They found successfully targeted siRNAs delivery against the target bone inhibitory Plekho1 genes by observing increased bone mass both in normal and osteoporotic mouse models [Gene therapy is a new strategy that can be used to deliver small exogenous nucleic acid molecules like miRNAs and siRNAs to combat various diseases . Vectorseoblasts . Therefoe models .N-(2-hydroxypropyl)methacrylamide (HPMA) copolymers with d-aspartic acid octapeptide (d-Asp8) having siRNA against sema4D. Successful uptake and intracellular trafficking was found within osteoclasts that prevented the suppression of osteoblast activity [MITF  expression. It gives a clue that they can be utilized as most promising therapeutic targets to treat the disorders related to osteoclasts [Osteoclasts, also called bone resorption cells because they digest bone tissues by secreting acids and proteolytic enzymes during healing and growth. They are required in proper balance with osteoblasts to maintain adequate bone mass, otherwise it may lead to serious disorders like osteoporosis, bone tumors, and Paget’s disease . Therefoactivity . Recentleoclasts .Bone marrow mesenchymal stromal cells (BMSCs) are non-hematopoietic in origin having the ability to differentiate into different cell types that is, adipocytes chondrocytes and osteoblasts . TherefoSome therapeutic agents have been discovered that target specific molecules expressed in bone cells . By doinCathepsin K (Cat K), is basically a cystein protease member from cathepsin lysosomal protease family which is highly expressed in osteoclasts . It has The vacuolar ATPase (V-ATPase) is actually a multi-subunit enzyme that exploits ATP hydrolysis to pump the protons across a membrane. It is predominantly expressed in osteoclasts and plays a key role in regulating their extracellular acidity and bone resorption . Therefovβ3 integrins are expressed highly in osteoclasts where they act as receptors for various extracellular matrix proteins like osteopontin, vitronectin, and bone sialoprotein [vβ3 integrins receptors. They reported that this protein inhibited bone resorption by disturbing osteoclast activity in a mouse model with osteoporosis, hence it may act as a model therapeutic agent for bone disorders [Integrins constitute a family of cell surface receptors consisting of an α and β subunit, each having a short cytoplasmic and large extracellular domain. Actually they are heterodimeric glycoproteins responsible for mediating cell–cell and cell–matrix interactions . It has oprotein . They haoprotein ,111,112.isorders . Src is basically a gene that encodes an enzyme nonreceptor tyrosine kinase that phosphorylates other proteins on specific tyrosine residues. It consists of several domains including SH2 domain, SH3 domain, catalytic kinase domain, N-terminal domain, and C-terminal domain [l domain . They exl domain . Therefol domain .Prostaglandins (PG) E2 receptors are the most versatile prostanoids that are produced mainly in osteoblasts . They haRANKL is actually a transmembrane protein that belongs to tumor necrosis factor (TNF) cytokine family. It acts as a ligand for receptor RANK to induce the function, formation, and survival of osteoclasts . Therefo3−) and protons (H+) through reversible hydration of carbonic anhydride [Carbonic anhydrases constitute a superfamily of metalloenzymes that generate bicarbonate (HCOnhydride . They arnhydride .Usually, bone treatment with systemic administrations requires high doses and cause toxicities in non-skeletal tissues and organs. Therefore, various targeting delivery systems have been developed that target specifically bone or bone cells to minimize off-target effects and improve efficacy. But development of an absolute viral or non-viral carrier system that can deliver gene, siRNA, and drugs to specific sites is still in progress or under investigation. However, in an experiment siRNA was delivered to a mouse model against semaphorin 4D which resulted in increased number of active osteoblast cells and bone mass .Although non-viral vectors have low transfection efficiency they are safer and easily producible as compared to non-viral vectors. Non-viral vectors like liposome and cationic polymers are surprisingly too efficient in carrying cargo towards different cell organelles. They have been proven as a successful delivery system for bone disorder treatments in many of the in vitro and some of the in vivo experiments. However, they can be designed and engineered in different ways to encapsulate various types of therapeutic inhibitors and carry them to specific target sites to cure severe bone disorders. Moreover, gene therapy can play a tremendous role in targeting various bone locations more specifically and efficiently than any other technique in the future.


Real-time ultrasonography (RTU) has become an essential diagnostic value when assessing female swine reproduction in either individual or groups of animals. Diagnostic application of RTU is applied throughout most stages of production, including gilt development, breeding, gestation and farrowing. Along with its most common use in on-farm assessment of pregnancy status, RTU is also used to troubleshoot disruptions in reproductive performance such as delayed puberty, prolonged wean-to-estrus interval, absence of post-weaning estrus, decreased conception and farrowing rates, vulval discharge, peripartum and puerperal disorders. This review aims to provide an overview on principles and clinical uses of RTU in female reproduction on commercial swine farms.Within the past 30 years, through ongoing technology and portability developments, real-time (b-mode) ultrasonography (RTU) has increasingly become a valuable diagnostic tool in assessing the female reproductive tract in swine. Initially applied in swine production to visually determine pregnancy status, RTU use has expanded to include assessment of the peri-pubertal and mature non-pregnant females as well. Transabdominal and transrectal modalities to visualizing the reproductive tract in swine have been reported with the transabdominal approach more common due to the fact of its ease of accessibility, animal/personnel safety, and reduced time to perform. Adjustable frequency transducers are preferred as they allow optimization of image quality at various depths. If a single transducer frequency must be selected, a 5 MHz probe provides the best versatility for visualizing the reproductive tract in swine. Other basic requirements for ultrasound equipment which will be used on commercial swine farms include being light weight and easy to handle, readily cleanable and disinfectable, long battery-life, and good durability. When using RTU for pregnancy determination, diagnosis is based upon a combination of the animal’s breeding records, the presence of embryonic fluid, and, depending upon gestational stage, fetal structures. If RTU is used as a diagnostic tool in assessing reproductive problems in an individual or a group of animals, sonographic evaluation of both the uterus and ovaries is performed. Tissues are delineated and assessed based upon their echogenicity, echotexture, and size. Uses of RTU in clinical practice may include assessment of delayed puberty, prolonged wean-to-estrus interval, absence of post-weaning estrus, herd disruptions in conception and farrowing rates, vulval discharge, peripartum and puerperal disorders. This review aims to provide an overview on principles and clinical uses of RTU with respect to application to address female reproductive performance issues in commercial swine operations. Over the past 30 years, through ongoing technology and portability developments, real-time ultrasonography (RTU) has increasingly become a valuable diagnostic tool in assessing the female reproductive tract in swine. As comprehensively reviewed twelve years ago , RTU hasHigher quality RTU imagery has allowed transabdominal assessment of the ovary, with most physiological and pathological ovarian structures able to be delineated ,8. This Collectively, RTU has become an indispensable tool for troubleshooting disruptions to production parameters such as conception and farrowing rates, problems with delayed or failed puberty attainment, and with delayed or failed post-weaning estrus issues ,13. ThisBasic requirements for ultrasound equipment for use at swine facilities include being light weight and easy to handle , easy to clean and externally disinfectable, capable of high resolution, long battery-life, and durability . To optiIt is preferred that transducer probes be either a sector or convex  configuration, as linear probes may be difficult to hold appropriately when performing transabdominal scanning. If transrectal scanning is preferred by the operator, only linear probes are appropriate. With newer generation machines, transducer probes usually provide a frequency spectrum , with capabilities to adjust frequencies to tissue characteristics and depth in order to generate optimum images. If a single transducer frequency must be selected, a 5 MHz is preferred as lower frequencies typically provide for lower resolution insufficient for visualizing subtle structures , while higher frequencies may lack the ability to penetrate sufficiently to visualize the entire reproductive tract.Both transrectal and transabdominal scanning of the gilt/sow is possible ,16. TranReproductive organs typically assessed using RTU are the uterus and the ovaries ,7. The cUsing conventional RTU, the uterus is characterized based on intrauterine fluid echogenicity (if any), echotexture, and size . EchogenPregnancy determination has been diagnosed based on breeding records and the presence of embryonic fluid of approximately 1.0 mm in diameter as early as day 9 gestation . By day Real-time ultrasonography can be used to diagnose pathological conditions such as embryonic death and abortion based upon the lack of ultrasonographical characteristic patterns specific to the stage of pregnancy ,25. HydrSuccessful diagnosis of pregnancy status in early gestation is dependent upon the machine’s quality and the skills and experience of the person performing the examination . GeneralApplications of RTU peripartum have been described as well. Doppler RTU to assess fetal heart rate (FHR) antepartum is a good predictor of fetal viability , with se2 on day 2 to 3.53 ± 0.86 cm2 on day 7. Sizes were also categorized as “normal” or “enlarged” based on being below or above mean values, respectively. Not surprisingly, sows with “enlarged” uteri were older parity sows, had more total and stillborn piglets, and had been exposed to more obstetrical interventions. Uterine fluid accumulation, an indicator of metritis, was also positively associated with the number of total and stillborn piglets, and animal exposure to obstetrical interventions. These results support that both “enlarged” uteri and fluid accumulation may be used as ultrasonographical parameters to ascertain puerperal disorders and acceptable uterine involution. Real-time ultrasonography has gained recent attention in the characterization of the puerperal  uterus and uterine involution. In a study conducted in Finland, the uterus was investigated based on size  and intrauterine fluid between days 2 and 7 post-partum . The sizAdditional work has shown an association between duration of farrowing and puerperal complications . Sows wiThe normal, non-gravid, post-puerperal  uterus is typically devoid of intrauterine fluid and, thus, should be absent of anechogenicity. Observation of any fluid echogenicity, beyond that observed within the first 12 h post-breeding, is indicative of a diseased uterus . Ex. Ex13]. Timely attainment of puberty is important to gilt flow and breeding group management. It is critical that gilts be at a mature level by first breeding in order to facilitate higher production parameters throughout their breeding life . The aforementioned approach for disrupted conception rates is performed in order to determine if breeding errors are a root cause of low farrowing rates. Repeated assessment of pregnancy status using RTU in one or more breeding groups starting at the farm’s typical week of pregnancy testing , and then repeated weekly over the entire period of pregnancy may be required in order to delineate the extent  of the problem. These findings would drive precision timing of downstream diagnostics such as serology or management related issues . Lastly, farrowing rate problems  can also be the result from errors in a farm’s pregnancy testing program. If this is suspected, it is strongly advised to initiate the diagnostic workup starting with the estrus detection screening process along with assessing the capabilities and accuracy of any on-site personnel performing ultrasonography pregnancy confirmation .Vulval discharge is considered a normal component when it occurs ante- and post-farrowing and while an animal is in estrus. Additionally, it is typical for sows to show a vulval discharge shortly after artificial insemination, with no detrimental effects on fertility. In any other situation, vulval discharge is an indicator of an underlying disease process in the reproductive tract . DischarOther factors can contribute to genital inflammation. For instance, post-ovulatory inseminations performed at times under progesterone (P4) predominance may facilitate inflammation, as P4 is immunosuppressive in the genital tract. A similar immunosuppressive effect can be caused in animals exposed to feed contamination containing the mycotoxin deoxynivalenol. A puerperal endometritis that is left under- or non-treated can become chronic as the animal enters into the breeding pool, allowing the re-emergence of vulval discharge upon re-entry of pathogens at breeding. Commonly, affected animals start discharging any time from day 10 post-breeding, but earlier discharges are possible. RTU imagery can aid in defining the discharge’s origin refer to . when asSince its initial application in the 1980s, RTU has rapidly gained favor in its diagnostic value related to swine reproduction. With RTU, virtually all tissues of the female genital tract can be accurately assessed in all stages of production . Though RTU is most commonly used on-farm for assessing pregnancy status, it has become an essential diagnostic tool when troubleshooting disruptions to herd reproductive performance. Particular issues where RTU has repeatedly provided diagnostic value include delayed puberty, prolonged wean-to-estrus interval, absence of post-weaning estrus, herd disruptions in conception and farrowing rates, vulval discharge, peripartum and puerperal disorders. If combined with doppler, RTU can provide added value not just in fetal viability assessment, but also in assessing perfusion characteristics of the uterus and the ovaries that may aid in achieving an appropriate diagnosis.


Increasing evidence suggests that infection and persistent low-grade inflammation in peripheral tissues are important pathogenic factors in major depression. Major depression is frequently comorbid with systemic inflammatory diseases/conditions such as rheumatoid arthritis, allergies of different types, multiple sclerosis, cardiovascular disease, inflammatory bowel disease, chronic liver disease, diabetes, and cancer, in which pro-inflammatory cytokines are overexpressed. A number of animal studies demonstrate that systemic inflammation induced by peripheral administration of lipopolysaccharide increases the expression of pro-inflammatory cytokines in both the periphery and brain and causes abnormal behavior similar to major depression. Systemic inflammation can cause an increase in CNS levels of pro-inflammatory cytokines associated with glial activation, namely, neuroinflammation, through several postulated pathways. Such neuroinflammation can in turn induce depressive moods and behavioral changes by affecting brain functions relevant to major depression, especially neurotransmitter metabolism. Although various clinical studies imply a causal relationship between periodontitis, which is one of the most common chronic inflammatory disorders in adults, and major depression, the notion that periodontitis is a risk factor for major depression is still unproven. Additional population-based cohort studies or prospective clinical studies on the relationship between periodontitis and major depression are needed to substantiate the causal link of periodontitis to major depression. If such a link is established, periodontitis may be a modifiable risk factor for major depression by simple preventive oral treatment. It has been well documented that inflammation is closely related to major depression, even though it remains uncertain whether inflammation is a cause or a result of the mental illness. The association between inflammation and major depression has been supported by the well-known clinical observation that pro-inflammatory cytokines such as interferon (IFN)-α, which is used to treat hepatitis C, renal cancer, and multiple myeloma, and interleukin (IL)-2, which is used to treat renal cancer and angiosarcoma, frequently induce depressive symptoms as side effects . More thMajor depression seems to be a heterogeneous disease with different genetic and environmental factors contributing to the disease development . SystemiMajor depression is frequently comorbid with systemic inflammatory diseases/conditions such as rheumatoid arthritis, allergies of different types, multiple sclerosis, cardiovascular disease, inflammatory bowel disease, chronic liver disease, diabetes, and cancer, in which pro-inflammatory cytokines are overexpressed . For exaEmerging evidence indicates that systemic inflammation has a profound impact on behavior. It is well established that systemic inflammation causes the synthesis of pro-inflammatory cytokines, which communicate with the brain to induce the spectrum of behavioral changes known as sickness behavior . SicknesMycobacterium bovis, into mice elicits a prolonged increase in serum levels of the pro-inflammatory cytokine IFN-α for 26 days, and the initial episode of sickness behavior, which develops in a few days after BCG inoculation, is followed by a long-lasting decrease in sucrose preference and an increase of immobility in the FST and the TST.The animal studies mentioned above employed single systemic administration of LPS, which can cause acute and strong but ephemeral activation of peripheral immune system. However, inflammation-associated depression in human is rather linked to chronic inflammation . RecentlHow the periphery communicates with the brain, modulates neurotransmission, and consequently changes behavior is not entirely clear. Nevertheless, several routes of communication between the systemic immune system and the brain have been postulated . A systeOnce the pro-inflammatory cytokine signals reach the brain from the periphery, they could affect several pathophysiological domains, functions, and neurotransmission relevant to major depression, such as anterior cingulate cortex , basal ganglia (motivation and motor activity), neuroendocrine function , synaptic plasticity (impaired neurogenesis), and neurotransmitter metabolism . MicroglMicroglial activation has been linked to depressive behavior. A number of animal studies have demonstrated that mice whose microglia are activated by systemic LPS injection show sickness and depressive-like behavior . We confThe gut-microbiota to brain route has received increasing attention for its ability to modulate brain functions. A recent animal study has shown that blockade of peripheral IL-6 receptor by anti-mouse IL-6 receptor antibody promotes rapid and sustained antidepressant action thorough normalizing the altered composition of gut microbiota in susceptible mice after social defeat stress, suggesting that gut–microbiota–brain communication has a role in robust antidepressant actions of anti-IL-6 receptor therapy .per se is a low-grade chronic inflammation, but it causes or hastens the other chronic systemic inflammatory diseases, including atherosclerosis, cardiovascular diseases, diabetes, and rheumatoid arthritis , the major pathogen of periodontitis, and impaired delayed memory and calculation among patients older than 60 years, suggesting that periodontitis is associated with cognitive impairment in the elderly  than in the non-periodontitis group (N = 50832) with an adjusted hazard ratio of 1.73 when adjusted for sex, age, and comorbidity . Because these bacteria are capable of invading intact pocket epithelium, periodontal bacteria and their products can gain access to the circulation  neuroinflammation  induced by systemic inflammation associated with periodontitis; (2) neuroinflammation evoked by direct invasion of periodontal pathogen and their inflammatory products into the brain. Within the periodontal pocket, bacteria exist in a stratified dental biofilm, which is made of microorganisms and their components (e.g., endotoxin LPS and virulence factors; culation . This reculation , these cSystemic injection of LPS has been demonstrated to break down the BBB through abnormal activation of matrix metalloproteinase . TherefoFigure 1. Increasing evidence suggests that infection and persistent low-grade inflammation in peripheral tissues are important pathogenic factors that explain a possible pathophysiological mechanism of major depression. Systemic inflammation could induce depressive moods and behavioral changes by provoking neuroinflammation. Although various clinical studies imply a causal relationship between periodontitis and major depression, the notion that periodontitis is a risk factor for major depression is still a supposition. Additional population-based cohort studies or prospective clinical studies on the relationship between periodontitis and major depression are warranted to substantiate the causal link of periodontitis to major depression. If such a link is established, periodontitis may be a modifiable risk factor for major depression by simple preventive oral hygiene dealings.Presumed mechanisms of reciprocal connection between systemic inflammation, periodontitis, and major depression are summarized in SH wrote the manuscript. All authors discussed, edited, read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Precision oncologic medicine is an emerging approach for cancer treatment that has recently taken giant steps in solid clinical practice. Recent advances in molecular diagnostics that can analyze the individual tumor’s variability in genes have provided greater understanding and additional strategies to treat cancers. Although tumors can be tested by several molecular methods, the use of next-generation sequencing (NGS) has greatly facilitated our understanding of pediatric cancer and identified additional therapeutic opportunities. Pediatric tumors have a different genetic make-up, with a fewer number of actionable targets than adult tumors. Nevertheless, precision oncology in the pediatric population has greatly improved the survival of patients with leukemia and solid tumors. This review discusses the current status of pediatric precision oncology and the different clinical scenarios in which it can be effectively applied. The management of patients with cancer can sometimes be challenging. These challenges are frequently attributed to tumor recurrence/metastasis during or following treatment or initial therapy resistance. In such situations, the use of novel targeted therapy has greatly revolutionized cancer treatment and is largely based on blocking actionable gene mutations or over-expressed signaling transduction pathways. Recent advances in molecular diagnostics have fueled the personalized approach to such cancer treatment. The fact that cancer is a genomic disease is the basis or foundation of precision oncology and targeted therapy ,2. AlthoPrecision cancer treatment is largely based on matching the patient’s tumor mutations with the appropriate targeted therapy. A variety of genetic or molecular assays are currently available for testing on tumors including cytogenetic fluorescent in situ hybridization (FISH) tests, polymerase chain reaction (PCR) single gene testing, and single nucleotide polymorphism (SNP)-based microarrays . In receTP53, CDK12, NOTCH1, ARID1 and include amplification of other genes such as MYCN, MCL1, MDM2 and others [Most cancers harbor somatic gene mutations, variants, or fusions that can be detected by NGS in clinically approved platforms. In a recent genetic analysis of 439 adult patients with cancer, 90% had at least one actionable or targetable mutation in their tumor . Pediatrd others ,15.Several methods can be adopted for molecular precision analysis. Whole-exome, whole-genome, and RNA sequencing can be performed separately or complemented with methylation and expression microarray analysis. DNA methylation-based molecular diagnostics are currently being incorporated in the classification of pediatric central nervous system tumors . An incrAlthough comprehensive molecular testing has been advocated for all pediatric tumors , it is aPediatric cancers may occasionally recur or metastasize during or after completion of standard therapy. Relapsed tumors harbor more genetic alterations than primary tumors . Tumor rMultidisciplinary management of cancers in an institutional molecular tumor board has led to better implementation or translation of NGS results into clinical actions for patients with refractory and poor prognosis cancers . The majThe World Health Organization (WHO) diagnostic criteria for pediatric tumors, including most hematologic and brain tumors, are specified with integrated input from morphologic and molecular findings, reflecting the importance of molecular testing in clinical practice. Currently, many diagnostic genetic aberrations are tested by more routine molecular tests such as FISH and PCR. However, genomic sequencing is a sensitive technology for detecting diagnostic molecular aberrancies, can shed light on tumor heterogeneity and highlight differences (or similarities) between various pediatric and adult neoplasia. NGS can offer more accurate and comprehensive information on characteristic translocation partners, and identify targetable mutations that can lead to better treatments and cures. It has the potential to change or refine the morphologic diagnosis, offer prognostic information and direct care towards novel therapeutic agents . After gIn a rising number of cases, the treatment challenge stems from difficulties in classifying certain cancers that manifest with unusual or rare, non-characteristic morphology. Most often such cancers are labeled as undifferentiated malignancy or undifferentiated sarcoma after extensive histopathologic and immunophenotypic analysis. Undifferentiated sarcoma is commonly diagnosed when a soft-tissue tumor has no identifiable line of differentiation. These tumors represent a heterogeneous group of mesenchymal tumors with variable morphology that include spindle cell, pleomorphic, round cell, and epithelioid tumor variants. Undifferentiated sarcomas in children have variable outcomes and it is not possible to tailor the treatment to a specific disease type. In such patients, the routine use of NGS could increase diagnostic accuracy by determining the cell type of origin, identify molecularly-targeted therapies to try, and provide support for the use of other interventions, such as surgical resection and/or radiation therapy. Furthermore, it may help to better understand the patient’s prognosis for cure . In the de novo mutations, parental mosaicism, type of aberration , and the dysregulation of cancer pathways  [Recent studies indicate that a considerable percentage of childhood cancers are associated with CPS, some of which are inherited. The incidence of childhood cancers with genetic predisposition is estimated to be at least 10%, and the number of newly identified CPS has been steadily growing due to increased awareness and more practical use of NGS-based methods in the classification of pediatric cancer . WES of FA/BRCA) ,35. TumoFA/BRCA) ,37. ClinEarly trials of precision oncology have improved our understanding of chemoresistance during the course of therapy, and have highlighted cancer heterogeneity and genomic complexity, including the role(s) of epigenetic modifiers in determining disease outcome. NGS has revealed many cancers carry mutations in genes encoding for transcriptional control. Epigenetic dysfunction and transcriptional dysregulation is frequent in many pediatric cancers, particularly leukemias ,39. InhiAnother potential and novel area of precision medicine is the use of nanotechnology to overcome treatment resistance of cancer cells . DeliverThe use of antibodies against tumor antigens and stimulation of the patient’s own immune system to attack cancer cells are new forms of immunotherapy that have recently been used in patient treatments. A number of immunotherapy agents are in use or under investigation in pediatric cancer. Gemtuzumab ozogamicin has become part of the standard of care for pediatric patients with AML. Dinutuximab is an anti-GD2 antibody that is used in the treatment of neuroblastoma. Other immunotherapies under investigation include monoclonal antibodies , antibody-drug conjugates , bispecific T-cell engagers , immune modulators , and anti-cancer vaccines. Cellular therapies using modified T-cells have also been successfully employed. Based on their extraordinary ability to distinguish foreign peptides from self-antigens through their receptors, T cells are engineered with chimeric antigen receptors (CAR) designed for sustained proliferation and specific targeting of tumor cells. CAR-T cell therapy has been remarkably successful in treating patients with advanced refractory B cell malignancies through chimeric receptors targeting CD19 . The sucSeveral advances have been made in the last decade in the field of pediatric precision oncology heralding a new chapter in the fight against pediatric cancer. Although few pediatric institutions have started the initial journey towards precision oncology , several


Distributive (vasodilatory) shock is common in patients admitted to the intensive care unit (ICU). Treating distributive shock presents a challenge, especially if a patient is tachyphylactic to commonly used vasopressors. This case report illustrates the use of a newly approved vasopressor in a patient with vasodilatory shock resulting from a motor vehicle injury. A 56-year-old man was brought to our emergency department (ED) hemodynamically unstable requiring aggressive resuscitation. The results of his evaluation were consistent with multisystem trauma for which he required intubation on arrival, and he received multiple units of blood and blood product via transfusion. The patient’s condition declined despite receiving multiple vasopressors in the ICU. A few days after admission, the patient developed ischemic bowel requiring surgical resection. While his chance of survival was believed to be dismal, the use of angiotensin II (ATII) as a last resort proved to be helpful. Distributive (vasodilatory) shock is frequently encountered in intensive care unit (ICU) patients, and treating distributive shock can be difficult and puzzling, especially if tachyphylaxis to the commonly used vasopressors becomes an issue. Vasopressors are used in patients who remain hypotensive after adequate fluid resuscitation. There has been a paradigm shift in avoiding the use of dopamine in favor of norepinephrine augmented by vasopressin (an antidiuretic hormone) to minimize the dose of norepinephrine. In cases where a refractory shock is associated with poor cardiac output, dobutamine has been found to be useful. Responsiveness to commonly used drugs can decrease over time as a result of tachyphylaxis. This case illustrates the use of a newly approved vasopressor in vasodilatory shock.A 56-year-old male pedestrian was brought to our emergency department (ED) after being struck by a car at high speed resulting in an unstable pelvic fracture, massive retroperitoneal bleeding, pulmonary contusion, and traumatic rupture of the diaphragm which was not evident at the time of admission. He was admitted to our Level II trauma center ICU after initial resuscitation in the ED. Renal failure progressed rapidly due to rhabdomyolysis. His early post-injury course was complicated by refractory shock requiring high doses of vasopressors, hypoxic hypercapnia respiratory failure on ventilation support, ischemic colitis, septic shock, cardiogenic shock that required cardioversion on three different occasions, acute renal failure requiring continuous renal replacement therapy, and shocked liver. The patient required multiple visits to the operating room with initial resection of the terminal ileum and right colon, repair of the diaphragmatic hernia, chest tube insertion followed by washout, ileostomy, feeding gastro-jejunostomy tube, and biologic mesh closure.Despite receiving high-dose norepinephrine, vasopressin, and epinephrine, the patient’s condition continued to deteriorate with a mean arterial pressure <60 mmHg. Angiotensin II (ATII) was given as an infusion starting with 5 ng/kg/minute increments. The max maintenance dose of 15 ng/kg/minute was achieved in three hours reaching our target blood pressure (BP) for the first 24 hours, and was tapered to 10 ng/kg/minute during the next 12 hours and to 5 ng/kg/minutes during the last 12 hours. ATII was completed with no side effects. The patient’s condition dramatically improved, and he was weaned off of vasopressors within three days of the ATII use. He survived his injuries and was referred to acute rehabilitation.Shock  is a state of tissue and cellular hypoxia due to decreased delivery, increased consumption or inadequate utilization of oxygen. Distributive shock  is the result of severe peripheral vasodilation. Vasodilatory shock is multifactorial and characterized by peripheral vasodilation and preserved or increased cardiac output, high mortality, and limited therapeutic options -2. VasocATII has a vasoconstrictive effect on the venous and arterial smooth muscle , and exeSepsis also leads to an imbalance between vascular mediators. Vasodilating factors  are released, whereas the vasoconstrictor response to ATII and catecholamines might decrease -6. RefraWhen septic shock and respiratory failure coexist, failure of the lungs to convert ATI to ATII could be a factor leading to refractory septic shock, and patients might benefit from exogenous ATII . As partThe use of ATII had recently shown to be a possible adjunct to other frequently used vasopressors. Synthetic human ATII does not appear to have a contraindication for its use thus far; however, arterial and venous thromboembolic events have been reported. While further trials are warranted to confirm the benefits of ATII, intensivists should consider the use of ATII in hypotensive patients in distributive shock.


Geckos feature a large range of eye sizes, but what drives this phenotypic diversity is currently unknown. Earlier studies point towards diel activity patterns (DAPs) and locomotory mode, but phylogenetic comparative studies in support of the proposed adaptive mode of eye evolution are lacking. Here, we test the hypothesis of DAPs as the driver of eye size evolution with a dataset on 99 species of gecko. Results from phylogenetic generalized least-square analysis (PGLS) and multivariate model-fitting reveal smaller eyes in diurnal geckos consistent with different phenotypic optima. However, Bayesian analyses of selective regime shifts demonstrate that only two of nine transitions from nocturnal to diurnal activity are coupled with decreases in eye size, and two other regime shifts are not associated with DAP transitions. This non-uniform evolutionary response suggests that eye size is not the only functionally relevant variable. Evolutionary adaptations may therefore include different combinations of several traits (e.g. photoreceptors), all with the same functional outcome. Our results further demonstrate that DAP only partially explains eye size diversity in geckos. As open habitats favour the evolution of large eyes while obstructed habitats favour small eyes, the degree of habitat clutter emerges as another potential axis of eye diversification. IntegGeckos feature a large range of eye sizes, both in absolute and relative terms ,7. Eye sThe origin of geckos is nocturnal, but there are many independent evolutionary transitions to diurnal and cathemeral/crepuscular activity patterns , offerinWe tested the hypothesis that DAPs are the main drivers of eye size evolution in geckos, and predicted that evolutionary transitions to diurnality are coupled with reduction of eye size, reflecting the evolutionary trade-off between visual performance and the metabolic and biomechanical cost. Surprisingly, our phylogenetic perspective reveals that the morphological response to independent evolutionary DAP transitions is not uniform.2.n = 37) and new data collections on museum specimens , and compiled information on DAP . Comparative analyses were performed on species averages in a phylogenetic context [We used data on anteroposterior eye diameter (ED) and snout-vent-length (SVL) for 99 gecko species, representing a mix of the literature data , an agn3.a; [Sphaerodactylus/Gonatodes  and Phelsuma/Lygodactylus . The Naultinus clade represents a younger transition . All other transitions are represented by single lineages.Stochastic character mapping of DAPs confirms a nocturnal origin of geckos with several independent transitions to diurnal and cathemeral/crepuscular activity . Our daDiurnal geckos have smaller eyes for given SVL than other geckos. PGLS results support that the eyes of geckos with different DAPs scale with negative allometry , but hac). All diurnal bins contain smaller residual eye size than expected, except for Pristurus carteri (D3). All four cathemeral/crepuscular lineages feature relatively large eyes, whereas the tertiary nocturnal lineages have small residual eye size, similar to their diurnal relatives, D4. Ancestrally nocturnal geckos (ANC) have large residual eye sizes, but there is overlap with diurnal bins. The overlap suggests that DAP is not the only driver of eye size evolution, and/or gecko eyes follow different evolutionary trajectories in adapting to different light levels.We explored this overlap by an approach dictated by independent evolutionary transitions between DAPs. First, we calculated the residuals of all species from a simple PGLS model (eye size ∼ SVL) and then binned residuals according to DAP transitions c. All diNaultinus) and D4 (Sphaerodactylus/Gonatodes), but none of the other transitions to diurnality are characterized by a change in selection for smaller eyes. However, an additional shift towards smaller eyes falls on the branch leading to Lepidoblepharis, the taxon with the relatively smallest eyes in the data, nested within the Sphaerodactylus/Gonatodes clade. The fourth strongly supported regime shift is most surprising, situated within a radiation of nocturnal geckos, at the branch leading to the Palearctic naked-toe geckos. These geckos enter a regime that favours the evolution of even larger eyes than those of their immediate relatives . Our results, therefore, reveal a non-uniform evolutionary response in iterated DAP transitions and also suggest that DAP alone is insufficient to fully explain eye size evolution in geckos.The pattern of different evolutionary trajectories is reinforced by the analysis of the adaptive landscape. Four strongly supported selective regime shifts emerged, and while two of these shifts are congruent with our prediction, the other two are surprising. Geckos enter regimes selecting smaller eyes at D2  eye size in our data are ground-dwelling, and the mechanistic link between locomotor mode and visual performance is not well documented [What other factors determine the adaptive landscape of gecko eye size evolution? Locomotory mode may influence eye size, with ground-dwelling species having larger eyes than climbing species ,7. Howevcumented , especiacumented .Lepidoblepharis is active in leaf-litter on the ground [Lepidoblepharis, where leaf-litter constrains visual range. Precise habitat clutter data for geckos are unavailable, hence a formal test of this hypothesis is still wanted, but it is possible that the ground-dwelling/climbing axis of eye diversity [We propose that habitat clutter is an additional important environmental factor that impacts eye evolution. Palearctic naked-toe geckos occupy (semi-)arid habitats with little vegetation cover, providing an unobstructed, long-range view, whereas e ground , with obe ground , hence liversity  is relativersity . We propiversity ,25, and,iversity . Howeveriversity , and fut