Virtual pterygoid implant planning in maxillary atrophic patients: prosthetic-driven planning and evaluation.

PURPOSE: The study aims to use cone beam computed tomography (CBCT) to (1) define the virtual valid length of pterygoid implants in maxillary atrophic patients from the prosthetic prioritized driven position and (2) measure the implant length engaged in the pterygoid process according to the HU difference of the pterygoid maxillary junction. MATERIALS AND METHODS: Virtual pterygoid implants were planned with CBCT of maxillary atrophic patients in the software. The entry and angulation of the implant were planned according to the prosthetic prioritized driven position in the 3D reconstruction image. The planned implant length and the valid length defined as the implant between the pterygoid maxillary junction and pterygoid fossa were recorded. The relationship between the implant and sinus cavity was also evaluated. RESULTS: A total of 120 CBCT samples were enrolled and virtually planned. The mean age of the patients was 56.2 ± 13.2 years. One hundred and sixteen samples could successfully place virtual implants according to the criterion. The mean implant length and mean implant length beyond the pterygoid maxillary junction were 16.3 ± 4.2 mm (range, 11.5-18 mm) and 7.1 ± 3.3 mm (range, 1.5-11.4 mm), respectively. Ninety percent of virtually planned implants had a close relationship with the sinus cavity, and implants exhibited longer lengths when they had no relation with the sinus. CONCLUSION: From a prosthetic prioritized driven position with fixed entry and angulation, pterygoid implants achieve adequate bone anchorage length beyond the pterygoid maxillary junction. Due to the individual anatomy and the volume of the maxillary sinus, the implants presented a different positional relationship with the maxillary sinus.

Nucleolus-Targeted Photodynamic Anticancer Therapy Using Renal-Clearable Carbon Dots.

Photodynamic therapy (PDT), which utilizes light excited photosensitizers (PSs) to generate reactive oxygen species (ROS) and consequently ablate cancer cells or diseased tissue, has attracted a great deal of attention in the last decades due to its unique advantages. In order to further enhance PDT effect, PSs are functionalized to target specific sub-cellular organelles, but most PSs cannot target nucleolus, which is demonstrated as a more efficient and ideal site for cancer treatment. Here, an effective carbon dots (C-dots) photosensitizer with intrinsic nucleolus-targeting capability, for the first time, is synthesized, characterized, and employed for in vitro and in vivo image-guided photodynamic anticancer therapy with enhanced treatment performance at a low dose of PS and light irradiation. The C-dots possess high ROS generation efficiency and fluorescence quantum yield, excellent in vitro and in vivo biocompatibility, and rapid renal clearance, endowing it with a great potential for future translational research.

A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set.

Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements.

Growth differentiation factor 15 promotes blood vessel growth by stimulating cell cycle progression in repair of critical-sized calvarial defect.

Repair of large bone defects remains a challenge for surgeons, tissue engineering represents a promising approach. However, the use of this technique is limited by delayed vascularization in central regions of the scaffold. Growth differentiation factor 15(GDF15) has recently been reported to be a potential angiogenic cytokine and has an ability to promote the proliferation of human umbilical vein endothelial cells(HUVECs). Whether it can be applied for promoting vascularized bone regeneration is still unknown. In this study, we demonstrated that GDF15 augmented the expression of cyclins D1 and E, induced Rb phosphorylation and E2F-1 nuclear translocation, as well as increased HUVECs proliferation. Furthermore, we also observed that GDF15 promoted the formation of functional vessels at an artificially-induced angiogenic site, and remarkably improved the healing in the repair of critical-sized calvarial defects. Our results confirm the essential role of GDF15 in angiogenesis and suggest its potential beneficial use in regenerative medicine.

Chronic hepatitis B infection and risk of antituberculosis drug-induced liver injury: Systematic review and meta-analysis.

BACKGROUND: Antituberculosis drug-induced liver injury (ATDILI) is a major safety concern for the treatment of tuberculosis (TB). The impact of chronic hepatitis B infection (CHBI) on the risk of ATDILI is still controversial. In this study, we aimed to assess systematically the influence of CHBI on the susceptibility to ATDILI. METHODS: We reviewed all English-language medical literature with the medical subject search headings hepatitis B and antitubercular agents from the major medical databases. Thereafter, a systematic review and meta-analysis was performed on those publications that qualified. RESULTS: A total of 938 citations were retrieved on the initial major database search, from which 15 studies were determined to be eligible for analysis. While undergoing anti-TB treatment, 575 cases with drug-induced liver injury (DILI) and 4128 controls without DILI were enrolled into this analysis. The pooled odds ratio of all studies for the CHBI to ATDILI was 2.18 (95% confidence interval, 1.41-3.37). Among the studies with a strict definition of DILI (alanine aminotransferase > 5 × upper limit of normal value) and combination anti-TB regimen, the impact of CHBI on ATDILI was significant only in the prospective studies (odds ratio, 3.41; 95% confidence interval, 1.77-6.59), but not in the case-control studies. However, in the studies with a strict definition of DILI and isoniazid only treatment, the association between CHBI and ATDILI was not statistically significant. CONCLUSION: This meta-analysis suggests that CHBI may increase the risk of ATDILI in the standard combination therapy for active TB. Close follow-up and regular liver test monitoring are mandatory to treat TB in chronic hepatitis B carriers.

A block mixture model to map eQTLs for gene clustering and networking.

To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.

Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment.

By measuring gene expression at an unprecedented resolution and throughput, RNA-seq has played a pivotal role in studying biological functions. Its typical application in clinical medicine is to identify the discrepancies of gene expression between two different types of cancer cells, sensitive and resistant to chemotherapeutic treatment, in a hope to predict drug response. Here we modified and used a mechanistic model to identify distinct patterns of gene expression in response of different types of breast cancer cell lines to chemotherapeutic treatment. This model was founded on a mixture likelihood of Poisson-distributed transcript read data, with each mixture component specified by the Skellam function. By estimating and comparing the amount of gene expression in each environment, the model can test how genes alter their expression in response to environment and how different genes interact with each other in the responsive process. Using the modified model, we identified the alternations of gene expression between two cell lines of breast cancer, resistant and sensitive to tamoxifen, which allows us to interpret the expression mechanism of how genes respond to metabolic differences between the two cell types. The model can have a general implication for studying the plastic pattern of gene expression across different environments measured by RNA-seq.

A computational algorithm for functional clustering of proteome dynamics during development.

Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.

A model framework for identifying genes that guide the evolution of heterochrony.

Heterochrony, the phylogenic change in the time of developmental events or rate of development, has been thought to play an important role in producing phenotypic novelty during evolution. Increasing evidence suggests that specific genes are implicated in heterochrony, guiding the process of developmental divergence, but no quantitative models have been instrumented to map such heterochrony genes. Here, we present a computational framework for genetic mapping by which to characterize and locate quantitative trait loci (QTLs) that govern heterochrony described by four parameters, the timing of the inflection point, the timing of maximum acceleration of growth, the timing of maximum deceleration of growth, and the length of linear growth. The framework was developed from functional mapping, a dynamic model derived to map QTLs for the overall process and pattern of development. By integrating an optimality algorithm, the framework allows the so-called heterochrony QTLs (hQTLs) to be tested and quantified. Specific pipelines are given for testing how hQTLs control the onset and offset of developmental events, the rate of development, and duration of a particular developmental stage. Computer simulation was performed to examine the statistical properties of the model and demonstrate its utility to characterize the effect of hQTLs on population diversification due to heterochrony. By analyzing a genetic mapping data in rice, the framework identified an hQTL that controls the timing of maximum growth rate and duration of linear growth stage in plant height growth. The framework provides a tool to study how genetic variation translates into phenotypic innovation, leading a lineage to evolve, through heterochrony.

Systems mapping of genes controlling chemotherapeutic drug efficiency for cancer stem cells.

Cancer can be controlled effectively by using chemotherapeutic drugs to inhibit cancer stem cells, but there is considerable inter-patient variability regarding how these cells respond to drug intervention. Here, we describe a statistical framework for mapping genes that control tumor responses to chemotherapeutic drugs as well as the efficacy of treatments in arresting tumor growth. The framework integrates the mathematical aspects of the cancer stem cell hypothesis into genetic association studies, equipped with a capacity to quantify the magnitude and pattern of genetic effects on the kinetic decline of cancer stem cells in response to therapy. By quantifying how specific genes and their interactions govern drug response, the model provides essential information to tailor personalized drugs for individual patients.

A bi-Poisson model for clustering gene expression profiles by RNA-seq.

With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation­maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene­environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.

Towards a comprehensive picture of the genetic landscape of complex traits.

The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.

Delivering systems pharmacogenomics towards precision medicine through mathematics.

The latest developments of pharmacology in the post-genomic era foster the emergence of new biomarkers that represent the future of drug targets. To identify these biomarkers, we need a major shift from traditional genomic analyses alone, moving the focus towards systems approaches to elucidating genetic variation in biochemical pathways of drug response. Is there any general model that can accelerate this shift via a merger of systems biology and pharmacogenomics? Here we describe a statistical framework for mapping dynamic genes that affect drug response by incorporating its pharmacokinetic and pharmacodynamic pathways. This framework is expanded to shed light on the mechanistic and therapeutic differences of drug response based on pharmacogenetic information, coupled with genomic, proteomic and metabolic data, allowing novel therapeutic targets and genetic biomarkers to be characterized and utilized for drug discovery.

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon's mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments.

A quantitative model of transcriptional differentiation driving host-pathogen interactions.

Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems.

A multivalent three-point linkage analysis model of autotetraploids.

Because of its widespread occurrence and role in shaping evolutionary processes in the biological kingdom, especially in plants, polyploidy has been increasingly studied from cytological to molecular levels. By inferring gene order, gene distances and gene homology, linkage mapping with molecular markers has proven powerful for investigating genome structure and organization. Here we review and assess a general statistical model for three-point linkage analysis in autotetraploids by integrating double reduction, a phenomenon that commonly occurs in autopolyploids whose chromosomes are derived from a single ancestral species. This model does not require any assumption on the distribution of the occurrence of double reduction and can handle the complexity of multilocus linkage in terms of crossover interference. Implemented with the expectation-maximization (EM) algorithms, the model can estimate and test the recombination fractions between less informative dominant markers, thus facilitating its practical implications for any autopolyploids in most of which inexpensive dominant markers are still used for their genetic and evolutionary studies. The model was applied to reanalyze a published data in tetraploid switchgrass, validating its practical usefulness and utilization.

A maximum likelihood approach to functional mapping of longitudinal binary traits.

Despite their importance in biology and biomedicine, genetic mapping of binary traits that change over time has not been well explored. In this article, we develop a statistical model for mapping quantitative trait loci (QTLs) that govern longitudinal responses of binary traits. The model is constructed within the maximum likelihood framework by which the association between binary responses is modeled in terms of conditional log odds-ratios. With this parameterization, the maximum likelihood estimates (MLEs) of marginal mean parameters are robust to the misspecification of time dependence. We implement an iterative procedures to obtain the MLEs of QTL genotype-specific parameters that define longitudinal binary responses. The usefulness of the model was validated by analyzing a real example in rice. Simulation studies were performed to investigate the statistical properties of the model, showing that the model has power to identify and map specific QTLs responsible for the temporal pattern of binary traits.

Systems mapping of HIV-1 infection.

Mathematical models of viral dynamics in vivo provide incredible insights into the mechanisms for the nonlinear interaction between virus and host cell populations, the dynamics of viral drug resistance, and the way to eliminate virus infection from individual patients by drug treatment. The integration of these mathematical models with high-throughput genetic and genomic data within a statistical framework will raise a hope for effective treatment of infections with HIV virus through developing potent antiviral drugs based on individual patients' genetic makeup. In this opinion article, we will show a conceptual model for mapping and dictating a comprehensive picture of genetic control mechanisms for viral dynamics through incorporating a group of differential equations that quantify the emergent properties of a system.

Dynamic modeling of genes controlling cancer stem cell proliferation.

The growing evidence that cancer originates from stem cells (SC) holds a great promise to eliminate this disease by designing specific drug therapies for removing cancer SC. Translation of this knowledge into predictive tests for the clinic is hampered due to the lack of methods to discriminate cancer SC from non-cancer SC. Here, we address this issue by describing a conceptual strategy for identifying the genetic origins of cancer SC. The strategy incorporates a high-dimensional group of differential equations that characterizes the proliferation, differentiation, and reprogramming of cancer SC in a dynamic cellular and molecular system. The deployment of robust mathematical models will help uncover and explain many still unknown aspects of cell behavior, tissue function, and network organization related to the formation and division of cancer SC. The statistical method developed allows biologically meaningful hypotheses about the genetic control mechanisms of carcinogenesis and metastasis to be tested in a quantitative manner.