Rational design of oral drugs targeting mucosa delivery with gut organoid platforms.

Effective oral drugs and vaccines require high delivery efficiency across the gastrointestinal epithelia and protection of medically effective payloads (i.e., immunogens) against gastric damage. In this study, hollowed nanocarriers (NCs: silica nanospheres and gold nanocages) with poly-l-lysine (PLL) coating and mammalian orthoreovirus cell attachment protein σ1 functionalization (NC-PLL-σ1) were explored as functional oral drug delivery vehicles (ODDVs). The transport of these ODDVs to mucosal lymphoid tissues could be facilitated by microfold cells (M-cells) mediated transcytosis (via σ1-α2-3-linked sialic acids adherence) across gastrointestinal epithelia. PLL coating provided protection and slow-release of rhodamine 6 G (R6G), a model payload. The transport effectiveness of these ODDVs was tested on intestinal organoid monolayers in vitro. When compared with other experimental groups, the fully functionalized ODDV system (with PLL-σ1) demonstrated two significant advantages: a significantly higher transport efficiency (198% over blank control at 48 h); and protection of payloads which led to both better transport efficiency and extended-release of payloads (61% over uncoated carriers at 48 h). In addition, it was shown that the M cell presence in intestinal organoid monolayers (modulated by Rank L stimulation) was a determining factor on the transport efficiency of the ODDVs: more M-cells (induced by higher Rank L) in the organoid monolayers led to higher transport efficiency for ODDV-delivered model payload (R6G). The fully functionalized ODDVs showed great potential as effective oral delivery vehicles for drugs and vaccines.

TNFα regulates intestinal organoids from mice with both defined and conventional microbiota.

Cytokines are key factors affecting the fate of intestinal stem cells (ISCs) and effective reagents to manipulate ISCs for research purpose. Tumor necrosis factor alpha (TNFα) is a cytokine produced primarily by monocytes and macrophages. It can induce apoptotic cell death and inflammation, and to inhibit tumorigenesis and viral replication. Additionally, TNFα has been shown to play a critical role in the pathogenesis of inflammatory bowel disease (IBD). It is therefore important to identify the mechanism by which individual cytokines affect particular cell types. For this purpose, we used both conventional (CONV) and altered Schaedler flora (ASF) C3H/HeN mice to elucidate the effect of different microbial populations (complex versus defined) on growth of miniguts derived from two different intestinal environments. Furthermore, we studied the effects of different concentrations of TNFα extracted from the lymph and spleen on the growth and viability of ISCs recovered from mice bearing the ASF or CONV microbiota. The effect of TNFα on miniguts growth depends not only on the source and concentration, but also on the intestinal microenvironment from which the ISCs were derived. The findings suggest that TNFα influences the proliferation of miniguts derived from ISCs and, therefore, modulates mucosal homeostasis of the host.

Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control.

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.

Hypoglycemia Early Alarm Systems Based On Multivariable Models.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

BACKGROUND: Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. METHOD: A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. RESULTS: Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. CONCLUSIONS: The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance.

Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms.

Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject's continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

BACKGROUND: Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. RESULTS: This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (PM) effectively identified critical genes in these studies based on comparison with the current method (CM). The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. CONCLUSIONS: PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

A novel data mining method to identify assay-specific signatures in functional genomic studies.

BACKGROUND: The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. RESULTS: The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data--a study of classification of human tissue samples and a study of E. coli cultures with different medium formulations. CONCLUSION: We have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics.