Conjugate Family Research Articles

Early-onset group B streptococcal disease in African countries and maternal vaccination strategies.

Invasive group B streptococcal (GBS) disease is the commonest perinatally-acquired bacterial infection in newborns; the burden is higher in African countries where intrapartum antibiotic prophylaxis strategies are not feasible. In sub-Saharan Africa, almost one in four newborns with GBS early-onset disease will demise, and one in ten survivors have moderate or severe neurodevelopmental impairment. A maternal GBS vaccine to prevent invasive GBS disease in infancy is a pragmatic and cost-effective preventative strategy for Africa. Hexavalent polysaccharide protein conjugate and Alpha family surface protein vaccines are undergoing phase II clinical trials. Vaccine licensure may be facilitated by demonstrating safety and immunological correlates/thresholds suggestive of protection against invasive GBS disease. This will then be followed by phase IV effectiveness studies to assess the burden of GBS vaccine preventable disease, including the effect on all-cause neonatal infections, neonatal deaths and stillbirths.

Rational design of poly-L-glutamic acid-palbociclib conjugates for pediatric glioma treatment.

Brain tumors represent the second most common cause of pediatric cancer death, with malignant gliomas accounting for ∼75% of pediatric deaths. Palbociclib, a selective cyclin-dependent kinase 4/6 (CDK4/6) inhibitor, has shown promise in phase I clinical trials of pediatric patients with progressive/refractory brain tumors using the oral administration route; however, pharmacokinetic limitations and toxicity issues remain. We synthesized a family of well-defined linear and star-shaped polyglutamate (PGA)-palbociclib conjugates using redox-sensitive self-immolative linkers to overcome limitations associated with free palbociclib. Exhaustive characterization of this conjugate family provided evidence for a transition towards the formation of more organized conformational structures upon increased drug loading. We evaluated the activity of conjugates in patient-derived glioblastoma and diffuse intrinsic pontine glioma cells, which display differing reducing environments due to differential glutathione expression levels. We discovered that microenvironmental parameters and the identified conformational changes determined palbociclib release kinetics and therapeutic output; furthermore, we identified a star-shaped PGA-palbociclib conjugate with low drug loading as an optimal therapeutic approach in diffuse intrinsic pontine glioma cells.

Bayesian Inference of Parameters in Power System Dynamic Models Using Trajectory Sensitivities

We propose an analytically tractable Bayesian method to infer parameters in power system dynamic models from noisy measurements of bus-voltage magnitudes and frequencies as well as active- and reactive-power injections. The proposed method is computationally appealing as it bypasses the large number of system model simulations typically required in sampling-based Bayesian inference. Instead, it relies on analytical linearization of the nonlinear system differential-algebraic-equation model enabled by trajectory sensitivities. Central to the proposed method is the construction of a linearized model with the maximum probability of being (closest to) the actual nonlinear model that gave rise to the measurement data. The linear model together with Gaussian prior leads to a conjugate family where the parameter posterior, model evidence, and their gradients can be computed in closed form, markedly improving scalability for large-scale power systems. We illustrate the effectiveness and key features of the proposed method with numerical case studies for a three-bus system. Algorithmic scalability is then demonstrated via case studies involving the New England 39-bus test system.

Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model

This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion, the predictive distribution under assumption that the shape parameters are known. The prior distribution about the model parameters is represented by non-informative information, as well as the simulate on the generated data from the model by a suggested way based on different values ​​of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. It found that the posterior marginal probability distribution of the location matrix and the predictive probability distribution is a matrix-t distribution with different parameters, the posterior marginal probability distribution of the scale matrix is proper distribution but it does not belong to the conjugate family, Through the Bayes factor criterion, it was found that the sample that was used in the generation process was drawn from a population that does not belong to the generalized modified Bessel population.

Halogenation at the Phenylalanine Residue of Monomethyl Auristatin F Leads to a Favorable cis/trans Equilibrium and Retained Cytotoxicity.

Halogenation can be utilized for the purposes of labeling and molecular imaging, providing a means to, e.g., follow drug distribution in an organism through positron emission tomography (PET) or study the molecular recognition events unfolding by nuclear magnetic resonance (NMR) spectroscopy. For cancer therapeutics, where often highly toxic substances are employed, it is of importance to be able to track the distribution of the drugs and their metabolites in order to ensure minimal side effects. Labeling should ideally have a negligible disruptive effect on the efficacy of a given drug. Using a combination of NMR spectroscopy and cytotoxicity assays, we identify a site susceptible to halogenation in monomethyl auristatin F (MMAF), a widely used cytotoxic agent in the antibody–drug conjugate (ADC) family of cancer drugs, and study the effects of fluorination and chlorination on the physiological solution structure of the auristatins and their cytotoxicity. We find that the cytotoxicity of the parent drug is retained, while the conformational equilibrium is shifted significantly toward the biologically active trans isomer, simultaneously decreasing the concentration of the inactive and potentially disruptive cis isomer by up to 50%. Our results may serve as a base for the future assembly of a multifunctional toolkit for the assessment of linker technologies and exploring bystander effects from the warhead perspective in auristatin-derived ADCs.

A natural lead-in approach to response-adaptive allocation for continuous outcomes.

Response-adaptive (RA) allocation designs can skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued responses. While unstable estimators and increased variability can adversely affect adaptation in early trial stages, Bayesian methods can be implemented with decreasingly informative priors (DIP) to overcome these difficulties. DIPs have been previously used for binary outcomes to constrain adaptation early in the trial, yet gradually increase adaptation as subjects accrue. We extend the DIP approach to RA designs for continuous outcomes, primarily in the normal conjugate family by functionalizing the prior effective sample size to equal the unobserved sample size. We compare this effective sample size DIP approach to other DIP formulations. Further, we considered various allocation equations and assessed their behavior utilizing DIPs. Simulated clinical trials comparing the behavior of these approaches with traditional Frequentist and Bayesian RA as well as balanced designs show that the natural lead-in approaches maintain improved treatment with lower variability and greater power.

Multiple Target Tracking Based on Sets of Trajectories

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.

A Comparison of Bayes Estimators for the parameter of Rayleigh Distribution with Simulation

A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared error loss function and weighted squared error loss function) in the cases of the three different sets of prior distributions .Simulations is employed to obtain results. And determine the best estimator according to the smallest value of mean squared error and weighted mean squared error. We found that the best estimation for the parameter for all sample sizes (n) , when the double prior distribution for is SRIG - the natural conjugate family of priors distribution with values (a=5, b=0.5, =8, =0.5) and (a=8, b=1, =5, =1) for the true value of respectively .Also ,we obtained the best estimation for when the double prior distribution for is the natural conjugate family of priors-non-informative distribution with values(=0.5, =5, c=1) for the true value of (). 

Tumor microenvironment-targeted poly-L-glutamic acid-based combination conjugate for enhanced triple negative breast cancer treatment

The intrinsic characteristics of the tumor microenvironment (TME), including acidic pH and overexpression of hydrolytic enzymes, offer an exciting opportunity for the rational design of TME-drug delivery systems (DDS). We developed and characterized a pH-responsive biodegradable poly-L-glutamic acid (PGA)-based combination conjugate family with the aim of optimizing anticancer effects. We obtained combination conjugates bearing Doxorubicin (Dox) and aminoglutethimide (AGM) with two Dox loadings and two different hydrazone pH-sensitive linkers that promote the specific release of Dox from the polymeric backbone within the TME. Low Dox loading coupled with a short hydrazone linker yielded optimal effects on primary tumor growth, lung metastasis (∼90% reduction), and toxicological profile in a preclinical metastatic triple-negative breast cancer (TNBC) murine model. The use of transcriptomic analysis helped us to identify the molecular mechanisms responsible for such results including a differential immunomodulation and cell death pathways among the conjugates. This data highlights the advantages of targeting the TME, the therapeutic value of polymer-based combination approaches, and the utility of –omics-based analysis to accelerate anticancer DDS.

Anticancer Activity Driven by Drug Linker Modification in a Polyglutamic Acid‐Based Combination‐Drug Conjugate

AbstractCombination nanotherapies for the treatment of breast cancer permits synergistic drug targeting of multiple pathways. However, poor carrier degradability, poor synergism of the combined drugs, low drug release regulation, and a lack of control on final macromolecule solution conformation (which drives the biological fate) limit the application of this strategy. The present study describes the development of a family of drug delivery systems composed of chemotherapeutic (doxorubicin) and endocrine therapy (aromatase inhibitor aminoglutethimide) agents conjugated to a biodegradable poly‐l‐glutamic acid backbone via various linking moieties. Data from in vitro cytotoxicity and drug release assessments and animal model validation select a conjugate family member with optimal biological performance. Exhaustive physicochemical characterization in relevant media (including the study of secondary structure, size measurements, and detailed small‐angle neutron scattering analysis) correlates biological data with the intrinsic supramolecular characteristics of the conjugate. Overall, this study demonstrates how a small flexible Gly linker can modify the spatial conformation of the entire polymer–drug conjugate, promote the synergistic release of both drugs, and significantly improve biological activity. These findings highlight the need for a deeper understanding of polymer–drug conjugates at supramolecular level to allow the design of more effective polymer–drug conjugates.

1H-1,2,3-triazole-tethered uracil-ferrocene and uracil-ferrocenylchalcone conjugates: Synthesis and antitubercular evaluation.

Copper-catalyzed azide-alkyne [3+2] cycloaddition has been utilized for preparing a series of 1H-1,2,3-triazoles with the purpose of probing structure-activity relationships among a uracil-ferrocene-triazole conjugate family. The antitubercular evaluation studies revealed an improvement in activity with the introduction of a ferrocene nucleus among N-alkylazido-uracil precursors, with a preference for a bromo-substituent along with moderate chain lengths of n=2-6. The reported protocol is a successful approach for integrating uracil-ferrocene-chalcone functionalities tethered via 1H-1,2,3-triazole rings with apparent physicochemical stability.

A new prospective for Learning Automata: A machine learning approach

In the field of Learning Automata (LA), how to design faster learning algorithms has always been a key issue. Among solutions reported in the literature, the stochastic estimator reward-inaction learning automaton (SERI), which belongs to the Maximum Likelihood estimator based LAs, has been recognized as the fastest ϵ-optimal LA. In this paper, we first point out the limitations of the traditional Maximum Likelihood Estimator (MLE) based LAs and then introduce Bayesian estimator based approach, which is demonstrated to be equivalent to Laplace smoothing of the traditional method, to overcome these limitations. The key idea is that the Bayesian estimator, which estimates the probability of selecting each action in the LA, aims to reconstruct Bernoulli distribution from sequential data, and is formalized based on exponential conjugate family so that the LA has a relatively simple format for easy implementation. In addition, we also indicate that this Bayesian estimator could be applied to update almost all existing MLE estimator based LAs. Based on the proposed Bayesian estimator, a new LA, known as Generalized Bayesian Stochastic Estimator (GBSE) LA, is presented and proved to be ϵ-optimal. Finally, extensive experimental results on benchmarks demonstrate that our proposed learning scheme is more efficient than the current best LA SERI.

Visualizing attack of Escherichia coli by the antimicrobial peptide human defensin 5.

Human α-defensin 5 (HD5) is a 32-residue cysteine-rich host-defense peptide that exhibits broad-spectrum antimicrobial activity and contributes to innate immunity in the human gut and other organ systems. Despite many years of investigation, its antimicrobial mechanism of action remains unclear. In this work, we report that HD5ox, the oxidized form of this peptide that exhibits three regiospecific disulfide bonds, causes distinct morphological changes to Escherichia coli and other Gram-negative microbes. These morphologies include bleb formation, cellular elongation, and clumping. The blebs are up to ∼1 μm wide and typically form at the site of cell division or cell poles. Studies with E. coli expressing cytoplasmic GFP reveal that HD5ox treatment causes GFP emission to localize in the bleb. To probe the cellular uptake of HD5ox and subsequent localization, we describe the design and characterization of a fluorophore-HD5 conjugate family. By employing these peptides, we demonstrate that fluorophore-HD5ox conjugates harboring the rhodamine and coumarin fluorophores enter the E. coli cytoplasm. On the basis of the fluorescence profiles, each of these fluorophore-HD5ox conjugates localizes to the site of cell division and cell poles. These studies support the notion that HD5ox, at least in part, exerts its antibacterial activity against E. coli and other Gram-negative microbes in the cytoplasm.

Elicitation for Aggregation

We study the problem of eliciting and aggregating probabilistic information from multiple agents. In order to successfully aggregate the predictions of agents, the principal needs to elicit some notion of confidence from agents, capturing how much experience or knowledge led to their predictions. To formalize this, we consider a principal who wishes to learn the distribution of a random variable. A group of Bayesian agents has each privately observed some independent samples of the random variable. The principal wishes to elicit enough information from each agent, so that her posterior is the same as if she had directly received all of the samples herself. Leveraging techniques from Bayesian statistics, we represent confidence as the number of samples an agent has observed, which is quantified by a hyperparameter from a conjugate family of prior distributions. This then allows us to show that if the principal has access to a few samples, she can achieve her aggregation goal by eliciting predictions from agents using proper scoring rules. In particular, with access to one sample, she can successfully aggregate the agents' predictions if and only if every posterior predictive distribution corresponds to a unique value of the hyperparameter, a property which holds for many common distributions of interest. When this uniqueness property does not hold, we construct a novel and intuitive mechanism where a principal with two samples can elicit and optimally aggregate the agents' predictions.

Using Bayesian Statistics in Enterprise Demography

Knowledge of the number of different kinds of enterprises that will be created in a coming year is essential information. It can be used in macroeconomic analyses and as a constituent of the background for economic policy.From a demographics point of view, we consider the creation (birth) of some enterprise as a basic indicator. It can also be approached from the point of view of inference, as the creation of enterprise is influenced by a wide variety of inputs. Enterprise creation may therefore be thought of as a random process.The analytic tools Bayesian statistics provide make it possible involve more kinds of information into statistical analysis and gradually update the parameter estimations. We used the conjugate family Poisson / gamma to estimate the number of enterprises to be created in a coming year. The considerations were concerned with the mean square error, which was used as the main criterion of the point estimation quality. We solved two kinds of problems: to find a Bayesian point estimation that has a smaller mean square error than the classical one in a predetermined interval, and, along with it, to model prior information in a very simple way.In finding some connection among the variables contained in the conjugate family Poisson / gamma, we solved both presented problems and also developed a simple algorithm for optimal point estimation of the Poisson distribution parameter. This algorithm was used to estimate the number of enterprises created.

Bayesian Analysis of Mixture of Normal Factor Analysis Model

Factor analysis model belongs to a family of linear models and because the multivariate data sets do not vacate outliers . For this reason , this paper is concerned with Bayesian analysis of mixed normal factor analysis model . The mixture normal distribution treats the observations of a sample as they are taken from two different populations and none from single population . The model is analyzed when the common factor is (stochastic and non-stochastic) variable . We suppose that all the parameters of the model are unknown , some of them have prior distributions belong to the conjugate family.

Base-Promoted Expedient Access to Spiroisatins: Synthesis and Antitubercular Evaluation of 1H-1,2,3-Triazole-Tethered Spiroisatin–Ferrocene and Isatin–Ferrocene Conjugates

The use of sodium hydride provides a convenient access to the synthesis of C-5-functionalized spiroisatins with the absence of the typical drawbacks associated with conventional protocols. The synthesized precursors, viz. N-alkylazido spiroisatins and their unprotected counterparts, were explored in Cu-mediated azide–alkyne cycloaddition reactions to probe the antitubercular structure–activity relationships (SAR) within the isatin–ferrocene–triazole conjugate family. The antitubercular evaluation studies of the synthesized conjugates revealed an improvement in the minimal inhibitory concentration (MIC) with the introduction of ferrocene nucleus, as evidenced by spiroisatin–ferrocene and isatin–ferrocene hybrids.

Simple Marginally Noninformative Prior Distributions for Covariance Matrices

A family of prior distributions for covariance matrices is studied. Members of the family possess the attractive property of all standard deviation and correlation parameters being marginally noninformative for particular hyperparameter choices. Moreover, the family is quite simple and, for approximate Bayesian inference techniques such as Markov chain Monte Carlo and mean field variational Bayes, has tractability on par with the Inverse-Wishart conjugate family of prior distributions. A simulation study shows that the new prior distributions can lead to more accurate sparse covariance matrix estimation.

1H-1,2,3-Triazole-Tethered Isatin–Ferrocene and Isatin–Ferrocenylchalcone Conjugates: Synthesis and in Vitro Antitubercular Evaluation

A Cu-mediated azide–alkyne cycloaddition protocol has been employed for the synthesis of 16 different triazoles to probe the antitubercular structure–activity relationships within the isatin–ferrocene–triazole conjugate family. The antitubercular evaluation studies revealed a marked improvement in activity with the introduction of ferrocene nucleus among precursors N-alkylazido isatins with a prefernce for halogen (F, Cl) substituent at C-5 position of isatin as well as propyl chain length as a spacer. The induction of a chalcone nucleus resulted in the enhanced antimycobacterial efficacy irrespective of the subtituent and alkyl chain length as evidenced by the isatin–ferrocenylchalcone hybrids. The described protocol is the first successful attempt of the amalgamation of ferrocene–isatin nuclei tethered via a triazole linker.

Synthesis of 1H-1,2,3-triazole linked β-lactam–isatin bi-functional hybrids and preliminary analysis of in vitro activity against the protozoal parasite Trichomonas vaginalis

Twenty-two different triazoles were prepared to examine the anti-Trichomonas vaginalis structure–activity relationships (SAR) within the β-lactam–isatin–triazole conjugate family. The compounds were synthesized by copper-catalyzed ‘click chemistry.’ In vitro activity against T. vaginalis was determined at 10 and 100 μM for each compound, with eighteen of the synthesized hybrids showing 100% growth inhibition at 100 μM. The compound 5i, with no cytotoxicity on cultured CHO-K1 cells, is considered a good compound for further analysis.