Development of Alginate-Based Biodegradable Radioactive Microspheres Labeled with Positron Emitter through Click Chemistry Reaction: Stability and PET Imaging Study.

Biodegradable radioactive microspheres labeled with positron emitters hold significant promise for diagnostic and therapeutic applications in cancers and other diseases, including arthritis. The alginate-based polymeric microspheres offer advantages such as biocompatibility, biodegradability, and improved stability, making them suitable for clinical applications. In this study, we developed novel positron emission tomography (PET) microspheres using alginate biopolymer radiolabeled with gallium-68 (68Ga) through a straightforward conjugation reaction. Polyethylenimine (PEI)-decorated calcium alginate microspheres (PEI-CAMSs) were fabricated and further modified using azadibenzocyclooctyne-N-hydroxysuccinimide ester (ADIBO-NHS). Subsequently, azide-functionalized NOTA chelator (N3-NOTA) was labeled with [68Ga]Ga to obtain [68Ga]Ga-NOTA-N3, which was then reacted with the surface-modified PEI-CAMSs using strain-promoted alkyne-azide cycloaddition (SPAAC) reaction to develop [68Ga]Ga-NOTA-PEI-CAMSs, a novel PET microsphere. The radiolabeling efficiency and radiochemical stability of [68Ga]Ga-NOTA-PEI-CAMSs were determined using the radio-instant thin-layer chromatography-silica gel (radio-ITLC-SG) method. The in vivo PET images were also acquired to study the in vivo stability of the radiolabeled microspheres in normal mice. The radiolabeling efficiency of [68Ga]Ga-NOTA-PEI-CAMSs was over 99%, and the microspheres exhibited high stability (92%) in human blood serum. PET images demonstrated the stability and biodistribution of the microspheres in mice for up to 2 h post injection. This study highlights the potential of biodegradable PET microspheres for preoperative imaging and targeted radionuclide therapy. Overall, the straightforward synthesis method and efficient radiolabeling technique provide a promising platform for the development of theranostic microspheres using other radionuclides such as 90Y, 177Lu, 188Re, and 64Cu.

Assessing the impact of recombination on the estimation of isolation-with-migration models using genomic data: a simulation study.

Recombination events complicate the evolutionary history of populations and species and have a significant impact on the inference of isolation-with-migration (IM) models. However, several existing methods have been developed, assuming no recombination within a locus and free recombination between loci. In this study, we investigated the effect of recombination on the estimation of IM models using genomic data. We conducted a simulation study to evaluate the consistency of the parameter estimators with up to 1,000 loci and analyze true gene trees to examine the sources of errors in estimating the IM model parameters. The results showed that the presence of recombination led to biased estimates of the IM model parameters, with population sizes being more overestimated and migration rates being more underestimated as the number of loci increased. The magnitude of the biases tended to increase with the recombination rates when using 100 or more loci. On the other hand, the estimation of splitting times remained consistent as the number of loci increased. In the absence of recombination, the estimators of the IM model parameters remained consistent.

The effect of missing levels of nesting in multilevel analysis.

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

Recent advances in Bayesian inference of isolation-with-migration models.

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

Phylogeny Estimation by Integration over Isolation with Migration Models.

Phylogeny estimation is difficult for closely related populations and species, especially if they have been exchanging genes. We present a hierarchical Bayesian, Markov-chain Monte Carlo method with a state space that includes all possible phylogenies in a full Isolation-with-Migration model framework. The method is based on a new type of genealogy augmentation called a "hidden genealogy" that enables efficient updating of the phylogeny. This is the first likelihood-based method to fully incorporate directional gene flow and genetic drift for estimation of a species or population phylogeny. Application to human hunter-gatherer populations from Africa revealed a clear phylogenetic history, with strong support for gene exchange with an unsampled ghost population, and relatively ancient divergence between a ghost population and modern human populations, consistent with human/archaic divergence. In contrast, a study of five chimpanzee populations reveals a clear phylogeny with several pairs of populations having exchanged DNA, but does not support a history with an unsampled ghost population.

Patient-Reported Outcome Measures in the Food and Drug Administration Pilot Compendium: Meeting Today's Standards for Patient Engagement in Development?

BACKGROUND: In 2016, the Food and Drug Administration (FDA) released a Pilot Clinical Outcome Assessment Compendium (COA Compendium) intended to foster patient-focused drug development (PFDD). However, it is unclear whether patient perspectives were solicited during development or validation of the included patient-reported outcome (PRO) measures. OBJECTIVE: To examine the pedigree of a sample of measures included in the COA Compendium. METHODS: PROs included in chapters 1 or 2 of the COA Compendium were extracted and three reviewers independently searched PubMed and Google to identify information on measure pedigree. Data on method and stage of measure development where patient engagement took place were documented. RESULTS: Among the 26 evaluated PRO measures, we were unable to identify information on development or validation on nearly half the sample (n = 12). Among the remaining 14 measures, 5 did not include any evidence of patient engagement; 2 engaged patients during concept elicitation only; 1 engaged patients during psychometric validation only; and 6 engaged patients during both concept elicitation and cognitive interviewing. Measures either previously qualified or submitted for qualification were more likely to include patient engagement. CONCLUSIONS: For the FDA Pilot COA Compendium to fulfill its purpose of fostering PFDD, it needs fine-tuning to reflect today's standards, improving transparency and facilitating clear identification of included measures so that the level of patient engagement, among other factors, can be properly assessed. Suggested improvements include identifying clinical trials that correspond to the COA Compendium's use in drug development; more clearly identifying which measure is referred to; and including only those measures that already qualified or undergoing qualification.

Bayesian Analysis of Evolutionary Divergence with Genomic Data under Diverse Demographic Models.

We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation-with-Migration framework. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calculation of the joint posterior density for the parameters of a demographic model. In step 1, the MCMC sampling phase, the method uses a reduced state space, consisting of coalescent trees without migration paths, and a simple importance sampling distribution without the demography of interest. Once obtained, a single sample of trees can be used in step 2 to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. Because migration paths are not included in the state space of the MCMC phase, but rather are handled by analytic integration in step 2 of the analysis, the method is scalable to a large number of loci with excellent MCMC mixing properties. With an implementation of the new method in the computer program MIST, we demonstrate the method's accuracy, scalability, and other advantages using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes (P. t.) troglodytes and P. t. verus.

Ancestral-derived effects on the mutational landscape of laryngeal cancer.

Laryngeal cancer disproportionately affects more African-Americans than European-Americans. Here, we analyze the genome-wide somatic point mutations from the tumors of 13 African-Americans and 57 European-Americans from TCGA to differentiate between environmental and ancestrally-inherited factors. The mean number of mutations was different between African-Americans (151.31) and European-Americans (277.63). Other differences in the overall mutational landscape between African-American and European-American were also found. The frequency of C>A, and C>G were significantly different between the two populations (p-value<0.05). Context nucleotide signatures for some mutation types significantly differ between these two populations. Thus, the context nucleotide signatures along with other factors could be related to the observed mutational landscape differences between two races. Finally, we show that mutated genes associated with these mutational differences differ between the two populations. Thus, at the molecular level, race appears to be a factor in the progression of laryngeal cancer with ancestral genomic signatures best explaining these differences.

On the occurrence of false positives in tests of migration under an isolation-with-migration model.

The population genetic study of divergence is often carried out using a Bayesian genealogy sampler, like those implemented in ima2 and related programs, and these analyses frequently include a likelihood ratio test of the null hypothesis of no migration between populations. Cruickshank and Hahn (2014, Molecular Ecology, 23, 3133-3157) recently reported a high rate of false-positive test results with ima2 for data simulated with small numbers of loci under models with no migration and recent splitting times. We confirm these findings and discover that they are caused by a failure of the assumptions underlying likelihood ratio tests that arises when using marginal likelihoods for a subset of model parameters. We also show that for small data sets, with little divergence between samples from two populations, an excellent fit can often be found by a model with a low migration rate and recent splitting time and a model with a high migration rate and a deep splitting time.

Computing the joint distribution of tree shape and tree distance for gene tree inference and recombination detection.

Ancestral recombination events can cause the underlying genealogy of a site to vary along the genome. We consider Bayesian models to simultaneously detect recombination breakpoints in very long sequence alignments and estimate the phylogenetic tree of each block between breakpoints. The models we consider use a dissimilarity measure between trees in their prior distribution to favor similar trees at neighboring loci. We show empirical evidence in Enterobacteria that neighboring genomic regions have similar trees. The main hurdle in using such models is the need to properly calculate the normalizing function for the prior probabilities on trees. In this work, we quantify the impact of approximating this normalizing function as done in biomc2, a hierarchical Bayesian method to detect recombination based on distance between tree topologies. We then derive an algorithm to calculate the normalizing function exactly, for a Gibbs distribution based on the Robinson-Foulds (RF) distance between gene trees at neighboring loci. At the core is the calculation of the joint distribution of the shape of a random tree and its RF distance to a fixed tree. We also propose fast approximations to the normalizing function, which are shown to be very accurate with little impact on the Bayesian inference.