Patient-Facing Mobile Apps to Treat High-Need, High-Cost Populations: A Scoping Review

BACKGROUND: Self-management is essential to caring for high-need, high-cost (HNHC) populations. Advances in mobile phone technology coupled with increased availability and adoption of health-focused mobile apps have made self-management more achievable, but the extent and quality of the literature supporting their use is not well defined. OBJECTIVE: The purpose of this review was to assess the breadth, quality, bias, and types of outcomes measured in the literature supporting the use of apps targeting HNHC populations. METHODS: Data sources included articles in PubMed and MEDLINE (National Center for Biotechnology Information), EMBASE (Elsevier), the Cochrane Central Register of Controlled Trials (EBSCO), Web of Science (Thomson Reuters), and the NTIS (National Technical Information Service) Bibliographic Database (EBSCO) published since 2008. We selected studies involving use of patient-facing iOS or Android mobile health apps. Extraction was performed by 1 reviewer; 40 randomly selected articles were evaluated by 2 reviewers to assess agreement. RESULTS: Our final analysis included 175 studies. The populations most commonly targeted by apps included patients with obesity, physical handicaps, diabetes, older age, and dementia. Only 30.3% (53/175) of the apps studied in the reviewed literature were identifiable and available to the public through app stores. Many of the studies were cross-sectional analyses (42.9%, 75/175), small (median number of participants=31, interquartile range 11.0-207.2, maximum 11,690), or performed by an app\u27s developers (61.1%, 107/175). Of the 175 studies, only 36 (20.6%, 36/175) studies evaluated a clinical outcome. CONCLUSIONS: Most apps described in the literature could not be located on the iOS or Android app stores, and existing research does not robustly evaluate the potential of mobile apps. Whereas apps may be useful in patients with chronic conditions, data do not support this yet. Although we had 2-3 reviewers to screen and assess abstract eligibility, only 1 reviewer abstracted the data. This is one limitation of our study. With respect to the 40 articles (22.9%, 40/175) that were assigned to 2 reviewers (of which 3 articles were excluded), inter-rater agreement was significant on the majority of items (17 of 30) but fair-to-moderate on others

Protein design in a lattice model of hydrophobic and polar amino acids

A general strategy is described for finding which amino acid sequences have native states in a desired conformation (inverse design). The approach is used to design sequences of 48 hydrophobic and polar aminoacids on three-dimensional lattice structures. Previous studies employing a sequence-space Monte-Carlo technique resulted in the successful design of one sequence in ten attempts. The present work also entails the exploration of conformations that compete significantly with the target structure for being its ground state. The design procedure is successful in all the ten cases.Comment: RevTeX, 12 pages, 1 figur

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

Conformational Proofreading: The Impact of Conformational Changes on the Specificity of Molecular Recognition

To perform recognition, molecules must locate and specifically bind their targets within a noisy biochemical environment with many look-alikes. Molecular recognition processes, especially the induced-fit mechanism, are known to involve conformational changes. This raises a basic question: Does molecular recognition gain any advantage by such conformational changes? By introducing a simple statistical-mechanics approach, we study the effect of conformation and flexibility on the quality of recognition processes. Our model relates specificity to the conformation of the participant molecules and thus suggests a possible answer: Optimal specificity is achieved when the ligand is slightly off target; that is, a conformational mismatch between the ligand and its main target improves the selectivity of the process. This indicates that deformations upon binding serve as a conformational proofreading mechanism, which may be selected for via evolution

A highly selective, label-free, homogenous luminescent switch-on probe for the detection of nanomolar transcription factor NF-kappaB

Transcription factors are involved in a number of important cellular processes. The transcription factor NF-κB has been linked with a number of cancers, autoimmune and inflammatory diseases. As a result, monitoring transcription factors potentially represents a means for the early detection and prevention of diseases. Most methods for transcription factor detection tend to be tedious and laborious and involve complicated sample preparation, and are not practical for routine detection. We describe herein the first label-free luminescence switch-on detection method for transcription factor activity using Exonuclease III and a luminescent ruthenium complex, [Ru(phen)2(dppz)]2+. As a proof of concept for this novel assay, we have designed a double-stranded DNA sequence bearing two NF-κB binding sites. The results show that the luminescence response was proportional to the concentration of the NF-κB subunit p50 present in the sample within a wide concentration range, with a nanomolar detection limit. In the presence of a known NF-κB inhibitor, oridonin, a reduction in the luminescence response of the ruthenium complex was observed. The reduced luminescence response of the ruthenium complex in the presence of small molecule inhibitors allows the assay to be applied to the high-throughput screening of chemical libraries to identify new antagonists of transcription factor DNA binding activity. This will allow the rapid and low cost identification and development of novel scaffolds for the treatment of diseases caused by the deregulation of transcription factor activity

Local Gene Regulation Details a Recognition Code within the LacI Transcriptional Factor Family

The specific binding of regulatory proteins to DNA sequences exhibits no clear patterns of association between amino acids (AAs) and nucleotides (NTs). This complexity of protein-DNA interactions raises the question of whether a simple set of wide-coverage recognition rules can ever be identified. Here, we analyzed this issue using the extensive LacI family of transcriptional factors (TFs). We searched for recognition patterns by introducing a new approach to phylogenetic footprinting, based on the pervasive presence of local regulation in prokaryotic transcriptional networks. We identified a set of specificity correlations –determined by two AAs of the TFs and two NTs in the binding sites– that is conserved throughout a dominant subgroup within the family regardless of the evolutionary distance, and that act as a relatively consistent recognition code. The proposed rules are confirmed with data of previous experimental studies and by events of convergent evolution in the phylogenetic tree. The presence of a code emphasizes the stable structural context of the LacI family, while defining a precise blueprint to reprogram TF specificity with many practical applications.Ministerio de Ciencia e Innovación, Spain (Formación de Profesorado Universitario fellowship)Ministerio de Ciencia e Innovación, Spain (grant BFU2008-03632/BMC)Madrid (Spain : Region) (grant CCG08-CSIC/SAL-3651

Alarming rates of virological failure and HIV-1 drug resistance amongst adolescents living with perinatal HIV in both urban and rural settings: evidence from the EDCTP READY-study in Cameroon

Objectives: Adolescents living with perinatal HIV infection (ALPHI) experience persistently high mortality rates, particularly in resource-limited settings. It is therefore clinically important for us to understand the therapeutic response, acquired HIV drug resistance (HIVDR) and associated factors among ALPHI, according to geographical location. Methods: A study was conducted among consenting ALPHI in two urban and two rural health facilities in the Centre Region of Cameroon. World Health Organization (WHO) clinical staging, self-reported adherence, HIVDR early warning indicators (EWIs), immunological status (CD4 count) and plasma viral load (VL) were assessed. For those experiencing virological failure (VF, VL ≥ 1000 copies/mL), HIVDR testing was performed and interpreted using the Stanford HIV Drug Resistance Database v.8.9-1. Results: Of the 270 participants, most were on nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimens (61.7% urban vs. 82.2% rural), and about one-third were poorly adherent (30.1% vs. 35.1%). Clinical failure rates (WHO-stage III/IV) in both settings were < 15%. In urban settings, the immunological failure (IF) rate (CD4  < 250 cells/μL) was 15.8%, statistically associated with late adolescence, female gender and poor adherence. The VF rate was 34.2%, statistically associated with poor adherence and NNRTI-based antiretroviral therapy. In the rural context, the IF rate was 26.9% and the VF rate was 52.7%, both statistically associated with advanced clinical stages. HIVDR rate was over 90% in both settings. EWIs were delayed drug pick-up, drug stock-outs and suboptimal viral suppression. Conclusions: Poor adherence, late adolescent age, female gender and advanced clinical staging worsen IF. The VF rate is high and consistent with the presence of HIVDR in both settings, driven by poor adherence, NNRTI-based regimen and advanced clinical staging

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community

ZNF274 Recruits the Histone Methyltransferase SETDB1 to the 3′ Ends of ZNF Genes

Only a small percentage of human transcription factors (e.g. those associated with a specific differentiation program) are expressed in a given cell type. Thus, cell fate is mainly determined by cell type-specific silencing of transcription factors that drive different cellular lineages. Several histone modifications have been associated with gene silencing, including H3K27me3 and H3K9me3. We have previously shown that genes for the two largest classes of mammalian transcription factors are marked by distinct histone modifications; homeobox genes are marked by H3K27me3 and zinc finger genes are marked by H3K9me3. Several histone methyltransferases (e.g. G9a and SETDB1) may be involved in mediating the H3K9me3 silencing mark. We have used ChIP-chip and ChIP-seq to demonstrate that SETDB1, but not G9a, is associated with regions of the genome enriched for H3K9me3. One current model is that SETDB1 is recruited to specific genomic locations via interaction with the corepressor TRIM28 (KAP1), which is in turn recruited to the genome via interaction with zinc finger transcription factors that contain a Kruppel-associated box (KRAB) domain. However, specific KRAB-ZNFs that recruit TRIM28 (KAP1) and SETDB1 to the genome have not been identified. We now show that ZNF274 (a KRAB-ZNF that contains 5 C2H2 zinc finger domains), can interact with KAP1 both in vivo and in vitro and, using ChIP-seq, we show that ZNF274 binding sites co-localize with SETDB1, KAP1, and H3K9me3 at the 3′ ends of zinc finger genes. Knockdown of ZNF274 with siRNAs reduced the levels of KAP1 and SETDB1 recruitment to the binding sites. These studies provide the first identification of a KRAB domain-containing ZNF that is involved in recruitment of the KAP1 and SETDB1 to specific regions of the human genome

High Diversity at PRDM9 in Chimpanzees and Bonobos

BACKGROUND: The PRDM9 locus in mammals has increasingly attracted research attention due to its role in mediating chromosomal recombination and possible involvement in hybrid sterility and hence speciation processes. The aim of this study was to characterize sequence variation at the PRDM9 locus in a sample of our closest living relatives, the chimpanzees and bonobos. METHODOLOGY/PRINCIPAL FINDINGS: PRDM9 contains a highly variable and repetitive zinc finger array. We amplified this domain using long-range PCR and determined the DNA sequences using conventional Sanger sequencing. From 17 chimpanzees representing three subspecies and five bonobos we obtained a total of 12 alleles differing at the nucleotide level. Based on a data set consisting of our data and recently published Pan PRDM9 sequences, we found that at the subspecies level, diversity levels did not differ among chimpanzee subspecies or between chimpanzee subspecies and bonobos. In contrast, the sample of chimpanzees harbors significantly more diversity at PRDM9 than samples of humans. Pan PRDM9 shows signs of rapid evolution including no alleles or ZnFs in common with humans as well as signals of positive selection in the residues responsible for DNA binding. CONCLUSIONS AND SIGNIFICANCE: The high number of alleles specific to the genus Pan, signs of positive selection in the DNA binding residues, and reported lack of conservation of recombination hotspots between chimpanzees and humans suggest that PRDM9 could be active in hotspot recruitment in the genus Pan. Chimpanzees and bonobos are considered separate species and do not have overlapping ranges in the wild, making the presence of shared alleles at the amino acid level between the chimpanzee and bonobo species interesting in view of the hypothesis that PRDM9 plays a universal role in interspecific hybrid sterility