A Look Into the Cytotoxicity of Composite Fillings: Friend or Foe?

Dental resin composites are widely used restorative materials in dentistry for the treatment of carious and non-carious lesions as well as pit and fissure sealants, cavity liners, and endodontic sealers. They consist of two parts: an organic resin matrix and an inorganic/organic filler. The organic resin matrix phase is made up of multifunctional monomers and light-sensitive initiators, while the inorganic/organic filler phase is made up of micro/nano-sized fillers that primarily serve as reinforcement. Despite being a very promising dental material, its monomeric component has some drawbacks. It is well known for leaching out during incomplete polymerization, which can result in cytotoxicity. Bis-GMA (bisphenol A-glycidyl methacrylate) is the most cytotoxic of all monomeric components that exhibit synthetic estrogenic effects. The purpose of this article is to assess the cytotoxic effects of dental composite, understand the possible mechanism behind them, and explore ways to screen for and reduce this harmful effect, as well as shed light on its future prospects.

What Determines the District-Level Disparities in Immunization Coverage in India: Findings from Five Rounds of the National Family Health Survey.

India's Universal Immunization Programme has been performing at a sub-optimal level over the past decade, with there being a wide disparity in terms of immunization coverage between states. This study investigates the covariates that affect immunization rates and inequality in India at the individual and district levels. We used data from the five rounds of the National Family Health Survey (NFHS), conducted from 1992-1993 to 2019-2021. We used multilevel binary logistic regression analysis to examine the association between demographic, socio-economic and healthcare factors and a child's full immunization status. Further, we used the Fairlie decomposition technique to understand the relative contribution of explanatory variables to a child's full immunization status between districts with different immunization coverage levels. We found that 76% of children received full immunization in 2019-2021. Children from less wealthy families, urban backgrounds, Muslims, and those with illiterate mothers were found to have lower chances of receiving full immunization. There is no evidence that gender and caste disparities have an impact on immunization coverage in India. We found that having a child's health card is the most significant contributor to reducing the disparities that exist regarding children's full immunization between mid- and low-performing districts. Our study suggests that healthcare-related variables are more crucial than demographic and socio-economic variables when determining ways in which to improve immunization coverage in Indian districts.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI.

BACKGROUND AND PURPOSE: Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learning framework. MATERIALS AND METHODS: This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T1 perfusion and DWI data along with conventional MRI images. Analysis of T1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter. RESULTS: T1 perfusion parameter values were significantly different between the two groups. The combination of T1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error. CONCLUSION: The approach of using quantitative T1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

An In vitro Assessment of Retention Force of ZrO2, Polyether Ether Ketone, and ZrO2-Polyether Ether Ketone Telescopic Attachment for Mandibular Overdentures.

Objectives: The objective of this study was to compare ZrO2, polyether ether ketone (PEEK), and ZrO2-PEEK telescopic attachments in terms of retention of overdenture. Methodology: Forty-five acrylic resin models of the lower arch were divided into 3 groups of 15 each. In Group I, primary and secondary crowns were constructed from all zirconia (ZrO2), whereas all PEEK was used for Group II and Group III was made up of ZrO2 PEEK. Results: The mean retention value in Group I was 14.12 ± 3.4 N, in Group II was 15.86 ± 5.1, and in Group III was 22.40 ± 10.3 N. The mean final initial retention value in Group I was 14.50 ± 6.1 N, in Group II was 14.97 ± 8.2, and in Group III was 17.21 ± 9.3 N. A significant difference was observed in intergroup comparison (P < 0.05). Conclusion: Zirconia resulted in maximum retention as compared to other telescopic crown materials.

Locality and expectation effects in Hindi preverbal constituent ordering.

We investigate the relative impact of two influential theories of language comprehension, viz., Dependency Locality Theory (Gibson, 2000; DLT) and Surprisal Theory (Hale, 2001; Levy, 2008), on preverbal constituent ordering in Hindi, a predominantly SOV language with flexible word order. Prior work in Hindi has shown that word order scrambling is influenced by information structure constraints in discourse. However, the impact of cognitively grounded factors on Hindi constituent ordering is relatively underexplored. We test the hypothesis that dependency length minimization is a significant predictor of syntactic choice, once information status and surprisal measures (estimated from n-gram i.e., trigram and incremental dependency parsing models) have been added to a machine learning model. Towards this end, we setup a framework to generate meaning-equivalent grammatical variants of Hindi sentences by linearizing preverbal constituents of projective dependency trees in the Hindi-Urdu Treebank (HUTB) corpus of written text. Our results indicate that dependency length displays a weak effect in predicting reference sentences (amidst variants) over and above the aforementioned predictors. Overall, trigram surprisal outperforms dependency length and parser surprisal by a huge margin and our analyses indicate that maximizing lexical predictability is the primary driving force behind preverbal constituent ordering choices in Hindi. The success of trigram surprisal notwithstanding, dependency length minimization predicts non-canonical reference sentences having fronted direct objects over variants containing the canonical word order, cases where surprisal estimates fail due to their bias towards frequent structures and word sequences. Locality effects persist over the Given-New preference of subject-object ordering in Hindi. Accessibility and local statistical biases discussed in the sentence processing literature are plausible explanations for the success of trigram surprisal. Further, we conjecture that the presence of case markers is a strong factor potentially overriding the pressure for dependency length minimization in Hindi. Finally, we discuss the implications of our findings for the information locality hypothesis and theories of language production.

The first 100 days: how has COVID-19 affected poor and vulnerable groups in India?

In India, strict public health measures to suppress COVID-19 transmission and reduce burden have been rapidly adopted. Pandemic containment and confinement measures impact societies and economies; their costs and benefits must be assessed holistically. This study provides an evolving portrait of the health, economic and social consequences of the COVID-19 pandemic on vulnerable populations in India. Our analysis focuses on 100 days early in the pandemic from 13 March to 20 June 2020. We developed a conceptual framework based on the human right to health and the UN Sustainable Development Goals (SDGs). We analysed people's experiences recorded and shared via mobile phone on the voice platforms operated by the Gram Vaani COVID-19 response network, a service for rural and low-income populations now being deployed to support India's COVID-19 response. Quantitative and visual methods were used to summarize key features of the data and explore relationships between factors. In its first 100 days, the platform logged over 1.15 million phone calls, of which 793 350 (69%) were outbound calls related largely to health promotion in the context of COVID-19. Analysis of 6636 audio recordings by network users revealed struggles to secure the basic necessities of survival, including food (48%), cash (17%), transportation (10%) and employment or livelihoods (8%). Themes were mapped to shortfalls in 10 SDGs and their associated targets. Pre-existing development deficits and weak social safety nets are driving vulnerability during the COVID-19 crisis. For an effective pandemic response and recovery, these must be addressed through inclusive policy design and institutional reforms.

PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.

The inference of gene regulatory networks (GRNs) from expression data is a challenging problem in systems biology. The stochasticity or fluctuations in the biochemical processes that regulate the transcription process poses as one of the major challenges. In this paper, we propose a novel GRN inference approach, named the Probabilistic Extended Petri Net for Gene Regulatory Network (PEPN-GRN), for the inference of gene regulatory networks from noisy expression data. The proposed inference approach makes use of transition of discrete gene expression levels across adjacent time points as different evidence types that relate to the production or decay of genes. The paper examines three variants of the PEPN-GRN method, which mainly differ by the way the scores of network edges are computed using evidence types. The proposed method is evaluated on the benchmark DREAM4 in silico data sets and a real time series data set of E. coli from the DREAM5 challenge. The PEPN-GRN_v3 variant (the third variant of the PEPN-GRN approach) sought to learn the weights of evidence types in accordance with their contribution to the activation and inhibition gene regulation process. The learned weights help understand the time-shifted and inverted time-shifted relationship between regulator and target gene. Thus, PEPN-GRN_v3, along with the inference of network edges, also provides a functional understanding of the gene regulation process.

Awareness, attitude, and practice of dental practitioners toward management of endodontically treated teeth and factors associated with it: A questionnaire descriptive survey.

BACKGROUND: Endodontically treated teeth should be prosthodontically managed properly to add life to the teeth, and hence the main aim of this study was to explore awareness, attitude, and practice of dental practitioners toward management of endodontically treated teeth and factors associated with it. MATERIALS AND METHODS: It was a cross-sectional, descriptive questionnaire study conducted among dental practitioners working in private clinics. The study was conducted in August 2019. The survey was conducted among 239 participants. In this study, a close-ended interview schedule was prepared for perception and practice of dental specialists. RESULTS: Most of the dental professionals (31.79%) preferred full coverage crown for restoring the grossly decayed endodontically treated teeth. Awareness regarding prosthodontic management of endodontically treated teeth was better among specialists (13.75%) than bachelor's degree holders. Attitude was neutral among most of the study participants (46.86%) and practice scores toward prosthodontic management of endodontically treated teeth was good. Gender and degree were significantly associated (P = 0.02*) (P = 0.05*), (P = 0.00***) (P = 0.05*) with awareness and practice of study participants. CONCLUSION: A majority of study subjects in this study had moderate awareness, neutral attitude, and good practice regarding restoration of endodontically treated teeth.

Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT.

PURPOSE: High mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach. METHODS: Contrast-enhanced CT dataset of 40 patients (n = 40; M:F = 5:3; age = 25-55 years) with three groups of subjects: healthy (n = 14), cirrhosis (n = 12) and cirrhosis with HCC (n = 14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM). RESULTS: The proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p < 0.05) among healthy and diseased liver, and using logistic regression, classification accuracy obtained was 92.5%. For detecting cirrhosis and HCC liver, SVM with RBF kernel obtained highest slice-wise and patient-wise prediction accuracy of 86.9% (precision = 0.93, recall = 0.7) and 80% (precision = 0.86, recall = 0.75), respectively, than that of linear kernel (slice-wise: accuracy = 85.4%, precision = 0.92, recall = 0.67; patient-wise: accuracy = 73.33%, precision = 0.75, recall = 0.75). CONCLUSIONS: The proposed computer-aided diagnosis system for detecting cirrhosis and hepatocellular carcinoma (HCC) showed promising results and can be used as effective screening tool in medical image analysis.

Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components.

BACKGROUND: Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed. PURPOSE: To develop an artificial intelligence-based methodology for glioma grading using T1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients. STUDY TYPE: Retrospective. POPULATION: The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients. FIELD STRENGTH/SEQUENCE: Conventional MRI images (2D T1 -W, dual PD-T2 -W, and 3D FLAIR) and 3D T1 perfusion MRI data obtained at 3 T. ASSESSMENT: Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading. STATISTICAL TESTS: Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis. RESULTS: Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90th percentile value of T1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier. DATA CONCLUSION: It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306.

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.

PURPOSE: High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T2-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T1 perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor. MATERIAL AND METHODS: This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T1-W, T2-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T1 perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T1 perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing. RESULTS: Histology results showed that resected tissue consists largely of tumorous tissue with 7.21 ±â€¯4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing. CONCLUSION: The current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist's manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients.

Role of Expectation and Working Memory Constraints in Hindi Comprehension: An Eye-tracking Corpus Analysis.

We used the Potsdam-Allahabad Hindi eye-tracking corpus to investigate the role of wordlevel and sentence-level factors during sentence comprehension in Hindi. Extending previous work that used this eye-tracking data, we investigate the role of surprisal and retrieval cost metrics during sentence processing. While controlling for word-level predictors (word complexity, syllable length, unigram and bigram frequencies) as well as sentence-level predictors such as integration and storage costs, we find a significant effect of surprisal on first-pass reading times (higher surprisal value leads to increase in FPRT). Effect of retrieval cost was only found for a higher degree of parser parallelism. Interestingly, while surprisal has a significant effect on FPRT, storage cost (another predictionbased metric) does not. A significant effect of storage cost shows up only in total fixation time (TFT), thus indicating that these two measures perhaps capture different aspects of prediction. The study replicates previous findings that both prediction-based and memorybased metrics are required to account for processing patterns during sentence comprehension. The results also show that parser model assumptions are critical in order to draw generalizations about the utility of a metric (e.g. surprisal) across various phenomena in a language.

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years.

BACKGROUND: Incomplete immunisation coverage causes preventable illness and death in both developing and developed countries. Identification of factors that might modulate coverage could inform effective immunisation programmes and policies. We constructed a performance indicator that could quantitatively approximate measures of the susceptibility of immunisation programmes to coverage losses, with an aim to identify correlations between trends in vaccine coverage and socioeconomic factors. METHODS: We undertook a data-driven time-series analysis to examine trends in coverage of diphtheria, tetanus, and pertussis (DTP) vaccination across 190 countries over the past 30 years. We grouped countries into six world regions according to WHO classifications. We used Gaussian process regression to forecast future coverage rates and provide a vaccine performance index: a summary measure of the strength of immunisation coverage in a country. FINDINGS: Overall vaccine coverage increased in all six world regions between 1980 and 2010, with variation in volatility and trends. Our vaccine performance index identified that 53 countries had more than a 50% chance of missing the Global Vaccine Action Plan (GVAP) target of 90% worldwide coverage with three doses of DTP (DTP3) by 2015. These countries were mostly in sub-Saharan Africa and south Asia, but Austria and Ukraine also featured. Factors associated with DTP3 immunisation coverage varied by world region: personal income (Spearman's ρ=0·66, p=0·0011) and government health spending (0·66, p<0·0001) were informative of immunisation coverage in the Eastern Mediterranean between 1980 and 2010, whereas primary school completion was informative of coverage in Africa (0·56, p<0·0001) over the same period. The proportion of births attended by skilled health staff correlated significantly with immunisation coverage across many world regions. INTERPRETATION: Our vaccine performance index highlighted countries at risk of failing to achieve the GVAP target of 90% coverage by 2015, and could aid policy makers' assessments of the strength and resilience of immunisation programmes. Weakening correlations with socioeconomic factors show a need to tackle vaccine confidence, whereas strengthening correlations point to clear factors to address. FUNDING: UK Engineering and Physical Sciences Research Council.

Systems approaches in understanding evolution and evolvability.

Systems and network-based approaches are becoming increasingly popular in cellular biology. One contribution of such approaches has been to shed some light on the evolutionary origins of core organisational principles in biological systems, such as modularity, robustness, and evolvability. Models of interactions between genes (epistasis) have also provided insight into how sexual reproduction may have evolved. Additionally, recent work on viewing evolution as a form of learning from the environment has indicated certain bounds on the complexity of the genetic circuits that can evolve within feasible quantities of time and resources. Here we review the key studies and results in these areas, and discuss possible connections between them. In particular, we speculate on the link between the two notions of 'evolvability': the evolvability of a system in terms of how agile it is in responding to novel goals or environments, and the evolvability of certain kinds of gene network functionality in terms of its computational complexity. Drawing on some recent work on the complexity of graph-theoretic problems on modular networks, we suggest that modularity as an organising principle may have its raison d'etre in its ability to enhance evolvability, in both its senses.

How long is a piece of loop?

Loops are irregular structures which connect two secondary structure elements in proteins. They often play important roles in function, including enzyme reactions and ligand binding. Despite their importance, their structure remains difficult to predict. Most protein loop structure prediction methods sample local loop segments and score them. In particular protein loop classifications and database search methods depend heavily on local properties of loops. Here we examine the distance between a loop's end points (span). We find that the distribution of loop span appears to be independent of the number of residues in the loop, in other words the separation between the anchors of a loop does not increase with an increase in the number of loop residues. Loop span is also unaffected by the secondary structures at the end points, unless the two anchors are part of an anti-parallel beta sheet. As loop span appears to be independent of global properties of the protein we suggest that its distribution can be described by a random fluctuation model based on the Maxwell-Boltzmann distribution. It is believed that the primary difficulty in protein loop structure prediction comes from the number of residues in the loop. Following the idea that loop span is an independent local property, we investigate its effect on protein loop structure prediction and show how normalised span (loop stretch) is related to the structural complexity of loops. Highly contracted loops are more difficult to predict than stretched loops.

Hydronephrosis due to pelviureteric junction narrowing: Utility of urinary enzymes to predict the need for surgical management and follow-up.

AIM: To study the role of urinary enzymes N-acetyl-ß-glucosaminidase (NAG), alkaline phosphatase (AKP) and gamma glutamyl transferase (GGT) in the diagnosis and follow-up of patients with suspected pelviureteric junction obstruction (PUJO). MATERIALS AND METHODS: A total of 70 patients, 29 managed conservatively (group A) and 41 managed by pyeloplasty (group B), were studied prospectively. A serial measurement of urinary enzymes NAG, AKP and GGT level was performed in both the groups. The mean levels of these urinary enzymes were compared between the two groups and among the patients of the same group at presentation as well as during follow-up. RESULTS: There was a significant fall in the mean AKP level in patients managed conservatively at 8 months of follow-up. Similarly, in the operated group, there was a significant fall in the AKP levels at both 3 months and 8 months of follow-up. The mean level of GGT also showed a significant fall after 3 months of surgery but did not show further significant change at 8 months after surgery. The mean levels of NAG and GGT in the conservatively managed group were significantly low compared with that of patients requiring pyeloplasty at presentation as well as in the follow-up. The mean level of AKP was significantly low in the conservatively managed group when compared with the patients requiring surgery, but did not differ significantly in both the follow-ups after surgery. CONCLUSIONS: The level of urinary enzymes NAG, AKP and GGT are significantly high in the patients with hydronephrosis (HDN) requiring pyeloplasty when compared with the patients managed conservatively. The level of AKP significantly falls after pyeloplasty in the patients of HDN due to PUJO. There is a negative correlation with the preoperative level of enzyme NAG with split renal function in the patients of HDN requiring pyeloplasty.

Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.

The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM).

BACKGROUND: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification. RESULTS: The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively. CONCLUSION: The results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as an approach for ab initio identification of novel miRNAs.