A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh.

AIM: Attainment of sustainable development goal (SDG) targets requires reducing the rate of new hepatitis B virus (HBV)-induced infection and mortality rate to 90% and 65%, respectively, by 2030. Therefore, it is important to investigate the feasibility of reducing the required rates of HBV-induced infection and death incidents at the current rate of vaccination coverage in Bangladesh. Moreover, factors influencing vaccination coverage like negative bias toward girls during immunization can affect the current vaccination program and ultimately hinder the efforts to reduce HBV-induced infection and death rates. To investigate the possibility of reducing HBV-induced infection and death rates with current vaccination coverage, we adopted mathematical molding-based approach. MATERIALS AND METHODS: We developed a mathematical model based on the susceptible-infectious-recovered model to simulate the HBV-induced infection in children under the age of five at three different vaccination rates: 80, 90, and 95%. Additionally the impact of current vaccination coverage was assessed on HBV-induced death rates in the future. Moreover, we took advantage of the mathematical model to investigate the impact of negative bias toward girls in vaccination program on HBV-induced infection and death rates. RESULTS: The model simulations revealed that 10% increase in the vaccination rate from 80 to 90% can potentially contribute to the significant lowering (around 40%) of HBV-induced infection rate among children. When increased by 5% of vaccination rate from 90 to 95%, the HBV-infection rate is likely to be decreased by another 22%. Likewise, 44% reduction in HBV-induced death rate in the future (2050 onward) can potentially be achieved by 10% increase in the current vaccination rate from 80 to 90%, whereas 5% increase in the current vaccination rate (90-95%) may lead to 24% further reduction of death rate. These results underscored the significant impact of vaccination in reducing HBV-induced infection among children and future death rates in adults. Moreover, at 90% vaccination coverage, the negative bias of vaccination toward girls contributes to an increase of 15 and 12% of HBV-induced infection and death rates, respectively, in female subjects compared to their male counterparts. CONCLUSION: The current vaccination coverage (80-90%) is further aggravated by untimely vaccination, dropouts from vaccination program, and negative bias toward girls in vaccination program. Therefore, if the current situation persists, it will not be possible to accomplish the required reduction in HBV-induced infection and death rates by 2030, according to the SDG guidelines. Moreover negative bias in the vaccination program may intensify the HBV-induced infection and death rates in the future. CLINICAL SIGNIFICANCE: In light of the mathematical model, we suggest that the vaccination coverage should be increased to 95% without any negative bias toward girls. To accomplish this, the concerning authorities must ensure timely and full completion of the HBV vaccine schedules, reducing dropouts from vaccination program, and lastly preventing negative bias toward girls to uplift vaccination coverage to more than 95% with gender equality. Without these strategies, the necessary reduction in the HBV-induced infection and death rates in Bangladesh may not be attained per SDG directives. HOW TO CITE THIS ARTICLE: Chakraborty S, Chakravorty R, Alam S, et al. A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh. Euroasian J Hepato-Gastroenterol 2019;9(2):84-90.

Multi-scale classification based lesion segmentation for dermoscopic images.

This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.

Dermatologist-like feature extraction from skin lesion for improved asymmetry classification in PH2 database.

Asymmetry is one of key characteristics for early diagnosis of melanoma according to medical algorithms such as (ABCD, CASH etc.). Besides shape information, cues such as irregular distribution of colors and structures within the lesion area are assessed by dermatologists to determine lesion asymmetry. Motivated by the clinical practices, we have used Kullback-Leibler divergence of color histogram and Structural Similarity metric as a measures of these irregularities. We have presented performance of several classifiers using these features on publicly available PH2 dataset. The obtained result shows better asymmetry classification than available literature. Besides being a new benchmark, the proposed technique can be used for early diagnosis of melanoma by both clinical experts and other automated diagnosis systems.

A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition.

Advanced techniques in machine learning combined with scalable "cloud" computing infrastructure are driving the creation of new and innovative health diagnostic applications. We describe a service and application for performing image training and recognition, tailored to dermatology and melanoma identification. The system implements new machine learning approaches to provide a feedback-driven training loop. This training sequence enhances classification performance by incrementally retraining the classifier model from expert responses. To easily provide this application and associated web service to clinical practices, we also describe a scalable cloud infrastructure, deployable in public cloud infrastructure and private, on-premise systems.

In silico predicted mycobacterial epitope elicits in vitro T-cell responses.

Epitope-based vaccines permit the selection of only a specific subset of epitopes to induce the necessary immune response, thus providing a rational alternative to conventional design approaches. Using a range of immunoinformatics tools, we identified a novel, contiguous 28 amino acid multi-epitope cluster within the highly conserved secretory protein Ag85B of Mycobacterium tuberculosis, the causative agent of TB. This cluster, named Ep85B, is composed of epitopes which bind to three HLA Class I and 15 Class II molecules, and harbors the potential to generate 99% population coverage in TB-endemic regions. We experimentally evaluated the capacity of Ep85B to elicit T-cell immune responses using whole blood cells and, as predicted, observed significant increases in populations of both CD4+ and memory CD4+ CD45RO+ T-cells. Our results demonstrate the practical utility of an epitope-based design methodology - a strategy that, following further evaluation, may serve as an additional tool for the development of novel vaccine candidates against TB and other diseases.

Characterization of the Protective HIV-1 CTL Epitopes and the Corresponding HLA Class I Alleles: A Step towards Designing CTL Based HIV-1 Vaccine.

Human immunodeficiency virus (HIV) possesses a major threat to the human life largely due to the unavailability of an efficacious vaccine and poor access to the antiretroviral drugs against this deadly virus. High mutation rate in the viral genome underlying the antigenic variability of the viral proteome is the major hindrance as far as the antibody based vaccine development is concerned. Although the exact mechanism by which CTL epitopes and the restricting HLA alleles mediate their action towards slow disease progression is still not clear, the important CTL restricted epitopes for controlling viral infections can be utilized in future vaccine design. This study was designed for the characterization the HIV-1 optimal CTL epitopes and their corresponding HLA alleles. CTL epitope cluster distribution analysis revealed only two HIV-1 proteins, namely, Nef and Gag, which have significant cluster forming capacity. We have found the role of specific HLA supertypes such as HLA B∗07, HLA B∗58, and HLA A∗03 in selecting the hydrophobic and conserved amino acid positions within Nef and Gag proteins, to be presented as epitopes. The analyses revealed that the clusters of optimal epitopes for Nef and p24 proteins of HIV-1 could potentially serve as a source of vaccine.

Labour-efficient in vitro lymphocyte population tracking and fate prediction using automation and manual review.

Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

In silico QSAR analysis of quercetin reveals its potential as therapeutic drug for Alzheimer's disease.

UNLABELLED: Acetylcholine-esterase (AchE) inhibitors are one of the most potent drug molecules against Alzheimer's disease (AD). But, patients treated with current AchE inhibitors often experience severe side effects. Quercetin is a plant flavonoid compound which can act as AchE inhibitor and it may be a better alternative to current AchE inhibitors in terms of effectiveness with no or fewer side effects. AIMS: The aim of the study was to compare quercetin with conventional AchE inhibitors to search for a better drug candidate. METHODS AND MATERIALS: Physico-chemical properties of conventional drugs and quercetin were predicted using bioinformatics tools. Molecular docking of these compounds on the active site of AchE was performed using AutoDock and comparative analysis was performed. Later, modification on the basic structure of quercetin with different functional groups was done to perform QSAR analysis. RESULT AND DISCUSSION: Quercetin showed a similar drug likeness score to the conventional drugs. The binding strength for quercetin in the active site of the enzyme was -8.8 kcal/mol, which was considerably higher than binding scores for some of the drugs such as donepezil (binding score -7.9 kcal/mol). Fifteen hydrogen bonds were predicted between quercetin and the enzyme whereas conventional drugs had fewer or even no hydrogen bonds. It implies that quercetin can act as a better inhibitor than conventional drugs. To find out even better inhibitor, similar structures of quercetin were searched through SIMCOMP database and a methylation in the 4-OH position of the molecule showed better binding affinity than parent quercetin. Quantitative structure activity relationship study indicated that O-4 methylation was specifically responsible for better affinity. CONCLUSION: This in silico study has conclusively predicted the superiority of the natural compound quercetin over the conventional drugs as AchE inhibitor and it sets the need for further in-vitro study of this compound in future.

HLA supertypes contribute in HIV type 1 cytotoxic T lymphocyte epitope clustering in Nef and Gag proteins.

Induction of HIV-1-specific cytotoxic T lymphocyte (CTL) responses largely depends upon the presentation of CTL epitopes to the CD8(+) T cells aided by a large number of different HLA class I alleles. Although several studies showed the clustering pattern of HIV-1 CTL epitopes, the underlying reason for this tendency remains unresolved. Moreover, the hypothesis that the CTL epitope clusters tend to coincide with the conserved and hydrophobic regions of HIV-1 proteins has been challenged in recent times. The present study aims to characterize and compare the HIV-1 CTL epitope clusters in terms of restricting HLA alleles, hydrophobicity, and sequence conservation in a proteome-wide manner by including a large number of experimentally validated CTL epitopes from the HIV Molecular Immunology Database. CTL epitope cluster distribution analysis in a proteome-wide manner revealed that only two HIV-1 proteins, namely Nef and Gag, have significant cluster-forming capacity where their epitope localization coincides with the hydrophobic and conserved regions. Furthermore, analyses of proteasomal cleavage sites and HLA anchoring motif frequencies in the epitope-dense regions highlighted the role of specific HLA supertypes such as HLA B*07, HLA B*58, HLA A*02, and HLA A*03 in selecting the hydrophobic and conserved amino acid positions within Nef and Gag proteins to be presented as epitopes. Based on our results, we hypothesize that the cluster-forming tendency of HIV-1 CTL epitopes is not a proteome-wide feature confined to Nef and Gag proteins. Their cluster-forming tendency largely depends on the host HLA alleles that contribute significantly in selecting functionally constrained hydrophobic regions within the HIV-1 proteome.

Comparative network clustering of direct repeats (DRs) and cas genes confirms the possibility of the horizontal transfer of CRISPR locus among bacteria.

CRISPRs are a diverse family of DNA repeat sequences that are widely distributed among archaea and bacteria. The CRISPR locus is usually composed of three key elements; direct repeats (DRs), spacer sequences and the cas genes. Although recent studies have suggested that spacers may be of extrachromosomal origin, the evolutionary origin of the other two elements of the CRISPR locus has remained unresolved. With the aim to elucidate the evolutionary origin and association of DRs and cas genes of the CRISPR locus, a comparative analysis of the evolutionary network clusters of DRs, cas1 and 16S rRNA genes sequences from 100 different bacteria was conducted. Significant matches of DR and cas1 gene clades imply that these CRISPR components are evolutionary closely linked and potentially evolving simultaneously as a whole locus. On the contrary, the prominent discordance between the CASS (DR and cas1) clades and the 16S rRNA clusters indicates that CRISPR locus has been transferred horizontally as a complete package. Sequence analysis also revealed that DR and cas1 genes are coevolving under analogous evolutionary pressure. This atypical evolutionary pattern also signifies the possibility of horizontal transfer event of CRISPR locus.

A computational approach for identification of epitopes in dengue virus envelope protein: a step towards designing a universal dengue vaccine targeting endemic regions.

A major problem in designing vaccine for the dengue virus has been the high antigenic variability in the envelope protein of different virus strains. In this study, a computational approach was adopted to identify a multi-epitope vaccine candidate against dengue virus that may be suitable for large populations in the dengue-endemic regions. Different bioinformatics tools were exploited that helped the identification of a conserved immunological hot-spot in the dengue envelope protein. The tools also rendered the prediction of immunogenicity and population coverage to the proposed 'in silico' vaccine candidate against dengue. A peptide region, spanning 19 amino acids, was identified in the envelope protein which found to be conserved in all four types of dengue viruses. Ten proteasomal cleavage sites were identified within the 19-mer conserved peptide sequence and a total of 8 overlapping putative cytotoxic T cell (CTL) epitopes were identified. The immunogenicity of these epitopes was evaluated in terms of their binding affinities to and dissociation half-time from respective human leukocyte antigen (HLA) molecules. The HLA allele frequencies were studied among populations in the dengue endemic regions and compared with respect to HLA restriction patterns of the overlapping epitopes. The cumulative population coverage for these epitopes as vaccine candidates was high ranging from approximately 80% to 92%. Structural analysis suggested that a 9-mer epitope fitted well into the peptide-binding groove of HLA-A*0201. In conclusion, the 19-mer epitope cluster was shown to have the potential for use as a vaccine candidate against dengue.