Public health utility of cause of death data : applying empirical algorithms to improve data quality

Background: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD

Data_Sheet_1_Immunoinformatic Approach to Contrive a Next Generation Multi-Epitope Vaccine Against Achromobacter xylosoxidans Infections.docx

Achromobacter xylosoxidans, previously identified as Alcaligenes xylosoxidans, is a rod-shaped, flagellated, non-fermenting Gram-negative bacterium that has the ability to cause diverse infections in humans. As a part of its intrinsic resistance to different antibiotics, Achromobacter spp. is also increasingly becoming resistant to Carbapenems. Lack of knowledge regarding the pathogen’s clinical features has led to limited efforts to develop countermeasures against infection. The current study utilized an immunoinformatic method to map antigenic epitopes (Helper T cells, B-cell and Cytotoxic-T cells) to design a vaccine construct. We found that 20 different epitopes contribute significantly to immune response instigation that was further supported by physicochemical analysis and experimental viability. The safety profile of our vaccine was tested for antigenicity, allergenicity, and toxicity against all the identified epitopes before they were used as vaccine candidates. The disulfide engineering was carried out in an area of high mobility to increase the stability of vaccine proteins. In order to determine if the constructed vaccine is compatible with toll-like receptor, the binding affinity of vaccine was investigated via molecular docking approach. With the in silico expression in host cells and subsequent immune simulations, we were able to detect the induction of both arms of the immune response, i.e., humoral response and cytokine induced response. To demonstrate its safety and efficacy, further experimental research is necessary.</p

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, “Hantavax”, but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine’s immunogenic potential. The final vaccine construct’s three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings. Communicated by Ramaswamy H. Sarma</p

A chemoinformatic-biophysics based approach to identify novel anti-virulent compounds against <i>Pseudomonas aeruginosa</i> disulfide-bond protein A1

The conventional course of drug discovery is a lengthy, expensive and complex process and often experiences a high failure rate. This in-silico based study screened novel drug molecules against Pseudomonas aeruginosa disulfide-bond protein A1 (PaDsbA1; PDB ID of 4ZL7) using a variety of chemoinformatic and biophysics approaches. The structure-based virtual screening identified three antipseudomonal compounds (BDC_30129064, BDC_20699588 and BDC_25329008) that targeted PaDsbA1 enzyme with a binding energy score of −7.8 kcal/mol, −7.7 kcal/mol and −7.7 kcal/mol, respectively. The compounds revealed deep binding at the enzyme active pocket with close distance hydrogen bond interactions with Thr46, Pro55, Val58, Arg62, His88, and Asp180. The co-crystalized hexaethylene glycol revealed a binding energy of −6.02 kcal/mol. The docked compounds were further subjected to molecular dynamics simulation analysis in order to check the dynamic movements of docked complexes. The complexes reported no drastic changes during simulation time. In the simulation, stable compounds binding and docked conformation were accomplished. The docking and simulation results were validated using free binding energies calculation through molecular mechanics with generalized born surface area solvation and molecular mechanics Poisson Boltzmann surface area (MMGBSA/MMPBSA) approaches. The net binding energy estimated by MMGBSA for BDC_30129064, BDC_20699588 and BDC_25329008 was −75.07 kcal/mol, −77.87 kcal/mol and −59.1 kcal/mol, respectively while that of MMPBSA for the compounds was −72.47 kcal/mol, −78.99 kcal/mol and −60.991 kcal/mol, respectively. The physiochemical properties of the selected compounds indicated them to be physiochemically stable with good absorption, distribution, metabolism and elimination properties. From the above observations and predictions, the compounds can be recommended for further experimental validation in order to decipher their anti-virulence capacity in blocking disulfide bond formation in P. aeruginosa. Communicated by Ramaswamy H. Sarma</p

Structural characteristics of the vaccine model.

A) Three-dimensional model for multi-epitope vaccine model. B) physiochemical properties of the vaccine model.</p

Characteristics of designed vaccine construct.

A) Designed vaccine construct consisting of epitopes, adjuvants, and linkers. B) The predicted secondary structure of vaccine sequence. C) The Ramachandran chart depicting 86% residues in its most favoured region.</p

MHC Class-wise population coverage of the prioritized epitopes.

A) World-class MHC I population coverage. B) World-class MHC II population coverage. C) Class-combined global population coverage graph.</p

A novel therapeutic approach to prevent <i>Helicobacter pylori</i> induced gastric cancer using networking biology, molecular docking, and simulation approaches

Helicobacter pylori infects 50% of the world population and in 80% of cases, the infection progresses to the point where an ulcer develops leading to gastric cancer (GC). This study aimed to prevent GC by predicting Hub genes that are inducing GC. Furthermore, the study objective was to screen inhibitory molecules that block the function of predicted genes through several biophysical approaches. These proteins, such as Mucin 4 (MUC4) and Baculoviral IAP repeat containing 3 (BIRC3), had LogFC values of 2.28 and 3.39, respectively, and were found to be substantially expressed in those who had H. pylori infection. The MUC4 and BIRC3 inhibit apoptosis of infected cells and promote cancerous cell survival. The proteins were examined for their Physico-chemical characteristics, 3D structure and secondary structure analysis, solvent assessable surface area (SASA), active site identification, and network analysis. The MUC4 and BIRC3 expression was inhibited by docking eighty different compounds collected from the ZINC database. Fifty-seven compounds were successfully docked into the active site resulting in the lowest binding energy scores. The ZINC585267910 and ZINC585268691 compounds showed the lowest binding energy of −8.5 kcal/mol for MUC4 and −7.1 kcal/mol for BIRC3, respectively, and were considered best-docked solutions for molecular dynamics simulations. The mean root mean square deviation (RMSD) value for the ZINC585267910-MUC4 complex was 0.86 Å and the ZINC585268691-BIRC3 complex was 1.01 Å. The net MM/GBSA energy value of the ZINC585267910-MUC4 complex estimated was −46.84 kcal/mol and that of the ZINC585268691-BIRC3 complex was −44.84 kcal/mol. In a nutshell, the compounds might be investigated further as an inhibitor of the said proteins to stop the progress of GC induced by H. pylori. Communicated by Ramaswamy H. Sarma</p

Multiepitope peptide HEV vaccine docked with the human immune receptor.

TLR3-complex and protein-protein interaction between the chains.</p

Pairs of residues mutated as cysteine.

Hepatitis E virus (HEV) is one of the leading acute liver infections triggered by viral hepatitis. Patients infected with HEV usually recover and the annual death rate is negligible. Currently, there is no HEV licensed vaccine available globally. This study was carried out to design a multi-epitope HEV peptide-based vaccine by retrieving already experimentally validated epitopes from ViPR database leading to epitope prioritization. Epitopes selected as potential vaccine candidates were non-allergen, immunogenic, soluble, non-toxic and IFN gamma positive. The epitopes were linked together by AAY linkers and the linker EAAAK was used to join adjuvant with epitopes. Toll-like receptor (TLR)-4 agonist was used as an adjuvant to boost efficacy of the vaccine. Furthermore, codon optimization followed by disulfide engineering was performed to analyse the designed vaccine’s structural stability. Computational modeling of the immune simulation was done to examine the immune response against the vaccine. The designed vaccine construct was docked with TLR-3 receptor for their interactions and then subjected to molecular dynamic simulations. The vaccine model was examined computationally towards the capability of inducing immune responses which showed the induction of both humoral and cell mediated immunity. Taken together, our study suggests an In-silico designed HEV based multi-epitope peptide-based vaccine (MEPV) that needs to be examined in the wet lab-based data that can help to develop a potential vaccine against HEV.</div