In silico structure analysis and epitope prediction of E3 CR1-beta protein of Human Adenovirus E for vaccine design

Risk factors for invasive mechanical ventilation, extracorporeal membrane oxygenation, and mortality in children with severe adenovirus infection in the pediatric intensive care unit: a retrospective study

BACKGROUND

Adenovirus infection causes considerable morbidity and mortality in pediatric patients, primarily those affected by severe respiratory system involvement. Although prevalent, it often presents vague indications, making accurate diagnosis and management challenging. This study aims to set some risk factors for invasive mechanical ventilation, ECMO, and mortality in children with severe adenovirus infection admitted to PICU.

METHODS

We evaluated 66 children with severe adenovirus infection admitted to the PICU of Children's Hospital, Zhejiang University School of Medicine, from 2018 to 2019. Data on general conditions, clinical manifestations, laboratory findings, pathogenetic and radiological discoveries, treatments, therapeutic efficacy, and outcomes were collected. Machine learning models were used to predict the need for invasive mechanical ventilation, ECMO, and mortality.

RESULTS

Of the 66 patients, 5 died, and 61 survived. Significant factors related to mortality included heart failure (p = 0.005), pericardial effusion (p = 0.032), septic shock (p = 0.009), hemoglobin levels (p = 0.013), lactate dehydrogenase (p = 0.022), albumin (p = 0.035), normal creatinine levels (p = 0.037), and pneumothorax (p = 0.002). Additional risk factors for invasive mechanical ventilation included acute respiratory distress syndrome and encephalopathy. Low breath sounds were identified as a risk factor for ECMO. For predicting poor outcomes, including invasive mechanical ventilation, ECMO, or mortality, the random forest model using these factors demonstrated high accuracy, with an area under the curve of 0.968.

CONCLUSIONS

The study indicates poor prognosis in children with severe adenovirus infection is significantly related to comorbidities and clinical symptoms. Machine learning models can accurately predict adverse outcomes, providing valuable insights for management and treatment. Identifying high-risk patients using these models can improve clinical outcomes by guiding timely and appropriate interventions.

TRIAL REGISTRATION

The article is a retrospective study without a clinical trial number, so it is not applicable.

Computational prediction of B and T-cell epitopes of Kyasanur Forest Disease virus marker proteins towards the development of precise diagnosis and potent subunit vaccine

Kyasanur Forest Disease (KFD), also known as 'monkey fever', caused by KFD Virus (KFDV), is a highly neglected tropical disease endemic to Western Ghat region of Karnataka, India. Recently, KFD, which is fatal for both monkeys and humans with a mortality rate of 2-10% has been found to spread from its epicenter to neighboring districts and states also. The current ELISA based KFD detection method is very non-specific due to cross-reactivity with other flaviviruses. Further, presently available formalin-inactivated vaccine has been found to be less effective leading to disease susceptibility and severity. To address these, the present study was aimed at predicting the potent specific B and T-cell epitopes of KFDV immunogenic marker proteins using diverse computational tools aiming at developing precise diagnostic method and an effective subunit vaccine. Here, we have chosen E, NS1 and NS5 proteins as markers of KFDV by taking into account of their differential and non-overlapping sequences with selected arboviruses. Based on the linear and nonlinear epitope prediction tools and important biophysical parameters, we identified three potential linear and ten nonlinear B-cell epitopes. We also predicted T-cell epitope peptides which binds to MHC class-I and class-II receptors for the effective T-cell activation. Thus, our molecular docking and molecular dynamics simulation analysis has identified six different TH-cell epitopes based on the distribution frequency of MHC-II haplotypes in the human population and one TC-cell epitope from NS5 protein that has maximum interaction with class-I MHC. Overall, we have successfully identified potential B and T-cell epitope marker peptides present in the envelope and two non-structural proteins.Communicated by Ramaswamy H. Sarma.

Epitope-based universal vaccine for Human T-lymphotropic virus-1 (HTLV-1)

Human T-cell leukemia virus type 1 (HTLV-1) was the first oncogenic human retrovirus identified in humans which infects at least 10-15 million people worldwide. Large HTLV-1 endemic areas exist in Southern Japan, the Caribbean, Central and South America, the Middle East, Melanesia, and equatorial regions of Africa. HTLV-1 TAX viral protein is thought to play a critical role in HTLV-1 associated diseases. We have used numerous bio-informatics and immuno-informatics implements comprising sequence and construction tools for the construction of a 3D model and epitope prediction for HTLV-1 Tax viral protein. The conformational linear B-cell and T-cell epitopes for HTLV-1 TAX viral protein have been predicted for their possible collective use as vaccine candidates. Based on in silico investigation two B cell epitopes, KEADDNDHEPQISPGGLEPPSEKHFR and DGTPMISGPCPKDGQPS spanning from 324-349 and 252-268 respectively; and T cell epitopes, LLFGYPVYV, ITWPLLPHV and GLLPFHSTL ranging from 11-19, 163-171 and 233-241 were found most antigenic and immunogenic epitopes. Among different vaccine constructs generated by different combinations of these epitopes our predicted vaccine construct was found to be most antigenic with a score of 0.57. T cell epitopes interacted strongly with HLA-A*0201 suggesting a significant immune response evoked by these epitopes. Molecular docking study also showed a high binding affinity of the vaccine construct for TLR4. The study was carried out to predict antigenic determinants of the Tax protein along with the 3D protein modeling. The study revealed a potential multi epitope vaccine that can raise the desired immune response against HTLV-1 and be useful in developing effective vaccines against Human T-lymphotropic virus.

Structural Analysis and Epitope Prediction of MHC Class-1-Chain Related Protein-A for Cancer Vaccine Development

Major histocompatibility complex class 1 chain-related gene sequence A is a polymorphic gene found at about 46.6 kb centromeric to HLA-B. It encodes a transmembrane protein, which is a non-classical human leukocyte antigen whose expression is normally induced by stress conditions like cancer and viral infections. The expression of MIC-A leads to the activation of NKG2D receptors of natural killer and T cells, leading to the generation of innate immune response that can easily eliminate/cleanse tumour cells and other cells that express the protein. Several bioinformatics and immunoinformatics tools were used to analyse the sequence and structure of the MIC-A protein. These tools were used in building and evaluating modelled structure of MIC-A, and to predict several antigenic determinant sites on the protein. The MIC-A protein structure generated an average antigenic propensity of 1.0289. Additionally, the hydrophilic regions on the surface of the MIC-A protein where antibodies can be attached were revealed. A total of fourteen antigenic epitopes were predicted, with six found in the transmembrane protein topology, and are predicted to play a role in the development of vaccines that can reactivate the functionalities of the MIC-A protein on the surface of cancer cells in order to elicit a desired immune response.

Pralidoxime and pesticide poisoning: A question of severity?

In this issue of the Biomedical Journal, we highlight new data supporting the use of pralidoxime in the treatment of cases of organophosphate poisoning, which also suggest that WHO treatment guidelines should be updated. We also learn about a modified surgical technique to repair severe spinal injuries, as well as new insight into the structure of human adenovirus that could inform vaccine development.