Vaccines as tools for advancing more than public health: perspectives of a former director of the National Vaccine Program office

Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: Multi-modal analysis by variable vision transformer

OBJECTIVES

To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma.

MATERIALS AND METHODS

vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic features, and contrast-enhanced T1-weighted images (CE-T1WI). Radiomic features were extracted from each enhancing tumor (ET), necrotic tumor core (NCR), and peritumoral edematous/infiltrated tissue (ED). CE-T1WI were split into four images and input to vViT. In the training, internal test, and external test, 271 patients with 1070 images (535 IDH wildtype, 535 IDH mutant), 35 patients with 194 images (97 IDH wildtype, 97 IDH mutant), and 291 patients with 872 images (436 IDH wildtype, 436 IDH mutant) were analyzed, respectively. Metrics including accuracy and AUC-ROC were calculated for the internal and external test datasets. Permutation importance analysis combined with the Mann-Whitney U test was performed to compare inputs.

RESULTS

For the internal test dataset, vViT correctly predicted IDH status for all patients. For the external test dataset, an accuracy of 0.935 (95% confidence interval; 0.913-0.945) and AUC-ROC of 0.887 (0.798-0.956) were obtained. For both internal and external test datasets, CE-T1WI ET radiomic features and patient characteristics had higher importance than other inputs (p < 0.05).

CONCLUSIONS

The vViT has the potential to be a competent model in predicting IDH status among adult patients with diffuse glioma. Our results indicate that age, sex, and CE-T1WI ET radiomic features have key information in estimating IDH status.

MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Application of nanomaterials against SARS-CoV-2: An emphasis on their usefulness against emerging variants of concern

Researchers are now looking to nanomaterials to fight serious infectious diseases that cause outbreaks and even pandemics. SARS-CoV-2 brought chaos to almost every walk of life in the past 2 years and has challenged every available treatment method. Although vaccines were developed in no time against it, the most pressing issue was the emergence of variants of concern arising because of the rapidly evolving viral strains. The higher pathogenicity and, in turn, the higher mortality rate of infections caused by these variants renders the existing vaccines less effective and the effort to produce further vaccines a costly endeavor. While several techniques, such as immunotherapy and repurposed pharmaceutical research, are being studied to minimize viral infection, the fundamentals of nanotechnology must also be considered to enhance the anti-SARS-CoV-2 efforts. For instance, silver nanoparticles (AgNPs) have been applied against SARS-CoV-2 effectively. Similarly, nanomaterials have been tested in masks, gloves, and disinfectants to aid in controlling SARS-CoV-2. Nanotechnology has also contributed to diagnoses such as rapid and accurate detection and treatment such as the delivery of mRNA vaccines and other antiviral agents into the body. The development of polymeric nanoparticles has been dubbed a strategy of choice over traditional drugs because of their tunable release kinetics, specificity, and multimodal drug composition. Our article explores the potential of nanomaterials in managing the variants of concern. This will be achieved by highlighting the inherent ability of nanomaterials to act against the virus on fronts such as inhibition of SARS-CoV-2 entry, inhibition of RNA replication in SARS-CoV-2, and finally, inhibition of their release. In this review, a detailed discussion on the potential of nanomaterials in these areas will be tallied with their potential against the current and emerging future variants of concern.

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.

Excess hospital admissions for pneumonia, chronic obstructive pulmonary disease, and heart failure during influenza seasons in Hong Kong

It is widely held that Southern China is a hypothetical influenza epicentre for the emergence of pandemic influenza viruses. However, influenza is perceived as a relatively unimportant infection in this part of the world compared with western countries. Hong Kong is situated within the hypothetical epicentre and serves as a sentinel post for the region. In a retrospective study, the influenza-associated excess hospitalisations in a regional hospital for pneumonia, chronic obstructive pulmonary disease (COPD), heart failure, and asthma in persons aged > or = 65 years from 1998 to 2001 were each estimated by a model taking into consideration the confounding effect of other respiratory viral infections, seasonal factors, time trends, and weather and pollution indices. In the regression models, influenza activity is an independent significant factor affecting admission rates for pneumonia, COPD, and heart failure but not that for asthma. The variations in hospital admissions for pneumonia, COPD, and heart failure explained by influenza activity were 38.9, 7.5, and 45.6%, respectively. The adjusted rates of excess influenza-associated hospital admissions for the three diagnoses combined amounted to 58.5, 20.0, 29.2, and 13.4 per 10,000 populations aged > or = 65 years in 1998, 1999, 2000, and 2001, respectively. In conclusion, influenza activity is associated significant excess hospital admissions among elderly aged 65 or above in Hong Kong, comparable to the data reported in Western countries. The findings support a wider application of annual influenza vaccination in this region.

The molecular biologist against infectious disease. Infectious diseases, in both the developing and the developed world, still pose considerable challenges to biomedical research. But science alone is clearly not sufficient to overcome the burden of disease in the world

Infectious diseases, in both the developing and the developed world, still pose considerable challenges to biomedical research. But science alone is clearly not sufficient to overcome the burden of disease in the world

Advances DNA vaccines

Extraordinary advances in biotechnology make DNA vaccines the most promising area of vaccinology. This article reviews the public health impact of vaccines in the 20th century, summarizes immunologic concepts, and updates the status of DNA vaccine development and its impact on clinical practice.