Laboratory diagnosed microbial infection in English UK Biobank participants in comparison to the general population

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.

Combating Microbial Infections Using Metal-Based Nanoparticles as Potential Therapeutic Alternatives

The nature of microorganisms and the efficiency of antimicrobials have witnessed a huge co-dependent change in their dynamics over the last few decades. On the other side, metals and metallic compounds have gained popularity owing to their effectiveness against various microbial strains. A structured search of both research and review papers was conducted via different electronic databases, such as PubMed, Bentham, Springer, and Science Direct, among others, for the present review. Along with these, marketed products, patents, and Clinicaltrials.gov were also referred to for our review. Different microbes such as bacteria, fungi, etc., and their diverse species and strains have been reviewed and found to be sensitive to metal-carrying formulations. The products are observed to restrict growth, multiplication, and biofilm formation effectively and adequately. Silver has an apt use in this area of treatment and recovery, and other metals like copper, gold, iron, and gallium have also been observed to generate antimicrobial activity. The present review identified membrane disruption, oxidative stress, and interaction with proteins and enzymes to be the primary microbicidal processes. Elaborating the action, nanoparticles and nanosystems are shown to work in our favor in well excelled and rational ways.