HLA Alleles, COVID-19 Vaccine Antibody Response and Real-World Breakthrough Outcomes (preprint)

The rapid development, approval, and global distribution of COVID-19 vaccines represent an unprecedented intervention in public health history, with over 13 billion doses administered worldwide in two years. However, our understanding of the HLA genetic underpinnings of COVID-19 vaccine-induced antibody responses and their clinical implications for breakthrough outcomes remain limited. To bridge this knowledge gap, we designed and performed a series of genetic and epidemiological analyses among 368,098 vaccinated individuals, and a subset of 194,371 participants who had antibody serology tests. Firstly, we corroborated earlier findings that SNPs associated with antibody response were predominantly located in Major Histocompatibility Complex region, and that the expansive HLA-DQB1*06 allele family was linked to better antibody responses. However, our findings contest the claim that DQB1*06 alleles alone significantly impact breakthrough risks. Additionally, our results suggest that the specific DQB1*06:04 subtype could be the true causal allele, as opposed to the previously reported DQB1*06:02. Secondly, we identified and validated six new functional HLA alleles that independently contribute to vaccine-induced antibody responses. Moreover, we unravelled additive effects of variations across multiple HLA genes that, concurrently, change the risk of clinically relevant breakthrough COVID-19 outcomes. Finally, we detangled the overall vaccine effectiveness and showed that antibody positivity accounts for approximately 20% protection against breakthrough infection and 50% against severe outcomes. These novel findings provide robust population evidence demonstrating how variations within HLA genes strongly, collectively, and causally influence vaccine-induced antibody responses, and the risk of COVID-19 breakthrough infection and related outcomes, with implications for subsequent functional research and personalised vaccination.

Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks (preprint)

Continuous diagnosis and prognosis are essential for intensive care patients. It can provide more opportunities for timely treatment and rational resource allocation, especially for sepsis, a main cause of death in ICU, and COVID-19, a new worldwide epidemic. Although deep learning methods have shown their great superiority in many medical tasks, they tend to catastrophically forget, over fit, and get results too late when performing diagnosis and prognosis in the continuous mode. In this work, we summarized the three requirements of this task, proposed a new concept, continuous classification of time series (CCTS), and designed a novel model training method, restricted update strategy of neural networks (RU). In the context of continuous prognosis, our method outperformed all baselines and achieved the average accuracy of 90%, 97%, and 85% on sepsis prognosis, COVID-19 mortality prediction, and eight diseases classification. Superiorly, our method can also endow deep learning with interpretability, having the potential to explore disease mechanisms and provide a new horizon for medical research. We have achieved disease staging for sepsis and COVID-19, discovering four stages and three stages with their typical biomarkers respectively. Further, our method is a data-agnostic and model-agnostic plug-in, it can be used to continuously prognose other diseases with staging and even implement CCTS in other fields.

Predicting COVID-19 Disease Progression and Patient Outcomes based on Temporal Deep Learning (preprint)

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident. Methods: : We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19. Results: : Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98%, 95% and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. Conclusions: : To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.