ABSTRACT
To determine the antibody levels at 6 months in SARS-CoV-2 vaccinated individuals in COVID-recovered versus non-infected groups to determine the need to administer booster COVID vaccine in each group. Prospective longitudinal study. Pathology Department, Combined Military Hospital, Lahore for a period of eight months from July 2021 to February 2022. Two hundred and thirty three study participants in both COVID recovered and non-infected groups (105 participants in infected group, 128 participants in non-infected group) were subjected to blood sampling at 6 months post-vaccination. Anti-SARS-CoV-2 IgG antibody test was done using Chemiluminescence method. Comparison of antibody levels between COVID-recovered and non-infected groups was made. Results were compiled and statistically analyzed using SPSS version 21. Out of 233 study participants, males were 183 (78%) while females were 50 (22%), mean age being 35.93 years ± 8.298. Mean Anti-SARS-CoV-2 S IgG levels among COVID-recovered group was 1342 U/ml and among non-infected group was 828 U/ml at 6 months post-vaccination. Mean antibody titers in COVID-19 recovered group are higher than in non-infected group at 6 months post-vaccination in both groups.
ABSTRACT
Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared a pandemic. However, data on the poor or non-responders to SARS-CoV-2 vaccines in the general population are limited. The objective of this study was to comprehensively compare the immunological characteristics of poor or non-responders to SARS-CoV-2 vaccines in the 18-59-year group with those in the ≥60-year group using internationally recognized cut-off values. The main outcome was effective seroconversion characterized by an anti-SARS-CoV-2 spike IgG level of at least a four-fold increase from baseline. Profiling of naïve immune cells was analyzed prior to vaccination to demonstrate baseline immunity. The outcomes of effective seroconversion in patients aged 18-59 years with those in patients aged ≥60 years were compared. The quantitative level of anti-spike IgG was significantly lower in individuals aged ≥60 and men aged 18-59 years. There were 7.5% of poor or non-responders among the 18-59 years and 11.7% of poor or non-responders in the ≥60 years using a four-fold increase parameter. There were 37.0-58.1% with low lymphocyte count (<1000/mm3), 33.3-45.2% with low CD4 cell counts (<500/mm3), and 74.1-96.8% with low B cell counts (<100/mm3) in the non-seroconversion group. An individual with an anti-SARS-CoV-2 spike IgG titer below 50 BAU/mL might be considered a poor or non-responder between 14 and 90 days after the last vaccine dose. Booster vaccination or additional protective measures should be recommended to poor or non-responders as soon as possible to reduce disease severity and mortality.
ABSTRACT
Digital transformation is impacting every facet of science and society, not least because there is a growing need for digital services and products with the COVID-19 pandemic. But the need for digital transformation in diagnostics and personalized medicine field cuts deeper. In the past, personalized/precision medicine initiatives have been unable to capture the patients' experiences and clinical outcomes in real-time and in real-world settings. The availability of wearable smart sensors, wireless connectivity, artificial intelligence, and the Internet of Medical Things is changing the personalized/precision medicine research and implementation landscape. Digital transformation in poised to accelerate personalized/precision medicine and systems science in multiple fronts such as deep real-time phenotyping with patient-reported outcomes, high-throughput association studies between omics and highly granular phenotypic variation, digital clinical trials, among others. The present expert review offers an analysis of these systems science frontiers with a view to future applications at the intersection of digital health and personalized medicine, or put in other words, signaling the rise of "digital personalized medicine."