Biotechnological Interventions for the Production of Subunit Vaccines Against Group A Rotavirus
Bibliographic record
Abstract
Group A rotavirus (RVA) is a major cause of severe gastroenteritis in infants and young children globally, despite the availability of live-attenuated vaccines. Challenges such as limited efficacy in low-income regions, safety concerns for immunocompromised individuals, and cold-chain dependency necessitate alternative vaccine strategies. Subunit vaccines, which use specific viral proteins to elicit immunity, provide a safer and more adaptable approach. This review highlights biotechnological advancements in producing subunit vaccines, focusing on recombinant expression systems like bacterial, yeast, insect, mammalian, and plant-based platforms for scalable and cost-effective production of viral proteins. Key innovations include molecular engineering, adjuvant development, and delivery system improvements to enhance vaccine immunogenicity and efficacy. Subunit vaccines and virus-like particles expressed in various systems have demonstrated promising preclinical and clinical results, with some candidates nearing commercial readiness. Reverse vaccinology, combined with Artificial Intelligence and Machine Learning, is driving the development of innovative multiepitope vaccines and antivirals. Strategies such as passive immunization, single-chain antibodies, immunobiotics, and novel antivirals are also explored as alternative management options. The review also underscores advanced genome editing and reverse genetics approaches to improve vaccine design and antiviral therapies. These biotechnological interventions offer hope for equitable and effective control of rotavirus diarrhea, particularly in resource-limited settings, and represent significant progress toward addressing current vaccine limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".