METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.
RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.
CONCLUSION: While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.
Materials and methods: We developed a mathematical model based on the susceptible-infectious-recovered model to simulate the HBV-induced infection in children under the age of five at three different vaccination rates: 80, 90, and 95%. Additionally the impact of current vaccination coverage was assessed on HBV-induced death rates in the future. Moreover, we took advantage of the mathematical model to investigate the impact of negative bias toward girls in vaccination program on HBV-induced infection and death rates.
Results: The model simulations revealed that 10% increase in the vaccination rate from 80 to 90% can potentially contribute to the significant lowering (around 40%) of HBV-induced infection rate among children. When increased by 5% of vaccination rate from 90 to 95%, the HBV-infection rate is likely to be decreased by another 22%. Likewise, 44% reduction in HBV-induced death rate in the future (2050 onward) can potentially be achieved by 10% increase in the current vaccination rate from 80 to 90%, whereas 5% increase in the current vaccination rate (90-95%) may lead to 24% further reduction of death rate. These results underscored the significant impact of vaccination in reducing HBV-induced infection among children and future death rates in adults. Moreover, at 90% vaccination coverage, the negative bias of vaccination toward girls contributes to an increase of 15 and 12% of HBV-induced infection and death rates, respectively, in female subjects compared to their male counterparts.
Conclusion: The current vaccination coverage (80-90%) is further aggravated by untimely vaccination, dropouts from vaccination program, and negative bias toward girls in vaccination program. Therefore, if the current situation persists, it will not be possible to accomplish the required reduction in HBV-induced infection and death rates by 2030, according to the SDG guidelines. Moreover negative bias in the vaccination program may intensify the HBV-induced infection and death rates in the future.
Clinical significance: In light of the mathematical model, we suggest that the vaccination coverage should be increased to 95% without any negative bias toward girls. To accomplish this, the concerning authorities must ensure timely and full completion of the HBV vaccine schedules, reducing dropouts from vaccination program, and lastly preventing negative bias toward girls to uplift vaccination coverage to more than 95% with gender equality. Without these strategies, the necessary reduction in the HBV-induced infection and death rates in Bangladesh may not be attained per SDG directives.
How to cite this article: Chakraborty S, Chakravorty R, Alam S, et al. A Dynamic Mathematical Modeling Revelation about the Impact of Vaccination on Hepatitis B Virus-induced Infection and Death Rate in Bangladesh. Euroasian J Hepato-Gastroenterol 2019;9(2):84-90.
METHODS: We first generated 14 primary human subject-derived ASCs and stable immortalized CD10 knockdown and overexpression lines for 4 subjects by the lentiviral transduction system. To evaluate the role of CD10 in adipogenesis, the adipogenic potential of the human subject samples were scored against their respective CD10 transcript levels. Assessment of UCP1 expression levels was performed to correlate CD10 levels to the browning potential of mature ASCs. Quantitative polymerase chain reaction (qPCR) and Western blot analysis were performed to determine CD10-dependent regulation of various targets. Seahorse analysis of oxidative metabolism and lipolysis assay were studied. Lastly, as a proof-of-concept study, we used CD10 as a prospective marker for screening nuclear receptor ligands library.
RESULTS: We identified intrinsic CD10 levels as a positive determinant of adipocyte maturation as well as browning potential of ASCs. Interestingly, CD10 regulates ASC's adipogenic maturation non-canonically by modulating endogenous lipolysis without affecting the classical peroxisome proliferator-activated receptor gamma (PPARγ)-dependent adipogenic pathways. Furthermore, our CD10-mediated screening analysis identified dexamethasone and retinoic acid as stimulator and inhibitor of adipogenesis, respectively, indicating CD10 as a useful biomarker for pro-adipogenic drug screening.
CONCLUSION: Overall, we establish CD10 as a functionally relevant ASC biomarker, which may be a prerequisite to identify high-quality cell populations for improving metabolic diseases.