Relative Effectiveness of Cell-Cultured versus Egg-Based Seasonal Influenza Vaccines in Preventing Influenza-Related Outcomes in Subjects 18 Years Old or Older: A Systematic Review and Meta-Analysis

Avian mutations in vaccine strains obtained from embryonated eggs could impair vaccine effec-tiveness. We performed a systematic review and meta-analysis of the adjusted relative vaccine effectiveness (arVE) of seed cell-cultured influenza vaccines (ccIV) compared to egg-based influ-enza vaccines (eIV) in preventing laboratory-confirmed influenza related outcomes (IRO) or IRO by clinical codes, in subjects 18 and over. We completed the literature search in January 2021; ap-plied exclusion criteria, evaluated risk of bias of the evidence, and performed heterogeneity, pub-lication bias, qualitative, quantitative and sensitivity analyses. All estimates were computed us-ing a random approach. International Prospective Register of Systematic Reviews, CRD42021228290. We identified 12 publications that reported 26 adjusted arVE results. Five publications reported 13 laboratory confirmed arVE and seven reported 13 code-ascertained arVE. Nine publications with 22 results were at low risk of bias. Heterogeneity was explained by season and risk of bias. We found a significant 11% (8 to 14%) adjusted arVE favoring ccIV in preventing any IRO in the 2017-2018 influenza season. The arVE was 3% (-01 to 7%) in the 2018-2019 influenza season. We found moderate evidence of a significant advantage of the ccIV in preventing IRO, compared to eIV, in a well-matched A(H3N2) predominant season.

Relative Effectiveness of Cell-Cultured versus Egg-Based Seasonal Influenza Vaccines in Preventing Influenza-Related Outcomes in Subjects 18 Years Old or Older: A Systematic Review and Meta-Analysis

Avian mutations in vaccine strains obtained from embryonated eggs could impair vaccine effec-tiveness. We performed a systematic review and meta-analysis of the adjusted relative vaccine effectiveness (arVE) of seed cell-cultured influenza vaccines (ccIV) compared to egg-based influ-enza vaccines (eIV) in preventing laboratory-confirmed influenza related outcomes (IRO) or IRO by clinical codes, in subjects 18 and over. We completed the literature search in January 2021; ap-plied exclusion criteria, evaluated risk of bias of the evidence, and performed heterogeneity, pub-lication bias, qualitative, quantitative and sensitivity analyses. All estimates were computed us-ing a random approach. International Prospective Register of Systematic Reviews, CRD42021228290. We identified 12 publications that reported 26 adjusted arVE results. Five publications reported 13 laboratory confirmed arVE and seven reported 13 code-ascertained arVE. Nine publications with 22 results were at low risk of bias. Heterogeneity was explained by season and risk of bias. We found a significant 11% (8 to 14%) adjusted arVE favoring ccIV in preventing any IRO in the 2017-2018 influenza season. The arVE was 3% (-01 to 7%) in the 2018-2019 influenza season. We found moderate evidence of a significant advantage of the ccIV in preventing IRO, compared to eIV, in a well-matched A(H3N2) predominant season.

Identification of rockfall source areas using the seed cell concept and bivariate susceptibility modelling

The objective of this research was to prepare a rockfall susceptibility map. Explorations were conducted in the Dubračina River basin (Croatia). The input data included a geological map, an orthophoto and a 1-m digital terrain model (DTM). After a talus inventory was prepared, the seed cell concept was applied to define the rockfall source areas. The contributing factors (predictors) of rockfalls were evaluated by the chi-squared test. The analysis confirmed the following predictors: CORINE land cover, lithology, slope, aspect, distance from a spring, distance from a road, distance from a fault, distance from a stream, and distance from the rock-soil geological boundary. A matrix pairwise comparison of the predictor ratings was used to define the most significant contributing factors. The predictors that affected the susceptibility map in the share of 86.3% were the slope (61.6%), lithology (13.4%), CORINE land cover (6.2%), and distance from the rock-soil geological boundary (5.1%). Two susceptibility maps were prepared: one using all nine contributing factors and another using the four most significant factors. The analysis showed that both maps were good, with the same areas under the receiver operating characteristic (ROC) curves. The map prepared with only four contributing factors can be considered a better map due to its more precise spatial definition of critical areas. It can be concluded that geological map, 1-m DTM and orthophoto provide enough data to prepare reliable rockfall susceptibility map. The application of the bivariate statistical zonation method called the “frequency ratio method” was proven to be successful. This research demonstrates that the application of the seed cell concept can be useful to speed up the process of rockfall source area detections in large research regions.

U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement

Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2D cell-based assays by measuring initial seed cell numbers. These algorithms do not, however, quantitatively test in three-dimensional (3D) cell-based assays using extracellular matrix (ECM), because cells are aggregated and overlapped in the 3D structure of the ECM such as Matrigel, collagen, and alginate. Such overlapped and aggregated cells make it difficult to segment cells and to count the number of cells accurately. It is important, however, to determine the number of cells to standardize experiments and ensure the reproducibility of 3D cell-based assays. In this study, we apply a 3D cell-counting method using U-net deep learning to high-density aggregated cells in ECM to identify initial seed cell numbers. The proposed method showed a 10% counting error in high-density aggregated cells, while the contour and watershed segmentations showed 30% and 40% counting errors, respectively. Thus, the proposed method can reduce the seed cell-counting error in 3D cell-based assays by providing the exact number of cells to researchers, thereby enabling the acquisition of quality control in 3D cell-based assays.

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.