Parametric analysis of the transmission dynamics during indigenous aggregated outbreaks caused by five SARS-CoV-2 strains in Nanjing, China.

Background: SARS-CoV-2 strains have been of great concern due to their high infectivity and antibody evasion. Methods: In this study, data were collected on indigenous aggregated outbreaks in Nanjing from January 2020 to December 2022, caused by five strains including the original strain, the Delta variant, and the Omicron variant (BA.2, BA.5.2, and BF.7). The basic epidemiological characteristics of infected individuals were described and then parametric analysis of transmission dynamics was performed, including the calculation of incubation period, serial interval (SI), the basic reproductive number (R0), and the household secondary attack rate (HSAR). Finally, we compared the trends of transmission dynamic parameters of different strains. Results: The incubation period for the original strain, the Delta variant, Omicron BA.2, Omicron BA.5.2, and Omicron BF.7 were 6 d (95% CI: 3.5-7.5 d), 5 d (95% CI: 4.0-6.0 d), 3 d (95% CI: 3.0-4.0 d), 3 d (95% CI: 3.0-3.0 d), and 2 d (95% CI: 2.0-3.0 d), respectively; Also, the SI of the five strains were 5.69 d, 4.79 d, 2.7 d, 2.12 d, and 2.43 d, respectively. Notably, the incubation period and SI of the five had both a progressive shortening trend (p < 0.001); Moreover, R0 of the five were 2.39 (95% CI: 1.30-4.29), 3.73 (95% CI: 2.66-5.15), 5.28 (95% CI: 3.52-8.10), 5.54 (95% CI: 2.69-11.17), 7.39 (95% CI: 2.97-18.76), with an increasing trend gradually (p < 0.01); HSAR of the five were 25.5% (95% CI: 20.1-31.7%), 27.4% (95% CI: 22.0-33.4%), 42.9% (95% CI: 34.3-51.8%), 53.1% (95% CI: 45.0-60.9%), 41.4% (95% CI, 25.5-59.3%), also with an increasing trend (p < 0.001). Conclusion: Compared to the original strain, the incubation period and SI decreased while R0 and HSAR increased, suggesting that transmission in the population was faster and the scope of the population was wider. Overall, it's crucial to keep implementing comprehensive measures like monitoring and alert systems, herd immunization plans, and outbreak control.

Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma.

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.