Microbial community succession and changes of volatile compounds in the fermentation process of bamboo shoots.

Sour bamboo shoots are a traditional fermented delicacy that has garnered appreciation both domestically and internationally. This study investigates the intricate dynamics of microbial communities and volatile flavor compounds primarily derived from salted and pickled bamboo shoots during the fermentation process of Phyllostachys purpurea (PP). The dynamics of microorganisms and volatile flavor compounds were thoroughly examined initially using conventional isolation and cultivation methods in conjunction with high-throughput sequencing (HTS), headspace solid-phase microextraction (HS-SPME), and gas chromatography-mass spectrometry (GC-MS). In addition, we analyzed the core microorganisms responsible for modulating the volatile flavor profile. Our findings revealed 60 volatile compounds, 14 of which were the predominant contributors to the aroma of fermented PP. This group primarily comprised alcohols, aldehydes, and olefins. Notably, our investigation identified Lactobacillus and Candida as the dominant microbial genera during the middle and late stages of fermentation. These two genera exert a significant influence on the formation of characteristic aromas. Furthermore, we discovered that acids, sugars, and proteins pivotally influence the succession of microorganisms. Specifically, acids and soluble sugars drove the transition of Lactococcus to Lactobacillus and Pediococcus, whereas soluble proteins facilitated fungal succession from Candida to Kazachstania and Issatchenkia. These insights shed light on the community structure and succession patterns of flavor compounds throughout the PP fermentation process. Ultimately, they provide a foundation for optimizing the fermentation process and ensuring quality control in the production of sour bamboo shoots.

Effects of PEF on Cell and Transcriptomic of Escherichia coli.

Pulsed electric field (PEF) is an up-to-date non-thermal processing technology with a wide range of applications in the food industry. The inactivation effect of PEF on Escherichia coli was different under different conditions. The E. coli inactivated number was 1.13 ± 0.01 lg CFU/mL when PEF was treated for 60 min and treated with 0.24 kV/cm. The treatment times were found to be positively correlated with the inactivation effect of PEF, and the number of E. coli was reduced by 3.09 ± 0.01 lg CFU/mL after 100 min of treatment. The inactivation assays showed that E. coli was inactivated at electrical intensity (0.24 kV/cm) within 100 min, providing an effective inactivating outcome for Gram-negative bacteria. The purpose of this work was to investigate the cellular level (morphological destruction, intracellular macromolecule damage, intracellular enzyme inactivation) as well as the molecular level via transcriptome analysis. Field Emission Scanning Electron Microscopy (TFESEM) and Transmission Electron Microscope (TEM) results demonstrated that cell permeability was disrupted after PEF treatment. Entocytes, including proteins and DNA, were markedly reduced after PEF treatment. In addition, the activities of Pyruvate Kinase (PK), Succinate Dehydrogenase (SDH), and Adenosine Triphosphatase (ATPase) were inhibited remarkably for PEF-treated samples. Transcriptome sequencing results showed that differentially expressed genes (DEGs) related to the biosynthesis of the cell membrane, DNA replication and repair, energy metabolism, and mobility were significantly affected. In conclusion, membrane damage, energy metabolism disruption, and other pathways are important mechanisms of PEF's inhibitory effect on E. coli.

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.

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.