Intratumor microbiome shapes the immune system and influences the outcome of various tumors. Porphyromonas gingivalis ( P. gingivalis ), the keystone periodontal pathogen, is highly epidemically connected with pancreatic cancer (PC). However, its causative role and the underlining mechanism in promoting PC oncogenesis remain unclear. Here, we illustrated the landscape of intratumor microbiome and its bacterial correlation with oral cavity in PC patients, where P. gingivalis presented both in the oral cavity and tumor tissues. When exposed to P. gingivalis , tumor development was accelerated in orthotopic and subcutaneous PC mouse model, and the cancerous pancreas exhibited a neutrophils-dominated proinflammatory tumor microenvironment. Mechanistically, the intratumoral P. gingivalis promoted PC progression via elevating the secretion of neutrophilic chemokines and neutrophil elastase (NE). Collectively, our study disclosed the bacterial link between periodontitis and PC, and revealed a previously unrecognized mechanism of P. gingivalis in PC pathophysiology, hinting at therapeutic implications.

Background Pulmonary embolism (PE) is a leading cause of cardiovascular mortality worldwide. Rapid and accurate diagnosis and risk stratification are crucial for timely treatment options, especially in high‐risk PE. Objectives The study aims to profile the comprehensive changes of plasma proteomes in PE patients and identify the potential biomarkers for both diagnosis and risk stratification. Patients/Methods Based on the data‐independent acquisition mass spectrometry and antibody array proteomic technology, we screened the plasma samples (13 and 32 proteomes, respectively) in two independent studies consisting of high‐risk PE patients, non‐high‐risk PE patients, and healthy controls. Some significantly differentially expressed proteins were quantified by ELISA in a new study group with 50 PE patients and 26 healthy controls. Results We identified 207 and 70 differentially expressed proteins in PE and high‐risk PE. These proteins were involved in multiple thrombosis‐associated biological processes including blood coagulation, inflammation, injury, repair, and chemokine‐mediated cellular response. It was verified that five proteins including SAA1, S100A8, TNC, GSN, and HRG had significant change in PE and/or in high‐risk PE. The receiver operating characteristic curve analysis based on binary logistic regression showed that the area under the curve (AUC) of SAA1, S100A8, and TNC in PE diagnosis were 0.882, 0.788, and 0.795, and AUC of S100A8 and TNC in high‐risk PE diagnosis were 0.773 and 0.720. Conclusion As predictors of inflammation or injury repair, SAA1, S100A8, and TNC are potential plasma biomarkers for the diagnosis and risk stratification of PE.

BackgroundSmartphone-based online education gained popularity during and after the COVID-19 pandemic. Although recent studies have highlighted the association between problematic smartphone use (PSU) and mental health symptoms, the potential role of online learning in this relationship remains unclear. This study aimed to analyze the relationships between higher education modes, PSU, and related psychological symptoms in university students.MethodsA total of 1,629 Chinese university students from five provinces completed a web-based questionnaire survey between March 2020 and October 2021. Demographic characteristics and learning conditions were recorded. All participants completed the Smartphone Addiction Scale-Short Version, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Athens Insomnia Scale. Multiple regressions models and stratified analyses were used to examine the association between online education mode, PSU, and symptoms of depression, anxiety, and insomnia.ResultsThe prevalence of PSU was 58.5%. Students who relied primarily on online learning had a higher prevalence of depressive symptoms (29.95% vs. 22.24%), anxiety symptoms (25.13% vs. 18.91%), and insomnia symptoms (75.89% vs. 70.27%) than those who relied on traditional face-to-face learning (Ps < 0.05). After adjusting for covariates, subjects with PSU were more likely to report depressive symptoms (AdjOR = 3.14, 95% CI = 2.26–4.37), anxiety symptoms (AdjOR = 3.73, 95% CI = 2.13–4.59), and insomnia symptoms (AdjOR = 2.96, 95% CI = 2.23–3.92) than those without PSU. Furthermore, the associations of PSU with depressive symptoms (OR = 4.66 vs. 2.33, P for interaction = 0.015) and anxiety symptoms (OR = 6.05 vs. 2.94, P for interaction = 0.021) were more pronounced in the online learning group.ConclusionOur study provides preliminary evidence that Chinese university students have serious smartphone addiction problems, which are associated with depressive, anxiety, and insomnia symptoms. Online learning is found to exacerbate PSU and mental health problems. Our findings provide valuable information for targeted psychological interventions in the post-COVID-19 era.

The integration of HBV DNA into the human genome can disrupt its structure in hepatocellular carcinoma (HCC), but the complexity of HBV genomic integration remains elusive. Here we applied long-read sequencing to precisely elucidate the HBV integration pattern in the human hepatocellular genome. The DNA library was sequenced using the long-read sequencing on GridION and PacBio Sequel II, respectively. The DNA and mRNA were sequenced using next-generation sequencing on Illumina NextSeq. BLAST (Basic Local Alignment Search Tool) and local scripts were used to analyze HBV integration patterns. We established an analytical strategy based on the long-read sequences, and analyzed the complexity of HBV DNA integration into the hepatocellular genome. A total of 88 integrated breakpoints were identified. HBV DNA integration into human genomic DNA was mainly fragmented with different orientations, rarely with a complete genome. The same HBV integration breakpoints were identified among the three platforms. Most breakpoints were observed at P, X, and S genes in the HBV genome, and observed at introns, intergenic sequences, and exons in the human genome. Tumor tissue harbored a much higher integrated number than the adjacent tissue, and the distribution of HBV integrated into human chromosomes was more concentrated. HBV integration shows different patterns between cancer cells and adjacent normal cells. We for the first time obtained the entire HBV integration pattern through long-read sequencing and demonstrated the value of long-read sequencing in detecting the genomic integration structures of viruses in host cells.

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