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Alveolar echinococcosis (AE) may affect the composition of the host's gut microbiota, potentially disrupting the balance between the gut microbiota and metabolites. Metagenomics and untargeted metabolomics were employed to characterize changes in the gut microbiota and metabolites in mouse models infected with E. multilocularis. Pearson correlation coefficients were calculated to compare the distribution of microbiota and metabolites, revealing synergistic or mutually exclusive relationships. Functional outputs of the gut microbiota were explored using the CAZy database and six enzymes involved in carbohydrate metabolism were identified with statistically significant differential expression between infected and control groups. The resistome was characterized by identifying antibiotic resistance genes annotated in the Comprehensive Antibiotic Resistance Database from the metagenomes of the groups. Firmicutes are the main carrier of ARGs in the host gut with tetQ being most prevalent. Antibiotic efflux, inactivation and target modification were the principal mechanisms of resistance. Comparison and analysis of two sets of antibiotic metabolic pathways allowed the identification of enzyme reactions unique to infected mice. KEGG pathway overview shows phenazine biosynthesis involving phzG to be one of them. In conclusion, infection with AE in mice leads to an overall disruption of gut microbiota and metabolites with the involvement of enzymes related to carbohydrate metabolism. Furthermore, antibiotic-resistance genes may play a role in disease progression, offering potential insights into the relationship between antibiotic use in AE and treatment outcomes.

Abstract Background Low-level viremia (LLV) has been identified as a potential precursor to virologic failure (VF), yet its clinical implications, particularly within the context of Integrase Strand Transfer Inhibitors (INSTIs)-based regimens, remain insufficiently explored. The study aimed to investigate the relationship between LLV and VF within ART-naïve patients on INSTIs-based regimens in China. Methods A longitudinal cohort study was conducted with ART-naïve patients aged ≥ 18 years at Beijing Ditan Hospital, under the Chinese National Free Antiretroviral Treatment Program (NFATP). The LLV was defined as a viral load (VL) ranging from 50 to 199 copies/mL after six months of ART initiation, and VF as a VL ≥ 200 copies/mL. Sensitive analyses were also performed, defining LLV as 50–999 copies/mL and VF as exceeding 1000 copies/mL. Multivariate logistic regression, Kaplan-Meier (KM) curve, and Generalized Estimating Equation (GEE) models were used to evaluate the risk factors associated with LLV and VF events. Results The study involved 830 ART-naïve patients, comprising 600 in the INSTIs group and 230 in the protease inhibitors (PIs) group. LLV events were observed in 10.4% of patients on PIs-based regimens and and 3.2% on INSTIs-based regimens (P

Pulmonary artery stenosis represents a group of disorders involving main, branch or peripheral pulmonary arteries with pain, dyspnea, hemoptysis or even no symptoms. Early diagnosis and timely intervention are crucial for reducing mortality, but timely diagnosis is challenging due to the non-specific symptoms. Computed tomography pulmonary angiography (CTPA) is useful in the diagnosis because it can provide more details about abnormal changes in the lumen, vessel wall and adjacent mediastinal structures. Congenital and acquired pulmonary artery anomalies have some characteristics on CTPA, which can be useful for differential diagnosis. Awareness of these conditions is important for radiologists. This pictorial review provides an overview of CTPA imaging features of pulmonary artery stenosis.

The potential distribution range and management strategies for P. viburni are poorly understood. Based on historical distribution data and environmental factors, the present study predicted the potentially suitable areas for P. viburni spread under different climate change scenarios using MaxEnt (maximum entropy). The results showed that precipitation of the coldest quarter (Bio19), precipitation seasonality (Bio15), and mean temperature of the wettest quarter (Bio8) were the most important environmental factors determining the distribution of P. viburni. Under the current climate conditions, its potential suitable areas are southern China, the whole of Japan, North America (especially the eastern part of the United States), the southwestern part of South America, the Mediterranean coast and most of Europe, the central part of Africa, i.e., the south of the Sahara Desert, and most of the southern coast of Australia. The total area of habitats suitable for this insect pest is predicted to be increased in the future. In order to prevent P. viburni transmission and spread, there is a need to strengthen the monitoring and quarantine measures against this pest at the Southern ports.

Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings. [ABSTRACT FROM AUTHOR]