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BACKGROUND: This study aimed to investigate the differences in the microbiota composition of serum exosomes from patients with acute and chronic cholecystitis. METHOD: Exosomes were isolated from the serum of cholecystitis patients through centrifugation and identified and characterized using transmission electron microscopy and nano-flow cytometry. Microbiota analysis was performed using 16S rRNA sequencing. RESULTS: Compared to patients with chronic cholecystitis, those with acute cholecystitis exhibited lower richness and diversity. Beta diversity analysis revealed significant differences in the microbiota composition between patients with acute and chronic cholecystitis. The relative abundance of Proteobacteria was significantly higher in exosomes from patients with acute cholecystitis, whereas Actinobacteria, Bacteroidetes, and Firmicutes were significantly more abundant in exosomes from patients with chronic cholecystitis. Furthermore, functional predictions of microbial communities using Tax4Fun analysis revealed significant differences in metabolic pathways such as amino acid metabolism, carbohydrate metabolism, and membrane transport between the two patient groups. CONCLUSIONS: This study confirmed the differences in the microbiota composition within serum exosomes of patients with acute and chronic cholecystitis. Serum exosomes could serve as diagnostic indicators for distinguishing acute and chronic cholecystitis.
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Colecistitis Aguda , Colecistitis , Exosomas , Microbioma Gastrointestinal , Microbiota , Humanos , ARN Ribosómico 16S/genética , Microbioma Gastrointestinal/genética , Heces/microbiología , Microbiota/genéticaRESUMEN
Background: Elemene injection could provide clinical benefit for the treatment of various cancers, but the clinical evidence is weak. Thus, its wide use in China has raised concerns about the appropriateness of its use. Methods: This was a multicenter retrospective study to evaluate the prevalence of inappropriateness of elemene injection for hospitalized cancer patients. Patients who met the inclusion criteria were retrospectively included, and demographic characteristics were extracted from the hospital information systems. The inappropriateness of elemene injection use was assessed using the preset criteria, and the prevalence was calculated. Multivariate logistic analysis was applied to identify any factors associated with inappropriate use. Results: A total of 275 patients were included in the analysis. The median age was 62 years, and 30.9% were females. The most common cancer was lung cancer (24.0%), and 68.2% of the patients were receiving chemotherapy. The overall prevalence of inappropriateness was 61.8%. The most common reason for inappropriateness was inappropriate indications, and the second was inappropriate doses. Age and oncological department were significant risk factors associated with inappropriate use, while lung cancer, liver cancer and admission to cardiothoracic surgery were associated with a low risk of inappropriate use. Conclusion: The prevalence of inappropriateness among hospitalized elemene injection users was high. More efforts, especially those to improve the appropriateness of indications, should be made to improve the rational use of elemene, as well as other complementary medicines. Physicians should take caution to avoid inappropriate use when prescribing drugs with limited clinical evidence.
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Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in a practical re-id deployment, due to the lack of exhaustive identity labelling of positive and negative image pairs for every camera-pair. In this work, we present an unsupervised re-id deep learning approach. It is capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data end-to-end. We formulate an Unsupervised Tracklet Association Learning (UTAL) framework. This is by jointly learning within-camera tracklet discrimination and cross-camera tracklet association in order to maximise the discovery of tracklet identity matching both within and across camera views. Extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art unsupervised learning and domain adaptation person re-id methods on eight benchmarking datasets.
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Ahead of Print article withdrawn by publisher.
