Plasma Circulating Cell-Free DNA Facilitated the Detection of an Alveolar Echinococcosis Patient Initially Misdiagnosed as Cystic Echinococcosis: A Case Report.

Echinococcosis, especially alveolar echinococcosis (AE), is becoming an emerging/re-emerging disease with a growing number of cases reported globally. The diagnosis of echinococcosis is based mainly on imaging, which may be challenging when the image presentation is atypical. We reported one patient with suspected cystic echinococcosis (CE) by imaging. The cell-free DNA (cfDNA) obtained from sequencing the patient's plasma before the operation showed that this patient probably had AE with 45 reads mapped to the Echinococcus multilocularis reference genome (Read-Pairs Per Million = 0.24). The patients underwent surgery, and the pathological result showed that the patient had AE. The conventional polymerase chain reaction (PCR) of her lesion sample extraction also indicated that the infection was caused by Echinococcus multilocularis. The follow-up ultrasound after three months indicated no recurrence. We demonstrated that the differentiation of CE and AE by imaging may not be that easy, with further elaboration on the differentiation between AE and CE in different aspects. We demonstrated that it is possible to use patients' plasma cfDNA mapped to Echinococcus references before the operation to obtain the objective clue of the lesion to facilitate diagnosis.

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.

A real-world disproportionality analysis of anti-VEGF drugs from the FDA Adverse Event Reporting System.

BACKGROUND: The association between anti-vascular endothelial growth factor (VEGF) drugs and ocular adverse events (AEs) has been reported, but large real-world studies of their association with systemic AEs are still lacking. METHODS: A disproportionality analysis of reports from the FDA Adverse Event Reporting System from January 2004 to September 2021 was conducted to detect the significant ADR signals with anti-VEGF drugs (including aflibercept, bevacizumab, brolucizumab, pegaptanib, and ranibizumab). RESULTS: A total of 2980 reported cases with 7125 drug-AEs were included. Five drugs were all associated with eye disorders, and pegaptanib and ranibizumab were also associated with cardiac disorders. For ranibizumab, pegaptanib, bevacizumab and aflibercept, the proportions of cardiac AEs were 8.57%, 5.62%, 3.43% and 3.20%, respectively, and the proportions of central nervous AEs were 8.81%, 7.41, 5.86% and 5.68%, respectively. In multiple comparisons, ranibizumab was significantly higher than bevacizumab and aflibercept in the proportion of cardiac AEs (P < 0.001), and ranibizumab was significantly higher than aflibercept in central nervous AEs (P < 0.001). CONCLUSIONS: Our findings support the associations between anti-VEGF drugs and ocular AEs, cardiac AEs, and central nervous AEs. After intravitreal injection, attention should not only be paid to ocular symptoms, but also to systemic symptoms.

Polyacrylamide gel as a new embedding medium for the enhancement of metabolite MALDI imaging.

In this study, polyacrylamide gel (PAAG) was successfully used as a new embedding medium to provide the more effective maintenance of biological tissues during the sectioning process, enhancing the tissue imaging of metabolites via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, PAAG, agarose, gelatin, optimal cutting temperature compound (OCT), and ice media were used to embed rat liver and Atlantic salmon (Salmo salar) eyeball samples. These embedded tissues were then sectioned into thin slices and thaw-mounted on conductive microscope glass slides for MALDI-MSI detection to evaluate the embedding effects. The results showed that PAAG embedding has characteristics superior to those of commonly-used embedding media (e.g., agarose, gelatin, OCT, and ice) with the advantages of one-step operation without heating, a better performance of morphology maintenance, the absence of PAAG polymer-ion-related interference below m/z 2000, and the more efficient in situ ionization of metabolites, providing a significant enhancement of both the numbers and intensities of the metabolite ion signals. Our study demonstrates the potential of PAAG embedding as a standard practice for metabolite MALDI tissue imaging, which will lead to an expanded application scope of MALDI-MSI.

Michler's ethylketone as a novel negative-ion matrix for the enhancement of lipid MALDI tissue imaging.

Michler's ethylketone (MEK, 4,4'-bis(diethylamino)benzophenone), commonly-known as an intermediate in the synthesis of dyes and pigments, was successfully screened and optimized as a novel matrix for the enhancement of lipid in situ detection and imaging in tissues by MALDI-MSI. The results show several properties of MEK as a powerful MALDI matrix, including strong UV absorption, µm-sized crystals and uniform matrix-coating, super high vacuum chemical stability, low matrix-related ion interference, super soft ionization, and high lipid ionization efficiency.