Search

Abstract This study aimed to explore the gut microbiota characteristics of ischemic and hemorrhagic stroke patients. A case–control study was conducted, and high-throughput sequencing of the V4–V5 region of 16S rRNA was used to analyze the differences in gut microbiota. The results showed that Proteobacteria was significantly increased in the ischemic stroke group compared with the healthy control group, while Fusobacteria was significantly increased in the hemorrhagic stroke group. In the ischemic stroke group, Butyricimonas, Alloprevotella, and Escherichia were significantly more abundant than in the healthy control group. In the hemorrhagic stroke group, Atopobium, Hungatella, Eisenbergiella, Butyricimonas, Odonbacter, Lachnociostridium, Alistipes, Parabacteroides, and Fusobacterium were significantly more abundant than in the healthy control group. Additionally, Alloprevotella, Ruminococcus, and Prevotella were significantly more abundant in the ischemic stroke group than in the hemorrhagic stroke group. The gut microbiota of ischemic and hemorrhagic stroke patients has significant diversity characteristics. These results provide new theoretical basis for exploring the prevention and treatment of different types of stroke through gut microbiota research.

Abstract The African swine fever virus (ASFV) has caused severe economic losses in the pig industry. To monitor ASFV spread, the p30 protein has been identified as an ideal infection marker due to its early and long-term expression during the ASFV infection period. Timely monitoring of ASFV p30 enables the detection of ASFV infection and assessment of disease progression. Aptamers are an outstanding substitute for antibodies to develop an efficient tool for ASFV p30 protein detection. In this study, a series of aptamer candidates were screened by in vitro magnetic bead-based systematic evolution of ligands by exponential enrichment (MB-SELEX). An aptamer (Atc-20) finally showed high specificity and affinity (Kd = 140 ± 10 pM) against ASFV p30 protein after truncation and affinity assessment. Furthermore, an aptamer/antibody heterogeneous sandwich detection assay was designed based on Atc20, achieving a linear detection of ASFV p30 ranging from 8 to 125 ng/ml and a detection limit (LOD) of 0.61 ng/ml. This assay showed good analytical performances and effectively detected p30 protein in diluted serum samples, presenting promising potential for the development of ASFV biosensors.

Abstract Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach in the rapid detection and identification of different mixed pesticides in agricultural products.

Abstract The role of RNA N6-methyladenosine (m6A) modification in the regulation of the immune microenvironment in ischaemic cardiomyopathy (ICM) remains largely unclear. This study first identified differential m6A regulators between ICM and healthy samples, and then systematically evaluated the effects of m6A modification on the characteristics of the immune microenvironment in ICM, including the infiltration of immune cells, the human leukocyte antigen (HLA) gene, and HALLMARKS pathways. A total of seven key m6A regulators, including WTAP, ZCH3H13, YTHDC1, FMR1, FTO, RBM15 and YTHDF3, were identified using a random forest classifier. A diagnostic nomogram based on these seven key m6A regulators could effectively distinguish patients with ICM from healthy subjects. We further identified two distinct m6A modification patterns (m6A cluster-A and m6A cluster-B) that are mediated by these seven regulators. Meanwhile, we also noted that one m6A regulator, WTAP, was gradually upregulated, while the others were gradually downregulated in the m6A cluster-A vs. m6A cluster-B vs. healthy subjects. In addition, we observed that the degree of infiltration of the activated dendritic cells, macrophages, natural killer (NK) T cells, and type-17 T helper (Th17) cells gradually increased in m6A cluster-A vs. m6A cluster-B vs. healthy subjects. Furthermore, m6A regulators, including FTO, YTHDC1, YTHDF3, FMR1, ZC3H13, and RBM15 were significantly negatively correlated with the above-mentioned immune cells. Additionally, several differential HLA genes and HALLMARKS signalling pathways between the m6A cluster-A and m6A cluster-B groups were also identified. These results suggest that m6A modification plays a key role in the complexity and diversity of the immune microenvironment in ICM, and seven key m6A regulators, including WTAP, ZCH3H13, YTHDC1, FMR1, FTO, RBM15, and YTHDF3, may be novel biomarkers for the accurate diagnosis of ICM. Immunotyping of patients with ICM will help to develop immunotherapy strategies with a higher level of accuracy for patients with a significant immune response.

Abstract To evaluate the “flow void” diameter in patients with pregnancy-related diseases with and without uterine AVMs and assess the diagnostic performance of unenhanced MRI for uterine AVMs. From May 2014 to April 2019, 79 patients with pregnancy-related diseases were included, including 36 with and 43 without uterine AVMs confirmed by DSA. On MRI, the diameter of the most prominent “flow void” (hereinafter referred to as fv-D) was measured and compared between patients with and without uterine AVMs. The diagnostic performance of fv-D was estimated with receiver operating characteristic curves. The “flow void” sign was observed in patients with and without uterine AVMs (P > 0.05). The fv-D was significantly larger in patients with uterine AVMs in the myometrium and parametrium than in patients without uterine AVMs (P 1.33 mm) and parametrium (sensitivity 97.2%, specificity 67.4%, negative predictive value 96.7%, positive predictive value 71.4%, AUC 0.881, cut-off > 2.6 mm). On MRI, fv-D could diagnose uterine AVMs. The fv-D had a much higher diagnostic efficiency in the parametrium than in the myometrium. The parametrium fv-D greatly improved the diagnostic sensitivity and provides a more accurate, noninvasive method of investigating possible uterine AVMs.