Antifungal activity of synthetic peptides derived from Impatiens balsamina antimicrobial peptides Ib-AMP1 and Ib-AMP4

Seeds of Impatiens balsamina contain a set of related antimicrobial peptides (Ib-AMPs). We have produced a synthetic variant of Ib-AMP1, oxidized to the bicyclic native conformation, which was fully active on yeast and fungal strains; and four linear 20-mer Ib-AMP variants, including two all-d forms. We show that the all-d variants are as active on yeast and fungal strains as native peptides. In addition, fungal growth inhibition nor salt-dependency of Ib-AMP4 could be improved by more than two-fold via replacement of amino acid residues by arginine or tryptophan. Native Ib-AMPs showed no hemolytic nor toxic activity up to a concentration of 100 µM. All these data demonstrate the potential of the native Ib-AMPs to combat fungal infections

Impact of EUS in liver transplantation workup for patients with unresectable perihilar cholangiocarcinoma

Background and Aims: For a highly selected group of patients with unresectable perihilar cholangiocarcinoma (pCCA), liver transplantation (LT) is a treatment option. The Dutch screening protocol comprises nonregional lymph node (LN) assessment by EUS, and whenever LN metastases are identified, further LT screening is precluded. The aim of this study is to investigate the yield of EUS in patients with pCCA who are potentially eligible for LT. Methods: In this retrospective, nationwide cohort study, all consecutive patients with suspected unresectable pCCA who underwent EUS in the screening protocol for LT were included from 2011 to 2021. During EUS, sampling of a “suspicious” nonregional LN was performed based on the endoscopist's discretion. The primary outcome was the added value of EUS, defined as the number of patients who were precluded from further screening because of malignant LNs. Results: A total of 75 patients were included in whom 84 EUS procedures were performed, with EUS-guided tissue acquisition confirming malignancy in LNs in 3 of 75 (4%) patients. In the 43 who underwent surgical staging according to the protocol, nonregional LNs with malignancy were identified in 6 (14%) patients. Positive regional LNs were found in 7 patients in post-LT-resected specimens. Conclusions: Our current EUS screening for the detection of malignant LNs in patients with pCCA eligible for LT shows a limited but clinically important yield. EUS with systematic screening of all LN stations, both regional and nonregional, and the sampling of suspicious lymph nodes according to defined and set criteria could potentially increase this yield.</p

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics : a pilot study

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003–2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician’s and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10585-021-10119-6