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Knot theory, a subfield in geometric topology, is the study of the embedding of closed circles into three-dimensional Euclidean space, motivated by the ubiquity of knots in daily life and human civilization. However, focusing on topology, the current knot theory lacks metric analysis. As a result, the application of knot theory has remained largely primitive and qualitative. Motivated by the need of quantitative knot data analysis (KDA), this work implemented the evolutionary Khovanov homology (EKH) to facilitate a multiscale KDA of real-world data. EKH considers specific metrics to filter links, capturing multiscale topological features of knot configurations beyond traditional invariants. It is demonstrated that EKH can reveal non-trivial knot invariants at appropriate scales, even when the global topological structure of a knot is simple. The proposed EKH holds great potential for KDA and machine learning applications related to knot-type data, in contrast to other data forms, such as point cloud data and data on manifolds.

Abstract Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.

The ZIP family (SLC39A) is involved in transition metal homeostasis. Here, authors use integrated approaches to conclude that the ZIP from Bordetella bronchiseptica (BbZIP) utilizes the elevator-type transport mechanism to achieve alternating access.

Objective To investigate the concentration of soluble CD40 ligand (sCD40L) and interleukin (IL)-35 in peripheral blood of patients with deep vein thrombosis (DVT), and to study the protective effect of IL-35 on sCD40L-induced vascular endothelial cell injury. Methods Peripheral venous blood 3 mL was collected respectively from 30 patients with acute deep vein thrombosis patients (DVT group) and 30 healthy controls (HC group). Enzyme-linked immunosorbent assay (ELISA) was used to detect the level of serum IL-35, sCD40L, IL-1β, and IL-18. Human umbilical vein endothelial cells (HUVECs) were cultured and divided into three groups: control group (phosphate buffer), sCD40L group (25 μg/mL sCD40L), and IL-35 group (20 ng/mL IL-35 and 25 μg/mL sCD40L). CCK8 kit was used to detect the viability of HUVECs and ELISA was used to detect the level of IL-1β and IL-18 in the cell culture supernatant. The protein expression of GSDMD-N and cleaved caspase-1 in HUVECs was detected by Western blot. Results The level of IL-35 in peripheral blood of DVT patients was signifi- cantly lower than that in HC group, while the level of sCD40L, IL-1β, and IL-18 was significantly higher(P＜0.05). Furthermore, there was a negative correlation between IL-35 and sCD40L in DVT patients. In vitro, IL-35 inhibited the damage of cells viability induced by sCD40L, and reduced the concentration of IL-1β and IL-18 in culture supernatant from sCD40L group. The protein expression of GSDMD-N and cleaved caspase-1 in cells was decreased too. Conclusions The level of IL-35 in peripheral blood of DVT patients is decreased, while the level of sCD40L is increased. IL-35 has a protective effect on sCD40L-induced vascular endothelial cell injury, which may have some impacts on the pathogenesis of thrombosis.

Summary: Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.