Conditioned media of glial cell lines induce alkaline phosphatase activity in cultured artery endothelial cells Identification of interleukin-6 as an induction factor

AbstractConditioned media of human glial cell lines induced alkaline phosphatase activity in cultured calf artery endothelial cells. The maximal alkaline phosphatase activity in the culture was comparable to the level in isolated brain capillary endothelial cells. An induction factor in the conditioned media was purified and identified as interleukin-6 from its amino-terminal sequence, molecular weight, amino acid composition and immunoreactivity. Recombinant interleukin-6 had similar induction activity. Our findings raise the possibility that interleukin-6 induces and modulates alkaline phosphatase activity in endothelial cells during normal development of the blood—brain barrier and under certain pathological conditions

Road Network Representation Learning with Vehicle Trajectories

Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks

Multi-fair Capacitated Students-Topics Grouping Problem

Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences. The grouping should reflect students’ aspirations as much as possible. Usually, the resulting groups should also be balanced in terms of protected attributes like gender, as studies suggest that students may learn better in mixed-gender groups. Moreover, to allow a fair workload across the groups, the cardinalities of the different groups should be balanced. In this paper, we introduce a multi-fair capacitated (MFC) grouping problem that fairly partitions students into non-overlapping groups while ensuring balanced group cardinalities (with a lower and an upper bound), and maximizing the diversity of members regarding the protected attribute. To obtain the MFC grouping, we propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. Experimental results on a real dataset and a semi-synthetic dataset show that our proposed methods can satisfy students’ preferences and deliver balanced and diverse groups regarding cardinality and the protected attribute, respectively

MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks

Accurate spatio-temporal prediction is essential for capturing city dynamics and planning mobility services. State-of-the-art deep spatio-temporal predictive models depend on rich and representative training data for target regions and tasks. However, the availability of such data is typically limited. Furthermore, existing predictive models fail to utilize cross-correlations across tasks and cities. In this paper, we propose MetaCitta, a novel deep meta-learning approach that addresses the critical challenges of data scarcity and model generalization. MetaCitta adopts the data from different cities and tasks in a generalizable spatio-temporal deep neural network. We propose a novel meta-learning algorithm that minimizes the discrepancy between spatio-temporal representations across tasks and cities. Our experiments with real-world data demonstrate that the proposed MetaCitta approach outperforms state-of-the-art prediction methods for zero-shot learning and pre-training plus fine-tuning. Furthermore, MetaCitta is computationally more efficient than the existing meta-learning approaches

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Human Dental Pulp Cells Differentiate toward Neuronal Cells and Promote Neuroregeneration in Adult Organotypic Hippocampal Slices In Vitro

The adult mammalian central nerve system has fundamental difficulties regarding effective neuroregeneration. The aim of this study is to investigate whether human dental pulp cells (DPCs) can promote neuroregeneration by (i) being differentiated toward neuronal cells and/or (ii) stimulating local neurogenesis in the adult hippocampus. Using immunostaining, we demonstrated that adult human dental pulp contains multipotent DPCs, including STRO-1, CD146 and P75-positive stem cells. DPC-formed spheroids were able to differentiate into neuronal, vascular, osteogenic and cartilaginous lineages under osteogenic induction. However, under neuronal inductive conditions, cells in the DPC-formed spheroids differentiated toward neuronal rather than other lineages. Electrophysiological study showed that these cells consistently exhibit the capacity to produce action potentials, suggesting that they have a functional feature in neuronal cells. We further co-cultivated DPCs with adult mouse hippocampal slices on matrigel in vitro. Immunostaining and presto blue assay showed that DPCs were able to stimulate the growth of neuronal cells (especially neurons) in both the CA1 zone and the edges of the hippocampal slices. Brain-derived neurotrophic factor (BDNF), was expressed in co-cultivated DPCs. In conclusion, our data demonstrated that DPCs are well-suited to differentiate into the neuronal lineage. They are able to stimulate neurogenesis in the adult mouse hippocampus through neurotrophic support in vitro

Human Dental Pulp Cells Differentiate toward Neuronal Cells and Promote Neuroregeneration in Adult Organotypic Hippocampal Slices In Vitro

The adult mammalian central nerve system has fundamental difficulties regarding effective neuroregeneration. The aim of this study is to investigate whether human dental pulp cells (DPCs) can promote neuroregeneration by (i) being differentiated toward neuronal cells and/or (ii) stimulating local neurogenesis in the adult hippocampus. Using immunostaining, we demonstrated that adult human dental pulp contains multipotent DPCs, including STRO-1, CD146 and P75-positive stem cells. DPC-formed spheroids were able to differentiate into neuronal, vascular, osteogenic and cartilaginous lineages under osteogenic induction. However, under neuronal inductive conditions, cells in the DPC-formed spheroids differentiated toward neuronal rather than other lineages. Electrophysiological study showed that these cells consistently exhibit the capacity to produce action potentials, suggesting that they have a functional feature in neuronal cells. We further co-cultivated DPCs with adult mouse hippocampal slices on matrigel in vitro. Immunostaining and presto blue assay showed that DPCs were able to stimulate the growth of neuronal cells (especially neurons) in both the CA1 zone and the edges of the hippocampal slices. Brain-derived neurotrophic factor (BDNF), was expressed in co-cultivated DPCs. In conclusion, our data demonstrated that DPCs are well-suited to differentiate into the neuronal lineage. They are able to stimulate neurogenesis in the adult mouse hippocampus through neurotrophic support in vitro

The Detection and Negative Reversion of Circulating Tumor Cells as Prognostic Biomarkers for Metastatic Castration-Resistant Prostate Cancer with Bone Metastases Treated by Enzalutamide

Enzalutamide is a second-generation androgen receptor inhibitor that increases overall survival (OS) rates in patients with metastatic castration-resistant prostate cancer (mCRPC). This study evaluates the efficacy of circulating tumor cell (CTC) status as a prognostic biomarker following enzalutamide administration. A retrospective subgroup analysis and prognostic survey were conducted on 43 patients with mCRPC and bone metastases treated in Juntendo University-affiliated hospitals from 2015 to 2022. Patients were treated with 160 mg enzalutamide daily. CTC analyses on blood samples were performed regularly before and every three months after treatment. The relationship between the patients’ clinical factors and the OS rate was analyzed using the log-rank test; the median OS was 37 months. Patients with no detected CTCs at baseline showed significantly longer OS than those with detectable CTCs at baseline. Furthermore, patients demonstrating negative reversion of CTCs during enzalutamide treatment had significantly longer OS than patients with CTC-positivity. Two biomarkers—higher hemoglobin at baseline and achieving negative reversion of CTCs—were significantly associated with prolonged OS. This study suggests that patients achieving CTC-negative reversion during treatment for mCRPC with bone metastases exhibit improved long-term OS. Chronological measurement of CTC status might be clinically useful in the treatment of mCRPC