Adhesion analysis via a tumor vasculature-like microfluidic device identifies CD8+ T cells with enhanced tumor homing to improve cell therapy.

CD8+ T cell recruitment to the tumor microenvironment is critical for the success of adoptive cell therapy (ACT). Unfortunately, only a small fraction of transferred cells home to solid tumors. Adhesive ligand-receptor interactions have been implicated in CD8+ T cell homing; however, there is a lack of understanding of how CD8+ T cells interact with tumor vasculature-expressed adhesive ligands under the influence of hemodynamic flow. Here, the capacity of CD8+ T cells to home to melanomas is modeled ex vivo using an engineered microfluidic device that recapitulates the hemodynamic microenvironment of the tumor vasculature. Adoptively transferred CD8+ T cells with enhanced adhesion in flow in vitro and tumor homing in vivo improve tumor control by ACT in combination with immune checkpoint blockade. These results show that engineered microfluidic devices can model the microenvironment of the tumor vasculature to identify subsets of T cells with enhanced tumor infiltrating capabilities, a key limitation in ACT.

Trihexyphenidyl in young children with dystonic cerebral palsy: A single arm study.

PURPOSE: The incidence of dystonic cerebral palsy causing significant morbidity is on the rise. There is a paucity of evidence for the management of dystonia in children. METHODS: Forty-one children aged 6 months-5 years with predominantly dystonic cerebral palsy were started on a predetermined protocol of trihexyphenidyl (0.25-0.52 mg/kg) and followed up at 3, 6 and 12 weeks. Dystonia severity, motor function and developmental age at baseline and 12 weeks were compared using the Global Dystonia Scale (GDS), the Gross Motor Function Measure (GMFM), and Fine Motor/Perceptual Subscale of the Early Developmental Profile-2. Thirty-four children completed the entire 12 weeks of intervention. RESULTS: The mean age of participants was 25±11 months. A significant decrease in median total dystonia scores on the GDS was observed post-intervention (74.5 to 59, p < 0.0001), and 64% of participants gained motor milestones. GMFM scores increased significantly from a median of 19.8% pre-intervention to 26.5% post-intervention (p < 0.0001). There was improvement in the fine motor domain as compared to the baseline (p < 0.0001). The number of children classified at Gross Motor Function Classification System levels 1 and 2 increased to 47.05% from 5.88% in the pre-intervention group. CONCLUSION: Trihexyphenidyl significantly improved dystonia, motor function and development in children with dystonic cerebral palsy in this study. Additional studies are needed to clarify its role in larger numbers of children with this condition.

A message passing framework with multiple data integration for miRNA-disease association prediction.

Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at  http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.

MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction.

Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in recent years. A big challenge in building reliable machine learning models is that of data scarcity. In particular, existing approaches trained on the available small datasets, even when combined with precalculated handcrafted input features, often suffer from bad generalization and data leakage problems. We overcome the limitations of existing works by proposing a novel multitask graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (associations between miRNAs/diseases and protein-coding genes (PCGs), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multitask setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we conduct large-scale experiments on the standard benchmark datasets as well as on our proposed large independent testing sets and case studies. MuCoMiD obtains significantly higher Average Precision (AP) scores than all benchmarked models on three large independent testing sets, especially those with many new miRNAs, as well as in the detection of false positives. Thanks to its capability of learning directly from raw input information, MuCoMiD is easier to maintain and update than handcrafted feature-based methods, which would require recomputation of features every time there is a change in the original information sources (e.g., disease ontology, miRNA/disease-PCG associations, etc.). We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.

A Review of Anonymization for Healthcare Data.

Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, and reduced cost, and it drives innovative solutions within the healthcare sector. However, health data are highly sensitive and subject to regulations such as the General Data Protection Regulation, which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, although the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this article, we review the existing anonymization techniques and their applicability to various types (relational and graph based) of health data. Besides, we provide an overview of possible attacks on anonymized data. We illustrate via a reconstruction attack that anonymization, although necessary, is not sufficient to address patient privacy and discuss methods for protecting against such attacks. Finally, we discuss tools that can be used to achieve anonymization.

A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.

BACKGROUND: Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. RESULTS: We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. CONCLUSIONS: Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer .