scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.

Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.

Oligodendrocytes that survive acute coronavirus infection induce prolonged inflammatory responses in the CNS.

Neurotropic strains of mouse hepatitis virus (MHV), a coronavirus, cause acute and chronic demyelinating encephalomyelitis with similarities to the human disease multiple sclerosis. Here, using a lineage-tracking system, we show that some cells, primarily oligodendrocytes (OLs) and oligodendrocyte precursor cells (OPCs), survive the acute MHV infection, are associated with regions of demyelination, and persist in the central nervous system (CNS) for at least 150 d. These surviving OLs express major histocompatibility complex (MHC) class I and other genes associated with an inflammatory response. Notably, the extent of inflammatory cell infiltration was variable, dependent on anatomic location within the CNS, and without obvious correlation with numbers of surviving cells. We detected more demyelination in regions with larger numbers of T cells and microglia/macrophages compared to those with fewer infiltrating cells. Conversely, in regions with less inflammation, these previously infected OLs more rapidly extended processes, consistent with normal myelinating function. Together, these results show that OLs are inducers as well as targets of the host immune response and demonstrate how a CNS infection, even after resolution, can induce prolonged inflammatory changes with CNS region-dependent impairment in remyelination.

Microglia depletion exacerbates demyelination and impairs remyelination in a neurotropic coronavirus infection.

Microglia are considered both pathogenic and protective during recovery from demyelination, but their precise role remains ill defined. Here, using an inhibitor of colony stimulating factor 1 receptor (CSF1R), PLX5622, and mice infected with a neurotropic coronavirus (mouse hepatitis virus [MHV], strain JHMV), we show that depletion of microglia during the time of JHMV clearance resulted in impaired myelin repair and prolonged clinical disease without affecting the kinetics of virus clearance. Microglia were required only during the early stages of remyelination. Notably, large deposits of extracellular vesiculated myelin and cellular debris were detected in the spinal cords of PLX5622-treated and not control mice, which correlated with decreased numbers of oligodendrocytes in demyelinating lesions in drug-treated mice. Furthermore, gene expression analyses demonstrated differential expression of genes involved in myelin debris clearance, lipid and cholesterol recycling, and promotion of oligodendrocyte function. The results also demonstrate that microglial functions affected by depletion could not be compensated by infiltrating macrophages. Together, these results demonstrate that microglia play key roles in debris clearance and in the initiation of remyelination following infection with a neurotropic coronavirus but are not necessary during later stages of remyelination.

AtCircDB: a tissue-specific database for Arabidopsis circular RNAs.

Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.

Prostaglandin D2 signaling in dendritic cells is critical for the development of EAE.

Priming of autoreactive T cells in lymph nodes by dendritic cells (DCs) is critical for the pathogenesis of experimental autoimmune encephalitis (EAE). DC activation reflects a balance of pro- and anti-inflammatory signals. One anti-inflammatory factor is prostaglandin D2 signaling through its cognate receptor, D-prostanoid receptor 1 (PTGDR), on myeloid cells. Loss of PTGDR signaling might be expected to enhance DC activation and EAE but here we show that PTGDR-/- mice developed only mild signs of MOG35-55 peptide immunization-induced EAE. Compared to wild type mice, PTGDR-/- mice exhibited less demyelination, decreased leukocyte infiltration and diminished microglia activation. These effects resulted from increased pro-inflammatory responses in the lymph nodes, most notably in IL-1ß production, with the unexpected consequence of increased activation-induced apoptosis of MOG35-55 peptide-specific T cells. Conditional deletion of PTGDR on DCs, and not other myeloid cells ameliorated EAE. Together, these results demonstrate the indispensable role that PGD2/PTGDR signaling on DCs has in development of pathogenic T cells in autoimmune demyelination.

A Mechanical Evaluation of a Robot-Assisted Cutting Cornea Based on Force Response.

The aim of this paper is to propose laws of trephine operation based on a robot-assisted cutting cornea in order to obtain better microsurgical effects for keratoplasty. Using a trephine robot integrated with a microforce sensor and a handheld trephine manipulator, robotic and manual experiments were performed, with porcine corneas as the test subjects. The effect of trephine operational parameters on the results reflected by the biomechanical response is discussed, and the parameters include linear velocity, rotating angle, and angular velocity. Using probability density functions, the distributions of the manual operational parameters show some randomness, and there is a large fluctuation in the trephine force during the experiments. The biomechanical response shows regular trends in the robotic experiments even under different parameters, and compared to manual trephination, the robot may perform the operation of trephine cornea cutting more stably. Under different operational parameters, the cutting force shows different trends, and the optimal initial parameters that result in better trephine effects can be obtained based on the trends. Based on this derived law, the operational parameters can be set in robotic trephination, and surgeons can also be specially trained to achieve a better microsurgical result.

Screw Analysis, Modeling and Experiment on the Mechanics of Tibia Orthopedic with the Ilizarov External Fixator.

The Ilizarov external fixator plays an important role in the correction of complex malformed limbs. Our purpose in this work was to reveal the transmission of adjustable forces between the external fixator and the broken bone, and express the stress distribution at the end of the broken bone during the orthopedic treatment. Firstly, the screw model of the fixator was established and the theoretical relationship between the adjustable force and the stress was obtained. A sheep tibia was taken as a representative research object and its ediTable 3D entity was obtained by CT scanning. Then the mechanical model of the fixator and tibia was built using the ABAQUS software. Correction experiments were performed on the sheep tibia to measure the adjustable/support forces and tensions of the tibia. The measured results were imported to the screw and mechanical model, and the theoretical and simulation values were calculated. The theoretical tensions calculated by the screw model had a similar shape and doubled the value compared with that of the measured results. The transfer efficiency between the two results was improved and kept at about 50% after the initial 2~3 periods. The maximum stress occurring at the surface of the broken bone end was near the Kirschner wire pinhole. The simulation results for the tensions from the mechanical model showed a similar change trend, and the value was slightly higher. A biomechanical model of the Ilizarov external fixator was derived and verified through calculations, simulations and experiments. The change law of the adjustable forces and the tensions existing in the broken sheep tibias is presented herein, and offers a helpful contribution to orthopedic treatment.

Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.

Background: This study aimed to develop an algorithm using the explainable artificial intelligence (XAI) approaches for the early prediction of mortality in intensive care unit (ICU) patients with acute kidney injury (AKI). Methods: This study gathered clinical data with AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) in the US between 2008 and 2019. All the data were further randomly divided into a training cohort and a validation cohort. Seven machine learning methods were used to develop the models for assessing in-hospital mortality. The optimal model was selected based on its accuracy and area under the curve (AUC). The SHapley Additive exPlanation (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithm were utilized to interpret the optimal model. Results: A total of 22,360 patients with AKI were finally enrolled in this study (median age, 69.5 years; female, 42.8%). They were randomly split into a training cohort (16770, 75%) and a validation cohort (5590, 25%). The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.890. The SHAP values showed that Glasgow Coma Scale (GCS), blood urea nitrogen, cumulative urine output on Day 1 and age were the top 4 most important variables contributing to the XGBoost model. The LIME algorithm was used to explain the individualized predictions. Conclusions: Machine-learning models based on clinical features were developed and validated with great performance for the early prediction of a high risk of death in patients with AKI.

DNA Methylation Basis in the Effect of White Matter Integrity Deficits on Cognitive Impairments and Psychopathological Symptoms in Drug-Naive First-Episode Schizophrenia.

Background: Mounting evidence from diffusion tensor imaging (DTI) and epigenetic studies, respectively, confirmed the abnormal alterations of brain white matter integrity and DNA methylation (DNAm) in schizophrenia. However, few studies have been carried out in the same sample to simultaneously explore the WM pathology relating to clinical behaviors, as well as the DNA methylation basis underlying the WM deficits. Methods: We performed DTI scans in 42 treatment-naïve first-episode schizophrenia patients and 38 healthy controls. Voxel-based method of fractional anisotropy (FA) derived from DTI was used to assess WM integrity. Participants' peripheral blood genomic DNAm status, quantified by using Infinium® Human Methylation 450K BeadChip, was examined in parallel with DTI scanning. Participants completed Digit Span test and Trail Making test, as well as Positive and Negative Syndrome Scale measurement. We acquired genes that are differentially expressed in the brain regions with abnormal FA values according to the Allen anatomically comprehensive atlas, obtained DNAm levels of the corresponding genes, and then performed Z-test to compare the differential epigenetic-imaging associations (DEIAs) between the two groups. Results: Significant decreases of FA values in the patient group were in the right middle temporal lobe WM, right cuneus WM, right anterior cingulate WM, and right inferior parietal lobe WM, while the significant increases were in the bilateral middle cingulate WM (Ps < 0.01, GRF correction). Abnormal FA values were correlated with patients' clinical symptoms and cognitive impairments. In the DEIAs, patients showed abnormal couple patterns between altered FA and DNAm components, for which the enriched biological processes and pathways could be largely grouped into three biological procedures: the neurocognition, immune, and nervous system. Conclusion: Schizophrenia may not cause widespread neuropathological changes, but subtle alterations affecting local cingulum WM, which may play a critical role in positive symptoms and cognitive impairments. This imaging-epigenetics study revealed for the first time that DNAm of genes enriched in neuronal, immunologic, and cognitive processes may serve as the basis in the effect of WM deficits on clinical behaviors in schizophrenia.

Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients.

Glioma is the most common and most serious form of brain tumors that affects adults. Accurate prediction of survival and phenotyping of low-grade glioma (LGG) patients at high or low risk are the key to achieving precision diagnosis and treatment. This study is aimed to integrate both magnetic resonance imaging (MRI) data and gene expression data to develop a new integrated measure that represents a LGG patient's disease-specific survival (DSS) and classify subsets of patients at low and high risk for progression to cancer. We first construct the gene regulatory network by using gene expression data. We obtain twelve network modules and identify eight image biomarkers by using the Cox regression model with MRI data. Furthermore, correlation analysis between gene modules and image features identify four radiomic features. The least absolute shrinkage and selection operator (Lasso) method is applied to predict these image features with gene expression data when lacking MRI data or image segmentation technology. Furthermore, the support vector machine (SVM)-based recursive feature elimination method has been established to predict DSS using gene signatures. Finally, 4 image signatures and 43 gene signatures are recognized to be associated with the patient's prognosis. An integrated measure for combining image and gene signatures is obtained through the PSO algorithm. The concordance index (C-index) and the time-dependent receiver operating characteristic (ROC) analysis are used to evaluate prediction accuracy. The C-index obtained for this integrated measure is 0.8071 and the area under the curve (AUC) of the ROC curve is 0.79, which are higher than any other single features. The 72.1% accuracy of classification of patients is better than the accuracy associated with the published work. These results demonstrate that integration analysis of radiomic and genomic data can improve the accuracy of the prediction of survival for lower grade gliomas.