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BackgroundTraditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. ObjectiveThe objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets. MethodsWe approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning–based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models. ResultsThe F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding–based RCNN was 10% higher than that of the character encoding–based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models. ConclusionsMedical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning–based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer.

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion.

BACKGROUND: Hypertrophic response to pathological stimuli is a complex biological process that involves transcriptional and epigenetic regulation of the cardiac transcriptome. Although previous studies have implicated transcriptional factors and signaling molecules in pathological hypertrophy, the role of RNA-binding protein in this process has received little attention. METHODS: Here we used transverse aortic constriction and in vitro cardiac hypertrophy models to characterize the role of an evolutionary conserved RNA-binding protein Lin28a in pathological cardiac hypertrophy. Next-generation sequencing, RNA immunoprecipitation, and gene expression analyses were applied to identify the downstream targets of Lin28a. Epistatic analysis, metabolic assays, and flux analysis were further used to characterize the effects of Lin28a and its downstream mediator in cardiomyocyte hypertrophic growth and metabolic remodeling. RESULTS: Cardiac-specific deletion of Lin28a attenuated pressure overload-induced hypertrophic growth, cardiac dysfunction, and alterations in cardiac transcriptome. Mechanistically, Lin28a directly bound to mitochondrial phosphoenolpyruvate carboxykinase 2 ( Pck2) mRNA and increased its transcript level. Increasing Pck2 was sufficient to promote hypertrophic growth similar to that caused by increasing Lin28a, whereas knocking down Pck2 attenuated norepinephrine-induced cardiac hypertrophy. Epistatic analysis demonstrated that Pck2 mediated, at least in part, the role of Lin28a in cardiac hypertrophic growth. Furthermore, metabolomic analyses highlighted the role for Lin28a and Pck2 in promoting cardiac biosynthesis required for cell growth. CONCLUSIONS: Our study demonstrates that Lin28a promotes pathological cardiac hypertrophy and glycolytic reprograming, at least in part, by binding to and stabilizing Pck2 mRNA.

In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method. [ABSTRACT FROM AUTHOR]

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.