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BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.
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Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors. To properly evaluate the best predictive approaches currently available and avoid prejudice, this study employed three different prognosis prediction models including a statistical logistic regression model, commonly used clinical-based scores, and a latest high-performance ML-based XGBoost model. The study revealed that the XGBoost model outperformed other two traditional models, achieving an AUROC of 0.929 in predicting the prognosis changes of stroke patients followed for 3 months. In addition, the XGBoost model maintained remarkably high precision even when using only selected 20 most relevant clinical features compared to full clinical datasets used in the study. These selected features closely correlated with significant changes in clinical outcomes for stroke patients and showed to be effective for predicting prognosis changes after discharge, allowing physicians to make optimal decisions regarding their patients' recovery.
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AVC Isquêmico , Aprendizado de Máquina , Sistema de Registros , Humanos , Prognóstico , AVC Isquêmico/mortalidade , AVC Isquêmico/diagnóstico , Feminino , Masculino , Idoso , Pessoa de Meia-Idade , Modelos Logísticos , Idoso de 80 Anos ou mais , Acidente Vascular Cerebral/diagnóstico , Acidente Vascular Cerebral/mortalidadeRESUMO
Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.
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Lesões Acidentais , Vasos Coronários , Humanos , Vasos Coronários/diagnóstico por imagem , Angiografia Coronária , Benchmarking , Exame Físico , Processamento de Imagem Assistida por ComputadorRESUMO
One of the most important properties of human embryonic stem cells (hESCs) is related to their primed and naïve pluripotent states. Our previous meta-analysis indicates the existence of heterogeneous pluripotent states derived from diverse naïve protocols. In this study, we have characterized a commercial medium (RSeT)-based pluripotent state under various growth conditions. Notably, RSeT hESCs can circumvent hypoxic growth conditions as required by naïve hESCs, in which some RSeT cells (e.g., H1 cells) exhibit much lower single cell plating efficiency, having altered or much retarded cell growth under both normoxia and hypoxia. Evidently, hPSCs lack many transcriptomic hallmarks of naïve and formative pluripotency (a phase between naive and primed states). Integrative transcriptome analysis suggests our primed and RSeT hESCs are close to the early stage of post-implantation embryos, similar to the previously reported primary hESCs and early hESC cultures. Moreover, RSeT hESCs did not express naïve surface markers such as CD75, SUSD2, and CD130 at a significant level. Biochemically, RSeT hESCs exhibit a differential dependency of FGF2 and co-independency of both Janus kinase (JAK) and TGFß signaling in a cell-line-specific manner. Thus, RSeT hESCs represent a previously unrecognized pluripotent state downstream of formative pluripotency. Our data suggest that human naïve pluripotent potentials may be restricted in RSeT medium. Hence, this study provides new insights into pluripotent state transitions in vitro.
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BACKGROUND AND OBJECTIVES: Multimorbidity is common in patients who experience stroke. Less is known about the effect of specific multimorbidity patterns on long-term disability in patients with stroke. Furthermore, given the increased poststroke disability frequently seen in female vs male patients, it is unknown whether multimorbidity has a similar association with disability in both sexes. We assessed whether specific multimorbidity clusters were associated with greater long-term poststroke disability burden overall and by sex. METHODS: In the Taiwan Stroke Registry, an ongoing nationwide prospective registry, patients with first-ever ischemic stroke were enrolled; this analysis is restricted to those individuals surviving to at least 6 months poststroke. Using a hierarchical clustering approach, clusters of prestroke multimorbidity were generated based on 16 risk factors; the algorithm identified 5 distinct clusters. The association between clusters and 12-month poststroke disability, defined using the modified Rankin Scale (mRS), was determined using logistic regression models, with additional models stratified by sex. The longitudinal association between multimorbidity and functional status change was assessed using mixed-effects models. RESULTS: Nine-thousand eight hundred eighteen patients with first-ever ischemic stroke were included. The cluster with no risk factors was the reference, "healthier" risk group (N = 1,373). Patients with a cluster profile of diabetes, peripheral artery disease (PAD), and chronic kidney disease (CKD) (N = 1882) had significantly greater disability (mRS ≥ 3) at 1 month (OR [95% CI] = 1.36 [1.13-1.63]), 3 months (OR [95% CI] = 1.27 [1.04-1.55]), and 6 months (OR [95% CI] = 1.30 [1.06-1.59]) but not at 12 months (OR [95% CI] = 1.16 [0.95-1.42]) than patients with a healthier risk factor profile. In the sex-stratified analysis, the associations with this risk cluster remained consistent in male patients (OR [95% CI] = 1.42 [1.06-1.89]) at 12 months, who also had a higher comorbidity burden, but not in female patients (OR [95% CI] = 0.95 [0.71-1.26]), who had higher proportions of severe strokes and severe disability (p-interaction = 0.04). DISCUSSION: Taiwanese patients with multimorbidity, specifically the concurrent presence of diabetes, PAD, and CKD, had higher odds of a worse functional outcome in the first 6 months poststroke. Clusters of multimorbidity may be less informative for long-term disability in female patients. Further studies should evaluate other mechanisms for worse disability in female patients poststroke.
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Diabetes Mellitus , AVC Isquêmico , Insuficiência Renal Crônica , Acidente Vascular Cerebral , Humanos , Masculino , Feminino , Multimorbidade , Caracteres Sexuais , Taiwan/epidemiologia , Acidente Vascular Cerebral/complicações , Acidente Vascular Cerebral/epidemiologia , Diabetes Mellitus/epidemiologia , Sistema de RegistrosRESUMO
Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.
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COVID-19 , Humanos , COVID-19/diagnóstico , Computação em Nuvem , SARS-CoV-2/genética , Fluxo de Trabalho , GenômicaRESUMO
Among kidney cancers, clear cell renal cell carcinoma (ccRCC) has the highest incidence rate in adults. The survival rate of patients diagnosed as having metastatic ccRCC drastically declines even with intensive treatment. We examined the efficacy of simvastatin, a lipid-lowering drug with reduced mevalonate synthesis, in ccRCC treatment. Simvastatin was found to reduce cell viability and increase autophagy induction and apoptosis. In addition, it reduced cell metastasis and lipid accumulation, the target proteins of which can be reversed through mevalonate supplementation. Moreover, simvastatin suppressed cholesterol synthesis and protein prenylation that is essential for RhoA activation. Simvastatin might also reduce cancer metastasis by suppressing the RhoA pathway. A gene set enrichment analysis (GSEA) of the human ccRCC GSE53757 data set revealed that the RhoA and lipogenesis pathways are activated. In simvastatin-treated ccRCC cells, although RhoA was upregulated, it was mainly restrained in the cytosolic fraction and concomitantly reduced Rho-associated protein kinase activity. RhoA upregulation might be a negative feedback effect owing to the loss of RhoA activity caused by simvastatin, which can be restored by mevalonate. RhoA inactivation by simvastatin was correlated with decreased cell metastasis in the transwell assay, which was mimicked in dominantly negative RhoA-overexpressing cells. Thus, owing to the increased RhoA activation and cell metastasis in the human ccRCC dataset analysis, simvastatin-mediated Rho inactivation might serve as a therapeutic target for ccRCC patients. Altogether, simvastatin suppressed the cell viability and metastasis of ccRCC cells; thus, it is a potentially effective ccRCC adjunct therapy after clinical validation for ccRCC treatment.
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Carcinoma de Células Renais , Neoplasias Renais , Humanos , Sinvastatina/farmacologia , Carcinoma de Células Renais/tratamento farmacológico , Ácido Mevalônico/metabolismo , Neoplasias Renais/tratamento farmacológico , Lipídeos , Proteína rhoA de Ligação ao GTP/metabolismoRESUMO
Patients with intracranial artery stenosis show high incidence of stroke. Angiography reports contain rich but underutilized information that can enable the detection of cerebrovascular diseases. This study evaluated various natural language processing (NLP) techniques to accurately identify eleven intracranial artery stenosis from angiography reports. Three NLP models, including a rule-based model, a recurrent neural network (RNN), and a contextualized language model, XLNet, were developed and evaluated by internal-external cross-validation. In this study, angiography reports from two independent medical centers (9614 for training and internal validation testing and 315 as external validation) were assessed. The internal testing results showed that XLNet had the best performance, with a receiver operating characteristic curve (AUROC) ranging from 0.97 to 0.99 using eleven targeted arteries. The rule-based model attained an AUROC from 0.92 to 0.96, and the RNN long short-term memory model attained an AUROC from 0.95 to 0.97. The study showed the potential application of NLP techniques such as the XLNet model for the routine and automatic screening of patients with high risk of intracranial artery stenosis using angiography reports. However, the NLP models were investigated based on relatively small sample sizes with very different report writing styles and a prevalence of stenosis case distributions, revealing challenges for model generalization.
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Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to existing vaccines and therapeutics. Meanwhile, analyzing many raw sequencing data repeatedly with currently available code-based bioinformatics tools is tremendously challenging to be implemented in this unprecedented pandemic time due to the fact of limited experts and computational resources. Therefore, in order to hasten variant surveillance efforts, we developed an installation-free cloud workflow for robust mutation profiling of SARS-CoV-2 variants from multiple Illumina sequencing data. Herein, 55 raw sequencing data representing four early SARS-CoV-2 variants of concern (Alpha, Beta, Gamma, and Delta) from an open-access database were used to test our workflow performance. As a result, our workflow could automatically identify mutated sites of the variants along with reliable annotation of the protein-coding genes at cost-effective and timely manner for all by harnessing parallel cloud computing in one execution under resource-limitation settings. In addition, our workflow can also generate a consensus genome sequence which can be shared with others in public data repositories to support global variant surveillance efforts.
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COVID-19 , SARS-CoV-2 , COVID-19/genética , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Mutação , SARS-CoV-2/genética , Glicoproteína da Espícula de Coronavírus/genética , Fluxo de TrabalhoRESUMO
Methylprednisolone (MP) is an anti-inflammatory drug approved for the treatment of acute spinal cord injuries (SCIs). However, MP administration for SCIs has become a controversial issue while the molecular effects of MP remain unexplored to date. Therefore, delineating the benefits and side effects of MP and determining what MP cannot cure in SCIs at the molecular level are urgent issues. Here, genomic profiles of the spinal cord in rats with and without injury insults, and those with and without MP treatment, were generated at 0, 2, 4, 6, 8, 12, 24, and 48 h post-injury. A comprehensive analysis was applied to obtain three distinct classes: side effect of MP (SEMP), competence of MP (CPMP), and incapability of MP (ICMP). Functional analysis using these genes suggested that MP exerts its greatest effect at 8~12 h, and the CPMP was reflected in the immune response, while SEMP suggested aspects of metabolism, such as glycolysis, and ICMP was on neurological system processes in acute SCIs. For the first time, we are able to precisely reveal responsive functions of MP in SCIs at the molecular level and provide useful solutions to avoid complications of MP in SCIs before better therapeutic drugs are available.
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Anti-Inflamatórios/farmacologia , Metilprednisolona/farmacologia , Traumatismos da Medula Espinal/patologia , Transcriptoma/efeitos dos fármacos , Animais , Anti-Inflamatórios/uso terapêutico , Modelos Animais de Doenças , Feminino , Metilprednisolona/uso terapêutico , Ratos , Ratos Long-Evans , Medula Espinal/metabolismo , Traumatismos da Medula Espinal/tratamento farmacológico , Fatores de TempoRESUMO
BACKGROUND: Effectively utilizing disease-relevant text information from unstructured clinical notes for medical research presents many challenges. BERT (Bidirectional Encoder Representation from Transformers) related models such as BioBERT and ClinicalBERT, pre-trained on biomedical corpora and general clinical information, have shown promising performance in various biomedical language processing tasks. OBJECTIVES: This study aims to explore whether a BERT-based model pre-trained on disease-related clinical information can be more effective for cerebrovascular disease-relevant research. METHODS: This study proposed the StrokeBERT which was initialized from BioBERT and pre-trained on large-scale cerebrovascular disease related clinical text information. The pre-trained corpora contained 113,590 discharge notes, 105,743 radiology reports, and 38,199 neurological reports. Two real-world empirical clinical tasks were conducted to validate StrokeBERT's performance. The first task identified extracranial and intracranial artery stenosis from two independent sets of radiology angiography reports. The second task predicted the risk of recurrent ischemic stroke based on patients' first discharge information. RESULTS: In stenosis detection, StrokeBERT showed improved performance on targeted carotid arteries, with an average AUC compared to that of ClinicalBERT of 0.968 ± 0.021 and 0.956 ± 0.018, respectively. In recurrent ischemic stroke prediction, after 10-fold cross-validation on 1,700 discharge information, StrokeBERT presented better prediction ability (AUC±SD = 0.838 ± 0.017) than ClinicalBERT (AUC±SD = 0.808 ± 0.045). The attention scores of StrokeBERT showed better ability to detect and associate cerebrovascular disease related terms than current BERT based models. CONCLUSIONS: This study shows that a disease-specific BERT model improved the performance and accuracy of various disease-specific language processing tasks and can readily be fine-tuned to advance cerebrovascular disease research and further developed for clinical applications.
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Transtornos Cerebrovasculares , Processamento de Linguagem Natural , Transtornos Cerebrovasculares/diagnóstico por imagem , Humanos , IdiomaRESUMO
The ground or naive pluripotent state of human pluripotent stem cells (hPSCs), which was initially established in mouse embryonic stem cells (mESCs), is an emerging and tentative concept. To verify this vital concept in hPSCs, we performed a multivariate meta-analysis of major hPSC datasets via the combined analytic powers of percentile normalization, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and SC3 consensus clustering. This robust bioinformatics approach has significantly improved the predictive values of our meta-analysis. Accordingly, we revealed various similarities or dissimilarities between some naive-like hPSCs (NLPs) generated from different laboratories. Our analysis confirms some previous studies and provides new evidence concerning the existence of three distinct naive-like pluripotent states. Moreover, our study offers global transcriptomic markers that define diverse pluripotent states under various hPSC growth protocols.
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Genômica/métodos , Células-Tronco Pluripotentes/metabolismo , Transcriptoma , Diferenciação Celular , Proliferação de Células , Humanos , Células-Tronco Pluripotentes/citologiaRESUMO
Aim: The ability to predict outcomes can help clinicians to better triage and treat stroke patients. We aimed to build prediction models using clinical data at admission and discharge to assess predictors highly relevant to stroke outcomes. Methods: A total of 37,094 patients from the Taiwan Stroke Registry (TSR) were enrolled to ascertain clinical variables and predict their mRS outcomes at 90 days. The performances (i.e., the area under the curves (AUCs)) of these independent predictors identified by logistic regression (LR) based on clinical variables were compared. Results: Several outcome prediction models based on different patient subgroups were evaluated, and their AUCs based on all clinical variables at admission and discharge were 0.85-0.88 and 0.92-0.96, respectively. After feature selections, the input features decreased from 140 to 2-18 (including age of onset and NIHSS at admission) and from 262 to 2-8 (including NIHSS at discharge and mRS at discharge) at admission and discharge, respectively. With only a few selected key clinical features, our models can provide better performance than those previously reported in the literature. Conclusion: This study proposed high performance prognostics outcome prediction models derived from a population-based nationwide stroke registry even with reduced LR-selected clinical features. These key clinical features can help physicians to better focus on stroke patients to triage for best outcome in acute settings.
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INTRODUCTION: Being able to predict functional outcomes after a stroke is highly desirable for clinicians. This allows clinicians to set reasonable goals with patients and relatives, and to reach shared after-care decisions for recovery or rehabilitation. The aim of this study was to apply various machine learning (ML) methods for 90-day stroke outcome predictions, using a nationwide disease registry. METHODS: This study used the Taiwan Stroke Registry (TSR) which has prospectively collected data from stroke patients since 2006. Three known ML models (support vector machine, random forest, and artificial neural network), and a hybrid artificial neural network were implemented and evaluated by 10-time repeated hold-out with 10-fold cross-validation. RESULTS: ML techniques present over 0.94 AUC in both ischemic and hemorrhagic stroke using preadmission and inpatient data. By adding follow-up data, the prediction ability improved to 0.97 AUC. We screened 206 clinical variables to identify 17 important features from the ischemic stroke dataset and 22 features from the hemorrhagic stroke dataset without losing much performance. Error analysis revealed that most prediction errors come from more severe stroke patients. CONCLUSION: The study showed that ML techniques trained from large, cross-reginal registry datasets were able to predict functional outcome after stroke with high accuracy. The follow-up data is important which can further improve the predictive models' performance. With similar performances among different ML techniques, the algorithm's characteristics and performance on severe stroke patients will be the primary focus when we further develop inference models and artificial intelligence tools for potential medical.
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Aprendizado de Máquina , Avaliação de Processos e Resultados em Cuidados de Saúde/métodos , Sistema de Registros , Acidente Vascular Cerebral , Idoso , Inteligência Artificial , Feminino , Previsões , Humanos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Desempenho Físico Funcional , Reabilitação do Acidente Vascular Cerebral , Máquina de Vetores de Suporte , TaiwanRESUMO
INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction. Therefore, the quality of the measurements is crucial for training and validation of these models. The objective of this study was to apply and evaluate density-based outlier detection methods for identifying potentially incorrect measurements in multiple large stroke datasets to assess the measurement quality. METHOD: We applied three density-based outlier detection methods including density-based spatial clustering of applications (DBSCAN), hierarchical DBSCAN (HDBSCAN) and local outlier factor (LOF) based on a large dataset obtained from a nationwide prospective stroke registry in Taiwan. The testing of each method was done by using four different NINDS funded stroke datasets. RESULT: The DBSCAN achieved a high performance across all mRS values where the highest average accuracy was 99.2⯱â¯0.7 at mRS of 4 and the lowest average accuracy was 92.0⯱â¯4.6 at mRS of 3. The LOF also achieved similar performance, however, the HDBSCAN with default parameters setting required further tuning improvement. CONCLUSION: The density-based outlier detection methods were proven to be promising for validation of stroke outcome measures. The outlier detection algorithm developed from a large prospective registry dataset was effectively applied in four different NINDS stroke datasets with high performance results. The tool developed from this detection algorithm can be further applied to real world datasets to increase the data quality in stroke outcome measures.
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Algoritmos , Aprendizado de Máquina , Avaliação de Resultados em Cuidados de Saúde/métodos , Avaliação de Resultados em Cuidados de Saúde/estatística & dados numéricos , Acidente Vascular Cerebral/patologia , Idoso , Análise por Conglomerados , Conjuntos de Dados como Assunto , Feminino , Humanos , Masculino , Avaliação de Resultados em Cuidados de Saúde/normas , Estudos Prospectivos , Projetos de Pesquisa , Acidente Vascular Cerebral/epidemiologia , Acidente Vascular Cerebral/terapia , Taiwan/epidemiologia , Resultado do Tratamento , Estudos de Validação como AssuntoRESUMO
INTRODUCTION: Precision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease-based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care. METHODS: The LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care. RESULTS: The implementation of a shared-design, full-cycle LHS with an enhanced CIMS, combined with hospital-based real-world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high-quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full-cycle LHS for TCVGH's dementia research to improve the care of elderly patients. CONCLUSIONS: Knowledge generation requires high-quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.
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Informática Médica/métodos , Avaliação de Resultados em Cuidados de Saúde , Assistência ao Paciente/métodos , Doença Aguda , Algoritmos , Ensaios Clínicos como Assunto , Cuidados Críticos/métodos , Processamento Eletrônico de Dados , Humanos , Leucemia/terapia , Transplante de Fígado , Doadores Vivos , Oncologia , Neoplasias/psicologia , Neoplasias/terapia , Qualidade de Vida , Cirurgia Assistida por ComputadorRESUMO
Alternative splicing (AS) of pre-messenger (m)RNA is a pivotal mechanism in expanding proteomic diversity, which determines the functions of mammalian cells. By conducting transcriptome analyses to profile splicing events in human colorectal cancer (CRC) tissues compared to adjacent normal counterparts, we noted differential splicing profiles of serine/arginine-rich splicing factor 3 (SRSF3) and mitogen-activated protein 4 kinase 4 (MAP4K4) in cancerous tissues of CRC compared to adjacent normal tissues. In addition to SRSF3-mediated autoregulation, RNA-binding motif protein 4 (RBM4) constituted another mechanism in reprogramming the splicing profile of SRSF3. Upregulated expressions of SRSF3 in CRC cells modulated utilization of MAP4K4 exon 16 in a sequence-dependent manner. Alternatively spliced MAP4K4 variants exhibited differential effects on the phosphorylation of c-Jun N-terminal protein kinase 1 (JNK1) which subsequently modulated expression profiles of E-cadherin, N-cadherin, and vimentin, all of which are involved in the migration and invasion of CRC cells. Collectively, RBM4-SRSF3-MAP4K4 constitutes a novel mechanism for manipulating the metastasis of CRC cells through the JNK1 signaling pathway.
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Neoplasias Colorretais/metabolismo , Peptídeos e Proteínas de Sinalização Intracelular/metabolismo , Sistema de Sinalização das MAP Quinases , Proteínas de Neoplasias/metabolismo , Proteínas Serina-Treonina Quinases/metabolismo , Splicing de RNA , Proteínas de Ligação a RNA/metabolismo , Fatores de Processamento de Serina-Arginina/metabolismo , Neoplasias Colorretais/genética , Feminino , Humanos , Peptídeos e Proteínas de Sinalização Intracelular/genética , Masculino , Metástase Neoplásica , Proteínas de Neoplasias/genética , Proteínas Serina-Treonina Quinases/genética , Proteínas de Ligação a RNA/genética , Fatores de Processamento de Serina-Arginina/genéticaRESUMO
BACKGROUND: Inherited Retinal dystrophy (IRD) is a broad group of inherited retinal disorders with heterogeneous genotypes and phenotypes. Next generation sequencing (NGS) methods have been broadly applied for analyzing patients with IRD. Here we report a novel approach to enrich the target gene panel by microdroplet PCR. METHODS: This assay involved a primer library which targeted 3071 amplicons from 2078 exons comprised of 184 genes involved in retinal function and/or retinal development. We amplified the target regions using the RainDance target enrichment PCR method and sequenced the products using the MiSeq NGS platform. RESULTS: In this study, we analyzed 82 samples from 67 families with IRD. Bioinformatics analysis indicated that this procedure was able to reach 99% coverage of target sequences with an average sequence depth of reads at 119×. The variants detected by this study were filtered, validated, and prioritized by pathogenicity analysis. Genotypes and phenotypes were correlated by determining a consistent relationship in 38 propands (56.7%). Pathogenic variants in genes related to retinal function were found in another 11 probands (16.4%), but the clinical correlations showed inconsistencies and insufficiencies in these patients. CONCLUSIONS: The application of NGS in IRD clinical molecular diagnosis provides a powerful approach to exploring the etiology and pathology in patients. It is important for the clinical laboratory to interpret the molecular findings in the context of patient clinical presentations because accurate interpretation of pathogenic variants is critical for delivering solid clinical molecular diagnosis to clinicians and patients and improving the standard care of patients.
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Análise Mutacional de DNA/métodos , Mutação , Reação em Cadeia da Polimerase/métodos , Distrofias Retinianas/genética , Adulto , Idoso , Feminino , Predisposição Genética para Doença , Humanos , Masculino , Pessoa de Meia-Idade , Distrofias Retinianas/diagnósticoRESUMO
A growing body of studies has demonstrated that dysregulated splicing profiles constitute pivotal mechanisms for carcinogenesis. In this study, we identified discriminative splicing profiles of colorectal cancer (CRC) cells compared to adjacent normal tissues using deep RNA-sequencing (RNA-seq). The RNA-seq results and cohort studies indicated a relatively high ratio of exon 4-excluded neuro-oncological ventral antigen 1 (Nova1-4) and intron 2-retained SRSF6 (SRSF6+intron 2) transcripts in CRC tissues and cell lines. Nova1 variants exhibited differential effects on eliminating SRSF6 expression in CRC cells by inducing SRSF6+intron 2 transcripts which were considered to be the putative target of alternative splicing-coupled nonsense-mediated decay mechanism. Moreover, the splicing profile of vascular endothelial growth factor (VEGF)165/VEGF165b transcripts was relevant to SRSF6 expression, which manipulates the progression of CRC calls. These results highlight the novel and hierarchical role of an alternative splicing cascade that is involved in the development of CRC.