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General General

Enhancing behavioral sleep care with digital technology: study protocol for a hybrid type 3 implementation-effectiveness randomized trial.

In Trials

BACKGROUND : Insomnia affects almost one in four military service members and veterans. The first-line recommended treatment for insomnia is cognitive-behavioral therapy for insomnia (CBTI). CBTI is typically delivered in-person or online over one-to-four sessions (brief versions) or five-to-eight sessions (standard versions) by a licensed doctoral or masters-level clinician with extensive training in behavioral sleep medicine. Despite its effectiveness, CBTI has limited scalability. Three main factors inhibit access to and delivery of CBTI including restricted availability of clinical expertise; rigid, resource-intensive treatment formats; and limited capacities for just-in-time monitoring and treatment personalization. Digital technologies offer a unique opportunity to overcome these challenges by providing scalable, personalized, resource-sensitive, adaptive, and cost-effective approaches for evidence-based insomnia treatment.

METHODS : This is a hybrid type 3 implementation-effectiveness randomized trial using a scalable evidence-based digital health software platform, NOCTEM™'s Clinician-Operated Assistive Sleep Technology (COAST™). COAST includes a clinician portal and a patient app, and it utilizes algorithms that facilitate detection of sleep disordered patterns, support clinical decision-making, and personalize sleep interventions. The first aim is to compare three clinician- and system-centered implementation strategies on the reach, adoption, and sustainability of the COAST digital platform by offering (1) COAST only, (2) COAST plus external facilitation (EF: assistance and consultation to providers by NOCTEM's sleep experts), or (3) COAST plus EF and internal facilitation (EF/IF: assistance/consultation to providers by NOCTEM's sleep experts and local champions). The second aim is to quantify improvements in insomnia among patients who receive behavioral sleep care via the COAST platform. We hypothesize that reach, adoption, and sustainability and the magnitude of improvements in insomnia will be superior in the EF and EF/IF groups relative to the COAST-only group.

DISCUSSION : Digital health technologies and machine learning-assisted clinical decision support tools have substantial potential for scaling access to insomnia treatment. This can augment the scalability and cost-effectiveness of CBTI without compromising patient outcomes. Engaging providers, stakeholders, patients, and decision-makers is key in identifying strategies to support the deployment of digital health technologies that can promote quality care and result in clinically meaningful sleep improvements, positive systemic change, and enhanced readiness and health among service members.

TRIAL REGISTRATION : NCT04366284 . Registered on 28 April 2020.

Germain Anne, Markwald Rachel R, King Erika, Bramoweth Adam D, Wolfson Megan, Seda Gilbert, Han Tony, Miggantz Erin, O’Reilly Brian, Hungerford Lars, Sitzer Traci, Mysliwiec Vincent, Hout Joseph J, Wallace Meredith L


Behavioral sleep medicine, Cognitive-behavioral therapy for insomnia, Digital health technologies, Effectiveness, Implementation facilitation, Insomnia, Military personnel, Veterans

Surgery Surgery

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning.

In Journal of orthopaedic surgery and research

BACKGROUND : Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA.

METHODS : The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes.

RESULTS : Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC.

CONCLUSION : The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.

Liu Jianyong, Chen Ningjie


Bioinformatics analysis, Diagnostic signature, Differentially expressed genes, Random forest model, Rheumatoid arthritis

General General

PVT1 signals an androgen-dependent transcriptional repression program in prostate cancer cells and a set of the repressed genes predicts high-risk tumors.

In Cell communication and signaling : CCS

BACKGROUND : Androgen receptor (AR) and polycomb repressive complex 2 (PRC2) are known to co-occupy the loci of genes that are downregulated by androgen-stimulus. Long intergenic non-coding RNA (lincRNA) PVT1 is an overexpressed oncogene that is associated with AR in LNCaP prostate cancer cells, and with PRC2 in HeLa and many other types of cancer cells. The possible involvement of PVT1 in mediating androgen-induced gene expression downregulation in prostate cancer has not been explored.

METHODS : LNCaP cell line was used. Native RNA-binding-protein immunoprecipitation with anti-AR or anti-EZH2 was followed by RT-qPCR with primers for PVT1. Knockdown of PVT1 with specific GapmeRs (or a control with scrambled GapmeR) was followed by differentially expressed genes (DEGs) determination with Agilent microarrays and with Significance Analysis of Microarrays statistical test. DEGs were tested as a tumor risk classifier with a machine learning Random Forest algorithm run with gene expression data from all TCGA-PRAD (prostate adenocarcinoma) tumors as input. ChIP-qPCR was performed for histone marks at the promoter of one DEG.

RESULTS : We show that PVT1 knockdown in androgen-stimulated LNCaP cells caused statistically significant expression upregulation/downregulation of hundreds of genes. Interestingly, PVT1 knockdown caused upregulation of 160 genes that were repressed by androgen, including a significantly enriched set of tumor suppressor genes, and among them FAS, NOV/CCN3, BMF, HRK, IFIT2, AJUBA, DRAIC and TNFRSF21. A 121-gene-set (out of the 160) was able to correctly predict the classification of all 293 intermediate- and high-risk TCGA-PRAD tumors, with a mean ROC area under the curve AUC = 0.89 ± 0.04, pointing to the relevance of these genes in cancer aggressiveness. Native RIP-qPCR in LNCaP showed that PVT1 was associated with EZH2, a component of PRC2. PVT1 knockdown followed by ChIP-qPCR showed significant epigenetic remodeling at the enhancer and promoter regions of tumor suppressor gene NOV, one of the androgen-repressed genes that were upregulated upon PVT1 silencing.

CONCLUSIONS : Overall, we provide first evidence that PVT1 was involved in signaling a genome-wide androgen-dependent transcriptional repressive program of tumor suppressor protein-coding genes in prostate cancer cells. Identification of transcriptional inhibition of tumor suppressor genes by PVT1 highlights the pathway to the investigation of mechanisms that lie behind the oncogenic role of PVT1 in cancer. Video Abstract.

Videira Alexandre, Beckedorff Felipe C, daSilva Lucas F, Verjovski-Almeida Sergio


Genome-wide transcriptional repression, LincRNA PVT1, PVT1 knockdown, Prostate cancer, Tumor suppressor genes

General General

Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD.

In BMC psychiatry

BACKGROUND : Despite the effectiveness of methylphenidate for treating ADHD, up to 30% of individuals with ADHD show poor responses to methylphenidate. Neuroimaging biomarkers to predict medication responses remain elusive. This study characterized neuroanatomical features that differentiated between clinically good and poor methylphenidate responders with ADHD.

METHODS : Using a naturalistic observation design selected from a larger cohort, we included 79 drug-naive individuals (aged 6-42 years) with ADHD without major psychiatric comorbidity, who had acceptable baseline structural MRI data quality. Based on a retrospective chart review, we defined responders by individuals' responses to at least one-month treatment with methylphenidate. A nonparametric mass-univariate voxel-based morphometric analysis was used to compare regional gray matter volume differences between good and poor responders. A multivariate pattern recognition based on the support vector machine was further implemented to identify neuroanatomical indicators to predict an individual's response.

RESULTS : 63 and 16 individuals were classified in the good and poor responder group, respectively. Using the small-volume correction procedure based on the hypothesis-driven striatal and default-mode network masks, poor responders had smaller regional volumes of the left putamen as well as larger precuneus volumes compared to good responders at baseline. The machine learning approach identified that volumetric information among these two regions alongside the left frontoparietal regions, occipital lobes, and posterior/inferior cerebellum could predict clinical responses to methylphenidate in individuals with ADHD.

CONCLUSION : Our results suggest regional striatal and precuneus gray matter volumes play a critical role in mediating treatment responses in individuals with ADHD.

Chang Jung-Chi, Lin Hsiang-Yuan, Lv Junglei, Tseng Wen-Yih Issac, Gau Susan Shur-Fen


ADHD, Methylphenidate, Striatum, Support vector machine, Treatment response, VBM

Radiology Radiology

A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.

In BMC neurology

BACKGROUND : Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promising but its diagnostic results highly depends on doctors' experiences. This study intends to explore a way of deploying a deep learning model for muscle MRI images to diagnose dystrophinopathies.

METHODS : This study collected 2536 T1WI images from 432 cases who had been diagnosed by genetic analysis and/or muscle biopsy, including 148 cases with dystrophinopathies and 284 cases with other diseases. The data was randomly divided into three sets: the data from 233 cases were used to train the CNN model, the data from 97 cases for the validation experiments, and the data from 102 cases for the test experiments. We also validated our models expertise at diagnosing by comparing the model's results on the 102 cases with those of three skilled radiologists.

RESULTS : The proposed model achieved 91% (95% CI: 0.88, 0.93) accuracy on the test set, higher than the best accuracy of 84% in radiologists. It also performed better than the skilled radiologists in sensitivity : sensitivities of the models and the doctors were 0.89 (95% CI: 0.85 0.93) versus 0.79 (95% CI:0.73, 0.84; p = 0.190).

CONCLUSIONS : The deep model achieved excellent accuracy and sensitivity in identifying cases with dystrophinopathies. The comparable performance of the model and skilled radiologists demonstrates the potential application of the model in diagnosing dystrophinopathies through MRI images.

Yang Mei, Zheng Yiming, Xie Zhiying, Wang Zhaoxia, Xiao Jiangxi, Zhang Jue, Yuan Yun


Computer-Assisted Diagnosis, Deep Learning, Magnetic Resonance Imaging, Muscular Diseases

General General

JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.

In BMC genomics ; h5-index 78.0

BACKGROUND : Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.

RESULTS : In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.

CONCLUSIONS : Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.

Cui Tao, Wang Tingting


DEG, Deep learning, Dropout, JOINT, Parallel computing, Probability, RNA-Seq, Single-cell, Soft-clustering