Tag: nevin manimala

Mol Cancer Res. 2023 Apr 17:OF1-OF12. doi: 10.1158/1541-7786.MCR-22-0623. Online ahead of print.

ABSTRACT

Adult-type granulosa cell tumors (aGCT) are rare ovarian sex cord tumors with few effective treatments for recurrent disease. The objective of this study was to characterize the tumor microenvironment (TME) of primary and recurrent aGCTs and to identify correlates of disease recurrence. Total RNA sequencing (RNA-seq) was performed on 24 pathologically confirmed, cryopreserved aGCT samples, including 8 primary and 16 recurrent tumors. After read alignment and quality-control filtering, DESeq2 was used to identify differentially expressed genes (DEG) between primary and recurrent tumors. Functional enrichment pathway analysis and gene set enrichment analysis was performed using “clusterProfiler” and “GSVA” R packages. TME composition was investigated through the analysis and integration of multiple published RNA-seq deconvolution algorithms. TME analysis results were externally validated using data from independent previously published RNA-seq datasets. A total of 31 DEGs were identified between primary and recurrent aGCTs. These included genes with known function in hormone signaling such as LHCGR and INSL3 (more abundant in primary tumors) and CYP19A1 (more abundant in recurrent tumors). Gene set enrichment analysis revealed that primarily immune-related and hormone-regulated gene sets expression was increased in recurrent tumors. Integrative TME analysis demonstrated statistically significant depletion of cancer-associated fibroblasts in recurrent tumors. This finding was confirmed in multiple independent datasets.

IMPLICATIONS: Recurrent aGCTs exhibit alterations in hormone pathway gene expression as well as decreased infiltration of cancer-associated fibroblasts, suggesting dual roles for hormonal signaling and TME remodeling underpinning disease relapse.

PMID:37068116 | DOI:10.1158/1541-7786.MCR-22-0623

Hosp Pract (1995). 2023 Apr 17:1-14. doi: 10.1080/21548331.2023.2203051. Online ahead of print.

ABSTRACT

OBJECTIVES: This systematic review was conducted to investigate the characteristics and effects of clinical decision support systems (CDSSs) on clinical and process-of-care outcomes of patients with kidney disease.

METHODS: A comprehensive systematic search was conducted in electronic databases to identify relevant studies published until November 2020. Randomized clinical trials evaluating the effects of using electronic CDSS on at least one clinical or process-of-care outcome in patients with kidney disease were included in this study. The characteristics of the included studies, features of CDSSs, and effects of the interventions on the outcomes were extracted. Studies were appraised for quality using the Cochrane risk-of-bias assessment tool.

RESULTS: Out of 8722 retrieved records, 11 eligible studies measured 32 outcomes, including 10 clinical outcomes and 22 process-of-care outcomes. The effects of CDSSs on 45.5% of the process-of-care outcomes were statistically significant, and all the clinical outcomes were not statistically significant. Medication-related process-of-care outcomes were the most frequently measured (54.5%), and CDSSs had the most effective and positive effect on medication appropriateness (18.2%). The characteristics of CDSSs investigated in the included studies comprised automatic data entry, real-time feedback, providing recommendations, and CDSS integration with the Computerized Provider Order Entry system.

CONCLUSION: Although CDSS may potentially be able to improve processes of care for patients with kidney disease, particularly with regard to medication appropriateness, no evidence was found that CDSS affects clinical outcomes in these patients. Further research is thus required to determine the effects of CDSSs on clinical outcomes in patients with kidney diseases.

PMID:37068105 | DOI:10.1080/21548331.2023.2203051

PLoS Comput Biol. 2023 Apr 17;19(4):e1010137. doi: 10.1371/journal.pcbi.1010137. Online ahead of print.

ABSTRACT

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primers sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

PMID:37068103 | DOI:10.1371/journal.pcbi.1010137

PLoS Comput Biol. 2023 Apr 17;19(4):e1011047. doi: 10.1371/journal.pcbi.1011047. Online ahead of print.

ABSTRACT

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

PMID:37068100 | DOI:10.1371/journal.pcbi.1011047

PLoS Comput Biol. 2023 Apr 17;19(4):e1011044. doi: 10.1371/journal.pcbi.1011044. Online ahead of print.

ABSTRACT

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.

PMID:37068097 | DOI:10.1371/journal.pcbi.1011044

PLoS One. 2023 Apr 17;18(4):e0282349. doi: 10.1371/journal.pone.0282349. eCollection 2023.

ABSTRACT

Sex hormones can affect cellular physiology and modulate synaptic plasticity, but it is not always clear whether or how sex-dependent differences identified in vitro express themselves as functional dimorphisms in the brain. Historically, most experimental neuroscience has been conducted using only male animals and the literature is largely mute about whether including female mice in will introduce variability due to inherent sex differences or endogenous estrous cycles. Though this is beginning to change following an NIH directive that sex should be included as a factor in vertebrate research, the lack of information raises practical issues around how to design experimental controls and apply existing knowledge to more heterogeneous populations. Various lines of research suggest that visual processing can be affected by sex and estrous cycle stage. For these reasons, we performed a series of in vivo electrophysiological experiments to characterize baseline visual function and experience-dependent plasticity in the primary visual cortex (V1) of male and female mice. We find that sex and estrous stage have no statistically significant effect on baseline acuity measurements, but that both sex and estrous stage have can modulate two mechanistically distinct forms of experience dependent cortical plasticity. We also demonstrate that resulting variability can be largely controlled with appropriate normalizations. These findings suggest that V1 plasticity can be used for mechanistic studies focusing on how sex hormones effect experience dependent plasticity in the mammalian cortex.

PMID:37068089 | DOI:10.1371/journal.pone.0282349

PLoS One. 2023 Apr 17;18(4):e0284433. doi: 10.1371/journal.pone.0284433. eCollection 2023.

ABSTRACT

INTRODUCTION: We aim to understand the factors that drive citizens of different countries to adhere to recommended self-protective behaviors during the COVID-19 pandemic.

METHODS: Survey data was obtained through the COVID-19 Impact project. We selected countries that presented a sufficiently complete time series and a statistically relevant sample for running the analysis: Cyprus, Germany, Greece, Ireland, Latvia, Spain, Switzerland, the United Kingdom, and the United States of America. To identify country-specific differences in self-protective behaviors, we used previous evidence and change-point detection analysis to establish variations across participating countries whose effect was then assessed by means of interrupted series analysis.

RESULTS: A high level of compliance with health and governmental authorities’ recommendations were generally observed in all included countries. The level of stress decreased near the period when countries such as Cyprus, Greece or the United Kingdom relaxed their prevention behavior recommendations. However, this relaxation of behaviors did not occur in countries such as Germany, Ireland, or the United States. As observed in the change-point detection analysis, when the daily number of recorded COVID-19 cases decreased, people relaxed their protective behaviors (Cyprus, Greece, Ireland), although the opposite trend was observed in Switzerland.

DISCUSSION: COVID-19 self-protective behaviors were heterogeneous across countries examined. Our findings show that there is probably no single winning strategy for exiting future health crises, as similar interventions, aimed to promote self-protective behaviors, may be received differently depending on the specific population groups and on the particular geographical context in which they are implemented.

PMID:37068083 | DOI:10.1371/journal.pone.0284433

PLoS One. 2023 Apr 17;18(4):e0279216. doi: 10.1371/journal.pone.0279216. eCollection 2023.

ABSTRACT

Leadership development is a universally important goal across the agricultural plant science disciplines. Although previous studies have identified a need for leadership skills, less is known about leadership skill development in graduate programs. To address this, we constructed a mixed-method study to identify the most significant graduate school leadership experiences of scientists in the agricultural plant science disciplines. The survey was deployed to 6,728 people in the U.S. and received 1,086 responses (16.1% response rate). The majority of respondents reported that they were from one of the major agricultural states and employed at one of the agricultural plant science related doctoral universities, industries, or government. Results from this survey suggest that recent graduates were more engaged in graduate school activities that offered leadership development. Key experiences in graduate school were also identified that may be used to develop future leaders. Additionally, respondents reported the greatest barrier to providing leadership development for graduate students was that it is not part of their program curriculum, however current graduate students responded differently, and identifying lack of funding to support experiences as the greatest barrier. This survey also identified the top ranked professional skills considered most important for effective leaders in agricultural plant sciences as well as respondent-driven recommendations on how graduate programs can improve leadership development. Collectively, these results can be used in the future to identify priorities for skill development and opportunities for leadership training among graduate students within the plant science disciplines.

PMID:37068080 | DOI:10.1371/journal.pone.0279216

Blood Cancer Discov. 2023 Apr 17:OF1-OF18. doi: 10.1158/2643-3230.BCD-22-0156. Online ahead of print.

ABSTRACT

RNA splicing dysregulation underlies the onset and progression of cancers. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ∼20% of patients. However, the mechanism for splicing defects in spliceosome-unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover that proteins involved in RNA splicing are posttranscriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing complexes is an independent risk factor for poor prognosis. Moreover, increased splicing factor expression is highly correlated with the abundance of METTL3, an RNA methyltransferase that deposits N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification-mediated ribosome recycling and decoding. Our results uncover METTL3-mediated m6A modification as a novel regulatory axis in driving splicing dysregulation and contributing to aggressive CLL.

SIGNIFICANCE: METTL3 controls widespread splicing factor abundance via translational control of m6A-modified mRNA, contributes to RNA splicing dysregulation and disease progression in CLL, and serves as a potential therapeutic target in aggressive CLL. See related commentary by Janin and Esteller.

PMID:37067905 | DOI:10.1158/2643-3230.BCD-22-0156

Stat Atlases Comput Models Heart. 2022 Sep;13593:302-316. doi: 10.1007/978-3-031-23443-9_28. Epub 2023 Jan 28.

ABSTRACT

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.

PMID:37067883 | PMC:PMC10103081 | DOI:10.1007/978-3-031-23443-9_28