Category: Nevin Manimala

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

Proc Lond Math Soc. 2023 Jan;126(1):334-389. doi: 10.1112/plms.12493. Epub 2022 Oct 26.

ABSTRACT

We consider the nonlinear Schrödinger equation on the half-line $x⩾0$ with a Robin boundary condition at $x=0$ and with initial data in the weighted Sobolev space $H^{1,1}\left(R_{+}\right)$ . We prove that there exists a global weak solution of this initial-boundary value problem and provide a representation for the solution in terms of the solution of a Riemann-Hilbert problem. Using this representation, we obtain asymptotic formulas for the long-time behavior of the solution. In particular, by restricting our asymptotic result to solutions whose initial data are close to the initial profile of the stationary one-soliton, we obtain results on the asymptotic stability of the stationary one-soliton under any small perturbation in $H^{1,1}\left(R_{+}\right)$ . In the focusing case, such a result was already established by Deift and Park using different methods, and our work provides an alternative approach to obtain such results. We treat both the focusing and the defocusing versions of the equation.

PMID:37067878 | PMC:PMC10091827 | DOI:10.1112/plms.12493

J Raman Spectrosc. 2022 Dec;53(12):2044-2057. doi: 10.1002/jrs.6447. Epub 2022 Sep 12.

ABSTRACT

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS “spectral unmixing” from a multiplexed mixture of 7 SERS-active “nanorattles” loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE_label = 6.42 × 10^-2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.

PMID:37067872 | PMC:PMC10087982 | DOI:10.1002/jrs.6447

JMIR Ment Health. 2023 Apr 17;10:e44064. doi: 10.2196/44064.

ABSTRACT

BACKGROUND: Digital technologies play an increasingly important role in the lives of young people and have important effects on their mental health.

OBJECTIVE: We aimed to explore 3 key areas of the intersection between digital technology and mental health: the views and experiences of young people and clinicians about digital technology and mental health; implementation and barriers to the UK national guidance recommendation-that the discussion of digital technology use should form a core part of mental health assessment; and how digital technology might be used to support existing consultations.

METHODS: Two cross-sectional web-based surveys were conducted in 2020 between June and December, with mental health clinicians (n=99) and young people (n=320). Descriptive statistics were used to summarize the proportions. Multilinear regression was used to explore how the answers varied by gender, sexuality, and age. Thematic analysis was used to explore the contents of the extended free-text answers. Anxiety was measured using the Generalized Anxiety Disorder Questionnaire-7 (GAD-7).

RESULTS: Digital technology use was ubiquitous among young people, with positive and negative aspects acknowledged by both clinicians and young people. Negative experiences were common (131/284, 46.1%) and were associated with increased anxiety levels among young people (GAD-7 3.29; 95% CI 1.97-4.61; P<.001). Although the discussion of digital technology use was regarded as important by clinicians and acceptable by young people, less than half of clinicians (42/85, 49.4%) routinely asked about the use of digital technology and over a third of young people (48/121, 39.6%) who had received mental health care had never been asked about their digital technology use. The conversations were often experienced as unhelpful. Helpful conversations were characterized by greater depth and exploration of how an individual’s digital technology use related to mental health. Despite most clinicians (59/83, 71.1%) wanting training, very few (21/86, 24.4%) reported receiving training. Clinicians were open to viewing mental health data from apps or social media to help with consultations. Although young people were generally, in theory, comfortable sharing such data with health professionals, when presented with a binary choice, most reported not wanting to share social media (84/117, 71.8%) or app data (67/118, 56.8%) during consultations.

CONCLUSIONS: Digital technology use was common, and negative experiences were frequent and associated with anxiety. Over a third of young people were not asked about their digital technology use during mental health consultations, and potentially valuable information about relevant negative experiences on the web was not being captured during consultations. Clinicians would benefit from having access to training to support these discussions with young people. Although young people recognized that app data could be helpful to clinicians, they appeared hesitant to share their own data. This finding suggests that data sharing has barriers that need to be further explored.

PMID:37067869 | DOI:10.2196/44064

Genetics. 2023 Apr 17:iyad063. doi: 10.1093/genetics/iyad063. Online ahead of print.

ABSTRACT

Numerous studies over the last decade have demonstrated the utility of machine learning methods when applied to population genetic tasks. More recent studies show the potential of deep learning methods in particular, which allow researchers to approach problems without making prior assumptions about how the data should be summarized or manipulated, instead learning their own internal representation of the data in an attempt to maximize inferential accuracy. One type of deep neural network, called Generative Adversarial Networks (GANs), can even be used to generate new data, and this approach has been used to create individual artificial human genomes free from privacy concerns. In this study, we further explore the application of GANs in population genetics by designing and training a network to learn the statistical distribution of population genetic alignments (i.e. data sets consisting of sequences from an entire population sample) under several diverse evolutionary histories-the first GAN capable of performing this task. After testing multiple different neural network architectures, we report the results of a fully differentiable Deep-Convolutional Wasserstein GAN with gradient penalty that is capable of generating artificial examples of population genetic alignments that successfully mimic key aspects of the training data, including the site frequency spectrum, differentiation between populations, and patterns of linkage disequilibrium. We demonstrate consistent training success across various evolutionary models, including models of panmictic and subdivided populations, populations at equilibrium and experiencing changes in size, and populations experiencing either no selection or positive selection of various strengths, all without the need for extensive hyperparameter tuning. Overall, our findings highlight the ability of GANs to learn and mimic population genetic data and suggest future areas where this work can be applied in population genetics research that we discuss herein.

PMID:37067864 | DOI:10.1093/genetics/iyad063

J Med Internet Res. 2023 Apr 17;25:e42777. doi: 10.2196/42777.

ABSTRACT

BACKGROUND: Internet-based intervention platforms may improve access to mental health care for women with perinatal depression (PND). Though the majority of platforms in the market lack an evidence base, a small number of them are supported by research evidence.

OBJECTIVE: This study aims to assess the current status of internet-based PND intervention platforms supported by published evidence, understand the reasons behind the disappearance of any of these previously accessible platforms, examine adjustments made by those active platforms between research trials and market implementation, and evaluate their current quality.

METHODS: A cohort of internet-based PND intervention platforms was first identified by systematic searches in multiple academic databases from database inception until March 26, 2021. We searched on the World Wide Web and the iOS and Android app stores to assess which of these were available in the marketplace between April and May 2021. The basic characteristics of all platforms were collected. For inaccessible platforms, inquiries were made via email to the authors of publications to determine the reasons for their unavailability. We compared the intervention-related information of accessible platforms in the marketplace with that reported in original publications and conducted quality assessments using the App Evaluation Model of the American Psychiatric Association. Fisher exact tests were used to compare the functional characteristics in publications of available and unavailable platforms and to investigate potential associations between functional adjustments or quality indices and platform survival time.

RESULTS: Out of 35 platforms supported by research evidence, only 19 (54%) were still accessible in the marketplace. The main reason for platforms disappearing was the termination of research projects. No statistically significant differences were found in functional characteristics between available and unavailable platforms. A total of 18 (95%) platforms adapted their core functions from what was reported in related publications. The adjustments included changes to intervention methods (11/19, 58%), target population (10/19, 53%), human resources for intervention support (9/19, 47%), mood assessment and monitoring (8/19, 42%), communication modality (4/19, 21%), and platform type (2/19, 11%). Quality issues across platforms included low frequency of update, lack of crisis management mechanism, poor user interactivity, and weak evidence base or absence of citation of supporting evidence. Platforms that survived longer than 10 years had a higher tendency to use external resources from third parties compared to those that survived less than 10 years (P=.04). No significant differences were found for functional adjustments or other quality indices.

CONCLUSIONS: Internet-based platforms supported by evidence were not effectively translated into real-world practice. It is unclear if adjustments to accessible platforms made during actual operation may undermine the proven validity of the original research. Future research to explore the reasons behind the success of the implementation of evidence-based platforms in the marketplace is warranted.

PMID:37067855 | DOI:10.2196/42777

J Med Econ. 2023 Apr 17:1-53. doi: 10.1080/13696998.2023.2202600. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: To assess the cost-utility of palivizumab versus no prophylaxis in preventing severe respiratory syncytial virus (RSV) infection in Canadian moderate-to-late preterm (32-35 weeks’ gestational age) infants using an: i) International Risk Scoring Tool (IRST); ii) Canadian RST (CRST).

METHODS: A decision tree was developed to assess cost-utility. Infants assessed at moderate- and high-risk of RSV-related hospitalization (RSVH) by the IRST or CRST received palivizumab or no prophylaxis and then progressed to either: i) RSVH; ii) emergency room/outpatient medically attended RSV-infection (MARI); or, iii) were uninfected/non-medically attended. Infants admitted to intensive care could incur mortality (0.43%). Respiratory morbidity was accounted in all uninfected surviving infants for 6 years or 18 years (RSVH/MARI). Palivizumab efficacy (72.2% RSVH reduction) and hospital outcomes were from the Canadian CARESS, PICNIC and RSV-Quebec studies. Palivizumab costs (50mg: CAN$752; 100mg: $1,505) were calculated from Canadian birth statistics combined with a growth algorithm. Healthcare/payer and societal costs (May 2022; 1.5% discounting) were included.

RESULTS: Cost per quality-adjusted life year (QALY) was $29,789 with the IRST (0.79 probability of being <$50,000) and $15,833 with the CRST (0.96 probability). The model was most sensitive to utility scores, long-term sequelae, and palivizumab cost. Vial sharing improved the incremental cost-utility ratio (IRST: $22,319; CRST: $9,231).

CONCLUSIONS: Palivizumab was highly cost-effective (vs no prophylaxis) in Canadian moderate-to-late preterm infants using either the IRST or CRST. The IRST has fewer risk factors than the CRST (3 vs 7, respectively), captures more potential RSVHs (85% vs 54%) and provides another option to guide cost-effective RSV prophylaxis in Canada.

PMID:37067826 | DOI:10.1080/13696998.2023.2202600