Stepwise Excited-State Intramolecular Double Proton Transfer in 1,8-Dihydroxynaphthalene-2,7-dicarbaldehyde.

We theoretically investigate the salient features of stepwise excited-state intramolecular double proton transfer (ESIDPT) in 1,8-dihydroxynaphthalene-2,7-dicarbaldehyde (DHDA). Surface trajectory simulations using the TD-B3LYP/6-31G(d) level of theory reveal that the proton transfer primarily happens via S1, wherein about ∼42% of trajectories (40 out of 95) show the single proton transfer alone and another ∼32% (30 out of 95) show double proton transfer. The average time scale for the single proton transfer originating from those ∼42% trajectories is ∼147 fs. In the case of double proton transfer, the average time for the first step, i.e., single proton transfer, is ∼54 fs, and the subsequent step, i.e., double proton transfer, completes in ∼151 fs. All three tautomers, i.e., normal, single, and double proton-transferred tautomers, possess a stable minimum in their first singlet excited state. This state has a ππ* character in the former two tautomers, resulting in a dual fluorescence emission phenomenon upon photoexcitation of DHDA.

Improved meta-analysis pipeline ameliorates distinctive gene regulators of diabetic vasculopathy in human endothelial cell (hECs) RNA-Seq data.

Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.

Experiences of stigma, psychological distress, and facilitative coping among pregnant people with gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) has been rising in the United States, and it poses significant health risks to pregnant individuals and their infants. Prior research has shown that individuals with GDM also experience prevalent stress and mental health issues, which can further contribute to glucose regulation difficulties. Stigma associated with GDM may contribute to these mental health challenges, yet there is a lack of focused research on GDM-related stigma, its impact on psychological health, and effective coping mechanisms. Thus, this qualitative study aims to understand individuals' experiences related to GDM stigma, mental health, and facilitative coping. METHODS: In-depth, semi-structured interviews were conducted with 14 individuals with a current or recent (within the last year) diagnosis of GDM. Thematic analysis was employed to guide data analysis. RESULTS: Four themes emerged from data analysis: (1) experience of distal GDM stigma including stigmatizing provider interactions, stigma from non-medical spaces, and intersecting stigma with weight, (2) internalized GDM stigma, such as shame, guilt, and self-blame, (3) psychological distress, which included experiences of stress and overwhelm, excessive worry and fear, and loneliness and isolation, and (4) facilitative coping mechanisms, which included diagnosis acceptance, internet-based GDM community, active participation in GDM management, social and familial support, and time for oneself. CONCLUSIONS: Findings demonstrate the relevance of GDM stigma in mental health among people with GDM and the need for addressing GDM stigma and psychological health in this population. Interventions that can reduce GDM stigma, improve psychological wellness, and enhance positive coping may facilitate successful GDM management and healthy birth outcomes. Future quantitative, theory-driven research is needed to understand the prevalence of GDM stigma experiences and mechanisms identified in the current study, as well as among marginalized populations (e.g., individuals of color, sexual and gender minorities).

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature's size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

Improved downstream functional analysis of single-cell RNA-sequence data using DGAN.

The dramatic increase in the number of single-cell RNA-sequence (scRNA-seq) investigations is indeed an endorsement of the new-fangled proficiencies of next generation sequencing technologies that facilitate the accurate measurement of tens of thousands of RNA expression levels at the cellular resolution. Nevertheless, missing values of RNA amplification persist and remain as a significant computational challenge, as these data omission induce further noise in their respective cellular data and ultimately impede downstream functional analysis of scRNA-seq data. Consequently, it turns imperative to develop robust and efficient scRNA-seq data imputation methods for improved downstream functional analysis outcomes. To overcome this adversity, we have designed an imputation framework namely deep generative autoencoder network [DGAN]. In essence, DGAN is an evolved variational autoencoder designed to robustly impute data dropouts in scRNA-seq data manifested as a sparse gene expression matrix. DGAN principally reckons count distribution, besides data sparsity utilizing a gaussian model whereby, cell dependencies are capitalized to detect and exclude outlier cells via imputation. When tested on five publicly available scRNA-seq data, DGAN outperformed every single baseline method paralleled, with respect to downstream functional analysis including cell data visualization, clustering, classification and differential expression analysis. DGAN is executed in Python and is accessible at https://github.com/dikshap11/DGAN .

Adverse Event Following Immunization (AEFI) in Children: An Analysis of Reporting in VigiAccess.

INTRODUCTION: It is an indubitable fact that vaccination has been instrumental in the eradication and prevention of the deadliest diseases worldwide. Continuous vaccine safety surveillance is helpful to counter the negative perception and thus allay the fear of Adverse Events Following Immunization (AEFI) in the general public. VigiAccess, the WHO global database of reported side effects of medicinal products, can be accessed by the public at large. The objective of this study is to assess the characteristics of AEFIs of the commonly used vaccines in children in VigiAccess. MATERIALS AND METHODS: VigiAccess was thoroughly explored for the categories, number, and types of AEFIs of commonly used vaccines among children that are reported in five continents between 2011 and 2021. RESULTS: After a comprehensive analysis in VigiAccess, 27 kinds of AEFIs were discovered. For the nine vaccines, a total of 1,412,339 AEFIs were found. The most prevalent AEFIs were general disorder and administration site condition (436,199 or 30%). The majority of AEFIs are found in America, with Europe, Oceania, Asia, and Africa following closely behind. Girls of age from 27 days to 23 months had the highest number of AEFIs. The highest number of AEFIs was recorded in the year 2018. CONCLUSION: America has the maximum, whilst Africa has the least AEFI. Few AEFIs were caused by the measles vaccination, while the majority were related to the general disorder and administration site condition. Data synchronization in VigiAccess needs to be enhanced to improve its dependability.

Theoretical approach to modeling the early nonadiabatic events of ESIPT originating from three-state conical intersection in quinophthalone.

We explore the excited-state intramolecular proton transfer process of quinophthalone theoretically. This molecule possesses three low-lying singlet excited states ([Formula: see text] and [Formula: see text]) in a narrow energy gap of less than the N-H stretching frequency. Dynamics simulations show nonadiabatic wavepacket transfer to [Formula: see text] and [Formula: see text] upon initiating the wavepacket on [Formula: see text]. Multiple accessible conical intersections that lie in the Franck-Condon region facilitate the nonadiabatic wavepacket transfer. Nuclear densities associated with the proton transfer promoting vibrations would start accumulating on [Formula: see text] and [Formula: see text] within a few tens of femtoseconds, validating the involvement of these vibrations in the nonadiabatic events that occur before the proton transfer process. Our findings emphasize the necessity of refined kinetic models for assigning the time constants of ultrafast transient spectroscopy measurements due to the simultaneous evolution of nonadiabatic events and proton transfer kinetics in quinophthalone.