Diverse Databases Research Articles

Statistical Reappraisal of the Wax and Mercury Methods for Shrinkage Limit Determinations of Fine-Grained Soils

Because of the hazards associated with handling mercury, most standards organizations have withdrawn the conventional mercury (displacement) method (MM) for shrinkage limit (SL) determination of fine-grained soils. Despite attempts to substantiate the wax (coating) method (WM), which is presently the only standardized MM-testing alternative, the geotechnical community remains somewhat hesitant of its adoption in routine practice. To encourage more widespread use of WM-testing, this study re-examines the level of agreement between the MM- and WM-deduced SL parameters (i.e., SLMM and SLWM, respectively). This was achieved by performing comprehensive statistical analyses on the largest and most diverse database of its kind, to date, entailing SLMM:SLWM measurements for 168 different fine-grained soils having wide ranges of plasticity characteristics (i.e., liquid limit = 31.6–362.0%, plasticity index = 8.2–318.0% and SLMM = 7.1–42.0%). Furthermore, an attempt was made to evaluate the SLWM (in lieu of the SLMM) parameter for performing preliminary soil expansivity assessments using existing SLMM-based classification approaches. It was demonstrated that the MM and WM methods do not produce identical SL values for a given fine-grained soil under similar testing conditions, with their discrepancy being systematic and hence likely arising from the differences between the materials (mercury versus wax) and methodologies involved in performing these tests. New SLWM → SLMM conversion relationships were established, allowing SLMM to be deduced as a function of SLWM with high accuracy. Hence, when inputting SLWM in SLMM-based empirical correlations to predict other geoengineering design parameters, the newly proposed conversion relationships can be employed to minimize systematic prediction errors. It was also demonstrated that plasticity-based correlations, at best, can only provide a rough approximation of SLMM. Hence, when the SL is desired, WM-testing or any other alternative method that directly and reliably measures the soil shrinkage factors should be retained. Finally, the same soil-expansivity rankings, as obtained for existing classification systems employing SLMM results, are achieved using SLWM measurements (i.e., without the need of applying SLWM → SLMM conversion equations).

Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery

Lithium-ion batteries play a significant role in modern energy storage systems for a number of applications, including renewable energy installations, electric cars, and portable devices. In order to guarantee the long-term dependability and safety of these battery packs, comprehensive modelling and monitoring approaches are required for accurate State of Health (SoH) evaluation. This research suggests an innovative strategy that combines the strengths of Random Forest Regression (RFR) and Artificial Neural Networks (ANN) for reliable Li-ion battery SoH modelling and monitoring. The research starts with the collection of thorough Li-ion battery data, which includes voltage, current, temperature, and cycling characteristics. The battery dataset's nonlinear relationships are captured utilising RFR as the main modelling method. In terms of feature selection and prediction precision, RFR's ensemble of decision trees shines, making it a strong contender for modelling complex battery behaviour. The SoH prediction is then enhanced by the use of ANN, a deep learning framework renowned for its ability to extract detailed patterns from data. The characteristics of RFR are complemented by ANN's capacity to discover hidden features and nonlinear correlations, improving the overall prediction performance. The suggested hybrid approach, that combines RFR and ANN, is trained and verified utilising a diverse database acquired from Li-ion battery sources under operational circumstances. To assess the prediction accuracy of a suggested framework measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values are employed. The outcomes show that the hybrid RFR-ANN model works better than the individual RFR and ANN models, producing better SoH predictions. A viable solution for real-time Li-ion battery health monitoring is provided by this combination of conventional ML and deep learning approaches, which demonstrates the synergy among complexity and interpretability. This research advances battery SoH assessment and paves the way for the practical incorporation of precise monitoring systems into applications like electric vehicles, where timely and accurate SoH data is essential for maximising battery performance and extending operational lifespan.

Bioinformatics-driven discovery of novel EGFR kinase inhibitors as anti-cancer therapeutics: In silico screening and in vitro evaluation.

Epidermal growth factor receptor EGFR inhibitors are widely used as first line therapy for the treatment of non-small-cell lung cancer (NSCLC) in patients harboring EGFR mutation. However, the acquisition of a second-site mutation (T790 M) limited the efficacy and developed resistance. Therefore, discovery and development of specific drug target for this mutation is of urgent needs. In our study we used the ChemDiv diversity database for receptor-based virtual screening to secure EGFR-TK inhibitors chemotherapeutics. We identified four compounds that bind to the ATP-binding region of the EGFR-TK using AutoDock 4.0 and AutoDock Vina1.1.2 and post-docking investigations. The ligand showed hydrophobic interactions to the hydrophobic region of the binding site and engaged in hydrogen bonding with Met793. The ligands also explored π-cation interactions between the π-system of the ligand-phenyl ring and the positive amino group of Lys745. Molecular mechanics Poisson-Boltzmann surface area MM/PBSA per-residue energy decomposition analyses revealed that Val726, Leu792, Met793, Gly796, Cys797, Leu798, and Thr844 contributed the most to the binding energy. Biological evaluation of the retrieved hit compounds showed suppressing activity against EGFR auto phosphorylation and selective apoptosis-induced effects toward lung cancer cells harboring the EGFR L858R/T790M double mutation. Our work anticipated into novel and specific EGFR-TKIs and identified new compounds with therapeutic potential against lung cancer.

Reducing airline accident risk and saving lives: financial health, corporate governance, and aviation safety

PurposeThis study investigates the impact of financial health and corporate governance on aviation safety, aiming to fill a critical gap in existing research. The purpose of this study is to identify how these factors influence the safety records of airlines and provide insights for regulators, airlines and stakeholders to enhance aviation safety.Design/methodology/approachUsing a comprehensive international sample spanning 1950–2009 and later, this empirical analysis draws on diverse databases. The authors examine 372 airlines across 70 countries from 1990 to 2016. The research uses statistical models to analyze the relationship between financial indicators, corporate governance quality and aviation safety, addressing limitations of prior single-country studies.FindingsThe findings reveal a significant inverse relationship between financial health and accident propensity, with profitable airlines exhibiting lower accident rates. Additionally, airlines with higher corporate governance quality, characterized by qualified directors and stable leadership, experience fewer accidents. The study identifies key factors such as pilot errors, mechanical failures and adverse weather, contributing to approximately 75% of accidents, emphasizing the importance of organizational control.Practical implicationsThis research has crucial implications for aviation safety policies and practices. Regulators and international organizations, such as International Civil Aviation Organization and International Air Transport Association, should allocate resources to supervise financially vulnerable airlines and those with lower governance quality. Governments might consider incentivizing safety practices through tax deductibility for relevant expenses. Shareholders are encouraged to prioritize qualified, younger and less busy directors, recognizing their impact on safety performance.Originality/valueThis study contributes to existing literature by addressing methodological biases and offering a comprehensive international perspective. The identification of a link between financial health, corporate governance and accident rates in the aviation industry provides valuable insights. The research informs policymakers, regulators and industry stakeholders on effective strategies to improve safety by considering financial and governance factors under their control.

Sentimental analysis of social networks: A comprehensive review (2018-2023)

The field of sentiment analysis, a burgeoning field dedicated to deciphering emotions, opinions, and attitudes within text data, has experienced remarkable growth in research interest. This review paper embarks on a comprehensive journey through the dynamic landscape of sentiment analysis, encapsulating its multifaceted dimensions across three prominent academic databases—Scopus, the IEEE Xplore Digital Library, and the Wiley Online Library—spanning from 2018 to 2023. This paper establish the significance of sentiment analysis, charting its evolution from 2008 and elucidating its multifarious applications across domains. We underscore the necessity of drawing insights from diverse databases, transcending the confines of a single source. "On the causality of temporal sentiment analysis," delves into the intricate interplay between sentiment signals and other variables, echoing Granger causality theory from econometrics. We unveil the burgeoning adoption of Granger causality in examining stock market dynamics and sentiment analysis, shedding light on the predictive causality landscape. The temporal dimension takes centre stage "On the temporal dynamics of sentiment analysis," where we explore how sentiment fluctuates over time, particularly during transformative events such as the COVID-19 pandemic. We scrutinize the evolution of emotional appeals in political campaigns and the pivotal role of sentiment analysis in detecting online bullying and suicidal ideation. "On the reproducibility and practical application," we deliberate on the challenges and prospects of implementing sentiment analysis techniques in real-world scenarios. We navigate the intricate landscape of reproducibility and translate research insights into actionable strategies."Discussion and conclusion," encapsulates the essence of our journey. We highlight content variations across databases and unravel the evolution of techniques. We contemplate the bridge between academia and industry by presenting an integrated view of sentiment analysis. As the sentiment analysis field evolves, this review paper offers a panoramic view of its past, present, and future. It is a valuable resource for researchers and practitioners seeking to navigate the ever-expanding horizons of sentiment analysis across diverse databases and domains.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

Computer-Assisted Drug Discovery of Potential African Anti-SARS-CoV-2 Natural Products Targeting the Helicase Protein

Objectives: In our continuous efforts to combat COVID-19, our objective was to conduct a comprehensive computer-aided drug design study utilizing 4924 African natural metabolites sourced from diverse databases across various African regions from 1962 to 2019. The primary goal was to target the SARS-CoV-2 helicase, a crucial enzyme in viral replication. Methods: We employed structural fingerprint and molecular similarity studies with VXG, the co-crystallized ligand, as a reference. Subsequently, docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were conducted. Results: The structural fingerprint analysis identified 200 structurally similar compounds, then the molecular similarity studies selected 40 compounds. Among them, 15 metabolites with low free energies, favorable binding modes, and promising ADMET properties were identified. Four compounds were excluded according to the toxicity studies. Compound 1552, 1-(( S)-2,3-dihydro-2-(( R)-1-hydroxypropan-2-yl)benzofuran-5-yl)ethenone, exhibited the most favorable docking results. Molecular dynamics simulations conclusively demonstrated its stable binding to the SARS-CoV-2 helicase, characterized by low energy and optimal dynamics. Conclusion: The findings suggest promising avenues for potential COVID-19 cures, encouraging further exploration through in vitro and in vivo studies of the identified compounds, particularly compound 1552.

261 Providing Equity in Immunizations and Research

OBJECTIVES/GOALS: Disparities persist among minority and marginalized populations in immunizations and research. The Clinical Research Vehicle Team aimed to bring research to our community’s doorstep, building generalizability of research outcomes. Our mission adapted due to the pandemic to include increasing COVID-19 vaccinations and health education. METHODS/STUDY POPULATION: Community Engagement Events are planned when event organizers submit a request, then the proposal is discussed by the Clinical Research Vehicle (CRV) Team and UF HealthStreet. We go to underserved communities throughout Florida, food distributions, churches, community centers, rural areas, etc. At these events we offer services at no cost, including: COVID-19 and flu vaccines, as well as health screenings. We provide health education to individuals and refer them to UF HealthStreet for community resources, as appropriate. Research events are planned when researchers submit a request for the CRV, then the request is discussed by the CRV Team. The studies covered topics from COVID-19 research to the development of a diverse health database. Our fleet consists of two Community Health Vehicles and the CRV. RESULTS/ANTICIPATED RESULTS: As Community Engagement Events are ongoing, the presented numbers are reflective of the data available at the time of submission. Our impact includes administering 1,606+ COVID vaccines, conducting 1,081+ health screenings, collecting data from 944 screenings, providing 265 Flu vaccines, supporting 267+ community health events, and serving 25+ communities. The Clinical Research Vehicle facilitated 6 IRB-approved studies, administration of monoclonal antibody infusions, extended the capabilities of the Clinical Research Center, and enabled clinical trials aimed for novel treatments for COVID-19 positive participants. DISCUSSION/SIGNIFICANCE: Championing this initiative has shown an impact on vaccination rates and health education in communities. Experience and knowledge gained by our team throughout the pandemic while engaging with community members provided a platform from which to build generalizability of research outcomes and support translational science.

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.

BREATHING TECHNIQUES FOR PULMONARY REHABILITATION

Pulmonary rehabilitation (PR) programs, enriched by interdisciplinary approaches, play a pivotal role in enhancing the well-being of subjects with chronic respiratory conditions. Breathing techniques within these programs emerge as key components. Daily deep breathing exercises (DBE) and meditation practices stand out among these techniques. The role of these breathing techniques in addressing depression and anxiety among the elderly is particularly noteworthy, underscoring their potential as valuable non-pharmacological interventions. Employing a comprehensive search strategy, our research integrates evidence from diverse databases and delves into the benefits of breathing techniques in elderly populations, including those with multimorbidities. The findings reveal positive outcomes not only in pulmonary function but also in mental health. This underscores the critical role of such techniques in the holistic well-being of the elderly, shedding light on their potential as impactful elements in comprehensive healthcare strategies.

Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean.

Scientific advances in environmental data coverage and machine learning algorithms have improved the ability to make large-scale predictions where data are missing. These advances allowed us to develop a spatially resolved proxy for predicting numbers of tropical nearshore marine taxa. A diverse marine environmental spatial database was used to model numbers of taxa from ∼1000 field sites, and the predictions were applied to all 7039 6.25-km2 reef cells in 9 ecoregions and 11 nations of the western Indian Ocean. Our proxy for total numbers of taxa was based on the positive correlation (r2=0.24) of numbers of taxa of hard corals and 5 highly diverse reef fish families. Environmental relationships indicated that the number of fish species was largely influenced by biomass, nearness to people, governance, connectivity, and productivity and that coral taxa were influenced mostly by physicochemical environmental variability. At spatial delineations of province, ecoregion, nation, and strength of spatial clustering, we compared areas of conservation priority based on our total species proxy with those identified in 3 previous priority-setting reports and with the protected area database. Our method identified 119 locations that fit 3 numbers of taxa (hard coral, fish, and their combination) and 4 spatial delineations (nation, ecoregion, province, and reef clustering) criteria. Previous publications on priority setting identified 91 priority locations of which 6 were identified by all reports. We identified 12 locations that fit our 12 criteria and corresponded with 3 previously identified locations, 65 that aligned with at least 1 past report, and 28 that were new locations. Only 34% of the 208 marine protected areas in this province overlapped with identified locations with high numbers of predicted taxa. Differences occurred because past priorities were frequently based on unquantified perceptions of remoteness and preselected priority taxa. Our environment-species proxy and modeling approach can be considered among other important criteria for making conservation decisions.

Fast and accurate 3-D spine MRI segmentation using FastCleverSeg

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.

The usefulness of the MALDI-TOF MS technique in the determination of dairy samples' microbial composition: comparison of the new EXS 2600 system with MALDI Biotyper platform.

This study compared the EXS 2600 system with the MALDI Biotyper for identifying microorganisms in dairy samples. Of the 196 bacterial isolates from milk, whey, buttermilk, cream, and dairy wastewater, the species and genus consistent identification between two systems showed 74% and 99%, respectively. However, the level of species identification rate exhibited a difference, which was higher in Zybio than in Bruker-76.0% and 66.8%, respectively. Notably, the EXS 2600 system performed better with certain yeast species and H. alvei, while the Biotyper excelled with Pseudomonas bacteria. Unique microbial compositions were found in 85% of dairy samples, with whey and buttermilk having the highest diversity. This research highlights the EXS 2600's potential as a reliable dairy microbial identification tool and underscores the need for a more diverse and comprehensive spectral database, despite the database's focus on clinical applications (as announced).

Faster nonconvex low-rank matrix learning for image low-level and high-level vision: A unified framework

This study introduces a unified approach to tackle challenges in both low-level and high-level vision tasks for image processing. The framework integrates faster nonconvex low-rank matrix computations and continuity techniques to yield efficient and high-quality results. In addressing real-world image complexities like noise, variations, and missing data, the framework exploits the intrinsic low-rank structure of the data and incorporates specific residual measurements. The optimization problem for low-rank matrix learning is effectively solved using the nonconvex Proximal Block Coordinate Descent (PBCD) algorithm, resulting in nearly unbiased estimators. Rigorous theoretical analysis ensures both local and global convergence. The PBCD algorithm updates blocks of variables iteratively with closed-form solutions, adeptly handling nonconvexity and promoting faster convergence. Notably, the framework incorporates the randomized singular value decomposition (RSVD) technique and introduces a continuous strategy for adaptive model parameter updates. These strategic choices reduce computational complexity while maintaining result quality. They offer fine-tuned control over the desired rank of the learned matrix and enhance robustness in a straightforward manner. Furthermore, the versatility of the proposed nonconvex PBCD algorithm extends to handling problems with multiple variables, as supported by theoretical analysis. Experimental evaluations, spanning various image low-level and high-level vision tasks such as inpainting, classification, and clustering, validate the effectiveness and efficiency of our framework across diverse databases. The source code is available at https://github.com/ZhangHengMin/FNPBCD_LR.In a nutshell, our framework provides a unified solution to tackle both low-level and high-level vision tasks in images. By combining fast nonconvex low-rank matrix learning with adaptive parameter updates, we achieve efficient computation, yielding high-quality results that demonstrate robustness against various types of noise. The evaluations further endorse the reliability and applicability of our proposed framework.

NcRS: A resource of non-coding RNAs in sepsis

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.

A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness

Background Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. Method A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. Results Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. Conclusions The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.

Long noncoding RNAs and circular RNAs as potential diagnostic biomarkers of inflammatory bowel diseases: a systematic review and meta-analysis.

Inflammatory bowel disease (IBD) poses a growing global burden, necessitating the discovery of reliable biomarkers for early diagnosis. The clinical significance of dysregulated expression of long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in diagnosing IBD has not been well established. Thus, our study aimed to investigate the diagnostic value of lncRNAs and circRNAs for IBD based on currently available studies. A comprehensive search was carried out in diverse electronic databases, such as PubMed, Embase, Scopus, Science Direct and Wiley Online Library to retrieve articles published until October 30, 2023. Stata 17.0 software was employed to determine pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), and area under the curve (AUC). Heterogeneity, subgroup analysis, and meta-regression were explored, and publication bias was assessed using Deeks' funnel plot. Fagan's nomogram and likelihood ratio scattergram were employed to evaluate the clinical validity. A total of 11 articles encompassing 21 studies which involved 1239 IBD patients and 985 healthy controls were investigated. The findings revealed lncRNAs exhibit high level of pooled sensitivity 0.94 (95% CI: 0.87-0.97) and specificity 0.99 (95% CI: 0.89-1.00), along with PLR, NLR, DOR, and AUC values of 64.25 (95% CI: 7.39-558.66), 0.06 (95% CI: 0.03-0.13), 1055.25 (95% CI: 70.61-15770.77), and 0.99 (95% CI: 0.97-0.99), respectively. Conversely, CircRNAs showed moderate accuracy in IBD diagnosis, with sensitivity of 0.68 (95% CI: 0.61-0.73), specificity of 0.73 (95% CI: 0.65-0.79), PLR of 2.47 (95% CI: 1.94-3.16), NLR of 0.45 (95% CI: 0.38-0.53), DOR of 5.54 (95% CI: 3.88-7.93), and AUC value of 0.75 (95% CI: 0.71-0.79). Moreover, findings from subgroup analysis depicted heightened diagnostic efficacy when employing lncRNA H19 and a large sample size (≥100), with notable efficacy in diagnosing both ulcerative colitis (UC) and Crohn's disease (CD). LncRNAs exhibit high diagnostic accuracy in distinguishing patients with IBD from healthy controls signifying their possible use as potential biomarkers, while circRNAs showed moderate diagnostic accuracy. Nevertheless, to validate our findings and confirm the clinical utility of lncRNAs and circRNAs in IBD diagnosis, a large pool of prospective and multi-center studies should be undertaken. https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023491840.

Integrating virtual screening, pharmacoinformatics profiling, and molecular dynamics: identification of promising inhibitors targeting 3CLpro of SARS-CoV-2.

Introduction: The pursuit of effective therapeutic solutions for SARS-CoV-2 infections and COVID-19 necessitates the repurposing of existing compounds. This study focuses on the detailed examination of the central protease, 3-chymotrypsin-like protease (3CLpro), a pivotal player in virus replication. The combined approach of molecular dynamics simulations and virtual screening is employed to identify potential inhibitors targeting 3CLpro. Methods: A comprehensive virtual screening of 7120 compounds sourced from diverse databases was conducted. Four promising inhibitors, namely EN1036, F6548-4084, F6548-1613, and PUBT44123754, were identified. These compounds exhibited notable attributes, including high binding affinity (ranging from -5.003 to -5.772Kcal/mol) and superior Induced Fit Docking scores (ranging from -671.66 to -675.26Kcal/mol) compared to co-crystallized ligands. Results: In-depth analysis revealed that F6548-1613 stood out, demonstrating stable hydrogen bonds with amino acids His41 and Thr62. Notably, F6548-1613 recorded a binding energy of -65.72kcal/mol in Molecular Mechanics Generalized Born Surface Area (MMGBSA) simulations. These findings were supported by Molecular Dynamics simulations, highlighting the compound's efficacy in inhibiting 3CLpro. Discussion: The identified compounds, in compliance with Lipinski's rule of five and exhibiting functional molecular interactions with 3CLpro, present promising therapeutic prospects. The integration of in silico methodologies significantly expedites drug discovery, laying the foundation for subsequent experimental validation and optimization. This approach holds the potential to develop effective therapeutics for SARS-CoV-2.

Exploring the impact of information and communication technologies on loneliness and social isolation in community-dwelling older adults: a scoping review of reviews

BackgroundRecognizing the escalating public health concerns of loneliness and social isolation in aging populations, this study seeks to comprehensively explore the potential of information and communication technology (ICT)-based interventions to address these issues among older adults. This scoping review of reviews aims to map and synthesize existing evidence on the effectiveness and scope of ICT interventions targeting loneliness and social isolation in community-dwelling older adults, elucidating types of technology, impacts, facilitators, barriers, and research gaps.MethodsFollowing the Joanna Briggs Institute framework, we systematically searched eight diverse databases identifying relevant published reviews. We included English-written, peer-reviewed reviews of all types, with no limits regarding time of publication about ICTs targeting loneliness and/or social isolation for community-dwelling older adults. Eligible reviews were analysed and summarized, offering a holistic narrative of the reported types of ICTs and their impact, the identified facilitators and barriers influencing the implementation and adoption of ICT interventions, and the research gaps identified in the literature.ResultsThe review included 39 publications published between 2012 and 2024, spanning systematic, scoping, and reviews of reviews. Various ICTs were reported, primarily social media virtual communities, followed by video-mediated friendly visits, conversational agents, social robots, exergames and online gameplay. Predominantly positive impacts on mitigating social isolation and loneliness were evident for these ICTs, although methodological diversity and contradictory findings complicated definite conclusions. Facilitators and barriers encompassed individual competencies, access and usage, and intervention design and implementation. Research gaps involved targeting specific subgroups, exploring innovative technologies, incorporating diverse study designs, improving research methodologies, and addressing usability and accessibility. Future research should focus on identifying elderly individuals who can benefit the most from ICT use, exploring novel technologies, using a wider range of study designs, and enhancing usability and accessibility considerations.ConclusionsThis review sheds light on the diverse range of ICTs, their impact, and the facilitators and barriers associated with their use. Future investigations should prioritize refining outcome measures, addressing gender differences, and enhancing the usability and accessibility of interventions. The involvement of older adults in the design process and the exploration of technological training interventions hold promise in overcoming barriers.