Browse publications by year: 2024

  1. Yang Q, Wu F, Peñuelas J, Sardans J, Peng Y, Wu Q, et al.
    Environ Res, 2024 Dec 01;262(Pt 2):119963.
    PMID: 39251176 DOI: 10.1016/j.envres.2024.119963
    The significance of intermittent streams in nutrient loss within forest ecosystems is becoming increasingly critical due to changes in precipitation patterns associated with global climate change. However, few studies have focused on nutrient export from intermittent streams. We conducted continuous sediment collection from intermittent streams from March 2022 to February 2023 to investigate the export pattern and mechanism of sediment-associated nitrogen (N) from intermittent streams of different forest types (composed forest of Castanopsis carlesii (Cas. carlesii) and Cunninghamia lanceolata (C. lanceolata) forests, compared to Cas. carlesii forests). We measured the N concentrations and calculated the export amounts of four common forms of N associated with sediments: total N (TN), dissolved N (DN), nitrate, and ammonia. Our results showed that (1) the annual average exports of TN, DN, nitrate, and ammonia associated with sediments from intermittent streams from both forest types were 273, 1.62, 0.26, and 0.84 kg ha-1, respectively; (2) N export was significantly higher in composite forests of Cas. carlesii and C. lanceolata, compared to Cas. carlesii forests; (3) stream sediment export amount positively affected N export both in composite forests and Cas. carlesii forests; and (4) N export was also controlled by rainfall amount and stream characteristics. Our study quantified sediment-associated N export from intermittent streams among different subtropical forest types, which will enhance our understanding of N dynamics associated with stream hydrological processes in subtropical forests.
    MeSH terms: China; Environmental Monitoring; Nitrates/analysis; Forests*
  2. Chan KS, Farah NM, Yeo GS, Teh KC, Lee ST, Makbul IAA, et al.
    PMID: 39251408 DOI: 10.1139/apnm-2023-0621
    Increased cardiometabolic risk among children is increasingly becoming a concern, with evidence indicating that obesity, diet, and serum 25-hydroxyvitamin D (25(OH)D) are associated with cardiometabolic risk. However, such studies among Malaysian children are scarce. Thus, this study explores the associations between adiposity, dietary quality, and 25(OH)D, with cardiometabolic risk factors among Malaysian children aged 4-12 years. Data of 479 children (mean age: 8.2 ± 2.3 years old, 52% females) from the South East Asian Nutrition Surveys (SEANUTS II) Malaysia, were included in this analysis. Adiposity (percentage of body fat) was assessed with bioelectrical impedance technique. Dietary quality was assessed using 24 h dietary recall and calculated as mean adequacy ratio. Vitamin D was assessed based on serum 25-hydroxyvitamin D (25(OH)D). Measurements of cardiometabolic risk factors included waist circumference (WC), mean arterial pressure (MAP), fasting blood glucose (FBG), high-density lipoprotein (HDL), triglyceride, and high-sensitivity C-reactive protein, and cardiometabolic risk cluster score (siMS) was calculated. Overall, higher adiposity was positively associated with all cardiometabolic risk factors (WC, ß = 0.907; 95% CI = 0.865, 0.948; MAP, ß = 0.225; 95% CI = 0.158, 0.292; HDL, ß = -0.011; 95% CI = -0.014, -0.009; Triglyceride, ß = 0.012; 95% CI = 0.009, 0.016; FBG, ß = 0.006; 95% CI = 0.002, 0.011) and siMS score (ß = 0.033; 95% CI = 0.029, 0.037). Serum 25(OH)D was inversely associated with siMS score (ß = -0.002; 95% CI = -0.004, -0.000008) and positively associated with HDL (ß = 0.002; 95% CI = 0.0001, 0.003). Our findings suggest that adiposity is a key determinant of adverse cardiometabolic risk factors in children, while serum 25(OH)D may be associated with overall cardiometabolic health. Interventions to reduce obesity are needed to mitigate the deleterious consequences of cardiometabolic dysregulation in children.
  3. Ramanathan S, Lau WJ, Goh PS, Gopinath SCB, Rawindran H, Omar MF, et al.
    Mikrochim Acta, 2024 Sep 10;191(10):586.
    PMID: 39251454 DOI: 10.1007/s00604-024-06662-0
    A unique method for determining chlorophyll content in microalgae is devised employing a gold interdigitated electrode (G-IDE) with a 10-µm gap, augmented by a nano-molecularly imprinted polymer (nano-MIP) and a titanium dioxide/multiwalled carbon nanotube (TiO2/MWCNT) nanocomposite. The nano-MIP, produced using chlorophyll template voids, successfully trapped chlorophyll, while the TiO2/MWCNT nanocomposite, synthesized by the sol-gel technique, exhibited a consistent distribution and anatase crystalline structure. The rebinding of procured chlorophyll powder, which was used as a template for nano-MIP synthesis, was identified with a high determination coefficient (R2 = 0.9857). By combining the TiO2/MWCNT nanocomposite with nano-MIP, the G-IDE sensing method achieved a slightly better R2 value of 0.9892 for detecting chlorophyll in microalgae. The presented G-IDE sensor showed a significant threefold enhancement in chlorophyll detection compared with commercially available chlorophyll powder. It had a detection limit of 0.917 mL (v/v) and a linear range that spanned from 10-6 to 1 mL. The effectiveness of the sensor in detecting chlorophyll in microalgae was confirmed through validation of its repeatability and reusability.
    MeSH terms: Electrodes*; Molecular Imprinting; Limit of Detection
  4. Mukherjee S, Chopra H, Goyal R, Jin S, Dong Z, Das T, et al.
    Discov Nano, 2024 Sep 10;19(1):144.
    PMID: 39251461 DOI: 10.1186/s11671-024-04100-x
    The exploration of targeted therapy has proven to be a highly promising avenue in the realm of drug development research. The human body generates a substantial amount of free radicals during metabolic processes, and if not promptly eliminated, these free radicals can lead to oxidative stress, disrupting homeostasis and potentially contributing to chronic diseases and cancers. Before the development of contemporary medicine with synthetic pharmaceuticals and antioxidants, there was a long-standing practice of employing raw, natural ingredients to cure a variety of illnesses. This practice persisted even after the active antioxidant molecules were known. The ability of natural antioxidants to neutralise excess free radicals in the human body and so prevent and cure a wide range of illnesses. The term "natural antioxidant" refers to compounds derived from plants or other living organisms that have the ability to control the production of free radicals, scavenge them, stop free radical-mediated chain reactions, and prevent lipid peroxidation. These compounds have a strong potential to inhibit oxidative stress. Phytochemicals (antioxidants) derived from plants, such as polyphenols, carotenoids, vitamins, and others, are central to the discussion of natural antioxidants. Not only may these chemicals increase endogenous antioxidant defenses, affect communication cascades, and control gene expression, but they have also shown strong free radical scavenging properties. This study comprehensively summarizes the primary classes of natural antioxidants found in different plant and animal source that contribute to the prevention and treatment of diseases. Additionally, it outlines the research progress and outlines future development prospects. These discoveries not only establish a theoretical groundwork for pharmacological development but also present inventive ideas for addressing challenges in medical treatment.
  5. Mohd Zulkifli SWH, Samsudin HB, Majid N
    Sci Rep, 2024 Sep 09;14(1):21030.
    PMID: 39251631 DOI: 10.1038/s41598-024-63591-x
    Numerous studies have been conducted in other countries on the health effects of exposure to particulate matter with a diameter of 10 microns or less P M 10 , but little research has been conducted in Malaysia, particularly during the haze season. This study intends to investigate how exposure of P M 10 influenced hospital admissions for respiratory diseases during the haze period in peninsula Malaysia and it was further stratified by age group, gender and respiratory diseases categories. The study includes data from all patients with respiratory diseases in 92 government hospitals, as well as P M 10 concentration and meteorological data from 92 monitoring stations in Peninsula Malaysia starting from 1st January 2000 to 31st December 2019. A quasi-poison time series regression with distributed lag nonlinear model (DLNM) was employed in this study to examine the relationship between exposure of P M 10 and hospital admissions for respiratory diseases during the haze period. Haze period for this study has been defined from June to September each year. According to the findings of this study, P M 10 was positively associated with hospitalisation of respiratory disease within 30 lag days under various lag patterns, with lag 25 showing the strongest association (RR = 1.001742, CI 1.001029,1.002456). Using median as a reference, it was discovered that females were more likely than males to be hospitalized for P M 10 exposure. Working age group will be the most affected by the increase in P M 10 exposure with a significant cumulative RR from lag 010 to lag 030. The study found that P M 10 had a significant influence on respiratory hospitalisation in peninsula Malaysia, particularly for lung diseases caused by external agents(CD5). Therefore, it is important to implement effective intervention measures to control P M 10 and reduce the burden of respiratory disease admissions.
    MeSH terms: Adolescent; Adult; Aged; Air Pollutants/adverse effects; Air Pollutants/analysis; Air Pollution/adverse effects; Child; Child, Preschool; Female; Humans; Malaysia/epidemiology; Male; Middle Aged; Seasons; Young Adult
  6. Manoharan P, Ravichandran S, Kavitha S, Tengku Hashim TJ, Alsoud AR, Sin TC
    Sci Rep, 2024 Sep 09;14(1):20979.
    PMID: 39251720 DOI: 10.1038/s41598-024-71223-7
    In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ξ 1 , ξ 2 , ξ 3 , ξ 4 , R C , λ , and b . The fuel cells (FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs.
  7. Riberholt CG, Olsen MH, Milan JB, Hafliðadóttir SH, Svanholm JH, Pedersen EB, et al.
    BMC Med Res Methodol, 2024 Sep 09;24(1):196.
    PMID: 39251912 DOI: 10.1186/s12874-024-02318-y
    BACKGROUND: Systematic reviews and data synthesis of randomised clinical trials play a crucial role in clinical practice, research, and health policy. Trial sequential analysis can be used in systematic reviews to control type I and type II errors, but methodological errors including lack of protocols and transparency are cause for concern. We assessed the reporting of trial sequential analysis.

    METHODS: We searched Medline and the Cochrane Database of Systematic Reviews from 1 January 2018 to 31 December 2021 for systematic reviews and meta-analysis reports that include a trial sequential analysis. Only studies with at least two randomised clinical trials analysed in a forest plot and a trial sequential analysis were included. Two independent investigators assessed the studies. We evaluated protocolisation, reporting, and interpretation of the analyses, including their effect on any GRADE evaluation of imprecision.

    RESULTS: We included 270 systematic reviews and 274 meta-analysis reports and extracted data from 624 trial sequential analyses. Only 134/270 (50%) systematic reviews planned the trial sequential analysis in the protocol. For analyses on dichotomous outcomes, the proportion of events in the control group was missing in 181/439 (41%), relative risk reduction in 105/439 (24%), alpha in 30/439 (7%), beta in 128/439 (29%), and heterogeneity in 232/439 (53%). For analyses on continuous outcomes, the minimally relevant difference was missing in 125/185 (68%), variance (or standard deviation) in 144/185 (78%), alpha in 23/185 (12%), beta in 63/185 (34%), and heterogeneity in 105/185 (57%). Graphical illustration of the trial sequential analysis was present in 93% of the analyses, however, the Z-curve was wrongly displayed in 135/624 (22%) and 227/624 (36%) did not include futility boundaries. The overall transparency of all 624 analyses was very poor in 236 (38%) and poor in 173 (28%).

    CONCLUSIONS: The majority of trial sequential analyses are not transparent when preparing or presenting the required parameters, partly due to missing or poorly conducted protocols. This hampers interpretation, reproducibility, and validity.

    STUDY REGISTRATION: PROSPERO CRD42021273811.

    MeSH terms: Humans; Research Design/standards; Meta-Analysis as Topic*
  8. Rahman MS, Islam KR, Prithula J, Kumar J, Mahmud M, Alam MF, et al.
    BMC Med Inform Decis Mak, 2024 Sep 09;24(1):249.
    PMID: 39251962 DOI: 10.1186/s12911-024-02655-4
    BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.

    METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction.

    RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.

    CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

    MeSH terms: Machine Learning*; Aged; Female; Humans; Intensive Care Units; Male; Middle Aged; Prognosis; Biomarkers; Nomograms
  9. Wang C, Yuan Y, Ji X
    BMC Public Health, 2024 Sep 09;24(1):2451.
    PMID: 39252015 DOI: 10.1186/s12889-024-20001-1
    OBJECTIVE: The university period is a critical stage of personal development, and improving the physical fitness of university students is crucial to their academic performance, quality of life, and future. However, in recent years, the physical fitness level of Chinese university students has shown a decreasing trend. This study aimed to investigate the effects of a blended learning model on the physical fitness of Chinese university students through a 16-week intervention.

    METHODS: A total of 78 first-year students from a public university in Henan Province were recruited for this study via a cluster randomized controlled trial (CRCT) design. The participants were divided into an experimental group (blended learning) and a control group (traditional learning). The intervention lasted for 16 weeks, and physical fitness indices such as body mass index (BMI), lung capacity, sit and reach, pull-ups/sit-ups, standing long jumps, 50-meter runs, and 1000/800-meter runs were measured before and after the intervention. Statistical analyses were conducted via generalized estimating equation (GEE) modeling, with the significance level set at P 

    MeSH terms: Adolescent; China; Female; Humans; Male; Universities; Models, Educational; Young Adult
  10. Lim L, Ab Majid AH
    Data Brief, 2024 Oct;56:110811.
    PMID: 39252776 DOI: 10.1016/j.dib.2024.110811
    The draft genome data for Cimex hemipterus obtained through Illumina HiSeq sequencing were presented. The raw genomic data was deposited in GenBank under BioProject (PRJNA722579) with the BioSample accession number SAMN18780126. Software, including FLASH, SPADES, and QUAST, were used to merge, assemble, and qualify the raw dataset. The assembled genome was available in the Figshare repository. The assembled genomic data was compared to C. hemipterus data obtained using 454 Roche shotgun sequencing (BioProject, PRJNA308532), downloaded from NCBI. The draft genome data from this work demonstrated larger data volumes and an updated assembly of the C. hemipterus genome with better scaffolding compared to genome data obtained from 454 Roche shotgun sequencing.
  11. Tan JH, Liew KJ, Goh KM
    Data Brief, 2024 Oct;56:110829.
    PMID: 39252782 DOI: 10.1016/j.dib.2024.110829
    This data report presents prokaryotic metagenome-assembled genomes (MAGs) from a hot spring stream with temperatures between 64 and 100°C. The stream water was filtered and the extracted total DNA was sequenced using the Illumina HiSeq 2500 platform. Approximately 80 Gb of raw data were generated, which were subsequently assembled using MEGAHIT v1.2.9. The MAGs were generated using MetaWRAP with binning approaches of MetaBAT2, CONCOCT and MaxBin2. We constructed 25 medium-quality and 24 high-quality archaeal MAGs, and 152 medium-quality and 112 high-quality bacterial MAGs. The fasta files of these MAGs are available in the NCBI database as well as Mendeley Data. Major phyla identified include Bacteroidota, Chloroflexota, Desulfobacterota, Firmicutes, Patescibacteria, Proteobacteria, Spirochaetota, Verrucomicrobiota, Armatimonadota, Nitrospirota, Acidobacteriota, Elusimicrobiota, Planctomycetota, Candidate division WOR-3, Aquificota, Thermoproteota, and Micrarchaeota. This dataset is valuable for studies on thermophilic genomes, reconstruction of biochemical pathways and gene discovery.
  12. Shamrat FMJM, Shakil R, Idris MYI, Akter B, Zhou X
    Data Brief, 2024 Oct;56:110821.
    PMID: 39252785 DOI: 10.1016/j.dib.2024.110821
    Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the "FruitSeg30_Segmentation Dataset & Mask Annotations", a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications.
  13. Watowich MM, Arner AM, Wang S, John E, Kahumbu JC, Kinyua P, et al.
    medRxiv, 2024 Aug 26.
    PMID: 39252903 DOI: 10.1101/2024.08.26.24312234
    BACKGROUND: Many subsistence-level and Indigenous societies around the world are rapidly experiencing urbanization, nutrition transition, and integration into market-economies, resulting in marked increases in cardiometabolic diseases. Determining the most potent and generalized drivers of changing health is essential for identifying vulnerable communities and creating effective policies to combat increased chronic disease risk across socio-environmental contexts. However, comparative tests of how different lifestyle features affect the health of populations undergoing lifestyle transitions remain rare, and require comparable, integrated anthropological and health data collected in diverse contexts.

    METHODS: We developed nine scales to quantify different facets of lifestyle (e.g., urban infrastructure, market-integration, acculturation) in two Indigenous, transitioning subsistence populations currently undergoing rapid change in very different ecological and sociopolitical contexts: Turkana pastoralists of northwest Kenya (n = 3,692) and Orang Asli mixed subsistence groups of Peninsular Malaysia (n = 688). We tested the extent to which these lifestyle scales predicted 16 measures of cardiometabolic health and compared the generalizability of each scale across the two populations. We used factor analysis to decompose comprehensive lifestyle data into salient axes without supervision, sensitivity analyses to understand which components of the multidimensional scales were most important, and sex-stratified analyses to understand how facets of lifestyle variation differentially impacted cardiometabolic health among males and females.

    FINDINGS: Cardiometabolic health was best predicted by measures that quantified urban infrastructure and market-derived material wealth compared to metrics encompassing diet, mobility, or acculturation, and these results were highly consistent across both populations and sexes. Factor analysis results were also highly consistent between the Turkana and Orang Asli and revealed that lifestyle variation decomposes into two distinct axes-the built environment and diet-which change at different paces and have different relationships with health.

    INTERPRETATION: Our analysis of comparable data from Indigenous peoples in East Africa and Southeast Asia revealed a surprising amount of generalizability: in both contexts, measures of local infrastructure and built environment are consistently more predictive of cardiometabolic health than other facets of lifestyle that are seemingly more proximate to health, such as diet. We hypothesize that this is because the built environment impacts unmeasured proximate drivers like physical activity, increased stress, and broader access to market goods, and serves as a proxy for the duration of time that communities have been market-integrated.

  14. Li J, Ju SY, Zhu C, Yuan Y, Fu M, Kong LK, et al.
    Heliyon, 2024 Aug 30;10(16):e36437.
    PMID: 39253112 DOI: 10.1016/j.heliyon.2024.e36437
    The development of a Digital Intelligence Quotient (DQ) scale for primary school students is the basis for research on the DQ of primary school students, which helps to scientifically diagnose the level and the current average DQ among Chinese primary school students. This study developed and validated a scale applicable to the assessment of DQ in Chinese primary school students where, the initial scale was first constructed; Then 1109 valid datasets were collected through purposive sampling and divided into Sample A and Sample B; Sample A was subjected to exploratory factor analysis and Sample B was tested by confirmatory factor analysis; The final validated scale consists of 22 items in 7 dimensions: digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital literacy and digital rights. The scale has high reliability and validity and thus can be used as a reliable instrument for assessing DQ in Chinese primary school students.
  15. Zainul R, Basem A, J Alfaker M, Sharma P, Kumar A, Al-Bahrani M, et al.
    Heliyon, 2024 Aug 30;10(16):e35171.
    PMID: 39253151 DOI: 10.1016/j.heliyon.2024.e35171
    In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector. A detailed system analysis and thermodynamic evaluation based on thermodynamic principles were carried out using the Engineering Equation Solver (EES) software. Key factors such as wind speed, solar radiation, and collector area were critical in determining system performance. To enhance the system's effectiveness, we conducted a comprehensive comparison of optimization algorithms, incorporating the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and utilizing a Pareto front for value optimization. This approach significantly outperformed other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of system efficiency and cost-effectiveness. The developed system achieved an exergy efficiency of 14.46 % and a cost rate of $74.98 per hour, demonstrating its suitability for its intended functions. Moreover, exergoenvironmental evaluation was conducted for the proposed plant. The findings revealed that key component HEX has a high exergoenvironmental factor due to their use of hot water, which has zero unit exergoenvironmental impact. Additionally, pumps demonstrated a zero exergoenvironmental impact factor, indicating negligible component-related environmental impacts. Sensitivity analysis further evaluated critical performance parameters, revealing that increases in solar irradiation lead to decreased total system cost rates, while higher turbine temperatures resulted in a remarkable 14.08 % reduction in the system's cost rate. These results underscore the economic viability of operating the system at higher temperatures and strengthen the argument for its adoption from a financial perspective.
  16. Mohd Haniff NS, Ng KH, Kamal I, Mohd Zain N, Abdul Karim MK
    Heliyon, 2024 Aug 30;10(16):e36313.
    PMID: 39253167 DOI: 10.1016/j.heliyon.2024.e36313
    The aim of this systematic review and meta-analysis is to evaluate the performance of classification metrics of machine learning-driven radiomics in diagnosing hepatocellular carcinoma (HCC). Following the PRISMA guidelines, a comprehensive search was conducted across three major scientific databases-PubMed, ScienceDirect, and Scopus-from 2018 to 2022. The search yielded a total of 436 articles pertinent to the application of machine learning and deep learning for HCC prediction. These studies collectively reflect the burgeoning interest and rapid advancements in employing artificial intelligence (AI)-driven radiomics for enhanced HCC diagnostic capabilities. After the screening process, 34 of these articles were chosen for the study. The area under curve (AUC), accuracy, specificity, and sensitivity of the proposed and basic models were assessed in each of the studies. Jamovi (version 1.1.9.0) was utilised to carry out a meta-analysis of 12 cohort studies to evaluate the classification accuracy rate. The risk of bias was estimated, and Logistic Regression was found to be the most suitable classifier for binary problems, with least absolute shrinkage and selection operator (LASSO) as the feature selector. The pooled proportion for HCC prediction classification was high for all performance metrics, with an AUC value of 0.86 (95 % CI: 0.83-0.88), accuracy of 0.83 (95 % CI: 0.78-0.88), sensitivity of 0.80 (95 % CI: 0.75-0.84) and specificity of 0.84 (95 % CI: 0.80-0.88). The performance of feature selectors, classifiers, and input features in detecting HCC and related factors was evaluated and it was observed that radiomics features extracted from medical images were adequate for AI to accurately distinguish the condition. HCC based radiomics has favourable predictive performance especially with addition of clinical features that may serve as tool that support clinical decision-making.
  17. Zou W, Othman A
    Heliyon, 2024 Aug 30;10(16):e36106.
    PMID: 39253180 DOI: 10.1016/j.heliyon.2024.e36106
    This study investigates the influence of accounting conservatism on corporate innovation investment through the lens of information asymmetry theory. While existing literature acknowledges the importance of accounting conservatism in corporate decision-making, there remains a gap in understanding how it specifically affects innovation investment, particularly in varied market environments and regulatory contexts. Specifically, current research often overlooks the heterogeneity of the impact of accounting conservatism on innovation investment under different market environments and regulatory frameworks. Additionally, there is a lack of specialized studies on the unique group of Chinese listed companies. This study fills this gap by empirically analyzing data from Chinese A-share listed companies, revealing a negative correlation between accounting conservatism and corporate innovation investment. Through empirical analysis of the financial reports and research and development (R&D) investment data of Chinese A-share listed companies from 2015 to 2022, this study finds a significant negative correlation between accounting conservatism and corporate innovation investment. Specifically, as accounting conservatism increases, corporate investment in R&D shows a decreasing trend, with a correlation coefficient of -0.364. This result is further validated by hierarchical regression analysis, where the regression coefficient is -0.465, indicating that accounting conservatism has a significant inhibitory effect on corporate innovation investment. This study is pioneering in its examination of the relationship between accounting conservatism and corporate innovation investment within the unique market environment of China, taking into account its distinctive characteristics and rapidly evolving technological industry background. To quantify accounting conservatism, the research employs the C-Score and G-Score models, while employing a range of indicators to measure corporate innovation investment, including proportions of R&D expenditure, number of new products or services, patent applications, total R&D personnel, capital investments, and progress in innovation projects. This comprehensive evaluation method enhances the accuracy and reliability of the study. The contribution of this study is significant as it offers a fresh perspective on how accounting conservatism influences corporate innovation investment. By providing empirical data support, it assists investors and corporate managers in making informed financial decisions and shaping innovation strategies. Through hierarchical regression analysis, the study substantiates the detrimental impact of accounting conservatism on corporate innovation investment, thereby establishing new theoretical and practical foundations for further research and application in related fields.
  18. Xia Y, Md Johar MG
    Heliyon, 2024 Aug 30;10(16):e36399.
    PMID: 39253266 DOI: 10.1016/j.heliyon.2024.e36399
    Digital innovation activities are data-driven, and the process of organizational digital innovation is inevitably influenced by their key participants, employees, as well as changes in the social institutional environment. How government support and employee structure impact organisational digital innovation was examined in this study. Since digital innovation activities are data-driven, the mediating role of data flows within digital innovation ecosystems was explored. A quantitative research design was employed, and data were collected by a survey from 299 firms in China. Results of structural equation modelling using SPSS and AMOS reveal that government support for enterprises in terms of policies and services, as well as the employee structure within enterprises, have a direct impact on organisational digital innovation. Data flows within digital innovation ecosystems mediate the relationship between government support and organisational digital innovation activities. Our findings provided evidence for theories of digital innovation ecosystems and employee-driven digital innovation. The results and conclusions in this study can provide reference for enterprises to achieve digital innovation breakthroughs, and for policymakers to formulate digital-related policies and regulations.
  19. Fan PC, Chiou LC, Lai TH, Sharmin D, Cook J, Lee MT
    Front Pharmacol, 2024;15:1451634.
    PMID: 39253381 DOI: 10.3389/fphar.2024.1451634
    INTRODUCTION: The α6 subunit-containing GABAA receptors (α6GABAARs) are highly expressed in the trigeminal ganglia (TG), the sensory hub of the trigeminovascular system (TGVS). Hypo-GABAergic transmission in the TG was reported to contribute to migraine-related behavioral and histopathological phenotypes. Previously, we found that Compound 6, an α6GABAAR-selective positive allosteric modulator (PAM), significantly alleviated TGVS activation-induced peripheral and central sensitization in a capsaicin-induced migraine-mimicking model.

    METHODS: Here, we tested whether the deuterated analogues of Compound 6, namely DK-1-56-1 and RV-I-29, known to have longer half-lives than the parent compound, can exert a similar therapeutic effect in the same model. The activation of TGVS was triggered by intra-cisternal (i.c.) instillation of capsaicin in male Wistar rats. Centrally, i.c. capsaicin increased the quantity of c-Fos-immunoreactive (c-Fos-ir) neurons in the trigeminal cervical complex (TCC). Peripherally, it increased the calcitonin gene-related peptide immunoreactivity (CGRP-ir) in TG, and caused CGRP release, leading to CGRP depletion in the dura mater.

    RESULTS: DK-I-56-1 and RV-I-29, administered intraperitoneally (i.p.), significantly ameliorated the TCC neuronal activation, TG CGRP-ir elevation, and dural CGRP depletion induced by capsaicin, with DK-I-56-1 demonstrating better efficacy. The therapeutic effects of 3 mg/kg DK-I-56-1 are comparable to that of 30 mg/kg topiramate. Notably, i.p. administered furosemide, a blood-brain-barrier impermeable α6GABAAR-selective antagonist, prevented the effects of DK-I-56-1 and RV-I-29. Lastly, orally administered DK-I-56-1 has a similar pharmacological effect.

    DISCUSSION: These results suggest that DK-I-56-1 is a promising candidate for novel migraine pharmacotherapy, through positively modulating TG α6GABAARs to inhibit TGVS activation, with relatively favourable pharmacokinetic properties.

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