Browse publications by year: 2024

  1. Dagli N, Haque M, Kumar S
    Cureus, 2024 Apr;16(4):e58891.
    PMID: 38659710 DOI: 10.7759/cureus.58891
    This bibliometric analysis investigates the research landscape concerning the impact of rheumatoid arthritis (RA) on oral health through a comprehensive literature review. The study includes all English-language articles retrieved from the PubMed database, focusing on the relationship between RA and various aspects of oral health without any filter. The analysis of 261 publications revealed fluctuations in publication patterns from 1987 to 2024, with notable surges and declines in research activity. Collaborative networks among authors and institutions were identified, highlighting key contributors and prolific institutions such as Karolinska Institutet. The themes prevalent in the research included demographics, oral microbiota, biomarkers, treatment outcomes, and molecular mechanisms. Trend topic and thematic evolution analyses elucidated shifts in research focus from traditional concerns to emerging areas such as oral microbiology and immunological mechanisms. Key findings underscored the need for more clinical trials to comprehend the impact of RA on oral health, enhanced interdisciplinary collaboration, exploration of emerging areas, and longitudinal studies. This analysis provides valuable insights into the evolving research landscape, informing future investigations and interventions to improve oral health outcomes in individuals with RA.
  2. Hajim WI, Zainudin S, Mohd Daud K, Alheeti K
    PeerJ Comput Sci, 2024;10:e1903.
    PMID: 38660174 DOI: 10.7717/peerj-cs.1903
    Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
  3. Humayun MA, Shuja J, Abas PE
    PeerJ Comput Sci, 2024;10:e1984.
    PMID: 38660189 DOI: 10.7717/peerj-cs.1984
    Social background profiling of speakers is heavily used in areas, such as, speech forensics, and tuning speech recognition for accuracy improvement. This article provides a survey of recent research in speaker background profiling in terms of accent classification and analyses the datasets, speech features, and classification models used for the classification tasks. The aim is to provide a comprehensive overview of recent research related to speaker background profiling and to present a comparative analysis of the achieved performance measures. Comprehensive descriptions of the datasets, speech features, and classification models used in recent research for accent classification have been presented, with a comparative analysis made on the performance measures of the different methods. This analysis provides insights into the strengths and weaknesses of the different methods for accent classification. Subsequently, research gaps have been identified, which serve as a useful resource for researchers looking to advance the field.
  4. Hossain T, Shamrat FMJM, Zhou X, Mahmud I, Mazumder MSA, Sharmin S, et al.
    PeerJ Comput Sci, 2024;10:e1950.
    PMID: 38660192 DOI: 10.7717/peerj-cs.1950
    Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.
  5. Khairuddin MZF, Sankaranarayanan S, Hasikin K, Abd Razak NA, Omar R
    PeerJ Comput Sci, 2024;10:e1985.
    PMID: 38660193 DOI: 10.7717/peerj-cs.1985
    BACKGROUND: This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational injury severity assessment.

    METHODS: Natural language processing (NLP) techniques were harnessed to preprocess the occupational injury narratives obtained from the US Occupational Safety and Health Administration (OSHA) from January 2015 to June 2023. The methodology involved meticulous preprocessing of textual narratives to standardize text and eliminate noise, followed by the innovative integration of Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) word embeddings for effective text representation. The proposed predictive model adopts a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture and is further refined through model optimization, including random search hyperparameters and in-depth feature importance analysis. The optimized Bi-LSTM model has been compared and validated against other machine learning classifiers which are naïve Bayes, support vector machine, random forest, decision trees, and K-nearest neighbor.

    RESULTS: The proposed optimized Bi-LSTM models' superior predictability, boasted an accuracy of 0.95 for hospitalization and 0.98 for amputation cases with faster model processing times. Interestingly, the feature importance analysis revealed predictive keywords related to the causal factors of occupational injuries thereby providing valuable insights to enhance model interpretability.

    CONCLUSION: Our proposed optimized Bi-LSTM model offers safety and health practitioners an effective tool to empower workplace safety proactive measures, thereby contributing to business productivity and sustainability. This study lays the foundation for further exploration of predictive analytics in the occupational safety and health domain.

  6. Alhazmi A, Mahmud R, Idris N, Mohamed Abo ME, Eke C
    PeerJ Comput Sci, 2024;10:e1966.
    PMID: 38660217 DOI: 10.7717/peerj-cs.1966
    The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.
  7. Zhang H, Mo Y, Wang L, Zhang H, Wu S, Sandai D, et al.
    Front Immunol, 2024;15:1339647.
    PMID: 38660311 DOI: 10.3389/fimmu.2024.1339647
    INTRODUCTION: Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.

    METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.

    RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.

    DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

    MeSH terms: Female; Gene Expression Regulation; Humans; Biomarkers; Computational Biology/methods; Gene Expression Profiling; Databases, Genetic; Gene Regulatory Networks; Transcriptome*; Protein Interaction Maps*
  8. Hossen MJ, Ramanathan TT, Al Mamun A
    Int J Telemed Appl, 2024;2024:8188904.
    PMID: 38660584 DOI: 10.1155/2024/8188904
    The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.
  9. Zheng X, Li C, Ali S, Adebayo TS
    Risk Anal, 2024 Apr 25.
    PMID: 38660914 DOI: 10.1111/risa.14310
    The allocation of budgets for renewable energy (RE) technology is significantly influenced by geopolitical risks (GPRs), reflecting the intricate interplay among global political dynamics, social media narratives, and the strategic investment decisions essential for advancing sustainable energy solutions. Against the backdrop of increasing worldwide initiatives to transition to RE sources, it is crucial to understand how GPR affects funding allocations, informing policy decisions, and fostering international collaboration to pursue sustainable energy solutions. Existing work probes the nonlinear effect of GPR on RE technology budgets (RTB) within the top 10 economies characterized by substantial research and development investments in RE (China, USA, Germany, Japan, France, South Korea, India, the United Kingdom, Australia, and Italy). Past research largely focused on panel data techniques to delve the interconnection between GPR and RE technology, overlooking the distinctive characteristics of individual economies. Contrarily, existing investigation implements the "Quantile-on-Quantile" tool to explore this association on an economy-particular basis, enhancing the precision of our analysis and offering both a comprehensive global perspective and nuanced perceptions for entire countries. The findings manifest a significant reduction in funding for RE technology associated with GPR across various quantile levels in the chosen economies. The disparities in results spotlight the necessity for policymakers to perform thorough assessments and carry out competent strategies to address the variations in GPR and RTB.
  10. Su Y, Chai XH, Tan CP, Liu YF
    J Food Sci, 2024 Apr 25.
    PMID: 38660921 DOI: 10.1111/1750-3841.17028
    In this paper, the compatibility, phase behavior, and crystallization properties of the binary blends of palm kernel stearin (PKS) and anhydrous milk fat (AMF) were investigated by analyzing the solid fat content (SFC), thermal properties, polymorphism, and microstructure, with the aim of providing theoretical guidance for the blending of oils. The results showed that the PKS content primarily determined the SFC trend of the binary blends. However, the binary blends demonstrated poor miscibility and eutectic behavior was predominantly observed in the system, particularly at higher temperatures. Only α and β' forms appeared in this blended system. Simultaneously, the addition of PKS elevated the liquid phase transition temperature of the binary blends, considerably significantly increased their crystallization rate when the addition of PKS was more than 20% and increased the density and size of the fat crystals. Finally, the mixing design optimization method was used to get the most suitable ratio of the binary blends in the refrigerated cream system with PKS:AMF to be 0.914:0.086. The cream prepared with the above binary blends was indeed superior in overrun and firmness and had high stability. PRACTICAL APPLICATION: Some fats with special advantages are often limited in their wide application because of their poor crystallization ability. In this paper, the crystallization ability of fats is improved, and their application scenarios are increased through the combination of fats, so as to provide reference for the production of special fats for food.
  11. Malbenia John M, Benettayeb A, Belkacem M, Ruvimbo Mitchel C, Hadj Brahim M, Benettayeb I, et al.
    Chemosphere, 2024 Jun;357:142051.
    PMID: 38648988 DOI: 10.1016/j.chemosphere.2024.142051
    Water purification using adsorption is a crucial process for maintaining human life and preserving the environment. Batch and dynamic adsorption modes are two types of water purification processes that are commonly used in various countries due to their simplicity and feasibility on an industrial scale. However, it is important to understand the advantages and limitations of these two adsorption modes in industrial applications. Also, the possibility of using batch mode in industrial scale was scrutinized, along with the necessity of using dynamic mode in such applications. In addition, the reasons for the necessity of performing batch adsorption studies before starting the treatment on an industrial scale were mentioned and discussed. In fact, this review article attempts to throw light on these subjects by comparing the biosorption efficiency of some metals on utilized biosorbents, using both batch and fixed-bed (column) adsorption modes. The comparison is based on the effectiveness of the two processes and the mechanisms involved in the treatment. Parameters such as biosorption capacity, percentage removal, and isotherm models for both batch and column (fixed bed) studies are compared. The article also explains thermodynamic and kinetic models for batch adsorption and discusses breakthrough evaluations in adsorptive column systems. The review highlights the benefits of using convenient batch-wise biosorption in lab-scale studies and the key advantages of column biosorption in industrial applications.
    MeSH terms: Adsorption; Ions/chemistry; Kinetics; Thermodynamics
  12. Rajasegaran S, Ahmad NA, Tan SK, Lechmiannandan A, Mohamed OM, Cheng JQ, et al.
    Arch Dis Child, 2024 Apr 22.
    PMID: 38649254 DOI: 10.1136/archdischild-2023-326724
    PURPOSE: Children with anorectal malformation (ARM) and Hirschsprung's disease (HD) often experience bowel symptoms into adulthood, despite definitive surgery. This study evaluates the quality of life (QOL) and bowel functional outcome of children treated for ARM and HD in comparison to healthy controls.

    METHODS: Between December 2020 and February 2023, we recruited patients with ARM and HD aged 3-17 years at four tertiary referral centres, who had primary corrective surgery done >12 months prior. Healthy controls were age-matched and sex-matched. All participants completed the Pediatric Quality of Life Inventory Generic Core Scales 4.0, General Well-Being (GWB) Scale 3.0 and Family Impact (FI) Module 2.0 Questionnaires. Bowel Function Score (BFS) Questionnaires were also administered. We also performed subgroup analysis according to age categories. Appropriate statistical analysis was performed with p<0.05 significance. Ethical approval was obtained.

    RESULTS: There were 306 participants: 101 ARM, 87 HD, 118 controls. Patients with ARM and HD had significantly worse Core and FI Scores compared with controls overall and in all age categories. In the GWB Scale, only ARM and HD adolescents (13-17 years) had worse scores than controls. ARM and HD had significantly worse BFSs compared with controls overall and in all age categories. There was significant positive correlation between BFS and Core Scores, GWB Scores and FI Scores.

    CONCLUSION: Patients with ARM and HD had worse QOL than controls. Lower GWB Scores in adolescents suggests targeted interventions are necessary. Bowel function influences QOL, indicating the need for continuous support into adulthood.

  13. Ferrero F, Lin CY, Liese J, Luz K, Stoeva T, Nemeth A, et al.
    Paediatr Drugs, 2024 Apr 22.
    PMID: 38649595 DOI: 10.1007/s40272-024-00625-x
    BACKGROUND: Respiratory syncytial virus (RSV) causes significant morbidity and mortality in children aged ≤ 5 years and adults aged ≥ 60 years worldwide. Despite this, RSV-specific therapeutic options are limited. Rilematovir is an investigational, orally administered inhibitor of RSV fusion protein-mediated viral entry.

    OBJECTIVE: To establish the antiviral activity, clinical outcomes, safety, and tolerability of rilematovir (low or high dose) in children aged ≥ 28 days and ≤ 3 years with RSV disease.

    METHODS: CROCuS was a multicenter, international, double-blind, placebo-controlled, randomized, adaptive phase II study, wherein children aged ≥ 28 days and ≤ 3 years with confirmed RSV infection who were either hospitalized (Cohort 1) or treated as outpatients (Cohort 2) were randomized (1:1:1) to receive rilematovir (low or high dose) or placebo. Study treatment was administered daily as an oral suspension from days 1 to 7, with dosing based on weight and age groups. The primary objective was to establish antiviral activity of rilematovir by evaluating the area under the plasma concentration-time curve of RSV viral load in nasal secretions from baseline through day 5. Severity and duration of RSV signs and symptoms and the safety and tolerability of rilematovir were also assessed through day 28 (± 3).

    RESULTS: In total, 246 patients were randomized, treated, and included in the safety analysis population (Cohort 1: 147; Cohort 2: 99). Of these, 231 were included in the intent-to-treat-infected analysis population (Cohort 1: 138; Cohort 2: 93). In both cohorts, demographics were generally similar across treatment groups. In both cohorts combined, the difference (95% confidence interval) in the mean area under the plasma concentration-time curve of RSV RNA viral load through day 5 was - 1.25 (- 2.672, 0.164) and - 1.23 (- 2.679, 0.227) log10 copies∙days/mL for the rilematovir low-dose group and the rilematovir high-dose group, respectively, when compared with placebo. The estimated Kaplan-Meier median (95% confidence interval) time to resolution of key RSV symptoms in the rilematovir low-dose, rilematovir high-dose, and placebo groups of Cohort 1 was 6.01 (4.24, 7.25), 5.82 (4.03, 8.18), and 7.05 (5.34, 8.97) days, respectively; in Cohort 2, estimates were 6.45 (4.81, 9.70), 6.26 (5.41, 7.84), and 5.85 (3.90, 8.27) days, respectively. A similar incidence of adverse events was reported in patients treated with rilematovir and placebo in Cohort 1 (rilematovir: 61.9%; placebo: 58.0%) and Cohort 2 (rilematovir: 50.8%; placebo: 47.1%), with most reported as grade 1 or 2 and none leading to study discontinuation. The study was terminated prematurely, as the sponsor made a non-safety-related strategic decision to discontinue rilematovir development prior to full recruitment of Cohort 2.

    CONCLUSIONS: Data from the combined cohort suggest that rilematovir has a small but favorable antiviral effect of indeterminate clinical relevance compared with placebo, as well as a favorable safety profile. Safe and effective therapeutic options for RSV in infants and young children remain an unmet need.

    CLINICAL TRIAL REGISTRATION: EudraCT Number: 2016-003642-93; ClinicalTrials.gov Identifier: NCT03656510. First posted date: 4 September, 2018.

  14. Triyanto A, Ali N, Salleh H, Setiawan J, Yatim NI
    PMID: 38649606 DOI: 10.1007/s11356-024-33360-4
    Dye-sensitized solar cell (DSSC) is a photovoltaic device that can be produced from natural source pigments or natural dyes. The selection of natural dyes for DSSC application is currently under research. The utilization of natural dye materials that are easy to obtain, cost-effective, and non-toxic can reduce waste during DSSC fabrication. Natural dyes can be extracted from plants through extraction and chromatography methods. The suitability and viability of utilizing natural dyes as photosensitizers in DSSCs can be predicted using appropriate software simulation by varying related parameters to produce high power conversion efficiency. In this context, the purpose of the review is to highlight the evolution of performance improvement in the development of DSSCs with consideration of natural dye extraction and software simulation. This review also focuses on the results of extracting natural dyes from herbal ingredients, which are still very limited in information, and several parts of herbal plants that can be used as natural dye sources in the future of solid-state DSSCs have been identified. Based on the results of this review, the highest efficiency was obtained for the DSSC that used chlorophyll pigments as natural dyes using Peltophorum pterocarpum leaves with 6.07%, followed by anthocyanin pigments as natural dyes using raspberries (black) fruits with 1.5%, flavonoid pigments as natural dyes using Curcuma longa herbs with 0.64%, and flavonoid pigments as natural dyes using Indigofera tinctoria flowers with 0.46%.
  15. Lim WX, Chen Z
    Med Biol Eng Comput, 2024 Aug;62(8):2571-2583.
    PMID: 38649629 DOI: 10.1007/s11517-024-03093-0
    Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixel is expensive because the number of pixels is high even at low resolution. In this work, we propose a hybrid image processing method. Simple Linear Iterative Clustering with Gaussian Filter (SLIC-G) for the purpose of overcoming pixel constraints. The SLIC-G image processing method is divided into two stages: (1) simple linear iterative clustering superpixel segmentation and (2) Gaussian smoothing operation. In such a way, a large number of new transformed datasets are generated and then used for model training. Finally, two performance evaluation metrics that are suitable for imbalanced diabetic retinopathy datasets were used to validate the effectiveness of the proposed SLIC-G. The results indicate that, in comparison to prior published works' results, the proposed SLIC-G shows better performance on image classification of class imbalanced diabetic retinopathy datasets. This research reveals the importance of image processing and how it influences the performance of deep learning networks. The proposed SLIC-G enhances pre-trained network performance by eliminating the local redundancy of an image, which preserves local structures, but avoids over-segmented, noisy clips. It closes the research gap by introducing the use of superpixel segmentation and Gaussian smoothing operation as image processing methods in diabetic retinopathy-related tasks.
    MeSH terms: Algorithms; Fundus Oculi*; Humans; Image Interpretation, Computer-Assisted/methods; Photography/methods; Normal Distribution; Neural Networks (Computer)
  16. Assaggaf H, Jeddi M, Mrabti HN, Ez-Zoubi A, Qasem A, Attar A, et al.
    Sci Rep, 2024 Apr 22;14(1):9195.
    PMID: 38649707 DOI: 10.1038/s41598-024-59708-x
    The development of novel antioxidant compounds with high efficacy and low toxicity is of utmost importance in the medicine and food industries. Moreover, with increasing concerns about the safety of synthetic components, scientists are beginning to search for natural sources of antioxidants, especially essential oils (EOs). The combination of EOs may produce a higher scavenging profile than a single oil due to better chemical diversity in the mixture. Therefore, this exploratory study aims to assess the antioxidant activity of three EOs extracted from Cymbopogon flexuosus, Carum carvi, and Acorus calamus in individual and combined forms using the augmented-simplex design methodology. The in vitro antioxidant assays were performed using DPPH and ABTS radical scavenging approaches. The results of the Chromatography Gas-Mass spectrometry (CG-MS) characterization showed that citral (29.62%) and niral (27.32%) are the main components for C. flexuosus, while D-carvone (62.09%) and D-limonene (29.58%) are the most dominant substances in C. carvi. By contrast, β-asarone (69.11%) was identified as the principal component of A. calamus (30.2%). The individual EO exhibits variable scavenging activities against ABTS and DPPH radicals. These effects were enhanced through the mixture of the three EOs. The optimal antioxidant formulation consisted of 20% C. flexuosus, 53% C. carvi, and 27% A. calamus for DPPHIC50. Whereas 17% C. flexuosus, 43% C. carvi, and 40% A. calamus is the best combination leading to the highest scavenging activity against ABTS radical. These findings suggest a new research avenue for EOs combinations to be developed as novel natural formulations useful in food and biopharmaceutical products.
    MeSH terms: Biphenyl Compounds/antagonists & inhibitors; Biphenyl Compounds/chemistry; Gas Chromatography-Mass Spectrometry; Free Radical Scavengers/pharmacology; Free Radical Scavengers/chemistry
  17. Mendes CP, Albert WR, Amir Z, Ancrenaz M, Ash E, Azhar B, et al.
    Ecology, 2024 Apr 22.
    PMID: 38650359 DOI: 10.1002/ecy.4299
    Information on tropical Asian vertebrates has traditionally been sparse, particularly when it comes to cryptic species inhabiting the dense forests of the region. Vertebrate populations are declining globally due to land-use change and hunting, the latter frequently referred as "defaunation." This is especially true in tropical Asia where there is extensive land-use change and high human densities. Robust monitoring requires that large volumes of vertebrate population data be made available for use by the scientific and applied communities. Camera traps have emerged as an effective, non-invasive, widespread, and common approach to surveying vertebrates in their natural habitats. However, camera-derived datasets remain scattered across a wide array of sources, including published scientific literature, gray literature, and unpublished works, making it challenging for researchers to harness the full potential of cameras for ecology, conservation, and management. In response, we collated and standardized observations from 239 camera trap studies conducted in tropical Asia. There were 278,260 independent records of 371 distinct species, comprising 232 mammals, 132 birds, and seven reptiles. The total trapping effort accumulated in this data paper consisted of 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal, and far eastern India. The relatively standardized deployment methods in the region provide a consistent, reliable, and rich count data set relative to other large-scale pressence-only data sets, such as the Global Biodiversity Information Facility (GBIF) or citizen science repositories (e.g., iNaturalist), and is thus most similar to eBird. To facilitate the use of these data, we also provide mammalian species trait information and 13 environmental covariates calculated at three spatial scales around the camera survey centroids (within 10-, 20-, and 30-km buffers). We will update the dataset to include broader coverage of temperate Asia and add newer surveys and covariates as they become available. This dataset unlocks immense opportunities for single-species ecological or conservation studies as well as applied ecology, community ecology, and macroecology investigations. The data are fully available to the public for utilization and research. Please cite this data paper when utilizing the data.
  18. Butt MD, Ong SC, Rafiq A, Kalam MN, Sajjad A, Abdullah M, et al.
    J Pharm Policy Pract, 2024;17(1):2322107.
    PMID: 38650677 DOI: 10.1080/20523211.2024.2322107
    INTRODUCTION: Diabetes increases preventative sickness and costs healthcare and productivity. Type 2 diabetes and macrovascular disease consequences cause most diabetes-related costs. Type 2 diabetes greatly costs healthcare institutions, reducing economic productivity and efficiency. This cost of illness (COI) analysis examines the direct and indirect costs of treating and managing type 1 and type 2 diabetes mellitus.

    METHODOLOGY: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, Cochrane, PubMed, Embase, CINAHL, Scopus, Medline Plus, and CENTRAL were searched for relevant articles on type 1 and type 2 diabetes illness costs. The inquiry returned 873 2011-2023 academic articles. The study included 42 papers after an abstract evaluation of 547 papers.

    RESULTS: Most articles originated in Asia and Europe, primarily on type 2 diabetes. The annual cost per patient ranged from USD87 to USD9,581. Prevalence-based cost estimates ranged from less than USD470 to more than USD3475, whereas annual pharmaceutical prices ranged from USD40 to more than USD450, with insulin exhibiting the greatest disparity. Care for complications was generally costly, although costs varied significantly by country and problem type.

    DISCUSSION: This study revealed substantial heterogeneity in diabetes treatment costs; some could be reduced by improving data collection, analysis, and reporting procedures. Diabetes is an expensive disease to treat in low- and middle-income countries, and attaining Universal Health Coverage should be a priority for the global health community.

  19. Tay YZ, Balasubbiah N, Awang RR, Retna Pandian BD, Sathiamurthy N
    Cureus, 2024 Mar;16(3):e56792.
    PMID: 38650780 DOI: 10.7759/cureus.56792
    Primary hyperparathyroidism (PHPT) usually presents with symptoms of hypercalcemia which is due to excessive secretion of parathyroid hormone (PTH). Surgical removal of the secreting tumor either adenoma or hyperplasia remains the mainstay of treatment. Around 2% to 25% of the lesions are located in the mediastinum. We reviewed our institution's surgical treatment and approach to mediastinal parathyroid adenoma (MPA). We retrospectively reviewed the demography, comorbidities, clinical presentation, surgical approach, and outcome for patients in our institution who underwent surgery for MPA from September 2019 until August 2023. All patients with MPA who underwent surgery were included in the review. The surgical approaches used were both video-assisted thoracoscopic surgery (VATS) and median sternotomy. There were three patients with PHPT due to MPA who underwent surgery. Out of the three patients, two were female. The mean age was 48.6 years old, ranging from 16 to 66 years old. All of them presented with PHPT with a raised mean serum calcium level of 3.52 mmol/L (range: 2.84-4.38 mmol/L) and a mean PTH or intact PTH (iPTH) level of 274.6 pmol/L (range: 8.87-695 pmol/L). Ultrasound of the neck was performed for all the patients before further investigations were done to look for the ectopic parathyroid gland. Computed tomography (CT) of the thorax showed mediastinal parathyroid mass in all the patients with an average size of 2.4 x 2.1 x 2.3cm (range: 1.3-3.8cm), which showed uptake in 99mTc-hexakis-2-methoxyisobuthylisonitrile (Tc99m-MIBI) scintigraphy. VATS was performed for two cases and an upper partial sternotomy was performed for one patient. Postoperatively, iPTH and serum calcium levels were reduced significantly for all patients. There were no post-operative complications in our study. Comprehensive diagnostic imaging and surgical planning are important for the localization of MPA. In our review, all cases were promptly diagnosed and underwent surgery without complication.
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