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Background: Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch. Result: An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure. Conclusions: Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures. [ABSTRACT FROM AUTHOR]

Background: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. Result: Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. Conclusions: The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented. [ABSTRACT FROM AUTHOR]

Background: Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. Results: We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). Conclusions: The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common cancer in Malaysia, with many diagnosed at late stages. The "Know Your Lemons" (KYL) visual educational tools were developed by KYL Foundation. This study aimed to evaluate participants' confidence levels and perceived knowledge in identifying BC symptoms before and after exposure to KYL tools., Materials and Methods: A cross-sectional study was carried out among 788 participants in three KYL health campaigns from 2017 to 2020. Perceived knowledge (a 5-item Likert scale was used, zero means "very poor" and 4 means "excellent knowledge") and confidence in identifying BC symptoms were studied. A Wilcoxon Matched-Paired Signed-Rank Test was performed to assess the perceived knowledge., Results: There was a significant improvement in the perceived knowledge Mean (±SD) score (2.84 ± 1.02) versus (4.31 ± 0.66) before and after the campaign ( P < 0.01). About 95.6% agreed that the language used in KYL materials was clear and understandable, 89.8% agreed it is acceptable in Malaysian culture, and 80% felt more confident in identifying BC symptoms. Therefore, 90.8% had the intention of breast self-examination and 90.8% would consult a doctor if symptomatic. The majority (92.7%) agreed that the KYL tools clarified the BC tests needed., Conclusion: The KYL tools enhanced perceived BC symptom recognition knowledge and confidence levels., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Journal of Education and Health Promotion.)

Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.

In this study, we successfully present the dual-target design hypothesis to inhibit both dihydropteroate synthase (DHPS) and dihydrofolate reductase (DHFR) enzymes using a novel scheme that integrates our previous antibiotic-phytochemical interaction data, fragment combination and knowledge-based methods. Both the enzymes are well established antibacterial targets from folate biosynthesis pathway and their synergistic modulation by a single hybrid entity may have profound therapeutic benefits. Evaluation of the designed hybrid compounds based on their physico-chemical properties has indicated them as promising drug candidates with drug-like pharmacotherapeutic profiles. In addition, the stereo-electronic properties such as HOMO, LUMO and MEP maps calculated by quantum chemical methods gave a good correlation with the common pharmacophoric features required for dual-site interactions. Furthermore, docking and dynamics simulation studies reveal that the designed hybrid compounds have favorable binding affinity and stability in both pterin-binding site of DHPS and folate-binding site of DHFR by forming strong hydrogen bonds and hydrophobic interactions with key active-site residues. Looking forward this study could serve as a prospective lead in the process of new natural-product based hybrid-drugs development.