Displaying publications 1 - 20 of 65 in total

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  1. [Invasion of Drosophila albomicans into Shanghai and areas nearby and a study on its mitochondrial DNA polymorphism]
    Chen W, Zhang J, Geng Z, Zhu D
    Yi Chuan Xue Bao, 1994;21(3):179-87.
    PMID: 7917431
    We report the fact that D. albomicans invaded into Shanghai suddenly in the autumn of 1991. Using 9 restriction enzymes, we analyse the RFLPs of mitochondrial DNA of 29 isofemale lines belonging to 4 populations of Shanghai, Jiading, Qinpu and Nanhui. We find that all 29 haplotypes are different from each other. Comparing with the populations of Canton, Kunming, Sanhutan (Taiwan), Sumoto (Japan), and Kuala Lumper (Malaysia), we come to the conclusion that D. albomicans caught in Shanghai and areas nearby is from a few of places in the south of China-mainland. This conclusion agrees with the viewpoint that this species is on the speciation stage of migration towards north. We also discuss the mtDNA polymorphism within the species.
  2. Effects of different types of biochar on methane and ammonia mitigation during layer manure composting
    Chen W, Liao X, Wu Y, Liang JB, Mi J, Huang J, et al.
    Waste Manag, 2017 Mar;61:506-515.
    PMID: 28117129 DOI: 10.1016/j.wasman.2017.01.014
    Biochar, because of its unique physiochemical properties and sorption capacity, may be an ideal amendment in reducing gaseous emissions during composting process but there has been little information on the potential effects of different types of biochar on undesired gaseous emissions. The objective of this study was to examine the ability and mechanism of different types of biochar, as co-substrate, in mitigating gaseous emission from composting of layer hen manure. The study was conducted in small-scale laboratory composters with the addition of 10% of one of the following biochars: cornstalk biochar, bamboo biochar, woody biochar, layer manure biochar and coir biochar. The results showed that the cumulative NH3 production was significantly reduced by 24.8±2.9, 9.2±1.3, 20.1±2.6, 14.2±1.6, 11.8±1.7% (corrected for initial total N) in the cornstalk biochar, bamboo biochar, woody biochar, layer manure biochar and coir biochar treatments, respectively, compared to the control. Total CH4 emissions was significantly reduced by 26.1±2.3, 15.5±2.1, 22.4±3.1, 17.1±2.1% (corrected for the initial total carbon) for cornstalk biochar, bamboo biochar, woody biochar and coir biochar treatments than the control. Moreover, addition of cornstalk biochar increased the temperature and NO3(-)-N concentration and decreased the pH, NH4(+)-N and organic matter content throughout the composting process. The results suggested that total volatilization of NH3 and CH4 in cornstalk biochar treatment was lower than the other treatments; which could be due to (i) decrease of pH and higher nitrification, (ii) high sorption capacity for gases and their precursors, such as ammonium nitrogen from composting mixtures, because of the higher surface area, pore volumes, total acidic functional groups and CEC of cornstalk biochar.
  3. Fulltext Health-care data collecting, sharing, and using in Thailand, China mainland, South Korea, Taiwan, Japan, and Malaysia
    Aljunid SM, Srithamrongsawat S, Chen W, Bae SJ, Pwu RF, Ikeda S, et al.
    Value Health, 2012 2 1;15(1 Suppl):S132-8.
    PMID: 22265060 DOI: 10.1016/j.jval.2011.11.004
    This article sought to describe the health-care data situation in six selected economies in the Asia-Pacific region. Authors from Thailand, China mainland, South Korea, Taiwan, Japan, and Malaysia present their analyses in three parts. The first part of the article describes the data-collection process and the sources of data. The second part of the article presents issues around policies of data sharing with the stakeholders. The third and final part of the article focuses on the extent of health-care data use for policy reform in these different economies. Even though these economies differ in their economic structure and population size, they share some similarities on issues related to health-care data. There are two main institutions that collect and manage the health-care data in these economies. In Thailand, China mainland, Taiwan, and Malaysia, the Ministry of Health is responsible through its various agencies for collecting and managing the health-care data. On the other hand, health insurance is the main institution that collects and stores health-care data in South Korea and Japan. In all economies, sharing of and access to data is an issue. The reasons for limited access to some data are privacy protection, fragmented health-care system, poor quality of routinely collected data, unclear policies and procedures to access the data, and control on the freedom on publication. The primary objective of collecting health-care data in these economies is to aid the policymakers and researchers in policy decision making as well as create an awareness on health-care issues for the general public. The usage of data in monitoring the performance of the heath system is still in the process of development. In conclusion, for the region under discussion, health-care data collection is under the responsibility of the Ministry of Health and health insurance agencies. Data are collected from health-care providers mainly from the public sector. Routinely collected data are supplemented by national surveys. Accessibility to the data is a major issue in most of the economies under discussion. Accurate health-care data are required mainly to support policy making and evidence-based decisions.
  4. Utility of Simulation via Instant Messaging - Birmingham Advance (SIMBA) in medical education during COVID-19 pandemic
    Morgan G, Melson E, Davitadze M, Ooi E, Zhou D, Hanania T, et al.
    J R Coll Physicians Edinb, 2021 06;51(2):168-172.
    PMID: 34131679 DOI: 10.4997/JRCPE.2021.218
    BACKGROUND: Simulation via Instant Messaging - Birmingham Advance (SIMBA) aimed to improve clinicians' confidence in managing various clinical scenarios during the COVID-19 pandemic.

    METHODS: Five SIMBA sessions were conducted between May and August 2020. Each session included simulation of scenarios and interactive discussion. Participants' self-reported confidence, acceptance, and relevance of the simulated cases were measured.

    RESULTS: Significant improvement was observed in participants' self-reported confidence (overall n = 204, p<0.001; adrenal n = 33, p<0.001; thyroid n = 37, p<0.001; pituitary n = 79, p<0.001; inflammatory bowel disease n = 17, p<0.001; acute medicine n = 38, p<0.001). Participants reported improvements in clinical competencies: patient care 52.0% (n = 106/204), professionalism 30.9% (n = 63/204), knowledge on patient management 84.8% (n = 173/204), systems-based practice 48.0% (n = 98/204), practice-based learning 69.6% (n = 142/204) and communication skills 25.5% (n = 52/204).

    CONCLUSION: SIMBA is a novel pedagogical virtual simulation-based learning model that improves clinicians' confidence in managing conditions across various specialties.

  5. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
    He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.
    Sci Total Environ, 2019 May 01;663:1-15.
    PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329
    Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
  6. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
    Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, et al.
    Sci Total Environ, 2018 Sep 01;634:853-867.
    PMID: 29653429 DOI: 10.1016/j.scitotenv.2018.04.055
    The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
  7. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods
    Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.
    Sci Total Environ, 2020 Jan 20;701:134979.
    PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979
    Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
  8. Fulltext Reliability, technical error of measurements and validity of length and weight measurements for children under two years old in Malaysia
    Jamaiyah H, Geeta A, Safiza MN, Khor GL, Wong NF, Kee CC, et al.
    Med J Malaysia, 2010 Jun;65 Suppl A:131-7.
    PMID: 21488474
    The National Health and Morbidity Survey III 2006 wanted to perform anthropometric measurements (length and weight) for children in their survey. However there is limited literature on the reliability, technical error of measurement (TEM) and validity of these two measurements. This study assessed the above properties of length (LT) and weight (WT) measurements in 130 children age below two years, from the Hospital Universiti Kebangsaan Malaysia (HUKM) paediatric outpatient clinics, during the period of December 2005 to January 2006. Two trained nurses measured WT using Tanita digital infant scale model 1583, Japan (0.01kg) and Seca beam scale, Germany (0.01 kg) and LT using Seca measuring mat, Germany (0.1cm) and Sensormedics stadiometer model 2130 (0.1cm). Findings showed high inter and intra-examiner reliability using 'change in the mean' and 'intraclass correlation' (ICC) for WT and LT. However, LT was found to be less reliable using the 'Bland and Altman plot'. This was also true using Relative TEMs, where the TEM value of LT was slightly more than the acceptable limit. The test instruments were highly valid for WT using 'change in the mean' and 'ICC' but was less valid for LT measurement. In spite of this we concluded that, WT and LT measurements in children below two years old using the test instruments were reliable and valid for a community survey such as NHMS III within the limits of their error. We recommend that LT measurements be given special attention to improve its reliability and validity.
    Study site: Paediatric clinic, Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM), Kuala Lumpur, Malaysia
  9. Fulltext A national database on children and adolescent with diabetes (e-DiCARE): results from April 2006 to June 2007
    Fuziah MZ, Hong JY, Zanariah H, Harun F, Chan SP, Rokiah P, et al.
    Med J Malaysia, 2008 Sep;63 Suppl C:37-40.
    PMID: 19230245
    In Malaysia, Diabetes in Children and Adolescents Registry (DiCARE) was launched nationwide in August 2006 to determine and monitor the number, the time trend of diabetes mellitus (DM) patients, their socio-demographic profiles, outcome of intervention and facilitate research using this registry. This is an on going real time register of diabetic patients < or = 20 years old via the e-DiCARE, an online registration system. To date were 240 patients notified from various states in Malaysia. The mean age was 12.51 years (1.08-19.75) and 46.4% were boys. The mean age at diagnosis was 8.31 +/- 4.13 years old with an estimated duration of diabetes of 4.32 +/- 3.55 years. A total of 166/240 (69.2%) have T1DM, 42/240 (17.5%) have T2DM and 18/240 (7.5%) have other types of DM. Basis of diagnosis was known in 162 patients with T1DM and 41 patients with T2DM. In T1DM patients, 6.0% of the girls and 19.1% boys were overweight or obese. As for T2DM, 64.3% had their BMI reported: 66.7% girls and 91.6% boys were overweight or obese. Most patients (80.4%) practiced home blood glucose monitoring. Patients were seen by dietitian (66.7%), diabetes educator (50.0%), and optometrist or ophthalmologist (45.0%). Only 10.8% attended diabetic camps. In the annual census of 117 patients, the mean HbAlc level was 10.0% + 2.2 (range 5.2 to 17.0%). The early results of DiCARE served as a starting point to improve the standard of care of DM among the young in the country.
  10. Fulltext Dengue antibodies in a suburban community in Malaysia
    Chen WS, Wong CH, Cillekens L
    Med J Malaysia, 2003 Mar;58(1):142-3.
    PMID: 14556343
  11. Fulltext Device-supported automated basal insulin titration in adults with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials
    Luo Y, Chang Y, Zhao Z, Xia J, Xu C, Bee YM, et al.
    Lancet Reg Health West Pac, 2023 Jun;35:100746.
    PMID: 37424694 DOI: 10.1016/j.lanwpc.2023.100746
    BACKGROUND: Technological advances make it possible to use device-supported, automated algorithms to aid basal insulin (BI) dosing titration in patients with type 2 diabetes.

    METHODS: A systematic review and meta-analysis of randomized controlled trials were performed to evaluate the efficacy, safety, and quality of life of automated BI titration versus conventional care. The literature in Medline, Embase, Web of Science, and the Cochrane databases from January 2000 to February 2022 were searched to identify relevant studies. Risk ratios (RRs), mean differences (MDs), and their 95% confidence intervals (CIs) were calculated using random-effect meta-analyses. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

    FINDINGS: Six of the 7 eligible studies (889 patients) were included in meta-analyses. Low- to moderate-quality evidence suggests that patients who use automated BI titration versus conventional care may have a higher probability of reaching a target of HbA1c <7.0% (RR, 1.82 [95% CI, 1.16-2.86]); and a lower level of HbA1c (MD, -0.25% [95% CI, -0.43 to -0.06%]). No statistically significant differences were detected between the two groups in fasting glucose results, incidences of hypoglycemia, severe or nocturnal hypoglycemia, and quality of life, with low to very low certainty for all the evidence.

    INTERPRETATION: Automated BI titration is associated with small benefits in reducing HbA1c without increasing the risk of hypoglycemia. Future studies should explore patient attitudes and the cost-effectiveness of this approach.

    FUNDING: Sponsored by the Chinese Geriatric Endocrine Society.

  12. Genetic variation of Japanese encephalitis virus in nature
    Chen WR, Tesh RB, Rico-Hesse R
    J Gen Virol, 1990 Dec;71 ( Pt 12):2915-22.
    PMID: 2273391
    Forty-six strains of Japanese encephalitis (JE) virus from a variety of geographic areas in Asia were examined by primer-extension sequencing of the RNA template. A 240 nucleotide sequence from the pre-M gene region was selected for study because it provided sufficient information for determining genetic relationships among the virus isolates. Using 12% divergence as a cutoff point for virus relationships, the 46 isolates fell into three distinct genotypic groups. One genotypic group consisted of JE virus isolates from northern Thailand and Cambodia. A second group was composed of isolates from southern Thailand, Malaysia, Sarawak and Indonesia. The remainder of the isolates, from Japan, China, Taiwan, the Philippines, Sri Lanka, India and Nepal, made up a third group. The implications of these findings in relation to the epidemiology of JE are discussed. Results of this study demonstrate that the comparison of short nucleotide sequences can provide insight into JE virus evolution, transmission and, possibly, pathogenesis.
  13. Fulltext Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
    Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, et al.
    Sensors (Basel), 2018 Nov 05;18(11).
    PMID: 30400627 DOI: 10.3390/s18113777
    The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
  14. Fulltext Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms
    Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, et al.
    Sensors (Basel), 2018 Jul 31;18(8).
    PMID: 30065216 DOI: 10.3390/s18082464
    In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
  15. Fulltext Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
    Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.
    Sci Rep, 2021 Jul 20;11(1):15152.
    PMID: 34285263 DOI: 10.1038/s41598-021-93957-4
  16. Fulltext Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
    Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.
    Sci Rep, 2018 Oct 18;8(1):15364.
    PMID: 30337603 DOI: 10.1038/s41598-018-33755-7
    Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
  17. Reliability and validity of the English and Malay versions of the Driving and Riding Questionnaire: a pilot study amongst older car drivers and motorcycle riders
    Ang BH, Chen WS, Ngin CK, Oxley JA, Lee SWH
    Public Health, 2018 Feb;155:8-16.
    PMID: 29274898 DOI: 10.1016/j.puhe.2017.11.003
    OBJECTIVES: This study aimed to examine the reliability and validity of the English and Malay versions of the Driving and Riding Questionnaire.

    STUDY DESIGN: An observational study with a mix-method approach by utilising both questionnaire and short debriefing interviews.

    METHODS: Forward and backward translations of the original questionnaire were performed. The translated questionnaire was assessed for clarity by a multidisciplinary research team, translators, and several Malay native speakers. A total of 24 subjects participated in the pilot study. Reliability (Cronbach's alpha) and validity (content validity) of the original and translated questionnaires were examined.

    RESULTS: The English and Malay versions of the Driving and Riding Questionnaire were found to be reliable tools in measuring driving behaviours amongst older drivers and riders, with Cronbach's alpha of 0.9158 and 0.8919, respectively. For content validity, the questionnaires were critically reviewed in terms of relevance, clarity, simplicity, and ambiguity. The feedback obtained from participants addressed various aspects of the questionnaire related to the improvement of wordings used and inclusion of visual guide to enhance the understanding of the items in the questionnaire. This feedback was incorporated into the final versions of the English and Malay questionnaires.

    CONCLUSION: The findings of this study demonstrated both the English and Malay versions of the Driving and Riding Questionnaire to be valid and reliable.
  18. Fulltext Loss-free tensile ductility of dual-structure titanium composites via an interdiffusion and self-organization strategy
    Liu L, Li S, Pan D, Hui D, Zhang X, Li B, et al.
    Proc Natl Acad Sci U S A, 2023 Jul 11;120(28):e2302234120.
    PMID: 37399391 DOI: 10.1073/pnas.2302234120
    The deformation-coordination ability between ductile metal and brittle dispersive ceramic particles is poor, which means that an improvement in strength will inevitably sacrifice ductility in dispersion-strengthened metallic materials. Here, we present an inspired strategy for developing dual-structure-based titanium matrix composites (TMCs) that achieve 12.0% elongation comparable to the matrix Ti6Al4V alloys and enhanced strength compared to homostructure composites. The proposed dual-structure comprises a primary structure, namely, a TiB whisker-rich region engendered fine grain Ti6Al4V matrix with a three-dimensional micropellet architecture (3D-MPA), and an overall structure consisting of evenly distributed 3D-MPA "reinforcements" and a TiBw-lean titanium matrix. The dual structure presents a spatially heterogeneous grain distribution with 5.8 μm fine grains and 42.3 μm coarse grains, which exhibits excellent hetero-deformation-induced (HDI) hardening and achieves a 5.8% ductility. Interestingly, the 3D-MPA "reinforcements" show 11.1% isotropic deformability and 66% dislocation storage, which endows the TMCs with good strength and loss-free ductility. Our enlightening method uses an interdiffusion and self-organization strategy based on powder metallurgy to enable metal matrix composites with the heterostructure of the matrix and the configuration of reinforcement to address the strength-ductility trade-off dilemma.
  19. Sustained clinical knowledge improvements from simulation experiences with Simulation via Instant Messaging-Birmingham Advance
    Zhou D, Davitadze M, Ooi E, Ng CY, Allison I, Thomas L, et al.
    Postgrad Med J, 2023 Mar 22;99(1167):25-31.
    PMID: 36947426 DOI: 10.1093/postmj/qgac008
    BACKGROUND: Simulation via Instant Messaging-Birmingham Advance (SIMBA) delivers simulation-based learning through WhatsApp and Zoom, helping to sustain continuing medical education (CME) for postgraduate healthcare professionals otherwise disrupted by the coronavirus (COVID-19) pandemic. This study aimed to assess whether SIMBA helped to improve clinical knowledge and if this improvement in knowledge was sustained over time.

    METHODS: Two SIMBA sessions-thyroid and pituitary-were conducted in July-August 2020. Each session included simulation of various real-life cases and interactive discussion. Participants' self-reported confidence, acceptance, and knowledge were measured using surveys and multiple-choice questions pre- and post-simulation and in a 6- to 12-week follow-up period. The evaluation surveys were designed using Moore's 7 Levels of CME Outcomes Framework.

    RESULTS: A total of 116 participants were included in the analysis. Significant improvement was observed in participants' self-reported confidence in approach to simulated cases (thyroid, n = 37, P 

  20. Fulltext Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology
    Niazi MKK, Abas FS, Senaras C, Pennell M, Sahiner B, Chen W, et al.
    PLoS One, 2018;13(5):e0196547.
    PMID: 29746503 DOI: 10.1371/journal.pone.0196547
    Automatic and accurate detection of positive and negative nuclei from images of immunostained tissue biopsies is critical to the success of digital pathology. The evaluation of most nuclei detection algorithms relies on manually generated ground truth prepared by pathologists, which is unfortunately time-consuming and suffers from inter-pathologist variability. In this work, we developed a digital immunohistochemistry (IHC) phantom that can be used for evaluating computer algorithms for enumeration of IHC positive cells. Our phantom development consists of two main steps, 1) extraction of the individual as well as nuclei clumps of both positive and negative nuclei from real WSI images, and 2) systematic placement of the extracted nuclei clumps on an image canvas. The resulting images are visually similar to the original tissue images. We created a set of 42 images with different concentrations of positive and negative nuclei. These images were evaluated by four board certified pathologists in the task of estimating the ratio of positive to total number of nuclei. The resulting concordance correlation coefficients (CCC) between the pathologist and the true ratio range from 0.86 to 0.95 (point estimates). The same ratio was also computed by an automated computer algorithm, which yielded a CCC value of 0.99. Reading the phantom data with known ground truth, the human readers show substantial variability and lower average performance than the computer algorithm in terms of CCC. This shows the limitation of using a human reader panel to establish a reference standard for the evaluation of computer algorithms, thereby highlighting the usefulness of the phantom developed in this work. Using our phantom images, we further developed a function that can approximate the true ratio from the area of the positive and negative nuclei, hence avoiding the need to detect individual nuclei. The predicted ratios of 10 held-out images using the function (trained on 32 images) are within ±2.68% of the true ratio. Moreover, we also report the evaluation of a computerized image analysis method on the synthetic tissue dataset.
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