MyMedR

Displaying publications 81 - 100 of 1460 in total

Abstract:

Sort:

Fulltext How can gastro-intestinal tuberculosis diagnosis be improved? A prospective cohort study

Lowbridge C, Fadhil SAM, Krishnan GD, Schimann E, Karuppan RM, Sriram N, et al.

BMC Infect Dis, 2020 Mar 30;20(1):255.
PMID: 32228479 DOI: 10.1186/s12879-020-04983-y

BACKGROUND: Gastrointestinal tuberculosis (TB) is diagnostically challenging; therefore, many cases are treated presumptively. We aimed to describe features and outcomes of gastrointestinal TB, determine whether a clinical algorithm could distinguish TB from non-TB diagnoses, and calculate accuracy of diagnostic tests.
METHODS: We conducted a prospective cohort study of hospitalized patients in Kota Kinabalu, Malaysia, with suspected gastrointestinal TB. We recorded clinical and laboratory characteristics and outcomes. Tissue samples were submitted for histology, microscopy, culture and GeneXpert MTB/RIF®. Patients were followed for up to 2 years.
RESULTS: Among 88 patients with suspected gastrointestinal TB, 69 were included in analyses; 52 (75%) had a final diagnosis of gastrointestinal TB; 17 had a non-TB diagnosis. People with TB were younger (42.7 versus 61.5 years, p = 0.01) and more likely to have weight loss (91% versus 64%, p = 0.03). An algorithm using age 26 g/L, platelets > 340 × 109/L and immunocompromise had good specificity (96.2%) in predicting TB, but very poor sensitivity (16.0%). GeneXpert® performed very well on gastrointestinal biopsies (sensitivity 95.7% versus 35.0% for culture against a gold standard composite case definition of confirmed TB). Most patients (79%) successfully completed treatment and no treatment failure occurred, however adverse events (21%) and mortality (13%) among TB cases were high. We found no evidence that 6 months of treatment was inferior to longer courses.
CONCLUSIONS: The prospective design provides important insights for clinicians managing gastrointestinal TB. We recommend wider implementation of high-performing diagnostic tests such as GeneXpert® on extra-pulmonary samples.

Matched MeSH terms: Algorithms
Ten-year fracture risk by FRAX and osteoporotic fractures in patients with systemic autoimmune diseases

Lai EL, Huang WN, Chen HH, Hsu CY, Chen DY, Hsieh TY, et al.

Lupus, 2019 Jul;28(8):945-953.
PMID: 31177913 DOI: 10.1177/0961203319855122

The Fracture Risk Assessment Tool (FRAX) has been used universally for the purpose of fracture risk assessment. However, the predictive capacity of FRAX for autoimmune diseases remains inconclusive. This study aimed to compare the applicability of FRAX for autoimmune disease patients. This retrospective study recruited rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and primary Sjögren syndrome (pSS) patients with bone mineral density (BMD) tests. Patients with any osteoporotic fractures were identified. Taiwan-specific FRAX with and without BMD were then calculated. In total, 802 patients (451 RA, 233 SLE and 118 pSS) were enrolled in this study. The cumulative incidences of osteoporotic fractures in the RA, SLE and pSS patients were 43.0%, 29.2% and 33.1%, respectively. For those with a previous osteoporotic fracture, T-scores were classified as low bone mass. Overall, the patients' 10-year probability of major fracture risk by FRAX without BMD was 15.8%, which then increased to 20.3% after incorporation of BMD measurement. When analyzed by disease group, the fracture risk in RA patients was accurately predicted by FRAX. In contrast, current FRAX, either with or without BMD measurement, underestimated the fracture risk both in SLE and pSS patients, even after stratification by age and glucocorticoid treatment. For pSS patients with major osteoporotic fractures, FRAX risks imputed by RA were comparable to major osteoporotic fracture risks of RA patients. Current FRAX accurately predicted fracture probability in RA patients, but not in SLE and pSS patients. RA-imputed FRAX risk scores could be used as a temporary substitute for SLE and pSS patients.

Matched MeSH terms: Algorithms
A new approach for arrhythmia classification using deep coded features and LSTM networks

Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:121-133.
PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004

BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

Matched MeSH terms: Algorithms
The effectiveness of the McIsaac clinical decision rule in the management of sore throat: an evaluation from a pediatrics ward

Thillaivanam S, Amin AM, Gopalakrishnan S, Ibrahim B

Pediatr Res, 2016 Oct;80(4):516-20.
PMID: 27331353 DOI: 10.1038/pr.2016.113

BACKGROUND: Sore throats may be due to either viral or group A beta hemolytic streptococcus (GABHS) infections; but diagnosis of the etiology of a sore throat is difficult, often leading to unnecessary antibiotic prescriptions and consequent increases in bacterial resistance. Scoring symptoms using the McIsaac clinical decision rule can help physicians to diagnose and manage streptococcal infections leading to sore throat and have been recommended by the Ministry of Health, Malaysia. In this paper, we offer the first assessment of the effectiveness of the McIsaac rule in a clinical setting in Malaysia.
METHOD: This study is a retrospective review of 116 pediatric patients presenting with sore throat. Group A comprised patients before the implementation of the McIsaac rule and Group B comprised patients after the implementation.
RESULTS: Unnecessary throat swab cultures were reduced by 40% (P = 0.003). Redundant antibiotic prescriptions were reduced by 26.5% (P = 0.003) and the overall use of antibiotics was reduced by 22.1% (P = 0.003). The pediatricians' compliance rate to McIsaac rule criteria was 45% before implementation of the McIsaac rule, but improved to 67.9% (P = 0.0005) after implementation.
DISCUSSION: The McIsaac rule is an effective tool for the management of sore throat in children in Malaysia.

Matched MeSH terms: Algorithms
Gene expression profiling of giant fibroadenomas of the breast

Yin Lee JP, Thomas AJ, Lum SK, Shamsudin NH, Hii LW, Mai CW, et al.

Surg Oncol, 2021 Jun;37:101536.
PMID: 33677364 DOI: 10.1016/j.suronc.2021.101536

INTRODUCTION: Fibroadenomas of the breast present as two phenotypic variants. The usual variety is 5 cm or less in diameter and there is another large variant called giant fibroadenoma which is greater than 5 cm in diameter. Despite of its large size, it is not malignant. The aim of our study is to determine whether this large variant is different from the usual fibroadenoma in terms of its biological pathways and biomarkers.
METHODS: mRNA was extracted from 44 fibroadenomas and 36 giant fibroadenomas, and transcriptomic profiling was performed to identify up- and down-regulated genes in the giant fibroadenomas as compared to the fibroadenomas.
RESULTS: A total of 40 genes were significantly up-regulated and 18 genes were significantly down-regulated in the giant fibroadenomas as compared to the fibroadenomas of the breast. The top 5 up-regulated genes were FN1, IL3, CDC6, FGF8 and BMP8A. The top 5 down-regulated genes were TNR, CDKN2A, COL5A1, THBS4 and BMPR1B. The differentially expressed genes (DEGs) were found to be associated with 5 major canonical pathways involved in cell growth (PI3K-AKT, cell cycle regulation, WNT, and RAS signalling) and immune response (JAK-STAT signalling). Further analyses using 3 supervised learning algorithms identified an 8-gene signature (FN1, CDC6, IL23A, CCNA1, MCM4, FLT1, FGF22 and COL5A1) that could distinguish giant fibroadenomas from fibroadenomas with high predictive accuracy.
CONCLUSION: Our findings demonstrated that the giant fibroadenomas are biologically distinct to fibroadenomas of the breast with overexpression of genes involved in the regulation of cell growth and immune response.

Matched MeSH terms: Algorithms
Phytomedicines are Efficient Complementary Therapies for the Treatment of Atopic Dermatitis: A Review of Mechanistic Insight and Recent Updates

Tumpang MA, Ramli NA, Hussain Z

Curr Drug Targets, 2018;19(6):674-700.
PMID: 28914203 DOI: 10.2174/1389450118666170913162147

BACKGROUND: Phytomedicines have been well-accepted alternative complementary therapies for the treatment of a wide range of acute and chronic skin inflammatory diseases including chronic herpes, prurigo, psoriasis, and atopic dermatitis (AD). A plethora of in vitro and in vivo studies have evidenced the therapeutic viability of phytomedicines, polyherbal formulations, plant-based materials and their decoctions for the treatment of mild-to-severe AD.
OBJECTIVE: This review was aimed to summarize and critically discuss the convincing evidence for the therapeutic effectiveness of phytomedicines for the treatment of AD and explore their anti-AD efficacy.
RESULTS: The critical analysis of a wide algorithm of herbal medicines revealed that their remarkable anti-AD efficacy is attributed to their potential of reducing erythema intensity, oedema, inflammation, transepidermal water loss (TEWL) and a remarkable suppression of mRNA expression of ADassociated inflammatory biomarkers including histamine, immunoglobulin (Ig)-E, prostaglandins, mast cells infiltration and production of cytokines and chemokines in the serum and skin biopsies.
CONCLUSION: In conclusion, herbal medicines hold great promise as complementary and alternative therapies for the treatment of mild-to-moderate AD when used as monotherapy and for the treatment of moderate-to-severe AD when used in conjunction with other pharmacological agents.

Matched MeSH terms: Algorithms
Predicting the Risk of Adverse Drug Reactions in Older Inpatients: External Validation of the GerontoNet ADR Risk Score Using the CRIME Cohort

Petrovic M, Tangiisuran B, Rajkumar C, van der Cammen T, Onder G

Drugs Aging, 2017 02;34(2):135-142.
PMID: 28000156 DOI: 10.1007/s40266-016-0428-4

BACKGROUND: Adverse drug reactions (ADRs) in older people are often preventable, indicating that screening and prevention programs aimed at reducing their rate are needed in this population.
OBJECTIVE: The aim of this study was to externally validate the GerontoNet ADR risk score and to assess its validity in specific subpopulations of older inpatients.
METHODS: Data from the prospective CRIteria to assess appropriate Medication use among Elderly complex patients (CRIME) cohort were used. Dose-dependent and predictable ADRs were classified as type A, probable or definite ADRs were defined according to the Naranjo algorithm, and diagnostic accuracy was tested using receiver operating characteristic (ROC) analyses. Sensitivity and specificity were calculated for a cut-off point of 4.
RESULTS: The mean age of the 1075 patients was 81.4 years (standard deviation 7.4) and the median number of drugs was 10 (range 7-13). At least one ADR was observed in 70 patients (6.5%); ADRs were classified as type A in 50 patients (4.7%) and defined as probable or definite in 41 patients (3.8%). Fair diagnostic accuracy to predict both type A and probable or definite ADRs was found in subpopulations aged <70 or ≥80 years with heart failure, diabetes, or a previous ADR. Good accuracy to predict type A ADRs was found in patients with a low body mass index (BMI; >18.5 kg/m2) and a Mini-Mental State Examination (MMSE) score of >24/30 points, as well as in patients with osteoarthritis. The cut-off point of 4 points yielded very good sensitivity but poor specificity results in these subpopulations.
CONCLUSION: This study suggests that the GerontoNet ADR risk score might represent a pragmatic approach to identifying specific subpopulations of older inpatients at increased risk of an ADR with a fair to good diagnostic accuracy.

Matched MeSH terms: Algorithms
The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach

Taha Z, Musa RM, P P Abdul Majeed A, Alim MM, Abdullah MR

Hum Mov Sci, 2018 Feb;57:184-193.
PMID: 29248809 DOI: 10.1016/j.humov.2017.12.008

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.

Matched MeSH terms: Algorithms
Emerging Trends in Therapeutic Algorithm of Chronic Wound Healers: Recent Advances in Drug Delivery Systems, Concepts-to-Clinical Application and Future Prospects

Shao M, Hussain Z, Thu HE, Khan S, de Matas M, Silkstone V, et al.

Crit Rev Ther Drug Carrier Syst, 2017;34(5):387-452.
PMID: 29256838 DOI: 10.1615/CritRevTherDrugCarrierSyst.2017016957

Chronic wounds which include diabetic foot ulcer (DFU), pressure ulcer, and arterial or venous ulcers compel a significant burden to the patients, healthcare providers, and the healthcare system. Chronic wounds are characterized by an excessive persistent inflammatory phase, prolonged infection, and the failure of defense cells to respond to environmental stimuli. Unlike acute wounds, chronic nonhealing wounds pose a substantial challenge to conventional wound dressings, and the development of novel and advanced wound healing modalities is needed. Toward this end, numerous conventional wound-healing modalities have been evaluated in the management of nonhealing wounds, but a multifaceted approach is lacking. Therefore, this review aims to compile and explore the wide therapeutic algorithm of current and advanced wound healing approaches to the treatment of chronic wounds. The algorithm of chronic wound healing techniques includes conventional wound dressings; approaches based on autografts, allografts, and cultured epithelial autografts; and recent modalities based on natural, modified or synthetic polymers and biomaterials, processed mutually in the form of hydrogels, films, hydrocolloids, and foams. Moreover, this review also explores the promising potential of advanced drug delivery systems for the sustained delivery of growth factors, curcumin, aloe vera, hyaluronic acid, and other bioactive substances as well as stem cell therapy. The current review summarizes the convincing evidence for the clinical dominance of polymer-based chronic wound healing modalities as well as the latest and innovative therapeutic strategies for the treatment of chronic wounds.

Matched MeSH terms: Algorithms
The role of neuron-glia interactions in the emergence of ultra-slow oscillations

Chan SC, Mok SY, Ng DW, Goh SY

Biol Cybern, 2017 Dec;111(5-6):459-472.
PMID: 29128889 DOI: 10.1007/s00422-017-0740-z

Ultra-slow cortical oscillatory activity of 1-100 mHz has been recorded in human by electroencephalography and in dissociated cultures of cortical rat neurons, but the underlying mechanisms remain to be elucidated. This study presents a computational model of ultra-slow oscillatory activity based on the interaction between neurons and astrocytes. We predict that the frequency of these oscillations closely depends on activation of astrocytes in the network, which is reflected by oscillations of their intracellular calcium concentrations with periods between tens of seconds and minutes. An increase of intracellular calcium in astrocytes triggers the release of adenosine triphosphate from these cells which may alter transmission at nearby synapses by increasing or decreasing neurotransmitter release. These results provide theoretical support for the emerging awareness of astrocytes as active players in the regulation of neural activity and identify neuron-astrocyte interactions as a potential primary mechanism for the emergence of ultra-slow cortical oscillations.

Matched MeSH terms: Algorithms
Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis

Al-Qazzaz NK, Ali SHBM, Ahmad SA, Islam MS, Escudero J

Med Biol Eng Comput, 2018 Jan;56(1):137-157.
PMID: 29119540 DOI: 10.1007/s11517-017-1734-7

Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA-WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects.

Matched MeSH terms: Algorithms
Fulltext A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Charoenkwan P, Chotpatiwetchkul W, Lee VS, Nantasenamat C, Shoombuatong W

Sci Rep, 2021 Dec 10;11(1):23782.
PMID: 34893688 DOI: 10.1038/s41598-021-03293-w

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

Matched MeSH terms: Algorithms
Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Abunama T, Othman F, Ansari M, El-Shafie A

Environ Sci Pollut Res Int, 2019 Feb;26(4):3368-3381.
PMID: 30511225 DOI: 10.1007/s11356-018-3749-5

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Matched MeSH terms: Algorithms
Fulltext A multi-resolution textural approach to diagnostic neuropathology reporting

Fauzi MF, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, et al.

J Neurooncol, 2015 Sep;124(3):393-402.
PMID: 26255070 DOI: 10.1007/s11060-015-1872-4

We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.

Matched MeSH terms: Algorithms
Fulltext The relationship between perceptions and self-paid hepatitis B vaccination: A structural equation modeling approach

Rajamoorthy Y, Radam A, Taib NM, Rahim KA, Wagner AL, Mudatsir M, et al.

PLoS One, 2018;13(12):e0208402.
PMID: 30521602 DOI: 10.1371/journal.pone.0208402

BACKGROUND: Malaysia has a comprehensive, publicly-funded immunization program for hepatitis B (HepB) among infants, but adults must pay for the vaccine. The number of HepB carriers among adults is expected to increase in the future; therefore, we examined the impact of five constructs (cues to action, perceived barriers, perceived benefit, perceived severity, and perceived susceptibility) on adults' willingness to pay (WTP) for HepB vaccine; secondarily, we examined the association between perceived barriers and perceived benefits.
METHODS: Adults were selected through a stratified, two-stage cluster community sample in Selangor, Malaysia. The reliability, convergent validity, and discriminant validity of the measurement model were assessed before implementing a partial least squares structural equation model (PLS-SEM) to evaluate the significance of the structural paths.
RESULTS: A total of 728 participants were enrolled. The five constructs all showed adequate internal reliability, convergent validity, and discriminant validity. There was a significant, positive relationship to WTP from constructs (perceived barriers [Path coefficient (β) = 0.082, P = 0.036], perceived susceptibility [β = 0.214, P<0.001], and cues to action [β = 0.166, P<0.001]), and the model all together accounted for 8.8% of the variation in WTP. There was a significant, negative relationship between perceived barriers and perceived benefit [β = -0.261, P<0.001], which accounted for 6.8% of variation in perceived benefit.
CONCLUSIONS: Policy and programs should be targeted that can modify individuals' thoughts about disease risk, their obstacles in obtaining the preventive action, and their readiness to obtain a vaccine. Such programs include educational materials about disease risk and clinic visits that can pair HepB screening and vaccination.

Matched MeSH terms: Algorithms
Fulltext The clustering effects of surfaces within the tooth and teeth within individuals

Masood M, Masood Y, Newton JT

J Dent Res, 2015 Feb;94(2):281-8.
PMID: 25421840 DOI: 10.1177/0022034514559408

The objectives of this study were 1) to provide an estimate of the value of the intraclass correlation coefficient (ICC) for dental caries data at tooth and surface level, 2) to provide an estimate of the design effect (DE) to be used in the determination of sample size estimates for future dental surveys, and 3) to explore the usefulness of multilevel modeling of cross-sectional survey data by comparing the model estimates derived from multilevel and single-level models. Using data from the United Kingdom Adult Dental Health Survey 2009, the ICC and DE were calculated for surfaces within a tooth, teeth within the individual, and surfaces within the individual. Simple and multilevel logistic regression analysis was performed with the outcome variables carious tooth or surface. ICC estimated that 10% of the variance in surface caries is attributable to the individual level and 30% of the variance in surfaces caries is attributable to variation between teeth within individuals. When comparing multilevel with simple logistic models, β values were 4 to 5 times lower and the standard error 2 to 3 times lower in multilevel models. All the fit indices showed multilevel models were a better fit than simple models. The DE was 1.4 for the clustering of carious surfaces within teeth, 6.0 for carious teeth within an individual, and 38.0 for carious surfaces within the individual. The ICC for dental caries data was 0.21 (95% confidence interval [CI], 0.204-0.220) at the tooth level and 0.30 (95% CI, 0.284-0.305) at the surface level. The DE used for sample size calculation for future dental surveys will vary on the level of clustering, which is important in the analysis-the DE is greatest when exploring the clustering of surfaces within individuals. Failure to consider the effect of clustering on the design and analysis of epidemiological trials leads to an overestimation of the impact of interventions and the importance of risk factors in predicting caries outcome.

Matched MeSH terms: Algorithms
Fulltext Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

Sweeti, Joshi D, Panigrahi BK, Anand S, Santhosh J

J Healthc Eng, 2018;2018:9213707.
PMID: 29808111 DOI: 10.1155/2018/9213707

This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

Matched MeSH terms: Algorithms
Fulltext The global flood protection savings provided by coral reefs

Beck MW, Losada IJ, Menéndez P, Reguero BG, Díaz-Simal P, Fernández F

Nat Commun, 2018 06 12;9(1):2186.
PMID: 29895942 DOI: 10.1038/s41467-018-04568-z

Coral reefs can provide significant coastal protection benefits to people and property. Here we show that the annual expected damages from flooding would double, and costs from frequent storms would triple without reefs. For 100-year storm events, flood damages would increase by 91% to $US 272 billion without reefs. The countries with the most to gain from reef management are Indonesia, Philippines, Malaysia, Mexico, and Cuba; annual expected flood savings exceed $400 M for each of these nations. Sea-level rise will increase flood risk, but substantial impacts could happen from reef loss alone without better near-term management. We provide a global, process-based valuation of an ecosystem service across an entire marine biome at (sub)national levels. These spatially explicit benefits inform critical risk and environmental management decisions, and the expected benefits can be directly considered by governments (e.g., national accounts, recovery plans) and businesses (e.g., insurance).

Matched MeSH terms: Algorithms
Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry

Wen W, Shu XO, Guo X, Cai Q, Long J, Bolla MK, et al.

Breast Cancer Res, 2016 12 08;18(1):124.
PMID: 27931260

BACKGROUND: Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry.
METHODS: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk.
RESULTS: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P

Matched MeSH terms: Algorithms
Fulltext Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)

Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL

J Digit Imaging, 2014 Feb;27(1):133-44.
PMID: 24100762 DOI: 10.1007/s10278-013-9640-5

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Matched MeSH terms: Algorithms

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links