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Fulltext Application of the Socio-Ecological Model to predict physical activity behaviour among Nigerian University students

Essiet IA, Baharom A, Shahar HK, Uzochukwu B

Pan Afr Med J, 2017;26:110.
PMID: 28533833 DOI: 10.11604/pamj.2017.26.110.10409

INTRODUCTION: Physical activity among university students is a catalyst for habitual physical activity in adulthood. Physical activity has many health benefits besides the improvement in academic performance. The present study assessed the predictors of physical activity among Nigerian university students using the Social Ecological Model (SEM).
METHODS: This cross-sectional study recruited first-year undergraduate students in the University of Uyo, Nigeria by multistage sampling. The International Physical Activity Questionnaire (IPAQ) short-version was used to assess physical activity in the study. Factors were categorised according to the Socio-Ecological Model which consisted of individual, social environment, physical environment and policy level. Data was analysed using the IBM SPSS statistical software, version 22. Simple and multiple logistic regression were used to determine the predictors of sufficient physical activity.
RESULTS: A total of 342 respondents completed the study questionnaire. Majority of the respondents (93.6%) reported sufficient physical activity at 7-day recall. Multivariate analysis revealed that respondents belonging to the Ibibio ethnic group were about four times more likely to be sufficiently active compared to those who belonged to the other ethnic groups (AOR = 3.725, 95% CI = 1.383 to 10.032). Also, participants who had a normal weight were about four times more likely to be physically active compared to those who were underweight (AOR = 4.268, 95% CI = 1.323 to 13.772).
CONCLUSION: This study concluded that there was sufficient physical activity levels among respondents. It is suggested that emphasis be given to implementing interventions aimed at sustaining sufficient levels of physical activity among students.

Matched MeSH terms: Models, Theoretical*
Optimisation of Reactive Black 5 dye removal by electrocoagulation process using response surface methodology

Mook WT, Aroua MK, Szlachta M, Lee CS

Water Sci Technol, 2017 02;75(3-4):952-962.
PMID: 28234295 DOI: 10.2166/wst.2016.563

In this work, a regression model obtained from response surface methodology (RSM) was proposed for the electrocoagulation (EC) treatment of textile wastewater. The Reactive Black 5 dye (RB5) was used as a model dye to evaluate the performance of the model design. The effect of initial solution pH, applied current and treatment time on RB5 removal was investigated. The total number of experiments designed by RSM amounted to 27 runs, including three repeated experimental runs at the central point. The accuracy of the model was evaluated by the F-test, coefficient of determination (R(2)), adjusted R(2) and standard deviation. The optimum conditions for RB5 removal were as follows: initial pH of 6.63, current of 0.075 A, electrolyte dose of 0.11 g/L and EC time of 50.3 min. The predicted RB5 removal was 83.3% and the percentage error between experimental and predicted results was only 3-5%. The obtained data confirm that the proposed model can be used for accurate prediction of RB5 removal. The value of the zeta potential increased with treatment time, and the X-ray diffraction pattern shows that iron complexes were found in the sludge.

Matched MeSH terms: Models, Theoretical*
Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach

Foong HF, Hamid TA, Ibrahim R, Haron SA, Shahar S

Aging Ment Health, 2018 Jan;22(1):109-120.
PMID: 27732054 DOI: 10.1080/13607863.2016.1231172

OBJECTIVES: The aim of this study was to identify the predictors of elderly's cognitive function based on biopsychosocial and cognitive reserve perspectives.
METHOD: The study included 2322 community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, biomarkers, psychosocial status, disability, and cognitive function. A biopsychosocial model of cognitive function was developed to test variables' predictive power on cognitive function. Statistical analyses were performed using SPSS (version 15.0) in conjunction with Analysis of Moment Structures Graphics (AMOS 7.0).
RESULTS: The estimated theoretical model fitted the data well. Psychosocial stress and metabolic syndrome (MetS) negatively predicted cognitive function and psychosocial stress appeared as a main predictor. Socio-demographic characteristics, except gender, also had significant effects on cognitive function. However, disability failed to predict cognitive function.
CONCLUSION: Several factors together may predict cognitive function in the Malaysian elderly population, and the variance accounted for it is large enough to be considered substantial. Key factor associated with the elderly's cognitive function seems to be psychosocial well-being. Thus, psychosocial well-being should be included in the elderly assessment, apart from medical conditions, both in clinical and community setting.

Matched MeSH terms: Models, Theoretical*
Fulltext An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Shukla S, Hassan MF, Khan MK, Jung LT, Awang A

PLoS One, 2019;14(11):e0224934.
PMID: 31721807 DOI: 10.1371/journal.pone.0224934

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

Matched MeSH terms: Models, Theoretical*
Modeling the effects of medial olivocochlear efferent stimulation at the level of the inferior colliculus

Kwan TJM, Zilany MSA, Davies-Venn E, Abdul Wahab AK

Exp Brain Res, 2019 Jun;237(6):1479-1491.
PMID: 30903206 DOI: 10.1007/s00221-019-05511-4

Various studies on medial olivocochlear (MOC) efferents have implicated it in multiple roles in the auditory system (e.g., dynamic range adaptation, masking reduction, and selective attention). This study presents a systematic simulation of inferior colliculus (IC) responses with and without electrical stimulation of the MOC. Phenomenological models of the responses of auditory nerve (AN) fibers and IC neurons were used to this end. The simulated responses were highly consistent with physiological data (replicated 3 of the 4 known rate-level responses all MOC effects-shifts, high stimulus level reduction and enhancement). Complex MOC efferent effects which were previously thought to require integration from different characteristic frequency (CF) neurons were simulated using the same frequency inhibition excitation circuitry. MOC-induced enhancing effects were found only in neurons with a CF range from 750 Hz to 2 kHz. This limited effect is indicative of the role of MOC activation on the AN responses at the stimulus offset.

Matched MeSH terms: Models, Theoretical*
Predicting Down syndrome and neural tube defects using basic risk factors

Khattak MT, Supriyanto E, Aman MN, Al-Ashwal RH

Med Biol Eng Comput, 2019 Jul;57(7):1417-1424.
PMID: 30877513 DOI: 10.1007/s11517-019-01969-0

Congenital anomalies are not only one of the main killers for infants but also one of the major causes of deaths under 5. Among congenital anomalies, Down syndrome or trisomy 21 (T-21) and neural tube defects (NTDs) are considered the most common. Expectant mothers in developing countries may not have access to or may not afford the advanced prenatal screening tests. To solve this issue, this paper explores the practicality of using only the basic risk factors for developing prediction models as a tool for initial risk assessment. The prediction models are based on logistic regression. The results show that the prediction models do not have a high balanced classification rate. However, these models can still be used as an effective tool for initial risk assessment for T-21 and NTDs by eliminating at least 50% of the cases with no or low risk. Graphical Abstract Prenatal Risk Assessment of Trisomy-21 and Neural Tube Defects.

Matched MeSH terms: Models, Theoretical*
Modelling the effect of intervention on the transmission of malaria in East Malaysia

Collett D, Lye MS

Stat Med, 1987 10 1;6(7):853-61.
PMID: 3321316

To assess the public health importance of malaria on Banggi Island, Sabah, baseline epidemiological and entomological data were obtained in a study of three villages. These data were used to model the transmission of malaria using a non-seasonal version of the deterministic model of Dietz, Molineaux and Thomas. The model provided a satisfactory description of prevalence rates of Plasmodium falciparum parasitaemia. Modifications to the basic model enable the effects of mass chemotherapy with various combinations of schizonticidal and gametocidal drugs to be simulated. In this way, the relative merits of different procedures of mass drug administration can be compared. The fitted model is also used to examine the relationship between the overall prevalence of infection and the vectorial capacity, and to predict the consequences of a reduction in the size of the vector population.

Matched MeSH terms: Models, Theoretical*
A supply model for nurse workforce projection in Malaysia

Abas ZA, Ramli MR, Desa MI, Saleh N, Hanafiah AN, Aziz N, et al.

Health Care Manag Sci, 2018 Dec;21(4):573-586.
PMID: 28822005 DOI: 10.1007/s10729-017-9413-7

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Matched MeSH terms: Models, Theoretical*
Mathematical modeling for estrogenic activity prediction of 17β-estradiol and 17α-ethynylestradiol mixtures in wastewater treatment plants effluent

Ting YF, Praveena SM, Aris AZ, Ismail SNS, Rasdi I

Ecotoxicology, 2017 Dec;26(10):1327-1335.
PMID: 28975452 DOI: 10.1007/s10646-017-1857-5

Steroid estrogens such as 17β-Estradiol (E2) and 17α-Ethynylestradiol (EE2) are highly potent estrogens that widely detected in environmental samples. Mathematical modelling such as concentration addition (CA) and estradiol equivalent concentration (EEQ) models are usually associated with measuring techniques to assess risk, predict the mixture response and evaluate the estrogenic activity of mixture. Wastewater has played a crucial role because wastewater treatment plant (WWTP) is the major sources of estrogenic activity in aquatic environment. The aims of this is to determine E2 and EE2 concentrations in six WWTPs effluent, to predict the estrogenic activity of the WWTPs effluent using CA and EEQ models where lastly the effectiveness of two models is evaluated. Results showed that all the six WWTPs effluent had relative high E2 concentration (35.1-85.2 ng/L) compared to EE2 (0.02-1.0 ng/L). The estrogenic activity predicted by CA model was similar among the six WWTPs (105.4 ng/L), due to the similarity of individual dose potency ratio calculated by respective WWTPs. The predicted total EEQ was ranged from 35.1 EEQ-ng/L to 85.3 EEQ-ng/L, explained by high E2 concentration in WWTPs effluent and E2 EEF value that standardized to 1.0 μg/L. The CA model is more effective than EEQ model in estrogenic activity prediction because EEQ model used less data and causes disassociation from the predicted behavior. Although both models predicted relative high estrogenic activity in WWTPs effluent, dilution effects in receiving river may lower the estrogenic response to aquatic inhabitants.

Matched MeSH terms: Models, Theoretical*
Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

Golkarian A, Naghibi SA, Kalantar B, Pradhan B

Environ Monit Assess, 2018 Feb 17;190(3):149.
PMID: 29455381 DOI: 10.1007/s10661-018-6507-8

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Matched MeSH terms: Models, Theoretical*
Towards reinforcing telemedicine adoption amongst clinicians in Nigeria

Adenuga KI, Iahad NA, Miskon S

Int J Med Inform, 2017 08;104:84-96.
PMID: 28599820 DOI: 10.1016/j.ijmedinf.2017.05.008

Telemedicine systems have been considered as a necessary measure to alleviate the shortfall in skilled medical specialists in developing countries. However, the obvious challenge is whether clinicians are willing to use this technological innovation, which has aided medical practice globally. One factor which has received little academic attention is the provision of suitable encouragement for clinicians to adopt telemedicine, in the form of rewards, motivation or incentives. A further consideration for telemedicine usage in developing countries, especially sub-Saharan Africa and Nigeria in particular, are to the severe shortage of available practising clinicians. The researchers therefore explore the need to positively reinforce the adoption of telemedicine amongst clinicians in Nigeria, and also offer a rationale for this using the UTAUT model. Data were collected using a structured paper-based questionnaire, with 252 physicians and nurses from six government hospitals in Ondo state, Nigeria. The study applied SmartPLS 2.0 for analysis to determine the relationship between six variables. Demographic moderating variables, age, gender and profession, were included. The results indicate that performance expectancy (p<0.05), effort expectancy (p<0.05), facilitating condition (p<0.01) and reinforcement factor (p<0.001) have significant effects on clinicians' behavioural intention to use telemedicine systems, as predicted using the extended UTAUT model. Our results showed that the use of telemedicine by clinicians in the Nigerian context is perceived as a dual responsibility which requires suitable reinforcement. In addition, performance expectancy, effort expectancy, facilitating condition and reinforcement determinants are influential factors in the use of telemedicine services for remote-patient clinical diagnosis and management by the Nigerian clinicians.

Matched MeSH terms: Models, Theoretical*
Fulltext The Need to Calculate Target Glucose Levels When Measuring Changes in Insulin Sensitivity During Interventions for Individuals With Type 2 Diabetes

Othman NA, Docherty PD, Krebs JD, Bell DA, Chase JG

J Diabetes Sci Technol, 2018 05;12(3):665-672.
PMID: 29295634 DOI: 10.1177/1932296817750402

BACKGROUND: Physiological models that are used with dynamic test data to assess insulin sensitivity (SI) assume that the metabolic target glucose concentration ( GTARGET) is equal to fasting glucose concentration ( G0). However, recent research has implied that irregularities in G0 in diabetes may cause erroneous SI values. This study quantifies the magnitude of these errors.
METHODS: A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks.
RESULTS: SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes.
CONCLUSIONS: Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.

Matched MeSH terms: Models, Theoretical*
Fulltext Impact of pre-dilution and flushing on continuous renal replacement therapy

Koh KH

Singapore Med J, 2006 Sep;47(9):785-95.
PMID: 16924361

Infusing the replacement solution before the filter (pre-dilution) and regular flushing have not been accounted for in conventional mathematical equations. Their effects on various continuous renal replacement therapy (CRRT) parameters, such as ultrafiltration fraction and urea clearance, have not been well studied. We incorporated these parameters into mathematical equations to help in understanding and prescribing CRRT.

Matched MeSH terms: Models, Theoretical*
Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach

Gul S, Zou X, Hassan CH, Azam M, Zaman K

Environ Sci Pollut Res Int, 2015 Dec;22(24):19773-85.
PMID: 26282441 DOI: 10.1007/s11356-015-5185-0

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Matched MeSH terms: Models, Theoretical*
Assessment of groundwater vulnerability to anthropogenic pollution and seawater intrusion in a small tropical island using index-based methods

Kura NU, Ramli MF, Ibrahim S, Sulaiman WN, Aris AZ, Tanko AI, et al.

Environ Sci Pollut Res Int, 2015 Jan;22(2):1512-33.
PMID: 25163562 DOI: 10.1007/s11356-014-3444-0

In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (<90), low (90-110), moderate (110-130), and high (>130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and manager with understanding needed to ensure sustainability of the island's aquifer.

Matched MeSH terms: Models, Theoretical*
Fulltext Self care interventions to advance health and wellbeing: a conceptual framework to inform normative guidance

Narasimhan M, Allotey P, Hardon A

BMJ, 2019 Apr 01;365:l688.
PMID: 30936087 DOI: 10.1136/bmj.l688

Manjulaa Narasimhan and colleagues argue that there is a pressing need for a clearer conceptualisation of self care to support health policy

Matched MeSH terms: Models, Theoretical*
Fulltext Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

Gill BS, Jayaraj VJ, Singh S, Mohd Ghazali S, Cheong YL, Md Iderus NH, et al.

Int J Environ Res Public Health, 2020 Jul 30;17(15).
PMID: 32751669 DOI: 10.3390/ijerph17155509

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

Matched MeSH terms: Models, Theoretical*
Fulltext A Method for Quantifying, Visualising, and Analysing Gastropod Shell Form

Liew TS, Schilthuizen M

PLoS One, 2016;11(6):e0157069.
PMID: 27280463 DOI: 10.1371/journal.pone.0157069

Quantitative analysis of organismal form is an important component for almost every branch of biology. Although generally considered an easily-measurable structure, the quantification of gastropod shell form is still a challenge because many shells lack homologous structures and have a spiral form that is difficult to capture with linear measurements. In view of this, we adopt the idea of theoretical modelling of shell form, in which the shell form is the product of aperture ontogeny profiles in terms of aperture growth trajectory that is quantified as curvature and torsion, and of aperture form that is represented by size and shape. We develop a workflow for the analysis of shell forms based on the aperture ontogeny profile, starting from the procedure of data preparation (retopologising the shell model), via data acquisition (calculation of aperture growth trajectory, aperture form and ontogeny axis), and data presentation (qualitative comparison between shell forms) and ending with data analysis (quantitative comparison between shell forms). We evaluate our methods on representative shells of the genera Opisthostoma and Plectostoma, which exhibit great variability in shell form. The outcome suggests that our method is a robust, reproducible, and versatile approach for the analysis of shell form. Finally, we propose several potential applications of our methods in functional morphology, theoretical modelling, taxonomy, and evolutionary biology.

Matched MeSH terms: Models, Theoretical*
Predicting the fluorescent enhancement rate by gold and silver nanospheres using finite-difference time-domain analysis

Centeno A, Xie F, Alford N

IET Nanobiotechnol, 2013 Jun;7(2):50-8.
PMID: 24046905

Metal-induced fluorescence enhancement (MIFE) is a promising strategy for increasing the sensitivity of fluorophores used in biological sensors. This study uses the finite-difference time-domain technique to predict the fluorescent enhancement rate of a fluorophore molecule in close proximity to a gold or silver spherical nanoparticle. By considering commercially available fluorescent dyes the computed results are compared with the published experimental data. The results show that MIFE is a complex coupling process between the fluorophore molecule and the metal nanoparticle. Nevertheless using computational electromagnetic techniques to perform calculations it is possible to calculate, with reasonable accuracy, the fluorescent enhancement. Using this methodology it will be possible to consider different shaped metal nanoparticles and any supporting substrate material in the future, an important step in building reliable biosensors capable of detecting low levels of proteins tagged with fluorescence molecules.

Matched MeSH terms: Models, Theoretical*
Fulltext Role of Travel Motivations, Perceived Risks and Travel Constraints on Destination Image and Visit Intention in Medical Tourism: Theoretical model

Khan MJ, Chelliah S, Haron MS, Ahmed S

Sultan Qaboos Univ Med J, 2017 Feb;17(1):e11-e17.
PMID: 28417022 DOI: 10.18295/squmj.2016.17.01.003

Travel motivations, perceived risks and travel constraints, along with the attributes and characteristics of medical tourism destinations, are important issues in medical tourism. Although the importance of these factors is already known, a comprehensive theoretical model of the decision-making process of medical tourists has yet to be established, analysing the intricate relationships between the different variables involved. This article examines a large body of literature on both medical and conventional tourism in order to propose a comprehensive theoretical framework of medical tourism decision-making. Many facets of this complex phenomenon require further empirical investigation.

Matched MeSH terms: Models, Theoretical*

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