Conectar
Stunting, wasting and underweight are the three widely recognised indicators of child undernutrition. This study aimed to simultaneously model all indicators while accounting for their association using a joint modelling technique to identify their risk factors.
This was a cross-sectional study design.
The anthropometric data of children were elicited from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019.
Stunting, wasting and underweight were the main outcome measures of child undernutrition. Initially, a generalised linear mixed model (GLMM) was developed for each indicator separately to identify the underlying risk factors by considering children within the cluster (district level) as hierarchically nested. Finally, a joint model was developed by combining the separate GLMMs with the condition of correlated cluster-specific (district-specific) random effects.
The developed joint model provided precise effects of the risk factors and quantified the association among stunting, wasting and underweight. The joint correlations of underweight with stunting
This study demonstrates the application of a joint model to simultaneously identify the risk factors associated with indicators of child undernutrition. The study findings reveal a substantial positive association between stunting and underweight, as well as between underweight and wasting, with shared risk factors contributing to the disparity in the prevalence of all forms of child undernutrition in Bangladesh.
by Kayab Khandakar, Jabin Tasnin Upoma, Taib Hasan, A. H. M. Iftekharul Ferdous, Diponkar Kundu, Md. Omar Faruk, Md. Feroz Ali, Md. Shahorin Islam Shaun
Excessive hormone release, the possibility of sleep disturbances, and a brief and quick improvement in the functioning of many organs, the physiological system, the nerves, etc. are all consequences of the abuse of incentive medications. Illegal narcotics have terrible long-term impacts on human health, including the possibility of death, in addition to their immediate effects. These consequences highlight the need for more obviousness and accuracy in the detection of illicit drugs, as well as for their detection to be done gently, effectively, and consistently. This work introduces an illicit drug sensor based on PCF, with an eye toward these as the primary targets. Three illegal drugs – ketamine, amphetamine, and cocaine – have been simulated for the sensor. Two types of circular air holes in cladding of varying sizes have been developed for a single core PCF. The cladding has three-layer chain and wind turbine-shaped air holes, and a circular air hole in the core region that will be used to field test drug samples, all included to achieve low confinement losses and high sensitivity. A maximum Relative Sensitivity (RS) of 99.92%, 99.12% and 98.83% at ketamine, amphetamine, and cocaine respectively is revealed by the recently established PCF analysis, which was presented out right away. Furthermore, we looked at the Confinement Loss (CL) associated with these illicit drugs, which was around 1.275 × 10−7 dB/m, 2.653 × 10−9 dB/m, and 4.106 × 10−10 dB/m, besides Effective Material Loss (EML) of 0.0042 cm-1, 0.0044 cm-1 and 0.0045 cm-1. Refractive index changes in PCF are usually the cause of action for PCF-based biosensors. These modifications have an impact on how light travels within the fiber. Drug molecules interact with light as a result of changes in the optical properties of the core that occur during light propagation through it.
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