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Fulltext Ecofriendly adsorption and sensitive detection of Hg (II) by biomass-derived nitrogen-doped carbon dots: process modelling using central composite design

Issa MA, Zentou H, Jabbar ZH, Abidin ZZ, Harun H, Halim NAA, et al.

Environ Sci Pollut Res Int, 2022 Dec;29(57):86859-86872.
PMID: 35802332 DOI: 10.1007/s11356-022-21844-0

In this study, luminescent bio-adsorbent nitrogen-doped carbon dots (N-CDs) was produced and applied for the removal and detection of Hg (II) from aqueous media. N-CDs were synthesized from oil palm empty fruit bunch carboxymethylcellulose (CMC) and urea. According to several analytical techniques used, the obtained N-CDs display graphitic core with an average size of 4.2 nm, are enriched with active sites, stable over a wide range of pH and have great resistance to photobleaching. The N-CDs have bright blue emission with an improved quantum yield (QY) of up to 35.5%. The effect of the variables including pH, adsorbent mass, initial concentration and incubation time on the removal of Hg (II) was investigated using central composite design. The statistical results confirmed that the adsorption process could reach equilibrium within 30 min. The reduced cubic model (R2 = 0.9989) revealed a good correlation between the observed values and predicted data. The optimal variables were pH of 7, dose of 0.1 g, initial concentration of 100 mg/L and duration of 30 min. Under these conditions, adsorption efficiency of 84.6% was obtained. The adsorption kinetic data could be well expressed by pseudo-second-order kinetic and Langmuir isotherm models. The optimal adsorption capacity was 116.3 mg g-1. Furthermore, the adsorbent has a good selectivity towards Hg (II) with a detection limit of 0.01 μM due to the special interaction between Hg (II) and carboxyl/amino groups on the edge of N-CDs. This work provided an alternative direction for constructing low-cost adsorbents with effective sorption and sensing of Hg (II).
Fulltext Knowledge and Perception of Bidirectional Relationship Between Periodontal Disease and Type 1 Diabetes Mellitus: A Questionnaire Study

Abidin ZZ, Halim RA, Noor E, Nor NSM, Nazari NSM, Zaini AA, et al.

J Int Soc Prev Community Dent, 2023;13(5):416-425.
PMID: 38124724 DOI: 10.4103/jispcd.JISPCD_123_23

AIM: The bidirectional relationship between type 1 diabetes mellitus (T1DM) and inflammatory periodontal disease (PD) is globally recognized. However, oral health components are often given lower priority, and studies related to knowledge and the bidirectional association are limited. This study assesses the knowledge and perceptions of PD and its associated risk factors among T1DM patients and/or their parents.
MATERIALS AND METHODS: Patients under 18 with T1DM at Universiti Teknologi MARA and Universiti Malaya were invited to participate. Structured interviews were conducted to assess participants' knowledge and perceptions of T1DM, and statistical analysis was performed to examine their associations using Pearson's chi-squared test and Fisher's exact test.
RESULTS: A total of 113 T1DM patients, with a mean age of 11.4 ± 4, completed the interviews. Poor knowledge was observed among parents and T1DM patients (P-value = 0.007) and those who exercised regularly (P-value = 0.047). A significant association with good perception was found among individuals with uncontrolled HbA1c levels (P-value = 0.0018) and those experiencing bleeding symptoms (P-value = 0.021).
CONCLUSIONS: The study highlights the importance of increasing awareness, a key factor in improving oral health knowledge. Interestingly, despite poor control of clinical parameters, the population displayed good perception, suggesting a potential lack of understanding regarding disease control.
Fulltext Via-less electromagnetic band-gap-enabled antenna based on textile material for wearable applications

Ashyap AYI, Elamin NIM, Dahlan SH, Abidin ZZ, See CH, Majid HA, et al.

PLoS One, 2021;16(1):e0246057.
PMID: 33508025 DOI: 10.1371/journal.pone.0246057

A compact fabric antenna structure integrated with electromagnetic bandgap structures (EBGs) covering the desired frequency spectrum between 2.36 GHz and 2.40 GHz for Medical Body-Area Networks (MBANs), is introduced. The needs of flexible system applications, the antenna is preferably low-profile, compact, directive, and robust to the human body's loading effect have to be satisfied. The EBGs are attractive solutions for such requirements and provide efficient performance. In contrast to earlier documented EBG backed antenna designs, the proposed EBG behaved as shielding from the antenna to the human body, reduced the size, and acted as a radiator. The EBGs reduce the frequency detuning due to the human body and decrease the back radiation, improving the antenna efficiency. The proposed antenna system has an overall dimension of 46×46×2.4 mm3. The computed and experimental results achieved a gain of 7.2 dBi, a Front to Back Ratio (FBR) of 12.2 dB, and an efficiency of 74.8%, respectively. The Specific Absorption Rate (SAR) demonstrates a reduction of more than 95% compared to the antenna without EBGs. Moreover, the antenna performance robustness to human body loading and bending is also studied experimentally. Hence, the integrated antenna-EBG is a suitable candidate for many wearable applications, including healthcare devices and related applications.
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.
Fulltext Integrating local and global information to identify influential nodes in complex networks

Mukhtar MF, Abal Abas Z, Baharuddin AS, Norizan MN, Fakhruddin WFWW, Minato W, et al.

Sci Rep, 2023 Jul 14;13(1):11411.
PMID: 37452080 DOI: 10.1038/s41598-023-37570-7

Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.