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Fulltext Effects of MyBFF@school, a multifaceted obesity intervention program, on anthropometry and body composition of overweight and obese primary schoolchildren

Zin RMWM, Mokhtar AH, Yahya A, Zain FM, Selamat R, Ishak Z, et al.

BMC Public Health, 2025 Jan 14;24(Suppl 1):3627.
PMID: 39810108 DOI: 10.1186/s12889-024-20724-1

BACKGROUND: Recently, there has been an increase in the prevalence of childhood obesity in Malaysia, raising concerns about increased cardiometabolic morbidity. MyBFF@school is a multifaceted program comprising physical activity, nutritional education, and psychological empowerment introduced to combat childhood obesity in Malaysia. The efficacy of a six-month intervention on the body composition of overweight and obese primary schoolchildren was evaluated.
METHODS: This is a school-based, cluster randomized controlled trial involving selected primary schools in Kuala Lumpur, Selangor, and Negeri Sembilan. A total of 1,397 primary-school students aged 9-11 with a body mass index (BMI) z -score (corrected for age) greater than + 1 standard deviation based on the World Health Organization 2007 Growth Reference were assigned to intervention ( n = 647 ) and control ( n = 750 ) groups. BMI z-score, waist circumference (WC), percentage body fat (PBF), and skeletal muscle mass (SMM) were assessed at baseline and after three and six months of the study. Analyses of all outcomes except for the baseline characteristics were conducted according to the intention-to-treat principle.
RESULTS: After three months, there was no significant difference in the BMI z-score or PBF between the control and intervention groups, but SMM and WC were significantly higher in the intervention group versus the control group with mean difference of 0.15 kg; 95% confidence interval [CI]: 0.07-0.22, p
Fulltext An introduction to MyBFF@school, a school-based childhood obesity intervention program: a cluster randomized controlled trial

Mokhtar AH, Ishak Z, Zain FM, Selamat R, Yahya A, Jalaludin MY

BMC Public Health, 2025 Jan 14;24(Suppl 1):3628.
PMID: 39810143 DOI: 10.1186/s12889-025-21382-7

Obesity trend among Malaysian children is on the rise. Noting that the tendency for them to grow into obese adults and the relationship of obesity to many non-communicable diseases, the My Body is Fit and Fabulous at School (MyBFF@school program) was designed to combat obesity among the schoolchildren. The program was piloted in 2014 in Putrajaya, Malaysia. There were several challenges during the pilot study which included strain in manpower, limited variation of physical activity, nutrition, and psychology modules, time-constraint after school hours, co-curriculum marks, contamination effect, and school selection. The main MyBFF@school in 2016 addressed the challenges and improvised the design which were elaborated in subsequent articles in this supplement. This cluster randomized controlled trial was conducted in three states; Federal Territory of Kuala Lumpur, Selangor and Negeri Sembilan in 23 primary and 15 secondary schools were selected through proportionate random sampling. The MyBFF@school intervention package consisted of physical activity, nutrition and psychology components were carried out for six months. Data were collected at baseline, mid (month-3) and end (month-6) of the study period. The effects of the program on body composition, clinical, physical fitness, nutrition, and psychology were assessed in primary schoolchildren aged 9 to 11 years old (children age group) and secondary schoolchildren (adolescent) aged 13 to 16 years old. The prevalence of overweight and obesity at screening (N=22,816) were 29.4% in primary and 26.8% in secondary schoolchildren. Outcomes of the trial is presented in this supplement. In summary, the MyBFF@school program is a school-based intervention for overweight and obese children and adolescent. It is a combination of physical activity, nutrition and psychology components. We present in this supplement, the rationale, methodology and the outcomes of this randomized control trial of the MyBFF@school program.
Fulltext 'Her shape' intervention programme for obese women with high breast adiposity

Juliana N, Shahar S, Sahar MA, Ghazali AR, Manaf ZA, Noah RM

Asia Pac J Clin Nutr, 2017 Mar;26(2):278-286.
PMID: 28244706 DOI: 10.6133/apjcn.122015.05

BACKGROUND AND OBJECTIVES: Nutrition and physical activity interventions is beneficial in reversing obesity. However far too little attention has been paid to the effect of these interventions on breast tissues. Thus, the aim of this study was to explore the effect of a home-based dietary and physical activity intervention (the Her Shape Program) on metabolic parameters, blood biomarkers and adiposity at the breast.
METHODS AND STUDY DESIGN: A randomized controlled study was conducted on obese women with high breast adiposity (<0.1 Sm-1), aged 40-60 years in Klang Valley, Malaysia. Subjects were assigned to intervention (n=16) and control group (n=15). Intervention group received a home based health education package with close monitoring weekly, personal diet consultation and physical training in group. Assessment was ascertained at three time points; baseline, weeks 8 and 16. Outcome measures were the energy intake, physical activity, body composition, blood tests, blood biomarkers and electrical impedance tomography (EIT) quantitative values. Analyses were done using 2-way repeated measures ANOVA.
RESULTS AND CONCLUSIONS: All subjects completed the program without any drop-out. The HSI group had 100% compliance towards the intervention program; their energy intake was reduced for approximately 35% and their activity score was increased for approximately 11%. A significant interaction effect was found in body weight, body mass index (BMI), total cholesterol/HDL, vitamin C intake and matrix metallopeptidase 9 (MMP-9) (p<0.05). Interestingly, their EIT extremum values were also significantly increased indicating a reduction of breast adiposity. The intervention program was successful in improving body composition, physical activities, MMP9 and breast adipose tissue composition.
Fulltext An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks

Wang Z, Zainal A, Siraj MM, Ghaleb FA, Hao X, Han S

Sci Rep, 2025 Jan 14;15(1):1917.
PMID: 39809850 DOI: 10.1038/s41598-024-85083-8

The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN's computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.
Mechanomyography responses characterize altered muscle function during electrical stimulation-evoked cycling in individuals with spinal cord injury

Islam MA, Hamzaid NA, Ibitoye MO, Hasnan N, Wahab AKA, Davis GM

Clin Biomech (Bristol), 2018 10;58:21-27.
PMID: 30005423 DOI: 10.1016/j.clinbiomech.2018.06.020

BACKGROUND: Investigation of muscle fatigue during functional electrical stimulation (FES)-evoked exercise in individuals with spinal cord injury using dynamometry has limited capability to characterize the fatigue state of individual muscles. Mechanomyography has the potential to represent the state of muscle function at the muscle level. This study sought to investigate surface mechanomyographic responses evoked from quadriceps muscles during FES-cycling, and to quantify its changes between pre- and post-fatiguing conditions in individuals with spinal cord injury.
METHODS: Six individuals with chronic motor-complete spinal cord injury performed 30-min of sustained FES-leg cycling exercise on two days to induce muscle fatigue. Each participant performed maximum FES-evoked isometric knee extensions before and after the 30-min cycling to determine pre- and post- extension peak torque concomitant with mechanomyography changes.
FINDINGS: Similar to extension peak torque, normalized root mean squared (RMS) and mean power frequency (MPF) of the mechanomyography signal significantly differed in muscle activities between pre- and post-FES-cycling for each quadriceps muscle (extension peak torque up to 69%; RMS up to 80%, and MPF up to 19%). Mechanomyographic-RMS showed significant reduction during cycling with acceptable between-days consistency (intra-class correlation coefficients, ICC = 0.51-0.91). The normalized MPF showed a weak association with FES-cycling duration (ICC = 0.08-0.23). During FES-cycling, the mechanomyographic-RMS revealed greater fatigue rate for rectus femoris and greater fatigue resistance for vastus medialis in spinal cord injured individuals.
INTERPRETATION: Mechanomyographic-RMS may be a useful tool for examining real time muscle function of specific muscles during FES-evoked cycling in individuals with spinal cord injury.
Fulltext Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP

Mehmood S, Amin R, Mustafa J, Hussain M, Alsubaei FS, Zakaria MD

PLoS One, 2025;20(1):e0312425.
PMID: 39869573 DOI: 10.1371/journal.pone.0312425

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.
Fulltext Dual-hybrid intrusion detection system to detect False Data Injection in smart grids

Mohammed SH, Singh MSJ, Al-Jumaily A, Islam MT, Islam MS, Alenezi AM, et al.

PLoS One, 2025;20(1):e0316536.
PMID: 39869576 DOI: 10.1371/journal.pone.0316536

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data's spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.
Fulltext Molecular Action of Hydroxytyrosol in Attenuation of Intimal Hyperplasia: A Scoping Review

Vijakumaran U, Yazid MD, Hj Idrus RB, Abdul Rahman MR, Sulaiman N

Front Pharmacol, 2021;12:663266.
PMID: 34093194 DOI: 10.3389/fphar.2021.663266

Objective: Hydroxytyrosol (HT), a polyphenol of olive plant is well known for its antioxidant, anti-inflammatory and anti-atherogenic properties. The aim of this systematic search is to highlight the scientific evidence evaluating molecular efficiency of HT in halting the progression of intimal hyperplasia (IH), which is a clinical condition arises from endothelial inflammation. Methods: A systematic search was performed through PubMed, Web of Science and Scopus, based on pre-set keywords which are Hydroxytyrosol OR 3,4-dihydroxyphenylethanol, AND Intimal hyperplasia OR Neointimal hyperplasia OR Endothelial OR Smooth muscles. Eighteen in vitro and three in vitro and in vivo studies were selected based on a pre-set inclusion and exclusion criteria. Results: Based on evidence gathered, HT was found to upregulate PI3K/AKT/mTOR pathways and supresses inflammatory factors and mediators such as IL-1β, IL-6, E-selectin, P-selectin, VCAM-1, and ICAM-1 in endothelial vascularization and functioning. Two studies revealed HT disrupted vascular smooth muscle cells (SMC) cell cycle by dephosphorylating ERK1/2 and AKT pathways. Therefore, HT was proven to promote endothelization and inhibit vascular SMCs migration thus hampering IH development. However, none of these studies described the effect of HT collectively in both vascular endothelial cells (EC) and SMCs in IH ex vivo model. Conclusions: Evidence from this concise review provides an insight on HT regulation of molecular pathways in reendothelization and inhibition of VSMCs migration. Henceforth, we propose effect of HT on IH prevention could be further elucidated through in vivo and ex vivo model.
Fulltext Psychometric properties of the social determinants of health questionnaire (SDH-Q): development and validation

Sabo A, Kuan G, Abdullah S, Kuay HS, Goni MD, Kueh YC

BMC Public Health, 2024 Sep 16;24(1):2507.
PMID: 39285351 DOI: 10.1186/s12889-024-19990-w

BACKGROUND: The influence of social determinants of health (SDH) on sustainable development goals (SDG) has gained attention in recent years. However, there is a scarcity in the availability of valid and reliable instruments to assess the multiple aspects of SDH. Hence, this study was conducted to develop a brief self-reported measure for assessing SDH.
METHOD: A cross-sectional survey was conducted among university undergraduate students in Nigeria. The study consisted of 300 participants in the EFA (males 55.7%, females 44.3%) and 430 participants in the CFA (males 54.0%, females 46.0%). Participants were selected using a convenience sampling approach to assess their perceptions regarding SDH. Content Validity Index (CVI), Face Validity Index (FVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Composite Reliability (CR), Average Variance Extracted (AVE), Cronbach's alpha, and Intraclass Correlation Coefficient (ICC) were computed to determine the psychometric properties of the newly developed SDH scale.
RESULTS: In the EFA, two factors were extracted (structural determinants of SDH and intermediary determinants of SDH), with all 20 items retained. The total variance explained by the EFA model was 61.8%, and the factor correlation was 0.178. The Cronbach's alpha values of the two factors were 0.917 and 0.939. In the CFA, the initial model did not fit the data well based on fit indices. After several re-specification of the model, the final re-specified measurement model demonstrated adequate fit factor structure of the SDH scale with two factors and 20 items (CFI = 0.943, TLI = 0.930, SRMR = 0.056, RMSEA = 0.053, RMSEA p-value = 0.220). The CR was 0.797 for structural determinants of SDH and 0.794 for intermediary determinants of SDH. The ICC was 0.938 for structural determinants of SDH and 0.941 for intermediary determinants of SDH.
CONCLUSION: The findings indicate that the SDH scale has adequate psychometric properties and can be used to assess the perceived level of SDH. We recommended that this tool be tested in other populations with diverse age groups and other demographic characteristics.
Fulltext Design and validation of ultra-compact metamaterial-based biosensor for non-invasive cervical cancer diagnosis in terahertz regime

Hamza MN, Tariqul Islam M, Lavadiya S, Ud Din I, Sanches B, Koziel S, et al.

PLoS One, 2025;20(2):e0311431.
PMID: 39899558 DOI: 10.1371/journal.pone.0311431

Cervical cancer belongs to the most dangerous types of cancers posing considerable threat to women's survival. It is most often diagnosed in the advanced stages as precancerous lesions are often symptom-free and difficult to identify. Microwave imaging, especially in terahertz (THz) range, is a convenient and noninvasive cancer detection tool. It enables characterization of biological tissues and discrimination between healthy and malignant ones. This study presents a novel triple-band biosensor based on metamaterials (MTMs). By leveraging unique properties of MTMs, the proposed biosensor operates as a perfect absorber. It exploits resonant modes in the THz spectrum to achieve remarkable sensitivity. Meticulous selection of the sensor geometry and dimensions enables efficient miniaturization. Meanwhile, utilization of frequency-domain data to detect refractive index changes improves resolution of cancerous tissue identification. Extensive numerical investigations corroborate its ability to carry out reliable early-stage cervical cancer diagnosis. This includes identification of the spatial extent of the malignant tissue. Excellent electrical properties of the sensor are accompanied by its compact size, which is highly desirable for non-invasive and portable applications.
Fulltext Urban water supply risks assessment under tropical climate

Shams S, Mubarak NM, Ismail NAB, Khan MMH, Al-Mamun A, Ahsan A

Sci Rep, 2025 Feb 12;15(1):5295.
PMID: 39939332 DOI: 10.1038/s41598-025-88922-4

The urban water supply system in tropical countries faces various physical risks, including pipe failures due to aging, material type, soil conditions, flooding, extreme weather events, and traffic loads. This study focuses on urban water supply risks for eight zones of Brunei-Muara district. A risk assessment using a data-driven matrix reveals Zones D2 and D6 as very high-risk areas, experiencing monthly average leaks of 880 and 471, respectively. These zones, characterized by low elevation and susceptibility to flooding during heavy rainfall, pose significant threats to water quality and public health due to the potential contamination of drinking water. Analysis of pipe data highlights that pipes with a diameter of 100 mm are more prone to leaks, with ductile iron pipes being particularly susceptible to failures. Brunei is actively exploring the implementation of digitalization and advanced technologies such as the application of GIS, deploying real-time water quality sensors, and real-time pressure monitoring integrated with SCADA systems to mitigate these risks.
Optimization of Polymers for Organic Solar Cells: Effects of Alkyl, Fluorinated and Thiophenated Chains

Ahmad Mustamin K, Sani Sarjadi M, Sarkar SM, Kumar S, Rahman ML

Chem Asian J, 2025 Jan 20.
PMID: 39831691 DOI: 10.1002/asia.202401406

This paper explores optimization strategies for polymeric materials in organic solar cells (OSCs) with the focus on varying alkyl side chain, addition of fluorine atom, and thiophenated derivatives onto polymer. As such, it outlines the significance of renewable energy sources and the potential of photovoltaic technologies, particularly organic photovoltaics (OPVs). Objectives include factors affecting power conversion efficiency (PCE), open-circuit voltage (Voc), aggregation tendencies, and optoelectronic properties in OPVs. The scope encompasses the impact of alkyl as well as the comparison between fluorinated and chlorinated polymers and the role of thiophene units to obtain an efficient organic solar cell. The review examines how alkyl chain structures influence thin film morphology, packing, and device performance, comparing linear and branched configurations. It also explores the role of halogenated polymers in modifying electronic properties and stability, focusing on the comparative performance between fluorinated and chlorinated polymers. The importance of thiophene units in polymer design for OPVs is discussed, along with performance comparisons based on different architectures. The paper summarizes key findings regarding the impact of various side chain modifications for OPVs device performance and outlines future research directions to enhance efficiency, stability, and scalability. It suggests exploring novel material design to further optimize OSCs.
Fulltext Comparison of the virulence of community- and hospital- isolated Acinetobacter baumannii in HeLa cell line and insect model, Galleria mellonella

Muzahid NH, Ramesh A, Siew TH, Hasan MZ, Narayanan K, Rahman S

Access Microbiol, 2025;7(2).
PMID: 39959467 DOI: 10.1099/acmi.0.000858.v3

Acinetobacter baumannii is an important nosocomial pathogen causing high infections and morbidity among affected individuals, and most studies focus on nosocomial strains. However, A. baumannii can also be isolated from healthy community individuals. This study compared the pathogenicity of hospital and community A. baumannii isolates using Galleria mellonella and human cell cultures. The insect model, G. mellonella, and in vitro HeLa cell line were used with ten A. baumannii isolates (six community and four hospital isolates from Segamat, Malaysia). G. mellonella killing assays and HeLa cell adherence, invasion and cytotoxicity assays were performed to investigate the virulence and invasion potential of the isolates. Out of the ten isolates investigated, three community and two hospital isolates were found to be highly virulent in the G. mellonella infection model, killing 100% of larvae within 96 h. These strains were also found to be invasive and have significant cytotoxicity in HeLa cells. Our study revealed that community- and hospital-isolated A. baumannii could be equally virulent judged by both model systems. Undoubtedly, besides hospital settings, the presence of highly virulent A. baumannii in community reservoirs poses a significant public health risk and requires additional investigation.
Outcome of Surgical Treatment for Tracheocutaneous Fistula in Paediatric Population: A Meta-Analysis

Saniasiaya J, van der Meer G, Toll E, McCaffer C, Barber C, Neeff M, et al.

Clin Otolaryngol, 2025 Feb 11.
PMID: 39932174 DOI: 10.1111/coa.14292

OBJECTIVE: The incidence of persistent tracheocutaneous fistula (TCF) in children has dramatically increased with the increasing number of tracheostomies performed earlier in the paediatric population. Despite the various emerging techniques, two fundamental surgical approaches are primary closure and healing by secondary intention. We aim to compare the surgical outcomes between the two procedures.
DATA SOURCE: PubMed, EMBASE and Scopus databases were searched from inception to 31 December 2023 with no restrictions on the setting or design of studies.
REVIEW METHODS: Data were pooled using a random effects model to assess the success and complication rates between the two surgical techniques.
RESULTS: A total of 26 studies were identified with a total of 1263 children. Persistent TCF was surgically treated with primary closure in 24 studies (n = 898), whereas healing by secondary intention was reported in 12 studies (n = 366). The success rate following primary closure and healing by secondary intention is 97.3% (95% CI: 95.7%-99.0%) and 94.0% (95% CI: 91.2%-96.7%), respectively. The pooled rate of complications following primary closure was 14.1% (95% CI: 8.9%-19.4%) and 8.4% (95% CI: 3.4%-13.3%) following healing by secondary intention.
CONCLUSION: Based on the pooled results, healing by secondary intention is a safer surgical option in children with persistent TCF.
Fulltext Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Pathan RK, Uddin MA, Paul AM, Uddin MI, Hamd ZY, Aljuaid H, et al.

PLoS One, 2023;18(8):e0290045.
PMID: 37611023 DOI: 10.1371/journal.pone.0290045

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.
Repurposing of dipeptidyl peptidase FDA-approved drugs in Alzheimer's disease using network pharmacology and in-silico approaches

Roney M, Uddin MN, Khan AA, Fatima S, Mohd Aluwi MFF, Hamim SMI, et al.

Comput Biol Chem, 2025 Feb 08;116:108378.
PMID: 39938415 DOI: 10.1016/j.compbiolchem.2025.108378

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) have similar clinical characteristics in the brain and islet, as well as an increased incidence with ageing and familial susceptibility. Therefore, in recent years there has been a great desire for research that elucidates how anti-diabetic drugs affect AD. This work attempts to first elucidate the possible mechanism of action of DPP-IV inhibitors in the treatment of AD by employing techniques from network pharmacology, molecular docking, molecular dynamic simulation, principal component analysis, and MM/PBSA. A total of 463 targets were identified from the SwissTargetPrediction and 784 targets were identified from the SuperPred databases. 79 common targets were screened using the PPI network. The GO and KEGG analyses indicated that the activity of DPP-IV against AD potentially involves the hsa04080 neuroactive ligand-receptor interaction signalling pathway, which contains 17 proteins, including CHRM2, CHRM3, CHRNB1, CHRNB4, CHRM1, PTGER2, CHRM4, CHRM5, TACR2, HTR2C, TACR1, F2, GABRG2, MC4R, HTR7, CHRNG, and DRD3. Molecular docking demonstrated that sitagliptin had the greatest binding affinity of -10.7 kcal/mol and established hydrogen bonds with the Asp103, Ser107, and Asn404 residues in the active site of the CHRM2 protein. Molecular dynamic simulation, PCA, and MM/PBSA were performed for the complex of sitagliptin with the above-mentioned proteins, which revealed a stable complex throughout the simulation. The work identifies the active component and possible molecular mechanism of sitagliptin in the treatment of AD and provides a theoretical foundation for future fundamental research and practical implementation.
Newly predicted halide perovskites Mg3AB3 (A = N, Bi; B = F, Br, I) for next-generation photovoltaic applications: a first-principles study

Khandaker MU, Osman H, Issa SAM, Uddin MM, Ullah MH, Wahbi H, et al.

RSC Adv, 2025 Feb 19;15(8):5766-5780.
PMID: 39980992 DOI: 10.1039/d4ra09093d

The research examines the exceptional physical characteristics of Mg3AB3 (A = N, Bi; B = F, Br, I) perovskite compounds through density functional theory to assess their feasibility for photovoltaic applications. Mechanical characterization further supports their stability where out of all the compounds, Mg3BiI3 demonstrates high ductility, while Mg3NF3 and Mg3BiBr3 possess a brittle nature. The calculated elastic constants and anisotropy factors also substantiate their mechanical stability, while there is an observed declining trend in Debye temperature with increase in atomic number. From the electronic point of view, Mg3NF3 can be considered as a wide-bandgap insulator with the bandgap of 6.789 eV, whereas Mg3BiBr3 and Mg3BiI3 can be classified as semiconductors suitable for photovoltaic applications bandgaps of 1.626 eV and 0.867 eV, respectively. The optical characteristics of such materials are excellent and pronounced by high absorption coefficients, low reflectivity, and good dielectrics, which are very important in the collection of solar energy. Among them, Mg3BiBr3 and Mg3BiI3 possess high light absorption coefficient, moderate reflectivity, and good electrical conductivity, indicating that they are quite suitable for applying the photoelectric conversion materials for solar cells. In addition, thermal analysis shows that Mg3NF3 is a good heat sink material, Mg3BiBr3 and Mg3BiI3 are favorable for thermal barrier coating materials. Due to their high absorption coefficients, low reflectance and suitable conductivity, both Mg3BiBr3 and Mg3BiI3 could be regarded as the most appropriate materials for the creation of the next generation of photovoltaic converters.
Fulltext Increased risks of cardiovascular diseases and insulin resistance among the Orang Asli in Peninsular Malaysia

Tuan Abdul Aziz TA, Teh LK, Md Idris MH, Bannur Z, Ashari LS, Ismail AI, et al.

BMC Public Health, 2016;16(1):284.
PMID: 27009064 DOI: 10.1186/s12889-016-2848-9

Despite the strategic development plan by the authorities for the Orang Asli, there are six subtribes of which their population numbers are small (less than 700). These minorities were not included in most of the health related studies published thus far. A comprehensive physiological and biomedical updates on these small subtribes in comparison to the larger subtribes and the urban Malay population is timely and important to help provide appropriate measures to prevent further reduction in the numbers of the Orang Asli.
Fulltext Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Alhasa KM, Mohd Nadzir MS, Olalekan P, Latif MT, Yusup Y, Iqbal Faruque MR, et al.

Sensors (Basel), 2018 Dec 11;18(12).
PMID: 30544953 DOI: 10.3390/s18124380

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
DNMT1 is predictive of survival and associated with Ki-67 expression in R-CHOP-treated diffuse large B-cell lymphomas

Loo SK, Ch'ng ES, Lawrie CH, Muruzabal MA, Gaafar A, Pomposo MP, et al.

Pathology, 2017 Dec;49(7):731-739.
PMID: 29074044 DOI: 10.1016/j.pathol.2017.08.009

DNMT1 is a target of approved anti-cancer drugs including decitabine. However, the prognostic value of DNMT1 protein expression in R-CHOP-treated diffuse large B-cell lymphomas (DLBCLs) remains unexplored. Here we showed that DNMT1 was expressed in the majority of DLBCL cases (n = 209/230, 90.9%) with higher expression in germinal centre B-cell-like (GCB)-DLBCL subtype. Low and negative DNMT1 expression (20% cut-off, n = 33/230, 14.3%) was predictive of worse overall survival (OS; p < 0.001) and progression-free survival (PFS; p < 0.001). Nonetheless, of the 209 DNMT1 positive patients, 33% and 42% did not achieve 5-year OS and PFS, respectively, indicating that DNMT1 positive patients showed considerably heterogeneous outcomes. Moreover, DNMT1 was frequently expressed in mitotic cells and significantly correlated with Ki-67 or BCL6 expression (r = 0.60 or 0.44, respectively; p < 0.001). We demonstrate that DNMT1 is predictive of DLBCL patients' survival, and suggest that DNMT1 could be a DLBCL therapeutic target due to its significant association with Ki-67.

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