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Fulltext Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique

Khalil A, Rahimi A, Luthfi A, Azizan MM, Satapathy SC, Hasikin K, et al.

Front Public Health, 2021;9:752509.
PMID: 34621723 DOI: 10.3389/fpubh.2021.752509

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.
Fulltext Impact of Substantive Staging and Communicative Staging of Sustainable Servicescape on Behavioral Intentions of Hotel Customers through Overall Perceived Image: A Case of Boutique Hotels

Khalil-Ur-Rehman, Adnan M, Ahmad N, Scholz M, Khalique M, Naveed RT, et al.

Int J Environ Res Public Health, 2021 08 30;18(17).
PMID: 34501713 DOI: 10.3390/ijerph18179123

Customers have become very sensitive regarding the innovative evaluation of services. Due to competition in the hospitality industry, it is a challenge for hotel marketers to understand customers' behavior. There is scant research in the hotel industry of Pakistan and especially on boutique hotels. This research seeks to measure the relationship between substantive, communicative elements of the sustainable servicescape and behavioral intentions (word of mouth) in a boutique hotel setting. However, the mediating effect of the overall perceived image is examined between these constructs. Responses of boutique hotel visitors were collected from Lahore, Islamabad, Faisalabad, and Murree. Data were analyzed by using structural equation modeling (SEM). Results display that both substantive and communicative servicescape elements positively affect the perceived image of customers, which has a positive influence on behavioral intentions such as word of mouth (WOM). Theoretical and practical implications are also discussed.
Fulltext An approach to develop clinical prediction rule for candidemia in critically ill patients: A retrospective observational study

Jameran AS, Cheah SK, Tzar MN, Musthafa QA, Low HJ, Maaya M, et al.

J Crit Care, 2021 10;65:216-220.
PMID: 34252648 DOI: 10.1016/j.jcrc.2021.06.018

PURPOSE: Early detection of candidemia in critically ill patients is important for preemptive antifungal treatment. Our study aimed to identify the independent risk factors for the development of a new candidemia prediction score.
METHODS: This single-centre retrospective observational study evaluated 2479 intensive care unit (ICU) cases from January 2016 to December 2018. A total of 76 identified candidemia cases and 76 matched control cases were analyzed. The patients' demographic characteristics and illness severity were analyzed, and possible risk factors for candidemia were investigated.
RESULTS: Multivariate logistic regression analysis identified renal replacement therapy (RRT) (odds ratio [OR]: 52.83; 95% confidence interval [CI]: 7.82-356.92; P < 0.0001), multifocal Candida colonization (OR: 23.55; 95% CI: 4.23-131.05; P < 0.0001), parenteral nutrition (PN) (OR: 63.67; 95% CI: 4.56-889.77; P = 0.002), and acute kidney injury (AKI) (OR: 7.67; 95% CI: 1.24-47.30; P = 0.028) as independent risk factors. A new prediction score with a cut-off value of 5.0 (80.3% sensitivity and 77.3% specificity) was formulated from the logit model equation.
CONCLUSIONS: Renal replacement therapy, AKI, PN, and multifocal Candida colonization were the independent risk factors for the new candidemia prediction score with high discriminatory performance and predictive accuracy.
Fulltext Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction

Radzi SFM, Karim MKA, Saripan MI, Rahman MAA, Isa INC, Ibahim MJ

J Pers Med, 2021 Sep 29;11(10).
PMID: 34683118 DOI: 10.3390/jpm11100978

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network-multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method's performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.
Fulltext A science framework (SF) for agricultural sustainability

Ahmed F, Al-Amin AQ, Masud MM, Kari F, Mohamad Z

An Acad Bras Cienc, 2015 Sep;87(3):1887-902.
PMID: 26221988 DOI: 10.1590/0001-3765201520130368

The significance of Science Framework (SF) to date is receiving more acceptances all over the world to address agricultural sustainability. The professional views, however, advocate that the SF known as Mega Science Framework (MSF) in the transitional economies is not converging effectively in many ways for the agricultural sustainability. Specially, MSF in transitional economies is mostly incapable to identify barriers in agricultural research, inadequate to frame policy gaps with the goal of strategizing the desired sustainability in agricultural technology and innovation, inconsistent in finding to identify the inequities, and incompleteness to rebuild decisions. Therefore, this study critically evaluates the components of MSF in transitional economies and appraises the significance, dispute and illegitimate issue to achieve successful sustainable development. A sound and an effective MSF can be developed when there is an inter-linkage within principal components such as of (a) national priorities, (b) specific research on agricultural sustainability, (c) adequate agricultural research and innovation, and (d) alternative policy alteration. This maiden piece of research which is first its kind has been conducted in order to outline the policy direction to have an effective science framework for agricultural sustainability.
HER2/neu-Based Peptide Vaccination-Pulsed with B-Cell Epitope Induced Efficient Prophylactic and Therapeutic Antitumor Activities in TUBO Breast Cancer Mice Model

Nordin ML, Mohamad Norpi AS, Ng PY, Yusoff K, Abu N, Lim KP, et al.

Cancers (Basel), 2021 Oct 01;13(19).
PMID: 34638441 DOI: 10.3390/cancers13194958

Breast cancer is the most common invasive cancer diagnosed among women. A cancer vaccine has been recognized as a form of immunotherapy with a prominent position in the prevention and treatment of breast cancer. The majority of current breast cancer vaccination strategies aim to stimulate antitumor T-cell responses of the HER2/neu oncogene, which is abnormally expressed in breast cancer cells. However, the role of the B-cell humoral response is often underappreciated in the cancer vaccine design. We have advanced this idea by elucidating the role of B-cells in cancer vaccination by designing a chimeric antigenic peptide possessing both cytotoxic T lymphocytes (GP2) and B-cell (P4) peptide epitopes derived from HER2/neu. The chimeric peptide (GP2-P4) was further conjugated to a carrier protein (KLH), forming a KLH-GP2-P4 conjugate. The immunogenicity of KLH-GP2-P4 was compared with KLH-GP2 (lacking the B-cell epitope) in BALB/c mice. Mice immunized with KLH-GP2-P4 elicited more potent antigen-specific neutralizing antibodies against syngeneic TUBO cells (cancer cell line overexpressing HER2/neu) that was governed by a balanced Th1/Th2 polarization in comparison to KLH-GP2. Subsequently, these immune responses led to greater inhibition of tumor growth and longer survival in TUBO tumor-bearing mice in both prophylactic and therapeutic challenge experiments. Overall, our data demonstrated that the B-cell epitope has a profound effect in orchestrating an efficacious antitumor immunity. Thus, a multi-epitope peptide vaccine encompassing cytotoxic T-lymphocytes, T-helper and B-cell epitopes represents a promising strategy in developing cancer vaccines with a preventive and therapeutic modality for the effective management of breast cancer.
Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Awan MJ, Bilal MH, Yasin A, Nobanee H, Khan NS, Zain AM

Int J Environ Res Public Health, 2021 Sep 27;18(19).
PMID: 34639450 DOI: 10.3390/ijerph181910147

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.
Fulltext Characterization of Inducible HSP70 Genes in an Antarctic Yeast, Glaciozyma antarctica PI12, in Response to Thermal Stress

Yusof NA, Charles J, Wan Mahadi WNS, Abdul Murad AM, Mahadi NM

Microorganisms, 2021 Sep 30;9(10).
PMID: 34683390 DOI: 10.3390/microorganisms9102069

The induction of highly conserved heat shock protein 70 (HSP70) is often related to a cellular response due to harmful stress or adverse life conditions. In this study, we determined the expression of Hsp70 genes in the Antarctic yeast, Glaciozyma antarctica, under different several thermal treatments for several exposure periods. The main aims of the present study were (1) to determine if stress-induced Hsp70 could be used to monitor the exposure of the yeast species G. antarctica to various types of thermal stress; (2) to analyze the structures of the G. antarctica HSP70 proteins using comparative modeling; and (3) to evaluate the relationship between the function and structure of HSP70 in G. antarctica. In this study, we managed to amplify and clone 2 Hsp70 genes from G. antarctica named GaHsp70-1 and GaHsp70-2. The cells of G. antarctica expressed significantly inducible Hsp70 genes after the heat and cold shock treatments. Interestingly, GaHsp70-1 showed 2-6-fold higher expression than GaHsp70-2 after the heat and cold exposure. ATP hydrolysis analysis on both G. antarctica HSP70s proved that these psychrophilic chaperones can perform activities in a wide range of temperatures, such as at 37, 25, 15, and 4 °C. The 3D structures of both HSP70s revealed several interesting findings, such as the substitution of a β-sheet to loop in the N-terminal ATPase binding domain and some modest residue substitutions, which gave the proteins the flexibility to function at low temperatures and retain their functional activity at ambient temperatures. In conclusion, both analyzed HSP70s played important roles in the physiological adaptation of G. antarctica.
Fulltext Conversational Task Increases Heart Rate Variability of Individuals Susceptible to Perceived Social Isolation

Feng YX, Roslan NS, Izhar LI, Abdul Rahman M, Faye I, Ho ETW

Int J Environ Res Public Health, 2021 09 18;18(18).
PMID: 34574777 DOI: 10.3390/ijerph18189858

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely to resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic has changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.
Predictor for unfavorable Johnsen scoring in non-obstructive azoospermia before intra cytoplasmic sperm insemination and its outcome

Abu MA, Tajuddin SA, Abdul Karim AK, Ahmad MF, Mohd Razi ZR, Omar MH

Minerva Ginecol, 2020 10;72(5):351-354.
PMID: 32720800 DOI: 10.23736/S0026-4784.20.04573-6
Fulltext Seroprevalence of hepatitis B virus and hepatitis C virus infection among Malaysian population

Muhamad NA, Ab Ghani RM, Abdul Mutalip MH, Muhammad EN, Mohamad Haris H, Mohd Zain R, et al.

Sci Rep, 2020 12 03;10(1):21009.
PMID: 33273475 DOI: 10.1038/s41598-020-77813-5

Malaysia is a country with an intermediate endemicity for hepatitis B. As the country moves toward hepatitis B and C elimination, population-based estimates are necessary to understand the burden of hepatitis B and C for evidence-based policy-making. Hence, this study aims to estimate the prevalence of hepatitis B and C in Malaysia. A total of 1458 participants were randomly selected from The Malaysian Cohort (TMC) aged 35 to 70 years between 2006 and 2012. All blood samples were tested for hepatitis B and C markers including hepatitis B surface antigen (HBsAg), anti-hepatitis B core antibody (anti-HBc), antibodies against hepatitis C virus (anti-HCV). Those reactive for hepatitis C were further tested for HCV RNA genotyping. The sociodemographic characteristics and comorbidities were used to evaluate their associated risk factors. Descriptive analysis and multivariable analysis were done using Stata 14. From the samples tested, 4% were positive for HBsAg (95% CI 2.7-4.7), 20% were positive for anti-HBc (95% CI 17.6-21.9) and 0.3% were positive for anti-HCV (95% CI 0.1-0.7). Two of the five participants who were reactive for anti-HCV had the HCV genotype 1a and 3a. The seroprevalence of HBV and HCV infection in Malaysia is low and intermediate, respectively. This population-based study could facilitate the planning and evaluation of the hepatitis B and C control program in Malaysia.
Study name: The Malaysian Cohort (TMC) project
The effectiveness of pure Stichopus horrens extract toothpaste in reducing plaque-induced gingivitis following orthodontic bonding: A triple-blinded randomized controlled trial

Bakar NA, Jayah NI, Mohamed NR, Ali SM, Nasir SH, Hashim R, et al.

J World Fed Orthod, 2020 03;9(1):3-8.
PMID: 32672665 DOI: 10.1016/j.ejwf.2019.11.004

INTRODUCTION: Gingivitis is one of the commonest problems faced by patients with fixed appliances (FA) as there is close relation between the appliances to gingival sulcus. Stichopus horrens (SH) is a sea cucumber from the Indo-Pacific that has medical healing properties which have been traditionally used.
OBJECTIVE: To assess the effects of toothpaste containing aqueous SH extract on plaque-induced gingivitis following orthodontic bond-up and to identify the optimal concentration of SH.
METHODS: A single-centred; triple-blinded randomized controlled trial conducted in 40 patients with FA. Participants were randomly assigned to one of the four groups with toothpaste which has concentration of SH extract of 0%, 3%, 6% or 9%. The statistician, the participants and the researchers involved in data collection were kept blinded from the allocation. Gingival Index (GI) and Bleeding on Probing (BOP) for each group were taken at day 0,7,14 and 30.
RESULTS: 9% of SH-containing toothpaste (SHCT) showed most substantial result as there were significance difference of GI (P = 0.020) from Day 7 to 14 and from Day 0 to 14 (P = 0.020). There was also significance difference of BOP from Day 0 to 14 (P = 0.022) and from Day 0 to 30 (P = 0.027). Significant difference was seen in 3% of SHCT group with the decrease of GI (P = 0.004) from Day 1 to 14. There were no significant difference noted for 0% and 6% SHCT.
CONCLUSION: The 9% SHCT is the most effective concentration to reduce both the gingival inflammation (up to day 14) and bleeding on probing (up to day 30).
Altered circulating concentrations of active glucagon-like peptide (GLP-1) and dipeptidyl peptidase 4 (DPP4) in obese subjects and their association with insulin resistance

Ahmed RH, Huri HZ, Muniandy S, Al-Hamodi Z, Al-Absi B, Alsalahi A, et al.

Clin Biochem, 2017 Sep;50(13-14):746-749.
PMID: 28288852 DOI: 10.1016/j.clinbiochem.2017.03.008

OBJECTIVES: Soluble DPP4 (sDPP4) is a novel adipokine that degrades glucagon-like peptide (GLP-1). We evaluated the fasting serum levels of active GLP-1 and sDPP4 in obese, overweight and normal weight subjects to assess the association between sDPP4 levels, active GLP-1 levels and insulin resistance in obese subjects.
METHODS: The study involved 235 Malaysian subjects who were randomly selected (66 normal weight subjects, 97 overweight, 59 obese subjects, and 13 subjects who were underweight). Serum sDPP4 and active GLP-1 levels were examined by enzyme-linked immunosorbent assay (ELISA). Also, body mass index kg/m(2) (BMI), lipid profiles, insulin and glucose levels were evaluated. Insulin resistance (IR) was estimated via the homeostasis model assessment for insulin resistance (HOMA-IR).
RESULTS: Serum sDPP4 levels were significantly higher in obese subjects compared to normal weight subjects (p=0.034), whereas serum levels of active GLP-1 were lower (p=0.021). In obese subjects, sDPP4 levels correlated negatively with active GLP-1 levels (r(2)=-0.326, p=0.015). Furthermore, linear regression showed that sDPP4 levels were positively associated with insulin resistance (B=82.28, p=0.023) in obese subjects.
CONCLUSION: Elevated serum sDPP4 levels and reduced GLP-1 levels were observed in obese subjects. In addition, sDPP4 levels correlated negatively with active GLP-1 levels but was positively associated with insulin resistance. This finding provides evidence that sDPP4 and GLP-1 may play an important role in the pathogenesis of obesity, suggesting that sDPP4 may be valuable as an early marker for the augmented risk of obesity and insulin resistance.
Comparative efficacy assessment of antiviral alone and antiviral-antibiotic combination in prevention of influenza-B infection associated complications

Ishaqui A, Hayat Khan A, Sulaiman SAS, Taher Alsultan M, Khan I

Expert Rev Anti Infect Ther, 2021 09;19(9):1165-1173.
PMID: 33567928 DOI: 10.1080/14787210.2021.1889369

OBJECTIVE: The study aimed to compare the efficacy of antiviral drug alone and antiviral-antibiotic combination therapy in prevention of complications associated with influenza B hospitalized patients.
METHOD: Laboratory confirmed influenza B hospitalized patients presented in emergency room after 48 hours of symptoms onset were identified and divided into two groups; Group-1 patients were initiated on Antiviral drug (oseltamivir) alone while Group-2 patients were initiated on Antiviral drug (oseltamivir) in combination with Antibiotic for at least 3 days. Patients were evaluated for different clinical outcomes among both treatment group.
RESULTS: A total of 153 and 131 patients were identified for Group-1 and Group-2, respectively. Clinical outcomes such as secondary bacterial infections (20.9%-vs-9.1%; P = 0.031), need of respiratory support (28.7%-vs-12.9%; P = 0.002), length of hospitalization stay (6.57-vs-4.95 days; P = <0.001), incidences of ICU admission (15.7%-vs-7.6%; P = 0.036), early clinical failure (32.6%-vs-16.1%; P = 0.01), and time to clinical stability (4.83-vs-4.1 days; P = 0.001) were found to be statistically less significant (P-value <0.05) for Group-2 patients.
CONCLUSION: Early initiation of antibiotic therapy in combination with oseltamivir was found to be more efficacious than oseltamivir alone in prevention of influenza B-associated complications especially in high-risk influenza patients.
Small Molecules as LIM Kinase Inhibitors

Bukhari SNA, Tandiary MA, Al-Sanea MM, Abdelgawad MA, Chee CF, Hussain MA

Curr Med Chem, 2021 Oct 26.
PMID: 34702151 DOI: 10.2174/0929867328666211026120335

LIMK1 and LIMK2 are involved in the regulation of cellular functions that depend on the dynamics of actin cytoskeleton. Disregulation of LIM kinases has been associated with diseases, such as tumor progression and metastasis, viral infection, and ocular diseases. Motivated by this, numerous studies have been carried out to discover small organic molecules capable of inhibiting LIM kinase effectively and selectively. In this review, a comprehensive survey of small organic molecules for LIM kinase inhibitors is reported, together with SAR study results, and the synthesis of these inhibitors.
Roles of Rho‑associated kinase in lung cancer (Review)

Zakaria MA, Rajab NF, Chua EW, Selvarajah GT, Masre SF

Int J Oncol, 2021 02;58(2):185-198.
PMID: 33491756 DOI: 10.3892/ijo.2020.5164

Lung cancer is one of the most lethal forms of cancer known to man, affecting millions of individuals worldwide. Despite advancements being made in lung cancer treatments, the prognosis of patients with the disease remains poor, particularly among patients with late‑stage lung cancer. The elucidation of the signaling pathways involved in lung cancer is a critical approach for the treatment of the disease. Over the past decades, accumulating evidence has revealed that Rho‑associated kinase (ROCK) is overexpressed in lung cancer and is associated with tumor growth. The present review discusses recent findings of ROCK signaling in the pathogenesis of lung cancer that were conducted in pre‑clinical studies. The significant role of ROCK in cancer cell apoptosis, proliferation, migration, invasion and angiogenesis is discussed. The present review also suggests the use of ROCK as a potential target for the development of lung cancer therapies, as ROCK inhibition can reduce multiple hallmarks of cancer, particularly by decreasing cancer cell migration, which is an initial step of metastasis.
Two-Dimensional Infrared Correlation Spectroscopy, Conductor-like Screening Model for Real Solvents, and Density Functional Theory Study on the Adsorption Mechanism of Polyvinylpolypyrrolidone for Effective Phenol Removal in an Aqueous Medium

Mohammad Alwi MA, Normaya E, Ismail H, Iqbal A, Mat Piah B, Abu Samah MA, et al.

ACS Omega, 2021 Oct 05;6(39):25179-25192.
PMID: 34632177 DOI: 10.1021/acsomega.1c02699

The discharge of industrial effluents, such as phenol, into aquatic and soil environments is a global problem due to its serious negative impacts on human health and aquatic ecosystems. In this study, the ability of polyvinylpolypyrrolidone (PVPP) to remove phenol from an aqueous medium was investigated. The results showed that a significant proportion of phenol (up to 74.91%) was removed using PVPP at pH 6.5. Isotherm adsorption experiments of phenol on PVPP indicated that the best-fit adsorption was obtained using Langmuir models. The response peaks of the hydroxyl groups of phenol (OH) and the carboxyl groups (i.e., C=O) of PVPP were altered, indicating the formation of a hydrogen bond between the PVPP and phenol during phenol removal, as characterized using 1D and 2D IR spectroscopy. The resulting complexes were successfully characterized based on their thermodynamic properties, Mulliken charge, and electronic transition using the DFT approach. To clarify the types of interactions taking place in the complex systems, quantum theory of atoms in molecules (QTAIM) analysis, reduced density gradient noncovalent interaction (RDG-NCI) approach, and conductor-like screening model for real solvents (COSMO-RS) approach were also successfully calculated. The results showed that the interactions that occurred in the process of removing phenol by PVPP were through hydrogen bonding (based on RDG-NCI and COSMO-RS), which was identified as an intermediate type (∇2ρ(r) > 0 and H < 0, QTAIM). To gain a deeper understanding of how these interactions occurred, further characterization was performed based on adsorption mechanisms using molecular electrostatic potential, global reactivity, and local reactivity descriptors. The results showed that during hydrogen bond formation, PVPP acts as a nucleophile, whereas phenol acts as an electrophile and the O9 atom (i.e., donor electron) reacts with the H22 atom (i.e., acceptor electron).
Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M

Sensors (Basel), 2021 Sep 30;21(19).
PMID: 34640888 DOI: 10.3390/s21196570

Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
Recent Advances in Chitin and Chitosan/Graphene-Based Bio-Nanocomposites for Energetic Applications

Ikram R, Mohamed Jan B, Abdul Qadir M, Sidek A, Stylianakis MM, Kenanakis G

Polymers (Basel), 2021 Sep 25;13(19).
PMID: 34641082 DOI: 10.3390/polym13193266

Herein, we report recent developments in order to explore chitin and chitosan derivatives for energy-related applications. This review summarizes an introduction to common polysaccharides such as cellulose, chitin or chitosan, and their connection with carbon nanomaterials (CNMs), such as bio-nanocomposites. Furthermore, we present their structural analysis followed by the fabrication of graphene-based nanocomposites. In addition, we demonstrate the role of these chitin- and chitosan-derived nanocomposites for energetic applications, including biosensors, batteries, fuel cells, supercapacitors and solar cell systems. Finally, current limitations and future application perspectives are entailed as well. This study establishes the impact of chitin- and chitosan-generated nanomaterials for potential, unexplored industrial applications.
The phytochemistry and biological diversity of Ferulago genus (Apiaceae): a systematic review

Salleh WMNHW, Abed SA, Taher M, Kassim H, Tawang A

J Pharm Pharmacol, 2021 Mar 01;73(1):1-21.
PMID: 33791809 DOI: 10.1093/jpp/rgaa034

OBJECTIVES: The genus Ferulago belonging to the family Apiaceae is a flora widely distributed in Central Asia and the Mediterranean and used in folk medicine. It is administered as a sedative, tonic, digestive, aphrodisiac, also as a treatment for intestinal worms and haemorrhoids. Herein, we reported a review on phytochemistry and its biological activities reported from 1990 up to early 2020. All the information and reported studies concerning Ferulago plants were summarized from the library and digital databases (e.g. Scopus, Medline, Scielo, ScienceDirect, SciFinder and Google Scholar).
KEY FINDINGS: The phytochemical investigations of Ferulago species revealed the presence of coumarins as the main bioactive compounds, including daucane derivatives, sesquiterpenes aryl esters, phenol derivatives, flavonoids and essential oils. Moreover, the therapeutic potentials of the pure compounds isolated from the genus Ferulago possess promising properties namely anticholinesterase, antimicrobial, anticoagulant, antileishmanial, antioxidant, antibacterial and antiproliferative.
SUMMARY: Today, significant advances in phytochemical and biological activity studies of different Ferulago species have been revealed. The traditional uses and reported biological results could be correlated via the chemical characterization of these plants. All these data will support the biologists in the elucidation of the biological mechanisms of these plants.

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