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Fulltext Advanced Nanoscale Surface Characterization of CuO Nanoflowers for Significant Enhancement of Catalytic Properties

Khan MA, Nayan N, Shadiullah, Ahmad MK, Fhong SC, Tahir M, et al.

Molecules, 2021 May 04;26(9).
PMID: 34064537 DOI: 10.3390/molecules26092700

In this work, advanced nanoscale surface characterization of CuO Nanoflowers synthesized by controlled hydrothermal approach for significant enhancement of catalytic properties has been investigated. The CuO nanoflower samples were characterized by field-emission scanning electron microscopy (FE-SEM), X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, high-resolution transmission electron microscopy (HR-TEM), selected-area electron diffraction (SAED), high-angular annular dark field scanning transmission electron microscopy (HAADF-STEM) with elemental mapping, energy dispersive spectroscopy (STEM-EDS) and UV-Vis spectroscopy techniques. The nanoscale analysis of the surface study of monodispersed individual CuO nanoflower confirmed the fine crystalline shaped morphology composed of ultrathin leaves, monoclinic structure and purified phase. The result of HR-TEM shows that the length of one ultrathin leaf of copper oxide nanoflower is about ~650-700 nm, base is about ~300.77 ± 30 nm and the average thickness of the tip of individual ultrathin leaf of copper oxide nanoflower is about ~10 ± 2 nm. Enhanced absorption of visible light ~850 nm and larger value of band gap energy (1.68 eV) have further supported that the as-grown material (CuO nanoflowers) is an active and well-designed surface morphology at the nanoscale level. Furthermore, significant enhancement of catalytic properties of copper oxide nanoflowers in the presence of H2O2 for the degradation of methylene blue (MB) with efficiency ~96.7% after 170 min was obtained. The results showed that the superb catalytic performance of well-fabricated CuO nanoflowers can open a new way for substantial applications of dye removal from wastewater and environment fields.
Fulltext Metal-Based Nanocomposite Materials for Efficient Photocatalytic Degradation of Phenanthrene from Aqueous Solutions

Chauhan HA, Rafatullah M, Ahmed Ali K, Siddiqui MR, Khan MA, Alshareef SA

Polymers (Basel), 2021 Jul 20;13(14).
PMID: 34301131 DOI: 10.3390/polym13142374

Polycyclic aromatic hydrocarbons (PAHs) are a class of naturally occurring chemicals resulting from the insufficient combustion of fossil fuels. Among the PAHs, phenanthrene is one of the most studied compounds in the marine ecosystems. The damaging effects of phenanthrene on the environment are increasing day by day globally. To lessen its effect on the environment, it is essential to remove phenanthrene from the water resources in particular and the environment in general through advanced treatment methods such as photocatalytic degradation with high-performance characteristics and low cost. Therefore, the combination of metals or amalgamation of bimetallic oxides as an efficient photocatalyst demonstrated its propitiousness for the degradation of phenanthrene from aqueous solutions. Here, we reviewed the different nanocomposite materials as a photocatalyst, the mechanism and reactions to the treatment of phenanthrene, as well as the influence of other variables on the rate of phenanthrene degradation.
The Impact of Private Standards on Corporate Social Responsibility Compliance and Rural Workers' Motivation in Developing Countries: A Study of Mango Farms in Pakistan

Tang H, Rasool Z, Khan AI, Khan AA, Khan MA, Azaz GA

Int J Food Sci, 2021;2021:9985784.
PMID: 34476257 DOI: 10.1155/2021/9985784

This study examines the role of a private standard on corporate social responsibility (CSR) compliance in the Pakistani mango industry and how this compliance affects rural workers' motivation. Pakistan is the fifth largest mango producer in the world and the fourth largest exporter in global mango trade; also, mango is the biggest fruit crop within the country. Mango trade is subject to trade terms, where buyers decide the conditions of trade agreements by means of codes of conduct. The key dimensions of the codes involved in agrofood trade are food safety, traceability, worker welfare, and environmental consideration, issues which are all connected with CSR. Private standards ensure compliance with these codes of conduct. This study draws on interviews and a questionnaire survey with certified mango producers and farm workers in Pakistan. The mango industry also involves other stakeholders such as government institutes and NGOs; interviews were also conducted with their representatives. Given that this study is an impact assessment research, the researcher designed a theoretical framework using a mixed method approach to investigate the rationale behind acquiring the standard by the mango growers in Pakistan and what impact (if any) this shift has generated with regard to the farm workers' job satisfaction and motivation. This study is the first to empirically examine good agricultural practices in Pakistan and evaluate their impact. This study shows that private standards play a significant role in ensuring compliance, and CSR practices implemented through them were found to be positively related to the rural workers' job satisfaction and motivation. Furthermore, this study has made separate contributions to theory, methodology, and practice. The production of the synergistic model for improving compliance is among the key highlights of the study. The findings of this study can extend to other agriculture and primary production industry workers in Pakistan and even beyond to other developing countries' rural agriculture workers.
Synthesis of CTAB intercalated graphene and its application for the adsorption of AR265 and AO7 dyes from water

Yusuf M, Khan MA, Otero M, Abdullah EC, Hosomi M, Terada A, et al.

J Colloid Interface Sci, 2017 05 01;493:51-61.
PMID: 28088121 DOI: 10.1016/j.jcis.2017.01.015

Environmental applications of graphene (GN) are limited by the occurrence of aggregation. Herein, graphene oxide (GO) was synthesized, reduced to GN by ascorbic acid, and intercalated with cetyltrimethylammonium bromide (CTAB). GN-CTAB was characterized by Boehm's titration, N2 adsorption/desorption, Fourier transform infrared spectroscopy, Raman spectroscopy, Fluorescence spectrophotometry, X-ray diffraction and Scanning electron microscopy. Then, GN-CTAB was used for the adsorptive removal of acid red 265 (AR265) and acid orange 7 (AO7) dyes from water both under batch and column operation. Under batch operation, the effect of pH, adsorbent dosage, initial dye concentration, contact time and temperature on dyes adsorption were assessed. Adsorption isotherms, kinetics, and thermodynamics were analyzed systematically. Regarding the fixed bed operation, the effect of both the bed height and flow rate were studied and experimental results fitted to the Thomas and BDST models. Then, the bed loss capacity along five adsorption-regeneration cycles was determined in order to further approach the practical application of GN-CTAB for wastewater treatment, namely for the removal of dyes.
Fulltext A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings

Safi A, Ahmad Z, Jehangiri AI, Latip R, Zaman SKU, Khan MA, et al.

Sensors (Basel), 2022 Nov 01;22(21).
PMID: 36366109 DOI: 10.3390/s22218411

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0-600 m) and heights above the ground (0-2 m) in an open environment and indoor (1st Floor-3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.
Ethyl acetate extract of Clausena excavata induces growth inhibition of non-small-lung cancer, NCI-H460, cell line via apoptosis

Fadhel Abbas Albaayit S, Khan MA, Abdullah R, Hezmee Mohd Noor M

J Appl Biomed, 2021 Mar;19(1):40-47.
PMID: 34907714 DOI: 10.32725/jab.2021.007

CONTEXT: Clausena excavata Burm. f is a plant used in folklore medicine for the treatment of various ailments in South East Asia. The plant parts contain chemical components that are cytotoxic to many cancer cells.
OBJECTIVE: The study investigated the cytotoxic effects of ethyl acetate, methanol and chloroform C. excavata leaf extracts on the non-small-lung cancer, NCI-H460, cell line.
METHODS: Based on the 3-(4,5-dimethylthiazol-2-yl)-2,5,-diphenyltetrazolium bromide (MTT) assay, among extracts, ethyl acetate C. excavata leaf extract (EACE) was the most potent anti-NCI-H460 cells, with IC50 value of 47.1 ± 6.1 μg/ml. The effects of EACE on NCI-H460 cells were also determined by clonogenic, 4', 6-diamidino-2-phenylindole (DAPI), and annexin-V-fluorescein isothiocyanate/propidium iodide-PI flow cytometric assays. Reactive oxygen species (ROS) production and apoptotic gene expressions was determined via flow cytometry and real-time quantitative PCR, respectively.
RESULTS: EACE-treated NCI-H460 cells after 48 h underwent apoptosis as evident by loss of cell viability, cell shrinkage, and chromatin condensation. The results also showed EACE mediated increase in ROS production by the NCI-H460 cells. After 48 h treatment, EACE increased the pro-apoptotic BAX and decreased the anti-apoptotic Bcl-2, Survivin and c-Myc gene expressions.
CONCLUSIONS: EACE is a potential anti-lung cancer by increasing cancer cell ROS production and apoptosis.
Fulltext Gelatin-based instant gel-forming volatile spray for wound-dressing application

Islam MS, Islam JMM, Rahman MF, Rahman MM, Khan MA

Prog Biomater, 2021 Sep;10(3):235-243.
PMID: 34542831 DOI: 10.1007/s40204-021-00166-3

This study was a successful endeavor to develop and investigate the suitability of a bioadhesive wound-healing gel based on gelatin for first-aid purposes. Polyethylene glycol (PEG) was used to prepare a denser phase of gelatin chains, and diethyl ether (DEE) was used to introduce high volatility to the solution. The prepared solution was stable in the storage container but rapidly formed (within 3 s) a protective and bioadhesive gel around the wound surface by being sprayed over the wound. Besides, it also suppressed pain and showed moderate antimicrobial activity against S. aureus. It was also found highly biocompatible and non-toxic. All the results revealed that the prepared solution could be an effective candidate for treating minor injuries or burn, especially for a first-aid purpose.
Fulltext Factors Affecting E-Shopping Behaviour: Application of Theory of Planned Behaviour

Tang H, Rasool Z, Khan MA, Khan AI, Khan F, Ali H, et al.

Behav Neurol, 2021;2021:1664377.
PMID: 34858540 DOI: 10.1155/2021/1664377

E-shopping is a rapidly growing phenomenon among different individuals who intend to shop online. However, a trust deficit in the E-shopping environment has always been a critical issue in the brick-and-click mode of shopping, being one of the main reasons for E-cart abandonment in E-commerce. This empirical study is aimed at investigating the perceived effect of website trust on E-shopping intentions and behaviour, drawing upon the theory of planned behaviour (TPB). Data were collected through self-administered questionnaires from working adults who shop for garments online. Structural equation modelling was used to evaluate the model fit and assumptions. Our findings suggest that website trust and E-shopping attitude play substantial roles in building E-shopping intentions and actual behaviours. Both are the significant predictors of the behaviour mediated by E-shopping intentions. However, E-shopping intentions did not mediate between subjective norms and E-shopping behaviour, when working adults decide to purchase garments online.
Fulltext An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression

Bukhari MM, Ghazal TM, Abbas S, Khan MA, Farooq U, Wahbah H, et al.

Comput Intell Neurosci, 2022;2022:3606068.
PMID: 35126487 DOI: 10.1155/2022/3606068

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.
Fulltext Self-medication dilemma in dengue fever

Khan MA, Tahir MJ, Ameer MA, Nawaz RA, Asghar MS, Ahmed A

Public Health Pract (Oxf), 2022 Dec;4:100298.
PMID: 36570398 DOI: 10.1016/j.puhip.2022.100298

This paper focuses on the trends of self-medication practices in treating symptoms that may lead to fatal complications in dengue. As dengue is a viral infection with increasing incidence, decision regarding its treatment is mostly affected by public health believes and practices to self-treat the condition by different home remedies, over-the-counter (OTC) drugs or using outdated prescription drugs that proved beneficial in the past experience. Poverty, lack of education, and poor access to health facilities pave the way for making such decisions. Future complications can be averted by raising awareness, counseling the patients and dispensing of pharmaceuticals with strict monitoring.
Fulltext Privacy-Aware Collaborative Learning for Skin Cancer Prediction

Ain QU, Khan MA, Yaqoob MM, Khattak UF, Sajid Z, Khan MI, et al.

Diagnostics (Basel), 2023 Jul 04;13(13).
PMID: 37443658 DOI: 10.3390/diagnostics13132264

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.
Fulltext Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, et al.

Diagnostics (Basel), 2023 Jul 11;13(14).
PMID: 37510084 DOI: 10.3390/diagnostics13142340

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.
Fulltext A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

Rehman A, Abbas S, Khan MA, Ghazal TM, Adnan KM, Mosavi A

Comput Biol Med, 2022 Nov;150:106019.
PMID: 36162198 DOI: 10.1016/j.compbiomed.2022.106019

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.
Fulltext A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

Nadeem MW, Goh HG, Ponnusamy V, Andonovic I, Khan MA, Hussain M

Healthcare (Basel), 2021 Oct 18;9(10).
PMID: 34683073 DOI: 10.3390/healthcare9101393

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.
Fulltext YOLO and residual network for colorectal cancer cell detection and counting

Haq I, Mazhar T, Asif RN, Ghadi YY, Ullah N, Khan MA, et al.

Heliyon, 2024 Jan 30;10(2):e24403.
PMID: 38304780 DOI: 10.1016/j.heliyon.2024.e24403

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models.
Uterotrophic, fetotoxic and abortifacient effect of a Malaysian variety of Plumbago rosea L. on isolated rat uterus and pregnant mice

Abdul Sattar M, Abdullah NA, Khan MA, Dewa A, Samshia D

Pak J Biol Sci, 2007 Mar 01;10(5):763-7.
PMID: 19069860

Traditionally Plumbago rosea L. is used as an abortifacient in the Southeast Asian region. Methanolic root extract of a local species of Plumbago rosea L. was studied to evaluate its traditional antifertility claim. Interestingly, it was found to possess dose related inhibitory effect on uterine contractile responses elicited by oxytocic agents on isolated uteri of pregnant and pseudo-pregnant rats. Furthermore, it was found to possess significant (p < 0.05) fetotoxic activity along with mild abortive potential in pregnant mice when given orally at high doses (400 and 800 mg kg(-1)) once daily for ten days starting from day 10 of gestation. The results derived indicated possible presence of utero-active compound (s) in this plant that inhibited oxytocic agents induced uterine motility. Moreover, pronounced fetotoxic and mild abortifacient potentials observed at higher doses in pregnant mice might support its accredited traditional use to avoid unwanted pregnancy.
Fulltext Exploring Patient Safety Culture: A Study of Community Pharmacies in Karachi, Pakistan

Khatri SA, Ahmad R, Osama M, Khan K, Khan MA, Ishaqui A, et al.

Cureus, 2024 Jan;16(1):e52135.
PMID: 38344495 DOI: 10.7759/cureus.52135

Background Community pharmacies are integral to the healthcare system, actively contributing to patient safety through accurate dispensing, education, collaboration, monitoring, and the implementation of safety protocols. Their accessibility and role as medication experts make them key partners in promoting positive health outcomes for individuals and communities. Objective The current study will evaluate the patient safety culture (PSC) among community pharmacies in Karachi, Pakistan. Additionally, this study will measure the association between patient safety culture in community pharmacies and the demographic characteristics of the pharmacy staff. Methods A cross-sectional survey of pharmacy staff was conducted using a survey instrument developed by the US Agency for Healthcare Research and Quality (AHRQ). Demographic variables and assessments of safety culture in pharmacies were studied. The data were analyzed using descriptive statistics. Results Among the 102 participants, positive responses ranged from 30% to 87.5%. The highest positive response was for the dimension "mistakes in communication" (86.3%), followed by "communication across shifts" (82.2%) and "communication openness" (81.7%). The dimensions "overall perceptions of patient safety" and "response to mistakes" had the lowest positive responses (56.0% and 60.9%, respectively). Furthermore, many staff did not regularly record the errors, even if they impacted the practices. Conclusion There was an overall unfavorable perception of patient safety culture among the surveyed pharmacies of Karachi, Pakistan. However, the communication dimensions showed the highest positive response. There is a strong need to improve the overall perception of patient safety among the staff and develop an optimistic response to mistakes.
Identification of Four Novel Candidate Genes for Non-syndromic Intellectual Disability in Pakistani Families

Ahmed I, Muzammal M, Khan MA, Ullah H, Farid A, Yasin M, et al.

Biochem Genet, 2024 Aug;62(4):2571-2586.
PMID: 37985543 DOI: 10.1007/s10528-023-10556-w

Intellectual disability, a genetically and clinically varied disorder and is a significant health problem, particularly in less developed countries due to larger family size and high ratio of consanguineous marriages. In the current genetic study, we investigate and find the novel disease causative factors in the four Pakistani families with severe type of non-syndromic intellectual disability. For genetic analysis whole-exome sequencing (WES) and Sanger sequencing was performed. I-TASSER and Cluspro tools were used for Protein modeling and Protein-protein docking. Sanger sequencing confirms the segregation of novel homozygous variants in all the families i.e., c.245 T > C; p.Leu82Pro in SLC50A1 gene in family 1, missense variant c.1037G > A; p.Arg346His in TARS2 gene in family 2, in family 3 and 4, nonsense mutation c.234G > A; p.Trp78Term and missense mutation c.2200G > A; p.Asp734Asn in TBC1D3 and ANAPC2 gene, respectively. In silico functional studies have found the drastic effect of these mutations on protein structure and its interaction properties. Substituted amino acids were highly conserved and present on highly conserved region throughout the species. The discovery of pathogenic variants in SLC50A1, TARS2, TBC1D1 and ANAPC2 shows that the specific pathways connected with these genes may be important in cognitive impairment. The decisive role of pathogenic variants in these genes cannot be determined with certainty due to lack of functional data. However, exome sequencing and segregation analysis of all filtered variants revealed that the currently reported variants were the only variations from the respective families that segregated with the phenotype in the family.
Fulltext Optimal control analysis of vector-host model with saturated treatment

Ullah S, Khan MF, Shah SAA, Farooq M, Khan MA, Mamat MB

Eur Phys J Plus, 2020;135(10):839.
PMID: 33101826 DOI: 10.1140/epjp/s13360-020-00855-1

Vector-host infectious diseases remain a challenging issue and cause millions of deaths each year globally. In such outbreaks, many countries especially developing or underdevelopment faces a situation where the number of infected individuals is getting larger and the medical facilities are limited. In this paper, we construct an epidemic model to explore the transmission dynamics of vector-borne diseases with nonlinear saturated incidence rate and saturated treatment function. This type of incidence rate, as well as the saturated treatment function, is also known as the Holling type II form and describes the effect of delayed treatment. Initially, we formulate a mathematical model and then present the basic analysis of the model including the positivity and boundedness of the solution. The threshold quantity R 0 is presented and the stability analysis of the system is carried out for the model equilibria. The global stability results are shown using the Lyapunov function of Goh-Voltera type. The existence of backward bifurcation is discussed using the central manifold theory. Further, the global sensitivity analysis of the model is carried out using the Latin Hypercube sampling and the partial rank correlation coefficient techniques. Moreover, an optimal control problem is formulated and the necessary optimality conditions are investigated in order to eradicate the disease in a community. Four strategies are presented by choosing different set of controls combination for the disease minimization. Finally, the numerical simulations of each strategy are depicted to demonstrate the importance of suggesting control interventions on the disease dynamics and eradication.
Fulltext IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

Abdulmalek S, Nasir A, Jabbar WA, Almuhaya MAM, Bairagi AK, Khan MA, et al.

Healthcare (Basel), 2022 Oct 11;10(10).
PMID: 36292441 DOI: 10.3390/healthcare10101993

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

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