Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023.

BACKGROUND: Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. MATERIALS AND METHODS: The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. RESULTS: Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). CONCLUSION: This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient's CVD prognosis.

A bibliometric review on electric vehicle (EV) energy efficiency and emission effect research.

Electric vehicles have received extensive attention due to their unique energy efficiency and good emission reduction effects. While a large-scale of electric vehicles are gradually replacing traditional fuel vehicles, it is necessary to ensure the energy efficiency of electric vehicles and the effectiveness of their emission reduction effects. This study conducted a bibliometric analysis of scientific publications on energy efficiency and emission reduction effects of electric vehicles from 2003 to 2022, using a variety of bibliometric tools such as R Studio, biblioshiny and VOSviewer. The results showed the gradual elimination of traditional energy vehicles, where electric vehicles play an important role in connecting energy efficiency and emission control. The results also showed the top publication outlets, citations trackers, authors with thematic evaluation of energy efficiency and emission reduction effects of electric vehicles. The contribution of the study is manifold. The academic contribution of the present study is the bibliometric analysis which will help academicians to get a quick overview of the most popular journals, top collaborators, documents, authors, and scientific knowledge structure. Secondly, policy makers, environmentalists, researchers, and academician will definitely get a pathway how they should go for future research. Finally, this study suggests more researches trend to focus on the sales of electric vehicles, automobile exhaust emissions, sensitivity analysis of electric vehicles, energy storage analysis to improve the energy efficiency of electric vehicles, and V2G related to the energy efficiency of electric vehicle clusters.

High-Speed Network DDoS Attack Detection: A Survey.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

The Covid-19 and online learning process in Bangladesh.

This study investigates the augmentation of students' engagement in the online learning process using Zoom platform. To engage students more in the online classes we have conducted a survey on four universities students in the four dimensions. To investigate effective online class, we have gone through descriptive statistics followed by principal component analysis (PCA) and factor regression model to identify predicted factors that engage students more in the Zoom online classes. The results of PCA confirmed that questions answer session, instructor asks question to them, break during the class, topic related examples, experience sharing scope, case studies, using Google classroom, screen share, screen annotation, video contents share, class recording, raise hand and reactions to topics can enhance students engagement in the Zoom online classes. The regression results validate all four dimensions have significant influence on effective zoom online class that enhance students learning process. Thus, findings of this study recommend educating course instructors for ensuring all the applications of online learning process while conducting online classes. We strongly believe this course of action will engage students in the online class to enhance learning activities using Zoom platform in Bangladesh.

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations.

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.

COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.

LR-AKAP: A Lightweight and Robust Security Protocol for Smart Home Environments.

For the betterment of human life, smart Internet of Things (IoT)-based systems are needed for the new era. IoT is evolving swiftly for its applications in the smart environment, including smart airports, smart buildings, smart manufacturing, smart homes, etc. A smart home environment includes resource-constrained devices that are interlinked, monitored, controlled, and analyzed with the help of the Internet. In a distributed smart environment, devices with low and high computational power work together and require authenticity. Therefore, a computationally efficient and secure protocol is needed. The authentication protocol is employed to ensure that authorized smart devices communicate with the smart environment and are accessible by authorized personnel only. We have designed a novel, lightweight secure protocol for a smart home environment. The introduced novel protocol can withstand well-known attacks and is effective with respect to computation and communication complexities. Comparative, formal, and informal analyses were conducted to draw the comparison between the introduced protocol and previous state-of-the-art protocols.

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

Linkage of SSR markers with rice blast resistance and development of partial resistant advanced lines of rice (Oryza sativa) through marker-assisted selection.

Rice blast disease is one of the major bottlenecks of rice production in the world including Bangladesh. To develop blast resistant lines, a cross was made between a high yielding but blast susceptible variety MR263 and a blast resistant variety Pongsu seribu 2. Marker-assisted backcross breeding was followed to develop F1, BC1F1, BC2F1, BC2F2, BC2F3, BC2F4 and BC2F5 population. DNA markers i.e., RM206, RM1359 and RM8225 closely linked to Pb1, pi21 and Piz blast resistant genes, respectively and marker RM276 linked to panicle blast resistant QTL (qPbj-6.1) were used in foreground selection. Calculated chi-square (χ2) value of phenotypic and genotypic segregation data of BC2F1 population followed goodness of fit to the expected ratio (1:1) (phenotypic data χ2 = 1.08, p = 0.701; genotypic data χ2 = range from 0.33 to 3.00, p = 0.08-0.56) and it indicates that the inheritance pattern of blast resistance was followed by a single gene model. Eighty-nine advanced lines of BC2F5 population were developed and out of them, 58 lines contained Piz, Pb1, pi21, and qPbj-6.1 while 31 lines contained Piz, Pb1, and QTL qPbj-6.1. Marker-trait association analysis revealed that molecular markers i.e., RM206, RM276, and RM8225 were tightly linked with blast resistance, and each marker was explained by 33.33% phenotypic variation (resistance reaction). Morphological and pathogenicity performance of advanced lines was better compared to the recurrent parent. Developed blast resistance advanced lines could be used as donors or blast resistant variety for the management of devastating rice blast disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-022-01141-3.

Cloud to cloud data migration using self sovereign identity for 5G and beyond.

The Coronavirus pandemic and the work-from-anywhere has created a shift toward cloud-based services. The pandemic is causing an explosion in cloud migration, expected that by 2025, 95% of workloads will live in the cloud. One of the challenges of the cloud is data security. It is the responsibility of cloud service providers to protect user data from unauthorized access. Historically, a third-party auditor (TPA) is used to provide security services over the cloud. With the tremendous growth of demand for cloud-based services, regulatory requirements, there is a need for a semi to fully automated self sovereign identity (SSI) implementation to reduce cost. It's critical to manage cloud data strategically and extend the required protection. At each stage of the data migration process, such as data discovery, classification, and cataloguing of the access to the mission-critical data, need to be secured. Cloud storage services are centralized, which requires users must place trust in a TPA. With the SSI, this can become decentralized, reducing the dependency and cost. Our current work involves replacing TPA with SSI. A cryptographic technique for secure data migration to and from the cloud using SSI implemented. SSI facilitate peer-to-peer transactions, meaning that the in-between presence of TPA needs no longer be involved. The C2C migration performance is recorded and found the background or foreground replication scenario is achievable. Mathematically computed encrypted and decrypted ASCII values for a word matched with the output by the algorithm. The keys generated by the algorithm are validated with an online validator to ensure the correctness of the generated keys. RSA based mutual TLS algorithm is a good option for SSI based C2C migration. SSI is beneficial because of the low maintenance cost, and users are more and more using a cloud platform. The result of the implemented algorithm shows that the SSI based implementation can provide a 13.32 Kbps encryption/decryption rate which is significantly higher than the TPA method of 1 Kbps.

A Comprehensive Review on the Optical Micro-Electromechanical Sensors for the Biomedical Application.

This study presented an overview of current developments in optical micro-electromechanical systems in biomedical applications. Optical micro-electromechanical system (MEMS) is a particular class of MEMS technology. It combines micro-optics, mechanical elements, and electronics, called the micro-opto electromechanical system (MOEMS). Optical MEMS comprises sensing and influencing optical signals on micron-level by incorporating mechanical, electrical, and optical systems. Optical MEMS devices are widely used in inertial navigation, accelerometers, gyroscope application, and many industrial and biomedical applications. Due to its miniaturised size, insensitivity to electromagnetic interference, affordability, and lightweight characteristic, it can be easily integrated into the human body with a suitable design. This study presented a comprehensive review of 140 research articles published on photonic MEMS in biomedical applications that used the qualitative method to find the recent advancement, challenges, and issues. The paper also identified the critical success factors applied to design the optimum photonic MEMS devices in biomedical applications. With the systematic literature review approach, the results showed that the key design factors could significantly impact design, application, and future scope of work. The literature of this paper suggested that due to the flexibility, accuracy, design factors efficiency of the Fibre Bragg Grating (FBG) sensors, the demand has been increasing for various photonic devices. Except for FBG sensing devices, other sensing systems such as optical ring resonator, Mach-Zehnder interferometer (MZI), and photonic crystals are used, which still show experimental stages in the application of biosensing. Due to the requirement of sophisticated fabrication facilities and integrated systems, it is a tough choice to consider the other photonic system. Miniaturisation of complete FBG device for biomedical applications is the future scope of work. Even though there is a lot of experimental work considered with an FBG sensing system, commercialisation of the final FBG device for a specific application has not been seen noticeable progress in the past.

Fischer Linear Discrimination and Quadratic Discrimination Analysis-Based Data Mining Technique for Internet of Things Framework for Healthcare.

The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry. Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.

A Predictive and Preventive Model for Onset of Alzheimer's Disease.

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

Industry 5.0: Ethereum blockchain technology based DApp smart contract.

The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.

An Empirical Model to Predict the Diabetic Positive Using Stacked Ensemble Approach.

Today, disease detection automation is widespread in healthcare systems. The diabetic disease is a significant problem that has spread widely all over the world. It is a genetic disease that causes trouble for human life throughout the lifespan. Every year the number of people with diabetes rises by millions, and this affects children too. The disease identification involves manual checking so far, and automation is a current trend in the medical field. Existing methods use a single algorithm for the prediction of diabetes. For complex problems, a single model is not enough because it may not be suitable for the input data or the parameters used in the approach. To solve complex problems, multiple algorithms are used. These multiple algorithms follow a homogeneous model or heterogeneous model. The homogeneous model means the same algorithm, but the model has been used multiple times. In the heterogeneous model, different algorithms are used. This paper adopts a heterogeneous ensemble model called the stacked ensemble model to predict whether a person has diabetes positively or negatively. This stacked ensemble model is advantageous in the prediction. Compared to other existing models such as logistic regression Naïve Bayes (72), (74.4), and LDA (81%), the proposed stacked ensemble model has achieved 93.1% accuracy in predicting blood sugar disease.

An enhanced dynamic transmission opportunity scheme to support varying traffic load over wireless campus networks.

Transmission opportunity (TXOP) is a key factor to enable efficient channel bandwidth utilization over wireless campus networks (WCN) for interactive multimedia (IMM) applications. It facilitates in resource allocation for the similar categories of multiple packets transmission until the allocated time is expired. The static TXOP limits are defined for various categories of IMM traffics in the IEEE802.11e standard. Due to the variation of traffic load in WCN, the static TXOP limits are not sufficient enough to guarantee the quality of service (QoS) for IMM traffic flows. In order to address this issue, several existing works allocate the TXOP limits dynamically to ensure QoS for IMM traffics based on the current associated queue size and pre-setting threshold values. However, existing works do not take into account all the medium access control (MAC) overheads while estimating the current queue size which in turn is required for dynamic TXOP limits allocation. Hence, not considering MAC overhead appropriately results in inaccurate queue size estimation, thereby leading to inappropriate allocation of dynamic TXOP limits. In this article, an enhanced dynamic TXOP (EDTXOP) scheme is proposed that takes into account all the MAC overheads while estimating current queue size, thereby allocating appropriate dynamic TXOP limits within the pre-setting threshold values. In addition, the article presents an analytical estimation of the EDTXOP scheme to compute the dynamic TXOP limits for the current high priority traffic queues. Simulation results were carried out by varying traffic load in terms of packet size and packet arrival rate. The results show that the proposed EDTXOP scheme achieves the overall performance gains in the range of 4.41%-8.16%, 8.72%-11.15%, 14.43%-32% and 26.21%-50.85% for throughput, PDR, average ETE delay and average jitter, respectively when compared to the existing work. Hence, offering a better TXOP limit allocation solution than the rest.

α7-Nicotinic Acetylcholine Receptor Agonist Ameliorates Nicotine Plus High-Fat Diet-Induced Hepatic Steatosis in Male Mice by Inhibiting Oxidative Stress and Stimulating AMPK Signaling.

α7-Nicotinic acetylcholine receptor (α7nAChR) agonists confer protection against a wide variety of cytotoxic insults and suppress oxidative stress and apoptosis in various cell systems, including hepatocytes. We recently demonstrated that nicotine, when combined with a high-fat diet (HFD), triggers oxidative stress, activates hepatocyte apoptosis, and exacerbates HFD-induced hepatic steatosis in male mice. This study evaluates whether PNU-282987 (PNU), a specific α7nAChR agonist, is effective in preventing nicotine plus HFD-induced hepatic steatosis. Adult C57BL6 male mice were fed a normal chow diet or HFD with 60% of calories derived from fat and received twice-daily intraperitoneal injections of 0.75 mg/kg body weight (BW) of nicotine, PNU (0.26 mg/kg BW), PNU plus nicotine, or saline for 10 weeks. PNU treatment was effective in attenuating nicotine plus HFD-induced increase in hepatic triglyceride levels, hepatocyte apoptosis, and hepatic steatosis. The preventive effects of PNU on nicotine plus HFD-induced hepatic steatosis were mediated by suppression of oxidative stress and activation of adenosine 5'-monophosphate-activated protein kinase (AMPK) together with inhibition of its downstream target sterol regulatory element binding protein 1c (SREBP1c), fatty acid synthase (FAS), and acetyl-coenzyme A-carboxylase (ACC). We conclude that the α7nAChR agonist PNU protects against nicotine plus HFD-induced hepatic steatosis in obese mice. PNU appears to work at various steps of signaling pathways involving suppression of oxidative stress, activation of AMPK, and inhibition of SREBP1c, FAS, and ACC. α7nAChR agonists may be an effective therapeutic strategy for ameliorating fatty liver disease, especially in obese smokers.

Nicotine plus a high-fat diet triggers cardiomyocyte apoptosis.

Cigarette smoking is an important risk factor for diabetes, cardiovascular disease and non-alcoholic fatty liver disease. The health risk associated with smoking can be aggravated by obesity. Smoking might also trigger cardiomyocyte (CM) apoptosis. Given that CM apoptosis has been implicated as a potential mechanism in the development of cardiomyopathy and heart failure, we characterize the key signaling pathways in nicotine plus high-fat diet (HFD)-induced CM apoptosis. Adult C57BL6 male mice were fed a normal diet (ND) or HFD and received twice-daily intraperitoneal (IP) injections of nicotine (0.75 mg/kg body weight [BW]) or saline for 16 weeks. An additional group of nicotine-treated mice on HFD received twice-daily IP injections of mecamylamine (1 mg/kg BW), a non-selective nicotinic acetylcholine receptor antagonist, for 16 weeks. Nicotine when combined with HFD led to a massive increase in CM apoptosis that was fully prevented by mecamylamine treatment. Induction of CM apoptosis was associated with increased oxidative stress and activation of caspase-2-mediated intrinsic pathway signaling coupled with inactivation of AMP-activated protein kinase (AMPK). Furthermore, nicotine treatment significantly (P < 0.05) attenuated the HFD-induced decrease in fibroblast growth factor 21 (FGF21) and silent information regulator 1 (SIRT1). We conclude that nicotine, when combined with HFD, triggers CM apoptosis through the generation of oxidative stress and inactivation of AMPK together with the activation of caspase-2-mediated intrinsic apoptotic signaling independently of FGF21 and SIRT1.