Background: Type 2 diabetes mellitus (T2DM) is a complex, multifactorial disease with a significant global burden. Although genome-wide association studies (GWAS) have identified many T2DM-associated variants, most lie in non-coding regions, making it difficult to interpret their functional roles. Methods: We aimed to identify genetically regulated Cytosine–phosphate–Guanine (CpG) sites associated with T2DM by conducting a methylome-wide association study (MWAS), followed by Mendelian randomization (MR) and functional validation using human pancreatic cells and mouse models. MWAS was performed using summary statistics from large-scale GWAS and a DNA methylation (DNAm) prediction model to test associations between genetically predicted DNAm and T2DM. Results: We identified 111 CpG sites significantly associated with T2DM in Europeans, including 8 novel sites near genes not previously linked to T2DM. These findings were replicated in independent datasets. Many CpGs also showed associations with cardiometabolic traits, highlighting shared epigenetic mechanisms. Trans-ethnic MR analysis confirmed consistent effects for six CpGs in East Asians. Functional analysis revealed that several CpGs regulate gene expression in human pancreatic α-and β-cells. Among them, 2´-5´-oligoadenylate synthetase like (OASL) expression, regulated by a significant CpG, was differentially expressed in α-cells of T2DM cases compared to controls. Supporting evidence from mouse models suggests a role for OASL in glucose regulation. Conclusion: Our study identifies novel genetically regulated CpG sites associated with T2DM risk and highlights OASL as a potential epigenetic regulator of glucose metabolism in α-cells. These findings provide mechanistic insights into the epigenetic architecture of T2DM and suggest potential targets for cross-ethnic biomarker development and therapeutic intervention.

Background: The rising global incidence of type 2 diabetes mellitus (T2DM) underscores the need for predictive models that enhance early detection and prevention across diverse populations. This study aimed to identify predictors of incident T2DM within a Han Chinese population, assess their impact across various age and sex demographics, and explore their applicability to European populations. Methods: Using data from about 65,000 participants in the Taiwan Biobank (TWB), we developed a predictive model, achieving an area under the receiver operating characteristic curve of 90.58%. Key predictors were identified through LASSO regression within the TWB cohort and validated using over 4 million records from Taiwan’s Adult Preventive Healthcare Services (APHS) program and the UK Biobank (UKB). Results: Our analysis highlighted 13 significant predictors, including established factors like glycosylated hemoglobin (HbA1c) and blood glucose levels, and less conventionally considered variables such as peak expiratory flow. Notable differences in the effects of HbA1c levels and polygenic risk scores between the TWB and UKB cohorts were observed. Additionally, age and sex-spe-cific impacts of these predictors, detailed through APHS data, revealed significant variances; for instance, waist circumference and diagnosed mixed hyperlipidemia showed greater impacts in younger females than in males, while effects remained uniform across male age groups. Conclusion: Our findings offer novel insights into the diagnosis and management of diabetes for the Han Chinese and potentially for broader East Asian populations, highlighting the importance of ethnic and demographic diversity in developing predictive models for early detection and personalized intervention strategies.

Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of (Formula presented.) for automatic localization, a mean Intersection over Union (mIoU) of (Formula presented.) for segmentation, and a mean (Formula presented.) score of (Formula presented.) for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images.

The history of how artificial intelligence (AI) has been integrated into the Medicare process has been responsible and contingent, aiming to achieve personalized and effective healthcare. Core AI concepts, such as machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and predictive analytics, have revolutionized conventional Medicare technologies. These advancements optimize disorder diagnosis, provide treatment plans tailored to individual cases, enhance medical imaging, and improve patient care. Current trends in AI adoption focus on disease detection, personalized treatment, remote access to healthcare, and they address major roadblocks such as data privacy, security, interoperability, mitigation of bias, regulatory compliance, and change resistance. Looking ahead, AI is poised to revolutionize not only drug discovery but also predictive analytics, promising the governance of ethical AI to further enhance healthcare accessibility and quality. To unlock the full potential of AI in Medicare services, ethical and regulatory considerations, including data privacy, transparency, mitigation of bias, and AI governance, must be carefully navigated. Additionally, stakeholders need to address change management, provide continuous education, and ensure a framework for ethical AI governance. These efforts will be essential for realizing the transformative benefits of AI in the realm of Medicare services.

Aims/Introduction: This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions. Materials and Methods: Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data. K-means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method. Results: We identified two stable clusters representing high- and low-risk diabetes groups in both biobanks. The high-risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low-risk clusters, respectively. Notably, males were predominant in the high-risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high-risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high-risk group (P < 0.001). Kaplan–Meier curves indicated significant differences in diabetes complication incidences between clusters. Conclusions: UL effectively identified risk-specific groups within prediabetes populations, with high-risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.

Objective: Kidney failure manifests in various forms, from sudden occurrences such as Acute Kidney Injury (AKI) to progressive like Chronic Kidney Disease (CKD). Given its intricate nature, marked by overlapping comorbidities and clinical similarities—including treatment modalities like dialysis—we sought to design and validate an end-to-end framework for clustering kidney failure subtypes. Materials and methods: Our emphasis was on dialysis, utilizing a comprehensive dataset from the UK Biobank (UKB). We transformed raw Electronic Health Record (EHR) data into standardized matrices that incorporate patient demographics, clinical visit data, and the innovative feature of visit time-gaps. This matrix structure was achieved using a unique data cutting method. Latent space transformation was facilitated using a convolution autoencoder (ConvAE) model, which was then subjected to clustering using Principal Component Analysis (PCA) and K-means algorithms. Results: Our transformation model effectively reduced data dimensionality, thereby accelerating computational processes. The derived latent space demonstrated remarkable clustering capacities. Through cluster analysis, two distinct groups were identified: CKD-majority (cluster 1) and a mixed group of non-CKD and some CKD subtypes (cluster 0). Cluster 1 exhibited notably low survival probability, suggesting it predominantly represented severe CKD. In contrast, cluster 0, with substantially higher survival probability, likely to include milder CKD forms and severe AKI. Our end-to-end framework effectively differentiates kidney failure subtypes using the UKB dataset, offering potential for nuanced therapeutic interventions. Conclusions: This innovative approach integrates diverse data sources, providing a holistic understanding of kidney failure, which is imperative for patient management and targeted therapeutic interventions.

Getting delicate information is the objective of the overwhelming majority. Cyber-attack programs target data driven information because majority of strategic and touchy information are available there. Thus, associations should focus on data set security, and the initial step is information knowledge—knowing what touchy information one has, how their data set framework is designed, and who approaches it. It involves a common sense that the web isn't secure. Many occasions have shown that there are individuals in this enormous interconnection of organizations that need to, with different aims, take others’ data, disturb the administration of an overall specialist co-op, and assault frameworks to get entrance or to cut them down. Network security has been a principal component of each association to guarantee secure web availability and insurance against information breaks. While numerous associations have turned towards data centre specialists to save their time and effort on obtaining, establishing and securing of equipments, servers, and gadgets, data centres themselves are not secure from hooligans on the web. It is time for the Data Centre to demonstrate its reliability to clients by getting their information and disconnection from different clients that share a similar framework and offering continuous assistance with a base measure of personal time. To get Data Centres organizations and forestall information breaks, various sellers and Data Centre experts have proposed different arrangements, of which some have been examined in this paper. Besides, as Data Centre innovation has been created to adjust to mechanization through programming reflection, virtualization has become an indivisible piece.

The devices of the Internet of Things (IoT) are integrated and interconnected by using the traditional wireless communication technology. The unavailability of spectrum sharing occurs in traditional wireless communication due to the presence of a massive number of IoT devices. Thus, the cognitive radio network (CRN) is introduced as a promising technology to utilize the spectrum efficiently. Channel hopping sequence (CHS) is used to establish communication among CRN users. However, the majority of CHS mechanisms only focus on multiuser single-hop scenarios, which can lead to bottleneck and throughput degradation problems. It is also found that few existing CHS mechanisms still have a low percentage of rendezvous that can cause difficulties for the secondary users (SUs) to communicate. Thus, it is a challenge to design efficient CHS for establishing fast communication among SUs under multiuser, multihop scenarios, and asymmetric asynchronous environments. In this article, asymmetric synchronous and asymmetric asynchronous (ASAA) channel hopping (CH) algorithms are designed for the multiuser CRN-enabled IoT devices to share the unused spectrum in the multihop scenario. The multiuser asymmetric synchronous (MUAS) and multiuser asymmetric asynchronous (MUAA) protocols are designed in the proposed ASAA CH mechanism. The simulation results show that the proposed ASAA CHS algorithms outperform the existing CHS mechanisms in terms of throughput, channel loading (CL), channel utilization (CU), maximum time to rendezvous (MTTR), average time to rendezvous (ATTR), and maximum inter rendezvous interval (MIRI).

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning.

Background: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern. Objective: This study aimed to comprehensively depict the concept of “informal caregiver burden” through bibliometric and content analyses. Methods: We searched the Web of Science (WoS) database to obtain bibliometric data and included only papers published between 2013 and 2022. We used content analysis to extract and identify the core concepts within the text systematically. Results: Altogether, 934 papers were included in the bibliometric analysis, from which we selected 19 highly impactful papers for content analysis. The results indicate that researchers have focused on exploring the factors that impact informal caregiver burden. Meanwhile, there has been a widespread discussion regarding the caregiver burden among those caring for recipients with specific illnesses, such as dementia, Alzheimer’s disease, and cancer, as these illnesses can contribute to varying levels of burden on informal caregivers. In addition, questionnaires and interviews emerged as the predominant methods for data collection in the realm of informal caregiver research. Furthermore, we identified 26 distinct assessment tools specifically tailored for evaluating burden, such as caregiver strain index (CSI). Conclusion: For future studies, we suggest considering the intersectionality of factors contributing to the burden on informal caregivers. This approach could enhance the well-being of both caregivers and older care recipients.

Activity Recognition (AR) is a method to identify a certain activity from the set of actions. It is commonly used to recognize a set of Activities of Daily Living (ADLs), which are performed by the elderly in a smart home environment. AR can be beneficial for monitoring the elder’s health condition, where the information can be further shared with the family members, caretakers, or doctors. Due to the unpredictable behaviors of an elderly person, performance of ADLs can vary in day-to-day life. Each activity may perform differently, which can affect the sequence of the sensor’s raw data. Due to this issue, recognizing ADLs from the sensor’s raw data remains a challenge. In this paper, we proposed an Activity Recognition for the prediction of the Activities of Daily Living using Artificial Intelligence approach. Data acquisition techniques and modified Naive Bayes supervised learning algorithm are used to design the prediction model for ADL. Our experiment results establish that the proposed method can achieve high accuracy in comparison to other well-established supervised learning algorithms.

Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks.

Internet of Things (IoT) has become one of the important components in developing interconnected smart IoT devices. Data generated from the IoT devices increases rapidly due to the increase in the number of connected devices. The current COVID-19 outbreak condition has led to the need of the Healthcare IoT (H-IoT), which can provide an automatic solution for monitoring. Therefore, IoT data is extremely crucial to be analyzed. Artificial Intelligence (AI) has gained a lot of attentions for automatizing applications based on the big data generated from the IoT devices. This chapter presents the current development of AI applications for monitoring the pandemic. The role of IoT, data acquisition, preprocessing, and analysis is also described here. In depth, we elucidate few methods of data preprocessing using conventional techniques and Machine Learning (ML) algorithms, and data analysis using ML and Deep Learning (DL) algorithms. We list all techniques in handling data preprocessing and analysis, and the challenges of IoT and AI in the new way of living during pandemic which is also known as the era of new normal.

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

Wastage of perishable and non-perishable products due to manual monitoring in shopping malls creates huge revenue loss in supermarket industry. Besides, internal and external factors such as calendar events and weather condition contribute to excess wastage of products in different regions of supermarket. It is a challenging job to know about the wastage of the products manually in different supermarkets region-wise. Therefore, the supermarket management needs to take appropriate decision and action to prevent the wastage of products. The fog computing data centers located in each region can collect, process and analyze data for demand prediction and decision making. In this paper, a product-demand prediction model is designed using integrated Principal Compo-nent Analysis (PCA) and K-means Unsupervised Learning (UL) algorithms and a decision making model is developed using State-Action-Reward-State-Action (SARSA) Reinforcement Learning (RL) algorithm. Our proposed method can cluster the products into low, medium, and high-demand product by learning from the designed features. Taking the derived cluster model, decision making for distributing low-demand to high-demand product can be made using SARSA. Experimental results show that our proposed method can cluster the datasets well with a Silhouette score of ≥ 60%. Besides, our adopted SARSA-based decision making model outperforms over Q-Learning, Monte-Carlo, Deep Q-Network (DQN), and Actor-Critic algorithms in terms of maximum cumulative reward, average cumulative reward and execution time.

Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney’s boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT). This study aimed to design an automatic localization model of ADPKD using Artificial Intelligence. A robust detection model using CCT images, image preprocessing, and Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model is designed here. The model is trained and evaluated with 110 CCT images that comprise 10,078 slices. The experimental results showed that our derived detection model outperformed other DL detectors in terms of Average Precision (AP) and mean Average Precision (mAP). We achieved mAP = 94% for image-wise testing and mAP = 82% for subject-wise testing, when threshold on Intersection over Union (IoU) = 0.5. This study proves that our derived automatic detection model can assist radiologist in locating and classifying the ADPKD kidneys precisely and rapidly in order to improve the segmentation task and TKV calculation.

Crowding in city public transportation systems is a primary issue that causes delay in the mobility of passengers. Moreover, scheduled and unscheduled events in a city lead to excess crowding situations at the metro or bus stations. The Internet of Things (IoT) devices could be used for data collection, which are related to crowding situations in a smart city. The fog computing data centers located in different zones of a smart city can process and analyze the collected data to assist the passengers how to commute smoothly with minimum waiting time in the crowded situation. In this paper, Q-learning based passengers assistance system is designed to assist the commuters in finding less crowded bus and metro stations to avoid long queues of waiting. The traffic congestion and crowded situation data are processed in the fog computing data centers. From our experimental results, it is found that our proposed method can achieve higher reward values, which can be used to minimize the passengers’ waiting time with minimum computational delay as compared to the cloud computing platform.

The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.