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The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model's performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition., (© 2024. The Author(s).)

Alcohol production and consumption have been prohibited in Iran for over four decades, leading to a typical underestimation of its consumption. This study aimed to assess the prevalence of alcohol consumption, its associated factors, and estimate per capita alcohol consumption among Iran's adult population. In this population-based survey, 27,874 adults from across Iran were selected using systematic proportional-to-size cluster sampling. Alcohol consumption was evaluated through a modified Persian version of the STEPS questionnaires from previous studies, applied over different timespans. Per capita consumption was calculated using the quantity-frequency method, expressed in liters of pure alcohol. Adjusted odds ratios were reported for associates of alcohol consumption concerning metabolic risk factors, sociodemographic elements, and lifestyle variables. The prevalence of lifetime alcohol consumption was 6.9% (95% CI 6.5-7.2) in the adult population, with a notable sex difference (males: 13.7% [95% CI 13-14.4]; females: 1.4% [95% CI 1.1-1.6]). The 12 month prevalence was 3.8% (95% CI 3.6-4.1). For individuals aged 18 and older, the per capita alcohol consumption in Iran was 0.12 L. Factors such as being a lifetime smoker, younger, wealthier, and having 7-12 years of education were significantly linked to higher alcohol consumption. Significant associations were also observed between alcohol consumption and having a history of heart attacks (OR = 2.04, 95% CI 1.44-2.89), and physical injuries (OR = 1.88, 95% CI 1.34-2.64). The estimated lifetime and 12-month prevalence of alcohol use in our study were higher among some of the subpopulations. The findings also revealed a complex relationship between alcohol consumption, behavioral risk factors, and metabolic profiles. Consequently, immediate preventive measures tailored to each factor's association with alcohol use are recommended., (© 2024. The Author(s).)

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification., (© 2023. The Author(s).)