Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.

Whole-grain cereal breakfast consumption has been associated with beneficial effects on glucose and insulin metabolism as well as satiety. Pearl millet is a popular ancient grain variety that can be grown in hot, dry regions. However, little is known about its health effects. The present study investigated the effect of a pearl millet porridge (PMP) compared with a well-known Scottish oats porridge (SOP) on glycaemic, gastrointestinal, hormonal and appetitive responses. In a randomised, two-way crossover trial, twenty-six healthy participants consumed two isoenergetic/isovolumetric PMP or SOP breakfast meals, served with a drink of water. Blood samples for glucose, insulin, glucagon-like peptide 1, glucose-dependent insulinotropic polypeptide (GIP), peptide YY, gastric volumes and appetite ratings were collected 2 h postprandially, followed by an ad libitum meal and food intake records for the remainder of the day. The incremental AUC (iAUC2h) for blood glucose was not significantly different between the porridges (P > 0·05). The iAUC2h for gastric volume was larger for PMP compared with SOP (P = 0·045). The iAUC2h for GIP concentration was significantly lower for PMP compared with SOP (P = 0·001). Other hormones and appetite responses were similar between meals. In conclusion, the present study reports, for the first time, data on glycaemic and physiological responses to a pearl millet breakfast, showing that this ancient grain could represent a sustainable alternative with health-promoting characteristics comparable with oats. GIP is an incretin hormone linked to TAG absorption in adipose tissue; therefore, the lower GIP response for PMP may be an added health benefit.

The first diagnostic tool for thyroid disease management is ultrasound. Despite its importance, ultrasound is an extremely subjective procedure that requires a high level of performance skill. Few studies have assessed thyroid ultrasound performance and its effectiveness, particularly the variability between observers in the assessment of ultrasound images. This study evaluated the variability in ultrasound assessments and diagnoses of thyroid nodules between 2 radiologists. In this retrospective study, 75 thyroid nodules in 39 patients were reviewed by 2 experienced radiologists. The nodule composition, margin, shape, calcification, and vasculitis were determined using echogenicity. The study evaluation included these 5 assessments and the final diagnosis. Interobserver variation was determined using Cohen kappa statistics. The interobserver agreements in the interpretation of echogenicity, shape, and margin were fair (κ = 0.21–0.40), whereas there were substantial agreements for vascularity and calcification (κ = 0.62–0.78). The agreements between the observers for individual ultrasound features in this study were the highest for vascularity and the presence/absence of calcification. The interobserver reproducibility for thyroid nodule ultrasound reporting was adequate, but the diagnostic evaluation ability of the observers was inconsistent. The variability in the interpretation of sonographic features could influence the level of suspicion of thyroid malignancy. This study emphasizes the need for consistency in the training of sonographic interpretation of thyroid nodules, particularly for echogenicity, shape, and margin.

Wheat is a globally important crop used as a main staple food in various countries of the world. The current study was conducted with the objective to evaluate the effect of a high temperature (HT) on osmolytes (starch, sucrose, total soluble sugars, total soluble proteins and proline), physiological parameters (Chl-a, Chl-b, photosynthesis rate, transpiration rate and stomatal conductance), antioxidant enzymes (superoxide dismutase, catalase and peroxidase) and agronomic traits (flag leaf area, spike length, and thousand grain weight) during the grain filling and anthesis stages of wheat cultivars (Fakhr-e-Bhakar, Raj-3765, Jimai-22 and Bayraktar-2000) collected from different regions of the world. Separate experiments for both stages were conducted in a glasshouse and treated with two different temperature regimes, i.e., optimum (OT) (24 °C day; 14 °C night) and high temperature (HT) (32 °C day; 22 °C night) in RCBD for two weeks. The data for osmolytes, antioxidant enzymes and physiological contents were collected at days 3, 5, 7, 9 and 13 after the start of plant stress, while the agronomic traits were collected at maturity. The data obtained were subjected to a statistical analysis using the statistix8.1 and R-program. HT stress significantly reduced all the traits except for the membrane damage, transpiration rate, proline and total soluble sugars, whose values increased considerably in the genotype Bayraktar-2000. However, under both regimes of temperature Fakhr-e-Bhakkar showed a high tolerance against HT stress, as revealed by physiological, biochemical and agronomic evaluations. Moreover, correlation, PCA and heat map analyses indicated that all types of traits are significantly interconnected in determining the crop potential to sustain its growth under HT stress.

Alzheimer’s disease is a neurodegenerative disease causing memory loss and brain protein accumulation. Early diagnosis is crucial for clinical trials and patient care. Magnetic resonance imaging (MRI) methods have improved diagnosis and prognosis, but doctors need to interpret images proficiently. Deep learning technology has shown potential in detecting Alzheimer’s disease, but the disease progresses slower in early phases. A new dual-attention convolutional autoencoder model is presented, offering improved detection abilities and potential for real-time use in Alzheimer’s disease diagnosis. The study utilized two datasets: the first ADNI dataset, which includes three classes (MCI, CN, and AD), and the second Alzheimer’s Disease Neuroimaging Dataset, which includes two distinct classes (AD and MCI). We analyze the effectiveness of our proposed model by evaluating key performance metrics such as accuracy, precision, sensitivity, specificity, F1 score, and AUC score. In addition, we utilize cross-validation and mean absolute error to validate our model while also fine-tuning the parameters. Based on experimental data, the proposed model accurately detected Alzheimer’s disease with an accuracy of 0.9902 ± 0.0139. Based on the results, the proposed model demonstrates excellent performance compared to the existing methods described in the literature. The proposed mode achieves precision, sensitivity, and specificity of 0.9882 ± 0.0587, 0.9898 ± 0.0865, 0.9912 ± 0.0872 respectively. The model achieved an AUC score of 0.9992 for MCI and 0.9919 for AD class. Furthermore, the proposed method can enhance the affordability of Alzheimer’s disease diagnostics and increase the rate of early AD detection by facilitating remote healthcare.