Background: Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods: Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results: The performance measures such as precision, accuracy, recall, F₁ score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion: The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

MiRNA-146a, a single-stranded, non-coding RNA molecule, has emerged as a valuable diagnostic and prognostic biomarker for numerous pathological conditions. Its primary function lies in regulating inflammatory processes, haemopoiesis, allergic responses, and other key aspects of the innate immune system. Several studies have indicated that polymorphisms in miRNA-146a can influence the pathogenesis of various human diseases, including autoimmune disorders and cancer. One of the key mechanisms by which miRNA-146a exerts its effects is by controlling the expression of certain proteins involved in critical pathways. It can modulate the activity of interleukin-1 receptor-associated kinase, IRAK1, IRAK2 adaptor proteins, and tumour necrosis factor (TNF) targeting protein receptor 6, which is a regulator of the TNF signalling pathway. In addition, miRNA-146a affects gene expression through multiple signalling pathways, such as TNF, NF-κB and MEK-1/2, and JNK-1/2. Studies have been carried out to determine the effect of miRNA-146a on cancer pathogenesis, revealing its involvement in the synthesis of stem cells, which contributes to tumourigenesis. In this review, we focus on recent discoveries that highlight the significant role played by miRNA-146a in regulating various defence mechanisms and oncogenesis. The aim of this review article is to systematically examine miRNA-146a’s impact on the control of signalling pathways involved in oncopathology, immune system development, and the corresponding response to therapy.

Cardiovascular diseases (CVDs) are still one of the leading causes of death globally, underscoring the importance of early and right risk prediction for effective preventive measures and therapeutic approaches. This study proposes an innovative hybrid ensemble learning framework that combines state-of-the-art machine learning models and explainable AI approaches to risk prediction for cardiovascular disease. Using a range of publicly accessible datasets, the suggested structure incorporates Gradient Boosting, CatBoost, and Neural Networks using a stacked ensemble architecture, resulting in more robust predictive performance than the constituent models. This is particularly interesting when visualised through techniques such as SHAP values, t-SNE and PCA projections which allows the study to explore the multidimensional aspects of the relationships between key risk factors including systolic/diastolic blood pressure, BMI, cholesterol-glucose ratio, alongside various lifestyle parameters. They build further on model interpretability through explainable AI methods so that clinicians can observe the involvement of each feature in generating the predictions. The hybrid model demonstrated strong predictive performance with an AUC-ROC score of 0.82, and confusion matrices showing a well-balanced classification of both positive and negative cases - achieving Precision: 81%, Recall: 83%, and F1-Score: 82% on the test dataset. The results highlight the potential of ensemble learning for addressing complex medical prediction problems and the need for models to be interpretable to ensure the trustworthiness of AI systems in healthcare settings. These findings provide an exciting opportunity toward better models of CVD risk prediction, potentially providing healthcare stakeholders with interpretable means to target treatments.

One of the fundamental steps toward malaria control is the use of antimalarial drugs. The success of antimalarial treatment can be affected by the presence of drug-resistant populations of Plasmodium falciparum. To assess resistance, we used molecular methods to examine 351 P. falciparum isolates collected from 4 sentinel sites in Mozambique for K13, pfmdr1, pfcrt, and pfdhps polymorphisms and for plasmepsin2 (pfpm2) and pfmdr1 copy numbers. We found multiple copies of pfpm2 in 1.1% of isolates. All isolates carried K13 wild-type alleles (3D7-like), except 4 novel polymorphisms (Leu619Leu, Phe656Ile, Val666Val, Gly690Gly). Prevalence of isolates with pfcrt mutant (K76T) allele was low (2.3%). Prevalence of isolates with pfdhps mutant alleles (A437G and K540E) was >80%, indicating persistence of sulfadoxine/pyrimethamine resistance; however, markers of artemisinin were absent, and markers of piperaquine resistance were low. Piperaquine resistance isolates may spread in Mozambique as dihydroartemisinin/piperaquine drug pressure increases.