Among the long list of age-related complications, Alzheimer’s disease (AD) has the most dreadful impact on the quality of life due to its devastating effects on memory and cognitive abilities. Although a plausible correlation between the phosphatidylinositol 3-kinase (PI3K) signaling and different processes involved in neurodegeneration has been evidenced, few articles reviewed the task. The current review aims to unravel the mechanisms by which the PI3K pathway plays pro-survival roles in normal conditions, and also to discuss the original data obtained from international research laboratories on this topic. Responses to questions on how alterations of the PI3K/Akt signaling pathway affect Tau phosphorylation and the amyloid cascade are given. In addition, we provide a general overview of the association between oxidative stress, neuroinflammation, alterations of insulin signaling, and altered autophagy with aberrant activation of this axis in the AD brain. The last section provides a special focus on the therapeutic possibility of the PI3K/Akt/mTOR modulators, either categorized as chemicals or herbals, in AD. In conclusion, determining the correct timing for the administration of the drugs seems to be one of the most important factors in the success of these agents. Also, the role of the PI3K/Akt signaling axis in the progression or repression of AD widely depends on the context of the cells; generally speaking, while PI3K/Akt activation in neurons and neural stem cells is favorable, its activation in microglia cells may be harmful.

• Lung cancer is the most common and deadliest human malignancies. • The alterations of PI3K/Akt/mTOR pathway are related to lung cancer progression. • PI3K axis regulates proliferation, apoptosis, metastasis, and EMT of lung cancer. • Agents inhibiting components of PI3K axis diminish lung tumor growth and invasion. • Low efficacy and off-target toxicity could be improved by nanoparticle application.

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

DNA methylation is critical for the normal development and functioning of the human brain, such as the proliferation and differentiation of neural stem cells, synaptic plasticity, neuronal reparation, learning, and memory. Despite the physical stability of DNA and methylated DNA compared to other epigenetic modifications, some DNA methylation-based biomarkers have translated into clinical practice. Increasing reports indicate a strong association between DNA methylation profiles and various clinical outcomes in neurological diseases, making DNA methylation profiles valuable as novel clinical markers. In this review, we aim to discuss the latest evidence concerning DNA methylation alterations in the development of neurodegenerative, neurodevelopmental, and neuropsychiatric diseases. We also highlighted the relationship of DNA methylation alterations with the disease progression and outcome in many neurological diseases such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, frontotemporal dementia, and autism.