Natural compounds targeting YAP/TAZ axis in cancer: Current state of art and challenges.

Cancer has become a burgeoning global healthcare concern marked by its exponential growth and significant economic ramifications. Though advancements in the treatment modalities have increased the overall survival and quality of life, there are no definite treatments for the advanced stages of this malady. Hence, understanding the diseases etiologies and the underlying molecular complexities, will usher in the development of innovative therapeutics. Recently, YAP/TAZ transcriptional regulation has been of immense interest due to their role in development, tissue homeostasis and oncogenic transformations. YAP/TAZ axis functions as coactivators within the Hippo signaling cascade, exerting pivotal influence on processes such as proliferation, regeneration, development, and tissue renewal. In cancer, YAP is overexpressed in multiple tumor types and is associated with cancer stem cell attributes, chemoresistance, and metastasis. Activation of YAP/TAZ mirrors the cellular "social" behavior, encompassing factors such as cell adhesion and the mechanical signals transmitted to the cell from tissue structure and the surrounding extracellular matrix. Therefore, it presents a significant vulnerability in the clogs of tumors that could provide a wide window of therapeutic effectiveness. Natural compounds have been utilized extensively as successful interventions in the management of diverse chronic illnesses, including cancer. Owing to their capacity to influence multiple genes and pathways, natural compounds exhibit significant potential either as adjuvant therapy or in combination with conventional treatment options. In this review, we delineate the signaling nexus of YAP/TAZ axis, and present natural compounds as an alternate strategy to target cancer.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis.

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers.

Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.

Prognostic importance of coincidental coronary artery calcification on FDG-PET/CT oncology studies.

Coronary artery calcification (CAC) on body CT imaging is considered a coincidental finding in cancer patients. In order to determine the significance of CAC in cancer patients we evaluated the prognostic utility of CAC detected on oncology FDG-PET/CT studies. A retrospective study was performed of consecutive FDG-PET/CT studies from January to March 2011. CAC was identified on the CT portion of FDG/PET-CT studies. Chart review documented statin use, the Framingham risk score (FRS) (includes age, diabetes, hypertension, dyslipidemia and smoking), the primary malignancy and metastases. The primary end point was a composite of death and cardiovascular (CV) events (non-fatal myocardial infarction (MI), PCI or coronary artery bypass surgery (CABG)). 266 patients had a median follow up of 41 months (95% CI 31-56 months). CAC was noted in 140 patients. Based on CAC, potentially 84 patients would have had a change in statin prescribing (p < 0.01). CAC was associated with the primary end point on univariable and multivariable analysis (OR 2.6 (95% CI 1.42-4.77) (p < 0.01). On univariable Kaplan-Meier survival analysis, CAC was associated with decreased survival only in the absence of metastases (p < 0.01). Cox proportional hazard modelling demonstrated CAC was associated with mortality and cardiac events in patients without metastases, whereas FRS was not (For CAC: HR 1.69 (95% CI 1.22-2.35), p = 0.002). CAC is commonly detected with oncology FDG-PET/CT. In cancer patients CAC was associated with an increased risk of clinical events. CAC reduced survival free time in patients without metastases. CAC might therefore be considered more than a coincidentaloma in patients without metastases.