Despite the great advancements made in cancer treatment, there are still many unsatisfied aspects, such as the wide palette of side effects and the drug resistance. There is an obvious increasing scientific attention towards nature and what it can offer the human race. Natural products can be used to treat many diseases, of which some plant products are currently used to treat cancer. Plants produce secondary metabolites for their signaling mechanisms and natural defense. A variety of plant-derived products have shown promising anticancer properties in vitro and in vivo. Rather than recreating the natural production environment, ongoing studies are currently setting various strategies to significantly manipulate the quantity of anticancer molecules in plants. This review focuses on the recently studied secondary metabolite agents that have shown promising anticancer activity, outlining their potential mechanisms of action and pathways.

The COVID-19 pandemic has significantly accelerated the adoption of telemedicine and digital health technologies, revealing their immense potential in managing cancer patients effectively. This article explores the impact of recent technological developments and widened consumer perspectives on personalised healthcare and patient awareness, particularly in oncology. Smartphones and wearable devices have become integral to daily life, promoting healthy lifestyles and supporting cancer patients through remote monitoring and health management. The widespread use of these devices presents an unprecedented opportunity to transform clinical trials and patient care by offering convenient and accessible means of collecting health data continuously and non-invasively. However, to fully harness their potential, it is crucial to establish standardised methods for measuring patient metrics to ensure data reliability and validity. This article also addresses the challenges of integrating these technologies into clinical practice, such as cost, patient and professional reluctance, and technological oversaturation. It emphasises the need for continuous innovation, the development of robust digital infrastructures, and the importance of fostering a supportive environment to integrate these advancements permanently. Ultimately, the convergence of technological innovation and personalised healthcare promises to enhance patient outcomes, improve quality of life, and revolutionise cancer management in the post-COVID era.

The most frequent primary brain malignancy is glioma. Alterations in several adhesion G-protein-coupled receptors (aGPCRs) are present in cancer as they regulate adhesion, migration, and guidance. Epidermal growth factor (EGF) module-containing mucin-like receptor 2 (EMR2) is included in group II GPCRs and functionally in a family of brain angiogenesis inhibitor molecules (BAIs). Recent studies have shown that BAIs regulate phagocytosis and synaptogenesis, and their extracellular domain inhibits angiogenesis and tumor growth. In neoplastic processes, EMR2 appears to play a role in disease aggressiveness, patient survival rates, and tumor grade. This review summarizes the EMR2 involvement in cellular mechanisms and pathologies, particularly in cancer. We searched the Pubmed Central, Google Scholar and Scopus databases for terms “EMR2” and “glioma”. The initial search yielded a total of 92 results. After excluding studies not written in English, based on design, and excluding duplicates and non-relevant studies, we included 38 studies in the review. EMR2 was shown to be expressed in various histologic grades of gliomas and to be linked to the PI3K pathway, as both are upregulated in glioblastoma after bevacizumab therapy. The PI3K-Akt pathway is involved in tumorigenesis, and upregulation of EMR2 may in turn upregulate PI3K, leading to increased tumor invasiveness. Indeed, overexpression of EMR2 was associated with the mesenchymal glioblastoma subtype, tumor invasiveness, and poor survival. EMR2 also regulates neutrophil function by producing reactive oxygen species (ROS) and degranulation. Possible therapeutic approaches have been studied, such as the stimulation of microglia and monocytes to inhibit tumor-initiating cells by down-regulating the EMR2 gene or through an antibody against EMR2. The current review summarizes the knowledge about the EMR2 receptor that can serve as motivation for future studies on its role in the clinical evolution and tumor biology of gliomas in order to find new modulator therapeutic approaches.

Glioblastoma (GBM) is a severe brain cancer in which angiogenesis is controlled by G protein-coupled receptors (GPCRs), such as Epidermal Growth Factor Latrophilin and seven transmembrane domain-containing protein 1 (ELTD1), which are crucial for tumor progression. ELTD1 is an understudied GPCR with a broad expression profile in various tissues, including the human brain, especially in the cerebral cortex. It plays a significant role in angiogenesis and tumorigenesis and is regulated by interconnected VEGF and DLL4/Notch pathways. ELTD1 also modulates the JAK/STAT3/HIF-1α signaling axis, affecting the response of cells to low-oxygen conditions and promoting cell proliferation. However, their specific ligands and functional mechanisms remain unclear. ELTD1 expression is associated with different outcomes in various cancers. For example, in GBM, higher ELTD1 levels are linked to more mature and less leaky blood vessels, potentially enhancing drug delivery and therapeutic success. It also has divergent prognostic implications in renal, ovarian, and colorectal cancer. Additionally, ELTD1 overexpression in central nervous system endothelial cells suggests that it is a potential biomarker for multiple sclerosis. Therapeutically, blocking ELTD1 inhibits vessel formation, possibly slowing tumor growth. Initial therapies used polyclonal antibodies, but the shift has been towards more targeted monoclonal antibodies, particularly in preclinical glioma models. This review aimed to translate these insights into effective clinical treatments. However, several gaps remain in our knowledge regarding ELTD1 ligands and their potential involvement in other physiological or pathological processes that future research can address to elucidate the role of ELTD1 in cancer, through angiogenesis and other intracellular pathways.

Introduction The COVID-19 pandemic emerged globally in 2019, exposing healthcare vulnerabilities. This study delves into the impact of COVID-19 on cancer patients, a high-risk group with increased susceptibility and mortality rates. Recent research underscores cancer patients' vulnerability to severe disease, often due to compromised immunity. Materials and methods This retrospective study analyzed data from 474 adult COVID-19 patients, admitted between March 2020 and July 2023. Patients were categorized into two groups: those with a medically recorded oncological disease (237) and those without any malignant history (237). Demographic and hematologic analysis aim to unveil COVID-19 impact on individuals with cancer history. Results Statistically significant differences in blood parameters highlighted distinctions, with cancer patients exhibiting higher creatinine, leukocyte, and D Dimers levels as well as lower hemoglobin, neutrophile, lymphocyte, and Serum Glutamate-Pyruvate Transaminase (SGPT) levels. Non-significant differences in certain parameters prompted a nuanced exploration of metabolic and coagulation variations. Conclusion This study unveils global COVID-19 effects on cancer patients, emphasizing clinical and laboratory differences. Findings underscore the imperative need for targeted interventions and enhanced support for cancer patients during the pandemic. Study limitations stress careful interpretation, urging further exploration of COVID-19 and cancer interplay.

Introduction: This study aims to construct a predictive model that classifies the severity of coronavirus disease 2019 (COVID-19) in cancer patients by analyzing clinical, radiological, and demographic data. The goal is to assist healthcare providers in identifying high-risk patients and ensuring optimal resource allocation. Materials and methods: Data from 237 cancer patients diagnosed with COVID-19 was utilized to forecast disease severity based on various predictors. Key factors included cancer type and stage, intensive care unit admission, radiological assessments, ventilation status, obesity, and systemic inflammatory response syndrome (SIRS). The performance of multiple machine learning (ML) models, including Error-Correcting Output Codes (ECOC) frameworks built on Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Naive Bayes, Discriminant Analysis and an ensemble bagging method, was evaluated using 10-fold cross-validation. Model accuracy and receiver operating characteristic (ROC) curve scores served as the primary evaluation metrics. Results: KNN and ensemble bagging emerged as the most effective models, achieving accuracy rates of 100% and 98.3%, respectively, along with high area under the curve (AUC) values. These models excelled at identifying severe cases associated with intensive care unit (ICU) admission, ventilation, and metastatic cancer. Decision Trees demonstrated satisfactory performance with an accuracy of 82.55%, while SVM and Discriminant Analysis yielded moderate accuracy (64.26% and 65.11%). Naive Bayes underperformed, achieving only 40% accuracy, largely due to its assumption of feature independence. Conclusions: KNN and ensemble bagging models successfully predicted severe COVID-19 outcomes in cancer patients by capturing intricate relationships between factors such as ICU stays and ventilator support. Decision Trees also showed promise, but Naive Bayes was less reliable due to its simplified approach. The findings underscore the importance of non-linear models for predicting complex clinical outcomes. Despite robust evaluation through 10-fold cross-validation, the possibility of overfitting—particularly with KNN’s flawless accuracy—remains a concern. Additionally, the absence of external validation constrains the broader applicability of these findings to different patient populations.