The central nervous system (CNS) represents a complex network of different cells, such as neurons, glial cells, and blood vessels. In tumor pathology, glial cells result in the highest number of cancers, and glioblastoma (GB) is considered the most lethal tumor in this region. The development of GB leads to the infiltration of healthy tissue through the interaction between all the elements of the brain network. This results in a GB microenvironment, a complex peritumoral hallo composed of tumor cells and several non-tumor cells (e.g., nervous cells, stem cells, fibroblasts, vascular and immune cells), which might be the principal factor for the ineffective treatment due to the fact that the microenvironment modulates the biologic status of the tumor with the increase in its evasion capacity. Crosstalk between glioma cells and the brain microenvironment finally inhibits the beneficial action of molecular pathways, favoring the development and invasion of the tumor and its increasing resistance to treatment. A deeper understanding of cell–cell interactions in the tumor microenvironment (TME) and with the tumor cells could be the basis for a more efficient therapy.

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycaemia. Plant-based therapies are characterised by low tissue bioavailability. The study aimed to investigate the effect of gold nanoparticles phytoreduced with Rutin (AuNPsR), as a possible solution. Insulin, Rutin, and AuNPsR were administered to an early, six-week rat model of DR and CA. Oxidative stress (MDA, CAT, SOD) was assessed in serum and eye homogenates, and inflammatory cytokines (IL-1 beta, IL-6, TNF alpha) were quantified in ocular tissues. Eye fundus of retinal arterioles, transmission electron microscopy (TEM) of lenses, and histopathology of retinas were also performed. DM was linked to constricted retinal arterioles, reduced endogen antioxidants, and eye inflammation. Histologically, retinal wall thickness decreased. TEM showed increased lens opacity and fibre disorganisation. Rutin improved retinal arteriolar diameter, while reducing oxidative stress and inflammation. Retinas were moderately oedematous. Lens structure was preserved on TEM. Insulin restored retinal arteriolar diameter, while increasing MDA, and amplifying TEM lens opacity. The best outcomes were obtained for AuNPsR, as it improved fundus appearance of retinal arterioles, decreased MDA and increased antioxidant capacity. Retinal edema and disorganisation in lens fibres were still present.

Background: Prostate cancer (PCa) represents a matter at the forefront of healthcare, being divided into clinically significant (csPCa) and indolent PCa based on prognostic and treatment options. Although multi-parametric magnetic resonance imaging (mpMRI) has enabled significant advances, it cannot differentiate between the aforementioned categories; therefore, in order to render the initial diagnosis, invasive procedures such as transrectal prostate biopsy are still necessary. In response to these challenges, artificial intelligence (AI)-based algorithms combined with radiomics features offer the possibility of creating a textural pixel pattern-based surrogate, which has the potential of correlating the medical imagery with the pathological report in a one-to-one manner. Objective: The aim of the present study was to develop a machine learning model that can differentiate indolent from csPCa lesions, as well as individually classifying each nodule into corresponding ISUP grades prior to prostate biopsy, using textural features derived from mpMRI T2WI acquisitions. Materials and Methods: The study was conducted in 154 patients and 201 individual prostatic lesions. All cases were scanned using the same 1.5 Tesla mpMRI machine, employing a standard protocol. Each nodule was manually delineated using the 3D Slicer platform (version 5.2.2) and textural parameters were derived using the PyRadiomics database (version 3.1.0). We compared three machine learning classification models (Random Forest, Support Vector Machine, and Logistic Regression) in full, partial and no correlation settings, in order to differentiate between indolent and csPCa, as well as between ISUP 2 and ISUP 3 lesions. Results: The median age was 65 years (IQR: 61–69), the mean PSA value was 10.27 ng/mL, and 76.61% of the segmented lesions had a PI-RADS score of 4 or higher. Overall, the highest performance was registered for the Random Forest model in the partial correlation setting, differentiating between indolent and csPCa and between ISUP 2 versus ISUP 3 lesions, with accuracies of 88.13% and 82.5%, respectively. When the models were trained on combined clinical data and radiomic signatures, these accuracies increased to 91.11% and 91.39%, respectively. Conclusions: We developed a machine learning decision support tool that accurately predicts the ISUP grade prior to prostate biopsy, based on the textural features extracted from T2 MRI acquisitions.

The global incidence of cutaneous squamous cell carcinoma (cSCC), a prevalent and aggressive skin cancer, has risen significantly, posing a substantial public health challenge. This study investigates the tumor microenvironment (TME) of cSCC by focusing on the spatial distribution patterns of immune and vascular markers (CD31, CD20, CD4, and CD8) using fractal dimension (FD) analysis. Our analysis encompassed 141 cases, including 100 invasive cSCCs and 41 specimens with pre-invasive lesions exclusively, and the rest were peripheral pre-invasive lesions from the invasive cSCC class. The FD values for each marker were computed and compared between pre-invasive and invasive lesion classes. The results revealed significant differences in FD values between the two classes for CD20 and CD31 markers, suggesting distinct alterations in B cell distribution and angiogenic activity during cSCC progression. However, CD4 and CD8 markers did not exhibit significant changes individually. Still, the CD4/CD8 ratio showed a significant difference, suggesting a potential shift in the balance between T helper and cytotoxic T cell responses, impacting the immune landscape as lesions progressed from pre-invasive to invasive stages. These findings underscore the complexity and heterogeneity of the TME in cSCC and highlight the potential of FD analysis as a quantitative tool for characterizing tumor progression. Further research is needed to elucidate the implications of these differences in the clinical management of cSCC.

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycemia. Plant-based therapies are characterized by low bioavailability in ocular tissues. The study aimed to investigate the effect of gold nanoparticles phytoreduced with Rutin (AuNPsR), as a possible solution. Insulin, Rutin, and AuNPsR, were administered to an early, six-week rat model of DR and CA. Oxidative stress (MDA, CAT, SOD) was assessed in serum and eye homogenates, and inflammatory cytokines (IL-1 beta, IL-6, TNF alpha) were quantified in ocular tissues. Fundus of retinal arterioles, retina histopathology and lens transmission electron microscopy (TEM), were also performed. DM was linked to constricted retinal arterioles, reduced endogen antioxidants, and eye inflammation. Histologically, retinal wall thickness decreased. TEM showed increased lens opacity and fiber disorganization. Rutin improved retinal arteriolar diameter, while reducing oxidative stress and inflammation. Retinas were moderately edematous. Lens structure was preserved on TEM. Insulin restored retinal arteriolar diameter, while increasing MDA, and amplifying TEM lens opacity. The best outcomes were obtained for AuNPsR, as it improved fundus appearance of retinal arterioles, decreased MDA formation and increased antioxidant capacity. Retinal edema and disorganization in lens fibers were still present.

The global incidence of cutaneous squamous cell carcinoma (cSCC), a prevalent and aggressive skin cancer, has risen significantly, posing a substantial public health challenge. This study investigates the tumor microenvironment (TME) of cSCC by focusing on the spatial distribution patterns of immune and vascular markers (CD31, CD20, CD4, and CD8) using fractal dimension (FD) analysis. Our analysis encompassed 141 cases, including 100 invasive cSCC and 41 specimens with pre-invasive lesions exclusively, and the rest being peripheral pre-invasive lesions from the invasive cSCC class. The FD values for each marker were computed and compared between pre-invasive and invasive lesion classes. The results revealed significant differences in FD values between the two classes for CD20 and CD31 markers, suggesting distinct alterations in B cell distribution and angiogenic activity during cSCC progression. However, CD4 and CD8 markers did not exhibit significant changes individually. Still, the CD4/CD8 ratio showed a significant difference, indicating a potential shift in the immune landscape as lesions progress from pre-invasive to invasive stages. These findings underscore the complexity and heterogeneity of the TME in cSCC and highlight the potential of FD analysis as a quantitative tool for characterizing tumor progression. Further research is needed to elucidate the implications of these differences in the clinical management of cSCC.