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A unitary model of drug release dynamics is proposed, assuming that the polymer-drug system can be assimilated into a multifractal mathematical object. Then, we made a description of drug release dynamics that implies, via Scale Relativity Theory, the functionality of continuous and undifferentiable curves (fractal or multifractal curves), possibly leading to holographic-like behaviors. At such a conjuncture, the Schrödinger and Madelung multifractal scenarios become compatible: in the Schrödinger multifractal scenario, various modes of drug release can be "mimicked" (via period doubling, damped oscillations, modulated and "chaotic" regimes), while the Madelung multifractal scenario involves multifractal diffusion laws (Fickian and non-Fickian diffusions). In conclusion, we propose a unitary model for describing release dynamics in polymer-drug systems. In the model proposed, the polymer-drug dynamics can be described by employing the Scale Relativity Theory in the monofractal case or also in the multifractal one.

Background and Objectives : A positive pathological circumferential resection margin is a key prognostic factor in rectal cancer surgery. The point of this prospective study was to see how well different MRI parameters could predict a positive pathological circumferential resection margin (pCRM) in people who had been diagnosed with rectal adenocarcinoma, either on their own or when used together. Materials and Methods : Between November 2019 and February 2023, a total of 112 patients were enrolled in this prospective study and followed up for a 36-month period. MRI predictors such as circumferential resection margin (mCRM), presence of extramural venous invasion (mrEMVI), tumor location, and the distance between the tumor and anal verge, taken individually or combined, were evaluated with univariate and sensitivity analyses. Survival estimates in relation to a pCRM status were also determined using Kaplan-Meier analysis. Results : When individually evaluated, the best MRI predictor for the detection of a pCRM in the postsurgical histopathological examination is mrEMVI, which achieved a sensitivity (Se) of 77.78%, a specificity (Sp) of 87.38%, a negative predictive value (NPV) of 97.83%, and an accuracy of 86.61%. Also, the best predictive performance was achieved by a model that comprised all MRI predictors (mCRM+ mrEMVI+ anterior location+ < 4 cm from the anal verge), with an Se of 66.67%, an Sp of 88.46%, an NPV of 96.84%, and an accuracy of 86.73%. The survival rates were significantly higher in the pCRM-negative group ( p < 0.001). Conclusions : The use of selective individual imaging predictors or combined models could be useful for the prediction of positive pCRM and risk stratification for local recurrence or distant metastasis.

Oncolytic viruses (OVs) are emerging as potential treatment options for cancer. Natural and genetically engineered viruses exhibit various antitumor mechanisms. OVs act by direct cytolysis, the potentiation of the immune system through antigen release, and the activation of inflammatory responses or indirectly by interference with different types of elements in the tumor microenvironment, modification of energy metabolism in tumor cells, and antiangiogenic action. The action of OVs is pleiotropic, and they show varied interactions with the host and tumor cells. An important impediment in oncolytic virotherapy is the journey of the virus into the tumor cells and the possibility of its binding to different biological and nonbiological vectors. OVs have been demonstrated to eliminate cancer cells that are resistant to standard treatments in many clinical trials for various cancers (melanoma, lung, and hepatic); however, there are several elements of resistance to the action of viruses per se. Therefore, it is necessary to evaluate the combination of OVs with other standard treatment modalities, such as chemotherapy, immunotherapy, targeted therapies, and cellular therapies, to increase the response rate. This review provides a comprehensive update on OVs, their use in oncolytic virotherapy, and the future prospects of this therapy alongside the standard therapies currently used in cancer treatment.

This study aimed to investigate the behavior of chitosan/quaternized chitosan fibers in media mimicking wound exudates to understand their capacities as wound dressing. Fiber analysis of the fibers using dynamic vapor sorption proved their ability to adsorb moisture up to 60% and then to desorb it as a function of humidity, indicating their outstanding breathability. Dissolution analyses showed that quaternized chitosan leached from the fibers in water and PBS, whereas only small portions of chitosan were solubilized in water. In media containing lysozyme, the fibers degraded with a rate determined by their composition and pH, reaching a mass loss of up to 47% in media of physiologic pH. Notably, in media mimicking the wound exudate during healing, they adsorbed moisture even when their mass loss due to biodegradation was high, whereas they were completely degraded in the media of normal tissues, indicating bioabsorbable dressing capacities. A mathematical model was constructed, which characterized the degradation rate and morphology changes of chitosan/quaternized chitosan fibers through analyses of dynamics in scale space, using the Theory of Scale Relativity. The model was validated using experimental data, making it possible to generalize it to the degradation of other biopolymeric systems that address wound healing.

Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota's composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.

Aim: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs, by drawing a parallel between statistical and AI algorithms., Methods: Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs from 84 patients and correlated with clinicopathological parameters and survival. The results were compared with those obtained by using various AI algorithms., Results: the Kaplan-Meier univariate survival analysis showed a significant correlation between survival and Claudin-7 intensity in the invasive front ( p = 0.00), a higher expression being associated with a worse prognosis, while Claudin-7 intensity in the tumor core had no impact on survival. In contrast, AI models could not predict the same outcome on survival., Conclusion: The study showed through statistical means that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs, contrary to AI models which could not predict the same outcome, probably because of the small number of patients included in our cohort.

Immunotherapy represents a promising strategy for the treatment of cancer, which functions via the reprogramming and activation of antitumor immunity. However, adverse events resulting from immunotherapy that are related to the low specificity of tumor cell-targeting represent a limitation of immunotherapy's efficacy. The potential of nanotechnologies is represented by the possibilities of immunotherapeutical agents being carried by nanoparticles with various material types, shapes, sizes, coated ligands, associated loading methods, hydrophilicities, elasticities, and biocompatibilities. In this review, the principal types of nanovectors (nanopharmaceutics and bioinspired nanoparticles) are summarized along with the shortcomings in nanoparticle delivery and the main factors that modulate efficacy (the EPR effect, protein coronas, and microbiota). The mechanisms by which nanovectors can target cancer cells, the tumor immune microenvironment (TIME), and the peripheral immune system are also presented. A possible mathematical model for the cellular communication mechanisms related to exosomes as nanocarriers is proposed.