Lyapunov exponent (LE), Poincaré map, bifurcation and Hamiltonian diagrams are used to analyze the dynamical behavior of the non-Hamiltonian conservative chaotic system. The frequency and initial values of the system have an extensive variable range. Through the mechanism adjustment, instead of trial-and-error, the maximum LE of the system can even reach an incredible value of 963. An analog circuit is implemented to verify the existence of the non-Hamiltonian conservative chaotic system, which overcomes the challenge that a little bias will lead to the disappearance of conservative chaos.Substantial progress in recent years has dramatically increased our knowledge of the molecular basis of cancer, revealing new potential therapeutic targets and paving the way for effective personalised medicine for the treatment of many tumour types. However, pancreatic cancer has been lagging behind in this success and continues to be one of the most lethal solid malignancies. Its molecular heterogeneity and the unselected design of the majority of clinical trials to date can in part explain the reason for our failure to make a significant change in the survival outcomes for patients with pancreatic cancer. A changing paradigm in drug development is required to validate the new molecular taxonomy and to rapidly translate preclinical discovery into clinical trials. Here, we review the molecular subtyping of pancreatic cancer, the challenges in identifying effective treatment regimens according to defined low-prevalence molecular subgroups and we illustrate a new model of translational therapeutic development that was established in the U.K. (Precision-Panc) as a potentially effective solution to improve outcomes for patients with pancreatic cancer.Nearly half of patients with advanced and metastatic melanomas harbor a BRAF mutation. Vemurafenib (VEM), a BRAF inhibitor, is used to treat such patients, however, responses to VEM are very short-lived due to intrinsic, adaptive and/or acquired resistance. In this context, we present the action of the B-Raf serine-threonine protein kinase inhibitor (vemurafenib) on the glycans structure and metallomics profiles in melanoma cells without (MeWo) and with (G-361) BRAF mutations. The studies were performed using α1-acid glycoprotein (AGP), a well-known acute-phase protein, and concanavalin A (Con A), which served as the model receptor. The detection of changes in the structure of glycans can be successfully carried out based on the frequency shifts and the charge transfer resistance after interaction of AGP with Con A in different VEM treatments using QCM-D and EIS measurements. These changes were also proved based on the cell ultrastructure examined by TEM and SEM. The LA-ICP-MS studies provided details on the metallomics profile in melanoma cells treated with and without VEM. The studies evidence that vemurafenib modifies the glycans structures and metallomics profile in melanoma cells harboring BRAF mutation that can be further implied in the resistance phenomenon. Therefore, our data opens a new avenue for further studies in the short-term addressing novel targets that hopefully can be used to improve the therapeutic regiment in advanced melanoma patients. The innovating potential of this study is fully credible and has a real impact on the global patient society suffering from advanced and metastatic melanomas.COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.Breast cancer is the most common type of cancer. In the developmental stages of breast cancer, estrogens are strongly involved. As estrogen synthesis is regulated by the enzyme aromatase, targeting the activity of this enzyme represents a therapeutic option. The pineal hormone melatonin may exert a suppressive role on aromatase activity, leading to reduced estrogen biosynthesis.  https://www.selleckchem.com/products/gw4869.html  A melatonin-mediated decrease in the expression of aromatase promoters and associated genes would provide suitable evidence of this molecule's efficacy as an aromatase inhibitor. Furthermore, melatonin intensifies radiation-induced anti-aromatase effects and counteracts the unwanted disadvantages of chemotherapeutic agents. In this manner, this review summarizes the inhibitory role of melatonin in aromatase action, suggesting its role as a possible oncostatic molecule in breast cancer.Traumatic brain injury (TBI) has been described to be man's most complex disease, in man's most complex organ. Despite this vast complexity, variability, and individuality, we still classify the severity of TBI based on non-specific, often unreliable, and pathophysiologically poorly understood measures. Current classifications are primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. Brain imaging results have also been used, yet there are multiple ways of doing brain imaging, at different timepoints in this very dynamic injury. Severity itself is a vague concept. All prediction models based on combining variables that can be assessed during the acute phase have reached only modest predictive values for later outcome. Yet, these early labels of severity often determine how the patient is treated by the healthcare system at large. This opinion paper examines the problems and provides caveats regarding the use of current severity labels and the many practical and scientific issues that arise from doing so.