The objective of this paper was to evaluate the efficacy of a hypofractionated radiation protocol for feline facial squamous cell carcinoma (SCC). Twenty-five histologically confirmed SCCs in 15 cats were treated with four fractions of 7.6-10Gy each, with 1 week intervals. The equipment used was a linear accelerator Clinac 2100 delivering electron beam of 4 or 6MeV, and a bolus of 5 or 10mm was used in all lesions. Of the lesions, 44% were staged as T4, 16% as T3, 8% as T2 and 32% as T1. Of the irradiated lesions, 40% had complete response, 12% had partial response and 48% had no response (NR) to the treatment. For T1 tumors, 62.5% had complete remission. Mean overall survival time was 224 days. Owners requested euthanasia of cats having NR to the treatment. Mean disease free time was 271 days. Side effects observed were skin erythema, epilation, ulceration and conjunctivitis, which were graded according to Veterinary Radiation Therapy Oncology Group (VRTOG) toxicity criteria. Response rates found in this study (52%) were lower when compared to other protocols, probably due to technique differences, such as fractionation schedule, bolus thickness and energy penetration depth. However, the hypofractionated radiation protocol was considered safe for feline facial SCC. Modifications of this protocol are being planned with the objective of improving the cure rates in the future.

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In this work, we propose a mathematical model that describes how the mesothalamic dopamine pathway modulates the attentional focus via the thalamocortical loop, and how mesothalamic dopamine alterations can promote inattention symptoms in patients with Parkinson's disease (PD) and attention deficit hyperactivity disorder (ADHD). We model the thalamocortical loop with a neuronal network where each thalamic neuron is described by a system of coupled differential equations reflecting neurophysiological properties. The computational simulations reflect neurochemical features of PD and ADHD. Our results suggest that the mesothalamic dopamine hypoactivity causes difficulties in attentional shifting. Conversely, the mesothalamic dopamine hyperactivity hinders the attentional focus consolidation. Furthermore, regardless of the amount of mesothalamic dopamine activity, the mesocortical dopamine hypoactivity leads to loss of attentional focus. Finally, we identify a unique neuronal mechanism underlying attention deficits in PD and ADHD and relate different inattention symptoms in ADHD to different dopaminergic levels in the brain circuit modeled.

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

RESUMEN La Atención Domiciliaria se presenta en Brasil como una modalidad de atención a la salud con potencial substitutivo y capacidad de ofrecer cuidado con calidad, garantizando a la familia y al usuario la oportunidad de manejar salud y enfermedad desde una óptica innovadora. Este artículo presenta una reflexión sobre el analizador-disputa de planes de cuidado-identificado en uno de los estudios de caso de la investigación sobre implantación de la atención domiciliaria en el Sistema Único de Salud (SUS). El foco del análisis fue la micropolítica del trabajo en salud en la producción del cuidado, involucrando al equipo y a las/los cuidadores.

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Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system’s development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system’s dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.