Neuronal Coherence Agent for Shared Intentionality : A Hypothesis of Neurobiological Processes Occurring during Social Interaction

Funding Information: No foundation that funded this research. Publisher Copyright: © 2021 by the author.The present interdisciplinary study discusses the physical foundations of the neurobiological processes occurring during social interaction. The review of the literature establishes the difference between Intentionality and Intention, thereby proposing the theoretical basis of Shared Intentionality in humans. According to the present study, Shared Intentionality in humans (Goal-directed coherence of biological systems), which is the ability among social organisms to instantly select just one stimulus for the entire group, is the outcome of evolutionary development. Therefore, this interaction modality should be the preferred, archetypal, and most propagated modality in organisms, attributed to the Model of Hierarchical Complexity Stage 3. This characteristic of biological systems facilitates the training of the new members of the group and also ensures efficient cooperation among the members of the group without requiring communication. In humans, Shared Intentionality contributes to the learning of newborns. The neurons of a mature organism may teach the neonate neurons regarding the fitting reactions to the excitatory inputs of the specific structural organization. This enables the neonate neurons to develop a Long-Term Potentiation that links particular stimuli with specific embodied sensorimotor neural networks. The present report discusses three possible neuronal coherence agents that could involve quantum mechanisms in cells, thereby enabling the distribution of the quality of goal-directed coherence in biological systems (Shared Intentionality in humans). Recently reported case studies conducted online with the task of conveying the meaning of numerosity to the children of age 18–33 months revealed the occurrence of Shared Intentionality in mother-child dyads in the absence of sensory cues between the two, which promoted cognitive development in the children. The findings of these case studies support the concept of physical foundations and the hypothesis of the neurophysiological process of social interaction proposed in the present study.publishersversionPeer reviewe

Is there an optimal diet for weight management and metabolic health?

Individuals can lose body weight and improve health status on a wide range of energy (calorie) restricted dietary interventions. In this paper, we have reviewed the effectiveness of the most commonly utilized diets, including low-fat, low-carbohydrate and Mediterranean approaches in addition to commercial slimming programmes, meal replacements and newly-popularized intermittent fasting diets. We also consider the role of artificial sweeteners in weight management. Low-fat diets tend to improve LDL-cholesterol most, whilst lower-carbohydrate diets may preferentially improve triglycerides and HDL-cholesterol, however differences between diets are marginal. Weight loss improves almost all obesity related co-morbidities and metabolic markers, regardless of the macronutrient composition of the diet, but individuals do vary in preferences and ability to adhere to different diets. Optimizing adherence is the most important factor for weight loss success, and this is enhanced by regular professional contact and supportive behavioral change programs. Maintaining weight losses in the long-term remains the biggest challenge, and is undermined by an ‘obesogenic’ environment and biological adaptations that accompany weight loss

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Bayesian Design in Clinical Trials

In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented

Hodological Resonance, Hodological Variance, Psychosis, and Schizophrenia: A Hypothetical Model

Schizophrenia is a disorder with a large number of clinical, neurobiological, and cognitive manifestations, none of which is invariably present. However it appears to be a single nosological entity. This article considers the likely characteristics of a pathology capable of such diverse consequences. It is argued that both deficit and psychotic symptoms can be manifestations of a single pathology. A general model of psychosis is proposed in which the informational sensitivity or responsivity of a network (“hodological resonance”) becomes so high that it activates spontaneously, to produce a hallucination, if it is in sensory cortex, or another psychotic symptom if it is elsewhere. It is argued that this can come about because of high levels of modulation such as those assumed present in affective psychosis, or because of high levels of baseline resonance, such as those expected in deafferentation syndromes associated with hallucinations, for example, Charles Bonnet. It is further proposed that schizophrenia results from a process (probably neurodevelopmental) causing widespread increases of variance in baseline resonance; consequently some networks possess high baseline resonance and become susceptible to spontaneous activation. Deficit symptoms might result from the presence of networks with increased activation thresholds. This hodological variance model is explored in terms of schizo-affective disorder, transient psychotic symptoms, diathesis-stress models, mechanisms of antipsychotic pharmacotherapy and persistence of genes predisposing to schizophrenia. Predictions and implications of the model are discussed. In particular it suggests a need for more research into psychotic states and for more single case-based studies in schizophrenia

A population model of deep brain stimulation in movement disorders from circuits to cells

Copyright © 2020 Yousif, Bain, Nandi and Borisyuk.For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson's disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient's brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.Peer reviewe

Protein nanoparticle: A unique system as drug delivery vehicles

Over the past three decades, there has been a considerable research interest in the area of developing drug delivery using nanoparticles (NPs) as carriers for small and large molecules. Targeting delivery ofdrugs to the diseased lesions is one of the most important aspects of drug delivery system. They have been used in vivo to protect the drug entity in the systemic circulation, restrict access of the drug to thechosen sites and to deliver the drug at a controlled and sustained rate to the site of action. Various polymers have been used in the formulation of nanoparticles for drug delivery research to increase therapeutic benefit, while minimizing side effects. This review presents the most outstandingcontributions in the field of protein nanoparticles used as drug delivery systems. Methods of preparation of protein nanoparticles, characterization, drug loading, release and their applications in delivery of drug molecules and therapeutic genes are considered