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The Recommended Dietary Allowances (RDA) by the National Academy of Sciences are revised approximately every five years. The RDA are compromise opinions of highly qualified nutritional scientists based on interpretations of available data. As with any interpretations of available data. As with any, interpretations and opinions not found on definitive information, they are subject to challenges. The RDA for protein for 7-to 9-uear-old children have been adjusted downward from 60 g in 1958 to 36 g in 1974, a 40% reduction. Data from our laboratories have shown positive apparent nitrogen balances on intakes as low as 18 g daily when no allowances were made for integumental and other nitrogen losses, however, based on accumulative data over several years we calculate the protein requirement to be 45 g daily from a typical American diet. If a safety factor of 30% is added the allowance would become 58.5 g. Currently the RDA for protein for the 7- to 10-year-old child supplies 6% of the RDA for Calories which contrasts to 8.30 and 9.20% for adult males and females, respectively. For comparison, energy from protein as a percentage of total energy for some common foods are: white bread, 12%; corn meal 10%; white rice 7%; and wheat flour 13%.

The water content of paper and its time dependence are of great importance for the use of paper and cardboard as packaging materials. The physical process of water sorption and the mechanisms of water "binding" in paper, as fundamental principles of the kinetics, are discussed. Water exists in up to four different "states" (i.e. domains of mobility) in paper: the first layer at the primary sites on the surface of the fibers; the multimolecular layers, with less "binding" energy, on these inner surfaces; condensed water in the voids; and dissolved water in the cellulose fibers. All these "states" influence the shape of the sorption isotherm and correspond to a different water content and to different transport mechanisms. The various types of water "binding" on paper were studied by measuring the transversal relaxation rate, T2, using a pulsed Nuclear Magnetic Resonance spectrometer (minispec pc 120). In addition, a non-destructive, fast method of determining the water content in packaging materials with NMR spectroscopy is described.

Various lipids, present as thin films on polar filter paper supports, were evaluated for resistance to the transmission of water vapor (rH2O) and oxygen (rO2). Beeswax exhibited the largest r(H2O), followed in order by fully-hydrogenated soy-rapeseed oil, stearyl alcohol, acetylated monoglycerides, hexatriacontane, tristearin, and stearic acid. Most of the lipids exhibited negative activation energies, E, for resistance to transmission of water vapor and positive Es for resistance to transmission of oxygen. The type of lipid support (hydrophobic or hydrophilic) also influenced E for resistance to water vapor transmission. Differences in r(H2O) for the various lipids, comparative r(H2O) and r(O2) values, and the temperature dependence of these values can be explained, in part, by the degree of hydrophilicity of the lipid molecule. Tempering at 48 degrees C of stearyl alcohol caused a substantial decrease in its permeability to oxygen and water vapor. The polymorphic form of a blend of fully-hydrogenated soybean and rapeseed oil had a moderate influence on its permeability to oxygen and water vapor. This information will be useful for formulating lipid-containing films with controlled barrier properties to the passage of water vapor and oxygen.

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.

One of the critical Global challenges in achieving the UN Sustainable Development Goals 3 Good Health and Well Being is optimizing drug discovery and translational research for unmet medical need in both communicable and non-communicable diseases. Recently, the WHO reports there has been a shift from communicable diseases to non-communicable diseases with respect to being the leading cause of death globally and particularly in low- and middle-income countries such as South Africa. Hence, there is current drive to establish functional precision medicine program that addresses the unmet medical need using high throughput drug sensitivity and drug repurposing platform. Here, this paper serves as a perspective to showcase the recent development in high throughput drug sensitivity screening platform for the cancer precision medicine. We also elaborate on the benefit and applications of high-throughput drug screening platform for Precision Medicine. From a future perspective, this paper aims to showcase the possibility to integrate existing high-throughput drug sensitivity screening platform with the newly developed platform technologies such as microfluidics based single cell drug screening.

Anatomical knowledge, such as gross anatomy, neuroanatomy, histology, and embryology, involve three-dimensional (3D) learning and interpretation. Virtual 3D models especially have been used in the anatomical sciences both as a supplement to traditional anatomical education with cadaveric specimens and as a substitute for cadavers at institutions that do not utilize human donors for educational purposes. This paper discusses the methods used to assess the models' validation and accuracy, as well as suggestions for the models' improvement. This paper also aims to describe students' learning of anatomy using stereoscopic 3D models and provides a summary of the results from the literature concerning students' performance outcomes using virtual stereoscopic models as well as both students' and experts' perceptions of their utilization. There have been mixed results in the literature concerning the effectiveness of virtual 3D anatomical models in general, but there is limited research on stereoscopic anatomical models specifically. Stereoscopic anatomical models have shown to improve the learning of students, particularly for the students with low spatial ability, and they have the potential to enhance students' understanding of 3D relationships.

Dementia describes a group of symptoms linked with cognitive decline. Alzheimer's disease (AD) is the most common form of dementia. Identifying accurate diagnostic biomarkers is a key goal. Technological advancements result in the generation of an ever-increasing volume of data. An interdisciplinary field of bioinformatics, known as machine learning (ML), allows scientists to explore and analyse said data. ML is broadly categorized into two groups: (i) unsupervised learning and (ii) supervised learning. This paper focuses on supervised learning methodologies. These approaches are not only helpful for biomarker discovery but for neuroimaging studies as well since they are able to analyse many variables simultaneously and to identify patterns in neuroimaging data. Furthermore, this paper lists several other computational approaches used for dementia care.

Taking as a starting point that the only proving method in mathematics is logic, as, for example, is used in the logical setup of the paper (arguments, i.e., premises leading to conclusions, logical inferences), we describe a new tool for analysis and validation of publications in the field of bioinformatics. In striking contrast to the abundance of statistical programs and packages available to bioinformatics researchers, no such logical tool is available to them. Therefore the creation of such a tool is necessary; a form of expert system that may do the following procedures: Evaluate papers giving a score of logical consistency and completeness (i.e., how many conclusions were investigated in comparison to all possible conclusions based on the set of premises used by the researchers). Help researchers and academics better plan their protocols by automating the process of analysis of the premises given. Be able to locate and analyze arguments directly from the text of the scientific paper, from certain parts of it (e.g., the "conclusions" section, or even from the whole text, based on advanced AI algorithms). Provide a new standard for future publications in any form and, later, in any scientific field.

In type 1 diabetes mellitus (T1DM) pancreas beta-cells do not segregate insulin. This hormone is necessary to convert glucose into energy. Thus, people with diabetes are required to maintain blood glucose (BG) levels within a safe range using external control solutions. Insulin recommender systems (IRS's) provide the precise amount of insulin to the patient when needed, reducing the effects of the disease. The goal of this paper is to review and summarize all current proposals of IRS's and, with this purpose, 70 papers have been analysed. The analysis of the works was performed taking the following aspects into account: (i) technology of the recommendation process, (ii) control procedures, (iii) complementary processes, (iv) hardware, testing and assessment, (v) pricing and (vi) results. Those are our main conclusions after the review: There is a lack of published research works providing real experimentation together with simulation processes. Information about the IRS's features is also lacking in a remarkable percentage of the publications. Due to the variability in how experiments are performed and results are presented, research work comparisons become difficult. In summary, this topic requires standards to be able to perform comparison analysis of published papers and therefore, progress adequately.

Over recent years, there has been an explosion of digital technologies and infrastructure, and many biomedical education contexts have taken advantage of these new tools and platforms for teaching and learning visualisation practices. However, there are uncertainties, concerns, and debates in the literature, regarding the impact of digital technology on biomedical visualisation, and education more broadly. In this discussion paper, a multimodal social semiotics perspective is applied to examining visual representations, and the visual language we use to make meaning with these representations, in four different ways. Firstly, in terms of the function, role, and positionality of visual representations within the biomedical sciences. Secondly, within the context of a disciplinary Discourse, wherein visual language functions as just one of many socially specific languages, or modes, such as the written, verbal, and symbolic modes, and through which we animate the discourse and make disciplinary meaning. Thirdly, consideration is given to the meaning-making affordances and limitations of biomedical visualisation practices, affirming that no single mode or media is superior and that multimodal integration is a necessity. Finally, there is a discussion of the approaches to teaching and learning for the acquisition of a disciplinary Discourse, which highlights a need to focus on explicating the hidden curriculum. This discussion presents a perspective that offers some valuable insights into the uncertainties expressed in the literature and empowers educators with some productive pedagogical strategies.

Periodontitis is one of the world's most common chronic human diseases and has a significant impact on oral health. Recent evidence has revealed a link between periodontitis and certain severe systemic conditions. Moreover, periodontal patients remain so for life, even following successful therapy, requiring ongoing supportive care to prevent the disease's recurrence. The first challenge in treating the condition is ensuring a timely and accurate diagnosis since the loss of periodontal bone and soft tissue is progressive and largely irreversible. Although current clinical and radiographic parameters are the best available for identifying and monitoring the disease, the scientific community has a particular interest in finding quantifiable biomarkers in oral fluids that can improve early detection rates of periodontitis and evaluations of its severity. It is widely accepted that periodontitis is associated with polymicrobial dysbiosis and a chronic inflammatory immune response in the host. This response causes the generation of mediators like cytokines. Higher concentrations of cytokines are involved in inflammation and disease progression, acting as a network of biological redundancy. Most of the cytokines investigated concerning the periodontitis pathogenesis are proinflammatory. Of all of them, interleukin (IL) 1beta has been studied the most, followed by tumor necrosis factor (TNF) alpha and IL6. In contrast, only a few papers have evaluated antiinflammatory cytokines, with the most researched being IL4 and IL10. Several systemic reviews have concluded that the specific cytokines present in patients with periodontitis have a distinctive profile, which may indicate their possible discriminatory potential. In this chapter, the focus is on analyzing studies that investigate the accuracy of diagnoses of periodontitis based on the cytokines present in gingival crevicular fluid and saliva. The findings of our research group are also described.

Periodontitis is an inflammatory disease triggered by the infection of the periodontal sulcus by microbes. Together with the abundant eubacterial microbiota, at least two parasites have often been identified: the amoeba Entamoeba gingivalis and the flagellate Trichomonas tenax. The role of these protists in the pathophysiology of periodontal disease remains to be deciphered. A high diversity in their measured prevalence, mainly due to methodological concerns, prevents further analysis of the aetiological link between these parasites and periodontitis.To determine E. gingivalis and T. tenax prevalence in periodontal pockets as compared to healthy sulci, we have conducted a systematic review, searching 3 remote databases (Pubmed, LILACS, and Google Scholar), restricting to papers in which the diagnostic of the parasite was made using molecular methods. A total of 5 studies for E. gingivalis and 2 studies for T. tenax were included for the meta-analysis.In the periodontal pockets, the prevalence of parasites is 76.9% (95%-CI: 71.5-81.7%) for E. gingivalis and 38.6% (95%-CI: 27.2-50.0%) for T. tenax . Both parasites are more abundant in periodontal pockets as compared to healthy sulci, with a risk ratio of 3.96 (95%-CI: 1.57-9.98) for E. gingivalis and 21.82 (95%-CI: 6.71-70.96) for T. tenax . The two subtypes of E. gingivalis exhibited the same risk ratio: 3.30 (95%-CI: 1.27-8.55) for ST1 and 3.30 (95%-CI: 0.42-26.03) for ST2, but ST1 was more prevalent (70.6%, 95%-CI: 65.0-76.2%) than ST2 (43.9%, 95%-CI: 35.5-52.4%) in periodontal pockets.Altogether, the data show that parasites are more prevalent in the diseased than in the healthy. However, the differences in prevalence between species and subtypes call for more studies to be able to conclude about their individual contributions in the pathophysiology of periodontal diseases. The heterogeneity in prevalence estimation should be investigated further, in particular to make out biological from methodological heterogeneity.

The objective of the present review paper was to comprehensively introduce the pain symptom and comorbidities of depression, anxiety, and learning and memory dysfunctions in the central poststroke pain (CPSP) of human and animal models. CPSP is a disease in which the lesion or dysfunction of the spinothalamocortical circuits is due to thalamic stroke hemorrhage. According to previous literature, CPSP patients experience impaired explicit and implicit learning and memory in addition to the pain symptom. Moreover, there are associated depression and anxiety comorbidities for CPSP. However, the data from some clinical studies were not supportive of the notion that CPSP patients also experienced induced comorbid depression and anxiety. On the other hand, the motor function test was likely to be inconsistent in terms of the results of human and animal models. The review paper provides some implications for further development of animal models for examinations of CPSP comorbidities of depression, anxiety, learning and memory dysfunction, and motor functions, aside from the central pain symptom. In human models, some conflicting data related to comorbid depression, anxiety, explicit and implicit learning memory, and motor dysfunctions should be re-elucidated in further studies.

Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form-concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley crew of notable theoretical and cognitive neuroscientists, which have served to define much of the landscape of whole-brain modelling as it stands at the start of the 2020s. At the same time, the field has over the past decade expanded in a dozen or more fascinating new methodological, theoretical, and clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.

Graphene family nanomaterials have interesting electronic structures which determine their electrical, optical, and mechanical properties. Especially, their unique chemical properties enable interactions with biological substances and chemical reagents, and the interactions have further an influence on the observable properties of the graphene family nanomaterials. Such aspects render graphene family nanomaterials versatile for various types of biosensing as a target recognition unit and a recognition-to-signal transduction unit. In this chapter, we look over the recent progress on the graphene-based biosensors, which is categorized in terms of (1) the role of graphene family nanomaterials (target recognition, signal transduction), (2) the sensing mechanisms and modes (electrochemical, electrical, fluorescent, Raman scattering), and (3) the formats of sensing devices (paper, lab-on-a-chip, wearable devices).

Dramatic advancements in interdisciplinary research with the fourth paradigm of science, especially the implementation of computer science, nourish the potential for artificial intelligence (AI), machine learning (ML), and artificial neural network (ANN) algorithms to be applied to studies concerning mechanics of bones. Despite recent enormous advancement in techniques, gaining deep knowledge to find correlations between bone shape, material, mechanical, and physical responses as well as properties is a daunting task. This is due to both complexity of the material itself and the convoluted shapes that this complex material forms. Moreover, many uncertainties and ambiguities exist concerning the use of traditional computational techniques that hinders gaining a full comprehension of this advanced biological material. This book chapter offers a review of literature on the use of AI, ML, and ANN in the study of bone mechanics research. A main question as to why to implement AI and ML in the mechanics of bones is fully addressed and explained. This chapter also introduces AI and ML and elaborates on the main features of ML algorithms such as learning paradigms, subtypes, main ideas with examples, performance metrics, training algorithms, and training datasets. As a frequently employed ML algorithm in bone mechanics, feedforward ANNs are discussed to make their taxonomy and working principles more readily comprehensible to researchers. A summary as well as detailed review of papers that employed ANNs to learn from collected data on bone mechanics are presented. Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations; avoid and/or minimize injuries; and, encourage early detection of such injuries in the first place.

As the world has witnessed three severe coronavirus outbreaks in the past two decades, including the recent pandemic COVID19, caused by SARS-CoV2, it has become of utmost importance to develop drugs and vaccines against coronaviruses. The previous two outbreaks, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) emerged in China and Saudi Arabia in 2003 and 2012, respectively. COVID19 is considered the worst of all and has taken more than 4 million lives so far and crippled the socioeconomic life of human beings in the entire world. Extensive research is being carried out to find out a solution that will not only help us to fight the current situation but also prepare us to prevent further intervention by similar viruses in the future. Here, we aim to highlight potential drug target sites in coronavirus infection or life cycle in general.We have gone through the research papers published on coronavirus, with special emphasis on SARS-CoV, MERS-CoV, and SARS-CoV2, in peer-reviewed journals and tried to identify the possible sites in the coronavirus life cycle which can be used as potential drug targets.Studies showed that there are several unique enzymes and mechanisms involved in the coronavirus life cycle which can be manipulated to develop drugs against it. However, it has been always a challenge to develop drugs or vaccines against viruses as they utilize the host cell machinery and more difficult against RNA viruses because of their high mutation rate.Effective control of the current (2020) pandemic necessarily depends on the development of either a vaccine or an effective therapeutic agent. In the past, many attempts were taken to develop vaccines after the outbreak of SARS-CoV and MERS-CoV, though no successful vaccine reached to the market as the situation came under control. In the current scenario, many laboratories have developed effective vaccines against SARS-CoV2, which have reduced both the severity of the infection and the rate of mortality considerably. However, world needs to be prepared for similar viral outbreaks in future and research must be continued to develop more effective vaccines and therapeutics against coronaviruses.

This article aims to determine the effectiveness of hyperbaric oxygen therapy (HBOT) for treating symptoms of Parkinson's disease. We present a brief review of the relevant literature and general information about HBOT. This paper describes evidence that HBOT has crucial effects to the three specific brain circuits possibly involved in "Kinesia Paradoxa" (noradrenergic system, basal ganglia, and the cerebellum circuit). Moreover, we are presenting clues supporting "Norepinephrine Hypothesis" according to which HBOT increases norepinephrine levels and restores motor deficits in Parkinson's disease patients.

ALS is increasingly perceived as a multisystem neurodegenerative disorder, and the identification of a panel of biomarkers that accurately reflect features of pathology is a priority, not only for diagnostic purposes but also for prognostic or predictive applications [1]. Thus, as a multisystemic disease, it is likely that a panel of biomarkers will be needed to fully capture the features of ALS pathology. Taking also into consideration the fact that its causes remain unknown to their majority, it remains a complex disease driven by a combination of several systemic parameters [2]. Beyond the monogenic causes, representing the 15% of the ALS cases, which list up to 30 gene mutations with the most frequent being C9orf72, SOD1, FUS, and TARDBP/TDP43 [3-5], much research is being done to identify and associate possible causes for the 80% of ALS cases (sALS and fALS combined) which at the moment are not explained by a known mutation [2, 4]. ALS sporadic cases are related to a multigenic component, and/or involve epigenetic modification, and/or result from DNA damage, environmental risk factors, behavioural factors, oxidative stress or viral infections leading to cellular dysfunctions [4, 6-10]. ALS diagnosis is lengthy and there is a typical diagnostic delay of 9-15 months from onset to diagnostic confirmation based on clinical and electrophysiological criteria as well as the exclusion of diseases with similar behaviour to ALS. Three major exclusion criteria are involved in the diagnosis process: the Revised El Escorial Criteria (REEC), the Airlie House Criteria (AHC) and the Awaji Criteria [11, 12]. Taking into consideration that the average survival is 2-4 years, it makes it urgent for the researchers to improve diagnostic speed and accuracy for ALS [13, 14]. In the absence of a reliable diagnostic test for ALS, biomarkers are a strong weapon not only for its diagnosis but also for understanding the pathomechanism as well as a basis for the development of therapeutics. Recent global research has accepted the fact that biomarkers will facilitate the combination of therapeutics with diagnostics and will thus play an important role in the development of personalized medicine [15]. This paper proposes a combination of diagnostic and prognostic biomarkers to meet the scope of global research. Thus, biomarkers specific to ALS pathology need to be identified towards the development of a reliable fast diagnostic test, while at the same time prognostic biomarkers should also be identified to monitor the status of the pathology as various candidates may serve both purposes. Finally, since different sub-cohorts of ALS patients respond differently to treatments [16], the identification of ALS biomarkers will contribute to a better understanding of the disease pathogenesis and permit targeted drug development and patient stratification leading to more efficient clinical trials.

The Strengthening Families Program for Parents and Youth 10-14 (SFP10-14) is an evidence-based, internationally recognized program designed in the USA for prevention of youth substance abuse and other behavioral problems. The program aims to build young adolescents' skills to reduce risk, improve parenting practices, and promote positive family relationships that are known to reduce high-risk behaviors in youth. The SPF 10-14 is a universal program developed for ethnically diverse populations. The license to adapt and apply the SFP10-14 was granted to the First Department of Pediatrics, Medical School of the National and Kapodistrian University of Athens in Greece by the Iowa State University of Science and Technology in the USA. The program was approved by the School Division of the Municipality of Athens and the Greek Ministry of Education. This paper presents Phases I and II of the Greek adaptation of the SFP10-14 project. In Phase I, the original US SFP10-14 was initially translated into Greek and was subsequently implemented to 14 families with adolescents attending the Greek school. Phase II endeavored extensive adaptation of the SFP10-14 tools based on survey results from 57 independent advisory participants. Phases I and II provided safe grounds to warrant reshooting of the SFP10-14 DVDs. The Greek adaptation pointed to substantial cross-cultural convergence as to what the parents evaluate as "unacceptable." With respect to role models, however, Greek parents often came up as overprotective. The Greek families welcomed the intervention as a path to receive help, when general healthcare was often not accessible.

The objective of this paper was to explore whether inflammatory biomarkers, especially C-reactive protein (CRP) and high-sensitivity CRP (hsCRP), can be used as a diagnostic tool for possible late-life cognitive decline and dementias. Twelve studies were identified since 2010 involving 13,174 subjects, and one study recruited only healthy population. Inflammatory biomarkers CRP and hsCRP were obtained from blood plasma. Cognitive function was measured using the Mini-Mental State Examination (MMSE) as a basic test in 11 studies. Results were conflicted, five studies revealed no significant differences between higher CRP levels and cognitive decline, four studies demonstrated significant association even in healthy population, and three studies showed significant correlation of lower CRP levels to cognitive deficits. Inflammatory reaction is possibly associated with the pathogenesis of some cognitive impairment and dementias; however, as the results were conflicted, further research should be made, including the normal aging factor, both in healthy and in demented or cognitive declined population.

Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.

In the modern world of rapidly increasing autistic spectrum disorder case rates, medical costs, societal impact, and long-waiting times from initial screening, there is a need for an easy, early screening of autistic spectrum disorder risk in children. In this paper, a mobile application was developed with these requirements, using machine learning algorithms achieving high performance compared to other applications that use simple rule approaches. A recently published autistic spectrum disorder dataset is used to train the model, containing hundreds of screening data from children in the ages 4 to 11.

With expanding applications for dance in geriatrics, including rehabilitation and pre-habilitation of neurodegenerative conditions, an examination of how elements of dance may contribute to specific improvements is called for. "Dance" can refer to a vast number of diverse practices; dancing may be rehearsed or improvised, performed with or without music, and may include a wide variety of styles of movement and accompanying sounds; some dances, such as tango and other social dance forms, rely on a partner, while many folk and cultural forms are group-based; artistic and contemporary forms may consist of solo exploration/performance. Research on the impacts of dance-based interventions has spanned in many different varieties of dance, and often the specifics of dance interventions involved are sparsely provided. This paper offers a brief review of elements central to many dance forms that may contribute to rehabilitation, suggesting hypotheses for future research and training, examining how elements of dance may foster adaptive plasticity. Primary topics include motor learning, motor control, spatial navigation, action observation, and entrainment, with suggested future considerations as to how imagery, imagination, multisensory integration, mental rehearsal, volitional movement, and creativity in nonexpert populations contribute to a multinetwork toolbox that may be neuroprotective.

The Surgical Safety Checklist (SSC) has been created based on the recommendations of the WHO and obligatorily introduced worldwide. SSC is used to increase the patient's safety and reduce complications while in the hospital, especially in the perioperative period. The original SSC template was of a multicolor polychromatic design. However, an achromatic black-and-white or gray-gray design on plain printer paper appears often used in clinical practice. This review aims to assess the level of SSC use in the polychromatic versus achromatic versions and the pros and cons of using either in practice. We used the Google browser for the identification and collection of SSC graphic images available as of June 2021 using the following search commands: "surgical safety checklist WHO" or "surgical safety checklist" or "SSC WHO." The commands were repeated in 103 languages representing the five continents with the back answers provided in 41 languages. The successive top 10 thematically relevant images or fewer if not available in the cases of some foreign languages were considered for analysis, providing a mean of 5 ±2 images per language. The numbers of achromatic and polychromatic two-color or multicolor images were calculated. The number of images corresponding to the respective color designs ranged as follows: 0-6 (27.6%), 0-9 (41.6%), and 0-6 (27.6%) We conclude that polychromatic imaging of SSC documents predominates in practical use. The polychromatic SSC design catches the doctor's eye, which likely increases the effectiveness of completing the document.

Coronary heart disease is a serious and common disease that affects a large part of the population. There is a tendency to use machine learning techniques for the punctual and valid diagnosis, which can determine the effectiveness of treatment and the health of the individual. In this paper, we apply popular machine learning algorithms, achieving satisfactory performance (over 70%), coinciding with corresponding studies in the literature and at some points overriding them. Moreover, we provide a user-friendly web-based decision support system, which is suitable for specialized medical staff and, based on some easily accessible and available data, contributes to the diagnosis of the disease.

Nowadays, modern lifestyle along with the ever-growing technological and scientific advancement, especially in developed countries, has led to a significant increase of the middle age and elderly population. As a consequence, a substantial rise in neurodegenerative diseases has been reported, such as mild cognitive impairment, Alzheimer's disease, and other types of dementia. The aim of this paper is to study the impact of mathematical problem-solving on cognitive enhancement of adults. Moreover, the possible applicability of mathematical problem-solving as part of a treatment in cases of adults suffering from different levels of cognitive decline due to neurodegenerative diseases has been taken into consideration. For the needs of this study, a qualitative research was conducted in a sample of 16 participants, in order to confirm the effect of the mathematical problem-solving process on their cognition and its reinforcement, as stated by the participants. The results of this research, as interpreted, indicate a positive effect of mathematical problem-solving on adult cognition and on cognitive processes in general. Nonetheless, there are certain limitations to this research, deriving from its nature, while others derive from the measures enforced during the SARS-CoV-2 outbreak. Therefore, this chapter suggests that a more thorough quantitative research should be conducted in order to specifically measure the magnitude, as well as the time span of the impact of mathematical problem-solving on adult cognitive enhancement. Moreover, it is proposed that future studies should concentrate on the usefulness of mathematical problem-solving as a component in both invasive and noninvasive cognitive treatments used to cure or alleviate the aforementioned cognitive impairments.

We live in the big data era in the biomedical field, where machine learning has a very important contribution to the interpretation of complex biological processes and diseases, since it has the potential to create predictive models from multidimensional data sets. Part of the application of machine learning in biomedical science is to study and model complex cellular systems such as biological networks. In this context, the study of complex diseases, such as Alzheimer's diseases (AD), benefits from established methodologies of network science and machine learning as they offer algorithmic tools and techniques that can address the limitations and challenges of modeling and studying cellular AD-related networks. In this paper we analyze the opportunities and challenges at the intersection of machine learning and network biology and whether this can affect the biological interpretation and clarification of diseases. Specifically, we focus on GRN techniques which through omics data and the use of machine learning techniques can construct a network that captures all the information at the molecular level for the disease under study. We record the emerging machine learning techniques that are focus on ensemble tree-based techniques in the area of classification and regression. Their potential for unraveling the complexity of model cellular systems in complex diseases, such as AD, offers the opportunity for novel machine learning methodologies to decipher the mechanisms of the various AD processes.

Clinical simulation encompasses a broad range of methods and techniques that allow clinical skills to be rehearsed and practiced away from the clinic before being applied to real patients. As such, preparation of doctors and other healthcare professionals for safe clinical practice is one of its main aims. The objective of this paper was to review the evidence regarding the use of clinical simulation training in geriatric medicine education and consider how the findings may be translated to education in the closely related field of psychiatry of old age. Original papers and descriptive case studies of clinical simulation training programs for medical professionals were considered for inclusion. Papers were grouped according to the participants' level of training: (1) undergraduate medical education; (2) postgraduate medical education; and (3) multiple levels of medical learners. A diverse range of effective simulation modalities for teaching geriatric medicine was identified across all levels of learning. The evidence suggests that there is much fertile ground for trainees in geriatric medicine and psychiatry of old age to participate in joint simulation training programs, thereby maximising their reach while minimising associated resource requirements and financials costs. Given the prominent position of psychiatry of old age at the interface between psychiatry and medicine, old age psychiatrists potentially have much to offer in advancing the field of clinical simulation while simultaneously improving patient care.

An increased emphasis in recent years on psychiatrists as healthcare leaders has not only drawn attention to the skills they can bring to this role but has also raised questions about how to best train and prepare them to assume leadership responsibilities. Such training should not be conducted in isolation from, and oblivious to, the wide-ranging expertise in human behaviour and relationships that psychiatrists can bring to the leadership arena. The aim of this theoretical paper is to draw attention to how psychiatrists can use their existing knowledge and skill set to inform their understanding of leadership theory and practice. In particular, the Psychodynamic Leadership Approach and Leader-Member Exchange theory are compared and contrasted to illustrate this point. The former represents a less well-known approach to leadership theory and practice whereas the latter is a widely familiar, conventional theory that is regularly taught in leadership courses. Both are underpinned by their emphasis on leader-follower relationships-and human relationships more broadly-and are intuitively appealing to psychiatrists endeavouring to understand aspects of organisational behaviour in the healthcare settings in which they work and lead. The application of these theories to assist reflection on and understanding of professional and personal leadership behaviours through leadership-oriented Balint-style groups and 360-degree appraisal is proposed. It is hoped that this paper will serve to stimulate thought and discussion about how leadership training for future psychiatrists can be tailored to better harness their existing competencies, thereby developing richer formative learning experiences and, ultimately, achieving superior leadership outcomes.