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This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

Older people with dementia (OPD) have specific housing and technology-related needs, for which various design principles exist. A model for designing environments and its constituting items for people with dementia that has a firm foundation in neurology may help guide designers in making design choices. The paper aims to discuss these issues.

A general design model is presented consisting of three principles for OPD, namely designing for ageing people; designing for a favourable state and designing for beautiful moments. The neurosciences as a whole give shape to an eminent framework explaining the behaviour of OPD. One of the objectives of this paper is to translate the design principles into design specifications and to show that these specifications can be translated in a design.

Philosophical concepts are introduced which are required to understand design for OPD. Four case studies from Dutch nursing homes are presented that show how the theory of modal aspects of the philosopher Dooyeweerd can be used to map design specifications in a systematic way.

These examples of design solutions illustrate the applicability of the model developed in this article. It emphasises the importance of the environment for supporting the daily life of OPD.

There is a need for a design model for OPD. The environment and technology should initiate positive behaviours and meaningful experiences. In this paper, a general model for the designing of environments for OPD was developed that has a firm foundation in neurology and behavioural sciences. This model consists of six distinct steps and each step can be investigated empirically. In other words, this model may lay the foundation for an evidence-based design.

Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

Research into the neurobiological origins of same-sex attraction is inconclusive. A recent theory of homosexuality posited that maternal thyroid dysfunction during pregnancy is associated with an increased rate of homosexual orientation in offspring. Relevant studies from the prenatal thyroid model perspective were reviewed, the major findings of which are as follows: i) An increased prevalence of Hashimoto's disease in lesbian women suggests a maternal and even familial presence of the same autoimmune thyroid disease. Female-to-male transsexuals and lesbian women were also reported to have higher rates of polycystic ovary syndrome (PCOS). Over the last several years, reports suggesting a strong link between PCOS and thyroid autoimmunity have accumulated. ii) The increased risk of autism spectrum disorders (ASD) in the offspring of mothers with thyroid autoimmunity in pregnancy and the association between ASD and gender dysphoria indicate a link between maternal thyroid dysfunction and gender dysphoria/same-sex attraction in the off-spring. iii) The high risk of miscarriage and retarded fetal growth in pregnancies of mothers who give birth to homosexual off-spring can be explained by the impact of maternal thyroid dysfunction during pregnancy. This perspective review highlights relevant research findings and integrates them into the prenatal thyroid model of homosexuality. A better understanding of the mechanisms involved in the generation of same-sex orientation will contribute to the betterment of individual lives, as well as of society.

Although first rank symptoms focus on positive symptoms of psychosis they are shared by a number of psychiatric conditions. The difficulty in differentiating bipolar disorder from schizophrenia with affective features has led to a third category of patients often loosely labeled as schizoaffective. Research in schizophrenia has attempted to render the presence or absence of negative symptoms and their relation to etiology and prognosis more explicit. A dichotomous population is a recurring theme in experimental paradigms. Thus, schizophrenia is defined as process or reactive, deficit or non-deficit and by the presence or absence of affective symptoms. Laboratory tests confirm the clinical impression showing conflicting responses to dexamethasone suppression and clearly defined differences in autonomic responsiveness, but their patho-physiological significance eludes mainstream theory. Added to this is the difficulty in agreeing to what exactly constitutes useful clinical features differentiating, for example, negative symptoms of a true deficit syndrome from features of depression. Two recent papers proposed that the general and specific cognitive features of schizophrenia and major depression result from a monoamine-cholinergic imbalance, the former due to a relative muscarinic receptor hypofunction and the latter, in contrast, to a muscarinic hypersensitivity exacerbated by monoamine depletion. Further development of these ideas will provide pharmacological principles for what is currently an incomplete and largely, descriptive nosology of psychosis. It will propose a dimensional view of affective and negative symptoms based on relative muscarinic integrity and is supported by several exciting intracellular signaling and gene expression studies. Bipolar disorder manifests both muscarinic and dopaminergic hypersensitivity. The greater the imbalance between these two receptor signaling systems, the more the clinical picture will resemble schizophrenia with bizarre, incongruent delusions and increasingly disorganized thought. The capacity for affective expression, by definition a non-deficit syndrome, will remain contingent on the degree of preservation of muscarinic signaling, which itself may be unstable and vary between trait and state examinations. At the extreme end of muscarinic impairment, a deficit schizophrenia subpopulation is proposed with a primary and fixed muscarinic receptor hypofunction.

The genomic profile of bipolar disorder and schizophrenia overlap and both have a common dopaminergic intracellular signaling which is hypersensitive to various stressors. It is proposed that the concomitant muscarinic receptor upregulation differentiates the syndromes, being marked in bipolar disorder and rather less so in schizophrenia. From a behavioral point of view non-deficit syndromes and bipolar disorder appear most proximate and could be reclassified as a spectrum of affective psychosis or schizoaffective disorders. Because of a profound malfunction of the muscarinic receptor, the deficit subgroup cannot express a comparable stress response. None -theless, a convergent principle of psychotic features across psychiatric disorders is a relative monoaminergic-muscarinic imbalance in signal transduction.

How does the human brain absorb information and turn it into skills of its own in psychotherapy? In an attempt to answer this question, the authors will review the intricacies of processing channels in psychotherapy and propose the term transprocessing (as in transduction and processing combined) for the underlying mechanisms. Through transprocessing the brain processes multimodal memories and creates reparative solutions in the course of psychotherapy. Transprocessing is proposed as a stage-sequenced mechanism of deconstruction of engrained patterns of response. Through psychotherapy, emotional-cognitive reintegration and its consolidation is accomplished. This process is mediated by cellular and neural plasticity changes.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Taurine (2-aminoethane sulfonic acid; C2H7NO3S) is a nonprotein sulfur-containing β-amino acid present in nearly all mammalian tissues and the most ubiquitous free endogenous biomolecule in human cells. Taurine is commonly known as a conditionally essential amino acid because taurine is one of the few amino acids that are not incorporated in protein synthesis. The purpose of this study is to review the existing articles related to taurine and to give an account how useful is taurine to the different body systems. In this thorough overview, taurine is covered in terms of its essentiality, sources, advantages for neonates and the elderly, the effects of taurine deficiency, and the safety and toxicity of taurine supplements.

This is a narrative review into the subject matter. Published articles were searched on different portals like PubMed, EMBASE, Scopus, Google Scholar, PubChem etc. The authors also evaluated the availability of taurine in commercially available energy drinks.

This comprehensive review, presents the potential clinical benefits and functional properties of taurine as a conditionally essential amino acid. Energy drinks containing taurine (and their concentration) are also reported in this review.

This is the first data that the authors are aware of that shows taurine content in a variety of energy drinks on the market.

The mass exodus of the professional healthcare workforce has become a cankerworm for a developing nation like Nigeria, and this worsens the already depleted healthcare systems in underdeveloped nation. This study investigated the rationale behind medical workers' brain-drain syndrome and the quality healthcare delivery in the Nigerian public healthcare sector.

To stimulate an understanding of the effect of the phenomenon called brain drain, the study adopted a diagnostic research design to survey the public healthcare personnel in government hospitals. The study administered a battery of adapted research scales of different measures to confirm the variables of interest of this study on a probability sampling strategy. The study surveyed 450 public healthcare sector employees from four government hospitals to gather pertinent data. The study used a structural equation model (SEM) and artificial neural networks (ANNs) to analyse the collected data from the medical personnel of government hospitals.

The findings of this study are significant as postulated. The study discovered that poor quality worklife experienced by Nigerian medical personnel was attributed to the brain-drain effect and poor healthcare delivery. The study further demonstrated that job dissatisfaction suffered among the public healthcare workforce forced the workforce to migrate to the international labour market, and this same factor is a reason for poor healthcare delivery. Lastly, the study discovered that inadequate remuneration and pay discouraged Nigerian professionals and allied healthcare workers from being productive and ultimately pushed them to the global market.

Practically, this study has shown three major elements that caused the mass movement of Nigerian healthcare personnel to other countries of the world and that seems novel given the peculiarity of the Nigerian labour market. The study is original and novel as much study has not been put forward in the public healthcare sector in Nigeria concerning this phenomenon.