Background: One way to demonstrate the existence of nursing is to develop a nursing theory model through nursing research which can ultimately be implemented in nursing practice. RAM is one of the most frequently used models in guiding nursing research. Roy's Adaptation Model (RAM) is one of the most useful conceptual frameworks that guide nursing practice, direct research, and influence education. Theory-guided nursing practice is fundamental in providing the framework for developing superior and quality nursing care.Objectives: This systematic review aims to critically analyze recent studies using RAM as a conceptual framework to identify the effectiveness of this model in guiding nursing research.Methods: A literature search was conducted on five databases, namely SCOPUS, PubMed, ProQuest, ScienceDirect, and SAGEPub. There were no population boundaries and diagnoses involved in the study. The study is a quantitative design focused on publication between 2015-2021. The methodological quality of applying the Cochrane and JBI bias tools. The analysis uses narrative synthesis.Results: 20 studies were found out of 1,315 studies. The research population found is very diverse. The given intervention follows the conceptual framework of RAM. RAM-based interventions effectively overcome the problems experienced by patients and reduce the perceived symptoms and improve the patient's quality of life.Conclusion: The conceptual use of RAM theory in nursing research has been widely reported. RAM-based interventions have a significant impact and have strong evidence-based practice in improving patient health status.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

Nowadays the freedom to make choices is considered of vital importance for decision-makers. Researchers investigating the psychological effects of choice provided empirical evidence about the many advantages of choice (i.e., increased intrinsic motivation, greater perception of self-determination, better performance, and greater life satisfaction). However, more recent studies demonstrated the downsides of choice. When the decision task difficulty exceeds the natural cognitive resources of human mind, the possibility to choose becomes more a source of unhappiness and dissatisfaction than an opportunity for a greater well-being. We here discuss in a detailed fashion the main findings of research in psychology and consumer behavior showing the bright sides and the downsides of choice.

Designed by a group of ME/CFS researchers and health professionals, the European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) has received funding from the European Cooperation is Science and Technology (COST) (https://www.cost.eu/cost-actions/what-are-cost-actions/ ) - COST action 15111 - from 2016 to 2020. The main goal of the Cost Action was to assess the existing fragmented knowledge and experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in European countries, and to enhance coordinated research and health care provision in this field. We report on the recommendations for clinical diagnosis, heath services and care for people with ME/CFS in Europe, as prepared by the group of clinicians and researchers from 22 countries and 55 European health professionals and researchers, who have been informed by people with ME/CFS (https://www.cost.eu/actions/CA15111/#tabs|Name:overview).

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

Introduction: An epidemic of Coronavirus Disease 2019 (COVID-19) begun in December 2019 in China, causing a Public Health Emergency of International Concern. Among raised questions, clinical, laboratory, and imaging features have been partially characterized in some observational studies. No systematic reviews have been published on this matter. Methods: We performed a systematic literature review with meta-analysis, using three databases to assess clinical, laboratory, imaging features, and outcomes of COVID-19 confirmed cases. Observational studies, and also case reports, were included and analyzed separately. We performed a random-effects model meta-analysis to calculate the pooled prevalence and 95% confidence interval (95%CI). Results: 660 articles were retrieved (1/1/2020-2/23/2020). After screening by abstract/title, 27 articles were selected for full-text assessment. Of them, 19 were finally included for qualitative and quantitative analyses. Additionally, 39 case report articles were included and analyzed separately. For 656 patients, fever (88.7%, 95%CI 84.5-92.9%), cough (57.6%, 40.8-74.4%) and dyspnea (45.6%, 10.9-80.4%) were the most prevalent manifestations. Among the patients, 20.3% (95%CI 10.0-30.6%) required intensive care unit (ICU), with 32.8% presenting acute respiratory distress syndrome (ARDS) (95%CI 13.7-51.8), 6.2% (95%CI 3.1-9.3) with shock and 13.9% (95%CI 6.2-21.5%) of hospitalized patients with fatal outcomes (case fatality rate, CFR).Conclusion: COVID-19 brings a huge burden to healthcare facilities, especially in patients with comorbidities. ICU was required for approximately 20% of polymorbid, COVID-19 infected patients and this group was associated with a CFR of over 13%. As this virus spreads globally, countries need to urgently prepare human resources, infrastructure, and facilities to treat severe COVID-19.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

With the growing hope that disease-modifying treatments could target the molecular basis of neurodegenerative diseases even before the onset of symptoms, there is mounting pressure to define disease entities based on pathophysiology rather than on clinical syndromes. The Alzheimer’s disease research community has recently transitioned from diagnostic criteria based on an amnestic syndrome to a purely biomarker-based disease definition, relying on the demonstration of amyloid-beta pathology, tau pathology, and neurodegeneration. In contrast, current diagnostic criteria for Parkinson’s disease still rely on the presence of the well-described clinical syndrome of parkinsonism, with the addition of characteristic motor- and non-motor signs and symptoms. However, there is now unequivocal evidence that Parkinson’s disease starts years before the onset of parkinsonism. Furthermore, neuropathologically defined Lewy body disease is clinically heterogeneous, combining a range of motor, non-motor, dopaminergic and non-dopaminergic features. Finally, clinically defined Parkinson’s disease has diverse underlying etiologies most, but not all, associated with α-synuclein positive Lewy pathology. In light of recent scientific advances, we propose a biologically based definition for the diagnosis of Parkinson's disease, initially to be used for research purposes. The criteria use a three-component ‘G-S-N’ system. The first is documentation of defined gene variants (‘G’), which cause or strongly predispose to PD as the most upstream component. The second is α-synuclein pathology (‘S’), currently defined as pathological α-synuclein deposition in tissue or positive α-synuclein seeding assays. The third is evidence of underlying neurodegeneration (‘N’), currently defined by specific neuroimaging procedures. The associated clinical syndrome (‘C’) is defined by a single high-specificity feature or multiple lower-specificity features. Initiating this transition will enable the field to fuel both basic and clinical research and move closer to the precision medicine required to develop clinically meaningful disease-modifying therapies. We acknowledge current limitations, ethical implications, and the need for prospective validation of this approach.

With the COVID-19 pandemic now ongoing for close to a year, people all over the world are still waiting for a vaccine to become available. The initial focus of accelerated global research and development efforts to bring a vaccine to market as soon as possible was on novel platform technologies that promised speed but had limited history in the clinic. In contrast, recombinant protein vaccines, with numerous examples in the clinic for many years, missed out on the early wave of investments from government and industry. Emerging data are now surfacing suggesting that recombinant protein vaccines indeed might offer an advantage or complement to the nucleic acid or viral vector vaccines that will likely reach the clinic faster. Here, we summarize the current public information on the nature and on the development status of recombinant subunit antigens and adjuvants targeting SARS-CoV-2 infections.

The treatment of peptides has played an important role in clinical practice since the discovery of insulin therapy in the 1920s. Over 60 peptide drugs are approved in the United States (US and other regional mar-kets, and peptides continue to undergo drug discovery steadily. Peptide research and development has lev-eraged a wider range of structures known from other plant sources, via pharmacology and medicinal molecular biology, beyond its conventional focus on individual endogenous peptides. We build a comprehensive database of peptides that have met scientific studies with more than 150 constantly evolving peptides. Here we provide a simple overview of the peptide-based drug therapy environment, comprising evolutionary points of view, structural properties, operational thresholds, and explanation of the therapeutic area.

Intuitionistic fuzzy set, which can be represented using the triangular intuitionistic fuzzy number (TIFN), is a more generalized platform for expressing imprecise, incomplete and inconsistent information when solving multi-criteria decision-making problems, as well as for reflecting the evaluation information exactly in different dimensions. In this paper, the TIFN has been applied for solving some multi-criteria decision-making problems by developing a new triangular intuitionistic fuzzy geometric aggregation operator, that is the generalized triangular intuitionistic fuzzy ordered weighted geometric averaging (GTIFOWGA) operator, and defining some triangular intuitionistic fuzzy geometric aggregation operators including the triangular intuitionistic fuzzy weighted geometric averaging (TIFWGA) operator, the ordered weighted geometric averaging (TIFOWGA) operator and the hybrid geometric averaging (TIFHWGA) operator. Based on these operators, a new approach for solving multicriteria decision-making problems when the weight information is fixed has been proposed. Finally, the proposed method has been compared with some similar existing computational approaches by virtue of a numerical example to verify its feasibility and rationality.

The technology-driven world of the 21^st century is currently confronted with a major threat to humankind in the form of the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As of April 22, 2020, COVID-19 has claimed 169, 006 human lives and had spread to over 200 countries with more than 2,471,136 confirmed cases. The perpetually increasing figures associated with COVID-19 are disrupting the social and economic systems globally. The losses are unmatched and significantly higher compared to those from previously encountered pathogenic infections. Previously, two CoVs (SARS-CoV and Middle East respiratory syndrome-CoV) affected the human population in 2002 and 2012 in China and Saudi Arabia, respectively. Based on genomic similarities, animal-origin CoVs, primarily those infecting bats, civet cats, and pangolins, were presumed to be the source of emerging human CoVs, including the SARS-CoV-2. The cohesive approach amongst virologists, bioinformaticians, big data analysts, epidemiologists, and public health researchers across the globe has delivered high-end viral diagnostics. Similarly, vaccines and therapeutics against COVID-19 are currently in the pipeline for clinical trials. The rapidly evolving and popular technology of artificial intelligence played a major role in confirming and countering the COVID-19 pandemic using digital technologies and mathematical algorithms. In this review, we discuss the noteworthy advancements in the mitigation of the COVID-19 pandemic, focusing on the etiological viral agent, comparative genomic analysis, population susceptibility, disease epidemiology, animal reservoirs, laboratory animal models, disease transmission, diagnosis using artificial intelligence interventions, therapeutics and vaccines, and disease mitigation measures to combat disease dissemination.

Coronavirus Disease 2019 (COVID-19), caused by a novel coronavirus named Severe Acute Respiratory Syndrome - Coronavirus-2 (SARS-CoV-2), emerged in early December 2019 in China and attained a pandemic situation worldwide by its rapid spread to nearly 167 countries with 287.239 confirmed cases and 11.921 human deaths with a case fatality rate (CFR) of around 4 per cent. Bats were considered as the reservoir host, and the search of a probable intermediate host is still going on. Animals have anticipated culprit of SARS-CoV-2 as of now. The disease is mainly manifested by pneumonia and related respiratory signs and symptoms, but the involvement of the gastrointestinal system and nervous system is also suggested. The severe form of the disease associated with death is mainly reported in older and immune-compromised patients with pre-existing disease history. Death in severe cases is attributed to respiratory failure associated with hyperinflammation. Cytokine storm syndrome associated with rampant inflammation in response to SARS-CoV-2 infection is considered as the leading killer of COVID-19 patients. COVID-19 patients were reported with higher levels of many pro-inflammatory cytokines and chemokines like IFN-g, IL-1b, IP-10, and MCP-1. Furthermore, severe cases of COVID-19 revealed higher levels of TNF-α, G-CSF, and MIP-1A. Blood profile of the COVID-19 patients exhibits lymphopenia, leucopenia, thrombocytopenia and RNAaemia along with increased levels of aspartate aminotransferase. SARS-CoV-2 infection in pregnant women does not lead to fetus mortalities unlike other zoonotic coronaviruses like SARS-CoV and MERS-CoV, with no evidence of intrauterine transmission to neonates. Rapid and confirmatory diagnostics have been developed, and high efforts are being made to develop effective vaccines and therapeutics. In the absence of any virus-specific therapeutic, internationally health care authorities are recommending adoption of effective prevention and control measures to counter and contain this pandemic virus. This paper is an overview of this virus and the disease with a particular focus on SARS-COV-2 / COVID-19 clinical pathology, pathogenesis and immunopathology along with a few recent research developments.

Noni juice is a globally popular health beverage that originates from the tropics. Traditional Tahitian healers believe the noni plant to be useful for a wide range of maladies, and noni juice consumers throughout the world have similar perceptions. Nevertheless, human clinical trials are necessary for an understanding of what the health benefits of noni juice truly are. A review of published human intervention studies suggest that noni juice may provide protection against tobacco smoke-induced DNA damage, blood lipid and homocysteine elevation, and systemic inflammation. Human interventions studies also suggest that noni juice may improve joint health, increase physical endurance, increase immune activity, inhibit glycation of proteins, aid weight management, help maintain bone health in women, help maintain normal blood pressure, and improve gum health. Further, these studies point to noni juice possessing notable antioxidant activity, more so than the other fruit juices that served as placebos. It is this antioxidant effect, and its interaction with the immune system and inflammation pathways, that may account for many of the observed health benefits of noni juice. However, the existing evidence does have some limitations in its application to noni juice products in general as all the peer-reviewed human interventions studies to-date have involved only one source of French Polynesian noni juice. Geographical factors and variations in processing methods are known to produce commercial noni juice products with divergent phytochemical and nutrient compositions. Therefore, other sources of noni products may have different toxicological and pharmacological profiles.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

This paper formulates the performance evaluation of cloud services as a multicriteria group decision making problem, and presents a fuzzy multicriteria group decision making method for evaluating the performance of cloud services. Interval-valued intuitionistic fuzzy numbers are used to model the inherent subjectiveness and imprecision of the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to ideal solution method and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented to demonstrate the applicability of the proposed fuzzy multicriteria group decision making method for solving the multicriteria group decision making problem in real world situations.

In considering the question of machine as artist, the art object can be analytically separated from its making, and its making can be dualistically conceptualized as process on one hand and experience on the other. One of the reasons we value art is that there was an experience of its making. To better understand what is meant by the experience of art-making, this paper presents results from a qualitative, phenomenological study of a group of artists. These results appear in three groups: feeling, intention and lifeworld. Machines cannot experience art-making, at least not in the same way as humans, and thus they cannot create art but only art-like objects. Even so, in the present century, we should not be asking whether machines can be artists, but rather how machines can help more people experience art-making for themselves.

The purposes of this study were to identify clinician’s characteristics associated with higher prescription rates of long-acting injectable (LAI) antipsychotics, as well as the information sources influencing medical decision-making about treatment of schizophrenia. We surveyed 202 psychiatrists during 6 regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, Strasbourg). Data on the characteristics of practice, prescription rates of antipsychotic and information sources about their clinical decisions were collected. Most of psychiatrists used second-generation antipsychotic (SGA), and preferentially an oral formulation, in the treatment of schizophrenia. SGA LAI was prescribed to 30.4% of schizophrenic patients. The duration and the type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with the higher use of LAI antipsychotics and the lower use of oral SGA. Personal experience, government regulatory approval and guidelines for the treatment of schizophrenia were the 3 main contributing factors guiding the clinical decision-making of clinicians about treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotic. The development of specialised programmes with top specialists should lead to better use of LAI in the treatment of schizophrenia.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

Multi-stakeholder based construction projects are subject to various risk factors due to dynamic business environments. These risks affect project activities which indirectly impact construction costs, resulting in delays and poor building quality. So, managing these project risks requires suitable risk mitigation strategies to evaluate and analyse their severity. Hence, risk evaluation and assessment of construction projects is a multi-criteria decision making (MCDM) problem. In present real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D number domain to handle three types of ambiguous evaluations, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D number based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster-Shafer (D-S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D number extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D number is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches (D-COPRAS, D-ARAS, D-MABAC, and D-TOPSIS). Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed for verify the robustness of the proposed method.

The COVID-19 pandemic is a devastating blow to the entire world community and changes the order of human life. All efforts and strategies are being carried out to contain and reduce the spread of the SARS-COV-2 virus, both by tightening the health protocol and using vaccines to the public. Currently, several vaccines are available and have passed phase 3 clinical trials, such as vector vaccines (Gamaleya Sputnik V Russia, University of Oxford/AstraZeneca, CanSino, and Janssen Pharmaceutical Companies), mRNA-based vaccines (Moderna/BioNTech/Fosun Pharma/Pfizer), inactivated vaccines (SinoVac and SinoPharm from China, Covaxin from Bharat Biotech India), and adjuvanted recombinant protein nanoparticles (Novavax from the USA) are expected to be able to suppress the spread of the virus and produce a minimum of 70 percent herd-immunity in a population. Each vaccine's efficacy varies from the lowest, namely the Sinovac vaccine (CoronaVac) 50% to the highest the Novavax vaccine (NVX-Cov2373) 96% effectivity value. Moreover, further rigorous research is still being carried out for the development of an effective and efficient vaccine.

There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from China and has now spread to 17 other countries. Governments are under increase pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites, and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.

Biosensors are measurement devices that can sense several biomolecules, and are widely used for the detection of relevant clinical pathogens such as bacteria and viruses, showing outstanding results. Because of the latent existing risk of facing another pandemic like the one we are living due to COVID-19, researchers are constantly looking forward to developing new technologies for diagnosis and treatment of infections caused by different bacteria and viruses. Regarding that, nanotechnology has improved biosensors design and performance through the development of materials and nanoparticles that enhance their affinity, selectivity, and efficacy in detecting these pathogens, such as employing nanoparticles, graphene quantum dots, and electrospun nanofibers. Therefore, this work aims to present a comprehensive review that exposes how biosensors work in terms of bacterial and viral detection, and the nanotechnological features that are contributing to achieving a faster yet still efficient COVID-19 diagnosis at the point-of-care.

Objective: To examine the burnout syndrome among the healthcare personnel in Puerto Rico during the COVID-19 pandemic. Methods: Descriptive study that pursues to understand burnout syndrome in the clinical personnel in Puerto Rico. The Maslach Burnout Inventory (MBI) was sent via email to healthcare professionals around the island. Furthermore, open questions were asked to the participants. Results: The overall burnout level on the clinical personnel was found to be moderate. Nonetheless, in physicians, 12.1% had severe burnout levels compared to a 13.1% score in nurses. Additionally, 92.4% of physicians and 100% of nurses had moderate to severe burnout. In the three subscales, nurses scored high levels in all of them, and physicians were high in Emotional Exhaustion and moderate level in Depersonalization and Personal Accomplishment at Work. There were high levels of burnout syndrome of the clinical personnel in Puerto Rico. Conclusion: Since the beginning of the COVID-19 pandemic, over 90% of healthcare professionals in Puerto Rico have been working with moderate to severe burnout syndrome, being the nurses the most affected. Key Words: Burnout syndrome, MBI, Clinical personnel, COVID-19, SARS-CoV-2

Early reports demonstrated the presence of cells with stem-like properties in bone marrow, with these cells having both hematopoietic and mesenchymal lineages. Over the years, various investigations have purified and characterized mesenchymal stromal/stem cells (MSCs) from different human tissues as cells with multi-lineage differentiation potential under the appropriate conditions. Due to their appealing characteristics and potential, MSCs are leveraged in many applications including medicine, oncology, bioprinting and as recent as treatment of COVID-19. To date, reports indicate mesenchymal stromal/stem cells have varied differentiation capabilities into different cell types and demonstrate immunomodulating and anti-inflammatory properties. Reports indicate that different MSCs microenvironments or niche and the resulting heterogeneity may influence their behavior and differentiation capacity. The potential clinical applications of mesenchymal stromal/stem cells have led to an avalanche of research reports on their properties and hundreds of clinical trials being undertaken. The future looks bright and promising for mesenchymal stem cell research with many clinical trials under way to prove their utility in many applications and in the clinic. This report provides an update on the potential broader use of mesenchymal stromal/stem cells, review early observations of the presence of these cells in the bone marrow and their magnificent differentiation capabilities and immunomodulation.

This article addresses reinsurance decision making process, which involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. In contrast to existing literature on pure proportional reinsurance or stop-loss reinsurance, this article focuses on the combination into Proportional-Stop-loss reinsurance design which better addresses interest of both parties. In terms of methodology, the significant contribution of the study is to incorporate Multiple Attribute Decision Making (MADM) into modelling the reinsurance selection. The Multi-Objective Decision Making (MODM) model is applied in designing reinsurance alternatives. Then MADM is applied to aid insurance companies in choosing the most appropriate reinsurance contract. To illustrate the feasibility of incorporating intelligent decision supporting system in reinsurance market, the study includes a numerical case study using simulation software @Risk in modeling insurance claims, and programming in MATLAB to realize MADM. Managerial implications could be drawn from the case study results. More specifically, when choosing the most appropriate reinsurance, insurance companies should base their decision on multiple measurements instead of single-criteria decision making models for their decisions to be more robust.

The Coronavirus disease due to SARS-CoV-2 emerged in Wuhan city, China in December 2019 and rapidly spread more than 200 countries as a global health pandemic. There are more 3 million confirmed cases and around 207,000 fatalities. The primary manifestation is respiratory and cardiac but neurological manifestations are being reported in the literature as case reports and case series. The most common reported symptoms to include headache and dizziness followed by encephalopathy and delirium. Among the complications noted are Cerebrovascular accident, Guillian barre syndrome, acute transverse myelitis, and acute encephalitis. The most common peripheral manifestation was hyposmia. It is further noted that sometimes the neurological manifestations can precede the typical features like fever and cough and later on typical manifestations develop in these patients. Hence a high index of suspicion is required for timely diagnosis and isolation of cases to prevent the spread in neurology wards. We present a narrative review of the neurological manifestations and complications of COVID-19. Our aim is to update the neurologists and physicians working with suspected cases of COVID-19 about the possible neurological presentations and the probable neurological complications resulting from this novel virus infection.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

ChatGPT is gaining widespread acceptance for its ability to generate natural language sentences in response to various inputs and is expected to become a supplementary tool for diagnosing and determining treatment policies in clinical settings. ChatGPT was used to evaluate its ability to perform clinical inference and accuracy in answering questions on the 117th Japanese National Medical Licensing Examination held in February 2023. The exam questions were manually inputted into ChatGPT's window, and the accuracy of ChatGPT's responses was determined based on answers provided by a preparatory school. ChatGPT provided answers for 389 out of 400 questions, and its overall correct answer rate was 55.0%. The correct answer rate for 5-choice-1, 5-choice-2, and 5-choice-3 were 57.8%, 42.9%, and 41.2%, respectively. The highest correct answer rate was for the compulsory exam (67.0%), followed by the specific knowledge exam (54.1%) and the cross category exam (47.9%). The correct answer rate for non-image questions was 56.2% and for image questions, it was 51.5%. The study suggests that ChatGPT has potential to support healthcare professionals in clinical decision-making in Japanese clinical settings, but caution should be exercised in interpreting and using the answers generated by ChatGPT due to room for improvement in performance.

Urban areas are centres of business and innovation. Freight transport is indispensable for the proper functioning of any modern urban society. Urban areas can’t function without an appropriate freight transport system, due to the need to replenish stocks of food and other goods in retail shops. The complexity of the decisions concerning implementation of measures to improve the movement of goods in the city requires tools designed to support this process. The purpose of this article is to introduce the possibility of applying the Analytic Hierarchy Process (AHP) as well as the Decision Making Trial and Evaluation Laboratory Method (DEMATEL) in choosing a set of measures and in analysing in the field of distribution logistics, which will help to solve the delivery problems and streamline the cargo flows in Szczecin, in the context of sustainable development.

Objectives: This study aims to identify, report, and analyze registered and published clinical trials and observational studies for the pharmacological treatment of COVID-19 conducted in China. Methods: A strategic search was conducted via the Chinese Clinical Trial Registry to identify and extract clinical trials and observational studies registered and conducted in China for the pharmacological treatment of COVID-2019 between January 1^st, 2020 and March 21^st, 2020. This was further supplemented by searches conducted via the China National Knowledge Infrastructure (CNKI) database, the MEDLINE database, the World Health Organization (WHO) database, and MedRxiv and BioRxiv electronic platforms for preprint articles, published up until April 8^th, 2020. Studies available in Chinese and English were included in the searches and extracted. A primary descriptive analysis was performed for registered clinical trials and observational studies identified in the Chinese Clinical Trial Registry based on the extraction of the following clinical study information: trial ID, planned date of enrollment, recruitment status, study design, population, sample size, intervention/exposure group, control /reference group, dosage, and primary outcomes. A secondary descriptive analysis was performed for published clinical trials and observational studies identified from the supplementary databases based on the extraction of the following published clinical study information: study design, population, intervention/exposure group, control /reference group and main results as appropriate. Results: A total of 221 clinical trials and observational studies were included from all databases searched. From the Chinese Clinical Trial Registry, 195 registered clinical studies including 170 clinical trials and 25 observational studies were identified and included for primary analysis. From the supplementary databases, 26 published clinical studies including 8 clinical trials and 18 observational studies were included for secondary analysis. Of these 26 published clinical studies, 18 studies, including 3 clinical trials and 15 observational studies were identified from CNKI, 2 studies including 1 clinical trial and 1 observational study from MEDLINE, 2 including 1 clinical trials and 1 observational studies from the WHO database, and 4 including 3 clinical trials and 1 observational studies from MedRxiv and BioRxiv platforms. In the primary analysis, among the 170 clinical trials included from the Chinese Clinical Trial Registry, 101 investigated western medicines (WMs), while 15 investigated Traditional Chinese Medicines (TCMs), and 54 investigated a combination of TCMs and WMs. Among the 25 included observational studies from the Chinese Clinical Trial Registry, 2 investigated WMs, 2 investigated TCMs, and 21 investigated a combination of TCMs and WMs. The total number of exposed patients in all 195 clinical studies from the Chinese Clinical Trial Registry amounted to 24,500. In the secondary analysis, treatment with Lopinavir-ritonavir and treatment with Hydroxychloroquine was not associated with a difference from standard of care in the rate of RT-PCR negativity; treatment with a combination of Lopinavir-ritonavir, interferon α, and Lian-Hua-Qing-Wen capsule was found to significantly improve the effective rate of treatment compared with Interferon α combined with Lian-Hua-Qing-Wen capsule. Conclusions: China is generating a massive source of evidence that is critical for defeating the COVID-19 pandemic. Not only the clinical experience, but also the scientific evidence should be shared with the global scientific community.

The County of Avon in England was abolished in 1996 and replaced by four unitary planning authorities. Recently the authorities have been working closely to develop a West of England Joint Spatial Plan to facilitate better integration of policies on transport, housing, the environment etc. The Joint Spatial Plan team commissioned a multidisciplinary study to investigate whether the West of England has special characteristics of 'place' that engender shared interest and regional affinity, i.e. ‘sense of place’, to which emerging planning policies might positively respond. In this regard the present article is particularly focused on identifying whether the West of England has unique and unifying landscape characteristics, relating to topography, rural scenery, and flora and fauna, which combine with human experiences to distinguish the Region from adjoining areas. It is concluded that the West of England does indeed have real geographical integrity, being bound on all sides by attractive and prominent landscape features - the Mendip Hills, Cotswold Hills and Severn Estuary – that contribute to a sense of identity and belonging among its inhabitants. The alternating limestone ridges and broad clay vales that prevail across the Region’s heart, combined with low lying flatlands on the Region’s western fringe, provide further contrast with neighbouring regions, reinforcing the emotional bond to the landscape. Furthermore, it is suggested that the inhabitants of the West England show a particularly special environmental consciousness that sets them apart from neighbouring populations, complementing the influence of the physical environment in making the Region a special place to live.

Human coronaviruses cause both upper and lower respiratory tract infections in humans. In 2012 a sixth human coronavirus (hCoV) was isolated from a patient presenting with severe respiratory illness. The 60-year-old man died as a result of renal and respiratory failure after admission to a hospital in Jeddah, Saudi Arabia. The aetiological agent was eventually identified as a coronavirus and designated Middle East respiratory syndrome coronavirus (MERS-CoV). MERS-CoV has now been reported in more than 27 countries across the Middle East, Europe, North Africa and Asia. As of July 2017, 2040 MERS-CoV laboratory confirmed cases, resulting in 712 deaths, were reported globally, with a majority of these cases from the Arabian Peninsula. This review summarises the current understanding of MERS-CoV, with special reference to the (i) genome structure, (ii) clinical features, (iii) diagnosis of infection and (iv) treatment and vaccine development.

The emergence of new type of viral pneumonia cases in China, on December 31, 2019; identified as the cause of human coronavirus, labeled as "COVID-19," took a heavy toll of death and reported cases of infected people all over the world, with the potential to spread widely and rapidly, achieved worldwide prominence but arose without the procurement guidance. There is an immediate need for active intervention and fast drug discovery against the 2019-nCoV outbreak. Herein, the study provides numerous candidates of drugs (either alone or integrated with another drugs) which could prove to be effective against 2019-nCoV, are under different stages of clinical trials. This review will offer rapid identification of a number of repurposable drugs and potential drug combinations targeting 2019-nCoV and preferentially allow the international research community to evaluate the findings, to validate the efficacy of the proposed drugs in prospective trials and to lead potential clinical practices.

Glioblastoma (GBM) is the most lethal form of brain tumor, characterized by rapid growth and surrounding tissue invasion. The current standard treatment is surgery followed by radiotherapy, and concurrent chemotherapy, typically with temozolomide. Although extensive research has been performed over the past years to develop an effective therapeutic strategy for the treatment of GBM, efforts have not provided major improvements in the overall survival of patients with GBM. Thus, new therapeutic approaches are urgently needed. A major challenge in the development of therapies for central nervous system (CNS) disorders is overcoming the blood–brain barrier (BBB). In this context, the intranasal (IN) route of drug administration has been proposed as a non-invasive alternative route to directly targeting the CNS. In fact, this route of drug administration may bypass the blood-brain barrier and reduce systemic side effects. Recently, formulations have been developed to further enhance nose-to-brain transport, mainly with the use of nano-sized and nanostructured drug delivery systems. The focus of this review will be on the strategies developed to deliver a number of anticancer compounds for the treatment of GBM using the nasal administration. In particular, the specific properties of nanomedicines proposed for the nose-to-brain delivery will be critically evaluated. The number of preclinical and clinical data reviewed support the idea that nasal delivery of anticancer drugs might represent a breakthrough advancement in the fight against GBM.

Recent findings show that preferences for food items can be modified without external-reinforcements using the cue-approach task. In the task, the mere association of food item images with a neutral auditory cue and a speeded button press, resulted in enhanced preferences for the associated stimuli. Here, in a series of 10 independent samples with a total of 255 participants, we show we can enhance preferences using this non-reinforced method for faces, fractals and affective images as well as snack foods, using auditory, visual and even aversive cues. This change was highly durable in follow-up sessions performed one to six months after training. Preferences were successfully enhanced for all conditions, except for negative valence items. These findings promote our understanding of non-reinforced change, suggest a boundary condition for the effect and lay the foundation for development of novel applications.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

Background: Handover is a critical process for ensuring quality and safety in healthcare. Considerable research suggests that poor handover results in significant morbidity, mortality, dissatisfaction, and excess financial costs. Despite this, little formal attention, education, and evaluation has been given to handover. There is also paucity of data on the opinions of practitioners on the safety of handover.Objectives: The aim of this study was to measure the perceived risk, degree of patient harm and the systems used to support handover, and to understand how this varied by care setting, type of clinical practice, location, or level of experience. Methods: An open, anonymous and confidential online questionnaire covering: (a) respondent characteristics; (b) peer-to-peer handover; (c) internal referrals; (d) discharges and transfers between organisations; and (e) leading and improving handover was conducted with healthcare practitioners and managers from various settings. Results: We gathered a total of 432 completed responses from 26 countries. The average reported performance of handover was rated as 3.9 out of 5.For each type of handover, 12 - 14% reported errors occurring more than weekly. Of those that knew the outcome of such errors, between 29% and 34% reported that they had witnessed moderate or severe harm. 12% and 17% of respondents believed that handover was high or very high risk (See table 4). These respondents were more likely to have witnessed moderate or severe harm, or to be more senior.A wide combination of handover systems was utilised by respondents. 28% - 32% relied exclusively on EPRs (with or without face-to-face contact). 21% used Office documents such as Word and Excel for peer-to-peer handover, and over 30% used hand-written or manual systems. Conclusions: This study suggests the need to do more — and go further — to improve communication and reduce risk during all types of handovers. Clinical leaders should find ways to train and support handover with effective systems, with less experienced staff being the primary focus. More research is needed to demonstrate the interventions that improve the safety of handover.

Urban development and planning are increasingly centered on matters of sustainability, balancing economic development with ecosystem services and biotic structures within urban environments. In addition to these institutional and structural factors, the decision-making process within individual households must be understood to address rising concerns about water use. Therefore, individual characteristics and preferences that influence the use of water also warrant examination. In response to a survey of occupants of single-family residences in the Fresno Clovis Metropolitan Area of California, contextual interviews and focus group interviews with a homeowner sub-sample, we find evidence of an interplay of social-structural, institutional, and cultural factors involved in influencing individual water use behaviors and landscape decision making. The complexity of residential behaviors and decision-making poses some potential issues with regards to the interactions between individual households and institutional actors in matters of water usage and landscaping, as survey respondents indicate relatively little confidence in institutions and groups to make wise water policy decisions. We conclude that the promotion and implementation of sustainable water use practices will require not only environmental education for the citizenry, but also a tailoring of information for environmental educational initiatives that address the particularities of individual neighborhoods and communities.

COVID-19 was identified in Wuhan, China in in December 2019, and rapidly spread worldwide, being declared global pandemic one month later on 30 January 2020. Since its emergence, COVID-19 has raised global concerns associated with drastic measures that were never adopted in any previous outbreak, to contain the situation as early as possible. The 2019 novel corona virus (2019-nCoV) or SARS-CoV-2 is the causative agent of COVID-19. 2019-nCoV genetic sequence was rapidly identified within few days since the first reported cases and RT-PCR kits became available for COVID-19 diagnosis. However, RT-PCR diagnosis carries a risk of false-negative results, therefore additional serologic test are needed. The most important approach in the battle against COVID-19 is rapid diagnosis of suspicious cases, timely therapeutic intervention and isolation to avoid community spread. In this review, we summarize the clinical scenario that raises suspicion of COVID-19 and available laboratory diagnostics.

(1) Background: the NGS based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization. (2) Methods: We implemented and validated the complete automation of the SOPHiA GENETICS’ CE-IVD Hereditary Cancer Solution™ (HCS) libraries preparation workflow on the Hamilton’s STARlet platform. (3) Results: We demonstrate that this automated workflow, used for more than 1000 samples achieved the same performances of manual setup in terms of coverages and reads uniformity, with extremely lower variability of reads mapping rate onto the regions of interest. (4) Conclusions: This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.

Clinical metagenomics (CMg), referred to as the application of next-generation sequencing (NGS) to clinical samples, is a promising tool for the diagnosis of hospital-acquired pneumonia (HAP). Indeed, CMg allows identifying pathogens and antibiotic resistance genes (ARGs), thereby providing the information required for the optimization of the antibiotic regimen. Hence, provided that CMg would be faster than conventional culture, the probabilistic regimen used in HAP could be tailored faster, which should lead to an expected decrease of mortality and morbidity. While the inference of the antibiotic susceptibility testing from metagenomic or even genomic data is challenging, a limited number of antibiotics are used in the probabilistic regimen of HAP (namely beta-lactams, aminoglycosides, fluoroquinolones, glycopeptides and oxazolidinones). Accordingly in the perspective of applying CMg to the early diagnostic of HAP, we aimed at reviewing the performances of whole genomic sequencing (WGS) of the main HAP-causing bacteria (Enterobacteriaceae, Pseudomonas aeruginosa, Acinetobacter baumannii, Stenotrophomonas maltophilia and Staphylococcus aureus) for the prediction of susceptibility to the antibiotic families advocated in the probabilistic regimen of HAP.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

The current discussion about a “risk culture” in financial services was triggered by the recent series of crisis (from the US subprime mortgage crisis to the lock-in trap of central banks in the aftermath of the sovereign debt crisis). The last decade saw a long list of hybris, misconduct and criminal activities by human beings on a single or even a collective basis in banks, in the industry or in the whole economy. As a counter-reaction, financial authorities called for a guidance by a “new” risk culture in financial institutions based on a set of abstract, formal and normative governance processes. In the humanities, culture is a paraphrase for the behaviour in collectives and dynamics of organization found in human societies. Therefore, a “risk culture” should be a link between those normative guidelines and the positive “real-world” decision-making in financial services. This paper will focus on “risk culture” from the perspective of human beings interacting in dynamical and intertemporal commercial relations. In this context, “risk” is perceived by economic agents ex-ante as the consequence of the time-lag between the present and the uncertain future development (compared to a probability distribution calculated by observers ex-post). For all those individual decisions – to be made under uncertainty – future “risk” includes the so-called “normal accidents”, i.e. failures that will happen at some uncertain point in time but inevitable, and it is only the questions, when failure will happen and how to maintain function in the first line of defence. Finally, the sum of all decisions with individual responsibility for things to come is the positive economical context of a “risk culture” in the sense of the (rational) behaviour of an honourable merchant in long-term and repeated commercial relations in a merchant community, as this is a heritage of European business culture.

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

Repeated air pollution exposure is one of the major threats to skin health. Air pollution causes skin damage and accelerates skin ageing mainly through oxidative stress mechanisms. Since it is difficult to minimize skin exposure from air pollutants, especially in urban areas, strategies to protect the skin are needed. On the other hand, plant phenolic compounds have been found to be effective in attenuating cellular oxidative stress and inflammation induced by different air pollutants. Therefore, we conducted a 12-week randomized, placebo-controlled, double blind study to assess the efficacy of a polyphenol-enriched dietary supplement (comprised of olive leaf, lemon verbena, rosemary and Sophora japonica extracts) in reducing pollution-induced oxidative stress and in improving different skin parameters related to skin ageing of Caucasian and Asian volunteers living in a polluted urban European area (Milan). One hundred healthy women were recruited and randomized in the placebo or in the test dietary supplement treatment arms. To assess the efficacy of the dietary supplement, the total antioxidant capacity on saliva (FRAP) was determined, as well as the oxidative damage on skin (lipoperoxides content), the skin moisture (corneometry), the trans-epidermal water loss (Tewameter), the skin radiance and the skin colour, (spectrophotometry), the skin elasticity (cutometry), the skin sebum content (sebumeter), and the skin roughness (image analysis). As a result, both inter-group and intra-group analysis proved that the dietary supplement improved all clinical and biochemical monitored parameters, in both Caucasian and Asian individuals. In conclusion, the results of the study indicate a reduced oxidative stress-induced skin damage in both Asian and Caucasian women living in a polluted urban area.

This paper reports the efforts made and experiences gained in developing the Flood Prevention and Emergency Response System (FPERS) powered by Google Earth Engine, with focus on its applications at the three stages of floods. At the post-flood stage, FPERS integrates various remote sensing imageries, including Formosat-2 optical imagery, to detect and monitor barrier lakes, synthetic aperture radar imagery to derive an inundation map, and high-spatial-resolution photographs taken by unmanned aerial vehicles to evaluate damage to river channels and structures. At the pre-flood stage, a huge amount of geospatial data are integrated in FPERS and are categorized as typhoon forecast and archive, disaster prevention and warning, disaster events and analysis, or basic data and layers. At the during-flood stage, three strategies are implemented to facilitate the access of the real-time data: presenting the key information, making a sound recommendation, and supporting the decision-making. The example of Typhoon Soudelor in August of 2015 is used to demonstrate how FPERS was employed to support the work of flood prevention and emergency response from 2013 to 2016. The capability of switching among different topographic models and the flexibility of managing and searching data through a geospatial database are also explained, and suggestions are made for future works.

Abstract. The COVID-19 has turned in to a global human tragedy and economic devastation. Governments have implemented lockdown measures, blocked international travel, and enforced other public containment measures to mitigate the virus morbidity and mortality. As of today, no drug has the power to fight the infection and bring normalcy to the utter chaos. This leaves us with only one choice namely an effective and safe vaccine that shall be manufactured as soon as possible and available to all countries and populations affected by the pandemic at an affordable price. There has been an unprecedented fast track path taken in R&D by the World community for developing an effective and safe vaccine. Platform technology has been exploited to develop candidate vaccines in a matter of days to weeks and as of now, 108 such vaccines are available. Six of these vaccines have entered clinical trials. As clinical trials are ‘rate-limiting’ and ‘time-consuming’, many innovative methods are in practice for a fast track. These include parallel phase I-II trials and obtaining efficacy data from phase IIb trials. Human ‘challenge experiments’ to confirm efficacy in humans is under serious consideration. The availability of the COVID-19 vaccine has become a race against time in the middle of death and devastation. There is an atmosphere of tremendous hype around the COVID-19 vaccine and developers are using every moment to make claims, which remain unverified. However, concerns are raised about a rush to deploy a COVID-19 vaccine. Applying ‘Quick fix’ and ‘short cuts’ can lead to errors with disastrous consequences.

A cross-sectional study was carried out to determine the prevalence of subclinical mastitis (SCM) among medium to large scale household dairy farms in southwestern district, Jhenaidah, Bangladesh during July to December 2019. A total of 78 (n=100) lactating cows from household dairy farms (N=32) having three or more dairy cows were selected randomly as sampled populations. Milk samples were screened for SCM by using Surf Field Mastitis Test (SFMT). The prevalence of SCM varied among farm level [71.9% (95% CI: 53.3-86.3)], individual animal level [67.9% (95% CI: 56.4-86.3)] and quarter level [29.5% (95% CI: 24.5-34.9)]. Descriptive statistics represented the farmers and farm demography, characteristics of the sampled population, and overall management feature. Random Effect Logistic Regression identified, Body Condition Score (BCS) [OR=3.8 and 2.9, at cows level and quarter level respectively (BCS-2 vs. BCS-≥3)], and breed [OR=5.1 and 2.9, at cows level and quarter level respectively (HF× Sahiwal vs. HF × Local)] as potential risk factors. This study shows that SCM is highly prevalent in the study area, which is a major threat to the dairy industry's production performance. Regular screening by SFMT, proper hygiene, improve the management system, and farmer’s awareness is required to control the disease.

Economic development and advancements in information technology contributed to the shift on accounting objective from commission responsibility view to decision-making useful view. The decision-making useful view claims that the accounting measurement methods should be changed to improve the usefulness of accounting information, to enable information users to make correct decisions. In addition, the development of financial instruments leads to the birth of fair value accounting. In recent years, fair value has been one of the most important measurement methods in the International Accounting Standard and US Generally Accepted Accounting Principles. However, China has been quite cautious when it comes to applying fair value full time, especially after the global financial crisis happened in 2008, wherein fair value accounting was exposed to strong suspicion. Hence, studying the value relevance of fair value is of great significance to exploring the accounting profession and reforming accounting standards. This paper attempts to investigate the value-relevance of fair value based on the data of listed financial companies and manufacturing companies in China. The results indicate that the fair value information in the financial industry has a strong explanatory influence to corporate share price. In contrast, the fair value information shows limited relevance in the manufacturing industry. Finally, consulting with the results of normative analysis and empirical study, this paper suggests several rational advices for the application of fair value in China.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false positive results), PFN (probability of false negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

Neutrosophic cubic set is the hybridization of the concept of neutrosophic set and interval neutrosophic set. Neutrosophic cubic set has the capacity to express the hybrid information of both the interval neutrosophic set and the single valued neutrosophic set simultaneously. As newly defined, little research on the operations and applications of neutrosophic cubic sets appear in the current literature. In the present paper we propose the score, accuracy functions for neutrosophic cubic sets and prove their basic properties. We firstly develop TODIM method to solve multi attribute group decision making in neutrosophic cubic set environment, which we call NC-TODIM. Also, we solve a MAGDM problem using the proposed NC-TODIM method to show the applicability and effectiveness of the developed method. We also conduct sensitivity analysis to show the impact of ranking order of the alternatives for different values of attenuation factor of losses for multi-attribute group decision making problem.

Scientific, technical and bioinformatics advances have made it possible to establish analytics-based molecular biosimilarity for the approval of biosimilars. If the molecular structure and other product- and process-related attributes are comparable within the limits of testing then a biosimilar candidate would have safe safety and efficacy as its reference products. The current model of animal and human testing becomes redundant since all of these studies have much lower sensitivity and reproducibility in confirming biosimilarity. The recent AI-based protein structure prediction model has confirmed that the 3D structure can be predicted from the amino acid sequence, reducing the need for structural analysis; however, the new test methods based on MS are millions of times more sensitive and accurate. While the regulatory agencies have begun waiving animal testing and, in some cases, clinical efficacy testing, removing clinical pharmacology profiling brings a dramatic paradigm shift, reducing development costs without compromising safety and efficacy. Also shared is a list of 160+ products ready to enter as biosimilars. Major actions from regulatory agencies and developers are required to make this paradigm shift.

A nutritional approach could be a promising strategy to prevent or slow the progression of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease, since there is no effective therapy for these diseases so far. The beneficial effects of omega-3 fatty acids are now well established by a plethora of studies through their involvement in multiple biochemical functions, including synthesis of antinflammatory mediators, cell membrane fluidity, intracellular signalling and gene expression. This systematic review will consider epidemiological studies and clinical trials that assessed the impact of supplementation or dietary intake of omega-3 polyunsaturated fatty acids on neurodegenerative diseases such as Parkinson’s and Alzheimer’s diseases. Indeed, treatment with omega-3 fatty acids, being safe and well tolerated, represent a valuable and biologically plausible tool in the management of neurodegenerative diseases in their early stages.

Leaders are failing to respond to the climate and environmental urgency the world is facing. A growing action gap, clearly visible during the recent CoP25, has been fueled by leaders' inability to respond efficiently to the mounting threats scientists—and increasingly society—are concerned about. Bridging this gap and tackling the growing polarization within society calls for leaders to accept the full complexity of the issues the world is facing. This will require them to question their understanding of these geopolitical affairs and embrace the dynamics at play, and avoid falling back on simplistic cognitive models. We propose a heuristic to convey the pathways available to decision-makers to make their way out of the current inaction impasse. By breaking free of this deadlock, a social transition will have the potential to take place, helping us to avoid crossing the climate system tipping points.

A face serum composed of a combination of biologically active compounds was evaluated for safety and efficacy in vitro, in a repeat insult patch test and in a human clinical efficacy trial. The serum inhibited tyrosinase activity modestly, decreased collagenase activity and exhibited notable free radical scavenging activity in vitro. It is gentle to the skin, as the serum did not irritate the skin or produce symptoms of allergic contact dermatitis in the 55 healthy adults that participated in the repeat insult patch test. In the efficacy trial, daily application of the face serum for 30 days significantly increased skin hydration, with all 35 volunteers experiencing improvement. Substantial improvements in skin elasticity, roughness (fine lines and wrinkles), and brightness also occurred during the trial. Dermatological examination also revealed a trend for reduced comedone count with use of the serum. Self-assessment responses revealed that all volunteers experienced improvements in multiple skin quality parameters and that participant perceptions are consistent with the results of the instrumental analyses. These findings indicated that the measured improvements in skin quality are not only statistically significant but are also clinically relevant as they were great enough for users of the face serum to feel and recognize.

Severe disease and uremia are risk factors for neurological complications of coronavirus disease-2019 (COVID-19). An in-depth analysis of a case series was conducted to describe the neurological manifestations of patients with COVID-19 and gain pathophysiological insights that may guide clinical decision-making – especially with respect to the cytokine release syndrome (CRS). Extensive clinical, laboratory, and imaging phenotyping was performed in five patients. Neurological presentation included confusion, tremor, cerebellar ataxia, behavioral alterations, aphasia, pyramidal syndrome, coma, cranial nerve palsy, dysautonomia, and central hypothyroidism. Neurological disturbances were remarkably accompanied by laboratory evidence of CRS. SARS-CoV-2 was undetectable in the cerebrospinal fluid. Hyperalbuminorachy and increased levels of the astroglial protein S100B were suggestive of blood-brain barrier (BBB) dysfunction. Brain MRI findings comprised evidence of acute leukoencephalitis (n = 3, of whom one with a hemorrhagic form), cytotoxic edema mimicking ischemic stroke (n = 1), or normal results (n = 2). Treatment with corticosteroids and/or intravenous immunoglobulins was attempted – resulting in rapid recovery from neurological disturbances in two cases. Patients with COVID-19 can develop neurological manifestations that share clinical, laboratory, and imaging similarities with those of chimeric antigen receptor-T cell-related encephalopathy. The pathophysiological underpinnings appear to involve CRS, endothelial activation, BBB dysfunction, and immune-mediated mechanisms.

Social plasticity, defined as the ability to adaptively change the expression of social behavior according to previous experience and to social context, is a key ecological performance trait that should be viewed as crucial for Darwinian fitness. The neural mechanisms for social plasticity are poorly understood, in part due to skewed reliance on rodent models. Fish model organisms are relevant in the field of social plasticity for at least two reasons: first, the diversity of social organization among fish species is staggering, increasing the breadth of evolutionary relevant questions that can be asked. Second, that diversity also suggests translational relevance, since it is more likely that “core” mechanisms of social plasticity are discovered by analyzing a wider variety of social arrangements than relying on a single species. We analyze examples of social plasticity across fish species with different social organizations, concluding that a “core” mechanism is the initiation of behavioral shifts through the modulation of a conserved “social decision-making network”, along with other relevant brain regions, by monoamines, neuropeptides, and steroid hormones. The consolidation of these shifts may be mediated via neurogenomic adjustments and regulation of the expression of plasticity-related molecules (transcription factors, cell cycle regulators, and plasticity products).

This study evaluated the effect of low-level laser irradiation induces by diode laser on the speed of orthodontic tooth movement of canines submitted to initial retraction. Twenty-four mandibular canines were retracted by using NiTi spring (force of 150 g/side). Thirteen of those were irradiated with a diode laser 980-nm diode laser (Wiser Laser Doctor Smile, Lambda) operating at an 810-nm wavelength (1 W of output power, continuous wave of 66.7 J/cm2) that was equipped with a 0.6-mm optical fiber in continuous-wave mode. The canine retraction was accomplished by using prefabricated coil springs. The right of the mandible was chosen to be irradiated with the laser, whereas the left side was considered the control without laser irradiation. The laser was applied with 0-, 3-, 7-, and 14-day intervals. The amount of canine retraction was measured with a digital electronic caliper while the pain level was prompted by a patient questionnaire. The speed of tooth movement was significantly greater in the laser group than in the control group. The pain intensity was also at a lower level in the laser group. Our findings suggest that diode laser therapy can highly accelerate tooth movement during orthodontic treatment and can also effectively reduce pain level.

Steroid analysis is important in the clinical assessment of endocrine function in health and disease. Although tandem mass spectrometry methods coupled with chromatographic separation are considered the gold standard analytical technique in this setting, enabling profiling of multiple steroids in a single sample, sample processing can be labour-intensive. Here we present a simple, efficient automated 96-well Supported Liquid Extraction method with dichloromethane/isopropanol as organic solvent, carried out on a Extrahera automated sample handler (Biotage), which completes sample preparation of 80 plasma samples (200µL) in 90 minutes. Compounds were separated on a Kinetex C18 column (150x3mm;2.6um) using a mobile phase of methanol and water (0.1% formic acid). The run time was 16 minutes on a Nexera uHPLC system (Shimadzu) with a QTrap 6500+ linear ion trap mass spectrometer (AB Sciex). Precisions ranged 8.1 to 18.1% RSD, bias -10.1-5.8%, and extraction recoveries 73.5-111.9%. LOQs ranged between 0.025–0.500 ng/mL.

The influence and importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrated a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, was analyzed as a case study. Initially, more than 30 variables were considered, and the adopted soft computing technique retrieved 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

Coastal areas are increasingly being damaged with the expansion of seawater intrusion areas. We suggest a three-step method for reducing seawater intrusion areas by predicting future damage to groundwater being used continuously. First, the area most vulnerable to seawater intrusion damage is selected from among 25 areas on the west coast of the Republic of Korea. Having identified the Taean area as the region in question in the second step, we use RCP 4.5 and 8.5 as future sea level rise scenarios and predict the future usage of groundwater using linear-regression analysis of data for the past 10 years. Consequently, for RCP 8.5 (groundwater-usage scenario 1.0), 68.5% of the total Taean area is projected to be influenced by seawater intrusion. In the third step, the effectiveness of seawater intrusion reduction measures is analyzed considering the projected future situation and the local characteristics of the Taean area. After considering the effects of alternative locations, as well as seawater intrusion related data, alternatives were prioritized using a multi-criteria decision-making method. Consequently, 3, 5, and 4 were prioritized in the listed order, and we judged that by applying seawater intrusion area reduction measures according to this result, we will achieve the biggest effect.

Increasing rates of infection by antibiotic resistant bacteria have led to a resurgence of interest in bacteriophage (phage) therapy. Several phage therapy studies in animals and humans have been completed over the last two decades. We conducted a systematic review of safety and toxicity data associated with phage therapy in both animals and humans reported in English-language publications from 2008 – 2021. Overall, 69 publications met our eligibility criteria including 20 animal studies, 35 clinical case reports or case series, and 14 clinical trials. After summarizing safety and toxicity data from these publications, we discuss potential approaches to optimizing safety and toxicity monitoring with the therapeutic use of phage moving forward. In our systematic review of the literature, we found few, but no serious, adverse events associated with phage therapy. Comprehensive and standardized reporting of potential toxicities associated with phage therapy has generally been lacking in the published literature. Structured safety and tolerability endpoints are necessary when phages are administered as anti-infective therapeutics.

AI is often looked at as a logical rational way to develop a games agent that methodically looks at options and delivers rational solutions. This paper is based on developing an AI agent that plays a game with a similar emotive content like a human. The purpose of the study was to see if the incorporation of this emotive content would influence the outcomes within the game Love Letter. In order to do this an AI agent with an emotive layer was developed to paly the game over a million times. A lower win/loss ratio demonstrates that to some extent this methodology was vindicated and a 100 per cent win for the AI agent did not happen. Machine learning techniques were modelled purposely so as to match extreme models of behavioural change. The results demonstrated a win/loss ration of 0.67 for the AI agent and in many ways reflectd the frustration that a normal player would exhibit during game play. As was hypothesised the final agent investment value was, on average, lower after matchplay than its initial value.

The objective of the paper is to introduce new similarity measure for single valued neutrosophic sets based on logarithm function. We define logarithm similarity measure and their weighted similarity measure for single valued neutrosophic sets. Then we define hybrid logarithm similarity measure and weighted hybrid logarithm similarity measure for single valued neutrosophic sets. We prove the basic properties of the proposed measures. We then define an entropy function using logarithm function to determine unknown attribute weights. We develop a novel multi attribute group decision making strategy for single valued neutrosophic sets based on the weighted hybrid logarithm similarity measure. We address an illustrative example to demonstrate the effectiveness and aptness of the proposed strategies. We conduct a sensitivity analysis of the developed strategy. We also make a comparison between the obtained results from proposed strategies and different existing strategies in the literature.

The Pythagorean fuzzy set (PFS), which is characterized by a membership and a non-membership degree and the square sum of them is less or equal to one, can act as an effective tool to express decision makers’ fuzziness and uncertainty. Considering that the Heronian mean (HM) is a powerful aggregation operator which can take the interrelationship between any two arguments, we study the HM in Pythagorean fuzzy environment and propose new operators for aggregating interval-valued Pythagorean fuzzy information. First, we investigate the HM and geometric HM (GHM) under interval-valued intuitionistic fuzzy environment and develop a series of aggregation operators for interval-valued intuitionistic fuzzy numbers (IVIFNs) including interval-valued intuitionistic fuzzy Heronian mean (IVIFHM), interval-valued intuitionistic fuzzy geometric Heronian mean (IVIFGHM), interval-valued intuitionistic fuzzy weighted Heronian mean (IVIFWHM) and interval-valued intuitionistic fuzzy weighted geometric Heronian mean (IVIFWGHM). Second, some desirable and important properties of these aggregation operators are discussed. Third, based on these aggregation operators, a novel approach to multi-attribute decision making (MADM) is proposed. Finally, to demonstrate the validity of the approach, a numerical example is provided and discussed. Moreover, we discuss several real-world applications of these operators within policy-making contexts.

In recent years, music education in Ibero-America has been losing ground within the school environment in favor of the development of curricular systems that benefit academic results in standardized tests. Despite this, several studies in the field of cognitive neurosciences have found evidence of great relevance and in which it can be observed how music education can favor cognitive development and performance in practically all stages of human development, with important results in language tasks, attention, and executive functions such as planning, inhibition, cognitive flexibility and working memory.

Various tissue resident stem cells are receiving attention from basic scientists and clinicians as they hold certain promise for regenerative medicine. This paper is intended to clarify and facilitate the understanding, development and adoption of regenerative medicine in general and specifically of therapies based on unmodified, autologous adipose-derived regenerative cells (UA-ADRCs). To this end, results of landmark experiments on stem cells and stem cell therapy performed in the labs of the authors are summarized, the most intriguing of which are the following: (i) vascular associated mesenchymal stem cells (MSCs) can be isolated from different organs (adipose tissue, heart, skin, bone marrow and skeletal muscle) and differentiated into ectoderm, mesoderm and endoderm, providing significant support for the hypothesis of the existence of a small, ubiquitously distributed, universal vascular associated stem cell with full pluripotency; (ii) the orientation and differentiation of MSCs are driven by signals of the respective microenvironment; and (iii) these stem cells irrespective of the tissue origin exhibit full pluripotent differentiation potential without any prior genetic modification or the need for culturing. They can be obtained from a small amount of adipose tissue when using the appropriate technology for isolating the cells, and can be harvested from and re-applied to the same patient at the point of care without the need for complicated processing, manipulation, culturing, expensive equipment, or repeat interventions. These findings demonstrate the potential of UA-ADRCs for triggering the development of an entire new generation of medicine for the benefit of patients and of healthcare systems.

The value of an NBA basketball player varies at each crucial point in time, depending on the player’s career and performance. This study constructs a player’s value assessment model. The model comprises two parts. First, from an objective perspective, entropy is employed to measure each player’s achievement in five categories—rebounds, assists, steals, blocked shots, and scores. The total entropy assessment value is calculated and then combined with the players’ scores to develop a player's value matrix to assess the relative value model among players of the same type.

Objectives: Data sharing has become a requirement of many funding bodies and is becoming a scientific standard in many disciplines. In medical research, however, data sharing can conflict with clinicians’ obligation to protect patients’ privacy. General recommendations on data sharing exist also for clinical research, but so far lack practical and Swiss-specific aspects. The objective of this document is to provide practical recommendations for all relevant aspects of data sharing in agreement with legislation in Switzerland. Methods: This document was written by members of the Swiss CTU Network, a network of academic clinical trial units. The process did not follow a formalized Delphi process. After an internal consensus round, this report is now published as pre-print for external review. A second version will incorporate external comments. We plan to publish this document as a text in progress, as we expect relevant changes in related fields such as the development of further dedicated medical repositories or methodological advances in anonymization techniques. Results: We developed principles and practical recommendations with respect to informed consent, data management plan, anonymization, data structure and format, coding of variables, metadata and documentation, version control, selection of repository, requesting and use of data. We also provide a summary of legal aspects relevant for the Swiss context. Conclusions: The intension to share data has an impact not only after a clinical trial or an observational study is completed, but also during the planning period, the conduct and the analysis phase. Clinical researchers need to be aware at the beginning of a study on how to inform patients and at least the amount of work related to preparing data for sharing, metadata, and any further documentation. This report provides details of aspects to be considered, suggests decision criteria, and provides examples and checklists, in order to support data sharing in practice.

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

Pharmacovigilance databases are showing evidence of injury in the context of the COVID-19 modified mRNA shots. According to recent publications, adverse event reports linked to the mRNA COVID-19 products largely point to the spike protein as an aetiological agent of adverse events, but we propose that the platform itself may be culpable. To assess the safety of current and future mRNA vaccines, further analysis on the risks due to the platform itself, and not specifically the expressed antigen. If harm can be exclusively and conclusively attributed to the spike protein, then it is possible that future mRNA vaccines expressing other antigens will be safe. If harms are attributable to the platform itself, then regardless of the toxicity, or lack thereof, of the chosen payload therein, the platform may be inherently unsafe, pending modification. In this work, we examine previous studies of RNA-based delivery by a lipid nanoparticle and break down the possible etiological elements of harm.

Neutrosophic cubic set (NCS) is one of the important family members of neutrosophic hybrid sets. Neutrosophic cubic set has more strength than other family members of neutrosophic hybrid sets to express incomplete information due to the presence of interval valued neutrosophic set (IVNS) and single valued neutrosophic set (SVNS) in its structure. Cross entropy measure is one of the best way to calculate the divergence of any variable from the priori one variable. In this paper we first define a new cross entropy measure under NCSs environment which we call NC- cross entropy measure. We investigate the basic properties of NC-cross entropy. We also propose weighted NC-cross entropy and investigate its basic properties. We develop a novel multi attribute decision making (MADM) strategy based on weighted NC-cross entropy. To show the feasibility and applicability, we solve a MADM problem using the proposed strategy.

During 2019, the number of patients suffering from cough, fever and reduction of WBC’s count increased. At the beginning, this mysterious illness was called “fever with unknown origin”. At the present time, the cause of this pneumonia is known as the 2019 novel coronavirus (2019-nCoV) or the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). The SARS-CoV-2 is one member of great family of coronaviruses. Coronaviruses can cause different kind of illnesses including respiratory, enteric, hepatic, and neurological diseases in animals like cat and bat. Coronaviruses are enveloped positive-stranded RNA viruses. The SARS-CoV-2 has some particular structures for binding to host cells, reproducing itself in cells and damaging human cells. The SARS-CoV-2 can bind angiotensin-converting enzyme 2 (ACE‐2) receptors and cause various difficulties for human. The SARS-CoV-2 can cause either not-serious issues like fever and cough or serious concerns such as multi-organ failure. Source(s) of SARS-CoV-2 is under debate. Malayan pangolin and bat are the most suspicious candidate for being sources of the SARS-CoV-2. The SARS-CoV-2 can be transmitted by various ways such as transmitting from infected human to healthy human and can make severe pneumonia, which can lead to death. The SARS-CoV-2 can infect different kind of people with different ages, races, and social and economic levels. The SARS‐CoV‐2 infection can cause various sorts of clinical manifestations like cough and fever and intensity of signs and symptoms depends on sufferer conditions. Clinicians use all of available documents and tests like laboratory, histopathological and radiological findings for diagnosing new cases and curing patients with high accuracy. At the present time, there is no particular way for treating SARS-CoV-2 infection; neither antiviral drugs nor palliative agents. It seems that the best way for standing against the SARS-CoV-2 infection is preventing from it by social distancing and vaccination. This review tries to prepare an essential brief update about SARS-CoV-2 infection for clinicians.

Biological drugs are inaccessible to more than 80% of the world population due mainly to their high costs; this is a significant concern of the World Health Organization. Biosimilars are supposed to reduce the cost burden, but their approval process is complex, including expensive and irrelevant studies. While the Stringent Regulatory Authorities (SRAs) have adopted the guidance of the FDA or EMA, such adoptions are neither necessary nor practical for the rest of the world (ROW). We present a science-driven, rational approach to formulate regulatory guidelines that will enable faster biosimilars' entry into the ROW without compromising their safety and efficacy. The key recommendations include removing animal and safety efficacy testing, making analytical assessment more robust, and cGMP compliance assured through third-party audits. The ROW countries are also recommended to initiate a rapporteur system available in the EU, to overcome the biases and assure state-of-the-science evaluation as the common understanding of the critical quality attributes evolves. It is anticipated that stronger regional agencies like those in the MENA region, with the leadership of the Kingdom of Saudi Arabia, will help propel the idea faster across the globe.

The paper investigates some similarity measures in interval bipolar neutrosophic environment for multi-attribute decision making problems. At first, we define Hamming and Euclidean distances measures between interval bipolar neutrosophic sets and establish their basic properties. We also propose two similarity measures based on the Hamming and Euclidean distance functions. Using maximum and minimum operators, we define new similarity measures and prove their basic properties. Using the proposed similarity measures, we propose a novel multi attribute decision making strategy in interval bipolar neutrosophic set environment. Lastly, we solve an illustrative example of multi attribute decision making and present comparison analysis to show the feasibility, applicability and effectiveness of the proposed strategy.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are amongst the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this case study. Actual locations of dams constructed in the area were used to verify the results of both methods.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

Single valued neutrosophic set has king power to express uncertainty characterized by indeterminacy, inconsistency and incompleteness. Most of the existing single valued neutrosophic cross entropy bears an asymmetrical behavior and produce an undefined phenomenon in some situations. In order to deal with these disadvantages, we propose a new cross entropy measure under single valued neutrosophic set (SVNS) environment namely SN- cross entropy and prove its basic properties. Also we define weighted SN-cross entropy measure and investigate its basic properties. We develop a new multi attribute group decision making (MAGDM) strategy for ranking of the alternatives based on the proposed weighted SN-cross entropy measure between each alternative and the ideal alternative. Finally, a numerical example of MAGDM problem of investment potential is solved to show the validity and efficiency of proposed decision making strategy. We also present comparative anslysis of the obtained result with the results obtained form the existing solution strategies in the solution.

In recent years, the consumption of over-the-counter probiotics used to promote health has grown rapidly worldwide and become an industry. In medicine, various studies have proven that probiotics can help improve the immune system and intestinal health. They are usually safe, but in some rare cases, they may cause concerning adverse reactions. Although the use of probiotics has been widely popularized in the public, the results of many probiotics clinical trials are contradictory. Especially for the cancer patients, the feasibility of probiotics management to provide benefits by targeting cancer and lessening anti-cancer side effects requires further investigations. And this review summarizes the interactions between probiotics and the host and current pros and cons of applying probiotics in the cancer patients.

Glioblastoma (GBM), the most common and aggressive primary brain tumor in adults, remains one of the least treatable cancers. Current standard of care—combining surgical resection, radiation, and alkylating chemotherapy—results in a median survival of only 15 months. Despite decades of investment and research into the development of new therapies, most candidate anti-glioma compounds fail to translate into effective treatments in clinical trials. One key issue underlying this failure of therapies that work in pre-clinical models to generate meaningful improvement in human patients is the profound mismatch between drug discovery systems—cell cultures and mouse models—and the actual tumors they are supposed to imitate. Indeed, current strategies that evaluate the effects of novel treatments on GBM cells in vitro fail to account for a wide range of factors known to influence tumor growth. These include secreted factors, the brain’s unique extracellular matrix, circulatory structures, the presence of non-tumor brain cells, and nutrient sources available for tumor metabolism. While mouse models provide a more realistic testing ground for potential therapies, they still fail to account for the full complexity of tumor-microenvironment interactions, as well as the role of the immune system. Based on the limitations of current models, researchers have begun to develop and implement novel culture systems that better recapitulate the complex reality of brain tumors growing in situ. A rise in the use of patient derived cells, creative combinations of added growth factors and supplements, may provide a more effective proving ground for the development of novel therapies. This review will summarize and analyze these exciting developments in 3D culturing systems. Special attention will be paid to how they enhance the design and identification of compounds that increase the efficacy of radiotherapy, a bedrock of GBM treatment.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

Cerebrospinal fluid (CSF)-contacting neurons (CSF-N) located in the surface of both brain ventricles and the central canal (cc) in the spinal cord. The cc and CSF maintain a proliferative niche for neural progenitor cells and play a vital role in development of the brain. The CSF circulates in the ventricles and the subarachnoid spaces with the CSF rhythmic flow: cardiac pulsation and respiratory fluctuation. A new concept of CSF motion may be contrary to the classical one that the direction of CSF motion may vary in direction and may be dynamic in its location. The CSF pressure may also depend on the body position. Moderate music-making has been considered a potential approach for rehabilitative and restorative therapy of brain dysfunctions. Recently, we find that the CSF-Ns are present in both the interior CFS in the cc and also exterior CSF around the surface of the spinal cord. We hypothesize that CSF-N as mechanical sensors in the spinal cord could sense motion of the spinal cord. The myodural bridge is a ligament connecting a pair of deep, upper-neck muscles to the dura mater, which envelops the arachnoid mater and contains the CSF surrounding the brain and the spinal cord. We figure out the term “CSF-static compartment” and classify CSF storage location as rostral pool and caudal pool to demonstrate our hypothesis. We presume that the somatic body movement with music-making and rehabilitation-based interventions would orchestrate the CSF motion with head movement, myodural bridge stretching and puling as well as spinal bending.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

COVID-19 is causing major turmoil around the globe, and the clinical trial industry is likely to face unprecedented challenges to health and business sectors. In an effort to find a suitable treatment and prevention options for COVID-19, several COVID-19 clinical trials are being planned and initiated, while a large number of clinical trials for non- COVID-19 indications are suffering delays. With over more than 1000 trials being disrupted and more trials being added to this category daily, there is a direct impact on trial site activation and patient enrolment. This analysis deals with the specific impacts of the COVID-19 pandemic on the clinical trial and pharmaceutical industry. The objective of this study is to provide an updated information of the disrupted clinical trials and its impact on various therapeutic areas and different drugs. Among the severely affected clinical trials, oncology and CNS trials are the hardest hit therapy areas.This article will certainly emphasize the need for advanced and innovative approaches to maintain the health of the clinical trial ecosystem by continuing the existing trials and the start of the new studies. We have to take and follow necessary actions to guarantee that the initiatives will not be locked during the COVID-19 pandemic, both for the treatment of patients and for the researchers to conduct decision-relevant clinical trials.

The science‐policy interface in climate change adaptation became better managed over the past decades. However, the scientists and other knowledge producers, as well as policy makers still need to take bolder steps to more effectively engage with others to apply science and shape up policies. This paper aims to provide practical recommendations, intended to promote conversations between science and policy sectors to address climate change issues. Here, I used two different approaches to synthesize experiences and identify recommendations: a literature review and a case study. The paper stress main findings: (1) The linear communication model is still commonly involved in the science - policy dialogue and proved to be useful to increase the relevance of science and data products to decision makers. (2) When a gap between knowledge producer and knowledge user or decision maker exists, the need for a third party to specialize in bridging the gap become essential. (3) Indigenous people and knowledge must be involved in adaptation policy making based on legitimation local and traditional knowledge, designing the consultation process to broadly engage local and indigenous people, facilitating meaningful dialogues between traditional knowledge and science, and developing initiatives to strengthen skills and capacity of indigenous communities.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analyzed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

Forests across the world stand at the crossroad with climate and land use changes shaping their future. Despite the demonstration of political will and global efforts, forest loss, fragmentation and land degradation continue unabated. No clear evidence exists that these initiatives are working. Why are policies designed to halt deforestation and increase restoration of forest landscapes failing? A key reason for this apparent ineffectiveness lies in the failure to recognize the agency of the stakeholders involved and the adaptive capacities of the systems we seek to steer. Landscapes do not happen. We make them. They are the result of the sum of individual actions and decisions made by all stakeholders, and the interactions between these and biophysical processes. Likewise, forest transitions are not ecological, but social and behavioral. They are a product of the way humans manage ecosystems. Decision-makers need to integrate better representations of people’s agency in their mental models. We suggest possible solution pathways to overcome this key current barrier. These involve eliciting mental models behind policy decision, changing perspectives to better understand divergent points of view and refining strategies through explicit theories of change. Games designed to represent the constraints and opportunities that exist in the landscapes can help decision makers in these task.

Coronavirus disease 2019 (COVID-19) caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has reached a global epidemic across the world after first reported in Wuhan, China’s Hubei province in December 2019. The pandemic is also associated with acute respiratory distress syndrome (ARDS) characterized by excess inflammation, progressive arterial hypoxemia and dyspnea. Mesenchymal stem/ stromal cells (MSCs) have been investigated as treatment for ARDS due to immunomodulatory property. Exosomes derived from MSCs play an important role in paracrine signaling of MSCs, thereby contributed to immunomodulation of the immune microenvironment. Exosomes are emerged as potential alternative to MSC cell therapy with superiority of safety. In this review, we will introduce MSC-derived exosomes and briefly discuss current progress on MSCs and exosomes in ARDS, which may have clinical implications in pathogenesis and treatment of COVID-19.

Recently, there have been revolutions in the development of both gene therapy and genome surgical treatments for inherited diseases. Much of this progress has been centered around hereditary retinal dystrophies, because the eye is an immune-privileged and anatomically ideal target. Gene therapy treatments, already demonstrated to be safe and efficacious in numerous clinical trials, are benefitting from the development of new viral vectors, such as dual and triple AAVs. CRISPR/Ca9, which revolutionized the field of gene editing, is being adapted into more precise “high fidelity” and catalytically dead variants. New CRISPR endonucleases, such as CjCas9 and Cas12a, are generating excitement in the field as well. Stem cell therapy has emerged as a promising alternative, allowing human embryo derived stem cells and induced pluripotent stem cells to be edited precisely in vitro and then reintroduced into the body. This article highlights recent progress made in gene therapy and genome surgery for retinal disorders, and it provides an update on precision medicine FDA treatment trials.

Decision-making is a complex process of selecting an option from the given choices by analyzing the background information like risk, loss, and gain within the alternative options presented. It has been observed in earlier studies that people are prompt to make less rational decisions when choices are given in a language less known to them. Therefore, to understand the effect of languages on decision-making, we have questioned native Hindi speakers in French and English. French being the foreign language, and English as their second language. Thus, this effect of a non-native language brings to light the important role that the native language plays routinely in judgment and decision-making. In this paper, we developed a Neuropsychological assessment to decipher the effects on decision-making between choices when given in foreign language and second language in comparison with the native language of an individual, which is termed as foreign language effect(Fle). We have explored various possible situations to understand the foreign language effect(Fle) in decision-making and does this change translates when the decision is to be made in the second language. Our study concludes that the Foreign language is least affected by the intuitive biases, followed by the second language, and the native language is most affected by it.

Financial capacity instruments are psychometric tools designed to evaluate individual decisional capacity based on financial decisions. As tests are complex and need special conditions for administration and evaluation, it is difficult to use them in daily geriatric clinics. Our scoping review objective was to evaluate existing financial capacity instruments from the perspective of simplicity and portability. We evaluated one English speaking knowledge database (Medline) using a dedicated MeSH terminology. The review yielded one independent instrument, The Semi-Structured Clinical Interview for Financial Capacity, that can be easy adapted for every-day clinical use. It is simple to understand and perform and do not need trained personnel for administration. It can be finalized in 15 minutes. Initially validated on 261 subjects (with different forms of cognitive impairment), it showed good accuracy and precision mainly in subjects with cognitive impairment. The test is less apt to detect early or fluctuating cognitive impairment. Simplicity, the main advantage of the test, allows gamification fact that increases portability. Familiar images (coins, money) that are used for performing simple tasks does not need complex translation and adaptation. In form of a game, the test is suitable for serial administration, increasing the chance for early capacity reduction detection. Results reflect a physician judgement related to the subjects’ capacity to understand and execute simple financial instructions and not financial proficiency scores. The main limitation of our review is that we investigated only one, English speaking, knowledge database. The scoping strategy generated a financial capacity instrument that can be used in geriatric clinics for early diagnostic of decisional capacity reduction. Further studies are needed to evaluate the reliability and validity of the test in conditions of serial administration and in populations having various financial experience.

Despite great strides being achieved in improving cancer patients’ outcomes through better therapies and combinatorial treatment, several hurdles still remain due to therapy resistance, cancer recurrence and metastasis. Drug resistance, culminating in relapse and metastatic disease continue to be associated with fatal disease. Cancer stem cells (CSCs) are a subpopulation of cancer cells known to be resistant to therapy and cause metastasis. Whilst the debate on whether CSCs are the origins of the primary tumor rages on, CSCs have been further characterised in many cancers with data illustrating that CSCs display great abilities to self-renew, withstand therapies due to enhanced epithelial to mesenchymal (EMT) properties, enhanced expression of ABC membrane transporters, activation of several survival signaling pathways and increased immune evasion DNA repair mechanisms. CSCs also display great heterogeneity with the consequential lack of specific CSC markers presenting a great challenge to their targeting. In this updated review we re-visit CSCs within the tumor microenvironment (TME) and present novel treatment strategies targeting CSCs. These promising strategies include targeting CSCs-specific properties using small molecule inhibitors, immunotherapy, microRNA mediated inhibitors, epigenetic methods as well as targeting CSC niche-microenvironmental factors and differentiation. Lastly, we present recent clinical trials undertaken to try to turn the tide against cancer by targeting CSC-associated drug resistance and metastasis.

This study aimed to explore how Indonesian clinical psychologists (CPs) address aspects of spirituality and religion (SR), particularly their attitudes towards and experience of it, on the mental health context. Semi-structured interviews were conducted with 43 CPs in public health centres in Yogyakarta Province, Indonesia. Data were anyalsed using deductive thematic analysis and they generated ten sub-themes which were merged into three central themes. The first theme was experiences related to SR, particularly in Indonesian sociocultural context. The second theme concentrated on participants’ clinical experience related to SR integration into clinical practice. The last theme highlighted the effort made by participants to create holistic mental health services. The originality of this study was represented by the interview quote in the title, “Doing my profession is also part of worship”. It was found that SR is part of culture and belief among Indonesian people, including CPs and mental health treatment clients. In summary, participants genuinely acknowledged that they were not able to completely detach SR from their professional practice. However, participants also pointed out that they were different with spiritual-religious healers (SRHs) and favourably welcomed future collaboration with credible SRHs. This positive attitude embodied a holistic care approach that recognises the diverse biopsycho-social-spiritual needs of clients. Therefore, professional organisations and psychology faculties should establish regulations and education of SR in psychology curricula and conventional psychotherapy to achieve this holistic mental health services in Indonesia.