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Alzheimer’s disease (AD) is the primary cause of dementia in elderly people and needs extensive professional care. The aim of our study was to assess the level of knowledge among primary health-care providers, primarily Family Physicians (FPs) and community pharmacists (CPs), about AD and its treatment.

A cross-sectional study using Google Forms sent by email or message to FPs and CPs in Istanbul in June–July 2023. Turkish modified the Alzheimer’s Disease Knowledge Scale (ADKS) and Alzheimer’s Medicines Knowledge Level Questionnaire (AMKLQ) were used in this study.

A total of 63 FPs with a mean age of 35.3 ± 7.8 and 138 CPs with a mean age of 38.6 ± 12.6 enrolled in the study. There was no statistically significant difference between FPs and CPs in terms of total ADKS score (19.82 ± 2.30 vs 19.23 ± 3.08, p = 0.136), but there was a significant difference in terms of total AMKLQ score (4.31 ± 1.40 vs 3.81 ± 1.49, p = 0.020). Health-care providers with Alzheimer’s training had a higher total AMKLQ score (OR =1.08 CI 95% [1.03–1.14], p = 0.012).

FPs’ knowledge of AD is on par with that of CPs. Nevertheless, it has been observed that FPs have more proficiency in delivering accurate responses to the AMKLQ and ADKS inquiries, which encompass crucial details regarding the treatment of AD. The sole determinant of the highest mean AMKLQ score was found to be professional education. Collectively, these arguments emphasize the need for primary health-care practitioners to obtain comprehensive and ongoing education regarding AD and its treatment.

There is increasing interest in the UK in developing environments that support people who are neurodiverse. This paper aims to report on a project to develop a cognitively supportive environmental assessment tool to improve the design of health centres, where the majority of National Health Service consultations take place, for all users, including people living with dementia and those who are neurodiverse.

A three-stage process was used: a literature review; the development of a matrix of key design features for people living with dementia, autism and other neurodiverse conditions; and the development of an environmental assessment tool and guide for users, which included easy-to-read versions to maximise service user involvement.

The overarching concepts of dementia-friendly design can be adapted to create designs for everyone, including those who are neurodiverse.

There is a paucity of research in environmental design for primary care from the patient’s perspective, and, more generally, further research on design for adults with learning disabilities and autism is needed.

The tools are free to download. With adaptation, they have potential applicability across health and care settings.

This project has confirmed that the principles of dementia-friendly design are applicable, with modifications, to a wider group of neurodiverse people. Critically, each person’s response to sensory stimuli is individual rather than determined by their condition.

This review paper delves into the comprehensive understanding of Ashwagandha, spanning its botanic occurrence, conventional applications, extraction techniques and pivotal role in addressing various disorders.

Introduction Ashwagandha, also known as Withania somnifera, is a remarkable botanical resource with a rich history of use in traditional medicine.

In botany, Withania somnifera thrives in diverse ecosystems, particularly in tropical and subtropical regions. Its extensive distribution across regions, the Canary Islands, South Africa, the Middle East, Sri Lanka, India and China underscores its adaptability and resilience. The traditional uses of Ashwagandha in Ayurvedic and indigenous medicine systems have persisted for over 3,000 years. With over 6,000 plant species utilized historically, India, often regarded as the “botanical garden of the world,” has firmly established Ashwagandha as a cornerstone in traditional healing practices.

Extraction methods play a pivotal role in harnessing the therapeutic potential of Ashwagandha. Ultrasonic-assisted extraction and high-performance liquid chromatography are among the techniques employed to obtain the key bioactive compounds. Ashwagandha’s significance in modern medicine is underscored by its potential to address a spectrum of health issues. The multifaceted bioactivity of Ashwagandha is attributed to its antioxidant, anti-inflammatory, heart conditions, metabolic disorders, renal ailments, hepatic diseases and adaptogenic properties, making it a subject of increasing interest in contemporary medical research. This review synthesizes the assorted perspectives of Ashwagandha, from its botanical roots and conventional employments to its advanced extraction strategies and its intention to basic well-being challenges, advertising important bits of knowledge for analysts, specialists and healthcare experts alike.

The focus of this study was to evaluate the relationship between research publications in the pesticide field, a country’s gross domestic product (GDP) and GDP per capita. The study aims to analyze pesticide use in association with a country’s population and research publications. The purpose of this study is to uncover the country’s contribution to pesticide research and assess the financial resources allocated to it as a percentage of their GDP by exploring these factors.

The Web of Science database was used to retrieve data for the period of 2001–2020. The use of scientometric indicators allowed for the analysis of the collaborative patterns and active performance of countries in pesticide research. Socio-economic analysis was used to determine the contribution of countries toward pesticide research.

This study demonstrated a strong association (0.952%) between a country’s GDP and its research publications in the field of pesticide research. Countries, such as Denmark, Belgium and Australia, have benefited from global collaboration, which has enhanced their research efforts. Despite ranking lower in pesticide utilization, India focused on pesticide research, as indicated by its high publication/GDP per capita ratio (0.26).

Research on pesticides directly impacts agricultural practices, which, in turn, influence the economic production of the agricultural sector. Changes in pesticide usage can have inference for crop yields, food price and, eventually, the GDP. Comparative analysis can assist in evaluating the efficiency of regulatory policies in balancing ecological concerns with economic interests. Changes in regulations may impact both pesticide usage and economic outcomes.

This study aims to find out how pomposia fruit powder (Syzygium cumini L.) mixed with roasted coffee (RCO) affected antioxidants, phytochemicals, lipid peroxidation inhibition and sensory attributes.

Pomposia fruits (Syzygium cumini L.) powder (PFP) was integrated with RCO at levels of 0.0%, 5.0%, 10.0%, 15.0% and 20.0%. RCO, PFP and binary mixtures containing RCO: PFP were evaluated for their total phenolics (TP), total flavonoids (TF), anthocyanin content (AC), antioxidant activity and phenolic compounds fractionation. The oxidative indices of coffee oil samples were measured throughout different storage intervals. Additionally, sensory characteristics of RCO enriched with different PFP doses were evaluated.

PFP samples exhibited the greatest TP content (1910 mg/100 g), whereas RCO samples had the lowest concentration (1090 mg/100 g). As more PFP was added, the corresponding coffee blends’ concentrations of TP, TF and AC were improved significantly. PFP possesses a radical-scavenging activity that is about 1.20 times more than RCO’s. DPPH (2,2-diphenyl-1-picrylhydrazyl) radical-scavenging activity jumped significantly from 74.13% in control (untreated) samples to 77.64%, 78.39% and 80.15% for samples enriched with 10.0%, 15.0% and 20% PFP, respectively. Significant increases in gallic acid, hesperidine, benzoic acid, chlorogenic acid, hispertin, catechol, quercetin, pyrogallol and rutin were detected when RCO was mixed with different quantities of PFP. At the end of storage trial, the coffee oil treated with 20% PFP had AV, PV and TBA values that were about 1.70, 1.95 and 1.66 times lower, respectively, than those of the control sample that had not had PFP addition. The RCO with 5.0% PFP achieved the greatest over acceptability grades.

To the best of the authors’ knowledge, this study was the first study to evaluate the effect of incorporating various level of pomposia fruit powder into RCO. The findings shows that adding different concentrations of pomposia fruit powder into RCO can indeed enhance the radical-scavenging activity of the coffee and potentially extend its shelf life.

This study aims to describe the current conceptualisation of self-management of dementia by family carers in the literature and from the views of dementia professionals and family carers, and to establish a more comprehensive concept of self-management of dementia by family carers.

A hybrid concept analysis included three phases: the theoretical phase reviewed the literature on self-management of dementia by family carers; the fieldwork phase interviewed professionals and family carers; and the analytical phase synthesised and discussed the findings from the previous two phases.

The findings revealed that self-management of dementia by family carers encompasses four domains: supporting care recipients, self-care, sustaining a positive relationship with care recipients, and personal characteristics and skills.

The findings highlighted the essential elements of the construct of self-management of dementia by family carers. The findings can be used as a conceptual framework of self-management and are useful in designing and evaluating self-management support interventions for family carers.

This study aims to explore the effects of cat-assisted therapy (Ca-AT) on a patient in their homes, specifically investigating the effects on patient’s memory, behavioral pathology and ability to perform activities of daily living, independently.

A case study design was used in patient’s homes using three measuring scales, namely, Mini-Mental State Examination (MMSE), Barthel index (BI) and Behavioral Pathology in Alzheimer’s Disease (AD) Rating Scale.

The MMSE and BI mean scores were increased, whereas the Behavioral Pathology mean score was decreased. Patient negative behaviors were improved specifically, aggressiveness, anxieties, phobias, and caregiver burden was decreased.

Patients with AD could significantly benefit from Ca-AT in their own homes, and it could decrease caregiving burden.

Ca-AT is a newly developed type of animal-assisted therapy that uses cats to treat patients, especially elderly people with AD, in their homes.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

The objective of this paper is to synthesize and extend the existing understanding of social health accounting (SHA) literature within the perspectives of social health disclosures (SHAD) and the effect of social health problems on public and private sector accounting.

This study provides a comprehensive and up-to-date systematic literature review (SLR) of past studies on social health within the accounting literature. This is done by employing a three-step SLR research design to investigate a sample of papers, made up of 62 mixed, qualitative and quantitative studies conducted in over 23 countries, drawn predominantly from the extant accounting literature from 2013 to 2023 and published in 25 peer-reviewed journals.

Our SLR offers several findings. First, we find that existing SHA studies apply theories in SHAD studies, but hardly apply them to explain the impact of health problems on business outcomes. Second, we show that the extant studies have focused predominantly on rigorous empirical studies on SHAD, while this is scarce for studies examining the impact of diseases/health problems on both public and private sector accounting. Third, we identify several research design weaknesses, including a lack of primary data analysis, mixed-methods approach and rigorous qualitative studies. Finally, we present directions for future SHA research.

In contrast to the ever-increasing general social and environmental accounting (SEA) research, existing studies examining global health issues and challenges (e.g. diseases, epidemics and pandemics), especially from an accounting perspective are rare. Nonetheless, the past decade has witnessed a steady increase in research on corporate accounting for, and reporting of, health issues; although the emerging literature remains fragmented thereby impeding the generation of useful empirical and theoretical insights for policymakers, practitioners and researchers. Consequently, this paper offers extensive and timely SLR of the existing studies on SHA; critically reviewing past findings published in a wide range of peer-reviewed international journals that discuss the current state of global SHA research, their weaknesses and set future research agenda.

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

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

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

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

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

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